esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.1 Field of View  
The field of view provided by the frame of the visor of a head-mounted device and the resolution of the shared picture were challenges reported in the literature by six studies [18; 31; 32; 34; 36; 49]. These six studies were conducted in lab-based environments. Conversely, two other studies that reported positive results were conducted in different contexts. [PERSON] et al. (2022) and [PERSON] (2019) both reported positive results related to the field of view. [PERSON] et al. (2022) stated that based on the perception of on-site and off-site users, the shared vision of the site through the use of Trimble XR10 (version of HoloLens-2 incorporated in a hard hat) was clear and that the device had the ability to share exactly what the on-site user is viewing and looking at [35].  
It is noteworthy to mention that reviewing 59 papers as part of this analysis of the literature, the authors found that 72% of the studies were conducted in controlled environments, whereas only 28% were conducted in uncontrolled environments. [PERSON] et al. (2022) implemented AR in live construction sites and [PERSON] and [PERSON] (2019) developed AR applications in controlled environments but then applied them in a real environment [35; 45]. While the field of view seems to be related intuitively to the type of device in use (i.e., mobile vs. head-mount device), in the case of communication, this attribute was also related to the type of environment in which a study was conducted. Within the construction context, the purpose of using AR and the field of view that needs to be shared is wider than when using AR in controlled environments where the vision can focus on a defined object or area. This divergence indicates that the vision transferred by the device during an AR application is highly affected by the surrounding of the users performing the call and the goals of using AR for the project at hand.
# Efficient QC-LDPC Encoder for 5G New Radio
## 4 Proposed 5G NR QC-LDPC Encoder Design
### Proposed QC-LDPC Encoder Architecture
It should be noted that the number of cyclic shifters is equal to the number of message blocks, and the size of the barrel shifters is equal to submatrix size \(Z\). (4) XOR Trees: in Modulo 2, the addition implementation is obtained by carrying out an XOR operation on all the elements. (5) Controller: this block generates control signals, such as \(data\_sel\) to indicate the step being processed; and \(mem\_en\), to enable write access to the \(\lambda\_\)memory.
## 5 Discussion
### Tectonic Setting and Geological Significance
On an Rb - Y + Nb diagram (Figure 10a), the data fall into the volcanic arc granite and syn-collisional granite areas, but are closer to the syn-collisional area. On an Rb - Yb + Ta diagram (Figure 10b), most data fall into the syn-collisional granite area. Thus, the Dahongliutan pluton may have formed in a syn-collisional tectonic setting. The Late Triassic tectonic setting was the extensional post-collisional orogenic stage of the Paleo-Tethys Ocean [57]. Peak magmatism occurred during the late Hercynian-early Yanshanian period in the Western Kunlun region. However, there is no chronological evidence of magmatic activity from 230 to 250 Ma [56], which may be because the strong compressive stress was not conducive to magmatism [58]. When the tectonic stress changes to post-collisional extension, decompression melting and magmatic upwelling are more likely to occur [58], leading to intense magmatism. With the development of extension and collapse of the orogenic belt, a substantial amount of heat was generated, thereby causing the partial melting of crustal material that ascended and was emplaced. With continuous extension and lithospheric thinning, large-scale magma upwelling formed the Late Triassic intrusive rocks in the Western Kunlun region. Figure 10: **(a)** Y + Nb vs. Rb and **(b)** Yb + Ta vs. Rb diagrams for the Dahongliutan pluton (according to [PERSON], et al. [17]) (WPG: within-plate granite; VAG: volcanic arc granite; syn-COLG: syn-collisional granite, ORG: ocean ridge granite). Figure 9: R1–R2 diagram for the Dahongliutan pluton (according to [PERSON], et al. [59]). 1 R1: 45i-11(Na + K) \(-\) 2(Fe + Ti); R2: 6 Ca + 2 Mg + Al; Mantle anorthosite granite; 2 Destructive active plate edge (before plate collision) granite; 3 Granite from the uplift stage after plate collision; 4 Late tectonic granite; 5 Non-orogenic A-type granite; 6 Syn-collisional (S-type) granite; 7 Post-orogenic A-type granite.
## 2 Materials and Methods
### Image Assessment  
MRIs performed both for tumor staging and 4-month follow-up after the end of CRT were independently reviewed by two radiologists with 12 (CN) and 7 (MP) years of experience in head and neck imaging, respectively.  
The following morphologic, DWI, and DCE-PWI features were assessed:  
* Maximum size of the primitive tumor and submucosal thickness of the residual tissue after CRT on contrast-enhanced T1 images.  
Signal intensity (SI), hyper- or hypointense, of the residual tissue after CRT on DWlb800 images;
* Mean ADC values of the primitive tumor (ADC\({}_{\text{pre}}\)), residual tissue after CRT (ADC\({}_{\text{post}}\)), and ipsilateral trapezius muscle as a normative reference on both pre- and post-CRT (ADC\({}_{\text{muscle pre}}\) and \({}_{\text{muscle post}}\));
* Mean AUC and K(trans) values of the primitive tumor (AUC\({}_{\text{pre}}\), K(trans)\({}_{\text{pre}}\)), residual tissue after CRT (AUC\({}_{\text{post}}\), K(trans)\({}_{\text{post}}\)), and ipsilateral trapezius muscle as a normative reference on both pre- and post-CRT (AUC, K(trans)\({}_{\text{muscle pre}}\) and \({}_{\text{muscle post}}\));
* Ratio between ADC\({}_{\text{post}}\) and ADC\({}_{\text{muscle post}}\) (ADC\({}_{\text{post}/\text{muscle post}}\));
* Ratio between AUC values of the residual tissue after CRT and primitive tumor (AUC\({}_{\text{post}/\text{pre}}\));
* Ratio between K(trans) values of the residual tissue after CRT and primitive tumor, standardized with respect to AUC and K(trans) values of the ipsilateral trapezius muscle as a normative reference (AUC\({}_{\text{post}/\text{pre}/\text{muscle}}\) and K(trans)\({}_{\text{post}/\text{pre}/\text{muscle}}\)), as follows:  
\[\frac{\text{AUCpost}}{\text{AUC}\text{ muscle post}}\text{: }\frac{\text{AUC}\text{ pre}}{\text{AUC}\text{ muscle pre}}\text{ and }\frac{\text{K(trans)post}}{\text{K(trans)}\text{ muscle post}}\text{: }\frac{\text{K(trans)}\text{ pre}}{\text{K(trans)}\text{ muscle pre}}\]  
where AUC\({}_{\text{muscle pre}}\), AUC\({}_{\text{muscle post}}\), K(trans)\({}_{\text{muscle pre}}\), and K(trans)\({}_{\text{muscle post}}\) are the AUC and K(trans) values of the ipsilateral trapezius muscle measured on pre- and post-CRT, respectively.  
The diagnosis of tumor response to CRT (post-treatment changes) or tumor persistence/recurrences (post-treatment residual cancer) was defined at the 2-year follow-up, with clinical examinations and cross-sectional imaging including MRI and 18F-FDG PET/CT. Post-treatment biopsy was performed only in case of positive 18F-FDG PET/CT during follow-up (12 patients). Clinical examinations and MRI were used to validate results as true negatives both in patients with negative 18F-FDG PET/CT (25) and in patients with positive 18F-FDG PET/CT and negative post-treatment biopsy (7).
## 5 System Servo Performance Evaluation and Verification
### Experimental Verification  
According to the above simulation results, friction torque and road disturbance torque are the internal and external factors that have the greatest impact on the servo performance, respectively. To verify the above conclusions, the torque closed-loop system composed of load motor, planetary reducer, and torque sensor is used as the electric load simulator to change the load torque within a certain range to accurately simulate friction torque and road disturbance torque.
## 3 Results and Discussion  
### Methodology Validation and Grid Refinement Study  
The employed turbulence analysis method (\(k\)-\(\epsilon\)) and the results were compared and verified with the different turbulent schemes. Figure 2a illustrates the velocity magnitude in the \(+4D\) separator model for the different turbulence models. The results were computed at point 1 using an air inlet velocity of 35 m/s and bed slanting angle (\(\theta\)) of 0\({}^{\circ}\). The \(k\)-\(\epsilon\) and the \(k\)-\(\omega\) models resulted in higher velocity magnitudes than other turbulence models. The Spalart-Allmaras and the shear stress transport (SST) models do not use any wall functions and tend to be most accurate when solving the flow near the wall. Thus, these two models do not accurately compute flow fields away from the walls especially for the shear flow, separated flow, or decaying turbulence, and they also require high mesh resolution near the wall [29]. The algebraic yPlus and L-VEL are algebraic turbulence models based on the distance to the nearest wall. These two turbulence models are robust and computationally inexpensive but also least accurate, because they do not solve any additional transport equations. The algebraic yPlus and L-VEL models provide good approximations for internal flow with low Reynolds number. Therefore, these models failed to precisely predict the flow in the density separator because of high Reynolds number (60,100). The standard \(k\)-\(\epsilon\) is very popular for industrial applications (complex geometries) due to its good convergence rate and relatively low memory requirements. The \(k\)-\(\omega\) model has more memory requirements and is useful in many cases in which the \(k\)-\(\epsilon\) model is not accurate. A reasonable agreement was found between the standard \(k\)-\(\epsilon\) and the \(k\)-\(\omega\) models, thus verifying the proposed turbulence method (\(k\)-\(\epsilon\)) for the separation process in the material density separator [28].  
Grid refinement study was performed to test the accuracy of the results and to ensure that the velocity magnitude did not change with mesh size (Figure 2b). The study adopted different mesh refinements by varying them with a factor of five: fine (29,509 domains and 352 boundary elements), extra fine (147,547 domains and 1717 boundary elements), and extremely fine (737,740 domains and 8585 boundary elements). The results were computed in the \(+4D\) separator model using an air inlet velocity of 35 m/s and bed slanting angle (\(\theta\)) of 0\({}^{\circ}\). A minimal difference was observed between the velocity magnitudes at point 1 for different mesh refinements. Thus, all the separator models were simulated using the extra fine (147,547 domains and 1717 boundary elements) mesh refinement.
# A Novel Optimization Design Method of Form Ginding Wheel for Screw Rotor
## 2 Theoretical Background
### Calculation Method of Profile Errors  
The rotor profile errors must be defined reasonably and scientifically; they must also be easily measurable in order to evaluate the profile errors of the screw rotor. Figure 4 shows the profile of the compressor rotor. In Figure 5, the rotor profile errors are calculated according to the machined profile \(C\) and the theoretical profile. \(C_{i}\) is a point on \(C\) and it is represented by \((x_{i},y_{i})\).  
Figure 4: Theoretical rotor profiles. (**a**) Male rotor. (**b**) Female rotor.  
Figure 5: The profile error of the screw rotor cross-section.  
To facilitate the calculation, the cubic parametric curve is selected to characterize the theoretical rotor profile:  
\[y=S(x) \tag{23}\]  
A point on \(C\) is denoted by \(C_{i}\) and is represented by \((x_{i},y_{i})\) (\(i=\{1,2,\ldots,m\}\)), where \(m\) is the number of measured profile data points. The profile error \(E_{i}\) is defined as the shortest distance between \(C_{i}\) and the theoretical profile. \(E_{i}\) is positive when \(C_{i}\) is outside the theoretical profile, and vice versa, and it can be calculated using the following equation:  
\[\begin{array}{l}E_{i}=\pm L_{i}(\min)\\ L_{i}(\min)=\sqrt{\left(x_{i}-x_{k}\right)^{2}+\left(y_{i}-S(x_{k})\right)^{2} }\end{array} \tag{24}\]  
where points \((x_{k},S(x_{k}))\) satisfy Equation (23). The sign in Equation (24) is chosen based on the relationship of the machined rotor profile and the theoretical rotor profile. Specifically, if \(y_{i}>S(x_{k})\), \"+\" is chosen; otherwise, \"-\" is chosen. The shortest distance, \(L_{i}(\min)\), can be easily determined using the numeric method, and the profile error can then be determined.
# Thorium Recovery with Crown Ether-Polymer Composite Membranes
## 3 Results and Discussion
### The Process Performance of the Prepared Membranes
#### 3.2.3 Determination of Membrane Retention and Flux over Time  
In order to follow the variation in the retention of the membranes depending on the operating time, the pH of the solution at the value of 4, the concentration of thorium ions of \(10^{-5}\) mol/L, and the stirring regime of 200 rpm were chosen as parameters. Both membranes show a slight decrease in retention (Figure 11), which can be estimated to be within the acceptable limit for the dead-end nanofiltration-type working system. The retention of the ABCE-sPEEK composite membrane decreases from 98% at the first hour of operation to 95% at eight hours. For the sPEEK membrane, the decrease is from 90% in the first hour of operation to 85% after eight hours. The obtained values show that in the first hour of operation, the retention mechanism is of the adsorption type, after which it switches to the ionic expulsion regime, which determines the acceptable decrease at a relatively long operating time.  
Depending on the operating regime, the evolution of the permeate flux for the thorium nitrate solution of concentration \(10^{-5}\) mol/L and pH 4, and with the stirring regime at 200 rpm, has the same pattern for both prepared membranes (Figure 12). From the initial flux, an important decrease is observed until the fourth hour of operation, after which the flux maintains a quasi-constant value.  
The performance of the ABCE-sPEEK composite membrane is superior to the performance of the sPEEK reference membrane, in all operating situations.  
Figure 10: Dependence of retention (R%) for sPEEK membrane (orange and gray curves) and ABCE-sPEEK composite membrane (green and blue curves) for Th\({}^{4+}\) solution concentration of \(10^{-4}\) and \(10^{-5}\) mol/L.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 3 Simulation and Signal Processing Tools  
We developed simulation and signal processing tools in order to support the study of the potential of LEO SV signals for the navigation of a high dynamic vehicle.
# A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization
## 4 Experiment
### Unimodal Unconstrained Optimization
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **Algorithm** & **Best** & **Worst** & **Average** & **Variance** & **Time(s)** \\ \hline \multirow{8}{*}{\(F_{1}\)} & PIO & \(-199.7120\) & \(-175.2841\) & \(-195.4801\) & \(19.4308\) & \(0.029\) \\  & SOA & \(-199.9893\) & \(-45.0261\) & \(-185.6132\) & \(658.3112\) & \(0.010\) \\  & **GWO** & \(\mathbf{-200}\) & \(\mathbf{-200}\) & \(\mathbf{-200}\) & \(\mathbf{10^{-28}}\) & \(0.011\) \\  & QBSO & \(\mathbf{-200}\) & \(-199.9999\) & \(\mathbf{-200}\) & \(10^{-10}\) & \(0.087\) \\  & BSO & \(\mathbf{-200}\) & \(-177.8722\) & \(-197.8545\) & \(10.3658\) & \(0.009\) \\  & BAS & \(-199.9965\) & \(-199.8666\) & \(-199.9398\) & \(10^{-4}\) & \(0.017\) \\  & PIO & \(10^{-4}\) & \(31.4922\) & \(6.2104\) & \(60.2032\) & \(0.027\) \\  & SOA & \(0.0020\) & \(10^{4}\) & \(10^{3}\) & \(10^{7}\) & \(0.018\) \\  & GWO & \(0.0017\) & \(0.0391\) & \(0.0134\) & \(10^{-5}\) & \(0.026\) \\  & **QBSO** & \(\mathbf{10^{-7}}\) & \(\mathbf{10^{-5}}\) & \(\mathbf{10^{-6}}\) & \(\mathbf{10^{-11}}\) & \(0.152\) \\  & BSO & \(0.1055\) & \(1.5872\) & \(1.2652\) & \(2.8857\) & \(0.018\) \\  & BAS & \(8.366\) & \(33.949\) & \(19.747\) & \(24.237\) & \(0.017\) \\  & PIO & \(-0.9998\) & \(-0.9291\) & \(-0.9509\) & \(10^{-4}\) & \(0.029\) \\  & SOA & \(-1\) & \(-0.0352\) & \(-0.7232\) & \(0.0831\) & \(0.010\) \\  & GWO & \(-1\) & \(-0.9362\) & \(-0.9754\) & \(10^{-4}\) & \(0.011\) \\  & **QBSO** & \(\mathbf{-1}\) & \(\mathbf{-1}\) & \(\mathbf{-1}\) & \(\mathbf{10^{-23}}\) & \(0.088\) \\  & BSO & \(-1\) & \(-0.9362\) & \(-0.9641\) & \(10^{-4}\) & \(0.010\) \\  & BAS & \(-0.996\) & \(-0.465\) & \(-0.897\) & \(10^{-3}\) & \(0.018\) \\  & PIO & \(10^{-6}\) & \(10^{7}\) & \(10^{5}\) & \(10^{12}\) & \(0.066\) \\  & SOA & \(0.0227\) & \(10^{45}\) & \(10^{43}\) & \(10^{88}\) & \(0.025\) \\  & GWO & \(10^{-4}\) & \(10^{3}\) & \(27.9445\) & \(10^{4}\) & \(0.037\) \\  & **QBSO** & \(\mathbf{10^{-19}}\) & \(\mathbf{10^{-15}}\) & \(\mathbf{10^{-16}}\) & \(\mathbf{10^{-31}}\) & \(0.181\) \\  & BSO & \(10^{-4}\) & \(10^{4}\) & \(10^{3}\) & \(10^{7}\) & \(0.025\) \\  & BAS & \(19.887\) & \(10^{4}\) & \(10^{3}\) & \(10^{8}\) & \(0.017\) \\ \hline \end{tabular}
\end{table} 
Table 2: Results of the unimodal benchmark function experiments (population size = 30).
# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
## 4 Experiments  
### Experiment Settings  
**Environment Information.** The experiments were executed on the CentOS 7.5 operating system, utilizing the Intel(R) Xeon(R) CPU E5-2697 v2 @ 2.70 GHz with 256 GB of memory and NVIDIA Quadro K4000 GPU. The programming languages and frameworks employed were Python, TensorFlow, and Torch.  
**Dataset.** This paper uses four 15 K bilingual datasets from OpenEA [28], covering multiple languages including English, French, and German. The data are sourced from well-known knowledge graphs such as YAGO 3 [29], Dbpedia [30], and Wikidata [31]. To assess the performance of the model on datasets of varying densities, OpenEA constructed sparse and dense versions of each dataset. The sparse dataset (V1 version) was directly generated using the IDS algorithm and has features that are more closely aligned with real-world datasets. The dense dataset (V2 version) is based on the V1 version but with low-degree entities (d < 5) removed from the source knowledge graph and then reconstructed using the IDS algorithm, making it more similar to real-world datasets. It should be noted that since the data collection methods for DBpedia, Wikidata, and YAGO 3 are similar, the labels between their entities are highly similar, which may have a certain impact on the evaluation of actual performance. Therefore, in the data preprocessing stage, the label information for the entities was removed. Table 1 presents the statistical details for each dataset, comprising the entity count, relationship count, as well as relationship and property triple counts.  
**Implementation details.** To ensure fairness in the evaluation, this study employed a five-fold cross-validation method. Specifically, the dataset was divided into five non-overlapping parts, with each part accounting for 20% of the total dataset. In each experiment, one part was chosen as the training data, 10% of the remaining data was allocated for validation, and the remaining 70% of the data was used for testing. The hyperparameters were set to a maximum epoch number of 2000; batch size of 5000; embedding dimension of 100; margin of 1.5; and learning rate of 0.001.  
**Evaluation metrics.** In this study, Hits@k (where k = 1, 5, 10) and mean rank (MR) were used as evaluation metrics to measure the performance of aligning entities. These metrics assess the proportion of correctly aligned entities within the top k predicted entities and overall performance: lower MR scores and Higher Hits@k scores indicate superior performance. Specifically, we focus on the Hits@1 performance metric, as it corresponds to the conventional accuracy metric in the field of traditional entity alignment.  
**Baseline.** To evaluate the performance of the proposed PCE-HGTRA model, it was compared against seven contemporary EA models. For an elaborate exposition of the model specifications, please refer to Section 2.
## 3 Overview of Vitamin D
### Vitamin D in Pregnancy  
Discovery of the nuclear VDR and 1\(\alpha\)-hydroxylase enzyme in pregnancy-specific tissues such as decidua and placenta has generated substantial interest into the role of vitamin D in pregnancy. In normal pregnancy, maternal 1,25(OH)2D3 levels are twice as high by 12 weeks of gestation compared to the non-pregnant state, and peak at levels 2-3 fold higher (>700 pmol/L\({}^{-1}\)) by the third trimester [60]. These concentrations would be toxic due to hypercalcaemia in a non-pregnant individual, yet, they appear to be essential in pregnancy with an apparent decoupling with calcium homeostasis. It is thought that the increase in 1,25(OH)2D3 may have an immunomodulatory rather than calcium-regulatory function (although it maintains the latter), and is highly dependent on bioavailability of the 25(OH)D substrate for conversion [60]. Thus, vitamin D deficiency in pregnancy is of particular importance because at no other time in life is 25(OH)D so closely linked with 1,25(OH)2D3 production.  
Low vitamin D status in pregnancy predisposes to hypocalcaemia in the immediate postpartum period, and then to rickets in the following months. Infants breastfed by vitamin D-deficient mothers remain at high risk as the amount of vitamin D in breast milk correlates with maternal vitamin D status [61]. Conversely, vitamin D supplementation during pregnancy improves maternal vitamin D status and may positively affect the availability of vitamin D to the fetus and neonate [62]. This has important clinical implications and as such, the IOM recommends daily intakes of 600 IU and 400 IU of vitamin D for pregnant [52] and lactating women [61], respectively. However, these doses are likely inadequate to sufficiently raise blood 25(OH)D concentrations, particularly in the case of existing deficiency and limited sun exposure. In 2011, guidelines released by the Endocrine Society [63] suggested that all pregnant and lactating women with vitamin D deficiency receive 1000-2000 IU of vitamin D daily in addition to the 400 IU provided in prenatal vitamins. These levels are consistent with current recommendations in Australia, where daily supplements containing 1000 or 2000 IU of vitamin D3 are recommended for pregnant women with 25(OH)D levels <50 and <30 nmol/L, respectively, while women with levels >50 nmol/L should consume 400 IU daily as part of a pregnancy multivvitamin [64].  
In addition to bone health and infant transfer of vitamin D, maternal vitamin D deficiency has been associated with an increased risk of several other adverse pregnancy outcomes including GDM [59; 65], pre-eclampsia [66], and preterm birth [67]. Although observational data supports a strong and consistent association between low vitamin D status and GDM, cause-effect relationships and the potential benefits of maternal vitamin D replenishment for the prevention and management of GDM remain unclear.