esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Aiming at the problems of the traditional MRC model in Chinese machine reading comprehension tasks, such as information loss, lack of long-distance dependence ability and inability to deal with unanswerable questions, this paper proposes the AT-CRF Reader model. This model uses Chinese RoBERTa as an embedding process to fully obtain the underlying semantic features of the context, combines depthwise separable convolution neural networks and attention mechanisms to obtain long-distance dependency ability, and uses the transition score in the conditional random field to judge the relationship between adjacent labels, and then obtains the global optimal sequence as the answer output. To verify the effect of this model, we conduct experiments on two Chinese reading comprehension datasets, CMRC2018 and DuReader-checklist, and the results demonstrate the effectiveness of the AT-CRF Reader. In future research, the problem of excessive parameters caused by the introduction of pre-trained models can be solved by Knowledge Distillation, which is trained through a lightweight network. This method can effectively reduce the number of model parameters and improve the training speed.  
Conceptualization, N.S. and H.W.; methodology, N.S.; validation, N.S.; formal analysis, N.S.; investigation, N.S.; data curation, N.S.; writing--original draft preparation, N.S.; writing--review and editing, H.W. and Y.C.; visualization, N.S.; supervision, H.W. and Y.C.; funding acquisition, N.S. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Southwest Minzu University Graduate Innovative Research Project Grant No. (CX2021 SZ31, Master Program), the Sichuan Science and Technology Applied Foundation Project Grant No. (No. 20220016), the National Natural Science Foundation of China (Grant No. 72174172, 71774134), the Fundamental Research Funds for the Central Universities, Southwest Minzu University (Grant No. 2021101).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: The effect of input length on model performance on the DuReader-checklist dataset. In this experiment, only the input length parameter is changed and other experimental parameters are kept constant.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 2 Full-Scale Test of Corrugated Steel  
### Full-Scale Test  
#### Specimen Design  
A full-scale test was conducted to characterize the stiffness of the CSP. The test specimen consisted of E235B (ISO standard [22]) steel plates configured with corrugations measuring 380 mm \(\times\) 140 mm \(\times\) 5 mm (pitch \(\times\) depth \(\times\) thickness). The physical dimensions of the specimen were adjusted to 1650 mm \(\times\) 830 mm \(\times\) 150 mm (l \(\times\) b \(\times\) h) to suit the loading system requirements (Figure 1). Steel supports were installed at both ends of the CSP to ensure uniform stress distribution under horizontal loading conditions. The connections between the supports and the CSP employed M20 high-strength bolts. Additionally, iron rollers were positioned at the lower portion of each support to provide constraints, effectively treating the corrugated steel plate as a simply supported beam.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver  
In this section, we detail the well-known differential process that is applied to the CSS modulation. Moreover, since our receiver has to deal with the time varying Doppler shift, we propose an additional processing to more precisely estimate the \(argmax\) of each FFT module. Finally, we detail all of the steps implemented by our synchronization algorithm.
# Discovering Trends in the Digitalization of Shipping:
## 2 Current State of the Maritime Industry: An Industry 4.0 Perspective
### Digitalization and Technological Innovations  
Digitalization in the maritime sector has been a game-changer, offering unprecedented opportunities for efficiency and sustainability. The integration of digital technologies, such as AI and big data analytics, is revolutionizing operations ranging from cargo handling to fleet management [2]. A notable aspect of this digital shift is the utilization of big data for predictive analytics, which significantly improves decision-making processes in maritime operations. Advanced algorithms are being employed to analyze vast datasets, enhancing route planning, fuel consumption optimization, and predictive maintenance of vessels, thus leading to cost savings, and increased operational efficiency [6, 7]  
Exemplary of the latter are the developments in vessel tracking and fuel optimization. AI algorithms are now utilized to provide more accurate Estimated Time of Arrival (ETA) predictions for vessels. A platform called PortXchange's Synchronizer, for instance, combines AIS data with proprietary information to monitor if vessels adhere to their planned routes and issue alerts about potential delays. This approach aims to tackle the challenge of schedule unreliability in the container shipping industry, which has historically led to significant financial losses [12]. AI, along with IoT and analytics, is also aiding the maritime sector in minimizing fuel consumption and reducing emissions through the optimization of vessel routes. For these cases, weather routing technologies enable captains to optimize vessel routes based on load, weather conditions, and currents, enhancing the efficiency of the entire supply chain and contributing to a significant reduction in carbon footprint [13].
## 5 Results and Discussion
### Comparison with KBC 2016  
We compared the mean and mean plus 1\(\sigma\) response spectra of the eight seismic stations for the horizontal components (vector sum of NS and EW components) in the Jeju region to those of KBC 2016, which is used as the seismic design standard in Korea for general buildings and structures such as hospitals and schools. In contrast with Reg. Guide 1.60, KBC 2016 does not distinguish between the vertical and horizontal spectra, and the design response spectrum of KBC 2016 considers both the short- (S\({}_{\rm{DS}}\)) and long-period (S\({}_{\rm{D1}}\)) seismic hazards based on site amplification of Fa (short) and Fv (long), respectively.  
As shown in Figure 12, we considered three categories of KBC 2016 design response spectra based on the soil type (SC, SD, and SE), following the processes suggested in the KBC 2016 codes. Site soil types--SC, SD, and SE--were selected considering they cover most soils in Korea. Figure 12 shows the response spectra corresponding to the soil types SC, SD, and SE in the order of distance from the x-axis. Based on an example in KBC 2016, a return period of 2400 years, which is determined by considering the importance of buildings and structures and the degree of socially mandated performance levels, was selected for this study. As the level of amplification of the ground increases in the order of SC, SD, and SE, the acceptance guideline of design hazard levels increases in the same order.  
For soil SE, the horizontal response spectra of the eight stations for mean plus 1\(\sigma\) were exceeded by a fairly large margin (\(\sim\)0.25-0.60 s or \(\sim\)1.33-4 Hz) compared to the design standard. On comparing the mean response spectra, it was observed that the range exceeding soil SE becomes narrow naturally, as shown in Figure 12.  
We obtained in this section the design response spectra by comprehensively considering both seismic coefficient and site amplification (Fa and Fv), following the regulation process (0306.3.3) by KBC 2016. In contrast, a simple calculation using the formula for a brief method (0306.5.6) suggested also by KBC 2016 was conducted in the previous section.  
The period of exceeding the design response spectra (\(\sim\)0.25-0.60 s) in this section corresponded to the resonance period of 2-7 story buildings from the brief formula (0306.5.6) by KBC 2016. This implies that the re-establishment of the general seismic design standards of buildings, including the site-specific design response spectra, is required in the Jeju region.
## 3 Results and Discussion
Figure 5 represents the charge transport properties of the AZO TF and AZO-RGO NCTF at room temperature compared to those of ZnO single crystals [31; 32; 33; 34] and epitaxial thin films [35; 36; 37; 38] in the literature. The empirical curve developed by [PERSON] et al., which describes the dependence of the single crystalline ZnO mobility versus carrier concentration, is also depicted as a green solid line in Figure 5a [39; 40]. The mobility of the AZO TF without RGO was 10.3 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\), which is far below that of single crystalline ZnO. This result indicates that the charge transport properties in the AZO TF were limited by numerous nanograins, as shown in Figure 4a. Generally, the charge transport in polycrystalline ZnO is strongly influenced by the Schottky barrier at the grain boundaries, which is an electrostatic potential barrier originating from the trapped electrons. This Schottky barrier induces severe charge scattering in polycrystalline ZnO [41]. The RGO could release the trapped electrons in the grain boundary regions in AZO-RGO NCs, leading to the single crystal-like charge transport caused by the relieved Schottky barrier [23]. A considerable improvement of mobility (29.1 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)) with increased carrier concentration was obtained in the AZO-RGO NCTF, leading to the reduction of resistivity (shown in Figure 5b). Since the grain size of the AZO TF and AZO-RGO NCTF (\(<\)10 nm) is very small, the charge transport behavior in our system could be strongly affected by grain boundary. Although the mobility of the AZO-RGO NCTF was still lower than that of the single crystalline ZnO due to the small size of nanograins (\(<\)10 nm), we confirmed that the incorporation of RGO in AZO TF is an effective strategy for enhancing the charge transport properties in AZO. Figure 4: (**a**) Bright-field and (**b**) high-resolution TEM micrographs of AZO TF. (**c**) Bright-field and (**d**) high-resolution TEM micrographs of AZO-RGO NCTF. Insets in (**a**) and (**c**) represent the selected area electron diffraction patterns. Inset in (**d**) shows the intensity profile obtained from the red rectangular box.
## 7 Challenges and Future Directions
The study underscored the lack of internal structure in the current processes and the need for defining roles and responsibilities to ensure the safety and efficacy of the procedures [81]. According to the Regulation (EU) 2017/745 of the European Parliament and of the Council dated 5 April 2017, all 3D-printed products are classified as custom-made devices [82]. As per this regulation, manufacturers of such custom-made devices are required to adhere to conformity assessment procedures in order to ensure compliance with safety and performance requirements [82]. Looking ahead, continued technological evolution is likely to further refine the precision, affordability, and accessibility of 3D printing and VSP in orthognathic and oral maxillofacial surgery. Integration of artificial intelligence and machine learning could automate aspects of surgical planning, making these technologies more user-friendly and efficient. Moreover, advances in biomaterials may lead to the production of bioresorbable or tissue-engineered 3D-printed implants, fostering innovation in patient-specific treatment. On the regulatory front, the establishment of clear guidelines and quality standards is necessary to ensure patient safety while supporting technological progress. As research progresses and the technology matures, these innovative tools are set to usher in a new era of precision and personalization in surgical care.
# Deep Reinforcement Learning-Based Dynamic Pricing for Parking Solutions
## 1 Introduction  
A major problem in many metropolitan areas, particularly in major cities such as London, Hong Kong and Kuala Lumpur, is the dramatic rise of private automobiles. The growing number of automobiles has detrimental effects on both the local population and the surroundings. The growing number of automobiles increases the need for parking spaces. A 2017 poll conducted by New Strait Times found that the average time spent seeking parking in certain urban areas is roughly 25 min per day. The cars continue to circle the location, wasting time looking for an open parking lot. This increases fuel consumption and emissions of carbon dioxide which devastate the climate and generate the greenhouse effect. The greater the time driver spent on driving, the greater the traffic jam in that region. This starts a doming effect that aggravates other drivers and causes more delays.  
Numerous academics have attempted to address the issues of traffic congestion and the enormous demand for parking lots after realising the difficulties brought on by the growth in the number of automobiles. Parking information, such as the availability of free parking spaces, can be received in real time using technologies such as sensors (loop or ultrasonic sensor), tickets or e-payment systems. This offers a chance to develop a smart parking system with dynamic pricing. To maximise the income and simultaneously improve parking space utilisation, the parking vendor can provide adjustable price regulation depending on various periods of time by adopting dynamic pricing.  
In this research, a deep reinforcement learning-based dynamic pricing (DRL-DP) model is proposed to manage parking prices depending on vehicle volume and parking occupancy rate. Because it does not require raw labelled data in environment modelling, reinforcement learning (RL) is recommended. RL makes it possible to make decisions sequentially and offers a complete series of wise choices throughout the experiments. Thedynamic pricing model keeps track of various price plans and how they are used in various contexts to reduce traffic congestion and increase profits for parking vendors. The dynamic pricing model distributes vehicle flows and predicts vehicle volume and traffic congestion. By suppressing drivers' visits to a certain region during peak hours at specified intervals of the day, the vehicle flows are distributed to non-peak hours to boost the parking utilisation rate and decrease traffic jams. This is accomplished by controlling prices through price reductions such as parking payment cash back. In order to improve the returned incentive in the following episode, the deep learning agent will learn from the sequential choice of dynamic pricing.  
The remainder of this article is structured as follows. Section 2 covers related research on topics including dynamic pricing and smart parking. The proposed approach is described in Section 3. The experiment's findings are presented in Section 4. The final section, Section 5, discusses further developments.
## 1 Introduction  
The micro-perforated panel (MPP) has recently become a popular sound absorber in the field of noise control and building acoustics. The attractive appearance, durability, and environmental friendliness of MPP relative to the conventional porous absorbing materials made from minerals and synthetics, which present problems of indoor air quality [1]. However, the cost of MPP is still high for the commercial market in Taiwan, so MPP is not yet popular in public buildings like public transportation stations because the costs are prohibitive to the government. In this study, we developed a micro-expanded metal mesh (MEMM) absorber with a lower cost and high sound-absorbing quality.  
Many studies have proposed prediction models for perforated panels and MPPs, but most of them have assumed that the shape of the perforations are circular [1; 2; 3; 4]. However, the MEMM expanding process makes the holes on both sides uneven (Figure 1). Therefore, before using these theoretical models to predict the absorption coefficient of MEMM, we have to make assumptions regarding transforming the geometric conditions of the perforation.  
Furthermore, machine learning (ML) can solve specific problems or perform certain tasks through relevant information and experience and has been widely used in various studies of prediction models, including image and speech recognition [5, 6], market analysis [7], etc. In the ML approach, a prediction model can be trained with input data to achieve a goal without solving theoretic equations. In the field of building acoustics, ML has been adopted to predict the reverberation time (RT) of the auditorium, thus demonstrating that using neural networks has a higher correlation coefficient (R\({}^{2}\)) than traditional prediction formulas, such as Sabine and Eyring [8]. [PERSON] [9] developed the prediction model of indoor acoustic parameters in a single room; compared with the Sabine formula, the neural network had a higher mean accuracy in predicting RT. In addition, using the speech recognition method to predict RT also provided accurate prediction results [10]. Most of such ML models have achieved higher accuracy than conventional models.  
The objective of this study is to develop a MEMM absorber with a lower cost that provides high sound-absorbing quality and propose a prediction model via the machine learning approach.
## 3 Design of Control Strategy
### Judgment of Vehicle Stability  
Under the extreme conditions, using formula (10) and formula (11) to determine whether the vehicle is unstable.  
Under extreme conditions, use Equations (10) and (11) to determine whether the vehicle is unstable. Equation (10) is the judgment condition for yaw rate, Equation (11) is the judgment condition for the side slip angle. When the vehicle state satisfies either one of (10), (11), it is in vehicle instability [16].  
\[|\Delta\omega|\leq C\omega_{NO} \tag{10}\]  
\[\Big{|}B_{1}\dot{\beta}+\beta\Big{|}\leq\mu B_{2} \tag{11}\]  
Among them, \(\Delta\omega\) is the difference between yaw rate and nominal yaw rate; \(\omega_{NO}\) is the nominal yaw rate. \(C\), \(B_{1}\), \(B_{2}\) are stability coefficients. According to experiences \(C=0.165\), \(B_{1}=0.4\), \(B_{2}=0.12\).
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 5 Conclusions
[PERSON], [PERSON] and [PERSON] planned and performed the channel measurement campaigns; [PERSON] performed the ray tracing simulations and pre-processed the simulation data; [PERSON] analyzed the data and wrote the paper; [PERSON] and [PERSON] supported the writing by providing material to Sections 1 and 3, respectively. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
3D three-dimensional  
3 GPP 3 rd Generation Partnership Project  
5G 3 fifth generation  
APDP averaged power delay profile  
CCDF complementary cumulative distribution function  
CIR channel impulse response  
deg angular degree  
DS delay spread  
FPGA field-programmable gate array  
GO geometrical optics  
GPU graphics processing unit  
GSCM geometry-based stochastic channel model  
HIRATE High Performance Digital Radio Testbed  
HPBW half-power beamwidth  
IF intermediate frequency  
IP intercept point  
ITU-R International Telecommunication Union Radiocommunication Sector  
LOS line-of-sight  
LS least-squares  
METIS Mobile and Wireless Communications Enablers for the Twenty-twenty Information Society  
MiWEBA Millimetre-Wave Evolution for Backhaul and Access  
mm-wave millimeter-wave  
MPC multipath component  
NLOS non-line-of-sight  
omni omdirectional  
OS-CC open square, city center  
PL path loss  
pol. polarization  
RMS root mean square  
RT ray tracing  
RX receiver  
SC-CC street canyon, city center  
SC-RA street canyon, residential area
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 4 Real-Time Energy Management Strategy
### Strategy Implementation in Energy Management Controller  
According to the real scenarios of electrical load use, the requirements of load powers in a vehicle can be divided into several grades, which are denoted as the load power set \(\mathbf{P}=\left\{P_{L^{\prime}}^{0},P_{L}^{1},\ldots,P_{L}^{m}\right\}\). For \(\forall P_{L}^{i}\in\mathbf{P}\), the algorithm in Figure 9 can be used to find its corresponding initial co-state \(\gamma_{0}^{i}\) if the driving cycle is given. Set \(\gamma_{0}=\left\{\gamma_{0}^{1},\gamma_{0}^{2},\ldots,\gamma_{0}^{m}\right\}\) is the initial co-state set, which corresponds to \(\mathbf{P}\). \(\mathbf{P}\) and \(\gamma_{0}\) are used to implement the aforementioned strategy in an energy management controller. The working process of the strategy is shown in Figure 10.  
In Figure 10, the BCM monitors the working state of electrical loads in the vehicle, and the SoC(t) of the battery is measured by the IBS in real-time. The EMS collects the rotational speed \(n(t)\) and torque \(T(t)\) of the engine, which are used to calculate the fuel consumption rate \(\dot{m}_{f}(t)\). First, the energy management controller calculates the current requirements of load power \(P_{L}^{i}\) based on the information from BCM and finds the corresponding initial co-state \(\gamma_{0}^{i}\) in \(\gamma_{0}\). Next, the Hamiltonian function \(H_{i}\) corresponding to each \(u_{i}\in[u_{\min},u_{\max}]\) is calculated through SoC(\(t\)), \(\dot{m}_{f}(t)\), and \(\gamma_{0}^{i}\). Then, the desired  
Figure 9: Algorithm of the initial co-state acquisition.  
output power of the battery is chosen as \(u^{*}\), which makes the Hamiltonian function \(H_{i}\) the minimum. Therefore, the desired output power of the alternator is obtained as \(P_{L}^{i}-u*\). Finally, this output power of the alternator is converted into the control signal of the voltage based on the model in Figure 4, and the control signal is sent to the intelligent alternator to adjust its output power.
###### Abstract  
The most important and emerging characteristic of Wireless Body Area Networks (WBANs), which differentiates them from other wired and wireless area networks, is mobility. Therefore, the routing protocols for WBAN are designed in such a way that they can deal with dynamic changes in topology and provide maximum throughput, packet delivery ratio, average end-to-end delay, and minimum energy consumption. Thus, achieving optimal values for every performance parameter becomes a big challenge. This work investigates the performance of three separate path discovery protocols, such as Destination-Sequenced Distance-Vector Routing (DSDV), Ad Hoc On-demand Distance Vector (AODV), and Ad Hoc On-demand Multipath Distance Vector Routing protocol (AOMDV), for two different mobility models with a fixed-positioned sink. During experimentation, the AOMDV routing protocol achieves a high packet delivery ratio (PDR), average end-to-end delay, and throughput as compared to other routing protocols.  
WBAN; routing protocols; mobility; performance analysis Article
## 3 Experimental Tests and Results Analysis
Therefore, four different adjustments were made by altering the values of gamma, contrast, Gaussian noise, and brightness. For each, a set of 20 values were uniformly selected, where gamma was varied between 0.1 and 4, contrast and Gaussian noise between 0.05 and 1 and brightness between 0.1 and 2. In total, the algorithm was performed on 1600 images. The results shown in Figure 16 demonstrate that the proposed solution is capable of adapting to different environmental conditions but is most affected by the Gaussian noise. Table 4 shows that only the Gaussian noise had a low proportion of correctly read gauges, with a result of 43.3 %, and the other adjustments all show results above 75 %.