esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Future Outlook
#### 5.2.2 Communication in Real-Time Object Detection Based on UAV Remote Sensing  
As we discussed in the previous section, edge computing is an ideal technology for achieving real-time object detection, except for improving lightweight models and offloading technology. In contrast, the development of communication technologies cannot be ignored.  
The limitation of 5G basically arises from the need to reach data rates of up to 100 Mbps and 50 Mbps for downlink and uplink, respectively [125]. Compared to 5G, 6G needs more significant improvements in performance, such as bandwidth, delay, and coverage, which expands 5G's requirements for scenarios such as ultra-low latency, massive connections, and ultra-large bandwidth to achieve higher peak transmission rates [148]. Benefiting from the large communication bandwidth and high transmission rate of the 6G mobile network, UAVs can achieve efficient data collection in a shorter period, which also overcomes the problem of insufficient collection time caused by the short battery life of UAVs. In addition, the challenge of real-time detection is not only solved by reducing latency and increasing network bandwidth but also by considering technologies such as pre-training and online learning to solve real-time tasks.  
The above-mentioned discussion points indicate that it is essential to support machine learning or deep learning algorithms at the edge. Future generations of wireless communication systems have the opportunity to solve this issue. The communication channels in THz and optical frequency regimes, while exploiting modulation methods capable of 10 b/symbol, can achieve data rates of around 100 Tb/s with such assumptions in mind to provide a communication link that can enable massive computations to be conducted remotely from the device or machine that is undertaking real-time processing at the edge of the network [149].  
However, there are also some challenges that need to be considered, such as how to enable the interaction between the data generated by UAVs in the airspace network and other heterogeneous networks and how to solve the problem of insufficient spectrum resources caused by massive UAV access to a 6G network. Additionally, even though there is automatic battery replacement technology for UAVs, it still cannot solve the root problem of short battery life [150].
## 2 A Mathematical Framework and Its Corresponding Solution for a Symmetric ABH Beam  
Referencing Figure 1, we examine a symmetric Euler-Bernoulli beam that experiences bending oscillations due to a localized force \(F_{t}\) applied at position \(x=x_{f}\). The beam's structure includes a segment with a uniform thickness \(h_{b}\) extending between \([0,x_{b1}]\) and \([x_{b3},x_{b4}]\), as well as a section with an ABH profile that features a thickness varying according to a power law from \(x_{b1}\) to \(x_{b3}\), i.e.,  
\[h(x)=\epsilon(x-x_{b2})^{m}+h_{0},\quad x\in[x_{b1},x_{b3}] \tag{1}\]  
where \(h_{0}\) is the thickness of the ABH structure at the location \(x=x_{b2},\epsilon\) and \(m\) represent the slope and the ABH order, respectively. Both the lines \(x=x_{b2}\) and \(z=0\) are axes of symmetry for the structure in Figure 1. To enhance the wave trapping capability of the structure, damping material is affixed to both sides of the beam within the coordinates \([x_{d1},x_{d4}]\), with a thickness designated as \(h_{d}(x)\). The beam's edges are secured by a rotational spring with stiffness \(K_{1}\) and a translational spring with stiffness \(K_{2}\), and a variety of distinct boundary conditions are emulated by varying the values assigned to \(K_{1}\) and \(K_{2}\). The structure presented in Figure 1 usually is considered as a unit cell in a periodic ABH beam. Considering the inherent damping dissipation of the beam and the additional damping material, the complex modulus of elasticity can be articulated as follows:  
\[E_{b,d}=E(1+i\eta_{b,d}), \tag{2}\]  
where \(\eta_{b,d}\) are the damping factors of the beam and the damping layers, respectively.
## 5 Limitations and Further Directions of Study  
The findings of the present study support the potential of using social robots in the diagnosis of autism and justifies the continuation of activities in this area as the relevant technologies progress. Currently, NAO with the proposed software applications can be treated as a supplementary tool, thanks to which specialists can acquire additional diagnostic intuition to be used with standard tools.  
The experience gained in this experimental study led us to the conclusion that the games we used for interactive diagnosis of turn-taking deficits need to be improved and further developed. This should be achieved by having close and extensive cooperation with the target users, including children, psychiatrists/psychologists and therapists. The ultimate goal is to identify a set of game scenarios that would be attractive for children, useful for specialists and technically feasible for engineers.  
First, automation of the robot-enabled diagnostic system discussed in this work is necessary to deliver an efficient tool for specialists that guarantees high objectivity of the diagnosis process. Recent progress in RGB-D vision and studies on deep learning suggest that it should be possible to develop a system that is capable of detecting and recording a child's spontaneous turn-taking behavior with a level of quality that is acceptable for diagnosis. The flexibility of the NAO SDK, in conjunction with a wide variety of tactile sensors, provides the possibility of developing a reliable sensing system for commanded turn-taking behavior. Development of eye-tracking systems presents a good basis for the detection of children's attention. Finally, it should be kept in mind that we are developing a component of a potential system that supports autism diagnosis but for which for no protocol has yet been defined. This implies the need for further research from the fields of robotics and psychology to implement our proposal (a component of the target diagnostic system).  
The work should culminate in a system based on artificial intelligence methods, which, on the basis of the knowledge acquired from specialists, competent judges and current observations, will enable automatic diagnosis with high reliability.
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 2 Overview of UOWC Systems
### System Architecture
However, APDs also require high bias voltage, complex control circuitry, and are more sensitive to ambient noise. Due to the relatively high path loss, UOWC systems require a sensitive receiver to increase the transmission distance. Hence, PMT has been explored, which has a high gain, low noise, and a large collection area [8]. Moreover, SPAD, which operates at a reverse voltage higher than the breakdown voltage to further increase the internal gain and sensitivity, has also been investigated [13]. With PMT and SPAD, a 50 m UOWC link with 500 kbps data rate and 117 m UOWC link with 2 Mbps data rate have been demonstrated [14; 90]. In UOWC systems, both the line-of-sight (LOS) link and non-line-of-sight (NLOS) link have been studied, as shown in Figure 4. Direct LOS link is the most simple link configuration and has been widely studied both theoretically and experimentally [82; 91]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ocean Water Type** & **a (m\({}^{-1}\))** & **b (m\({}^{-1}\))** & **c (m\({}^{-1}\))** \\ \hline Clear ocean & 0.114 & 0.037 & 0.151 \\ Coastal ocean & 0.179 & 0.220 & 0.339 \\ Turbid harbor & 0.366 & 1.829 & 2.195 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Typical values of absorption and scattering coefficients in three different ocean water types. However, in practical underwater environments, marine life and reefs can block the channel. To overcome this limitation, NLOS-based UOWC systems, which use the water-air surface or bubbles underwater to reflect the signal to avoid obstacles, are investigated in recent studies [80; 92; 93], including a few NLOS UOWC experimental demonstrations [46; 94]. Moreover, the NLOS link configuration is also applied in highly turbid water to achieve a high transmission data rate and bypass obstacles [95; 96].
## 2 Basic Concepts
### Mfcc  
The MFCC (Mel-frequency cepstral coefficients) is a method to obtain a parametric representation of a signal, which has been widely used in speech recognition tasks. To obtain this representation, a 1 st order FIR filter is used as a pre-emphasis filter to flatten the signal spectrum. Then, the STFT (short-time Fourier transform is applied to the signal, maintaining a time overlap between the frames. Its magnitude spectrum passes through a bank of filters formed by \(M\) triangular filters, where its linear frequency is assigned to Mel-frequency through Equation (2) [29].  
\[Mel\left(f\right)=2595 log_{10}\left(1+f/700\right). \tag{2}\]  
In the Mel domain, the filters are equally spaced in a frequency band [29].  
After signal filtering, the DCT (discrete cosine transform) is applied to the logarithm of the above data [30], in order to de-correlate them. Finally, a filter is applied to obtain the MFCC coefficients. The MFCC has been applied to speech recognition, audio signal processing and other scenarios [31; 32].
# Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)
As discussed in [9], the propagation delay of acoustic waves is 200,000 times higher than that of RF waves, which poses a great challenge and high delay. At the same time, due to water currents and extreme weather conditions, the noise in UWSN is very high; noise is introduced because of tides, water movement, and the variations on sea current. Another source of noise is various types of machinery, such as ships, water pumps, and power plants. Thus, removal of noise is a big challenge in designing a routing protocol. Geometric spreading, i.e., the spreading of sound energy, is another problem in UWSN. The reason for geometric spreading is expansion of wave fronts. Another problem introduced by the water currents is change of topology of sensors, which has a great effect on the performance of a routing protocol. On the other hand, the bandwidth of acoustic waves is very low, ranging between one and fifty kHz [10]. Further, the transmission power required in UWSN [11] is significantly higher as compared to terrestrial networks. Another problem is introduced by Doppler spread, which creates significant problems in high speed network as it introduces symbol interference [12]. Finally, sensors are subject to corrosion and fouling in UWSN, which results in failure [13]. The focus of this paper is the design of an efficient routing protocol for UWSN. The crux of the proposed routing protocol, SOSNET, is dividing the UWSN into different clusters and developing an effective dynamic clustering scheme for selection of a cluster head responsible for coordination with sink nodes. The selection of the cluster head will depend on available energy in a sensor node. Once the energy of a cluster head decreases from a given threshold, a new cluster head is selected through an election mechanism. The following are the brief contributions of this paper:* Optimization of UWSN's routing by employing the evolutionary algorithms. These algorithms have already been successfully used in other similar network types, such as (Mobile ad hoc Networks) MANETs, vehicular ad hoc networks (VANETs), and (Flying ad hoc Networks) FANETs to name a few. Our proposed technique, SOSNET, is a moth flame optimization-based technique that reduces the overall routing cost and makes the UWSN more efficient. * SOSNET's performance has been compared with other state of the art evolutionary algorithms and the results exhibit that the proposed SOSNET provides better results. The remainder of the paper is organized as follows. Section 2 discusses the related work in this area, Section 3 introduces the key concepts of moth flame optimization, Section 4 provides the detail regarding the proposed algorithm, and Section 5 provides details of the experimental setup. Section 6 provides simulation results and Section 7 concludes the discussion.
## 4 System Model
### Link Quality Estimation of Mobile Nodes
Also, a potential node is added to the forwarded set \(\text{C}^{s,\ d}\) when it decreases the value of \(EAX(s,\ d)\) by a factor \(\delta\), which is defined as a configurable parameter. This step iterates until the new potential node is added in the network. The selection of intermediate potential candidate nodes with ETX and EAX metrics is shown in Figure 1. It can be observed that source node \(s\), destination node \(d\), and next-hop neighbor nodes \(b\), \(e\), and \(f\) of node \(c\) possess a different ratio of delivery probabilities. The ETX metric selects all three neighbor nodes such as \(b,e\), and \(f\) because paths from these nodes to \(d\) have smaller ETX values than that from \(s\) to \(d\). These three neighbor's nodes are sorted in ascending order based on the ETX value obtained by using Equation (10). On the other hand, the EAX select only two nodes, such as \(b\) and \(e\), because with these two candidates, the EAX from \(s\) to \(d\) is less than the EAX from \(f\) to \(d\), adding \(f\) to the candidate set does not decrease EAX between \(s\) and \(d\). Furthermore, EAX prioritizes node \(b\) over node \(e\) as it possesses a smaller value of EAX based on Equation (11). Next hop selection comparison between ETX and EAX potential intermediate candidate's selection and prioritization from source to destination nodes by ETX and EAX is illustrated in Table 1.
## 2 Materials and Methods
### Platform Selection  
The CubeSat standard allows for several configurations. Currently, there are 1U up to 6U CubeSats in orbit, although future missions with 12U and 27U units are already planned. After a detailed study, the 6U unit is the smallest CubeSat platform to fit the payload's main requirements. Two arguments lead to this conclusion:  
1. High spatial resolution is achieved by using sensors with a small detector size and long focal lengths. This discards 1U and 2U CubeSats in favor of 3U and 6U ones. In a 3U, most of the inner space would be used for the optical sensor and the optical train, leaving little space for the GNSS-R payload (antenna and microwave receiver) and all other satellite subsystems.
2. The electrical power to be supplied to the subsystems and payload of the 3 Cat-3/MOTS cannot be supplied by a 3U CubeSat.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 2 Materials and Methods
### Characterization of Coatings  
The structures of the TiNb and TiNbN coatings were determined by XRD (D8 Advance, Bruker, GER), using Cu K\(\alpha\) radiation in glancing angle mode at 1\({}^{\circ}\). The scan speed was 2\({}^{\circ}\cdot\)min\({}^{-1}\) with a step size of 0.02\({}^{\circ}\). The surface morphologies of the coatings were characterized by SEM (Merlin, Zeiss Company, GER) equipped with EDS. To evaluate the chemical composition of the coatings, XPS (Escalab 250 XI, Thermo Fisher Scientific, UK) technique was used. The corrosion resistance of the bipolar plate material was tested by electrochemical methods including potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and potentiostatic polarization in 0.5 mol\(\cdot\)L\({}^{-1}\) H\({}_{2}\)SO\({}_{4}\) solutions saturated with O\({}_{2}\) at room temperature, using an electrochemical workstation (CHI760E, Shanghai Chenhua Instruments Limited, CN). The sample size was 20 by 20 mm for the tests. The testing container was a three-electrode cell. Working electrodes were SS samples, sealed by circular rings with a hole of 1 cm\({}^{2}\) exposure area. To avoid the possible addition of adverse reactive ions from other electrodes, a saturated mercurous sulfate electrode (MSE) and a platinum foil served as reference electrode and counter electrode, respectively. Prior to tests, samples were immersed in electrolyte solutions for 30 min to get stable open circuit potential (OCP). The scan rate of potentiodynamic polarization was set as 2 mV\(\cdot\)s\({}^{-1}\). EIS was carried out at both OCP and 0.19 V vs MSE, within a 0.01-10\({}^{5}\) Hz frequency range and with a 10 mV potential amplitude. Besides electrochemical evaluation, ICR measurements were performed. Two pieces of Toray conductive carbon papers were sandwiched between SS sample (30 by 30 mm) and two Au coated copper plates like [PERSON]'s method [25]. An electrical current of 2A was applied via the copper plates. The ICR value between the coating and carbon paper was calculated from the voltage drop and the current with the resistances between the other contact interfaces measured by the similar method subtracted.
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
###### Abstract  
Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a spatial hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids' height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.  
transmission tower; power line; feature extraction; pylon detection; reconstruction 2019 11 2600; doi:10.3390/rs11222600
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods  
### Neural Network Architecture  
Convolutional Neural Networks (CNNs) find extensive usage in real-time segmentation tasks due to their capability of recognizing local patterns and characteristics within images. They provide processing efficiency and a hierarchical feature structure enabling the identification of both intricate details and broader patterns. Specifically, the U-Net architecture stands out for its specialization in semantic and medical segmentation [20]. Its skip connections facilitate the fusion of information across various spatial scales, resulting in the creation of highly detailed segmented masks. This is particularly advantageous in medical imaging, like lung consolidation segmentation.  
With the aim of improving the behavior and response of our model, various U-Net network modifications were studied, including the standard U-Net architecture. A study was performed using the classical U-Net; the results can be found in [21], where it was shown that the performance was inferior compared to the architecture with attention blocks presented in this paper. For the modifications, we used the Python library Keras-unet, developed by [PERSON] and published on GitHub [22]. Finally, the chosen architecture is Attention U-Net, developed by [PERSON] in [23], where attention gates filters are added in the decoder part, which automatically learns to focus on main patterns with different shapes and sizes, improving the result of the prediction. To find a compromise between computational cost, performance, and network complexity, we chose to use 16, 32, 64, and 128 filters on the encoding layers and the inverse for the decoding layers. Higher filter counts increase the model's complexity and resource needs significantly, which may not translate into better performance for our task.  
Two implementation options were explored with the aim of seeing what is more suitable for the described problem: A different model for each pneumonia pattern, and a single model capable of predicting and segmenting all main lung ultrasound patterns (Pleura, A-line, B-line, Consolidation). Both solutions present advantages and disadvantages that were taken into account. The use of several models offers a more specific response and the possibility to tune the network according to each pattern to obtain the best behavior, but, on the other hand, when using several models, the inference time increases, making the real-time implementation more difficult. While using one global network with multiple outputs, the response could be less precise if we compare each output with a network specifically trained for that pattern, but the main advantage is that the inference time is reduced, thereby allowing real-time implementation.
## 3 Mapping Performances of the Four Models over the Arctic and Antarctic  
According to the existing research, the commonly used ionospheric TEC models have degraded accuracy in the polar regions [7]. Thus, in order to more reliably analyze its characteristics, the GPS ground observations were adopted for the mapping procedure.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## Network Model and Assumptions  
### Network Model  
The real-time cloud-based content-delivery network model is shown in Figure 3. The network model is based on four tiers: content provider tier, SD-WAN tier, data summarization tier, and end-user tier.  
The content provider tier produces the multimedia content for distribution over the network. The content can be a live stream or broadcast, music, video on demand, etc., although live video content was our main focus in this paper. The content is made to be accessible and processed by the various servers and forwarding modules in SD-WAN.  
The SD-WAN tier consists of a collection of interconnected network switches, controlled by one or more centralized controller(s) through a southbound protocol such as OpenFlow. The OpenFlow protocol is an open-source, standard protocol that governs communication between SDN controllers and switches. OpenFlow allows full control over packet flows, where the controller specifies the routing paths as well as how packets should be processed.  
The end-user tier contains a wide range of end devices that are used for accessing the content and provide continuous, real-time feedback of QoE in the form of perceived video quality through SD-WAN. The end-user tier connects to SD-WAN through a number of gateway servers, which form a data summarization tier and are responsible for delivering the content, as well as collecting and summarizing the crowd feedback to the controllers.  
Figure 2: The flow of a complete OneClick assessment procedure [12].  
The software-defined centralized control plane in SD-WAN allows efficient traffic engineering to meet dynamic service requirements. An SD-WAN controller can implement a global view of the network, making it an ideal physical substrate for cloud-based content-delivery network (CCDN) environments. Implementing QoE applications over SD-WAN allows us to further enhance the quality of streaming by adjusting video quality on the basis of user feedback, not just by relying on QoS SLA.