esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and discussion
### Validation of the Analytical Procedure
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & **Spiked Conc. (\(\upmu\)g/mL)** & **Found Conc. (\(\upmu\)g/mL)** & **Recovery (\%)** & **SD** & **RSD (\%)** \\ \hline \multirow{3}{*}{MULA} & 6.00 & 5.92 & 98.68 & 1.21 & 1.22 \\  & 15.00 & 15.08 & 100.52 & 1.50 & 1.50 \\  & 30.00 & 31.05 & 103.50 & 1.10 & 1.07 \\ \hline \multirow{3}{*}{AMY} & 6.00 & 5.88 & 97.99 & 2.49 & 2.54 \\  & 15.00 & 14.99 & 99.94 & 2.35 & 2.36 \\  & 30.00 & 30.18 & 100.61 & 1.81 & 1.80 \\ \hline \multirow{3}{*}{LIQA} & 4.00 & 4.02 & 100.54 & 0.95 & 0.95 \\  & 10.00 & 9.81 & 98.11 & 1.54 & 1.56 \\  & 20.00 & 20.45 & 102.27 & 0.71 & 0.69 \\ \hline \multirow{3}{*}{LIQ} & 2.00 & 1.97 & 98.39 & 2.14 & 2.17 \\  & 5.00 & 4.90 & 97.98 & 1.01 & 1.03 \\  & 10.00 & 10.00 & 100.05 & 0.62 & 0.62 \\ \hline \multirow{3}{*}{NAR} & 4.00 & 3.97 & 99.25 & 2.57 & 2.59 \\  & 10.00 & 9.72 & 97.18 & 1.08 & 1.11 \\  & 20.00 & 19.91 & 99.56 & 1.16 & 1.16 \\ \hline \multirow{3}{*}{HES} & 4.00 & 3.98 & 99.54 & 2.52 & 2.53 \\  & 10.00 & 10.09 & 100.94 & 1.94 & 1.92 \\  & 20.00 & 19.84 & 99.22 & 1.33 & 1.34 \\ \hline \multirow{3}{*}{RA} & 2.00 & 2.01 & 100.30 & 1.38 & 1.37 \\  & 5.00 & 4.93 & 98.66 & 1.12 & 1.13 \\  & 10.00 & 10.21 & 102.05 & 1.00 & 0.98 \\ \hline \multirow{3}{*}{GA} & 4.00 & 4.01 & 100.26 & 0.54 & 0.54 \\  & 10.00 & 9.80 & 98.01 & 1.62 & 1.65 \\ \cline{1-1}  & 20.00 & 20.65 & 103.26 & 1.11 & 1.07 \\ \hline \hline \end{tabular}  
* \(y\): peak area (mAU) of each analyte; \(x\): concentration (\(\upmu\)g/mL) of each analyte.  
\end{table} 
Table 2: Recovery test for the assay of the eight marker components in HPYJT (Haepyoyijin-tang). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{**Conc. (ug/mL)**} & \multicolumn{3}{c}{**Intraday (n = 5)**} & \multicolumn{3}{c}{**Interday (n = 5)**} & \multicolumn{3}{c}{**Repeatability (n = 6)**} \\ \cline{2-10}  & & \multicolumn{1}{c}{**Measured**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Measured**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**RSD (\%) of**} & \multicolumn{1}{c}{**RSD (\%) of**} \\  & & \multicolumn{1}{c}{**Conc. (ug/mL)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**Conc. (ug/mL)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**Retention Time**} & \multicolumn{1}{c}{**Peak Area**} \\ \hline \multirow{3}{*}{MULA} & 25.00 & 25.25 & 0.30 & 101.02 & 25.17 & 0.94 & 100.69 & 0.11 & 0.51 \\  & 50.00 & 50.79 & 0.75 & 101.57 & 51
## 1 Introduction
One way of making the feedback very low or almost zero is to utilize the car's position as a feature to foresee the acquired power of the different beams. Messages about safety in dedicated short-range communications (DSRC), or the corresponding functions in cellular systems, can provide us with vehicle-location information. First, different regression models are applied to the strongest beam powers. By using different degrees of situational-awareness results to compare to the results that were obtained by regression, the results show that, as the degree of situational awareness deepens, the accuracy of the prediction will also improve, and full situational awareness can obtain the most accurate prediction. With the help of specific datasets, we investigated the influence of the different quantization parameters of the channel-quality index (CQI) on the results. Studies have shown that dataset statistics determine the optimal parameters, and CQI quantization has little effect on the performance under the premise that the resolution can be guaranteed. Finally, the degree to which the power performance of all pairs of beams is affected by multiple regression was also evaluated. The results show that power-prediction-based beam selection can achieve higher throughput than classification-based beam selection.
## 2 Categories of Robotic Teleoperation  
### Collocated and Separated Teleoperation  
Based on the relative locations of the human and robot, robot teleoperation can be categorized into collocated and separated teleoperation. In collocated teleoperation [9; 10], the operator can directly observe the robot and its working environment in the same space, and a visual feedback system is not necessary; augmented reality technology can be used in collocated teleoperation to provide users with informative virtual content superimposed on the user-robot shared space [11]. Collocated robot teleoperation presents users with a robotic platform that shares a physical environment with the users, who have a natural and clear 3D view of the robot's workspace [12]. However, although humans and robots physically share the same space, users can often only observe from a third viewpoint alongside the robot and cannot inspect the robot's environment from the robot's main viewpoint.  
Spatially separated teleoperation is used in a wider range of scenarios where the user and the robotic system are far apart or where the user must be separated from the robot for safety reasons and cannot directly view the robot's movements. This type of robot teleoperation system typically requires visual and haptic feedback systems to show the user how the robot is working at a distance, allowing the operator to make timely adjustments. However, in the separated telemanipulation, users can typically only perceive the robot space through 2D camera streams and have difficulty synthesizing the 2D information collected by the robot with contextual knowledge of robot movements in its working space [13].
## 4 Simulation
### Low-Pass Filter  
From Figure 17, we can see that there are many harmonic components, which do not meet our design requirements. The signals that we want to obtain are zero medium frequency signals. Hence, we need to process the mixed signal effectively, which implies using a low-pass filter. The low-pass filter in this paper is implemented through an integration and accumulation manner. The main function of the low-pass filter is to filter high frequency components. The simulation result of the low-pass filter for the mixing output of Figure 16 is shown in Figure 18.
## 3 Materials and Methods
### HS-SPME Procedure  
The headspace solid phase microextraction (HS-SPME) was performed with a 23 GA Stablefex(tm) 2 cm-long 50/30 \(\upmu\)m DVB/CAR/PDS fiber from Supelco, attached in a manual SPME fiber holder (57330-U, Supelco, Germany). For the HS-SPME procedure, glass vials (15 mL) closed with polytetrafluoroethylene (PTFE) coated silicone rubber septum were used. Prior to analysis, the fibers were preconditioned in the injector port of the GC System according to the manufacturer's instructions. Under the optimum conditions, an aliquot of 2 mL of the nut-based beverage was transferred into the glass vial and 100 \(\upmu\)L of internal standard solution was added to the vial (final IS concentration 0.5 \(\upmu\)g g\({}^{-1}\)). The samples were heated at 60 \({}^{\circ}\)C and extraction was achieved in 40 min under agitation without any addition of salt. By the time that the incubation temperature was obtained, the fiber was immersed in the vial and one-stage simultaneous extraction and incubation was performed. Every sample was analyzed in triplicate.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 3 System Model  
In this paper, we consider a data transmission from a base station to a user. When a user request is sent to the base station, the base station shall send an amount of data to the user, supporting a target experience rate within a deadline, i.e., meeting the user-perceived capacity. Specifically, a target rate illustrates how fast the user reads the transmitted data at the receiver. Due to the randomness of the wireless channel, a careful channel resource arrangement shall be acquired to avoid any interruption for reading the data.
# Machine Learning-Based Malicious X.509 Certificates' Detection
## 3 Verification for Extraction (VFE)
### Certificate Chain Validation  
The certificate chain validation part is implemented for examining some fields for related certificates in the certificate chain. For instance, certificate policy mapping, path length constriction, name district, etc. Any implementation of certificate chain validation is required to be conforming to RFC 5280. Considering the time cost of implementing a tool  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Value Type** \\ \hline serial\_number\_not\_conforming & Whether serial number is positive and no longer than 20 octets & integer \\ after\_smaller\_than\_before & Whether after-time is small than before-time in validation & integer \\ extension\_exist\_in\_wrong\_version & Whether extensions are math with certificate version & integer \\ decipher\_and\_encipher\_error & Whether the appearance of decipher and encipher is conforming & integer \\ explicit\_text\_exceed & Whether the length of explicit text exceeds 200 & integer \\ only\_consist\_reasons\_error & Whether cRLDistributionPoint only consist reasons & integer \\ keycertsign\_not\_conform\_ca & Whether the set of keycertsign is conforming with ca field & integer \\ cA\_with\_empty\_subject & Whether subject information is empty and the subject is ca & integer \\ CRLissuer\_with\_empty\_subject & Whether subject information is empty and the subject is CRLissuer & integer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of checking items.  
for verifying the certificate chain, we accomplish the certificate chain validation part with the help of OpenSSL. Many trusted certificates and Certificate Revocation Lists (CRLs) are required to be added into the validation context during the validation process. It is worth mentioning that we validate the certificate chain of each certificate in the certificate chain to find more possible errors. The detail of certificate chain validation is recognized by checking results of OpenSSL. The details of validation values are displayed on the official website of OpenSSL (OpenSSL verify: [[https://www.openssl.org/docs/man1.0.2/man1/verify.html](https://www.openssl.org/docs/man1.0.2/man1/verify.html)]([https://www.openssl.org/docs/man1.0.2/man1/verify.html](https://www.openssl.org/docs/man1.0.2/man1/verify.html))).  
We record the necessary information for extracting as many as possible features during the processing flow of four parts. With the help of the VFE, comprehensive and useful traits of certificates are collected.
# Vibration Responses of a Coaxial Dual-Rotor System with Supporting Misalignment
## 3 Simulation and Discussions
### Effects of Bearing Damping  
In previous studies [26], it has been found that coupling misalignment can excite several backward and forward natural frequencies of a dual-rotor system when the bearing damping is zero. In the present analysis, the bearing damping is set to 200 Ns/m, and the backward and forward natural frequencies are not excited, which are described in Sections 3.1 and 3.2. In order to verify whether this problem is caused by the bearing damping factor, the following comparative research is performed. All parameters remain unchanged except that Bearing 1 is elevated for an intentional misalignment. The damping of all bearings is changed from 200 to 0 Ns/m. The frequency spectra of Bearings 1, 3, and 4 are shown in Figures 13 and 14. By comparing the two figures with Figures 7-9, the following can be stated:  
(1) From the observations of the frequency spectra, it can be seen that the bearing damping suppresses the natural frequency of forward and back precession. The spectra of Bearings 1 and 3 show the notable first-order backward natural frequency (BW1) and the second-order forward natural frequency (FW2) when the damping is zero. Meanwhile, the spectrum of Bearing 4 shows the frequency components of BW1, FW1, and BW2. These natural frequencies all disappear when the damping is increased to 200 Ns/m, only the frequency components such as 1\(\times\), 1.6\(\times\), and 2\(\times\) are left. The bearing damping does not change the relative magnitude of 1\(\times\), 1.6\(\times\), and 2\(\times\), but only suppresses the natural frequencies.  
(2) Regarding the orbit, when the damping is zero, the orbit becomes a complicated \"reticulated structure\" shape instead of a simple 8-shaped orbit. In the research, it is found that the shape of the orbit is affected by many factors, such as the damping of the bearing, the rotational speeds of the inner and the outer rotor, the unbalanced mass of the inner and the outer rotor, the amount of misalignment, and the position of other misalignments. Therefore, it is inaccurate to judge the misalignment error only by the shape of the orbit,  
Figure 11: The spectrum and the orbit of Bearing 3 (misalignment at Bearing 3).  
Figure 12: The spectrum and the orbit of Bearing 4 (misalignment at Bearing 3).  
especially for a dual-rotor system. It will be interesting to explore the changing law of the orbit of the dual-rotor system in the future.
# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 6 Conclusions  
This paper presented CrowdPower, a novel and comprehensive software platform that leverages service computing, smartphone sensors, and mobile application for crowdsensing and reporting real-time incidents in smart cities. A comprehensive description of the end-to-end system with architecture and design highlighted important message sequences and web service interfaces. The implementation was described using the user interface on both the server and client sides. We described our models to benchmark sensor quality, select participants for sensing campaigns, measure the quality of data submitted by sensors, and determine the reputation of participants in sensing campaigns. An approach to filter outliers was also proposed. The empirical evaluations were presented to test the feasibility of our proposed models. The models with equations, algorithms, and Python code of the simulation are provided. The results verified that the model was ideal for determining data quality and measuring participant reputation and intent. The complete CrowdPower system was also successfully tested for functionality.  
Currently, CrowdPower allows for data collection from Android-based devices. In the future, we are planning to extend the design to accommodate other operating systems such as iOS. The architecture of CrowdPower, designed based on a component-based pattern, allows for integrating devices belonging to other operating systems. Furthermore, the platform was tested on text and image-based data collected by producers. However, the current system does not support video-based data. In the future, we will investigate ways to assess video data quality. We will possibly integrate machine learning algorithms that can analyze video content. As future work, we also plan to assess security vulnerabilities of CrowdPower as producers share data using their personal mobile devices. Furthermore, we plan to conduct field testing of our solution in large-scale events (e.g., national sports  
Figure 26: Classification of data to filter outliers.  
events, adverse weather condition events, and road accidents) to assess the performance of our system in realistic settings.  
Conceptualization, S.S.M. and M.E.B.; methodology, S.S.M. and M.E.B.; software: S.S.M. and M.E.B.; validation: S.S.M.; formal analysis, S.S.M. and M.E.B.; investigation: S.S.M., M.E.B. and M.A.K.; data curation, S.S.M., M.E.B. and M.A.K.; writing, S.S.M., M.E.B. and M.A.K.; original draft, S.S.M.; writing--review and editing, S.S.M., M.E.B. and M.A.K.; supervision, S.S.M. and M.E.B.; project administration, S.S.M.; visualization, M.A.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Zayed University, UAE, under grant number R17069.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# Docker Performance Evaluation across Operating Systems
## 5 Conclusions and Future Work
### Linux Verdict  
Despite the preference for macOS as the most versatile host system for Docker containers, it might not always be the optimal choice. In instances where a program rarely utilizes write-to-disk functionality, Linux may prove as a more efficient solution. In-memory databases, data streaming platforms, database cache, static websites, FTP read-only servers are among the systems that infrequently require write operation and can benefit from being run in a Docker container on a Linux host.
## 3 Results
### Thermal Properties
#### 3.4.2 Specific Heat Capacity  
Specific heat capacity (\(c_{p}\) in J/kgK) is calculated as \(c_{p}\ =\ \frac{c}{\rho_{dy}}\), where \(c\) is volumetric heat capacity in J/m\({}^{3}\)K and \(\rho_{dy}\) is concrete dry density in kg/m\({}^{3}\). Further to the data in Table 9, the use of GRC raised \(c_{p}\) by 1.4% in N10/0 and 4.1% in N25/0 relative to NAC and by 1.8% in R10/50 and 4.8% in R25/50 relative to R0/50, an indication that the new mixes would be less affected by abrupt temperature changes than NAC [60]. Figure 6b plots the direct linear relationship (R\({}^{2}\geq\) 0.950) between \(c_{p}\) and GRC in NA and MRA mixes and shows that the use, in addition, of 50% MRA (MRA effect) raised \(c_{p}\) by a mean 4.5% in the MRA mixes relative to the NA mixes, a value similar to the mean 5.3% decline observed for \(\lambda\).  
The joint use of GRC and MRA induced a rise in \(c_{p}\) of 6.0% in R10/50 and 9.1% in R25/50 relative to the reference NAC. Those increases with the use of GRC and MRA infer greater thermal inertia in the new recycled material mixes than in the reference concrete and, consequently, greater thermal stability when exposed to changing outdoor temperatures.  
All the \(c_{p}\) values lay within the normal range (0.790 kJ/m\({}^{3}\cdot\)K to 0.960 kJ/m\({}^{3}\cdot\)K) for concrete used in residential building construction [13].
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.7 Named Entity Recognition (NER)  
In the NER NLP task, we measure the model's ability to identify and categorize named entities within unstructured text such as people's names, locations, dates, and times. For the NER task, we employed six datasets that contain only MSA text. Descriptions of the employed four datasets are provided below:  
* Wikipedia-based Fine-grained Arabic Named Entity (NewsFANEGold): NewsFANEGold is a fine-grained Arabic NER corpora that was manually annotated from the Arabic Newswire [75]. It contains 170 K tokens categorized into 88 classes.
* Wikipedia-based Fine-grained Arabic Named Entity Corpus (WikiFANEGold): WikiFANEGold is a fine-grained Arabic NER corpora that was manually annotated from the Arabic Wikipedia [75]. It contains about 500 K tokens that are annotated manually into 102 classes.
* Wikipedia-derived corpus (WDC): WDC is a dataset specifically designed for training models to identify named entities in Arabic text [76]. In the WDC corpus, only sentences with at least three annotated named entity tokens were included. It consists of 165,119 sentences consisting of around 6 M tokens categorized into 9 classes.
* WikiAnn: WikiAnn is a dataset collected from Wikipedia articles in 282 Languages. It was designed for the task of crosslingual naming and linking [77]. The Arabic subset contains 40 K sentences, while the tokens are categorized into 7 classes.
* American and Qatari Modeling of Arabic (AQMAR): AQMAR dataset was collected from Wikipedia for Arabic NER task [78]. It consists of 3000 sentences in four domains: history, science, sports, and technology. The tokens are categorized into 26 classes.  
* Arabic NER corpora (ANERcorp): ANERcorp is a dataset designed to be used in Arabic NER tasks [79]. The data were collected from news websites and other web sources [80]. It consists of 316 articles and 150,286 tokens. Each token was annotated based on four categories: person, location, organization, and miscellaneous or other.
## 2 Research Data and Methods
### Theoretical Backgrounds and Model Development
#### 2.2.4 Hybridization (PSO-ANN, PSO-ELM)  
[PERSON] et al. [38] showed a theory-based proof of the ability of ELM to perform as a universal approximator and can use several activation functions. ELM is extensively applied in the prediction task due to its fast learning capability, and adequate generalization performance [39; 40]. The combination of ELM with other techniques can enhance the generalization ability of ELM [41; 42; 43]. Some researchers have successfully used nature-inspired algorithms to optimize ELM. [PERSON] et al. [44] developed hybrid combination of cuckoo search and ELM to classify medical data. [PERSON] et al. [45] utilized a firefly algorithm to optimize ELM and to be applied in the stability analysis of photovoltaic interactive microgrid. The whale optimization algorithm was used to optimize ELM and was utilized for aging degree evaluation of insulated gate bipolar transistor [46]. The experimental results showed that optimized ELM gave good prediction accuracy compared to singleton ELM.  
Generally, due to the stochastic initialization of the network input weights and hidden biases in the basic ELM, ELM solution models can easily fall into local minima [36]. Therefore, this research paper utilized PSO to optimize the parameter set (input weights and hidden biases) of ELM to achieve better learning ability of ELM. According to literature studies, PSO with a combination of ELM models has been considered and developed in many areas with high reliability [47; 48; 49]; however, they have still not been considered and prepared for predicting \(\mathrm{M_{r}}\) values. The development of the hybrid PSO-ELM was used to design a prediction model for \(\mathrm{M_{r}}\), which was compared with the hybrid PSO-ANN [23] and KELM [22] to assess the performance of the designed model. Algorithm 2 describes the PSO-ELM model process. In PSO-ANN, all the parameters of the single hidden layer of ANN (input weights, hidden biases, hidden-output weights, and output neuron bias) were tuned using PSO.  
Figure 2: Single hidden layer feed-forward neural network called ELM.
# A Review of Blast Loading in the Urban Environment
## 3 Numerical Methods
### Mm-Ale Solver  
[PERSON] et al. (2017) present a blast pressure prediction model that utilises the Multi-Material Arbitrary Lagrange-Eulerian solver (MM-ALE) in LS-DYNA. Typically, this approach is restricted to the analysis of near-field scenarios, as the MM-ALE solver requires a rapidly increasing number of elements for larger configurations. To enable the testing of larger configurations, 1D to 2D to 3D mapping was used to reduce the computational cost considerably. This approach does not compromise the results compared to those obtained using the same mesh sizing without mapping. Furthermore, it can allow for greater accuracy in the prediction of peak pressures via the use of denser mesh sizing.  
Experimentally obtained results ([PERSON] et al., 2018) were compared to the numerical results from the MM-ALE analysis, see Table 2. It is seen that both sets of results are comparatively similar. A few data pairings contain some variations from each other, but these were somewhat overlooked. By using mean values, the approach was validated and _\"shown to achieve accuracy\"_; however, it is only computationally feasible for symmetric situations. [PERSON] et al. (2017) went on to develop a fast-running blast model through the use of regression analysis to rapidly estimate peak overpressures acting on buildings following the detonation of explosive devices in typical urban street configurations (dead-end and cross-road), taking inputs such as street width, explosive mass, and location. This approach was validated using the simulation results found earlier in the study.