esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition
## 1 Introduction
Ion track size measurement in the case of SAXS analysis is based on small differences in material density within the track and outside, which SAXS can successfully resolve [16, 18, 22]. The IR spectroscopy track size measurement relies on the changes in the Si-O tetrahedral network that occur when the O-Si-O bond angle decreases due to material compaction. This change in bond angle can be observed in IR spectra as a change in the TO3 band (ranging from 1000 cm\({}^{-1}\)-1120 cm\({}^{-1}\)[17]), which is an asymmetric vibrational stretching mode. The AFM measurements of thermally grown a-SiO\({}_{2}\) irradiated at a grazing incidence by high-energy heavy ions was also explored and was found to be a convenient way for ion track imaging [23, 24, 25, 26]. In our previous work, changes in IR spectra have been observed after irradiation with 23 MeV I, and also with much lighter and less energetic 3 MeV O and 1 MeV O beams [21]. This finding was not very surprising because a-SiO\({}_{2}\) is known to be very sensitive to ion irradiation, which is an observation in line with other amorphous materials as well [14]. However, to our knowledge, there is a lack of data about ion track formation in amorphous Al\({}_{2}\)O\({}_{3}\) and MgO. These two materials in the crystalline form are known to be very radiation hard, and previously we have not been able to make ion tracks in them except on their surfaces [12, 13]. Thus, the first aim of this study was to investigate by IR spectroscopy possible ion track formation in amorphous a-Al\({}_{2}\)O\({}_{3}\) and a-MgO prepared by magnetron sputtering deposition. The second aim was to investigate the possible differences in radiation hardness between thermally grown a-SiO\({}_{2}\) (reported previously [21]) and a-SiO\({}_{2}\) prepared with magnetron sputtering deposition in this study. Finally, a comparison between high-energy heavy ion and gamma irradiation effects was made, exploring the possible correlations between these two types of irradiations.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 6 Analysis of the Proposed Schemes
### Proposed Scheme-I (Key Recovery System Using Signcryption)
If some \(KRA\)s fail to recover the session key pieces, other \(KRA\)s recover the session key pieces instead of the failed \(KRA\) and send them to the \(KRC\). \(TT\) includes all \(c_{i},r_{i},s_{i}\) pieces and the \(SGN\) produced by the XOR operation. The other \(KRA\) (not the corresponding \(KRA\)) decrypts \(TT_{i}\) and sends it to the \(KRC\), allowing the \(KRC\) to recover the complete session key. \[ \begin{split} TT_{i}=(Tc_{i}\parallel Tr_{i}\parallel Ts_{i}) \end{split} \] (62) \[ \begin{split} TT_{i}\oplus SGN&=((c_{i}\oplus SGN )\parallel(r_{i}\oplus SGN)\parallel(s_{i}\oplus SGN))\\ &=((c_{i}\oplus SGN)\oplus SGN\parallel(r_{i}\oplus SGN) \oplus SGN\parallel(s_{i}\oplus SGN)\oplus SGN)\\ &=(c_{i}\parallel r_{i}\parallel s_{i})\end{split} \] (63)
* Med applied for support: \(Med\) should be able to view the encrypted data by acquiring the encrypted session key in the event of an emergency where it is necessary to view the device's data. Therefore, \(Med\) sends \(KRF\) to \(KRC\), and \(KRC\) decrypts \(KRF\) to obtain \(KRF\) pieces. The acquired \(KRF\) pieces are sent to \(M-KRA\) and requested for recovery. Then, \(M-KRA\) obtains session key pieces by decrypting the acquired \(KRF\) pieces. The obtained session key pieces are sent to \(KRC\), and \(KRC\) recovers the complete session key. After that, it sends the complete session key to the \(Med\), allowing message decryption. * Key escrow problem: The proposed scheme-I is based on a CL-PKC scheme. Therefore, as \(KGC\) can generate only a part of the private key during the private key genera tion process, the key escrow problem caused by \(KGC\) in ID-based encryption has been solved.
## 3 Model for Tracking User Interactions in AR-Based Serious Games
### Conceptual Model
#### 3.1.4 Accessibility  
Although previously mentioned features can affect accessibility (or reveal accessibility-related issues), in this section, we describe features and interaction parameters that can be additionally tracked. They are mostly related to the accessibility options that the user can apply or change. Changing a certain option in the accessibility menu can indicate that the user wants to improve accessibility, i.e., their user experience. Also, the context in which the change appeared needs to be recorded (e.g., the AR task or environmental context) as well as the final option with which the user ended the game.  
According to research conducted regarding the needs of people with different disabilities in the context of web accessibility and mobile accessibility [64,65], the accessibility options menu should contain options to change text size, change font type, choose between different combinations of text-background colour contrast, underline and highlight links, select grayscale images, turn off light/night view, and return to initial settings. Since handheld AR has some common components as mobile applications, e.g., parts of the user interface, it is useful to define interaction parameters related to accessibility options that can be selected from the accessibility menu and to track their changes. In addition to the usual accessibility options for mobile applications, it is desirable to define and track AR-specific accessibility options, e.g., a freeze option. Other accessibility features found in AR-based solutions for people with disabilities include switching on sound support, additional visual support, wider spacing between texts, a high-contrast option for displayed 3D objects, voice control, larger manipulation buttons, sliders instead of buttons, and others [22]. Examples of accessibility options whose change in AR-based games is useful to track are shown in Table 3. They are divided into two groups of related accessibility features (UI-related and AR-specific).
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 4 State-of-the-Art in IDS for IoT
### Game-Based Approaches to Intrusion Detection  
[PERSON] et al. [56] proposed an attack-defence game model to detect malicious nodes using repeated-game approach. The authors claim to have developed a tree model, which is used to formulate an optimal solution to the error detection problems focusing on the detection strategy and performance overhead for individual IoT devices. This is achieved by developing a repeated game method where attackers and defenders can alter strategies to achieve maximum pay-offs. The authors do not discuss challenges, such as placement of the IDS module, type of the detection engine and the level of visibility, which can contribute towards the overall effectiveness of an IDS for IoT networks.  
[PERSON] et al. [57] proposed a honeypot-based approach to improve defence against malicious attempts within an IoT infrastructure. In order to strengthen the defence, authors proposed using a game theoretic model with the defender using honeypot to deceive the attacker. The proposed system focuses on scenarios where the attacker is not known to the defender, therefore, the system does not take into account attempts, such as Rank Attack, which can be initiated by an insider node known to the system. The proposed system is designed and implemented in isolation to the specific challenges and requirements of the IoT infrastructures, such as the resource constraints, interactions between different IoT devices and the positioning of the system. The authors presented experimentation performed in Matlab, which models the overall concept of the system. We believe this to be a limitation, as an effective intrusion detection mechanism for IoT systems should ideally take into account the unique characteristics of such systems as those explained above.
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 3 Results and Discussion
### Comparison of Machine Learning Models with Physical Model  
After generating 5000 data points from the modified HLD-NAC model, we trained several machine learning algorithms to predict surfactant-oil-water phase behavior. Reference phase behavior generated based on the physical modified HLD-NAC model is shown in Figure 5. The cubic SVM emerged as the best-performing model, with its predictions shown in Figure 6.  
The cubic SVM consistently identified phase regions across various surfactant and oil compositions, particularly in the multiphase areas (two-phase in blue, three-phase in purple). While there were minor errors near phase transition boundaries, the model demonstrated strong performance overall, especially in capturing phase transitions.  
Other models demonstrated some limitations, see Figure 6. The medium tree model was too discretized, producing coarse predictions that missed fine details in phase boundaries. The linear SVM model struggled to capture the curvature of the two-phase region, leading to significant inaccuracies in multiphase areas. The fine tree model performed reasonably well but failed to classify phase regions beyond the two-phase boundary, particularly underpredicting the single-phase region. The boosted trees model also indicated several examples of misclassifying the single-phase region. The k-nearest neighbors algorithm performed well.  
Figure 4: Physical model performance compared to experimental data, salinity scan. Data from [PERSON] et al. (2015) [41].  
Overall, the cubic SVM provided the most accurate predictions. The consistency between the SVM model's predictions and the physical model highlights machine learning's potential for fast, accurate phase behavior prediction. This approach offers an efficient  
Figure 5: Reference phase behavior based on physical model, to be used for machine learning comparison.  
Figure 6: Machine learning phase behavior predictions (accuracy): fine tree (97.4%), medium tree (93.4%), linear SVM (90.6%), cubic SVM (99.5%), KNN (98.6%), boosted trees (96.9%).  
alternative to traditional physical modeling, reducing the need for extensive computational and experimental resources.
## 2 Numerical Model
### Model Setup
#### 2.2.5 Software Framework of the Numerical Model  
The model ist implemented in the open-source software DuMu\({}^{x}\)[41]. The simulation domain is spatially discretized with cell-centered finite volumes. The model results have been tested for grid convergence. For time discretization, the implicit Euler method is used. The coupling between the two adjacent domains is described in detail in Reference [41]. Both domains including all balance equations are solved monolithically.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Photoplethysmography (PPG)
#### 3.2.1 Wearable PPG Sensors  
Wearable devices can be wrist-worn, such as smart watches (Figure 3a), fitness bracelets, and smart rings (Figure 3b) with an integrated PPG sensor [49; 50], or portable PPG modules typically attached by clips to the driver's ear or finger [26] (Figure 3c). Wearable PPG sensors measure the intensity of light reflected from the biological tissue under the sensor. Usually, the light source emits in the green spectrum with a wavelength of 520 nm.  
In clinical practice, PPG signals with infrared (IR) and red wavelengths are used to measure oxygen saturation (SpO2), as it is well known that at these two wavelengths, the light absorption between oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) differs significantly [51]. Some commercial smart watches with integrated PPG sensors, in addition to HR and HRV, can measure SpO2, using two wavelengths (usually red 660 nm and IR 910 nm) for collecting PPG signals.
Despite the success stories about the temperature sensor employing reflective delay line pattern, they still suffer from the further improvement of the accuracy in a larger temperature range. In this work, an adaptive LMS filtering algorithm was utilized to eliminate the noise of the returned sensor signal and improve to accuracy. A method combining the multi-point parabola approximation and moving average approach was proposed to reduce the phase error. The sensing device operating at 433 MHz frequency was developed on YZ-cut LiNbO\({}_{3}\) using the photolithographic technique. Before device development, the coupling of modes (COM) model was performed to simulate the device performance. Referring to the FSCW-based transceiver and planar antenna, the wireless temperature sensing system was built, and wirelessly characterized by referring to the above algorithms. High sensitivity and excellent accuracy were achieved successfully. The rest of this paper is organized as follows. In Section 2, the optimal design parameters of the sensor of the reflective delay line pattern are presented. The simulation of the optimization is based on the COM theory. In Section 3, the reflective delay line SAW temperature and wireless system are introduced. In Section 4, the signal processing methods for temperature sensing data are presented, including the adaptive LMS algorithm to improve SNR and the combination method of multi-point parabola approximation and moving average algorithm for reducing phase fluctuation. In Section 5, the temperature experiment for obtaining sensing data is presented. Fitting and relative error analysis are utilized for data analysis. Finally, in Section 6, the results are concluded.
## 5 Results and Discussion
### Water Stability  
In this section, Vacuum Saturated Marshall Stability and Freeze-thaw Splitting tests shall be respectively executed by reference to the test method of T 0709-2011 and T 0729-2000 in Chinese specification JTG E20-2011 [32]. The material composition of specimens used in this test are based on the results of Figures 4, 6 and 7. In the vacuum saturated Marshall stability test, specimens were cylinders (\(\varphi\) 101.6 mm \(\times\) 63.5 mm), which were made according to T 0702-2011 [32]. Specimens were first placed in a vacuum dryer with the water inlet hose closed, then the vacuum pump was turned on to make the vacuum degree 97.3 kPa or above for 15 min. Thirdly, the water inlet hose was opened to let the cold-water flow enter to completely immerse the specimen in water for 15 min. At last, after normal pressure was restored, specimens were taken out from the vacuum dryer and put in a water bath with a constant temperature for 48 h. Test results are shown in Table 16. Residual stability MS\({}_{0}^{\prime}\) was calculated based on Equation (A5) in Appendix B. Original splitting strength (\(R_{\rm T1}\)), freeze-thaw splitting strength (\(R_{\rm T2}\)) and Tensile strength ratio (TSR) were calculated based on Equations (A2)-(A4) in Appendix B.  
Figure 11: Relationship between flexural stiffness modulus and filler-asphalt ratio of SMA-II-3 mixture at \(-\)10 \({}^{\circ}\)C, loading rate of 50 mm/min.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixture** & **Specimen 1** & **Specimen 2** & **Specimen 3** &
\begin{tabular}{c} **Mean Value of Dynamic** \\ **Stability/(Times/mm)** \\ \end{tabular} \\ \hline SMA-II-1 & 3182 & 2930 & 3029 & 3047 \\ SMA-II-2 & 2540 & 2461 & 2944 & 2648 \\ SMA-II-3 & 2930 & 3316 & 3264 & 3170 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Test results of dynamic stability of SMA-II.  
The results in Tables 16 and 17 show that all the performance indicators of the water stability of SMA-II-1, SMA-II-2 and SMA-II-3 can meet Chinese specification JTG F40-2004 [39]. Among the test values, the test value of the water stability of SMA-II-2 is the highest.
## 3 Experimental Activity: Set-Up
### Set-Up of Experimental Equipment  
Baseboards were installed during the spring 2017; thus, the compared measurements refer to the winter 2017-2018. During the monitoring period, the operation of heating emitters was alternated with continuous monitoring as in the following schedule:  
* 21 December 2017-03 January 2018 with baseboard heating;
* 20 January 2018-02 February 2018 with fan-coil units;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Type** & **Model** & **Range** & **Accuracy** \\ \hline
**Rain Gauge** & Cylindrical body of 400 cm\({}^{3}\) &
\begin{tabular}{c} NESA-ANS- \\ PL400-N \\ \end{tabular} & 0 \(\div\) 300 mm/h & \(\pm\)2\% \\
**Wind speed** & Ultrasonic anemometer & 0 \(\div\) 60 m/s & \(\pm\)2\% \\
**Wind direction** & Ultrasonic anemometer & 0 \(\div\) 359.9\% & \(\pm\)2\% \\
**Air temperature** & P100 & DELTA OHM & \(-\)40 \(\div\) 60 \({}^{\circ}\)C & \(\pm\)0.1\% \\
**Relative humidity** & Capacitive transducer & HD52.3D & 0 \(\div\) 100\% & \(\pm\)1.5\% Rt at 15 \(\div\) 35 \({}^{\circ}\)C \\
**Atmospheric pressure** & Piezoresistive transducer & 600 \(\div\) 1100 hPa & \(\pm\)0.5 hPa at 20 \({}^{\circ}\)C \\
**Global solar radiation** & Class II thermopile pyranometer & 0 \(\div\) 2000 W/m\({}^{2}\) & \(<\)10\% day\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of the external climatic station.  
* 03 January 2018-16 February 2018 with radiant heating floor. To carry out the measurement campaign, the test-room was divided into four areas (about 9 m\({}^{2}\)), and in each one, air temperature and relative humidity sensors were installed at different heights: ankles (0.1 m), head (1.1 m) for a man sitting for a typical office work, and 2.1 m and 3.1 as representative points of thermal stratifications. The 16 sensors (Pt100 platinum thermo-resistance) were of two different types, comparable in terms of technical specifications as shown in Table 3. In more detail, at heights of 1.1 m and 2.1 m, sensors produced by NESA were used, and TESTO devices were used at 0.1 m and 3.1 m.  
At the room center, a globe thermometer (emission equal to 0.95 and diameter of 15 cm) is positioned and also an air temperature and relative humidity sensor (1.1 m) and the anemometer. Technical details are reported in Table 3. The measurement of global temperature and knowing the air temperature allowed us to obtain the mean radiant temperature starting from a non-linear equation. On the floor, 5 thin-film flowmeters were positioned in correspondence with air temperature sensors; these were used for recording surface temperature when the radiant system was turned on (T\({}_{\text{s\_floor}}\) in the following). During the monitoring period, the primary air flow rate was set to 0.30 ACH. All the sensors were connected to a cr3000 data acquisition unit, and these were shielded from direct radiation coming from the window. For all internal and external devices, the sampling time was set to 1 min.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 4 Evaluation  
Various experiments have been made to evaluate the effectiveness and efficiency of Skywalker. This section introduces our experimental environment and test files. Then it discusses the scan registration time and scan result time of executables. Finally, it discusses the total extra time, called total preparation time, that Skywalker takes to start the execution of a file. It also compares the total preparation time of an executable when it is first executed and the total preparation time of an executable when it has been executed before.  
We implemented Skywalker on a Windows 7 32-bit computer, and the specification of our host is shown in Table 2. Moreover, we chose VirusTotal as our cloud-based scanner.  
There are 12 executables used in our experiments. Table 3 shows the names and file sizes of these files. We want to learn the time spent on scan requests and report requests. The file size is one of the main factors that decides the time, because it takes more time to transmit and scan a larger file. Hence, what kinds of files we choose are not important, but what sizes of files we choose may lead to different results. The sizes of test files vary between 0.018 MB and 17.405 MB.  
The major impact introduced by Skywalker to a system is the extra time, called total preparation time hereafter, to start an executable. When Skywalker is enabled, for an executable that is first executed, the total preparation time comes from two major sources, scan registration time and scan result time. Scan registration time consists of the time to retrieve and upload a local executable to VirusTotal and the time to wait for VirusTotal to send a preliminary report. The preliminary report contains a scan ID to identify a scan task. After obtaining a scan ID for an executable, Skywalker repeats using the scan ID to ask VirusTotal whether the final report of the executable is available. The time spending on the above operation is called scan result time. However, for an executable that is not executed for the first time, its total preparation time comes from the time to calculate the hash of the executable. The following subsections discuss the registration time, the scan result time, and total preparation time of test files.
## 1 Introduction
[17] developed a mathematical model to evaluate the performance of a modified heat pump system using an R32/R290 mixture for district heating. They employed an internal auto-cascade and an ejector to enhance the performance of the system. Their theoretical study revealed that their modified system had a 9-19% higher COP and a 12-37% higher volumetric heating capacity than conventional heat pumps operated under the same condenser outlet temperature. [PERSON] et al. [18] conducted experimental and theoretical studies to investigate the influence of the R744/R290 blend on the performance of a heat pump water heater with a large heat sink temperature lift. They found that both the COP and heating capacity increased when using the blend R744/R290 with the mass fraction of 12/88% compared with a case that used R22. According to the literature, the simultaneous use of energy and exergy analysis provides valuable information about the performance of the refrigeration systems and the sources and amounts of exergy losses [19; 20; 21]. Exergy analysis can identify the design modification required to improve the system's performance, as it can reveal the relationship between losses of different components of the system. In addition to the refrigeration system's performance, the system's environmental impact must be investigated, as the contribution of refrigeration systems to climate change is currently increasing. Total equivalent warming impact (TEWI) is a useful metric to facilitate the decision-making process when choosing a refrigerant with a low GWP [22]. In this study, three different configurations for the refrigeration system were investigated and compared. These configurations include a single-stage cycle, a single-stage cycle equipped with an internal heat exchanger, and a two-stage cycle with vapor injection. Hydrocarbons with suitable critical temperatures, R290, R600a, and R1270, were investigated as low GWP alternative fluids to R134a. This article aims to better understand the refrigeration system's performance with different configurations using hydrocarbon refrigerants by the simultaneous employment of energy, exergy, and environmental (3E) analyses. It helps to theoretically identify ways to improve the performance of the refrigeration system by modifying the design of the system and properly selecting low GWP refrigerants. The cycle configuration and refrigerant impact on direct and indirect CO\({}_{2}\)-eq (CO\({}_{2}\) equivalent) emissions were studied comprehensively. The results will be helpful to examine the possibility of employing hydrocarbons as a replacement for R134a. The intention of this study is to investigate solutions to mitigate the global warming impact of the refrigeration system while retaining efficient exergy and energy performances.