esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Corrosion Morphology  
The sample's exterior (Figure 2) exhibited distinct signs of corrosion following the immersion experiments, particularly noticeable at the interface between the solution and Sc-CO\({}_{2}\). The corrosion morphology showed significant alterations to the steel, with some corrosion products formed due to being in contact with the solution appearing dark and uniform, while those exposed to Sc-CO\({}_{2}\) exhibited various shades of color and were uneven. Based on the EDS image analysis (Figure 3), the distribution of elements in the corrosion products on the surface of N80 steel samples in the solution environment was relatively uniform, whereas the distribution of elements in the corrosion products in the Sc-CO\({}_{2}\) environment was non-uniform, particularly with a significant presence of Ca elements in localized areas. Since the N80 steel used in the experiment did not contain Ca elements, the Ca in the corrosion products originated from the Ca\({}^{2+}\) in the solution. The occurrence of this phenomenon might be attributed to the migration of formation water from the solution phase to the Sc-CO\({}_{2}\) phase through the pores within the corrosion products. Due to pore structure variation at different locations on the corrosion product layer, some locations had more water accumulation than others, causing pitting corrosion in water-rich regions. As a result, some regions are heavily corroded and abundant Ca is left in those regions in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Temperature** & **Phase** & **Immersion** & **Ca\({}^{2+}\)** & **Mg\({}^{2+}\)** \\  & **(\({}^{\circ}\)C)** & **Time (d)** & **(mol/L)** & **(mol/L)** \\ \hline
1 & 40 & Formation water & 7 & & \\
2 & 80 & and Sc\(<\)CO\({}_{2}\) & 3.5, 7, 14, 28 & 0.18 & 0.01 \\
3 & 120 & two-phase & 7 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Immersion experiment conditions (P\({}_{\text{CO}_{2}}\) = 10 MPa).  
Sc-CO\({}_{2}\) phase. Regrettably, we did not obtain solid evidence to validate this hypothesis, and future investigation was needed. Because the specimen was not immediately stored under vacuum conditions upon removal from the reactor, some corrosion products might have interacted with H\({}_{2}\)O and O\({}_{2}\) in the air, leading to a darkening of the specimen's surface color. However, this did not mean that these darker samples were more severely corroded in the autoclave, which was an error in the experiment.  
Throughout the 28-day corrosion period, the color of the resulting corrosion constituents on the exterior of the N80 sample gradually deepened, with significant corrosion detected at the interface between the formation water and Sc-CO\({}_{2}\). The corrosion severity at the interface was significantly higher compared to that observed in the Sc-CO\({}_{2}\) and formation water environments. Identical corrosion patterns were noted at various temperatures.
# A Comparative Analysis of Big Data Frameworks:
## 5 Comparative Analysis of Big Data Frameworks
### Feature Vector Related to Fault Tolerance  
To improve the reliability and performance of big data systems, the adoption of fault tolerant systems has grown over the years. The need for highly fault tolerant systems arise due to the frequent failures caused by the complexity, scale and heterogeneity of underlying systems. Fault detection is the starting point of any fault-tolerant mechanism, that enables faults to be detected as soon as they appear within the system. In large-scale systems, stable fault detection methods such as heartbeat mechanism and fault prediction are used. Most big data frameworks are based on the heartbeat detection approach [18]. After fault detection, fault recovery is used to restore the faulty component's normal behavior. Data replication is typically used to ensure the reliability of storage systems. Google File System and HDFS both storage systems employed this method for fault tolerance and high availability. Redundant processing and redundant storage of in-processing metadata is used to recover faults that occur during the data processing. Checkpointing is another approach to ensure fault tolerance in big data systems which is used by stream processing engines or real time transactions where low latency is desired. Table 7 presents a comparison of frameworks on the basis of feature vector related to fault tolerance.
# Prosthesis Embodiment in Lower Extremity Limb Loss: A Narrative Review
## 4 Results
### TIME and C-FINE Devices
Energy efficiency is crucial for amputees, who often expend more energy than non-amputees when performing basic movements such as basic ambulation [59]. In [27], [PERSON] et al. designed various physical tasks that demonstrated the ability of TIME devices to overcome challenges when cutaneous sensation is not present, as outlined in Section 2.2. * Ascending and descending stairs, testing posture on a complex surface. * Walking on a straight path with obstacles, testing balance on an uneven surface. * Walking with both feet in a straight line, testing agility. For C-FINE devices, a study by [PERSON] et al. [30] performed the sensory organization test, a balance test, and calculated the maximum postural sway from the test results. Subjects with C-FINE devices were able to decrease sway and improve balance after interruptions to their visual and somatosensory systems. Cutaneous sensory feedback also helps users reduce the perceived weight of their prosthetic limb, improving comfort and ease of use during extended periods of activity [31]. Blindfolded study subjects were asked to compare an ankle weight versus the prosthesis' weight. Amputees who had their cutaneous sensation restored by TIME devices more accurately identified which one was heavier [31]. In addition, this study used a split focus activity similar to the one in [59] to conclude that reduced weight contributed to increased embodiment [31]. In addition to measuring improved embodiment through reduced phantom limb pain and improved functional outcomes, PROMs were used in [27; 31; 59] to directly assess the impact of restoring cutaneous sensory feedback on embodiment. However, the questionnaire used was their own and was not standardized. Participants noted improvements in naturalness, control, and integration of the prosthesis with their phantom limb. These qualitative insights, combined with behavioral data, provided evidence of increased embodiment facilitated by cutaneous sensory feedback. The findings demonstrated the importance of a holistic approach to the research and development of prostheses and also indicate a lack of universal measurement systems for concepts such as embodiment and proprioception.
# Designation of the Quality of EGNOS+SDCM Satellite Positioning in the Approach to Landing Procedure
## 2 Research Method
### Mathematical Model of Multi-SBAS Solution for Parameters of Quality Positioning
- for the continuity parameter:  
\[C_{m}=\frac{C_{EGNOS}\cdot\alpha_{E}\;+\;C_{SDCM}\cdot\alpha_{S}}{\alpha_{E}\;+ \;\alpha_{S}} \tag{8}\]  
where:  
\(C_{m}\)--resultant continuity of Multi-SBAS positioning,  
\(C_{EGNOS}\)--positioning continuity in SBAS/EGNOS solution,  
\(C_{EGNOS}=\frac{P_{E}}{\Delta t_{E}}\cdot t_{obs_{E}}\),  
\(C_{SDCM}\)--positioning continuity in SBAS/SDCM solution,  
\(C_{SDCM}=\frac{P_{E}}{\Delta t_{S}}\cdot t_{obs_{E}}\),  
\(P_{E}\)--probability of maintaining continuity in SBAS/EGNOS solution,  
\(P_{S}\)--probability of maintaining continuity in SBAS/SDCM solution,  
\(\Delta t_{E}\)--time interval unit in SBAS/EGNOS solution,  
\(\Delta t_{S}\)--time interval unit in SBAS/SDCM solution,  
\(t_{obs_{E}}\)--total time of observations in SBAS/EGNOS solution,  
\(t_{obs_{E}}\)--total time of observations in SBAS/SDCM solutions. Actually, the determined value of the continuity parameter \(C_{m}\) defines the number of failures and interruptions in the functioning of the Multi-SBAS solution. What is worth noting is the fact that this value is dimensionless. * for the integrity parameter: \[\left\{ \begin{array}{c}HPL=K_{H}\cdot\sqrt{\delta B_{m}{}^{2}}\ +\ \delta L_{m}{}^{2}\\ VPL=K_{V}\cdot\delta h_{m}\end{array} \right.\] (9) where: \(K_{H}\)--proportionality coefficient in the horizontal plane for SBAS APV type approach: \(K_{H}=6.00\)[2], \(K_{V}\)--proportionality coefficient in the vertical plane for SBAS APV type approach: \(K_{V}=5.33\)[2], \((\delta B,\ \delta L,\ \delta h)\)--standard deviations for coordinates \((B_{m},\ L_{m},h_{m})\) from the solution of (3), \(HPL\)--level of positioning integrity in the horizontal plane, \(VPL\)--level of integrity of vertical positioning. Equation (9) describes the integrity of positioning for the Multi-SBAS solution with use of the HPL and VPL parameters.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 3 Passband Compensation of Chebyshev Sharpened Combs from [8; 10; 11]
### Steps of Design  
In this section, we present the design steps.  
STEP 1:  
Choose the Chebyshev sharpened comb.  
STEP 2:  
Choose narrowband or wideband design, depending on the passband of interest.  
In narrowband case, choose the filter \(C_{1}\) in (3).  
In the wideband case, choose between the filters \(C_{2}\) given in (6) or the simplified version \(C_{3}\), from (9).  
STEP 3:  
Find the optimal compensator parameters using PSO in MATLAB.  
STEP 4:  
Present the optimal compensator parameters in SPT forms using MATLAB, varying the number of adders.  
STEP 5:  
Compute the corresponding filter coefficients in the SPT form.
## 4 Simulation Verification and Physical Experiment
### Method Robustness Verification
#### 4.3.1 Double-Switch Fault Under Voltage Fluctuation  
To evaluate the efficacy of the proposed method under varying DC voltage conditions, Figure 8 illustrates the diagnostic results for \(T_{b2}\) and \(T_{b4}\) faults during the DC voltage rises from 300 V to 400 V. At \(t=0.1\) s, a sudden change in the DC voltage causes the output current amplitude to increase, accompanied by slight fluctuations in all diagnostic variables. However, \(R_{b}\) remains below \(T_{b}\), preventing any false alarms. When \(t=0.161\) s, the introduction of the fault causes \(R_{b}\) to promptly exceed \(T_{b}\). In the meantime, \(W_{b}>w_{b}\) identifies a fault in the negative-level switches, and combined with \(\eta_{b}\geq\delta_{2}\), further diagnoses that \(T_{b2}\) and \(T_{b4}\) have OC faults. The experimental results confirm that the proposed approach retains its diagnostic accuracy even under DC voltage fluctuations.
## 4 Results and Discussion
### Reflection Coefficient (dB)  
The simulated reflection coefficient results as seen in Figure 5 depicts that all four antenna elements radiate in the frequency band ranging from (84.67%) 2.37-5.85 GHz. The measured reflection coefficient results illustrate that all four antenna elements radiate in the frequency band ranging from (84.12%) 2.39-5.86 GHz. The measured S parameters agree very well with the simulated ones. The slight disparity may be due to the tolerance in size due to the fabrication process and losses across the UFL to SMA connector.
## 3 Results
### Dissociation State of Lithium Salts  
ATR-FTIR spectra of different compounds are presented in Figure 2. The dissociation state of LiTFSI salt in the PEGDM matrix can be observed by analyzing the shift of TFSI anion in the wavenumber range between 670 cm\({}^{-1}\) and 820 cm\({}^{-1}\). For example, the deformation band of CF\({}_{3}\) (\(\delta_{\rm CF3}\)) in LiTFSI appears at around 746 cm\({}^{-1}\)[27], this band is also observed at the same position for **ZN** zwitterion. After mixing LiTFSI with PEGDM, the \(\delta_{\rm CF3}\) peak shifts to 739 cm\({}^{-1}\) indicating the dissociation statute of TFSI anion due to the strong solvation of lithium ion by PEGDM. The shift of vibration bands at 799 cm\({}^{-1}\) and 772 cm\({}^{-1}\) related to vS-N-S and vC-S respectively is also observed. However, the addition of **ZN** zwitterion to LiTFSI/PEGDM mixtures does not change the position of these bands since the anion site of zwitterion has similar structure as TFSI anion of lithium salt.  
To further characterize the molecular environment of lithium cations, liquid \({}^{7}\)Li NMR measurements were performed. Figure 3 shows the \({}^{7}\)Li NMR spectra of **ZN**-0 and **ZN**-0.1 electrolytes mixtures. Broadening of the lithium peak is observed for both electrolyte samples that is caused by an increase of their viscosity in the electrolyte mixtures. Analyzing the chemical shift of these spectra reveals that the coordination of the lithium ion is not changed upon addition of **ZN** since the anion natures of the lithium salt and zwitterion compound are very similar. Indeed, the Li peak in **ZN**-0 and **ZN**-0.1 electrolytes are observed at \(-\)1 ppm and \(-\)0.98 ppm, respectively. In contrast, [PERSON] et al. [23] reported a pronounced change in the lithium ion coordination when the sulfon  
Figure 2: ATR-FTIR of **ZN** zwitterion, lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) and poly(ethylen glycol) (PEG)-based electrolytes in a wavenumber range from 670 cm\({}^{-1}\) to 820 cm\({}^{-1}\).
## 2 Materials and Methods
### MISTIC Winds Observing Concept, Requirements, and MISTIC Instrument Concept
MISTiC is optimized to provide accurate motion-vector wind observations, which requires high spectral resolution, high spatial resolution, and appropriate spatial sampling. The vertically resolved winds observation uses a constellation approach, and therefore requires a system-of-systems requirements framework involving instrument requirements, spacecraft requirements, and ground-processing requirements. MISTiC Winds targets both cloud and moisture-field motion. Key requirements developed under the IIP that relate to the vertically resolved vector wind observations and thermodynamic vertical profile are shown in Table 2. The purpose of these requirements is to guide the design of both the instrument and the constellation. One of the key requirements, listed first in Table 2, is the wind speed uncertainty. This requirement is assumed to include errors due to height assignment in the presence of wind shear (up to 20 m/s, as indicated in the table) well as errors in wind tracer position change. The requirements for wind vector and thermodynamic vertical profile errors are assumed to be relative to the actual atmosphere, rather than to radiosondes. The feasibility of these requirements and connections to radiosonde observations will be discussed in Section 4.
# Design Point Performance and Optimization of Humid Air Turbine Power Plants
## 3 Results and Discussion
### Impact of Overall Pressure Ratio  
The parametric design point performance analysis was conducted at an _OPR_ of 30 that represents a typical technology level for current power generation applications. An optimum cycle configuration with thermal efficiency of 60.7% is achieved when reducing the overall pressure ratio from 40 to 30 (\(A_{5}^{40}\)\(\rightarrow\)\(A_{5}^{30}\))--i.e., a reduction of 0.7 pp with regards to the baseline cycle with _OPR_ = 40. The inlet gas mass flow to deliver the prescribed power output of 40 MW increases by 9.8%. This is achieved with the maximum specified effectiveness of all heat exchanger and minimum pinch temperature difference in the humidifier as shown in Table 6. The humidity levels at the inlet of the main burner is reduced by 10.2% while saturator bypass \(\beta\) increases by 4 pp when reducing the overall pressure ratio from 40 to 30. This is attributed to the lower water temperature at the outlet of the intercooler, aftercooler, economizer and the reduction in temperature of the gas at the outlet of the high pressure compressor.  
Although the reduction in overall pressure ratio implies cheaper and more compact turbomachinery, the decrease in specific power results in a 11.2% and 10.3% increase in \(k\times A\) (Figure 8) of the intercooler and recuperator, respectively, whereas the aftercooler and economizer \(k\times A\) decrease by approximately 4%. This yields more expensive designs of the intercooler and recuperator with an increase in acquisition cost index of, respectively, 25.6% and 7% (Figure 9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimized Design Variable** & & **Units** & **Value** \\ \hline Thermal efficiency & \(\eta_{HAT}\) & (\%) & 60.7 \\ Overall pressure ratio & _OPR_ & (-) & 30 \\ Relative pressure ratio & _relPR_ & (-) & 0.53 \\ Intercooler effectiveness & \(\epsilon_{IC}\) & (-) & 0.95 \\ Aftercooler effectiveness & \(\epsilon_{AC}\) & (-) & 0.95 \\ Economizer effectiveness & \(\epsilon_{EC}\) & (-) & 0.95 \\ Saturator pinch temperature difference & \(\Delta T_{ps}\) & (K) & 5 \\ Recuperator effectiveness & \(\epsilon_{REC}\) & (-) & 0.9 \\ Intercooler heat capacity ratio & C\({}_{IC}^{c}\) & (-) & 0.95 \\ Aftercooler heat capacity ratio & C\({}_{AC}^{c}\) & (-) & 0.95 \\ Economizer heat capacity ratio & C\({}_{EC}^{c}\) & (-) & 0.70 \\ Saturator bypass & \(\beta\) & (-) & 0.47 \\ GT inlet gas mass flow & \(\dot{m}_{g,GT\ inlet}\) & (kg/s) & 44.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Design variables values of the baseline cycle \(A_{5}^{30}\) with maximum thermal efficiency.
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 2 Brillouin Sensing Principle
### Backward Brillouin Scattering  
Backward Brillouin scattering occurs when optical pump and probe waves counter-propagate through a medium with the probe being frequency shifted from the pump by the BFS (\(\upsilon_{B}\)). In this scenario, the pump and probe interference creates a moving refractive index grating, which back scatters the pump wave with a Doppler shift associated with the velocity of the moving grating, as shown in Figure 1. This process is called backward Brillouin scattering, which under the phase-matching and energy conservation conditions, results in the coherent energy transfer from the pump wave to the signal wave through the acoustic wave.  
The BFS in the backward scattering process (\(\upsilon_{B}\)) is defined by Equation (1):  
\[\upsilon_{B}=\frac{2n_{\text{eff}}\upsilon_{a}}{\lambda_{p}}, \tag{1}\]  
In this equation, BFS is a function of the effective refractive index (\(n_{\text{eff}}\)), the pump wavelength (\(\lambda_{p}\)), and the longitudinal acoustic mode velocity (\(v_{a}\)), which itself is dependent on material properties [43] and is sensitive to strain and temperature [54; 57]. The relation between the BFS and temperature and strain have been experimentally demonstrated in different platforms such as standard silica fibres [58; 59; 60], \(GeO_{2}\)-doped fibre [57], dispersion-shifted fibre (DSF) [61], and PCF [62].
# A Review of Machine Learning and IoT in Smart Transportation
## 1 Introduction
### Internet of Things
* **Environmental Conditions Monitoring:** Wireless sensors distributed in the city make the perfect infrastructure for a wide variety of environmental conditions monitoring. Barometers, humidity sensors, or ultrasonic wind sensors can help to create advanced weather stations. Moreover, smart sensors can monitor the air quality and water pollution levels across the city. * **Logistics and Supply Chain Management:** With the use of smart RFID tags, a product can be easily tracked from the production to the store, reducing cost and time significantly. In addition, smart packaging can offer features such as brand protection, quality assurance, and client personalization. * **Security and Surveillance Systems:** Smart cameras can obtain video input across the streets. With real-time visual object recognition, smart security systems can identify suspects or prevent hazardous situations. As discussed so far, although there is a lot to be done in terms of standardization when it comes to IoT infrastructure and technologies, Figure 1 can accurately describe the key elements of the infrastructure as they have been used in the majority of the applications. Figure 1 is organized by separating the infrastructure key elements in numbered blocks. Each block depicts a representative image of the described element, and arrows are linking the images with numbers, indicating how each element interconnects with the other blocks. Additionally, text blocks are included, giving the most important aspects of each key element in bullets. The IoT infrastructure consists of: (1) IoT nodes, namely the IoT devices (sensors and actuators) at the edge of the network. (2) The Fog nodes, servers that assist IoT devices by providing computing, storage, and advanced networking capabilities. (3) Cloud nodes that consist of data centers, which handle data storing, computationally intense data analysis using ML techniques, data sharing, etc. (4) IoT applications that use the collected and analyzed information to create services for the end user. Points (A) and (B)  
Figure 1: Key elements of the Internet of Things (IoT) infrastructure. symbolize the IoT platforms, which have the orchestrator's role and support the elements throughout the infrastructure in various ways as described in the figure. Moreover, Figure 1, presents the role of ML in the IoT infrastructure. ML techniques can be applied at the IoT nodes, the fog nodes, or the cloud nodes, depending on the application needs.
## 3 Relation of S/P Ratios of Light Sources Using 2-degree and 10-degree SSCs  
Monochromatic spectra calculated for the \(S/P\) ratios in Section 2 are considered extreme conditions. In this section, we investigated a set that contained more than 300 light sources. Most of these were commercial light sources. These light source data were presented in the Illuminating Engineering Society of North America (IES-30-15. 2015), including incandescent lamps, fluorescent lamps, high-intensity discharge lamps, and LEDs. The CCTs of these light sources ranged from 2000 to 8000 K. The SPDs were normalised to a maximum intensity of unity. The mean SPD is shown in Figure 3. We calculated  
Figure 1: Spectral sensitivity curves (SSCs) for scotopic vision and photopic vision using 2- and 10-degree systems.  
Figure 2: Scotopic/photopic ratios (\(R_{sp}\)) as a function of wavelength using (**a**) 2-degree and (**b**) 10-degree SSCs. Some special \(R_{sp}\) values have been marked. The \(R_{sp}\) at 555 nm is equal to unity. The \(R_{sp}\) values at 531.1/533.5 nm are 2.261/2.085, and are equal to those for equal energy white (EEW) light in the 2-degree/10-degree cases. (**c**) Comparison of \(S/P\) ratios using 2- and 10-degree SSCs. There is a considerable difference in the short-wavelength part, especially for values \(<\)500 nm, and a minor difference in the long wavelength part.  
\(S/P\) ratios of these light sources using both 2- and 10-degree SSCs. Figure 4 shows that the \(S/P\) ratio was calculated in the plot of the 10-degree against the 2-degree SSC system. The relation is fitted well by a linear function with a high coefficient of determination, \(R^{2}\) = 0.999. The slope of the linear function is approximately equal to the ratio of the EEW light, which is equal to 2.071/2.261 0.916. We can use 0.916 as the conversion coefficient for the transformation of the results calculated with the use of the 2- and the 10-degree SSCs.  
Due to the fact that there are no differences in the responses for scotopic vision, the \(S/P\) ratio difference is caused by the difference of \(\mathrm{LER_{P}}\). Although considerable differences may exist for \(S/P\) ratios, the differences for mesopic luminance values may not be as considerable. We should further examine the mesopic luminance values transformed from photopic adaptation luminance levels in mesopic vision states.