esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semantic and Knowledge-Based Approach for Handover Management
## 5 Conclusions and Future Work  
This paper introduced SIM-Know, an approach that performs context-aware, cognitive, and proactive handovers. SIM-Know includes SIM to provide context-awareness to handover decisions and KBP to incorporate cognition in HM. KBP distributes knowledge (local and global) to afford the proactivity capability in HM. The evaluation results showed that, thanks to the aforementioned SIM-Know capabilities, our approach overcomes SSF regarding the number of handovers and the number of throughput drops when the end-user device moves at any speed and, further, equals AHP-TOPSIS when it travels at low and moderate speeds. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. SIM-Know overcomes AHP-TOPSIS regarding all evaluated metrics when the end-user  
Figure 11: Impact on TCP Traffic.  
Figure 10: Impact on VoIP Traffic.  
device moves at a high speed, positively impacting the wireless network's performance in terms of the delay, throughput, packet loss and jitter metrics. Considering these results, we concluded that SIM-Know is an attractive and feasible solution for cognitive HM.  
For future work, we intend to enrich our approach with SDN and NFV capabilities to deal with the scalability issue imposed on HM by the Industrial IoT and the massive IoT 5G use case. We also plan to create an efficient model for communicating KBPs and evaluating SIM-Know when it makes handover decisions in scenarios with many end-user devices, high network traffic, and high load in APs.  
Conceptualization, F.Y.V., O.M.C. and J.C.N.; Investigation, F.Y.V., O.M.C. and J.C.N.; Methodology, F.Y.V., O.M.C. and J.C.N.; Writing--original draft, F.Y.V. and O.M.C.; Writing--review & editing, F.Y.V., O.M.C. and J.C.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the InnovAccion Cauca project of the Colombian Science, Technology, and Innovation Fund (SGR-CTI-ID-3848) throuho the PhD scholarship of the student [PERSON] (01-2016).  
Not applicable.  
The SIM-Know Project Repository. 2021. Available online: [[https://github.com/fyvivas/SIMKnow](https://github.com/fyvivas/SIMKnow)]([https://github.com/fyvivas/SIMKnow](https://github.com/fyvivas/SIMKnow)) (accessed on 11 June 2021).  
The authors would like to thank the Universidad del Cauca and the Umea University.  
The authors declare no conflict of interest. The financiers had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project 5G Fifth Generation 4 HP 5
## 4 Simulation Results
### Accuracy of Monitoring Environment  
Next, we focus on the accuracy of monitoring environment. Due to the prediction and representative mechanism, the possible difference between the forecasting/representative and concrete data readings comes from the reduced transmission. Therefore, in order to investigate 2 TC-cor accuracy, we predefine 4 check points to examine the difference. The check points are set at each 82 time intervals. The aim is to make the check points set at the \(\Delta\) period, seen in Figure 2.  
In order to collect the concrete data readings, 2 TC algorithm should be adaptively altered. It is worth noting that the representative mechanism should be ignored during the simulation of altered 2 TC-cor. That is to say, not only the representative node (_vch_) works but all of the cluster members in a virtual cluster have to work (_i.e._, sense the environment and report to _vch_). Besides, the prediction model should be ignored, too. Each _vch_ report continuously its data readings to its _pch_ instead of reporting only while \"violations\" appearing (reporting occasionally based on the prediction model).  
The difference between reported readings from virtual cluster member nodes (_i.e._, concrete data reading) and sensed readings of _vch_ (_i.e._, the representative node) is used to examine the 2 TC-cor accuracy. We denote the average difference (\(\bar{d}(t)\)) at _vch_ side as  
Figure 4: The time at which half of the node are dead.  
\[\bar{d}(t)=\frac{|VC|-\sum_{i}^{vc}\frac{|R(t)-R_{i}(t)|}{R(t)}}{|VC|} \tag{8}\]  
where \(R(t)\) is the sensed readings of \(vch\), \(R_{i}(t)\) is the concrete data reading from virtual cluster member node \(i\) and \(|VC|\) is the number of virtual cluster member nodes. Finally, the accuracy index of our algorithm is defined as:  
\[accuracy\ index=\sum_{x}^{N}\frac{\sum_{y}^{S}\bar{d}}{|N|} \tag{9}\]  
where \(x\) and \(y\) are \(pch\) and \(vch\), \(|N|\) and \(|S|\) are the number of physical and virtual cluster heads, respectively.  
Therefore, the accuracy index of our algorithm is obviously lower than LEACH because of the prediction and representative mechanism. As shown in Figure 5, the accuracy index is progressively increasing as the times goes by (or say nodes are dying). Naturally, nodes are gradually draining their energy. In order to accurately monitor the whole environment, more nodes are expected to work while more nodes are dead. In other words, as the working nodes are getting fewer, the accuracy index is getting higher due to the efficiency of representative mechanism is not as active.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{AWGN} \\ \hline  & Tree & Crowd & Cross & Duck & Park \\ \hline BCH-LDPC/QPSK & 96\% & 94.6\% & 93.8\% & 93.8\% & 92\% \\ \hline RS-CONV/QPSK & 84\% & 74.48\% & 77.09\% & 72.39\% & 69.16\% \\ \hline BCH-LDPC/16-QAM & 89\% & 85.4\% & 85.8\% & 84.2\% & 80\% \\ \hline RS-CONV/16-QAM & 75.93\% & 64.34\% & 67.92\% & 61.99\% & 56.6\% \\ \hline BCH-LDPC/64-QAM & 84\% & 75.4\% & 76\% & 73.8\% & 70.6\% \\ \hline RS-CONV/64-QAM & 67.74\% & 47.19\% & 54.36\% & 52.04\% & 48.6\% \\ \hline  & \multicolumn{4}{c}{Rayleigh} \\ \hline  & Tree & Crowd & Cross & Duck & Park \\ \hline BCH-LDPC/QPSK & 81.75\% & 73.15\% & 75.13\% & 72.49\% & 69.53\% \\ \hline RS-CONV/QPSK & 56.49\% & 37.12\% & 44.62\% & 40.6\% & 36.21\% \\ \hline BCH-LDPC/16-QAM & 78.38\% & 67.22\% & 68.61\% & 66.68\% & 63.8\% \\ \hline RS-CONV/16-QAM & 38.19\% & 22.15\% & 27.53\% & 21.8\% & 20.58\% \\ \hline BCH-LDPC/64-QAM & 72.93\% & 59.71\% & 63.14\% & 58.36\% & 56.42\% \\ \hline RS-CONV/64-QAM & 20.39\% & 12.65\% & 14.47\% & 13.6\% & 12.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Portion of the videos without loss. quality delivered to the end user. In contrast, metrics limited to the physical layer level, such as BER, do not consider important QoE parameters. In our future work, we plan to use the proposed methodology to assess other systems based on video quality, such as the techniques used by 5G, including FBMC and UFMC. These methods have been suggested as alternatives to the traditional OFDM transmission technique used in DVB-T2 systems, as mentioned in [10; 11].
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Bearing Estimation
#### 4.3.2 Effects of PCPA Tilt  
To analyze the angle estimation error caused by the tilt of the PCPA we conducted multiple measurements. In our experiments we utilized the setup and rotation pad depicted on Figures 6 and 7. The PCPA was assembled with multiple mounts, which provided tilt values of 0\({}^{\circ}\), 1\({}^{\circ}\), 3\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\).  
The bearing estimation errors can be seen in Figure 13, where the errors are plotted for both the LS and FD estimator. The errors were defined as the differences between the bearing estimates using the 1\({}^{\circ}\), 3\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\) tilted measurements, and the bearing estimates using the 0\({}^{\circ}\) tilt reference measurement.  
As Figure 13 shows, the angle errors due to the tilt of the PCPA are negligible, usually below 0.25\({}^{\circ}\) (except the 10\({}^{\circ}\) case), which is the same magnitude as the measurement error of the current system. Note, that reflective surfaces cause several times higher angle estimation inaccuracies.
## 3 Experimental Results and Analysis
### Performance Metrics
#### 3.2.3 Classification Measures  
The image forgery problem corresponds to the binary classification in which either the block is forged (True class) or authentic (False Class). Hence, the false positive rate _FP_, true positive rate _TP_, false negative rate _FN_, and true negative rate _TN_ are the main measures of forgery classification accuracy that are utilized in our experiments. The efficiency and robustness of the proposed approach can be quantified using the \(F1\) score, precision and recall, as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{19}\]  
\[Recall=\frac{TP}{TP+FN} \tag{20}\]  
\[F1\ Score=2\times\frac{Precision\ \times\ Recall}{Precision+Recall} \tag{21}\]
## 1 Introduction  
Since the first report on half-metal (HM) by [PERSON] et al. [1], various Heusler compounds have been verified by theoretical approaches and experiments to be HM materials. The HM Heusler alloys attract significant interest owing to their novel physical properties [2; 3; 4; 5; 6].  
Typically, Heusler-based HMs can be divided into three types: half-Heusler HMs [3], full-Heusler HMs [4], and quaternary Heusler HMs [5]. The quaternary Heusler alloys (XMYZ) can be regarded as a combination of two full-Heusler alloys: X\({}_{2}\)YZ and M\({}_{2}\)YZ (X, Y, M are individual 3\(d\) or 4\(d\) transition elements, while Z is an atom of the main group). Many quaternary Heusler HMs have been investigated by first-principle approaches. For example, [PERSON] et al. [6] have investigated a novel equiatomic quaternary Heusler (EQH) alloy YRhTiGe, concluding that it is an HM with a ferromagnetic ground state. Moreover, they studied its mechanical anisotropy, as well as dependence on the direction of shear modulus and Young's modulus in detail. New quaternary HMs, FeRuCrP and FeRhCrP [5], have been studied by [PERSON] et al. in 2017; strain has been introduced to investigate its effect on the HM states.  
In recent years, [PERSON] [7] has theoretically predicted a novel type of materials, referred to as spin-gapless semiconductors (SGSs). The SGSs can be categorized as new members of the zero-gapless material family. Following this study, many Heusler-based SGSs [8] were studied. Recently, [PERSON] et al. studied the LuCoCrGe EQH compound [9], revealing that it is a highly dispersive gapless HM under strain.  
Figure 1 illustrates a HM, SGS, and magnetic semiconductor (MS). Figure 1a shows that one of the channels (spin-down (minority) channel) is metallic, while the other one (spin-up (majority) channel)  
exhibits a semiconducting behavior. Figure 1b shows that there is a band gap for the spin-up channel between the two bands (conduction and valence bands). The behavior is different for the spin-down channel, exhibiting a zero-gap between these bands; this behavior corresponds to SGS. In Figure 1c, two semiconductor-type band gaps are observed in both channels, corresponding to MS properties.  
In this study, we employed first-principle calculations to investigate the crystal structure, electronic structure, and magnetic properties of a new rare-earth-containing EQH compound ScFeRhP. We demonstrate that this compound is HM at its equilibrium lattice constant. Rare physical transitions with an SGS feature can be observed at different strain magnitudes.
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 4 Results  
### Frequency Analysis  
The frequency analysis was performed from 0 to 5 kHz. The modal shapes of the first six predicted modes are presented in Figure 6. The mode shapes presented in Figure 6 are given in the form of the normalized relative displacement and provide a very important indication of how the structure behaves at a natural frequency. These results, the modal frequencies and the mode shapes, were very important, as a qualitative evaluation, in the preliminary setup design stages to decide on the optimal positioning of the actuators and how to operate them, e.g., which actuators should be excited in phase or out of phase and which should simply not be operated at all for each natural frequency of interest. For higher resonant modes, the anti-nodes are smaller than the actuator patches (Figure 7). The position of the first actuator patch is circled in red, showing that the patch overlaps two different anti-nodes. As observed in the previous study on the flat plate [17], an actuator overlapping two anti-nodes resulted in a suboptimal excitation of the resonant mode and when the structure was not optimally excited, the resulting vibration amplitude was weak, and the results of the numerical modeling were less accurate. Moreover, the power consumption increased with an increase in the frequency of excitation since power is linearly dependent on the frequency (see Equation (1)). For these reasons, only the first six resonant modes were studied to achieve the ice layer breaking in this part of the study (Table 2). Rotation did not affect the mode shapes, which was expected due to the very low rotation speed and high rigidity of the clamped leading edge structure, and this is the only vibrating part considered, as opposed to the complete blade.  
Figure 5: (**a**) Experimental ice accumulation in static (**b**) accumulated at 600 RPM rotation speed and (**c**) numerical model with ice layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mode** & **Numerical Frequency** \\
**(\#)** & **(Hz)** \\ \hline
1 & 559 \\
2 & 1136 \\
3 & 1828 \\
4 & 1865 \\
5 & 2038 \\
6 & 2149 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequencies of the blade resonant modes with the ice layer calculated with the numerical model.  
Figure 6: First six resonant modes of the blade structure: (**a**) mode 1 at 559 Hz; (**b**) mode 2 at 1136 Hz; (**c**) mode 3 at 1828 Hz; (**d**) mode 4 at 1865 Hz; (**e**) mode 5 at 2038 Hz; (**f**) mode 6 at 2149 Hz.
## 2 Mathematical Modelling of Ricochet
### Practical Application  
Normally, the size of the target and the impacting point are predefined, but the location of the bulletproof structure is unknown, although it plays a key role in the protection of key targets from damages caused by projectile ricocheting. The aforementioned analytical method may contribute to finalizing the exact location of the bulletproof wall when the ricochet range is certain. A linear bulletproof wall \(PG\) is simply considered in Figure 4, where \(B\) is the impacting point of the projectile ricocheting. The equation of \(PG\) can be expressed as:  
\[y=\tan\alpha\big{[}x-l^{\prime}\cdot\cos(\theta+\beta)-s_{1}\big{]} \tag{10}\]  
where \(\alpha\) is the angle between the \(PG\) and \(X\) axis, and \(s_{1}\) is the distance from the incidence point \(A\) to \(K\), which is the intersection of the \(PG\) and \(X\) axis. When Equations (9) and (10) are jointly computed, the relationship of \(t\), \(\alpha\) and \(s_{1}\) is derived as:  
\[\frac{\sin(\theta+\beta)-\sin(\theta+\beta-\omega_{2}t)}{\tan \alpha} =\cos(\theta+\beta-\omega_{2}t)-\cos(\theta+\beta)-\frac{s_{1}-v_{c }t}{l^{\prime}} \tag{11}\] \[t =f(\alpha,s_{1})>0 \tag{12}\]  
Figure 4: The diagram of a linear bulletproof structure.  
Figure 3: Diagram of the projectile trajectory in the ricochet process.  
The location of impacting point \(B\) can be obtained through trial calculations of \((\varpi,s_{1})\). The value of the inclination angle \(\varpi\) is limited by the site. When the bulletproof wall is set perpendicular to the target plate, \(\varpi\) reaches its maximum: \(\varpi=90-\beta\). During the trial calculations, \((\varpi,s_{1})\) needs to be adjusted until Equation (11) can be solved. When Equation (11) has only one solution, the intersection is the impacting point \(B(x_{B},y_{B})\). When Equation (11) has several solutions, the intersection matching the earliest moment is the impacting point \(B(x_{B},y_{B})\).  
Given that the incident angle may deviate from the designed value, \(B\) can be taken as the middle point of \(PG\) for security. When the intersection \(K\) is set as the middle point of \(BG\), the size of the bulletproof wall can be calculated conveniently, and its size can be adjusted by multiplying a safety coefficient, taking into account the varying diameters of the projectile and materials of the target plate. Concerning the value of the safety coefficient, it will be disclosed in the subsequent research.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Disease-Specific Research and Drug Discovery
#### 2.1.1 AlzGPS: A Platform for Alzheimer's Drug Discovery Using Multi-Omics  
By integrating genomics, transcriptomics, and proteomics data, AlzGPS focuses on Alzheimer's drug discovery [14]. Through its web-based interface, disease-associated gene-protein interactions and drug-target networks can be visualized, enabling researchers to map multi-omics data within the context of Alzheimer's disease (AD). The integration of genetic variants and gene expression data with protein interactions helps identify potential drug targets, utilizing network proximity analysis to uncover these connections.  
Figure 1: Tools and Data in Web Platforms for Multi-Omics Integration. This figure illustrates the integration of various omics data types—such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics—within web platforms designed for multi-omics re- search. These platforms utilize several computational tools to enable the comprehensive analysis of biological systems. Data Integration tools combine different omics layers, allowing platforms to present a unified view of biological processes. Statistical Analysis is used to evaluate relationships between datasets, identify patterns, and highlight significant changes in molecular expression across conditions. Visualization techniques, such as heatmaps and network diagrams, aid in exploring these relationships interactively. Additionally, Machine Learning tools are employed to predict disease outcomes, identify biomarkers, and support personalized medicine by leveraging large-scale omics data. These computational tools lead to Biological Insights, including a deeper Understanding of Biological Systems, Disease Prediction and Treatment Strategies, Biomarker Identification, and the development of Personalized Medicine Approaches. They also address challenges, including handling data heterogeneity, large dataset complexity, and the integration of multiple omics layers.  
Network proximity analysis reveals relationships between genes, proteins, and drug targets, offering insights into disease mechanisms and potential therapeutic interventions. With its emphasis on [PERSON]'s drug discovery, the platform serves as a specialized tool for AD researchers. By linking omics data with known drug compounds, drug repurposing efforts are supported, enhancing the efficiency of identifying therapeutic candidates. However, the platform's narrow focus on [PERSON]'s disease may reduce its utility for researchers working on other conditions. Additionally, the computational demands of network proximity analysis could limit accessibility for smaller research groups lacking the necessary resources.
## 3 Principals of Routing Protocols and Their Breakdowns  
In this section, we divide the multi-path routing propagation into three categories based on energy-based, data-based, and geographical information-based routing protocols. The total taxonomy of the underwater wireless sensor networks is illustrated in Figure 2.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref No.** & **Publisher** & **Year** & **Scope** & **No of Protocols** \\ \hline
[13] & MDPI & 2020 & Provided a holistic taxonomy of UWSN, about their requirements, recent advances and open challenges for UWSN. & Covers a limited number of protocols and based on their classification. & Covers a limited number of protocols and based on their classification. \\
[20] & IEEE & 2020 & State-of-the-art survey on Underwater Internet of Things (UIoT) in smart ocean. Focuses on the architecture and challenges of UIoT. & They did not propose or survey any particular routing protocols but rather gave an idea of how the future protocols should work \\
[21] & IEEE & 2021 & Classified the UWSN protocols into two categories: sender-based and receiver-based. Further categorized it into three types: energy-based, geographic information-based routing and hybrid routing. Focused on the application, deployment and routing protocols of UWSNs. The focus is mainly of deployment and geographical information-based routing protocols \\
[23] & Springer & 2016 & A new protocol. They also simulated all the protocols to compare their performance, lifetime and energy consumption. & Only covered the data forwarding routing protocols \\ Our Work & & The current study divides the protocols into three main categories: energy-based, geographical information-based, and data-based protocols. We further subdivide them into several subcategories, describe them, and compare them. The paper illustrates the probable application of UWSN and finally, puts forward open challenges. & Only covered the data forwarding routing protocols \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Taxonomy of Underwater Wireless Sensor Network.
# Innovative Approaches for Minimizing Disinfection
## 3 DBPs  
### DBPs Categories  
DBPs can be classified into two main groups based on their molecular composition: carbon-containing disinfection byproducts (C-DBPs) and nitrogen-containing disinfection byproducts (N-DBPs). C-DBPs, such as haloacetic acids (HAAs) and trihalomethanes (THMs), are the most detected DBPs in drinking water. On the other hand, N-DBPs, which include haloacetonitriles (HANs), halonitromethanes (HANMs), haloacetamides (HACAs), and nitrosamines (NAs), are generally more toxic than C-DBPs [1].  
Nitrogenous disinfection byproducts (N-DBPs) and iodinated disinfection byproducts (I-DBPs) are becoming increasingly concerning in water treatment. Among N-DBPs, halogenated types like HANs, HINMs, and HACAs have attracted attention due to reports indicating their higher toxicity compared to regulated DBPs. Iodinated disinfection byproducts (I-DBPs) are generally more cytotoxic and genotoxic than their chlorinated and brominated counterparts. I-THMs were first identified in drinking water in the 1980s and have since received more attention. Besides I-THMs, various other categories of I-DBPs have been found in drinking water, including iodinated acids, different polar I-DBPs, and iodinated N-DBPs, such as iodinated HAMs (I-HAMs) [7].  
Recently, heightened focus has been placed on water treatment processes, driven by growing public health and safety concerns. Various countries have implemented stringent regulations concerning both water quality and its sources. These regulations aim to guarantee drinking water's safety by eliminating hazardous substances or minimizing their concentrations to the lowest possible levels [6].