esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A low-noise multimodal reconfigurable sensor readout circuit for voltage/resistive/current/capacitive microsensors has been presented. The proposed circuit has a reconfigurable structure that can provide a voltage/resistive-sensing mode, a current-sensing mode, or a capacitive-sensing mode. The reconfigurable characteristics of the proposed circuit can be applied in an environmental monitoring system using various sensors. A low noise and wide bandwidth were achieved by applying multiple DOC techniques, including chopper stabilization and the CDS technique. The chopper-stabilized, multipath operational amplifier with an AC-coupled RRL resolved the band-limiting issue and the ripple caused by the chopper. The measurement input noise values in the voltage/resistive/current/capacitive-sensing modes were 5.25 mVrms, 2.9 m\(\Omega_{\text{rms}}\), 742 pArms, and 0.313 aFrms, respectively, from 1 Hz to 200 Hz. The proposed readout circuit was fabricated using  
Figure 18: The real-time measurement result using a capacitive z-axis accelerometer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **This Work** & **Ref. [20]** & **Ref. [21]** & **Ref. [10]** & **Ref. [12]** \\ \hline Supply (V) & 1.8 & 2.7 & 5 & 1.2 & 1.8 \\ Technology (\(\upmu\)m) & 0.18 & 0.18 & 0.35 & 0.18 & 0.35 \\ Power consumption (mW) & 2.552 & 0.02765 & 189.3 & 0.058 & 0.942 \\ Area (mm\({}^{2}\)) & 9.61 & N/A & 12.5 & N/A & 11.25 \\ Signal type & V/I/R/C & R & C & V/R & V/I/R/C \\ Noise cancellation & Chopping, CDS & Chopping & CDS & Chopping & Chopping \\ Input noise & 5.25 mVrms & 3.76 mVrms & N/A & 0.6 mVrms & 0.769 mVrms \\ Resistance noise floor & 2.9 m\(\Omega_{\text{rms}}\) & N/A & N/A & 8.6 m\(\Omega\)/kHz & N/A \\ Input current noise & 742 pArms & N/A & N/A & N/A & N/A \\ Capacitance noise floor & 0.313 aFrms & N/A & 0.41 aFrms & N/A & N/A \\ V: 70 mV & \(\text{V}_{\text{PP}}\) & & & & \\ Input range & \(\text{R}: 0.4-0.40\) & \(\text{V}:17.6\) mV & \(\text{C}:\pm 2.9\) pF & \(\text{V}:30\) mV\({}_{\text{PP}}\) & \(\text{C}:10\) pF \\ Input range & \(\text{I}: 1.65\) uA & & & & \\  & \(\text{C}:\pm 2\) pF & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance summary and comparison.  
0.18-\(\upmu\)m CMOS technology to fabricate a device with an active area of 9.61 mm\({}^{2}\). The total power consumption was 2.552 mW with a 1.8-V supply voltage.  
Conceptualization and supervision, H.K. ([PERSON]); investigation and writing--original draft preparation, D.Y.; writing--review and editing, H.K. ([PERSON]), J.K., K.H., H.H., Y.K., G.K., W.S.S., J.W.L. and B.J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the NanoMaterial Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2015M3A7B7045527). This research was also supported by the Brain Korea 21 Plus Project.  
The chip fabrication and EDA tool were provided by the IC Design Education Center (IDEC), Korea.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
The diamond ATR prism and the sample compartment were made suitable for cooling by using a bath from above the sample and also by a cooling/heating apparatus attached to the ATR bowl (Figure 3). The temperature variation was monitored with a thermocouple probe placed in contact with the sample next to the diamond crystal. The temperature variation was monitored with scans in real time, i.e., repeated scans as the temperature was changing. Due to the brightness of the Australian synchrotron THz source, attenuated total reflection (ATR) apparatus can produce a scan every 0.3-0.4 s at THz frequencies. Data were collected and presented with each datapoint being an average of 50 scans. This resulted in total data gathering times for each sample of 15 to 20 min. Data analysis was carried out with the OPUS 8.0 software. The experiments were conducted over the frequency range of 0.18 to 3 THz. In practice, the useful sensitivity of the detector was in the range of wavenumber 23 to 38 (-0.7 to 1.15 THz) and wavenumber 56.5 to 75.0 (-1.70 to 2.25 THz). The best sensitivity of the detector was in the range of 0.85 to 1.05 THz and 1.85 to 2.10 THz.
## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.5 Al-Enabled Networks

Table 3: Summary of future research challenges of supporting technologies for the 6G networks. While achieving high-speed communication technologies, the goal of constructing green, low-carbon 6G networks is to minimize environmental effect. Sixth-generation networks may greatly mitigate their impact on climate change by lowering their energy usage and carbon emissions. Maximizing the usage of renewable energy in network base stations and data centres is a primary goal in the design and implementation of 6G network equipment and infrastructure. This will improve energy efficiency and decrease waste. The research [139] offered a model for a 6G wireless environmentally friendly network design. This design's standout qualities are its ability to schedule tasks in an energy-efficient manner, its intelligent communication nodes, and the thorough separation of the user and control planes. Ensures energy efficiency and sustainability in 6G networks by discussing the difficulties and opportunities in [140].
## 3 Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Study** & \begin{tabular}{c} **No. of** \\ **Patients** \\ \end{tabular} & **Facial Skeleton** & **Entzonal Soft Tissues** & **Dentition** & **Superimposition Method** & **\begin{tabular}{c} **RMS** \\ **(Root Mean Squared of the Distance Measurements)** \\ \end{tabular} & **Mean Absolute Distance** \\ \hline [PERSON] et al. * (2018) & 6 & CBCT & - & IOS & \begin{tabular}{c} Foldical markers, \\ Point-based \\ \end{tabular} & - & \begin{tabular}{c} 0.13-0.19 mm on \\ intraoral markers \\ \end{tabular} \\ \hline [PERSON] et al. (2007) & 6 & CT & Stererophotogrammetry & - & Point-based & - & \(\pm\)1.5 mm \\ \hline [PERSON] et al. (2012) & 29 & CBCT & Stererophotogrammetry & - & Surface-based & 0.74 mm whole face error 0.04 mm. registration error & - \\ \hline [PERSON] et al. (2015) & 30 & CBCT & Stererophotogrammetry & - & Surface-based & \(1.8\pm 0.4\) mm & \(0.1\pm 0.07\) mm \\ \hline [PERSON] et al. (2014) & 4 & CBCT & Structured light scan & - & Surface-based & - & \(0.60\pm 0.12\) mm \\ \hline [PERSON] et al. (2011) & 30 & CBCT & - & DLS & Surface-based & - & 0.27-0.33 mm \\ \hline [PERSON] et al. (2018) & 20 & CBCT & - & DLS & \begin{tabular}{c} Voxel-based \\ vs. fiducial markers-point \\ based \\ \end{tabular} & - & \begin{tabular}{c} 0.39 upper jaw \\ 0.30 lower jaw \\ \end{tabular} \\ \hline [PERSON] et al. (2015) & 30 & CT & - & DLS & \begin{tabular}{c} Fiducial markers, \\ surface-based \\ \end{tabular} & 0.12-0.02 mm & - \\ \hline [PERSON] et al. (2013) & 5 & CBCT & - & CBCT & \begin{tabular}{c} Fiducial markers \\ surface-based \\ \end{tabular} & - & \begin{tabular}{c} 0.20 \(\pm\) 0.03 \\ 0.27 \(\pm\) 0.05 \\ \end{tabular} \\ \hline [PERSON] et al. (2014) & 10 & CT & - & DLS & \
\begin{tabular}{c} Point based vs. \\ surface-based \\ \end{tabular} & - &
\begin{tabular}{c} Point based: 0.16 mm \\ (seven points) to \(-\)0.20 \\ \end{tabular} \\ \hline [PERSON] et al. (2013) & 14 & CBCT & - & DLS & Surface-based & - & \(\pm\)0.5 mm \\ \hline [PERSON] et al. * (2009) & 10 & CBCT & - & CBCT & Triple voxel-based & - & \(0.08\pm 0.03\) mm \\ \hline \hline \end{tabular}  
* CT = Conventional computed tomography; CBCT = Cone Beam Computed Tomography; DLS = Dental Lab Scanner; IOS = Intraoral scanner,
* = Studies that provided a “gold standard” to measure the accuracy of the procedure.  
\end{table} 
Table 2: Information on the studies that analyzed the reliability of the registration.
# Performance of Warm-Mixed Flame Retardant Modified Asphalt Binder
## 1 Introduction
The results show that the mixture with Sasobit additive has the best rutting resistance [13]. [PERSON] carried out rotational viscosity tests and dynamic shear rheological tests on Sasobit modified asphalt binder at different concentrations. The results indicate that the phase angle of asphalt binder grows slowly at first, then decreases with the addition of Sasobit [14]. [PERSON] carried out conventional performance tests and Superpave Performance Graded (PG) grading tests respectively for Sasobit modified asphalt binder at different concentrations, and results show that Sasobit can significantly improve the high temperature performance and construction workability of asphalt binder. The study also found that Sasobit causes certain aging resistance performance and temperature susceptibility effects, but it has some adverse effects on low-temperature performance [15]. [PERSON] used magnesium hydroxide, melamine, ammonium polyphosphate, and titanate as raw materials to prepare flame retardants of different formulas [16]. [PERSON] found that the compound of flame retardant and WMA additives reduces heat and smoke release and can increase the high-temperature performance of asphalt mixtures [17]. [PERSON] believed that warm-mixed flame-retardant technology can improve the flame retardant properties of asphalt mixtures and reduce the mixing and paving temperatures, thus contributing to energy conservation, environmental protection and low-carbon construction [18]. In conclusion, although many studies have been made on warm mix technology and flame-retardant technology both in China and abroad, less are about the compounded technology of warm mix and flame retardants. In addition, the types of WMA additives and flame retardants are numerous, and there are differences in their research methods and evaluation means, and there are certain limitations in the conclusions of relevant research, so no corresponding technology specification and evaluation system have been formed in China at present, and it is necessary to conduct deeper and more comprehensive research on this synthesis technology so as to promote the extensive application of this technology in the large-scale construction of tunnel projects in China.
# Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
###### Abstract  
Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users' demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints.  
bike sharing systems; fleet sizing; Markov chains; modeling; effective rebalancing operations +
Footnote †: journal: Applied  
_articles_
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Q-Learning-Based Dynamic Almost Blank Subframe  
For performance comparison of our proposed schemes, the following simulation scenarios were considered (see Table 6). The purpose of designing the five scenarios is illustrated as follows.  
* **Scenario 1**: It is designed to verify the performance of the HCI cell selection scheme, which is compared with the SINR scheme.
* **Scenario 2**: It is designed to verify the performance of the MTS resource allocation scheme, which is compared with the Max-min scheme.
* **Scenario 3:** It is designed to verify the performance of the PF resource allocation scheme in dual connectivity environment, which is compared with our proposed MTS scheme. In addition, it is also compared with itself in single connectivity environment.
* **Scenario 4:** It is designed to verify the performance of the QD-ABS scheme in single connectivity, which is compared with the static ABS scheme.
* **Scenario 5:** It is designed to verify the performance of dual connectivity combined with CRE and ABS.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Connection** & **Cell Selection** & **Scheduler** & **E-ICIC** & **CRE Bias** \\ \hline \multirow{2}{*}{Scenario 1} & \multirow{2}{*}{DC} & HCI & \multirow{2}{*}{MTS} & Non-ABS & 0 dB \\  & & SINR & & & \\ \hline \multirow{2}{*}{Scenario 2} & \multirow{2}{*}{DC} & HCI & \multirow{2}{*}{Max-min} & \multirow{2}{*}{Non-ABS} & 0 dB \\  & & & & \\ \hline \multirow{2}{*}{Scenario 3} & \multirow{2}{*}{DC} & HCI & \multirow{2}{*}{MTS} & Non-ABS & 0 dB \\  & SC & HCI & PF & Non-ABS & 0 dB \\ \hline \multirow{2}{*}{Scenario 4} & \multirow{2}{*}{SC} & SINR & PF & \multirow{2}{*}{Q-ABS} & 0 dB \\  & & & & Static ABS = 0.5 & 6 dB \\ \hline \multirow{4}{*}{Scenario 5} & SC & SINR & PF & Non-ABS & 0 dB \\  & SC & SINR & PF & Static ABS = 0.5 & 6 dB \\ \cline{1-1}  & DC & HCI & MTS & Non-ABS & 0 dB \\ \cline{1-1}  & DC & HCI & MTS & QD-ABS & 6 dB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation scenarios.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Configuration** \\ \hline Cellular layout & Wrap around, 7 Macros, 3 sectors per site \\ Carrier frequency & 2.14 GHz \\ Bandwidth & 10 MHz \\ ISD & 500 m \\ Macro transmit power & 46 dBm \\ Pico transmit power & 30 dBm \\ Number of picos per sector & 2 \\ Number of UEs per sector & 15 \\ Cell selection & HCI and SINR \\ Scheduler & MTS and PF \\ ABS & Q-learning dynamic and Static \\ Thermal noise & \(-\)174 dBm/Hz \\ UE speed & 3 km/hr \\ Pathloss model & As in 3 GPP TS 36.942 \\ Traffic model & VolP (GBR for 1.5 Mbps) and \\  & Video (MBR for 3 Mbps–7 Mbps) \\ Duplex model & FDD \\ Number of RBs & \(C\) = 50 \\ Learning rate & \(\rho\) = 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: System parameters of the simulation.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 2 Proposed System
### Face Detection and Face Alignment  
Face detection is a process of detecting faces that appear in a given scene [18]. It is an indispensable part of most of the identity authentication systems. Face detection is a branch of general object detection. General object detection is divided into two types [19]: two stages and one stage object detection. General object detection can be used for a face detection task by training it on facial datasets. A two-stage detection algorithm divides the detection process into two steps: scan for interesting regions and classify these regions. There are some popular two-stage detection algorithms, such as R-CNN [20], Fast R-CNN [21], Faster R-CNN [22], R-FCN [23], and Mask R-CNN [24]. There are also specified algorithms for face detection, such as MTCNN [25]. Differing from two-stage detection, a one-stage detection algorithm directly maps the input image pixels to bounding box coordinates and class probabilities. Some recent one-stage detection algorithms are YOLO [26], YOLOv2 [27], YOLOv3 [28], SSD [29], RetinaNet [30], SSH [31] face detector, and RetinaFace [32] face detector. The face alignment is responsible for detecting facial landmarks. There are some separate facial landmark detectors like OpenCV landmarks detector [33] and DLib landmarks detector [34], and built-in facial landmark detectors, such as those of MTCNN and RetinaFace.
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 6 Conclusions  
This paper proposes the physical layer elements of 5G-NR for the evolution of non-terrestrial networks in 6G services based on the optimization of various 5G-NR numerologies, MCS levels, and MIMO schemes under pilot-aided perfect and DM-RS-based practical channel estimation schemes. The link-level simulations are carried out under 3 GPP NR-NTN channel models, such as single-path Rayleigh, single-path Rician, multipath NLOS (TDL-A), and LOS (TDL-E) fading channels, at stationary UE and high UE mobility of 500 km/h. The link-level performance of 5G-NR PDSCH is analyzed in terms of BER, throughput, and spectral efficiency versus SNR (dB). The simulation results show that NR-PDSCH can achieve 30 Mbps throughput at stationary UE under high MCS levels, such as 256-QAM for SCS of 15 kHz, using a pilot-aided perfect channel estimation. The optimization of numerologies shows that the practical channel estimation requires a high SCS of 120 kHz to support 500 km/h. The front-loaded DM-RS can enable fast decoding. Hence, the DM-RS-based channel estimation with a denser DM-RS symbol pattern with a front-loaded configuration is proposed to support the high mobility along with the high SCS and MCS levels, such as 120 kHz and 64-QAM, respectively, under the MIMO (8 \(\times\) 8) scheme, which provides the maximum achievable throughput of 368.832 Mbps and spectral efficiency of 368 bps/Hz at 500 km/h for the LOS (TDL-E) channel model. The utilization of SCS of 120 kHz, MCS 64-QAM, dense DM-RS symbol pattern-based channel estimation, and MIMO/3D-beamforming based on precoding shows improvement in the link reliability, which is proposed to utilize for NTN to support high mobility, which can also successfully enable transonic speed mobility in 6G-NTN services.  
**Author Contributions:** Supervision and investigation, K.C.; Conceptualization and methodology, C.J.P.; writing--original draft preparation, C.J.P.; writing--review and editing, K.C. and C.J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-00794, Development of 3D Spatial Mobile Communication Technology).  
Figure 10: Link-level performance of front-loaded DM-RS-based channel estimation with additional DM-RS symbol for TDL-A (NLOS): (**a**) BER, (**b**) throughput, (**c**) spectral Efficiency; and for TDL-E (LOS): (**d**) BER, (**e**) throughput, (**f**) spectral efficiency.
_Appl. Sci._**2015**, \(5\), 367-379; doi:10.3390/app5030367  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
An Investigation of the Thermal Expansion Coefficient for Resin Concrete with ZrW\({}_{2}\)O\({}_{8}\)  
**[PERSON] \({}^{\dagger}\), [PERSON] \({}^{\dagger}\), [PERSON] * and [PERSON] \({}^{\dagger}\)**  
Key Laboratory of Advanced Ceramics and Machining Technology, Ministry of Education, Tianjin University, Tianjin 300072, China; E-Mails: [EMAIL_ADDRESS] (K.L.); [EMAIL_ADDRESS] (S.Q.); [EMAIL_ADDRESS] (Y.W.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-22-2740-4915.  
Academic Editor: [PERSON]  
_Received: 23 June 2015 / Accepted: 6 August 2015 / Published: 14 August 2015_  
**Abstract:** This paper presents a novel resin concrete obtained by adding cubic zirconium tungstate (ZrW\({}_{2}\)Os) as filler. A prediction algorithm on the thermal expansion coefficient (CTE) of resin concrete (including filler) was established on the basis of the meso-mechanics method and a three-phase model for concrete. The concept of twice mixing was also proposed for prediction accuracy. Then, a 2D and 3D irregular polygon aggregate particles packing model was set up by Matlab and the properties of the packing model were simulated by finite element analysis. Finally, resin concrete samples were made and their CTE were measured. Mix proportion and addition of ZrW\({}_{2}\)O\({}_{8}\) as influencing factors were considered in this experiment. The CTE of resin concrete was verified by comparing results of the prediction model, simulation model and experiment. The optimum CTE obtained from the experiment was 1.504 \(\times\) 10\({}^{-6}\)/K. Compared with 6.817 \(\times\) 10\({}^{-6}\)/K without ZrW\({}_{2}\)O\({}_{8}\), it was found that the addition of ZrW\({}_{2}\)O\({}_{8}\) to resin concrete can make it perform significantly better in thermal expansion.  
**Keywords:** resin concrete; ZrW\({}_{2}\)O\({}_{8}\) powder; thermal expansion coefficient; prediction model; irregular polygon model
# Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm
## 2 Measurement of Rail Acoustic Roughness and Method
### Chord Offset-Based Measurement  
Although a measurement system using a displacement sensor can directly measure the displacement of a rail surface, the measured value differs from actual rail surface roughness since the sensor only measures the relative displacement while fixed on a moving platform. The system compensates for these errors by assuming the rail surface is a sinusoidal wave with amplitude Y and wavelength \(\lambda\) (\(y(x)=Ysin(kx)=Ysin(2\pi x/\lambda)\)), defining the ratio of the length of the measurement system (chord length, L) to the sensor position, and calculating the transfer function of rail roughness and the measured value [21]. However, specific wavelengths of rail surface can be unmeasureable or overamplified depending on the position of sensor on the platform. When considering that rolling noise has an important wavelength \(\lambda=5\!\sim\!250\) mm, an analytical method reflecting the measurement wavelength range is needed for accurate analysis of rail acoustic roughness.  
Therefore, the multi-sensor chord offset method, using the compensating method with multiple sensor's measurement, has been used in the analysis of acoustic rail roughness to get around being confined to measuring a specific wavelength range in a compact mobile measurement system [22]. While the conventional chord offset method is able to analyze measured values in the sinusoidal form, it has a limited correctable wavelength domain and lower measurement accuracy if there are irregular spikes or pits on the rail surface. Additionally, the multi-sensor chord offset method requires at least three sensors to measure the acoustic roughness of the rail surface correctly.  
A sliding surface contact method may be used for the platform instead of a drive wheel to reduce data distortion caused by movement of the mobile measurement system platform [23], but it is difficult to automate, and the measured value may vary greatly by user. Moreover, this increases the platform size rendering it more difficult to manufacture. The recently proposed chord offset synchronizing method has the design of a mobile measurement system that uses at least three sensors to increase data accuracy and reduce external disturbances from the driving wheel at the same time by mutually compensating for the amplitude and phase of the measurement value [17]. This paper proposes to increase the accuracy and reliability of acoustic roughness measurement through mutual compensation using only two displacement sensors in the mobile rail surface measurement system.