esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 EGCIR Protocol
### Design of the EGCIR Protocol  
The design of the EGCIR protocol was based on supervised clustering, routing setup and route management phases. In the first phase, the deployed sensor nodes were arranged into particular boundaries only once at the time of network initialization using a K-nearest neighbor (K-NN) algorithm. Unlike frequent network formulation at a particular time, it leads to a decrease in the fraction of energy exhaustion and node overheads. Within each boundary, a specific node was selected as a cluster head to transmit cluster data towards the BS. Initially, all nodes store their neighbor information in their local tables, which comprise residual energy, distance to the BS and distance to the source node. In addition, the BS creates a generalized table to store information about all sensors, i.e., position, energy, time edge and public and secret keys for future decisions. The stored information is also updated frequently to know the exact state of the network field. In the routing setup, the trusted and efficient routes in terms of energy, time edge, and communication distance were determined. The EGCIR protocol improves the route lifetime with the least transmission cost and selected cluster heads are mapped in an undirected graph model. Furthermore, the routing path in the undirected graph model is re-adjusted using routing updates and it increases the network performance for delivery ratio and energy efficiency. In the end, routing paths are protected against faulty or misbehaving nodes and retain data security with integrity. Along with the BS that secures intercluster communications, the cluster heads also act as a local node controller and protect the intracluster communications against threats. Moreover, based on routing requirements, an appropriate next-forwarder was selected to avoid unnecessary retransmissions and information damages. The design of the EGCIR protocol is illustrated in Figure 1.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Diagnostic Imaging  
In America, X-ray exams represent 60 percent of all imaging exams, and over 80 percent of health system visits include an imaging exam, placing significant pressure on healthcare providers. AI applications in diagnostic imaging not only generate results more efficiently but also more accurately by identifying problem areas or details that may be missed by the human eye. Current commercial clinical applications include general radiography, computed tomography, magnetic resonance imaging, fluoroscopy, and radiation therapy [44].  
For example, in lung cancer screening, AI systems are used to analyze chest CT scans, assisting doctors by automatically detecting small nodules. Studies have shown that these systems can identify potential cancerous lesions earlier and more accurately than traditional methods, thus improving the success rate of early treatment [45]. In brain imaging, AI algorithms are employed to analyze MRI images to detect and monitor the progression of neurodegenerative diseases such as Alzheimer's. These algorithms can quantify changes in different brain regions, providing clinicians with detailed information to create personalized treatment plans [46]. Additionally, in fracture detection, AI systems have been used to analyze X-rays in emergency rooms, automatically identifying subtle fractures that may be overlooked by rushed physicians. One study found that these AI systems have accuracy comparable to experienced radiologists, helping to improve the diagnostic efficiency and accuracy in emergency settings [47].  
AI applications play a pivotal role in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimization [48]. Moving forward, greater emphasis should be placed on the data-driven nature of this application. In addition to high-quality datasets, efforts should focus on generating more data and improving data quality [49].
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 5 Experiment Validations
### Parameters Selection  
In the proposed identification system, the real-time tracking of the cutting unit in infrared thermal images is the premise for accurate recognition of the shearer cutting state. A reasonable selection of parameters has great influence on the tracking effect, including the binarization threshold \(T_{1}\), the structure element [\(a_{1}\), \(b_{1}\)] of the corrosion operation, the structure element [\(a_{2}\), \(b_{2}\)] of the swell operation, and the maximum likelihood probability threshold \(T_{2}\).  
(1) Selection of parameter \(T_{1}\)  
In order to select a suitable \(T_{1}\), many experiments have been conducted, and some of the experimental results are shown in Figure 11. When \(T_{1}\) was set to 0.5, the cutting-unit characteristics  
Figure 10: Infrared thermal imaging video screenshots of shearer cutting unit.  
Figure 9: Built experimental system: (**a**) overall layout of experimental platform; (**b**) infrared thermal imager.  
As shown in Figures 12 and 13, if the corrosion-structure elements were too small, there were many residual uncoherent points after the corrosion operation, and when the corrosion-structure elements were too large, many useful characteristics of the shearer cutting unit were removed, which was not conducive to follow-up tracking. In addition, when the width and length of the swell structural elements increased, the external dimensions of the cutting unit were also augmented. By comparing this with the cutting unit in the original image, the corrosion-structure elements were selected as [4; 4], and the swell-structural elements were selected as [40; 50].  
(3) Selection of the maximum likelihood probability threshold \(T_{2}\)  
In order to select the optimal maximum likelihood probability threshold, the infrared thermal imaging video containing the covered cutting unit was selected to carry out the tracking experiment. The maximum likelihood probability value of the confidence map in each frame was calculated, and some results are shown in Figure 14.  
Figure 13: Processing results with different swell structural elements.  
It can be found that the maximum likelihood probability values of the confidence maps from the 11 th frame are lower than the first 10 frames in the video. The reason for this is that in the 11\(\sim\)15 frames, the shearer cutting unit is covered, resulting in target-tracking failure, and the target tracking from the 12 th frame is actually wrong. After many experiments, \(T_{2}\) was set to 0.001 in this paper. When \(T_{2}\) was less than 0.001, it was necessary to change the location of the cutting unit by morphology, and then perform tracking again.
## 1 Introduction  
Since the beginning of aviation, innovation in technologies and operational procedures have steadily increased comfort, performance and safety of air transport. Especially the latter aspect leads to a continuously decreasing number of fatal accidents even though the number of passengers is steadily increasing. In this context, it is important that also aging aircraft types are retrofit equipped with modern technologies whenever feasible.  
Besides new technological developments, the competition on the European as well as the global aviation market increases continuously. This pushes aircraft operators towards more efficient processes. In order to enhance efficiency, many key innovations focus on the reduction of scheduled and unscheduled grounding time (downtime) of the aircraft which is the time an aircraft is not available for flying. However, the procedure to remove liquid water from the fuel systems, called the draining procedure, requires time for the frozen water inside the tank to melt. The draining procedure makes use of the fact that liquid water, as well as ice, have a higher density than kerosene. Therefore, liquid water can be removed from the fuel tank by opening so-called drain valves at the lowest point of the tank and let the water run out of the tank. As this only removes liquid water from the tank, one must wait until all frozen water is molten. The time needed for the ice to melt depends on the inner temperature of the tank and on the outer surrounding temperature. Especially at cold winter times, scheduled downtimes are insufficient to melt all frozen water. A detailed description of the draining procedure and the problem occurring during the daily aircraft operation can be found in [PERSON] et al. [1].  
The real-time detection of remaining ice inside the fuel tank will thus contribute to the optimization of the draining procedure that is performed to remove liquid water from the fuel tank enabling in this way a considerable reduction of costly downtimes. For the operation in the hangar by aircraft technicians, a mobile, small and easy-to-use AE system that can monitor the melting ice from outside the tank is required. To match these requirements already in laboratory experiments, a low-priced system with off-the-shelf components is used to perform presented experiments.
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
###### Abstract  
A quad-element multiple-input-multiple-output (MIMO) antenna with ultra-wideband (UWB) performance is presented in this paper. The MIMO antenna consists of four orthogonally arranged microstrip line-fed hexagonal monopole radiators and a modified ground plane. In addition, E-shaped and G-shaped stubs are added to the radiator to achieve additional resonances at 1.5 GHz and 2.45 GHz. The reliability of the antenna in the automotive environment is investigated, with housing effects taken into account. The housing effects show that the antenna performs consistently even in the presence of a large metal object. The proposed MIMO antenna has potential for various automotive applications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), intelligent transport system (ITS), automatic vehicle identifier, and RFID-based electronic toll collection.  
antenna; diversity; MIMO; monopole; vehicular communication +
Footnote †: journal: Sensors  
0
Footnote 0: Copyright**: © 2021 by the authors. Licenses MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods  
Although approaches focused on reducing the amount of data transferred from a UAV to a GS using image compression methods [27] and compressive sensing [33; 34] exist, they usually demand rather significant computing power on the UAV to provide fast reduction (compression). More importantly, these data reduction techniques still (to some degree) decrease image quality and, therefore, the results of the image analysis. Thus, the goal of this research was to investigate solutions for transferring high-resolution images from a camera mounted on a UAV to the ground at a rate that will satisfy the demand for real-time coverage of the surveilled area.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 4 LoRaWAN Protocol
### LoRaWAN Key Features
#### 4.2.4 LoRa Gateway Role and Characteristics  
GW, base station or access point are different terminologies for the same device that is responsible for radio exchange with LoRa devices and connected to NS by IP backhauls (fiber optics, cellular, WiFi, etc.).  
GW receives LoRa-modulated RF messages from any ED at hearing distance and forwards these data messages to the NS. ED is not attached to any specific GW where it can be served by multiple gateways in the area. This arrangement significantly reduces the packet error rate (multiple receivers rather than one), reduces battery over consumption for mobile EDs, and opens the door for low-cost geo-location [44].  
GWs operate entirely at the physical layer and only check the data integrity of each incoming LoRa RF message. If the integrity is not complete, the message will be dropped. In addition, they are used for downlinks; GW executes transmission requests coming from the NS without any interpretation of the payload.  
GW's main characteristics can be summarized as below:  
* Main-powered most of the time, or solar where possible with backup batteries;
* Connected to external antennas (omni or directional);
* Installed in relatively high locations with the best line of sight;
* Have Ethernet or SimCard slots;
* Compact in design;
* Different types depending on the number of endpoints;
* Listening to eight different channels in parallel [48].  
The GW features, such as the main power, the high locations, and the IP connectivity with the NS make it suitable for performing spectrum sensing. Being main-powered, the GW does not suffer power consumption problems when continuously performing spectrum sensing. The high location helps GW accurately harvest the licensed channel's occupancy, since location may alleviate the channel conditions such as fading and shadowing. The IP connection ensures an existing support for reporting the spectrum sensing to the NS.
## 3 Results
### Phenology Estimated from Satellite- and Ground-Based SIF  
We compare dates of the phenophase transitions estimated from the two SIF data including \(\text{SIF}_{\text{GOME-2}}\) (740 nm) and \(\text{SIF}_{\text{Ground}}\) (760 nm) in this section. Although SIF radiance are at different wavelengths in these two data, the previous study [39] which was also implemented at Harvard Forest has shown that their measurements are highly linear correlated (Figure S1). Thus, their differences in the SIF wavelengths should have a minimal effect on extracting the phenological metrics.  
The temporal pattern of \(\text{SIF}_{\text{GOME-2}}\) is inconsistent with in situ \(\text{SIF}_{\text{Ground}}\) in some aspects. \(\text{SIF}_{\text{Ground}}\) shows a sharp springtime rise (within 20 days) and autumn decline (within 30 days). In contrast, \(\text{SIF}_{\text{GOME-2}}\) shows slower rates in both the rising and the falling part of the seasonal trajectory (Figure 2e). In comparison to \(\text{SIF}_{\text{Ground}}\), \(\text{SIF}_{\text{GOME-2}}\) also exhibits a shorter period of the summer plateau due to the later S3 and earlier A1 (Figure 2e). Again, these discrepancies can be explained by their difference in their spatial and temporal resolutions.  
Although \(\text{SIF}_{\text{GOME-2}}\) and \(\text{SIF}_{\text{Ground}}\) are largely different in spatial and temporal resolutions, their estimations of dates of S2 show an only four-day difference and both are close to the S2 from the GPP with an earlier estimation of three and seven days, respectively (Table 1). In the autumn, \(\text{SIF}_{\text{GOME-2}}\) and \(\text{SIF}_{\text{Ground}}\) estimate dates of A2 by seven days earlier and six days later than that of the GPP time series, respectively (Table 1). Due to its high temporal and spatial resolutions, \(\text{SIF}_{\text{Ground}}\) can provide similar estimations of S1, S3, A1 and A3 to those from the GPP data (Table 1), whereas the estimates from \(\text{SIF}_{\text{GOME-2}}\) clearly deviate from the GPP observations. Furthermore, the comparison using \(\text{SIF}_{\text{GOME-2}}\) and \(\text{SIF}_{\text{Ground}}\) data during 2014-2015 also confirms that both \(\text{SIF}_{\text{GOME-2}}\) and \(\text{SIF}_{\text{Ground}}\) are good predictors of S2, and \(\text{SIF}_{\text{Ground}}\) can further estimate A2 with a better accuracy (Table S4).
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 2 Routing in the Data Center
### Switching Overlay  
In addition to the infrastructure routing use case, migrating Virtual Machines (VM) between different servers keeping their IP addresses unchanged (i.e., maintaining their attachment to a given L3 subnet) is a major requirement from the application point of view, which is completely unaware of the underlying connectivity. It implies that the data center is connected by a transparent layer two fabric, which eventually implements L2 VLANs. Therefore, this requirement demands implementing layer two emulation over layer three environment, using solutions such as VXLAN [23] or Ethernet Virtual Private Networks (EVPN) [24].
## 2 State-of-the-Art
### Challenges for 5G and TI Adoption  
Although mobile operators began to roll out 5G in 2019, this does not mean the technology will be ready for widespread use by manufacturers. Certain 5G features relevant to manufacturing use cases may not be available for at least another 5 years, and coverage in rural areas will be a challenge. The following are the primary identified hurdles for industry adoption of 5G based on the foregoing: (a) the cost of procuring and installing the required equipment, (b) safety, (c) deployment knowledge, and (d) RF interference. Finally, the expanding usage of more complex collaborative robots in production is a topic to consider (cobots). Cobots are already on the market, but the incorporation of 5G networks is expected to make them even more valuable. For example, taking use of 5G networks' decreased latency allows for more instantaneous real-time proximity awareness, allowing the robot to work at quicker speeds, adjacent to a human, while reducing the danger of injury [40].
## 2 Method
### Objective Measurements
#### 2.1.3 Objective Analyses  
The ISO 3382-1 [17] parameters, that is, reverberation time (\(T_{30}\)), early decay time (\(EDT\)), clarity (\(C_{80}\)), definition (\(D_{50}\)), center time (\(T_{\mathrm{s}}\)), interaural cross-correlation (\(IACC\)) have been assessed by using the functions of ITA-Toolbox. Specifically, these parameters have been considered as a measure of reverberance and liveness (\(T_{30}\) and \(EDT\)), clarity and balance between early and late energy, or the balance between clarity and reverberance (\(C_{80}\), \(D_{50}\), and \(T_{\mathrm{s}}\)), and perceived spaciousness (\(IACC\)). This last parameter has been evaluated only for the binaural measurements at the head locations.  
Averaged values, as suggested in ISO 3382-1 [17], have been calculated over the 500 Hz and 1000 Hz octave bands, while the \(IACC\) values were averaged over 500 Hz, 1000 Hz and 2000 Hz octave band results since these frequencies concern the subjectively most important range. Besides the \(IACC\) for the full length of the impulse responses, the early-arriving (0-80 ms) and late-arriving (80 ms-inf) sound have been considered separately in the evaluation of \(IACC_{\mathrm{E}}\) and \(IACC_{\mathrm{L}}\), respectively. The JND values of each parameter have been used to compare the results for different configurations (Table 2).  
Figure 2: (**a**) Measurement positions (source, microphones, and artificial head) and main dimensions of the room (metric scale). (**b**) Schematic plan view of the diffusers location with respect to the source and artificial head positions.
_Applied_  
_sciences_  
Visibility Parameter in Sand/Dust Storms' Radio Wave Attenuation Equations: An Approach for Reliable Visibility Estimation Based on Existing Empirical Equations to Minimize Potential Biases in Calculations  
**[PERSON]\({}^{\copyright}\) and [PERSON] \({}^{\ast}\)**  
Auto ID Laboratory, School of Electrical and Electronic Engineering, Universiti Sains Malaysia,  
Nibong Tebal 14300, Pulau Pinang, Malaysia; [EMAIL_ADDRESS]  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
Received: 8 September 2020; Accepted: 19 October 2020; Published: 26 October 2020  
**Featured Application: Enhancing the techniques of existing empirical equations by relating visibility to Sand/Dust mass concentration for reliable estimations and to minimize potential biases in incorporating calculations.**  
**Abstract:** In efforts to structure an expression for wave attenuation under a sand/dust storm, most established calculations pronounce optical visibility as an essential parameter. Although visibility information can be retrieved from weather stations, other commonly encountered sources may present it differently, i.e., as total suspended particles (TSP). Consequently, several empirical equations linking visibility to TSP concentrations were evaluated to address offset tendencies in estimations. In addition to substantiating specific equations, the results revealed that averaging a pair of equations has a 46.09% chance of estimating visibilities with a probability of 37.27%, a relatively low error compared to that achieved by employing single equations, which were found to have a probability of 28.93% with a lesser chance (29.58%) of a low estimation error for the same set of data. The resulting enhancement was evaluated by considering a study on a wireless sensor network's (WSN's) signal performance under vaguely labelled meteorological conditions. The meteorological conditions were converted to visibility using the results' suggestions and were found to be in good agreement with an observation standard set by the China Meteorological Administration (CMA) for sand/dust storm outbreak classifications.  
**Keywords:** sand/dust storm (SDS); signal attenuation; visibility; total suspended particles (TSP)