esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Zeolite Samples
#### 3.1.4 XRD Analysis  
The X-ray crystallography patterns of fresh Z-A, fresh iron-Z-A, P-adsorbed Iron-Z-A, and P desorbed Iron-Z-A are shown in Figure 4. The XRD characteristic peak of Z-A appeared at 7.2\({}^{\circ}\), 10.3\({}^{\circ}\), 12.6\({}^{\circ}\), 16.2\({}^{\circ}\), 21.8\({}^{\circ}\), 24\({}^{\circ}\), 26.2\({}^{\circ}\), 27.2\({}^{\circ}\), 30\({}^{\circ}\), 30.9\({}^{\circ}\), 31.1\({}^{\circ}\), 32.6\({}^{\circ}\), 33.4\({}^{\circ}\), and 34.3\({}^{\circ}\)[49] in both fresh Z-A  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**The Vibration of the**}} & \multicolumn{4}{c}{**Band Peak Shift in IR Spectra of the Zeolite Backbone**} \\ \cline{2-5}  & **Fresh Z-A** & **Fresh Iron-Z-A** & **P-adsorbed Iron-Z-A** & **P-Desorbed Iron-Z-A** \\ \hline -OH strecching & 3467.78 & 3466.82 & 3462.96 & 3481.28 \\ -OH bending & 1653.85 & 1652.88 & 1652.88 & 1657.71 \\ -\(\gamma_{\rm{sa}}\) Si-O(Si) \& \(\chi_{\rm{sa}}\) Si-O(Al) & 993.27 & 995.20 & 1001.95 & 996.16 \\ -\(\gamma_{\rm{s}}\) Si-O-Si & 734.83 & 700.11 & 705.90 & 701.07 \\ -\(\gamma_{\rm{s}}\) Si-O-Al & 668.29 & 556.42 & 705.90 & 701.07 \\ -\(\gamma_{\rm{s}}\)Si-O-Si \& \(\&\)609.92 & 556.42 & & \\ -\(\delta\) O-Si-O & 441.67 & 459.02 & 450.34 & 452.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FT-IR bond stretching and vibrations of different band peak shifts in the IR spectra of the zeolite backbone.  
Figure 3: The FT-IR spectrum of different bonds on the fresh Iron-Z-A, P-adsorbed Iron-Z-A, and P-desorbed Iron-Z-A backbone.  
and Iron-Z-A. It suggested that in both the samples, the surface crystallinity of Z-A was adequately formed. The crystallinity of Z-A was found to be 84.529 whereas, in the case of Iron-Z-A, it was 78.343. It can be observed that there is a slight decline in the XRD peak intensity of iron-Z-A. The fall in the crystallographic pattern hints at the uniformity of iron in the zeolite [39]. However, a distinct XRD peak of Z-A spiked at 27.9\({}^{\circ}\), which is considered to be a sodalite peak. This peak also appeared in Iron-Z-A because sodalite was partially synthesized during the synthesis of Z-A. The XRD patterns of P-adsorbed and P desorbed Iron-Z-A showed no peaks. The surface of fresh Iron-Z-A transformed from the phase of crystallinity to amorphic after the adsorption of phosphate. The more is the surface amorphicity, the more will it have active sites [47] or adsorption capacity [50], which is also supported by the EPR results.
# Research on Optimization Design of Geometric Parameters of a Novel Frame-Embedded Track (NFET)
## 4 Comparison of Mechanical Characteristics of NFET Structure under Different Period Conditions
### Calculation and Analysis of the Mechanical Characteristics of Each Design Scheme in Running Stage
#### 4.2.1 Beam Width Comparison  
The displacement and stress values of each design scheme in the running period are shown in Figures 12 and 13, respectively. The displacement of each measuring point is actually in the negative direction.  
Figure 11: Layout of track slab displacement measuring points.  
Figure 12: The vertical displacement trend of the rail seats and the bottom of the cast-in-place concrete under the conditions of different beam widths during the running period. (**a**) Rail seats and (**b**) cast-in-place concrete.  
Figure 13: The change trend of the cast-in-place concrete stress under different beam widths during the running period. (**a**) Rail seats and (**b**) cast-in-place concrete.  
According to Figure 12, considering the uneven settlement of the subgrade and the load of the tram, the vertical displacements of the bottom of the rail seats and the bottom of the cast-in-place concrete slab at the same measuring point are almost the same as each other. The reason is that, in this period, through the cementation effect of cast-in-place concrete, the coupling of the track slab is greatly improved and results in transforming into a systematic body. In addition, it can also be found that increasing the width of the beam will hardly change the displacement at each position of the structure, indicating that the width of the beam is not a controlling factor for the vertical displacement of the track structure in this period.  
According to Figure 13, the stress of the existing concrete structure is generally greater than the stress of the rail seats. For the rail seats, when the beam width is 150 mm, the stress at the beam is relatively small. When the beam width changes from 185 to 300 mm, the stress at each position does not change significantly, and the maximum stress appears in TMU; for cast-in-place concrete, TEB is the most dangerous point. The maximum stress is 1.33 MPa, the beam width is 240 mm, and the TEB and stress at the SBP are the smallest.
## 2 Experimental Section
### Characterization Methods  
Fresh catalysts were characterized by X-ray power diffraction (XRD) recorded in a Philips X'Pert diffractometer equipped with a graphite monochromator, operating at 40 kV and 45 mA, and using nickel-filtered Cu K\(\alpha\) radiation (\(\lambda\) = 0.1542 nm).  
The bulk chemical composition of the catalysts was analyzed in a 715-ES ICP-Optical Emission spectrometer, after dissolution of the solids in a HNO\({}_{3}\)/HF solution.  
Textural properties were determined from the nitrogen adsorption isotherm, measured at 77 K on a Micrometrics ASAP 2010 volumetric adsorption analyzer. Surface area and micropore volume values were obtained by applying the BET equation [70] and from the t-plot graph [71], respectively.  
The relative concentration of acidic sites in the different samples was obtained by FT-IR spectroscopy using pyridine as the probe molecule. Pyridine adsorption-desorption experiments were carried out on self-supported wafers (10 mg/cm) of samples activated at 673 K and 10\({}^{-2}\) Pa for 2 h. After wafer activation, the base spectrum was recorded, and pyridine vapor (6.5 \(\times\) 10\({}^{2}\) Pa) was admitted into the vacuum IR cell and adsorbed onto the zeolite. Desorption was performed in vacuum over three consecutive 1-h periods of heating at 523, 623, and 673 K, each followed by an IR measurement at room temperature. All the spectra were scaled according to the sample weight. The amount of Brensted and Lewis acid sites (BAS and LAS, respectively) was determined from the intensities of the bands at ca. 1545 and 1450 cm\({}^{-1}\), respectively, using the molar extinction coefficients given by Emeis [72].  
Metal dispersion of the Mo/ZSM-5 catalysts was examined by field emission scanning electron microscopy (FESEM) equipped with backscatter electron (BSE) detector on a ZEISS ULTRA 55 microscope operating at 2 kW.  
Temperature-programmed reduction (H\({}_{2}\)-TPR) was performed in a Micromeritics Autochem 2910. The sample (100 mg) was pre-treated in argon flow at room temperature for 30 min, then the gas was switched to the reduction mixture (10 wt. % H\({}_{2}\) in Ar) with a flow rate of 50 mL/min, and temperature was increased to 1373 K at a heating rate of 10 K/min. The H\({}_{2}\) consumption was followed by a thermal conductivity detector (TCD). No sublimation of molybdenum species was observed during the H\({}_{2}\)-TPR experiments, in agreement with previous descriptions [73].
## 4 Characterization and Performance Analysis
### SQM Sensitivity and Effectiveness in Multipath Detection
For short-delay multipath, the effectiveness of the SQM metrics is almost the same for all tracking strategies and signaling schemes. In this range of multipath delays, the SQM detection outputs can be effective when the range error envelopes take non-zero values. In this area (especially for multipath delays less than 10 m) however, due to the low sensitivity of all the SQM metrics, it is possible that the resulting SQM values do not exceed the detection thresholds and thus multipath remains undetected for a realistic range of multipath power. For medium and long-delay multipath, the conclusions are as follows:  
* Under NC discriminator and BPSK(1) signaling, the deviations of the SQM metrics from nominal values (SQM sensitivity) coincide with the non-zero tracking range error envelopes. This means that the SQM detection output can be exploited effectively for multipath mitigation by de-weighting (or excluding) distorted measurements (SQM effectiveness). This is true for all SQM metrics analyzed here. * The effectiveness of the SQM metric for a NC discriminator with BOC(1,1) modulation is less than that of BPSK(1) (mostly for path delays longer than approximately 0.5 chips) where the corresponding ranging error is about one third of that of the BPSK(1) signal. In this scenario, according to the SQM profiles, the sensitivity of the metrics is also lower, which can be justified based on the different shapes of the correlation curves. * In the case of the HRC discriminator, for BPSK(1) signaling, the SQM methods are not effective for medium-delay multipath due to negligible ranging errors. In this scenario, relying on SQM detection to de-weight or exclude measurements may even increase position errors due to geometry degradation. For BOC(1,1), the SQM can be effective for some intervals of medium-delay multipath when tracking range error envelopes take non-zero values. For long-delay multipath, comparing BPSK(1) and BOC(1,1) schemes, a lower SQM performance is observed for BOC(1,1) where the sensitivity of the SQM metrics is less and the multipath performance of the BOC(1,1) is significantly better than that of the BPSK(1). * For a given SMR value, a higher C/N\({}_{0}\) value increases SQM sensitivity by reducing the nominal variance of SQM metrics and lowering the detection threshold. In all cases, lower signal to multipath ratios (SMR) result in higher SQM sensitivity as expected. Figure 6: SQM profiles for Double-Delta metric and corresponding multipath error envelopes for (**a**) Narrow Correlator (NC) tracking strategy and BPSK(1) signaling scheme, SMR = 3 (dB); (**b**) HRC tracking strategy and BPSK(1) signaling scheme, SMR = 3 (dB).
# Connecting the Elderly Using VR: A Novel Art-Driven Methodology
## 3 Materials and Methods  
### Methodology  
To address elderly people's needs, artists used technologies developed or provided by the engineers in order to collect seniors' topics of interests and create a useful VR tool for engagement with the goal of stimulating them intellectually, instigating further thoughts and communication. As already mentioned in the introduction of this paper, the VR public space for elders was developed with the strong involvement of artists, end users, architects, and software engineers throughout the design and development cycles, which by itself is an innovative design approach. The development methodology, which is depicted in Figure 1, included the following steps: (a) the creation of the first VR installation prototype, as a result of the requirement analysis and the synergy between the end users, artists, and software engineers. In this step, designers and artists were provided with 3D models of elderly people's living spaces as well as information sourced from interviews about habits their homes, etc.; (b) the implementation of the first prototype; (c) the collection of measurements in order to define the most visited VR areas; (d) analysis of stress and sentiment of subjects in the most visited areas (VR space hotspots); (e) evaluation of prototypes by the subjects via questionnaires; (f) assessment of prototypes by the artists after user feedback and sentiment analysis that led to innovative design proposals, responding to innate user needs; (g) adaption and redesign of the VR installation after assessment to meet end users' needs.  
In essence, artists and creative thinkers were asked to suggest imaginative ideas pertaining to the quality of the virtual reality (VR) social environment and its emotional and functional user-friendliness. The artistic proposal aims to concentrate on themes such as the emotional well-being of seniors, their social connections, personal expression, and memory. Consequently, the VR environment can be viewed as a work of art in its own right, potentially assisting seniors in cultivating resilience and engaging in active social interactions.  
In the following subsections, we provide a detailed description of the materials, methods, and data collection that supported our implementation.
# Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
## 1 Introduction
Hence, an efficiency optimization method with no mechanical sensor requirements was attained. This method has been further developed with varying frequency in [22], which utilizes the phase difference of main and auxiliary stator winding currents. Although this method might be appealing to SPIM drives due to the rather cheap and simple implementations, major deficiencies lie within it. Firstly, it lacks any sort of speed regulation due to its open loop speed control scheme. In addition, it is sensitive to motor parameters variations. In some of the proposed methods the optimum operating point of the motor is determined via utilizing machine learning approach [23]. Figure 1: Schematic of SPIM drive. The aforementioned method requires mechanical sensors for the aim of measurements, which make them rather impractical and uneconomical for the case of SPIMS. The motor parameters are prone to change due to environmental conditions, temperature variations, saturation and discrepancy of the same model motor parameters. These changes lead to deflection in the optimal efficiency point [24]. Therefore, in case of applying the methods which are offline, the controller would naturally be unable to track the deflected optimal efficiency point and henceforth the motor efficiency would never be the ultimate maximum. Furthermore, parameter variations can degrade the accuracy of speed estimation algorithms [12]. To deal with this issue, this paper proposes a novel speed sensorless and optimal efficiency control method for SPIMs. In the proposed method, the motor speed is estimated based on Stator Current Ratio using a neural network. Furthermore, the optimum efficiency conditions are derived using an online loss minimization method. This way, in addition to tracking the optimum operating point in online mode, accurate speed regulation is realized. The proposed method is verified by running a simulation using MATLAB software. The rest of the paper is organized as follows. The mathematical model of SPIM is addressed in Section 2. Speed estimation and efficiency optimization methods are explained in Sections 3 and 4, respectively. The proposed control scheme is addressed in Section 5. Simulation results are addressed in Section 6. Section 7 concludes the paper.
## 4 Hardware Customization
### Accessories Detection and Health Mote
#### 4.1.1 Helmet and Goggles' Detection Mote  
Wearing a helmet on the construction site is highly important to save people from unexpected injuries. The purpose of the helmet and goggle detection mote is to check whether the worker is wearing a helmet or not, using an eye blink sensor. Workers might remove their helmets after they enter the site, monitoring it on a regular basis is very important to notify the worker and the management and to reduce the risk. Eye protection is also important for workers since a lot of dust particles are present on the construction site. The goggles and helmet are attached to each other to protect the eyes from dust and an eye blink sensor is placed on the goggles to monitor the eye blink continuously, if the eyeblink sensor detects the eye lid movement regularly then it means the worker has worn the helmet, otherwise the helmet was removed. Every helmet is embedded with a RFID card through which it can be detected who has removed their helmet, a buzzer placed on the helmet rimngs continuously to alert the worker when eye lid movement is not detected and the same information is updated to management in the local server using the RF Transceiver, as shown in the Figure 8a. The customized board for helmet detection is shown in Figure 8b, an eye blink sensor on the goggles is integrated in this mote, which helps to detect whether the worker is wearing the helmet or not.  
Moreover, the RFID tag integrated with this helmet is for confirming the identity during entry into construction site. The IEEE 802.15.4-based Zigbee module works on 2.4 GHz frequency-based wireless communication and its use in detection mote allows the information to be sent to the local server with respect to the firmware instructions embedded in the controller. The technical specifications of the modules used in the helmet and goggle detection mote are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications** \\ \hline Microcontroller: & ATMega328P \\ Communication module: & nRF24L01+ Wireless module (2.4 GHz) \\ Voltage converter: & +5 V \\ PIR Sensor: & HC-SR501 \\ RFID Reader: & EM 18 (125 KHz) \\ LCD: & 20 \(\times\) 4 Alphanumeric Display \\ Buzzer: & Piezo Passive Buzzer \\ Power trails: & +5 V \\ Power jack: & +12 V External power supply \\ Programming pins: & FTDI and ICSP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical Specifications of Entry Node.  
Figure 8: (**a**) Helmet and Goggles’ Detection Mote; (**b**) Customized Hardware of Helmet and Goggles’ Detection Mote.
## 3 DC Circuit Breakers
### Hybrid Circuit Breakers
#### 3.4.2 Recent Developments of HCBs  
The following paragraphs have the purpose of showing the most recent developments for HCBs in terms of the type of semiconductor and material used:  
In the Ref. [96], the authors compared the general characteristics of press pack IGBT and injection-enhanced gate transistor (IEGT) used in HCBs. Basically, the maximum blocking voltage, maximum turn-off current, surge current, \(di/dt\), on-state voltage, drive power, failure mode, and voltage balance for series were compared. The IEGT was more suitable for natural commutation than HCBs when the system fault current was not very high. Comparatively, IGBT and IEGT were more suitable for very high current interruption, and IEGT was the superior selection based on their experiments. The advantage of HCBs is that they have very low on-state losses. Furthermore, the current can be turned off independently from a natural zero crossing. However, HCBs are very expensive and their speed is highly dependent on the mechanical parts of the system [94]. Consequently, a standard mechanical circuit breaker cannot be used because of its lack of speed [23], which represents a limitation for DCMG protection.  
To improve the speed of HCBs, several fast-acting mechanisms have been proposed to reduce the commutation time of MCBs to SSCB, such as Thomson coil and piezoelectric actuators, which act in hundreds of microseconds. However, in experiments performed with HCBs, the whole process takes between 0.5 and 5.5 ms [97; 98].
## 4 Results
### Experiment with Data-2
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PSNR (\(\uparrow\))** & **SSIM (\(\uparrow\))** & **ENL (\(\uparrow\))** & **CNR (\(\uparrow\))** \\ \hline NLM & 25.151 & 0.422 & 25.689 & 2.413 \\ BM3D & 27.584 & 0.559 & 50.503 & 2.926 \\ DnCNN & 27.739 & 0.605 & 161.126 & 2.789 \\ SDSR-OCT & 28.310 & 0.608 & 152.350 & 3.619 \\ DudeNet & 28.612 & 0.658 & 535.944 & 3.625 \\ MDGAN & 28.697 & 0.676 & 489.783 & 3.651 \\ MDRGAN & 28.706 & 0.661 & 613.285 & 3.767 \\ DCANet & 28.434 & 0.688 & 379.336 & 3.761 \\ DRANet & 28.425 & 0.611 & 182.153 & 3.818 \\ MFFA-GAN & 29.117 & 0.679 & 629.303 & 4.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different methods on Data-2. Figure 9: The OCT retinal image from Data-2 that was processed by different denoising methods: (**a**) noisy image, (**b**) ground-truth image, (**c**) NLM, (**d**) BM3D, (**e**) DnCNN, (**f**) SDSR-OCT, (**g**) DudeNet, (**h**) MDGAN, (**i**) MDGRAN, (**j**) DCANet, (**k**) DRANet, and (**l**) MFFA-GAN. The blue square represents the background area, and the red square represents the signal area.
## 5 Discussions and Future Perspectives
### Applications and Interdisciplinary Connections  
Advances in HDR reconstruction have implications beyond image processing:  
* Medicine: Images with higher exposure fidelity can reveal critical details for earlier and more accurate diagnoses, aiding medical imaging.
* VR/AR: HDR reconstruction algorithms can enhance immersion and realism in virtual environments, benefiting applications in entertainment, training, and simulation.
* Entertainment and Media: Televisions, monitors, and cameras using HDR technology provide richer visual experiences, increasing the impact of cinematic and advertising content.
* Autonomous Vehicles: Handling extreme lighting variations is crucial for vehicle vision systems, improving environmental interpretation under adverse conditions.
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 3 Calculation of Contact Force
### Normal Stiffness  
To correct the normal stiffness coefficient, a universally applicable initial value of the normal stiffness coefficient needs to be determined. Therefore, the formula for calculating the normal stiffness coefficient \(k_{n}\) derived from the collision between a spherical ball and a plane in reference [30] is used in this work. The formula takes into account the elastic modulus and Poisson's ratio of the material itself when calculating the contact force. The specific method for determining the normal stiffness coefficient is as follows:  
\[k_{n}=\frac{4}{3}\frac{E}{(1-\sigma^{2})}\sqrt{R} \tag{3}\]  
where \(E\) represents the elastic modulus of the material, \(\sigma\) represents the Poisson's ratio, and R represents the radius of the equivalent sphere.  
Since there are two different thicknesses of the specimens used in this experiment, substituting the values yields an equivalent sphere radius of 2.5325 \(\times\) 10\({}^{-2}\) m for the specimen with a thickness of 2 cm and an equivalent sphere radius of 3.6525 \(\times\) 10\({}^{-2}\) m for the specimen with a thickness of 6 cm. After substituting the equivalent sphere radius into Equation (3), the normal stiffness coefficients for the two types of specimens are 1.5234 \(\times\) 10\({}^{9}\) N/m and 1.8295 \(\times\) 10\({}^{9}\) N/m, respectively.