esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
By altering the cathode current, thin films of TiCrN were effectively deposited on WC substrates, allowing for subsequent tribology experiments to be conducted. The conclusions drawn from this work are as follows.  
1. The substrate current is shown to be linearly proportional to the chromium cathode current for a given coating time and Ti cathode current. The XRD patterns reveal that the phase constituents of the TiCrN thin films gradually change from cubic TiN to cubic CrN over the entire range of Cr current (80 to 175 A) in the coatings.
2. Thin films have much higher microhardness than the pure phases. Observations indicate that the high hardness is a result of the two-phase structure.
3. The findings of the wear test indicate that the TiCrN thin film with Cr/Ti ratio of 0.587 exhibits a higher wear resistance than thin films with Cr/Ti ratio of 0.428, 0.754, 0923, 1.083, and 2.571, as the wear rate of the counterpart (WC ball) is the highest (463 \(\upmu\)m\({}^{3}\)/Nm) of all coatings. The nitride thin film with Cr/Ti ratio of 0.587 exhibits maximum compressive residual stress.  
Conceptualization, S.M.; Validation, M.-S.O.; Formal analysis, S.M.; Investigation, S.M.; Resources, M.-S.O.; Data curation, M.-S.O.; Writing--original draft, S.M.; Writing--review & editing, S.M. and M.-S.O.; Supervision, M.-S.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Development Program (S3160560, 00221849) funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Figure 9: Comparison of compressive stress (\(\sigma_{\mathrm{x}}\) and \(\sigma_{\mathrm{y}}\)) developed in each sample.  
**Acknowledgments:** The authors thank ARCH Hyderabad, India for providing research facilities. The authors thank [PERSON] (IIT Bombay, India) for his support.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders 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.
# CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal
## 2 Related Work
### Labels Arrangements in Circles  
Forty-four articles published between 1999 and 2021 were collected to provide a comprehensive review of techniques for label arrangement using circular visual encoding. At a higher level, it is worth noting that the majority of papers discussed problem-driven rather than technique-driven approaches. Label arrangements in a circle can be broadly divided into three categories.  
The first category is **angular arrangement (A1)**, which is widely used in literature with different visual coding targets such as geographical information [13; 14], statistical information [15; 16; 17], and relationships [18; 16]. As an example, Topics [18] groups topics as small dots inside circles with labels outside the circle to explore relationships between labels and topics.  
**Path arrangement (A2)**[19; 20; 21; 22; 23] is the second proposed category. This arrangement is commonly used for small labels and long arc sizes, such as numbers and names. Emotion Cues [22], for example, is a visual analytical solution that analyzes students' emotions using classroom videos. It was created to help teachers and parents quickly determine student engagement in class. The circle view, which stores each child's feelings, was chosen from the four proposed views for the context of this paper. As a result, green represents surprise, yellow represents happiness, gray represents neutrality, magenta represents rage, blue represents sadness, purple represents disgust, and cyan represents fear.  
**Linear arrangement (A3)**[24] is the third proposed arrangement. Approaches using this arrangement [25; 26; 27; 28; 29] represent visualization of emotions, time, statistics [30], or topics. [PERSON] et al. [27], for example, proposed a circle view of negative feedback for exploratory search. As a result, labels were arranged in different circle layouts, from inner to outer circles. Keywords in the inner and outer rings on the same angle are similar. Their estimates of relevance represent the distance to the center, with high relevance in the center and linear decay to zero on the outer border. \(A3\) is commonly used inside circles to explain details or to present an overview of information.  
Table 1 summarizes the frequency with which the three label arrangements were used over time. We can see that between 1999 and 2013, A3 was the most commonly used in literature, whereas between 2014 and 2021, A1 and A2 were the most commonly used.
# Interplay Between Wear and Thermal Expansion in 6082 Aluminum: A Simulation and Experimental Study
## 3 Results and Discussion  
### Contact Area  
The contact area between friction pairs changes due to wear and temperature fluctuations. To assess the impact of thermal expansion and material softening, Figure 5 shows contact area variations with and without thermal effects. Significant differences in contact area are evident between scenarios with and without thermal effects. Thermal expansion is more pronounced at higher speeds and loads.  
Without thermal effects, the contact area between friction pairs gradually increases due to wear between the disc and pin [27]. As wear progresses, the pin penetrates further into the disc.  
In contrast, with thermal effects considered, the contact area is smaller and exhibits significant fluctuations. This results from dynamic contact behavior due to the combined  
Figure 4: (**a**) Wear experiment setup. (**b**) Wear scar profile measurement. (**c**) Temperature measurement using a thermal gun.  
effects of wear and thermal expansion. Under 15 N and 300 RPM, as the material absorbs frictional heat, thermal expansion becomes apparent (after 0.5 min), causing the interface to become more convex and reducing the actual contact area, as per Figure 6. In [10], reduced contact area is also observed and attributed to protrusions created by expansion, where their simulation used a flat pin surface sliding against a disc.  
Figure 5: Comparison of contact area with and without thermal effects at (**a**) 5 N and 100 RPM, (**b**) 10 N and 200 RPM, and (**c**) 15 N and 300 RPM (the results are from the FEM simulation).  
Figure 6: Protrusion at contact interface under 15 N and 300 RPM (the result is from the FEM simulation).  
This analysis shows that thermal expansion directly affects the contact area between the pin and disc, influencing other wear characteristics. Therefore, studies on the tribological behavior of the pin should consider the effects of thermal expansion.
# A Review of Medication Adherence Monitoring Technologies +
## 5 Challenges and Future Trends
### Challenges  
#### 5.1.1 System Accuracy and Data Fidelity  
Achieving better healthcare requires accurate systems that capture the user's activity. This also applies to medication adherence monitoring systems. In general, accuracy is determined by the device being used for capturing the medication taking activity. Furthermore, the setting of medication taking can affect and limit the technology advances in use. For example, the system might operate at low-sampling rates as a trade-off for energy consumption minimization. However, this comes at the cost of lower data quality. Accuracy includes data quality, data precision, or data fidelity [104; 146]  
Data fidelity can be characterized by the sampling frequency, the sensor operation mode (in case the systems supports multiple operation and power modes), and the duty cycling. Obtaining high accuracy data demands the system to be running at high-fidelity. However, high-fidelity systems deplete the battery energy at a fast rate (if it is battery-sourced), as their core should be set to run frequently for capturing the monitored event precisely. Thus, when engineering a medication tracking system, the energy consumption management should be considered carefully.
## 2 Literature Review
### Decision-Making Methods
#### 2.2.2 Evaluation Based on Distance to Average Solution  
Proposed by [PERSON] et al. in 2015 [PERSON] et al. (2015), it ranks alternatives based on the distance to the median solution, eliminating the need to calculate both the ideal and nadir solutions. Two measures are used to assess the desirability of alternatives: positive distance to the mean (PDA) and negative distance to the mean (NDA). These measures indicate the difference between each alternative and the mean solution. Higher PDA and lower NDA values mean the alternative is better than the median solution. Their application is summarized in the following steps:1. Construct the decision matrix \(X=\left[X_{ij}\right]_{nxm}\), \(X_{ij}\) is the value of the performance of alternative \(i\) in criterion \(j\).
2. Determine the average solution according to all criteria: \[AV_{J}=\frac{\sum_{i=1}^{n}X_{ij}}{n}\] (9)
3. Calculate the distance matrices according to the type of benefit or cost criterion: Benefit: \[PDA_{ij}=\frac{\max(0,\left(X_{ij}-AV_{j}\right))}{AV_{j}}\] (10) \[NDA_{ij}=\frac{\max(0,\left(AV_{j}-X_{ij}\right))}{AV_{j}}\] (11) Cost: \[PDA_{ij}=\frac{\max(0,\left(AV_{j}-X_{ij}\right))}{AV_{j}}\] (12) \[NDA_{ij}=\frac{\max(0,\left(X_{ij}-AV_{j}\right))}{AV_{j}}\] (13)
4. Determine the weighted sum for all alternatives: \[SP_{i}=\sum_{j=1}^{m}w_{j}PDA_{ij}\] (14) \[SN_{i}=\sum_{j=1}^{m}w_{j}NDA_{ij}\] (15) \(w_{j}\) is the weight of the criterion \(j\).
5. Normalize \(SP\) and \(SN\) values: \[NSP_{i}=\frac{SP_{i}}{max_{i}(SP_{i})}\] (16) \[NSN_{i}=1-\frac{SN_{i}}{max_{i}(SN_{i})}\] (17)
6. Calculate the evaluation score (\(AS\)) of all alternatives: \[AS_{i}=\frac{1}{2}(NSP_{i}+NSN_{i});\ 0\leq AS_{i}\leq 1\] (18) Alternatives are ranked in descending order of values (\(AS\)).
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Signature-Based Methods
#### 3.2.1 Traffic Pattern Analysis
Using various network environment datasets, the method was able to tackle the issue of dispersed traffic classification in high-dimensional unbalanced data and obtain 80% flow classification accuracy. For TCP traffic, however, the system's results are not very accurate. Some research investigations not only identify the attack but also pinpoint its source or traceback. In [53], attack detection and traceback were presented using decision tree and grey relation assessment. This study used a traffic-flow pattern-matching approach to pinpoint the attacker's location. The approach achieved detection and localization of attacks with a false positive ratio of 2.4% and a 2-10% false negative ratio during attack detection, while 8-12% false negative rate and 12-14% false positive rate was achieved during source attack tracing. Similar research was performed by [PERSON] and [PERSON] [22], who used independent component analysis to create a new pattern matching algorithm for DDoS attack source detection. The method bases its requirements for the attack traceback on variations in the number of packets over time. Thus, by comparing the geometries of input traffic patterns and the geometries of output traffic patterns seen at a network branch point, similar to a router, the source of the attack can be determined. In [54], an algorithm for detecting attacks via network traffic pattern assessment was introduced. A simple Hilbert operator was used to describe the traffic pattern over various time intervals. DDoS attack detection was then achieved using Bayesian decision theory. With a detection probability of 0.95, the algorithm detects IP fragments, TCP SYN floods, UDP floods, NTP amplification, and HTTP floods. The method identified a few missed cases. About 5% of network attacks in the dataset were not detected. The approach is also based on the signature of known attacks, which limits its applicability to detecting other attack vectors with dynamic patterns different from the classified data.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
###### Abstract  
In many areas of operation, application-specific logic implemented in FPGAs (Field Programmable Gate Arrays) is critical. In these situations, various mitigation methods are used to reduce or completely eliminate malfunctions in the circuit resulting from undesired physical phenomena (e.g., ionizing radiation). Such phenomena may occur, among others, in medicine, the military, nuclear power, and space systems. One of the most popular methods is the use of triple modular redundancy (TMR). Here, the FPGA provides a good basis for building TMR-based safety-critical systems due to its concurrent processing. This paper presents an overview of the implementation of logic structures using TMR. In this paper, the authors focus on different concepts for the implementation of FSMs. The different concepts differ in the way TMR voters are attached and the extent of redundancy of the individual FSM components. The article compares the efficiency of the different solutions. In order to evaluate this efficiency, it is crucial to determine the logic utilization or the power consumption of a given implementation. In the experimental part of the article, the authors show the results of the synthesis of FSM benchmarks, for different mitigation models. The synthesis was carried out for both commercial and academic tools.  
t pacs: 12
# Optimized Implementation and Analysis of CHAM in Quantum Computing
## 1 Introduction
#### Contribution  
Our contributions in this work are manifold and can be summarized as follows:  
1. Optimization to the linear layer of the CHAM block cipher: To the best of our knowledge/understanding, this type of optimization has never been applied to this cipher before. We show how the optimization affects the quantum footprint, by comparing the benchmarks of the naive versus the optimized implementation.
2. Optimization of CHAM's quantum circuit: Unlike classical computers, quantum computers have limited resources. For this reason, it is essential to optimize the number of qubits in optimizing quantum circuits. In addition, since the depth of a quantum circuit is related to speed and time, not only the number of qubits, but also the depth of the circuit is considered an important factor in optimizing the circuit. We apply two linear layer optimization techniques to CHAM's key schedule to reduce common unnecessary auxiliary qubits used for round key generation in existing structures. Moreover, we also reduce the number of CNOT gates and the depth of the circuit using the second improvement method, and so satisfied two important optimization factors.
3. Evaluation of post-quantum security about improved quantum circuit of CHAM: NIST provides criteria for evaluating the post-quantum security strength of existing codes in preparation for quantum computers. In order to evaluate the security strength, it is necessary to implement the cipher as a quantum circuit and estimate the cost of a [PERSON]'s algorithm attack. We estimate the cost of a [PERSON]'s algorithm attack about an improved quantum circuit of CHAM, compare it with the results of the previous one, and then evaluate the post-quantum security strength. Additionally, circuits are implemented for Revised CHAM, and security strength evaluations are conducted together.  
The organization of this paper is as follows. Section 2 presents the background of the CHAM cipher, quantum computers and quantum gates, linear layer optimization techniques, and the [PERSON]'s algorithm. In Section 3, the proposed quantum implementation is presented. In Section 4, we evaluate the proposed quantum circuit for CHAM. Section 5 estimates the cost of [PERSON]'s algorithm for the proposed CHAM quantum circuit, and evaluates the NIST post-quantum security level for CHAM based on the estimated cost. Finally, Section 6 concludes this article.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 3 Methodology
### Environmental Analysis of PV Systems  
One advantage of PV technology is reducing greenhouse gas (GHG) emissions. The annual rate of GHG emissions savings can be calculated using this formula:  
\[\text{GHG emissions = EF \times EP} \tag{4}\]  
where EF is the emissions factor (kg of CO\({}_{2}\) per kWh), and EP is the electricity produced from PV systems (kWh). In 2021, according to [PERSON] and [PERSON] [45], the CO\({}_{2}\) emissions density in electricity production in the KSA was 0.569 kg CO\({}_{2}\) per kWh.
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
## 4 Reliability Analysis of RLDV Tests  
The reliability of the test results generated by the Remote Laser Doppler Vibrometer (RLDV), a novel non-contact pavement deflection testing apparatus proposed in this study, is subject to scrutiny. As such, a comparative analysis of its testing results with other deflectometers is required to ascertain its reliability. Given the widespread usage and recognized reliability of the Heavy Weight Deflectometer (HWD) in current deflection tests, it was selected as the control group. This study conducted deflection tests using both HWD and RLDV on a full-scale outdoor concrete slab, with the measurement point situated at the edge of the HWD load plate. The horizontal distance between the measuring point and the Remote Laser Doppler Vibrometer was 35 m, and the vertical height between the measuring point and the optical head was 1 m. The deflection at the measuring points at five different load levels of 80 kN, 100 kN, 120 kN, 140 kN, and 160 kN was evaluated, with each load level repeated 10 times. The peak of the RLDV test data was obtained after angle compensation and vibration compensation had been applied.  
Figure 12 illustrates the correlation analysis between results obtained from the RLDV and HWD tests. A linear fitting equation was established, linking the deflection values derived from both testing methods. The coefficient of determination (\(R^{2}\)), serving as the evaluation metric for the linear regression model, yields a value of 0.94. This suggests a strong correlation between the RLDV and HWD test outcomes. Thus, the reliability of RLDV for detecting pavement deflection has been substantiated.  
Figure 11: Displacement of concrete slab (100 kN). (**a**) Displacement vibration of the experimental group; (**b**) Displacement vibration of the control group.
# Optical Transmitters without Driver Amplifiers--Optimal Operation Conditions
## 4 Influence of DPreD on the Electrical Eye-Opening
Figure 4 shows a measurement series for SRRC signals with a range of different symbol rates and roll-off factors with and without DPreD. The colors indicate different symbol rates. For each symbol rate, roll-off factors between zero to one were tested. For symbol rates beyond 50 Gbd, we swept the roll-off factor from zero until the highest possible, limited by \(f_{\mathrm{s}}/2\geq B=0.5(1+ROF)R\), where \(f_{\mathrm{s}}\) is the sampling frequency. Three effects can be seen in the results. Firstly, the saturation for SRRC signals at a roll-off factor above 0.4, as described in Section 2, can be seen. Secondly, the influence of the DAC's frequency response to the signals without DPreD can be seen in Figure 4a. That is why the measured RMS amplitude decreases for higher signal bandwidths. The slope of the amplitude decrease follows the evaluated response from Figure 3b. Thirdly, the difference between Figure 4a,b shows the influence of DPreD on the electrical eye-opening. In particular, for high symbol rates, there is a significant attenuation of the electrical eye-opening by DPreD upon comparing it to the case where no DPreD was used.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 4 Case Study
### Algorithm Comparisons
Based on the statistical results of the MED and HV indicators presented in Table 2, it is evident that SetGSO exhibits the best average convergence in comparison to SetGA. This further underscores the exceptional advantage of SetGSO in terms of solution convergence. Furthermore, a statistical analysis of the S metric reveals that the SetGA algorithm yields a more evenly distributed Pareto front, while the SetGSO algorithm exhibits denser clustering. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **HV** & **MED** & **S** & **RT (s)** \\ \hline GSO-MC & 273,530 & 79.51 & 2.39 & 11,484.0 \\ SetGA & 287,230 & 54.52 & **0.80** & **1599.0** \\ SetGSO & **305,480** & **44.03** & 1.71 & 1605.5 \\ \hline \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 1: Three objectives of results obtained using three methods on quantitative metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**HV**} & \multicolumn{2}{c}{**MED**} & \multicolumn{2}{c}{**S**} \\ \cline{2-6}  & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** & **SetGA** & **SetGSO** \\ \hline Average & 296,060 & **299,970** & 52.8 & **49.1** & 2.49 & **2.35** \\ Median & 295,810 & **300,090** & 53.2 & **48.9** & **1.43** & 2.02 \\ STD.Dev & 1486.2 & **355.9** & 2.3 & **1.2** & **1.42** & 1.47 \\ Worst & 294,560 & **299,570** & 53.4 & **49.5** & **47.3** & 5.10 \\ Best & 298,870 & **300,510** & 46.6 & **44.7** & 1.27 & **0.73** \\ \hline \hline \end{tabular} Bold text represents superior values.  
\end{table} 
Table 2: Statistical results for three metrics in the modified IEEE-30 Bus system.
## Materials and Methods
### Climatic Data  
Information on rainfall is available from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product [32]. Starting in 1981, CHIRPS is a 30-year quasi-global rainfall dataset. Spanning 50\({}^{\circ}\)S to 50\({}^{\circ}\)N (and all longitudes) CHIRPS incorporates 0.05\({}^{\circ}\) resolution satellite imagery with in-situ station data to create gridded monthly time series for seasonal drought monitoring and water resource management [33]. The monthly mean average of the CHIRPS dataset for 1981-2015 highlights the two dry seasons; a short dry period in July and a longer dry season in December-February characteristic of the north of the Republic of Congo.  
To highlight rainfall anomalies from these data, the Standardized Precipitation Index (SPI) is used. The SPI is the standard index for quantifying and reporting meteorological drought. SPI quantifies precipitation as a standardized departure from a selected probability distribution function that models the precipitation records [34]. Precipitation records are fitted to a gamma distribution, and then transformed to a normal distribution. The SPI values represent the number of standard deviations that the observed value would deviate from the long-term mean for a normally distributed variable. The SPI can be retrieved for different periods, generally ranging from 1 to 36 months, using monthly input data. For this study, the SPI is calculated at monthly time scales. On short timescales, the SPI is closely related to soil moisture.  
The strength of the 2015-2016 El Nino climatic event is characterized by the value of the Oceanic Nino Index (ONI). The ONI represents the three-month moving average of the Sea Surface Temperature (SST) anomalies in the Nino 3.4 regions (5\({}^{\circ}\)N-5\({}^{\circ}\)S, 120\({}^{\circ}\)-170\({}^{\circ}\)W). For the 2015-2016 period, the ONI indicates an El Nino event from February 2015 to June 2016 with maximum anomaly of 2.3\({}^{\circ}\)C which is considered as a very strong event. In the last decade, two weak El Nino events have been observed in 2004-2005 and 2006-2007 and two moderate events in 2002-2003 and 2009-2010. The last time an El Nino event was classified as very strong was during the 1997-1998 period [1].
## 3 Data and Methodology
### The Proposed Methodology
#### CostSensitiveClassifier Method  
This research applied a generic \"CostSensitiveClassifier\" method in order to minimize the expected misclassification cost of the MLP-ANN algorithm. In which it re-weights the training instances according to the total assigned cost to every class. The assigned cost is defined by a cost matrix \(C\).