esa-sceva/satcom-chunk-collection huggingface dataset:
# Dental Microstructural Imaging: From Conventional Radiology to In Vivo Confocal Microscopy
## 3 In Vitro Imaging Techniques
### Laser Speckle Imaging  
The _laser speckle imaging_ system is a non-destructive method based on the evaluation of the inhomogeneity of the microstructure of the dental surface in case of demineralization, erosion, or caries, accordingly with the different intensities of the backscattered coherent light detected from enamel and dentin irradiated with laser light [55]. Since the passage of light through the dental tissues changes according to the state of its regular/irregular mineralization, any change in intensity and direction of the light emitted from the dental surface helps to detect the degree of defect. The development of fiber-optic laser transmission systems has proven beneficial in the in vitro microscopic observation of carious lesions, which have a lower light transmission index, whereby a carious surface appears as a dark shadow that follows the spread of caries across the tooth, as reported by [PERSON] and colleagues [56]. Their in vitro analysis evaluated the remineralizing effects of different bioactive powders upon enamel blocks of bovine incisors with simulated caries lesions [56]. The specimens were irradiated by a HeNe Laser (at the wavelength of 633 nm, with a power of 40 mW), and the backscattered light was detected by a complementary metal oxide semiconductor (CMOS) sensor. The authors confirmed that their imaging methodology achieved microstructural data of the mineralization and remineralization status with good sensitivity. To date, preliminary in vivo works have demonstrated the applicability of a chairside prototype to discriminate gingival inflammation [57].
# Design Techniques for Low-Voltage RF/mm-Wave Circuits in Nanometer CMOS Technologies
## 3 The Body Biasing Technique  
The body biasing technique can be used to properly modify the threshold voltage, \(V_{\text{T}}\), according to the well-known expression reported below for an n-MOS device:  
\[V_{\text{T}}=V_{\text{T0}}+\gamma\Big{(}\sqrt{2\varnothing_{\text{F}}-V_{\text{ BS}}}-\sqrt{2\varnothing_{\text{F}}}\Big{)} \tag{1}\]  
where \(V_{\text{BS}}\) is the voltage between the body and the source, \(V_{\text{T0}}\) is the threshold voltage for zero \(V_{\text{BS}}\), \(\varnothing_{\text{F}}\) is the bulk Fermi potential, and \(\gamma\) is the body effect coefficient. Changing  
Figure 3: BEOL comparison between (**a**) mm-wave-optimized BiCMOS technology [17] and (**b**) 28-nm CMOS technology with standard BEOL [2], Reprinted from Ref [18].  
Figure 2: Integrated transformer configurations for RF/mm-wave operation. Reprinted with permission from Ref [9]. Copyright 2020, IEEE.  
modifies \(V_{\text{T}}\), and, thus, the transistor can be operated at a reduced bias voltage, with almost equivalent characteristics in terms of gain, linearity, and noise. Forward (reverse) biasing of n-MOS (p-MOS) transistors has been largely used at low frequencies, but it has also been proven to be effective for RF/mm-wave operations [19, 20, 21, 22, 23, 24]. It is worth mentioning that for n-MOS (p-MOS) transistors, a forward (reverse) body bias turns on the source-to-body diode, and, thus, an exponential DC current flows across the junction. This produces additional power consumption and can also cause latch-up failure. A simple, but effective, solution to limit the junction current is to add a resistor to the body terminal.  
The effectiveness of the body biasing technique is highly increased in the FD-SOI CMOS platform, thanks to a wider \(V_{\text{T}}\) variation with respect to bulk technologies and the lack of leakage currents. Figure 4a shows the simplified cross-section of a commercial FD-SOI CMOS technology [19, 20, 21, 22, 23, 24]. Transistors are fabricated in a 7-nm layer of silicon sitting over a 25-nm buried oxide (BOX). An ultra-thin body (UTB) guarantees an isolated electrical conduction channel between the source and drain, since the channel is fully depleted. Figure 4a shows flipped-well low-\(V_{\text{T}}\) (LVT) transistors that use an n-well body and p-well body for n-MOS and p-MOS devices, respectively. In these transistors, the BOX layer works as a second gate oxide, and the body as a gate terminal (i.e., back gate terminal) that allows the tuning of \(V_{\text{T}}\) by means of the body voltage. The body gain factor (i.e., the sensitivity of \(V_{\text{T}}\) to the body voltage) is almost four times that of the traditional bulk technology (i.e., 90 mV/V). The lower \(V_{\text{T}}\) characteristics of flipped-well devices are well exploited in low-voltage ICs.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Additional Experiments  
The remaining experiments considered different networking scenarios to evaluate the responsiveness and robustness of RSF MPTCP.  
In Scenario 7, we used iPerf3 to study the performance of short-lived MPTCP flows. For the network setup, we used RSF-DB and set equal bandwidth (20 Mbps), delay (150 ms), and loss (2%) for both paths. For the traffic, we generated short-lived flows with randomly generated durations between 1 and 30 s. These flows arrived in the network according to a Poisson arrival process (arrival rate = 8) and departed at random times, as well.
# Multiport Single Element Mimo Antenna Systems: A Review
## 4 Multiport Single Element System
### Single Element Two-Ports MIMO Antenna System
An edged rectangular slot placed in the radiator increases the isolation between the two ports for the operating frequency range from 3 to 12 GHz. The measured ECC in terms of far field is less than 0.01. In [75], a circular-shaped common radiator is connected by two tapered coplanar waveguide feeding structures as shown in Figure 13. It consists of a ground plane printed on the bottom layer corner of the substrate and the circular radiator placed on the top layer. In this antenna design, the implemented common radiating element decreases the overall size of the antenna to dimensions of 41 mm \(\times\) 41 mm and supports in creating the band notch elements in the radiator. Good isolation is achieved through the rectangular slot and inverted Y-shaped strip placed in the middle of the radiator and to the ground plane respectively. Wide bandwidth is obtained for UWB applications, which covers the frequency ranging from 2.9 GHz to 12 GHz. As discussed in this section, a CPW-feed provides easy integration and placement of multiple antennas with shared radiator with existing MMIC technology on a single-sided PCB board. The objective is to have compact antennas, space being the primary concern in most of the applications. Thus, an alternative feeding technique is required, the ACS [65; 66] feed antenna currently has been significantly explored and can still facilitate the basic antenna requirements. In [66], through proper placement of an I-shaped slot in the radiator along with a stub and a slot etched on ground plane achieve good isolation. In [67], by using a cross-shaped slot etched in a common radiator, mutual coupling is reduced, but the antenna performance is not identical for both ports due to asymmetrical radiator configuration seen from the two ports. In addition, most of the single element antennas reported in the literature are for UWB-MIMO applications and to achieve ample isolation between antenna ports, slots are used. The usage of a slot on a shared radiator is one technique for obtaining effective isolation between the antenna ports. To control the effect of mutual coupling, a chain-shaped parasitic isolation structure is used for the common element MIMO antenna and ground plane [61]. Similarly, a circular patch is shared by two co-planar waveguide (CPW) feeds and mutual coupling is reduced with the help of a single slot etched in the patch [74] and by introducing  
Figure 7.0 Fabricated two port PIFA MIMO antenna design with shared radiator [73]. Figure 7.1 Circular patch shared radiator [75]. a slot in patch and stub etched to the ground plane. In [75], two band-rejections are also achieved using elliptical split ring slots.
## 2 Materials and Methods  
### Preparation of Soil and Plant Material  
The soil for the experiment was taken from a long-term field experiment at the grassland research station of the University of Giessen (latitude N50\({}^{\circ}\)32\({}^{\prime}\), longitude E8\({}^{\circ}\)41.3\({}^{\prime}\), elevation 172 m a.s.l.), sieved to 10 mm, air-dried, and stored at 4 \({}^{\circ}\)C. The soil was classified as Fluvic Gleysol of clay loam texture (32% clay, 41% silt, and 27% sand) with a pH (CaCl\({}_{2}\)) of 5.67. Total soil N content was 0.42%, total soil C content was 4.2%, and \(\delta\)\({}^{13}\)C was \(-\)28.37%. Prior to the incubation experiment, the soil was pre-incubated in the dark for 5 weeks at 50% water holding capacity (WHC) and 20 \({}^{\circ}\)C.  
Maize plants (_Zea mays_ L. [PERSON]) were grown in nutrient solution [24] for 5 weeks. Leaves were cut from stems and left to wilt at room temperature for 4 h. Roots were rinsed with H\({}_{2}\)O\({}_{\text{dest}}\) and carefully dried with paper towels. Leaves and roots were stored at 4 \({}^{\circ}\)C and 90% relative humidity until experimental setup. Maize straw was collected from an experimental field site of the University of Gottingen after grain harvest in October 2018. Maize straw was shock frozen in liquid N\({}_{2}\) and stored at \(-\)20 \({}^{\circ}\)C. Prior to setting up the experiment, maize straw was unfrozen and all maize litter was cut to a size of 2 cm. A subsample of soil and maize litter was analyzed for total N, total C, and \(\delta\)\({}^{13}\)C using an elemental analyzer (NA1110, CE-Instruments, Rodano, Milano, Italy) linked to a gas-isotope ratio mass spectrometer (Delta Plus, Finnigan MAT, Bremen, Germany) via a Conflo III Interface (Finnigan MAT, Bremen, Germany). Further, plant litter was analyzed for water-extractable C and N content. Briefly, 0.2 g of finely ground plant litter were extracted in 100 mL H\({}_{2}\)O\({}_{\text{b}\text{d}\text{e}\text{t}}\), shaken for 1 h, filtered with 0.45 \(\upmu\)m polyether sulfone filters (Labsolute, Renningen, Germany), and stored at \(-\)20 \({}^{\circ}\)C. The extracts were analyzed for organic C and total N content using a multi N/C(r) Analyzer (Analytik Jena, Jena, Germany). Another subsample of finely ground plant litter was analyzed by \({}^{13}\)C solid-state cross polarization magic angle spinning nuclear magnetic resonance spectroscopy (\({}^{13}\)C-CPMAS NMR) using a Bruker AvanceIII 200 spectrometer (Bruker BioSpin GmbH, Karlsruhe, Germany). Samples were weighed into zircon oxide rotors and spun around a magic angle at a speed of 6.8 kHz. Contact time was 1 ms and the recycle delay time was set to 2 s, line broadening was set at 0. Peak integration areas were separated into \(-\)10-45 ppm (alkyl C), 45-110 ppm (O/N-alkyl C), 110-160 ppm (aryl C), and 160-220 ppm (carboxylic C).
## 5 Experiment and Result Analysis
### Feature Data Selection
#### 5.1.3 Validation of Feature Validity
most of the information of component content, the total information is less than that of other feature sets, resulting in a suboptimal prediction effect. In summary, while it is generally the case that more features can encompass more information, an excess of information can lead to feature redundancy. Simultaneously, spatial texture characteristics and extraction order can play a supportive role in predicting the component content of rare earth elements. Moreover, the overall performance of mixed features filtered by the RF-RFECV algorithm is superior to that of single color features, indicating that mixed features can provide more comprehensive and richer feature information.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Color Parameter**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Initial**} & \multirow{2}{*}{**After In-Office**} & \multirow{2}{*}{**After Home**} \\  & & & **Whitening** & & **Whitening** \\  & & & **Mean 95\% CI** & & **Mean 95\% CI** \\ \hline \multirow{4}{*}{**L\({}^{*}\)**} & \multirow{2}{*}{1S} & 80.5 & 82.1 & W test & 86.4 & W test \\  & & 78.9–82.0 & 80.6–83.6 & \(p=0.002\)* & 85.2–87.5 & \(p<0.001\)* \\ \cline{2-6}  & \multirow{2}{*}{2S} & 79.9 & 81.9 & W test & 85.1 & W test \\  & & 78.7–81.1 & 80.6–83.2 & \(p<0.001\)* & 83.8–86.3 & \(p<0.001\)* \\ \cline{2-6}  & \multirow{4}{*}{1S} & U test & U test & & U test \\  & & \(p=0.317\) & \(p=0.589\) & & \(p=0.154\) \\ \hline \multirow{4}{*}{**a\({}^{*}\)**} & \multirow{2}{*}{1S} & \(-0.43\) & \(-0.49\) & W test & \(-2.20\) & W test \\  & & \(-0.76\)—0.09 & \(-0.80\)—0.18 & \(p=0.471\) & \(-2.35\)—2.05 & \(p<0.001\)* \\ \cline{2-6}  & \multirow{2}{*}{2S} & \(-0.12\) & \(-0.50\) & W test & \(-1.89\) & W test \\  & & \(-0.45\)—0.20 & \(-0.80\)—0.20 & \(p=0.001\)* & \(-2.03\)—1.62 & \(p<0.001\)* \\ \cline{2-6}  & \multirow{4}{*}{1S} & U test & U test & & U test \\  & & \(p=0.165\) & \(p=0.935\) & & \(p=0.033\)* \\ \hline \multirow{4}{*}{**b\({}^{*}\)**} & \multirow{2}{*}{1S} & 21.2 & 20.1 & W test & 9.72 & W test \\  & & 19.5–22.9 & 18.6–21.5 & \(p<0.001\)* & 8.82–10.6 & \(p<0.001\)* \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{2S} & 22.8 & 20.9 & W test & 11.5 & W test \\  & & 21.2–24.5 & 19.4–22.2 & \(p<0.0001\)* & 10.4–12.7 & \(p<0.001\)* \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{1S} & U test & U test & & U test \\  & & \(p=0.176\) & \(p=0.548\) & & \(p=0.008\)* \\ \hline \hline \end{tabular}
* Significant difference \(p<0.05\).  
\end{table} 
Table 3: Values of color parameters L\({}^{*}\), a* and b* after in-office whitening and home whitening in relation to the number of in-office sessions (one or two). Mann–Whitney U test applied to identify intragroup differences. Figure 6: Survival curves (Kaplan–Meier method) of weeks required before reaching color stabilization in relation to number of in-office whitening sessions (one or two). N in-office sessions: blue line Group 1S; green line Group 2S. Figure 7: _Cont._  
Figure 8: _Cont._  
Figure 7: (**a**) Image of Group 1S patient before whitening treatment. (**b**) Photograph of Group 1S patient after in-office whitening. (**c**) Appearance of teeth of a Group 1S patient after home whitening.
# Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains
###### Abstract
In order to perform aerodynamic noise optimization, two problems have to be solved in advance: On the one hand, the efficiency of CAA calculation has to be improved, which means proper CAA algorithm should be determined to adapt the computing scale for high-speed trains; On the other hand, assessment of the noise level in the far field should be determined. Once the above problems have been solved, the aerodynamic noise optimization becomes feasible. It is very crucial to construct an aerodynamic noise optimization strategy for practical streamlined shape of high-speed trains. In the present paper, the authors present an aerodynamic noise optimization strategy for practical streamlined shape of high-speed trains. The NLAS approach [17] has been adopted to perform CAA analysis for each design sample. Meanwhile, the equivalent continuous SPL has been introduced to take all of the far field observation probes into consideration and taken as one objective for multi-objective optimization. With the use of Kriging surrogate model, the aerodynamic noise optimization of the streamlined shape of high-speed trains could be performed. In the present study, the noise level in the far field and the drag of the whole train are taken as the objectives. It is mainly based on the following considerations: The aerodynamic drag of high-speed trains can be up to 75% of the total drag at the speed of 300 km/h and the drag characteristics of the trains are directly related to the ability of energy saving and environmental protection. As a result, the aerodynamic drag reduction is very prominent and has become one of the key issues for aerodynamic shape optimization of high-speed trains. Meanwhile, aerodynamic noise increases with the sixth power of the running speed, and would be predominant over mechanical noise when the running speed is over 250 km/h. It is found that simply reducing aerodynamic drag cannot efficiently reduce the aerodynamic noise, indicating that aerodynamic noise optimization has to take aerodynamic noise as one optimization objective. After optimization, aerodynamic performance of the optimized shape and the prototype shape with three carariages is comparatively analyzed. Finally, sensitivity analysis has been performed, so that the nonlinear relationship between the objectives and design variables could be obtained. Current research pushes forward the cognition on how to reduce the noise level by modifying the streamlined shape of high-speed trains.
## 7 Results and Analysis
### Comparison with Advanced Baselines
Thus, despite its unparalleled security guarantees, the Blockchain-FL approach is impractical for applications demanding ultra-low latency and real-time responsiveness. Our proposed framework, on the other hand, achieves a balanced performance across all key metrics. It delivers 32% energy savings, the highest among the compared methods, while maintaining sub-millisecond latency at 1.2 milliseconds and achieving 94% poisoning resilience. This equilibrium between energy efficiency, ultra-low latency, and strong security resilience positions our framework as an optimal candidate for deployment in Near-Real-Time O-RAN control loops, where all three aspects are critical for operational viability and service quality assurance. In the broader discussion of trade-offs, while Transformer-based FL frameworks enhance predictive capabilities for tasks not tightly bound to temporal constraints, their high computational demands limit their usefulness in real-time RAN scenarios. Simi \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Energy Savings (\%)** & **Latency (ms)** & **Poisoning Resilience (\%)** \\ \hline Proposed Framework & 32 & 1.2 & 94 \\ FedAvg [8] & 25 & 9.1 & 67 \\ Centralized & 18 & 6.7 & 72 \\ Transformer-FL [64] & 28 & 2.5 & 82 \\ Blockchain-FL [43] & 18 & 15.3 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison with advanced baselines (poisoning resilience = 15% attackers). larly, Blockchain-enhanced FL solutions provide unmatched security and auditability, but their consensus and cryptographic operations impose latencies that are incompatible with stringent near-RT requirements. Our framework addresses these limitations by carefully balancing the O-RAN triad of energy efficiency, latency, and trustworthiness, enabling scalable, secure, and responsive intelligent control across diverse deployment environments. This balance is particularly critical as networks evolve toward more autonomous, self-optimizing architectures, where any compromise in one of these aspects can directly impact both performance and user experience.
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
## 1 Introduction
For instance, Honda's electric car \"Insight\" features a regenerative braking system based on an ISG motor and hydraulic system. By combining this system with throttle control methods, they developed a control strategy for the braking force distribution coefficient, achieving efficient operation of the regenerative braking system. Similarly, Ford's fuel cell car \"Focus\" is equipped with a regenerative braking system that enables the vehicle to achieve a range of 160-200 miles. Additionally, the HILS system developed by Chungham National University in South Korea is specifically designed for the braking force distribution system of hybrid vehicles. These examples illustrate that regenerative braking systems can significantly increase a vehicle's driving range. Therefore, it is feasible to install regenerative braking systems on electric mine cars to achieve energy conservation and emission reduction goals [9; 10; 11; 12]. To improve the working efficiency and range of underground mine cars, this paper suggests installing a regenerative braking device on the traction motor car. This device efficiently converts the generated potential energy during downhill travel and the energy produced during braking into electrical energy, which is then stored in a battery. The stored energy can subsequently power the motor car, enhancing its range. Moreover, the regenerative braking device can assist the vehicle during acceleration, further improving its range. Additionally, this device can contribute to braking by reducing wear on the vehicle's braking system.
## 1 Introduction
### Analysis of Demagnetization Fault Identified Signal
#### 2.3.1 Analysis of the Main Identification Signal
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Values** & **Unit** \\ \hline Rated power & 1.5 & kW \\ Rated current & 4 & Arms \\ Rated speed & 180 & rpm \\ Rated frequency & 66 & Hz \\ Number of phases & 3 & / \\ Number of parallel circuits per phase & 1 & / \\ Number of coils & 24 & / \\ Air-gap length & 0.9 & mm \\ Stator out diameter & 270 & mm \\ Stator inner diameter & 203 & mm \\ Permanent magnet thickness & 4.5 & mm \\ Permanent magnet width & 11 & mm \\ Permanent magnet length & 100 & mm \\ Permanent magnet material & NdFeB & / \\ Pole-arc coefficient & 0.73 & / \\ Slot–pole combination & 48-slot/44-pole & / \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The key specifications of the test DDPMSM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Demagnetization** & \multicolumn{3}{c}{**PM Number or Electrical**} & \multicolumn{3}{c}{**PM Number or Electrical**} \\ \multicolumn{3}{c}{**Period Number (\(i=1\))**} & \multicolumn{3}{c}{**Period Number (\(1<i\leq p\))**} \\ \cline{2-5}
**Fault Mode** & **2\(p\)** & **1** & **2** & **2\((i-1)\)** & **2\(i-1\)** & **2\(i\)** \\ \hline Mode 1(000) & 0 & 0 & 0 & 0 & 0 \\ Mode 2(001) & 0 & 0 & 1 & 0 & 0 & 1 \\ Mode 3(110) & 1 & 1 & 0 & 1 & 1 & 0 \\ Mode 4(011) & 0 & 1 & 1 & 0 & 1 & 1  
severity. Therefore, the \(Sl_{\text{m-2}}\) can discriminate six fault modes including Mode 1 to Mode 6, and then, determine the healthy condition of PM2, PM3, and PM4. As shown in Figure 2b, the waveform trends of \(Sl_{\text{m-2}}\) under the three fault modes including Mode 6, Mode 7, and Mode 8 are the same, but the amplitudes are different. The amplitude of \(Sl_{\text{m-2}}\) depends on demagnetization severity. Hence, in the following discussion, the effect of demagnetization severity on \(Sl_{\text{m-2}}\) is investigated.