esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 2 Related Work
Neural networks (NN), through proper training, can accomplish tasks such as structural damage detection [27], water quality monitoring [28], quality control for shuttlecock production [29] and the adaptive tracking control of nonlinear systems [30; 31]. As for road asset management, several studies have successfully employed deep neural networks to identify distress on roadway pavements. For example, [PERSON] et al. [32] proposed CrackNet, which essentially employed a modified CNN, to identify cracking on 3D asphalt surfaces. It can achieve pixel-level detection and outperforms conventional machine learning and imaging methods with a precision of 90.13% and an F-measure of 88.86%. [PERSON] et al. [33] modelled pavement crack detection as a multi-label problem and solved it based on structured prediction with CNN. [PERSON] et al. [34] developed a transfer learning-based framework to classify sealed and unsealed cracks and at the same time extract them at the pixel level. [PERSON] et al. [35] developed a mobile application to determine the damage status of roads, in terms of \"no damage\", \"minimal damage\" and \"damage needing repair\". However, few studies have focused on pavement markings and their condition assessment, except in [8], where [PERSON] et al. adopted end-to-end deep neural networks (i.e, Yolo) to identify blurry road markings. The mAP of detection reached 30% and the precision for blurred white lines was more than 65%. Another example is in the form of the study by [PERSON] and [PERSON] [9], who included blur detection of two types of markings, i.e., white line and crosswalk, into the proposed multi-classifier for road damage. The levels of precision and recall, using two different neural networks, were both greater than 0.77 and 0.71, respectively. To date, previous studies have neither focused on the inventory of pavement markings nor realized the quantification of their blurring condition. An efficient framework to identify pavement markings in images/videos and evaluate their condition with numeric indicators is still lacking in the literature. For this reason, this research proposes a vision-based framework for pavement marking detection and condition assessment, so as to automate the collection of relevant inventory data and facilitate maintenance decision-makings for road assets.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis More Affordable than Traditional Air Transportation Options  
It is difficult to say for certain whether flying taxis will be more affordable than traditional air transportation options. Currently, there are a number of companies and startups developing flying taxi technology, and the cost of using these services will depend on a variety of factors, such as the type of aircraft used, the distance traveled, and the level of demand for the service.  
However, some experts have suggested that flying taxis will eventually be able to offer lower prices than traditional air travel for certain routes, particularly those that are too short to be efficiently served by commercial airlines but too long to be comfortably traveled by car or public transportation. Additionally, flying taxis are able to offer greater flexibility and convenience than other transportation options, which could make them an attractive option for many travelers [154].
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 3 System and Network Model
### System  
The research problem in this paper is a problem encountered in a real industrial application. The system aims to monitor all working parameters of welder machines in production as shown in Figure 1. Therefore, we developed a WirelessCAN system, where the wireless network communication is based on the WirelessHART standard. The communication between welder machines (WMs) and sensor nodes is through the controller area network (CAN) which is the definition of a high performance communication protocol for serial data communication and the data delivery between sensor nodes and the welding parameter manager (WPM) is through a multi-hop wireless sensor network. A description of the main components in WirelessCAN system is presented in the following.  
* A W-CAN node is a wireless communication and processing device responsible for the data conversion between the CAN interface and wireless protocol. It can also relay packets to form a multi-hop mesh network.
* A Gateway is an intermediate device able to convert the protocol between wired and wireless. It classifies the data from all W-CAN nodes and sends the data to a server, such as a WPM or Network Manager.
* A Network Manager is a centralized controller of the whole IWSN. All the resources of communication between W-CAN nodes are scheduled in the Network Manager at the network joining process.  
A WirelessCAN system has provided reliable monitoring and control. The limitation is that it only provides for the static networks. However, some of the WMs need to be frequently moved to other weld sites in a real production scenario. This is liable to cause packet losses when mobile W-CAN nodes need to rejoin the network. Moreover, if a node is responsible for relaying packets to other nodes, it will result in more packet losses. Therefore, a new mechanism should be designed to solve the problem in a mobile scenario.
# The Use of Modern Technologies in Post-COVID-19 Cardiopulmonary Rehabilitation
## 4 Medical Technologies Used in Post-COVID-19 Cardiopulmonary Rehabilitation
### Hybrid Approach  
In order to facilitate the transition to telemedicine, there are studies currently testing the application of a post-COVID-19 cardiopulmonary rehabilitation program with a hybrid approach at the patient level. In this case, the patients are initially offered cardiac rehabilitation in a recovery center and then the long-term maintenance exercises are performed at the patient's home, using various medical technologies. The complicated part of these studies is the demonstration of the effectiveness of these hybrid systems. This involves active and ongoing contact between patients and professionals through traditional methods, such as home visits and telephone consultations, or the use of technology-based solutions, which include web-based video calling and social networking platforms [28].  
There are studies that aim to investigate the effectiveness and safety of the application of cardiopulmonary recovery programs at home, by the patient, without supervision. In this sense, issued application protocols include two parts: an aerobic training using an ergometer, which will be installed at the patients' homes, and patient education using an e-learning system. Cardiovascular parameters will be monitored remotely during exercise through video chats. An e-learning system that will promote a better understanding of cardiovascular disease is used. The necessary devices for cardiopulmonary recovery such as calibrated ergometers and tablets will be made available free of charge to the patients enrolled in the program. The patients will perform anaerobic exercises at home using the ergometer for 30-40 min at least 3 times a week. During the exercise, an instructor will monitor the patient in real time (using interactive video tools and monitoring tools for various vital data) [29].  
It is necessary to use monitoring systems of some important parameters in order to ensure the safety of the patient at home during the implementation of the rehabilitation program.  
[PERSON] et al. proposes the development of national public health programs designed to support health systems, in particular regional healthcare and patients curedof a COVID-19 infection, by providing a model for organizing a dedicated network for post-acute rehabilitation. The purpose of this network is to provide continuity of care from acute hospitalization to home rehabilitation. It proposes the evaluation of patients based on a minimum set of early assessment and onset tools of the treatment of post-COVID-19 disabilities. An important emphasis is placed on technological promotion in healthcare, and especially in tele-rehabilitation [30].
## 1 Introduction
### State of the Art
#### 1.1.1 Activity Recognition  
Most of the recent methods are based on machine learning approaches which would ideally have the following general framework: (i) gather a significant amount of referenced data, e.g., accelerometer signals and type of activity performed while measuring, (ii) preprocessing of the signals, for instance by denoising, correlation testing, etc., (iii) extract features vectors from the collected signals, although some neural network-based methods directly use the signals instead, (iv) train a classifier based on the collected data, usually by creation and use of data subsets for training, test and validation and finally (v) validate it on a new and independent set of data.  
For instance, [PERSON] et al. presented a k-means and Hidden Markov Model based method to recognize arm positions of a person holding a smartphone [10]. Here, the use case was to remotely teach arm movements to a learner, where both teacher and learner, used reconstructed traces in space. The article reported positive results in recognizing different basic motions, yet this is not exactly what one could refer to as daily activity. However, in the continuation of this study, [PERSON] et al. in 2018 published a proof of concept for the recognition of shoulder specific physiological exercises using machine learning and inertial signals from smartwatches [11]. Among the different classifiers tested, a Convolutional Recurrent Neural Network provided the best performance. The study was performed on healthy subjects and, due to daily life a use case was restricted to rather simple exercises. It is for instance similar to what [PERSON] et al. proposed [12].  
In that same perspective, [PERSON] et al. developed a smartwatch-based system able to classify gait types such as limping or walking with or without help, even including crutches and frames [13]. Data from a three-axis accelerometer and three-axis gyroscope of the smartwatch were collected, processed and used to train multiple machine learning algorithms. The best algorithm (Light Gradient Boosting Machine) performed with an accuracy of 94.9%.
## 5 Discussion
Another security-related challenge is associated with the authentication and authorization of client applications and end users, especially in architectures in which the Identity Server is part of the Gateway API, like the one presented in this manuscript. In these cases, any scale-up of the system needs to be seen in association with the scale-up of the Identity Server. Most importantly, the Gateway API shall incorporate a robust authentication and authorization mechanism. The Gateway API design and its proper maintenance, including regular updates, are crucial in ensuring immunity against security-associated issues for the Gateway API.
## 4 Summary and Conclusions  
The presented study tested the application of dynamic membrane adsorber (HAOPs) for the removal of phosphorus from surface water. The results showed that the maximum removal efficiency of 77% was achieved in the case of phosphorus while phosphate was removed below the detection limit. In the case of NOM, the results indicate that the process is very efficient, with the removal varying between 57.49% and 92%, depending on the dose of HAOPs.  
In the case of synthetic water, the maximum removal efficiency of 99.99% was achieved. As for the adsorption capacity, this study revealed that the Langmuir adsorption isotherm fits the experimental data best (0.9893). The kinetics of phosphorus adsorption on HAOPs was best described by the pseudo-second-order model.  
The best removal of phosphorus was achieved neutral pH (pH 6-7) and the increase in ionic strength did not significantly impact the efficiency of phosphorus removal.  
The presence of organic matter slightly decreases the removal efficiency of phosphorus and this applies mainly to highly contaminated waters.  
The evaluated process should be investigated further. The adsorption of phosphorus on DM involving adsorption on HAOPs and membrane filtration looks promising, but it needs optimization.  
Research has shown that HAOPs are highly effective when the phosphorus content in surface water is high, at the same time as the concentration in the raw water decreases, the efficiency of the removal of this element decreases.
## 6 Discussion  
### Accuracy Validation of Calculated Rock Mass Discontinuity Orientations  
To verify the accuracy of the calculated dominant groupings obtained using UAV photogrammetry technology, a comparison was conducted between the rock mass discontinuity orientation obtained using UAV photogrammetry technology and the field data obtained using a geological compass. The local slope surface for rock mass discontinuity orientation analysis is shown in Figure 11, where 172 discontinuities were measured on-site using a geological compass.  
The orientation of the rock mass discontinuities was obtained by calculating local images (i.e., Figure 11) captured by the UAV and the dominant groupings were obtained, as illustrated in Figure 12. The calculated results indicate that the rock discontinuities in the local slope surface were categorized into four dominant groupings (dip direction \(\angle\) dip angle), namely J1 (209.48\({}^{\circ}\)\(\angle\) 64.34\({}^{\circ}\)), J2 (15.64\({}^{\circ}\)\(\angle\) 63.43\({}^{\circ}\)), J3 (109.80\({}^{\circ}\)\(\angle\) 64.63\({}^{\circ}\)), and J4 (280.17\({}^{\circ}\)\(\angle\) 86.65\({}^{\circ}\)). The detailed results of each dominant group are provided in Table 3.  
Similarly, a contour map was also constructed by calculating the pole density, as shown in Figure 13a. The on-site measurements of rock discontinuity orientations were taken using a compass, and the measured local discontinuity orientation data were processed using the DIPS (V8.002) stereographic projection software to generate a density map of dip direction and dip angle, as illustrated in Figure 13b. A comparison between the discontinuity orientation information obtained from the 3D point cloud data and on-site measurements using a compass revealed that the maximum errors in dip direction and dip angle were 1.51\({}^{\circ}\) and 4.15\({}^{\circ}\), respectively. Therefore, the method of using UAV to collect and analyze the rock discontinuity orientations and dominant groupings for the steep slopes is feasible, offering improved efficiency in field rock discontinuity data collection while  
Figure 11: Analysis area of the local slope surface.
## Abbreviations  
The following abbreviations are used in this manuscript:  
MCNN Multi-scale Convolutional Neural Network  
CMCNN Cascaded Multi-scale Convolutional Neural Network  
BReLu Bilateral Rectified Linear Unit  
CMT Cascaded Multi-scale Network for Transmission map Estimation  
MRF Markov Random Field  
ICA Independent Component Analysis  
DCP Dark Channel Prior  
CNN Convolutional Neural Network  
DNN Deep Neural Network  
FR-IQA Full Reference Image Quality Assessment  
NR-IQA No Reference Image Quality Assessment
# Reconfigurable Intelligent Surface in Wireless-Powered Interference-Limited Communication Networks
###### Abstract  
The commercialization of future wireless communication systems faces crucial problems in terms of reduced network cost of deployment and high power consumption. As a result, reconfigurable intelligent surfaces (RIS) have been suggested as a promising approach to overcome these existing challenges. In this paper, the performance of RIS in a wireless-powered interference-limited communication network is investigated. In this network, an energy-constrained access point (AP) is powered by a dedicated multiantenna power beacon (PB) and communicates to a destination limited-interference node via the RIS. It is assumed that the RIS experiences generalized-K fading distribution while the PB and interferers links are subjected to Nakagami-m fading distributions. To evaluate the system performance, the analytical closed-form expression of the probability distribution function (PDF) for the concerned system is derived. Through this, the exact closed-form expressions of the systems outage probability, average delay-tolerance throughput and average bit error rate are obtained. The analysis quantifies the effects of the number of reflecting elements in the RIS, number of interferer nodes, fading parameters on the AP and interferer links and number of antennas on the PB. It is deduced from the results that there is a kind of symmetry relationship between the analytical and simulation results. Additionally, the results illustrate that the proposed system with an RIS outperforms the conventional system without an RIS. Finally, the accuracy of the derived analytical expressions is validated through a Monte-Carlo simulation.  
energy harvesting; interferer nodes; power beacon; reconfigurable intelligent surface IEEEexample:BSTcontrol
## 2 Model of the Chevron Type Microactuator
### Verification of the Numerical Model
The internal compressing force in a beam is equal to the reaction force in the anchor and can be calculated from [39]:  
\[R=\alpha\,\Delta T\,EA\,\frac{\cos^{2}\varphi}{\frac{\Delta L^{2}}{12L_{i}}\sin ^{2}\varphi+\cos^{2}\varphi}, \tag{15}\]  
whereas critical force is given by:  
\[F_{\sigma}=\frac{\pi^{2}EI_{\text{min}}}{\left(\beta L\right)^{2}}, \tag{16}\]  
where \(I_{\text{min}}\) is minor moment of inertia and \(\beta\) is buckling length coefficient  
For the verification, buckling analysis of the one actuator beam for the same FEM model was performed. Based on the same geometry, the boundary conditions for the single beam are as follows: the anchor is fixed in all degrees of freedom, while the end of the beam can move in vertical direction but cannot rotate. Critical force calculated for the geometry considered from the Equation (16) gives \(F_{\sigma}=419.5\) uN. While solving buckling analysis by means of FEM (13), the critical load factor (CLF) was calculated. That factor is a ratio between critical and applied load for the first form of stable model equilibrium. Assuming the same numerical data for the FEM-model simulated \(CLF=1.058\) indicates a good agreement between numerical and analytical results. Finally, compressive stress in the beam calculated analytically and numerically gives very similar results as well. Note, that the  
Figure 7: Pre-bending angle–contact force characteristics of the V-type actuator with straight arms. analytical solution incorporates only uniaxial stress and cannot provide information about local stress concentration (see Figure 2). The calculation performed allows us to state that the numerical FEM model developed has been successfully verified. This model was used to create a parametric model for the optimization task (details in Section 5). It is apparent that the relations between geometrical parameters and the actuation force, displacement generated, buckling factor and internal stresses, are non-obvious. Moreover, for the beams with non-uniform cross-sectional area, analytical formulas cannot be applied, so the relationships between criteria are more complex.
# Material Design in Implantable Biosensors toward Future Personalized Diagnostics and Treatments
## 3 Classification of Implantable Biosensors Based on Material Design
### Nanomaterial-Based Implantable Biosensors  
It is shown that textured compositions are more suitable than smooth materials for enhancing the performance of biosensors in vivo applications due to vascularity improvement around the implant [48]. One of the well-described examples is incorporating a textured angiogenic layer over an implantable glucose sensor's surface [49]. It is believed that a different hydrophobicity and hydrophilicity of the materials prohibits the proteins or cells' adsorption over the surface. Another common phenomenon in in vivo biosensors is the clotting process caused by electrons exchange between blood proteins and cells on the implant surface (Figure 4) [50]. It is believed that the lower time constant of nanomaterials can decrease the clotting process when compared to metal-containing materials. However, the working mechanism of nanomaterials in in vivo systems is still debatable, but there are various studies where nanostructures used as an active material alleviate host body responses. Some of the regularly used nanostructures include nanoporous silicon [51], nanoporous titania [52], nanoporous carbon, etc. It is widely accepted that nanomaterials with controlled shapes and spaces can optimize drug delivery kinetics and improve anti-fouling characteristics [53].  
Figure 4: Formation of a fibrous capsule following the implantation of a biosensor in tissue.  
As other important influencing factors, the permeability of the nanostructures is targeted in various studies. The output of these studies has led to the creation of nanoporous structures with smart responses to surrounding stimuli such as temperature, ionic strength, pH, and electromagnetic fields [54, 55]. Although these nanostructures could effectively improve the performance of the sensors, integrating these materials in miniaturized implantables may be costly and difficult. To deal with this problem, polymer matrices embedded with nanomaterials became a type of nanocomposite coating that attracted a lot of attention from the researchers [56]. Table 2 shows the list of the reported nanomaterial-based implantable devices.
## 3 Beamforming Antenna and Backhaul Line of Millimeter  
The path loss of millimeter wave propagation is higher than that of low-frequency band communication systems such as LTE or Wi-Fi, so it is necessary to compensate with a highly directional antenna. These antennas should also have directional control to support access and reconfigurable backhaul links.  
Figure 3: LTE-WLAN interconnection architecture in Release 13. IPsec: IP Security.
## 6 System Performance and Test Case Analysis
### Testing System Performance for Large-Scale Loads  
Large-scale arrays of nodes are at the heart of the test system, but they pose a challenge to the system performance. This subsection verifies the large-scale load performance of the software architecture. The test focuses on evaluating the performance of the master controller under high- and regular load conditions.  
Since hardware nodes are costly, developing virtual nodes with node interfaces and logic is necessary as an alternative. As shown in Figure 8, we set up a test environment, deploying the master controller and node simulators on a single test host. The load on the master controller was divided into two scenarios: the number of nodes accessed and the tasks executed by the nodes. The node simulators can simulate a specified number of nodes based on configurations and create virtually ideal communication channels within the environment.