esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Corrosion Effect on Yield Strength of PC-Bar  
Figure 15 depicts the stress-strain relationship for PC-bars. The rust posed the volumetric strain expansion to the PC-bar; therefore, stress concentration occurred on the PC-bar close to the rust when the tensile load was applied. As a result, the yield strength of the PC-bar decreased with time. After 75 years, the corrosion attacks had reduced the yield strength to 91.64% of its initial value.  
The following equation was proposed to predict the yield strength of the corroded PC-bar:  
\[f_{y,corr}=(1-0.4448\psi)f_{y,0} \tag{19}\]  
where \(f_{y,corr}\) is the yield strength of a corroded PC-bar; \(f_{y,0}\) is the initial PC-bar yield strength; and \(\psi\) is the corrosion degree.  
The yield strength reduction ratio was defined as the ratio of the yield strength of the corroded PC-bar to the initial yield strength. The relationship between the corrosion degree and the yield strength reduction ratio is shown in Figure 16. Additionally, Figure 16 shows the yield strength reduction ratio predicted by [PERSON] et al. [9], which is expressed as follows:  
\[f_{y,corr}=(1-0.005\psi)f_{y,0} \tag{20}\]  
where \(f_{y,corr}\) is the yield strength of a corroded rebar; \(f_{y,0}\) is the initial yield strength of a rebar; and \(\psi\) is the corrosion degree in %.  
The yield strength reduction ratio decreased linearly against the corrosion degree, which was in line with the result by [PERSON] et al. The high degree of fit of Equation (19) to the FEA results was indicated by a determination coefficient (\(R^{2}\)) value of 0.987.  
Figure 14: Relationship between corrosion degree and bond strength reduction ratio.
# Measurement and Model-Based Control of Solidification in Continuous Casting of Steel Billets
## 3 Results
### Dynamic Control of Crater End Position
* With the first increase in casting speed \(\mathrm{\Delta v_{c}}\), the crater end position increases by 1.6 m, and with the last increase by 1.8 m. * Thus, the increase in the crater end position by 0.8-0.9 m per increase of 0.1 m/min in casting speed is more homogenous than observed with decreasing spray water flow rates. All in all, the found decreases in surface temperatures and crater end position with incrementally increasing spray water flows as well as decreasing casting speeds have been  
Figure 13: Surface temperature and shell thickness for the middle of the strand top side simulated with DynSolidCC for billet casting at ESF with stationary reference casting conditions and variation of casting speed between \(\mathrm{v_{c}}^{\mathrm{min}}\) and \(\mathrm{v_{c}}^{\mathrm{max}}\). identified as an appropriate basis for a dynamic cooling strategy via the secondary cooling loops. 3. Variation of superheat of steel poured into the mould  
Finally, in a third parameter study, the initial temperature of the steel poured into the mould was varied between \(\mathrm{T_{0}}^{\mathrm{min}}\) and \(\mathrm{T_{0}}^{\mathrm{max}}\), which is between \(\mathrm{\Delta T}^{\mathrm{min}}\) and \(\mathrm{\Delta T}^{\mathrm{max}}\) above the liquidus temperature of the investigated steel grade. The other casting conditions were kept constant according to the defined reference case. Figure 14 shows the surface temperature and shell thickness for the middle of the strand top side resulting from the model simulations for the different initial steel temperatures. * At the end of the last spray water cooling loop, the mid-surface temperatures increase with each increase of 5 K regarding the initial steel temperature by about 3.4 K.
* The maximum difference in surface temperatures between the cases with initial steel temperature of \(\mathrm{T_{0}}^{\mathrm{min}}\) and \(\mathrm{T_{0}}^{\mathrm{max}}\) at the end of the last cooling loop is about 17 K;
* It decreases to about 13 K at a length of 23 m, where the cutting torch is located. * With each increase of 5 K regarding the initial steel temperature, the crater end position moves by about 0.2 m towards the cutting torch. * By increasing the initial steel temperature from \(\mathrm{T_{0}}^{\mathrm{min}}\) to \(\mathrm{T_{0}}^{\mathrm{max}}\), the crater end position moves from 18.0 m to 19.0 m.  
Thus, the initial superheat of the steel poured into the mould has only a minor influence on the temperature field and solidification behaviour in the strand, compared to the higher  
Figure 14: Surface temperature and shell thickness for the middle of the strand top side simulated with DynSolidCC for billet casting at ESF with stationary reference casting conditions and variation of initial steel temperature between \(\mathrm{T_{0}}^{\mathrm{min}}\) and \(\mathrm{T_{0}}^{\mathrm{max}}\). influence of the spray water flows in secondary cooling and the even stronger influence of the casting speed.
## 5 Research Challenges
Another issue highlighted in the article is the reasoning or querying of data. The sheer amount of data is huge, according to [PERSON] et al. Linked open data had created more than 50 billion RDFs as of 2015 [15]. Furthermore, some of those statements were incorrect or noisy, for example, when preamble information was omitted or when an instance was declared with disjointed classes [15]. As touched upon briefly in the fourth section of this article, there are also data quality concerns about semantic web applications. Trust is one of the key aspects of semantic web architecture because it helps take decisions over certain tasks [15]. A measure of trust degree is required, therefore, when taking data into account, such as provenance, reputation, or information quality [15]. But these features of trustworthiness must first be specified and secondly their definition must be ubiquitous. Herein lies part of the problem as highlighted by [PERSON] et al. since quality is subjective and it depends on human-driven criteria to judge whether the information is suitable or not [15]. All of these annotations must also be added to the data, and adding them manually would be impossible, so other machine learning tools are leveraged in order to simplify the process [15]. However, it is important to note that these annotations vary from implementation to implementation. While an attempt was made to define eighteen quality dimensions and metrics, only four were commonly used in the more than 1 billion triples analyzed by the authors [15]. The article by [PERSON] et al. does a very good job in highlighting many of the technical challenges of working with large sets of unregulated data, but there is one important aspect that is not addressed: security and privacy in the semantic web. Security issues have become increasingly prevalent in software development, with a cumulative total of nearly 230,000 vulnerabilities known between 1988 and 2022, according to IBM's IBM Security X-Force Threat Intelligence Index 2023 report [16]. Phishing is still the most prevalent technique in gaining unauthorized access to systems, and data theft is one of the most likely outcomes of a breach, accounting for 17% of total cybersecurity incidents in Europe according to the same report [16]. This represents a particular problem for semantic web applications as the interfacing capabilities of ontologies and RDFs can lead to privacy-leakage from one application to another, therefore exposing far more data in the case of a breach, than the regular web. Furthermore, there is the issue of compliance with regulations, most notably GDPR (General Data Protection Regulation), or, in the case of countries outside of Europe, HIPAA (Health Insurance Portability and Accountability Act). Finally, it is important to highlight that semantic web architecture must account for data privacy through more than just regular web means, as it has to minimize the amount of personal data exposed on several levels as well as ensure encryption of various layers and between various connections so that one successful attack does not expose the entire system and all of its dependents.
## 4 Results
### Description of the Case Study  
In the eHealth solutions field, there are plenty of specific sub-fields for applications; nonetheless, one of the most interesting is related to hepatic diseases, meaning those diseases connected to liver's failure. Actual statistics demonstrate that individuals increased their alcohol consumption in the years 2020 and 2021, worldwide [33; 34], which is generating several diseases linked to the liver [35; 36].  
We are going to test our approach on a dataset that defines people of different ages and genders to obtain a \"big picture\" and a \"fine view\" depending on the data used. Our data set includes 347 individuals with respect to five indicators of liver status. Four of them are enzymes and one is the Mean Corpuscular Volume (MCV). These variables are represented in Table 2 with their normal range.  
_SGOT enzyme_: is an enzyme delivered when there is a tissue damage. Its concentration increases drastically in cases of wound or damage. A magnitude in 50 units/L for men or 45 for women indicates damage.  
_SGPT enzyme_: is an enzyme that is produced in several organs, but its major concentration is found in the liver. The higher the concentration of this enzyme, the greater the tendency for liver damage.  
_ALKPHOS enzyme_: its function is to catalyze the hydrolysis of the phosphate in the serum at alkaline pH. ALKPHOS is a specific variable for the liver.  
_Gammagt enzyme_: measurements above 30 IU/L are directly connected to liver damage.  
_MCV_: it is a measurement of the volume of the cell (measured in femtoliters, fL). Above the 90 fL level is considered macrocytic anemia.  
To prove the capabilities of the 3 rd task in the CTM AC, we consider only the ALKPHOS and MCV, for several reasons, including that the selection criteria to identify liver damage is when the enzyme concentration (any of them) is abnormally high, which can be determined using ALKPHOS (chemical compound) and MCV (geometric feature). They represent two different things measured in the same system, giving heterogeneity to our approach, as well as handling less information and discarding possible sources of errors.  
Based on the design of the ARMNANO, a set of NSs is locally injected into the patient's liver to perform the measurements. The NS nodes will track the two variables previously  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Normal Level** \\ \hline
1. Mean Corpuscular Volume (MCV) & The normal range is between 80–100 fL \\
2. Alkaline phosphotase (alkphos) & The normal range is between 44 and 147 IU/L \\
3. Alanine aminotransferase (sgpt) & The normal range is between 7 to 56 units/L in serum. \\
4. Agarate aminotransferase (sgpt) & Normal range is between 8 to 45 units/L in the serum. \\
5. Gamma-glutamyl transpeptidase (gammagt) & The normal range in adults is between 0 and 30 IU/L \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured variables in the dataset to define the liver status.  
defined. The collected data concerning the context follow the characteristics required by the CTM AC regarding the format (units, etc.), etc.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 3 Components of Co-Simulation Tool
### Simulator System--Software  
#### 3.1.1 Driving Simulation Tool--CARLA  
CARLA is an open-source driving simulation platform developed by the Computer Vision Center at the Autonomous University of Barcelona [34]. It is designed to provide a highly customizable and accessible environment for autonomous driving research as well as human-driven vehicle simulations. Built on Unreal Engine 4, CARLA offers advanced 3D rendering capabilities and a comprehensive library of assets, including diverse road types, buildings, traffic signs, and terrain options. This flexibility allows researchers to create and modify virtual environments that accurately replicate real-world traffic conditions.  
The CARLA simulation system operates using a client-server architecture. The server manages all aspects of the simulation, including rendering the environment, computing vehicle and pedestrian movements, and managing interactions between agents. The client controls the simulation execution, making it possible to modify parameters in real-time. CARLA provides an API, available in both Python and C++, enabling users to interact with the simulation and control vehicles, pedestrians, and environmental settings.  
CARLA can be installed using two different methods. The first is a pre-packaged version, which comes with predefined maps and configurations, suitable for applications that do not require custom-built environments. The second option involves building CARLA from the source. This approach allows for custom map creation using external tools such as RoadRunner, a 3D road network editor available through MATLAB's academic licensing [35]. RoadRunner generates two key files needed for CARLA: an FBX file containing the 3D model of the environment and an OpenDRIVE file that encodes road network information, including lane configurations, intersections, traffic signals, and control mechanisms. The OpenDRIVE format ensures compatibility with PTV Vissim, allowing traffic simulation models to be seamlessly transferred between platforms. Also, it is possible to utilize the library of CARLA's blueprint library using Unreal Editor for placing 3D objects.  
Traffic modeling in CARLA can be handled using two different methods. The first option involves CARLA's built-in Traffic Manager, which is responsible for controlling vehicle movements, path planning, and traffic signal states. However, this module offers only basic functionality and lacks the advanced traffic control options necessary for realistic urban simulation. The second option involves customizing a buildable version of CARLA to integrate with external traffic simulation tools such as PTV Vissim and SUMO. This setup enables more sophisticated co-simulation features, ensuring that traffic conditions in CARLA accurately reflect real-world driving behaviors and infrastructure constraints.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 5 Results and Discussion
### Successful Packet Transmission (SPT)  
SPT defines the ratio among the total number of messages reached at the receiver node at the final stage effectively (\(M_{\textit{reched}}\)) and the total number of messages produced by the sender node at the initial stage (\(M_{\textit{produced}}\)). By the calculation of SPT, we can understand both the effectiveness of the data transmission as well as the number of messages lost during the process of communication in the network. The term SPT is mathematically derived as,  
\[SPT=\frac{M_{\textit{reched}}}{M_{\textit{produced}}} \tag{6}\]  
Figure 7 shows the comparison values of successful packet transmission with our MNUG-CLA protocol and other protocols. By analyzing the table values and graphs, our proposed MNUG-CLA can be understood.
## 4 Experimental Results and Discussion  
### Dataset and Experimental Setup  
The SQuAD Version 2.0 was used for verification of the proposed algorithm [41]. The dataset includes SQuAD 1.1, which is a QA paired machine reading comprehension (MRC) dataset generated through cloud sourcing of a more than 500 Wikipedia article dataset. Over 100,000 QA pairs comprise the dataset, enabling wide usage as a standard benchmark dataset in relevant research fields. Therefore, the context and QA randomly extracted from SQuAD was used as the input, the similarity of output questions among other generated questions and the input sentence are calculated, and the final question is selected through evaluating weight values. Experiments within this study used a high-capacity RAM-based TPU from Google Colab, which includes 40 CPUs with Intel(R) Xeon(R) 2.30 GHz specification, a memory of 35.25 GB, and Ubuntu 18.04.5 LTS operating system. Moreover, Python 3.7 version was utilized.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Evaluation Criteria for Machine Learning Methods Used in Detecting Anomalies in IoT  
The effectiveness of ML algorithms in detecting anomalies in IoT networks depends on several factors. There is no universally better algorithm, and experimenting with different approaches is often beneficial to identify the most appropriate one for specific use cases. When choosing an ML algorithm, consider the following factors:  
* Data characteristics [30];
* Computational resources [30];
* Accuracy and precision [31];
* False positive and false negative rates [31].
# Solvothermal Synthesis of Size-Controlled Monodispersed Superparamagnetic Iron Oxide Nanoparticles
## 1 Introduction  
Delivering a drug accurately and safely to its target site remains the yardstick for the design of drug delivery systems. Targeted drug delivery has attracted a significant amount of attention due to the unique advantage of precise control over a drug's release [1]. In particular, magnetic drug targeting is currently recognized as one of the most promising approaches to drug delivery [2; 3; 4]. A pharmaceutical drug is loaded onto the surface of the magnetic nanoparticles, which are released at the target site with an external localized magnetic field gradient, thereby achieving an accurate and targeted dose [5].  
During the process of delivering magnetic drug-loaded nanoparticles, the magnetic force that is exerted on the particles plays a crucial role and is strongly dependent on the size of the particle [6]. Furthermore, the magnetic force is proportional to \(d^{3}\), where \(d\) is the diameter of the particle [7]. In addition, to minimize the aggregation that is caused by the magnetic attractive forces, magnetic nanoparticles must have dependable superparamagnetic properties. As a result, superparamagnetic nanoparticles become magnetic in the presence of the external magnetic field, but revert to a nonmagnetic state that allows them to be excreted when the external magnetic field is removed [8]. Therefore, the size and superparamagnetism of magnetic nanoparticles are predominantly important to controlling the transport of a targeted drug.  
Superparamagnetic iron oxide (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles are frequently used as the drug carrier because of their low toxicity [9]. Among the reported synthetic methods, including ball-milling [10], co-precipitation [11; 12], thermal decomposition [13; 14], and microemulsion templating [15], the solvothermal method has been proven to be feasible for the synthesis of magnetic nanoparticles with a controllable morphology and microstructure [16; 17; 18; 19; 20]. A considerable amount of effort has been devoted to the size-controlled synthesis of magnetic nanoparticles, mainly focusing on changing the reaction time, temperature, surfactants, and the initial concentration of reagents [20; 21; 22; 23; 24; 25]. However, despite these advances, the synthesis of size-controllable magnetic nanoparticles based on changing the solvent deserves further research.  
In this study, size-tunable superparamagnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles were synthesized via a facile solvothermal method based on a binary solvent system with ethylene glycol (EG) and diethylene glycol (DEG). To obtain superparamagnetic nanoparticles with a high saturation magnetization, polyvinyl pyrrolidone (PVP) and sodium citrate (Na\({}_{3}\)Cit) were used together as structure guide agents. The Fe\({}_{3}\)O\({}_{4}\) nanoparticles have nearly monodispersed sizes that are tunable in the range of 60-800 nm by varying the volume ratio of EG/DEG. The reaction mechanism of the solvothermal system is discussed. These superparamagnetic ferrite nanoparticles may be candidate targeted drug carriers.
## 2 The FNA Method
### The Generation of Position Sampling Sequence  
In the flow of the FNA method shown in Figure 1, the machining trajectory predicting, the acceleration filtering, and the nominal acceleration solving, is based on discrete digital signals. Therefore, it is necessary to sample the continuous small line segments to generate a discrete position sampling sequence. The sampling process mainly consists of two steps: (1) Starting from the point \(O\), the small line segments are traversed in the backward/forward direction, respectively, until the traversed distance reaches the preset target value \(l_{\text{b}}\) and \(l_{\text{f}}\); and (2) the traversed region of the machining path is taken as the sampling interval, and \(N^{*}\) points are sampled along the small line segments in this interval, thus generating the position sampling sequence \(\mathbf{P}_{i}\):  
\[\mathbf{P}_{i}=\big{(}p_{x}(i),p_{y}(i),p_{z}(i)\big{)},i=0,\cdots,N^{*}-1 \tag{1}\]  
In Figure 1, the sampling interval of each part of the FNA method is a symmetric interval with point \(O\) as the midpoint for easy understanding, but, in fact, the sampling intervals of these parts are not the same and not all of them are symmetric, which depends on the specific needs of each part.
## 5 Conclusions  
A dynamic weighting selection system based on two modeling stages, W-MPM and MPM, applied to estimate the phase between signals was developed in this work. In this matter, an effective modeling stage was implemented in hardware to properly address highly nonlinear behavior. In this regard, the W-MPM required 55 and the MPM 67 coefficients to represent the device PA NXP 10 W at 2.34 GHz behavior. The adaptive modeling stage reaches for the NXP Semiconductor of 10 W GaN HEMT RF-PA an accuracy of \(-32.72\) dB NMSE using the MPM compared with \(-38.03\) dB NMSE for the W-MPM for the 10 W GaN HEMT RF-PA and of \(-44.9028\) dB NMSE based on the MPM and \(-44.349\) dB NMSE for the device ZHL-42W+ at 2000 MHz, respectively.  
The Halstead complexity analysis done establishes that the volume, complexity, and timing convergence to implement both models is \(11.85\) for the W-MPM and \(13.33\) s for the MPM, and the achieved time is relatively within the expected range due to the involved iterations in the whole developed design. It is also highlighted that the FPGA implementation required less than one percent of the available logical units; the correction in amplitude and phase based on the ILA scheme was properly performed. The EVM was estimated before and after the linearization algorithm and modeling stages based on MPM and was improved by \(13.07\%\) and \(15.48\%\) for the NXP 10 W at 2.34 GHz using the static MPM and dynamic W-MPM, respectively. In the case of the ZHL-42W+ at 2000 MHz 32.24 dBm, it was improved by \(24.1\%\) and \(24.22\%\) for the EVM in the same conditions before and after the proposed linearization algorithm.  
Conceptualization, [PERSON]-A., [PERSON]-R. and [PERSON]-V.; methodology, [PERSON]-A., C.V.-R., E.I.-G., [PERSON]-P. and J.R.C.-V.; software, [PERSON]-A., [PERSON]-R., [PERSON] and [PERSON]-V.; validation, [PERSON]-A. and [PERSON]-V.; formal analysis, [PERSON]-A., [PERSON]-P. and J.R.C.-V.; investigation, [PERSON]-A., [PERSON]-V. and [PERSON]-R.; resources, [PERSON]-R. and [PERSON]-P.; writing--original draft preparation, J.A.G.-A., [PERSON]-R. and [PERSON]-V.; writing--review and editing, [PERSON]-R. and [PERSON]-G.; supervision, [PERSON]-R.; funding acquisition, C.V.-R. All authors have read and agreed to the published version of the manuscript.  
The authors wish to thank TECNM for its support provided through the research projects 7926.20-P and 2021. Also, authors appreciate to [PERSON] that collaborate in a sabbatical year authorized by the Autonomous University of Baja California Academic Commission with official letter 21/2020.  
Not applicable.  
This work was supported in part by the SEP-CONACyT Research Project under grant 255387 and grant 256237, in part by the School of Engineering and Sciences, and in part by the Telecommunications Research Group at Tecnologico de Monterrey. In addition, the authors would like to express their gratitude to the IPN for its support by the project \"SIP-20210345. The authors would like to thank PRODEP for supporting the new generations and for innovating the application of knowledge.  
The authors declare no conflict of interest.