esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we compared differences between SYCL implementations. Although SYCL specification itself aims for single source heterogeneous computing, there are some implementations which do not support full specification of SYCL. Each platform had its characteristics in managing data and work-group sizes, even though for the same task. To achieve better results, selecting the most suitable SYCL implementation is inevitable to maximize kernel performance.  
For comparison purposes, we also measured the execution time of operations written in CUDA. The result shows that in most cases, CUDA showed better performance. This was mainly due to the fact that the native platform of CUDA provides the best optimization methods for their chipsets. From the view point of single source heterogeneous computing, SYCL is one of the best solutions to give flexibility in writing parallel tasks. SYCL also provides convenient nested parallelism by fully supporting native C++ standard lambda captures inside the kernel. Moreover, the SYCL implementation work groups are now focusing on optimizing their current implementations. Thus, we can expect performance enhancement as the SYCL implementations are optimized.  
Conclusively, SYCL implementations are still slower than the original parallel programming platforms, but the applications are rapidly moving to the new flexible solution of SYCL. This trend is especially important for large-scale parallel computing applications, i.e., the big-data computing and the big-data visualization applications. Our comparisons will be the starting point for selecting the most suitable SYCL implementation for a specific task.  
Conceptualization, W.S., N.B.; formal analysis, W.S.; investigation, W.S.; writing--original draft preparation, W.S.; writing--review and editing, K.-H.Y. and N.B.; supervision, N.B.; Funding Acquisition, K.-H.Y. and N.B. All authors have read and agreed to the published version of the manuscript. This work has supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grand No.NRF-2019R11 IA3A01061310). This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00448) supervised by the IITP (Institute for Information and communications Technology Promotion). This research was also supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005). The authors declare no conflict of interest.
## 3 Results and Discussion
### Compression Strength  
The average values for compression strength of longitudinally loaded LVL specimens (parallel to grain) are presented in Figure 10. The compression strength for the LVL samples made of geopolymer binder with no additives was about 52 MPa. A 25% reduction of compression strength was observed when the phenol was added into the binder. Compression strength was increased to about 60 MPa after adding grafted silica fume to the geopolymer binder. As explained earlier, the grafted silica fume created enhanced bonding mechanisms with wood cells that needs to be further explored.  
Failure modes were relatively consistent among groups of test specimens and can be seen in Figure 11. The common failure mode observed in most of the samples was splitting the layers at the geopolymer binder line that indicates a weak bond line quality. Specimens made with grafted silica fume generally started to fail in compression, after which they buckled to one side and compression creases were observed at the specimens' face. This result is also consistent with that trend observed in bending tests of LVL samples, showing an improved adhesion between wood and geopolymer binder with grafted silica fume.  
Figure 9: Failure mode in the LVL samples after face and edge bending tests.
# From Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives
## 2 Mathematical Background
### The TNS: From Barcodes to Trees
#### 2.4.2 Elongation  
We now describe how the synthesized trees are embedded in \(\mathbb{R}^{3}\). A _segment_ of a growing tree is the portion of the tree between a pair of consecutive vertices (parent and child). Each synthesized tree is grown segment by segment. The _direction_ of a segment, i.e., the vector \(\bar{d}\) from its starting point to its end point, is a weighted sum of three unit vectors: the cumulative _memory_\(\bar{m}\) of the directions of previous segments within a branch, a _target vector_\(\bar{t}\), and a random vector \(\bar{r}\)[26]. The memory term is a weighted sum of the previous directions of the branch, with the weights decreasing with distance from the tip. As long as the memory function decreases faster than linearly with the distance from the growing tip, the exact choice of function is not important [21]. The target vector is chosen at the beginning of each branch and depends on the bifurcation angles. The random component is a unit vector sampled uniformly from \(\mathbb{R}^{3}\) at each step. The direction of the segment  
\[\bar{d}=\rho\bar{r}+\tau\bar{t}+\mu\bar{m},\]  
then depends on three weight parameters \(\rho\), \(\tau\), and \(\mu\), where \(\rho+\tau+\mu=1\).  
An increase of the randomness weight \(\rho\) results in a highly tortuous branch, approaching the limit of a simple random walk when \(\rho=1\). If the targeting weight \(\tau=1\), the branch will be a straight line in the target direction. Different combinations of the three parameters \((\tau,\rho,\mu)\) generate more or less meandering branches and thus reproduce a large diversity of geometric trees.
# Technologies and Principles of Hot Recycling and Investigation of Preheated Reclaimed Asphalt
###### Abstract  
More and more recycled asphalt mixtures with high reclaimed asphalt pavement (RAP) content are used in road pavement. Having determined and evaluated RAP composition (aged bitumen content and aggregate gradation) and properties, a suitable recycling agent and virgin materials are selected in the design process. The gradation of hot mix asphalt (HMA) mixture that is recycled in an asphalt mixing plant (AMP) shall correspond to its optimal gradation set out in its job-mix formula (JMF). When RAP is recycled in an AMP, inevitable systematic and random errors of performed technological operations and inhomogeneity of virgin materials and RAP have a significant influence. These factors influence the variation of components quantities of recycled hot mix asphalt (RHMA) mixture and deviations from JMF. In this study, the principles of asphalt pavement hot recycling are systematized, which allows analysis of the factors of components' interaction influencing the results of the recycling process. The paper also presents and analyses asphalt recycling technologies in AMP and their comparative analysis. During the season of asphalt mixture production in 2014, statistical parameters were calculated according to the data obtained from one of the companies, which collected and systematized RAP batch masses, when before batching it was pre-dried and pre-heated in an additional dryer. These parameters of batch mass and RAP content in RHMA position and variation were used when evaluating the accuracy and precision of the recycling process in AMP. The obtained data showed that when RHMA mixtures are produced in a modern batch-type AMP, RAP is batched accurately, but not precisely enough.  
Keywords: asphalt pavement; recycling principles; asphalt mixing plant (AMP); reclaimed asphalt pavement (RAP); batching errors +
Footnote †: journal: Applied Sciences
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Data Processing
#### 2.3.1. PPP Processing Settings (RTKLIB)  
RTKLIB open-source package consists of a portable program library and several application libraries designed for GNSS standard and precise positioning. The package is distributed under the open source initiative. The current release is version 2.4.2, whereas the development version is 2.4.3b29. We used the latter one. In addition, RTKLIB supports various positioning modes: single, differential, baseline, static, kinematic, real-time, or post-processing. We exploited the PPP mode in this work. Furthermore, RTKLIB employs un-differenced measurement equations and Extended Kalman Filter (EKF) estimation in order to obtain the final solutions. The mathematical formulation is explained in detail in the Appendix E.8 of the package manual.  
As mentioned, PPP requires precise products. In this work, PPP solutions were produced using the final satellite orbits, final clock product with a sampling of 5 s, and weekly final Earth rotation parameter products from Centre for Orbit Determination in Europe (CODE). The products were taken from the homogeneous CODE reprocessing, release 2017 [52]. They were available until the end of 2014. Afterwards, we used the operational products [53]. Further corrections were derived using several mathematical models and standard conventions. The elevation cutoff angle was set to 10 degrees. This is a best compromise between the quantity and quality of data for high-latitude regions [39]. An elevation-dependent weighting scheme was adopted with separate  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Tool** & **Type** & **Examples** \\ \hline Software & scientific & Bernese, GIPSY-OASIS II, GAMIT/GLOBK/TRACK, NAPEOS, PANDA \\ \hline \multirow{2}{*}{package} & commercial & Trimble Business Centre, GrafNav, etc. \\  & open-source & RTKLIB, GPSTK, gLAB \\ \hline \multirow{4}{*}{Service} & free on-line & AUSPOS, OPUS, SCOUT, APPS, CSRS-PPP, GAPS, magicPPP, CenterPoint RTX \\ \cline{2-3}  & commercial & Atlas (Hemisphere), CORRECT-PPP (NOVATEL), OmniSTAR, RTX (Trimble), StarFire (Fugro), Ultra (Veripos), etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precise GPS observation data processing software packages and services.  
constant and elevation-dependent terms. The estimated parameters were station coordinates, receiver clock, troposphere parameters and float ambiguities. The estimated coordinates were obtained in ITRF2008 [54] before 29 January 2017 and ITRF2014 afterwards.  
Table 2 gives an overview of the various error components and corrections used in our PPP processing.
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 2 State of the Art
### Digital Twin Architectures
#### 2.5.1 Existing Digital Twin Concepts, Architectures and Platforms
Therefore, some researchers see a DT as merely the digital model inside a CPS [10],and this conversely implies that a DT is the prerequisite for a CPS [12]. [PERSON] et al. [83] state that the DT in the broad sense belongs to the CPS but has a higher fidelity and focuses more on data and models with ultra-high-fidelity simulations. Either way, DTs and CPSs are undoubtedly very similar, and thus, CPS architectures are also highly relevant for DT implementations. The prominent five-layer architecture (5C architecture) for CPSs proposed by [PERSON] et al. [84] consists of the five pyramid-like layers \"Smart Connection Level\", \"Data-to-Information Conversion Level\", \"Cyber Level\", \"Cognition Level\", and \"Configuration Level\", in order from bottom to top, bearing similarity with the 5D-DT architecture by [PERSON] et al. [72, 80]. The 5C architecture aims to guide the development and deployment of CPSs in manufacturing even though it cannot be considered to be a mature DT platform. In Ref. [85], an \"Intelligent Digital Twin\" architecture for implementation in CPSs was proposed. In the \"Cyber Layer\", a synchronization interface is introduced besides a data acquisition interface to keep simulation models of the DT consistent with the physical entity. Furthermore, a co-simulation interface is described as a component of the architecture to facilitate interaction between simulation models and to enable communication with other DTs. While focus of this architecture lies on automated synchronization and inseparability, the dimension of services seems underdeveloped, since the \"DT\" is only regarded as a virtual replication of physical functionalities in that article.
## 7 Black Start Capabilities through HVDC
### Soft-Start Capacity Using VSC-HVDC  
During certain blackstart processes, abruptly switching on circuit breakers causes inrush currents and transient over-voltages, possibly causing failures in the restoration. For that reason, the method proposed in [31] explores the ability of VSC-HVDC systems for soft-energizing major power systems, avoiding the previous voltages.  
With a method consisting of gradually increasing the AC bus voltage of the VSC adjacent to the affected grid and controlling the switching of certain circuit breakers on this ascending process, it is possible to achieve a restoration with no over-voltage or surge at all.  
The ability to control the active and reactive power absorbed by the VSC independently is fundamental in allowing the black start facility to apply a soft energization method.  
Consider \(P\) and \(Q\) as the active and reactive power absorbed by the VSC, respectively. From Equations (5) and (6) shown below, it can be concluded that \(P\) and \(Q\) can be controlled independently by \(\delta\) and \(U_{C}\). When a Pulse Width Modulation (PWM) is adopted, \(\delta\) is the phase angle of the fundamental component of modulation wave and \(U_{C}\) is proportional to the modulation index \(M\) of the PWM:  
\[P=\frac{U_{s}U_{c}}{X}\sin(\delta) \tag{5}\]  
\[Q=\frac{U_{s}(U_{s}-U_{c}\cos(\delta))}{X} \tag{6}\]  
\[U_{c}=\frac{M}{\sqrt{2}}U_{d} \tag{7}\]  
where:  
\(P,Q=\) Active and reactive power absorbed  
\(U_{s}=\) AC bus voltage  
\(U_{c}=\) VSC side AC voltage  
\(\delta=\) Lagging angle between \(U_{c}\) and \(U_{s}\).  
\(X=\) Converter reactance  
\(M=\) PWM modulation index  
This kind of method may be further developed for providing a safer and more reliable way of designing blackstart schemes.
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 4 Digital Twins and Virtual Nodes in Self-Organising Cyber-Physical Systems
### Augmented Collective Digital Twins in the Pulverised Aggregate Computing Meta-Model  
#### 4.2.1 Digital Twins and Collective Digital Twins  
The meta-model discussed in detail in [11], captures the execution relationship through a deployment relation specifying the cyber-physical mapping, but does not consider the twinning relationship, which is enforced implicitly depending on the deployment of the sensors \(\sigma\) and actuators \(\alpha\) components of each logical device (digital twin) to the designated host (physical twins). Indeed, whereas computation, communication, and state can be offloaded and disembodied, sensors and actuators should typically be situated together with their physical twins in order to properly perceive and act, in real time, on their very context. Instead, we propose to make the twinning relationship explicit--see Figure 4. Making the connection explicit, in terms of an identity correspondence between logical devices and physical hosts, has both a descriptive role and an operational role. For instance, it may be used to define custom deployment constraints or digital thread mechanisms.  
Moreover, the cyber subsystem is ultimately a network of logical devices, and as such it can be seen as the _collective digital twin_ of the _collective physical twin_ composed of the hosts of the platform subsystem. In aggregate computing, this is even more evident since the aggregate program defining the behaviours \(\beta\) is conceptually associated with the system as a whole. The benefit of considering the aggregate abstraction of a CDT lies in the possibility of getting and reasoning about collective descriptors of the system, expressed, for example, in terms of _computational fields_[22] mapping each device to a corresponding computational value (such that, e.g., a temperature field maps each device to the temperature it senses, a Boolean field represents a domain of devices, for instance denoting those available to provide a service, or those belonging to a team, and a field of percentages may denote the level of charge of the devices in the system).
## 4 Results
### Classification Results Using CCA Fusion
#### 4.5.6 Statistical Analysis of SVM on CCA Fusion  
In this section, we have discussed the reported statistics of the most famous traditional machine learning algorithm--support Vector Machine (SVM) classifier to classify the lesion on the vector of the fused feature. The statistics show that the accuracy of the AML class is 100 percent, ALL reached 90.909 percent, and MM's accuracy is around 94.44 percent. Sensitivity, precision, F1-score, etc., are cited below in Table 20 The overall accuracy of this classifier was very efficient and reached 95.19 percent. The overall combined results of the three classes are also shown in Table 20, which is also given below; also, the confusion matrix is shown in Figure 12.  
Overall accuracy achieved by SVM on ALL, AML and MM was 95.19%. The overall statistics of SVM are given below in Table 21.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** & **False Positive\%** & **F1-Score\%** \\ \hline ALL & 90.909 & 90.909 & 97.253 & 94.118 & 2.7473 & 92.486 \\ AML & 100 & 100 & 100 & 100 & 0 & 100 \\ MM & 94.444 & 94.444 & 95.556 & 91.398 & 4.4444 & 92.896 \\ \hline \end{tabular}
\end{table}
Table 20: Statistical Analysis of SVM on CCA Fusion.  
Figure 11: Confusion Matrix of Coarse KNN model.  
\begin{table}
\begin{tabular}{c c} \hline
**Statistics** & **Results \%** \\ \hline Accuracy & 95.19 \\ \hline Error & 4.81 \\ \hline Sensitivity & 95.14 \\ \hline Specificity & 97.60 \\ \hline Precision & 95.48 \\ \hline False Positive Rate & 2.40 \\ \hline \end{tabular}
\end{table}
Table 19: Overall Statistics of Coarse KNN Model of All Classes.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Dynamic Interaction Process
#### 3.5.1 Generation of Initial Scheduling Plan  
When an order arrives, its detailed information is transmitted to the scheduling layer. The scheduling layer, including the scheduling model and the corresponding optimization solution, generates an initial scheduling plan based on the established scheduling goals. This scheduling plan passes through the virtual system after the simulation and verification. If the simulation results show that the scheduling plan is unqualified, rescheduling will be triggered.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Initialization Phase  
In the initialization phase of our proposed scheme, the server initializes some parameters: it selects an elliptic curve equation \(E_{p}(a,b)\), a base point \(P\in E_{p}(a,b)\), a secure one-way hash function \(h(\cdot)\), and symmetric key encryption/decryption functions \(E_{s}(\cdot)/D_{s}(\cdot)\). In addition, it selects a high entropy integer \(k_{s}\) as its secret key and computes \(K_{s}=k_{s}\cdot P\).
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 2 Recent Studies
[24], the authors proposed a steganography technique using a Bit Invert System (BIS) with three control random parameters. The Henan Map Function (HMF) is used to make random selections. Both the Huffman Method and Affine Cipher are used for encryption as an added security measure. The method showed good security while maintaining image quality. In ref. [25], the authors presented various techniques to preserve the security and integrity of medical images using both steganography and watermarking, as well as the use of images for authentication. In ref. [26], the authors presented a review of remote user authentication methods used in healthcare to ensure that medical records are accessed by only authorized persons. They also propose a three-layer authentication method for use in healthcare. In ref. [27], the author proposed a method for the security of Electronic Health Records (EHRs) in a cloud-based environment. Biometric images are proposed for a unified identification across cloud environments. The proposed method is based on watermarking and encryption. The method was impermeable to repeated attacks. In ref. [28], the authors presented the importance of security in telemedicine as many medical services can now be offered remotely. They present a complete system for home care medical services while ensuring security and integrity. In ref. [29], the authors propose an optimized bias correction method that integrates both the variational denoised term and pre-piecewise fitting function into the energy function for low-contrast and low-intensityinhomogeneity images. They reported superior results for prime ACMs and deep learning methods in image segmentation. Medical images contain vital data that are used for diagnostics, so the image quality and image characteristics must be ensured to keep the vital information in the images intact. Otherwise, this might cause misdiagnosis. For example, medical images contain vital information for cancer diagnosis or other medical conditions [30,31], and these are just a few examples of the importance of medical images in diagnosis. Therefore, any proposed method for steganography must ensure that the vital image characteristic remains unchanged or at least ensure medical personnel use only the original cover image for diagnosis purposes.
## 4 Conclusions  
The production of microalgae biomass requires the selection of optimal cultivation conditions. The most important parameters are the type and the time of lighting, which will also be important for the economy of the process. All factors used in the research had a significant impact on the amount of biomass. Depending on the method used (direct, indirect), different results were obtained. For dry mass measurement, the optimal culture conditions were recorded using 18/6 h light/dark cycle and pH of 7--546 \(\pm\) 7.88 mg\(\cdot\)L\({}^{-1}\) for high-pressure sodium light and 543 \(\pm\) 1.92 mg\(\cdot\)L\({}^{-1}\) for light-emitting diode. The maximum content of chlorophyll a was determined in the 12/12 h light/dark cycle, pH 6, under light-emitting diode (136 \(\pm\) 14.13 mg m\({}^{-3}\)) and 18/6 h, pH 7, under sodium light (135 \(\pm\) 6.17 mg m\({}^{-3}\)). The biomass and chlorophyll productivity in optimal conditions under HPS and LED light was 27.08\(\pm\) 7.80 and 24.21 \(\pm\) 8.89 mg\(\cdot\)m\({}^{-3}\cdot\)d\({}^{-1}\) and 25.00 \(\pm\) 5.1 and 26.34 \(\pm\) 2.01 mg\(\cdot\)m\({}^{-3}\cdot\)d\({}^{-1}\), respectively. The highest number of microalgae cells (2.1 \(\times\) 10\({}^{6}\)) was obtained at pH 7 and photoperiod 18/6 h under sodium light, and 12/12 h under light-emitting diode. The results for all measurements suggest that particular methods can be used alternately during the determination of biomass content. However, if other indicators (cell number and chlorophyll a content) are used for biomass validation, the optimal culture parameters may change with different types of light. Considering the results, the most accurate method for biomass evaluation is gravimetric dry weight assessment > cell counting > chlorophyll content.  
Conceptualization, P.R. and M.H.-P.; methodology, M.H.-P.; validation, P.R. and M.H.-P.; formal analysis, P.R. and M.H.-P.; investigation, P.R.; writing--original draft preparation, P.R. and M.H.-P.; writing--review and editing, P.R. and M.H.-P.; visualization, P.R and M.H.-P.; supervision, M.H.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.