esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 4 Comparison between Reliable Author Profiles and Mis-Configured Author Profiles
### Features of an Author Profile
#### 4.1.1 Features of Basic Information  
* #papers: The total number of papers in the paper list represents the scholar's productivity. However, scholars who claimed a large number of papers are more likely to include mis-configured papers unconsciously if they are not senior enough.
* #Research fields: A list of interested research fields is displayed on the author profile. We count the number of interested research fields that the scholar claimed. Limited by the time and energy, there is little possibility for a decent scholar to focus on too many fields unless she works in interdisciplinary areas.
* Verified: A scholar can verify the author profile through an institutional email address. Whether the account is verified is also an important feature. Scholars who are verified by institutional email addresses will earn more reliability from the reputation of the institution.
* #co-authors: We count the number of co-authors with different names in the paper list. This feature might not reflect the exact number of co-authors, because different scholars may have the same name abbreviation. However, it can still provide an approximate size of the scholar's collaborators.
* Time span: Most of the papers claimed in the paper list have publication years. We calculate the time span of author \(a\) ranging from the first paper published to the latest paper as follows: \[timeSpan=\max_{i,j\in M^{a}}\left(|y_{i}^{a}-y_{j}^{a}|\right)\] (2) where \(y_{i}^{a}\) denotes the publication year of paper \(i\) authored by author \(a\). Due to the limited life span of the scholar, the author profile may be mis-configured if the value of this feature is too large. However, publication year of each paper in the paper list is automatically generated by Google Scholar according to its database, there may be some mistakes. So, it is unreasonable to set a clear threshold for this feature.
# Pothole Classification Model Using Edge Detection in Road Image
## 3 Pothole Classification Model Using Edge Detection in Road Image
### Pothole Classification Model Using Edge Detection
Features are extracted through the learning of image data from the generated model, and classification is performed according to the presence or absence of a _pothole_. The classification result  
Figure 5: The block diagram of the pothole classification model using edge Detection in road image. is output as 1 if there is a _pothole_ and 0 if not. In the image of road damage, most _potholes_ have a circular shape. Therefore, the determination of the _pothole_ compares the number of endpoints of the extracted edge with the sizes of the candidate area and the surrounding area of the bounding box. The size for _pothole_ classification is roughly estimated through the coordinates of the bounding box. Equation (2) represents a discriminant for discriminating the pot hole. \(w\), \(h\), \(a\) represents the thresholds. When the maximum length and height area are greater than the threshold, it is determined as a _pothole_, otherwise, it is determined as not a _pothole_. In this paper, the threshold values for the maximum length, height, and area are the average of the most accurately detected values through repeated experiments. Therefore, the threshold values for the maximum length, height, and area are 200, 150, and 300. \[\left\{ \begin{array}{cc}\textit{Pothole, if }(\textit{Maxwith}>w\ \wedge\textit{ Maxheight}>h\wedge\textit{MaxArea}>a\\ \textit{None}-\textit{Pothole, else}\end{array} \right. \tag{2}\]  
Algorithm 1 shows the algorithms for pothole classification based on YOLO. The input is road damage image data. The output is the classification result. ```
Input: Road Damage Image Data \(\to RD(n)\) Output: Classification Result \(\to CR\)  threshold w, h, a // Step 1. Road damage image data pre-processing for\(n\leftarrow\) 1 to the number of scales in road damage of images do  Covert n images RGB to Grayscale  Covert to 255 pexels after object detection  Create pre-processed \(RD(n)\) // Step 2. Road damage feature extraction using edge detection for\(RD(n)\gets 1\) to number of filter of stage f do  Filter is Laplacian mask  Multi-part loss function  Create optimized midel // Step 3. Pothole classification if the maximum length && height && area \(>\) threshold w, h, a then \(CR\) = Pothole else \(CR\) = None-Pothole endif endfor
```  
**Algorithm 1** Algorithms for pothole classification based on YOLO (you only look once). The algorithm in Algorithm 1 progress in three steps. The first step is the pre-processing of road damage image data. The second step is the road image feature extraction step. Finally, the third step is the pothole classification step. It is classified according to the presence or absence of a pothole in the road damage image data.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
###### Abstract  
Amphibious (air and water) drones, capable of both aerial and aquatic operations, have the potential to provide valuable drone applications in aquatic environments. However, the limited range of wireless data transmission caused by the low antenna height on water and reflection from the water surface (e.g., 45 m for vertical half-wave dipole antennas with the XBee 3C\({}^{\mathrm{TM}}\), estimated using the two-ray ground reflection model) persists as a formidable challenge for amphibious systems. To overcome this difficulty, we developed a wireless data relay system for amphibious drones using the mesh-type networking functions of the XBee\({}^{\mathrm{TM}}\). We then conducted field tests of the developed system in a large marsh pond to provide experimental evidence of the efficiency of the multiple-drone network in amphibious settings. In these tests, hovering relaying over water was attempted for extension and bypassing obstacles using the XBee 3C\({}^{\mathrm{TM}}\) (6.3 mW, 2.4 GHz). During testing, the hovering drone (<10 m height from the drone controller) successfully relayed water quality data from the transmitter to the receiver located approximately 757 m away, but shoreline vegetation decreased the reachable distance. A bypassing relay test for vegetation indicated the need to confirm a connected path formed by pair(s) of mutually observable drones.  
amphibious drone; field test; water quality; wireless data relay; XBee\({}^{\mathrm{TM}}\) +
Footnote †: journal: Advances in Mobile Computing  
Article
# Research on Polarization Coding Cooperative Communication Scheme for FSO System
## 6 Conclusions  
To improve the performance of the FSO communication system, we apply the cooperative communication mode based on system polarization coding to improve the bit-error rate and outage probability of the FSO communication system. The decoding and forwarding cooperation protocol is used at the relay node, and the forwarding threshold is set to effectively avoid the system performance loss caused by the noise signal. The interference of atmospheric turbulence on the performance of the bit-error rate and outage probability of the communication system can be suppressed by increasing the polarization code length and using the cooperative communication mode, which can be applied to solve the problems of strong turbulence and obvious performance degradation of long-distance communication systems. The simulation results show that the bit-error rate of the system can be reduced using the system polarization code and selecting a channel with good channel quality for information bit transmission; the use of cooperative communication not only obtains spatial diversity, but also reduces the bit-error rate and outage probability of the system. Therefore, cooperative communication based on system polarization coding can be effectively applied to the FSO communication system. In future research, we will focus on the research of system complexity under the conditions of fixed polarization code length in an FSO cooperative communication system. It is hoped that the system complexity can be reduced when the polarization code length is increased.  
Conceptualization, Y.C. and H.Q.; methodology, H.Q.; software, H.Q.; validation, X.P.; formal analysis, Y.W.; data curation, Z.Z.; writing--original draft preparation, H.Q.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chongqing Education Commission Fund (KJ120827); the Science and Technology Project of the Chongqing Education Commission (KJ1500934,KJ1709205); the Chongqing Graduate Scientific Research Innovation Project (CYS18311); the Chongqing Basic and Frontier Research Program (cstc2015 cyjA40051); and the Science and Technology Research Youth Project of the Chongqing Education Commission (KJQN202101124).  
The authors declare no conflict of interest.  
Figure 7: Outage probability of cooperative FSO communication system under different communication distances.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Single Element Flexible Antennas for the Sub-6 GHz 5G Band
#### 4.1.4 DRA-Based Flexible Antenna  
A dielectric resonator antenna (DRA) is another suitable candidate for 5G applications. A dielectric resonator can replace the conventional antenna since DRAs have a high radiation efficiency and a wide bandwidth and do not experience conduction losses. [58].  
In [59], a wearable dielectric resonator antenna in the form of a wristwatch is suggested for the 2.45 GHz ISM band and sub-6 GHz bands. As the feeding structure for the isolation, a vertical strip and a coplanar microstrip line are utilized. Figure 12a shows the schematic of the proposed antenna. To identify the conformal nature of the antenna, they are placed on the forearm and ankle model. There is a typical variation in the radiation due to the substrate radian changes. A slot is etched to suppress the substrate effect and because of the concentration of the electromagnetic energy around the slot, less backward radiation was emitted. The SAR is calculated by placing the DRA on multilayer biological tissue models with different electromagnetic properties and mass densities in each layer, as shown in Figure 12b. The average SAR of 1 g of tissue is below 1.22 W/kg. To check the performance variation when the DRA for users, a limp phantom model is considered, as shown in Figure 12c. The antenna has a simulated gain of 5.15 dBi and a measured gain of 4.6 dBi.  
Figure 12: DRA antenna for sub-6 GHz applications: (**a**) Schematic; (**b**) SAR analysis on the biological tissue models; (**c**) Analysis of the DRA on a limp model. Reprinted from ref. [59].  
In another design in [60], a compact button-shaped flexible antenna using a dielectric resonator is proposed for wearable applications. The antenna is in the shape of a button which consists of a ring-shaped resonator and is attached to a ground plane. A rectangular slit cut into the ground plane can be used to couple a microstrip line for the excitation. The antenna achieved a simulated efficiency of 50% to 60% in the specified bandwidth. At an operating frequency of 5.8 GHz, the antenna gives a gain of 5.4 dB. To understand the effect of the electromagnetic waves on the human body, the antenna is placed on a parallelepiped phantom. It is observed that for 1 g of tissue, the SAR is 0.105 and for 10 g of tissue, the SAR is 0.27, which is below the specified international limits.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Experimental Setup

Table 1: Training environment configuration. encoding process in the RSVQ-Diffusion model, the TransLocalBlock module replaces the original self-attention mechanism with a local awareness mechanism. This module divides long sequences into multiple short sequences and achieves parallel processing through the multi-head attention mechanism. Thus, the proposed improvements enhance the quality of the generated images while having minimal impact on the model's training and inference speed.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 6 Experimental Evaluation
### Sensitivity Analysis
#### 6.3.3 Summary  
The power consumption and reliability of WSNs were most affected by three factors in common: _Routing Protocol_ (RP), _Packet Size_ (PaS) and _Initial Energy Level_ (IEL). It should be given preference to routing protocols that consumes very few energy and do not use many sensor nodes to route packets (better reliability); sensor nodes should send small packets; and (when possible) put a battery with a higher load of energy in sensor nodes. As a fourth factor, the WSN reliability requires attention to the position of the sink node; while, for its power consumption, the sensor nodes should send a packet when it is necessary, i.e., only when an event occurs.
# Decision Support Using Machine Learning Indication for Financial Investment
## 2 Theoretical Background
### Machine Learning Techniques Used in Research
#### 2.4.1 Artificial Neural Networks  
Artificial Neural Networks (ANNs) are inspired by the biological neural networks existing in the human brain, which can perform complex tasks automatically, quickly, and simultaneously [21]. These structures served as the basis for the development of the models, which seek to simulate the learning capacity of the brain [22]. In other words, ANNs are non-linear mathematical systems that have neurons connected by connections that are associated with weights [23]. ANNs can recognize patterns, detecting relationships, performing operations with imprecise data, and predicting time series, among other functions [24].  
In addition to time series prediction, ANN shows promise for pattern classification [25; 26; 27], control [28; 29], and optimization. Optimization gains space in this context because of the need to improve the components necessary to maintain the system's operation [30; 31], especially considering the expansive growth of communication systems [32; 33], Internet of Things [34; 35; 36], the need for sustainability [37; 38], technology development [39], and data privacy [40; 41]. Figure 1 shows a representation of an artificial neuron from a Perceptron Neural Network.  
The output of the neuron is the result of the computation involving the inputs \(Xk\), bias \(Bk\) and synaptic weights called \(Wk\). Here, the inputs \(Xk\) are multiplied by a synaptic weight \(Wk\) added to a bias \(Bk\), which activates or not the neuron through the activation function. The activation function is responsible for controlling the activation of a neuron, and therefore, they are fundamental to the correct operation of the ANN [42]. Commonly, the Sigmoid and ReLu functions are used, which was the one used in the present work. Vanishing Gradient is used with the backpropagation algorithm.  
The interconnections between neurons in a neural network is what makes it possible to perform complex tasks [43]. The Multilayer Perceptron architecture organizes perceptrons into multiple layers, which are divided into three parts: the Input Layer, Hidden Layers, and Output Layer [44]. Networks with intermediate layers can implement continuous functions and can perform function approximation by using two intermediate layers [45], which served as the basis for the present work.
## 4 Parametric Studies
### Effect of the Maximum Diameter of the Conical Head (\(D\))
As _D/d_ grows, the axial force at the bottom of the conical head increases slowly, while that at the top of the conical head increases rapidly. It can be seen that the conical head can effectively reduce the axial force of the pile and the larger the _D/d_ is, the more obvious the effect will be. To judge the relative displacement direction of pile and soil, the \"plain function\" in the FLAC-3D is applied to slice the horizontal and vertical displacement nephogram along horizontal direction at one meter below the pile top, as is shown in Figure 14. When the settlement of soil is greater than that of the conical head, the soil would detach from the interface while the upper soil would move down to fill the gap. In this way, pressure and friction are generated on the interface. The relative displacement of the pile against the soil under the embankment is shown in Figure 15a and one under rigid foundation is shown in Figure 15b [39]. The above mentioned analysis elaborates that under the load of the embankment, the friction direction of soil against pile is downward at the conical head zone and the vertical stress of pile-soil interaction can be formulated as follows:  
\[F(z^{\star\star})=F_{Nz^{\star\star}}(z^{\star\star})-F_{zz^{\star\star}}(z^{ \star\star})=F_{N}(z^{\star\star})\sin\alpha-F_{\tau}(z^{\star\star})\cos\alpha \tag{40}\]  
It can be seen from Equation (39) that the vertical stress of soil against pile increases with \(\alpha\) (the cone angle of the conical head), which is consistent with the conclusion made in the numerical simulation. From Equation (14), we can see that when \(\alpha\) is constant, the vertical stress of the soil on the pile is only related to the soil stress \(P_{s}(z^{\star\star})\). This is consistent with the result shown in Figure 12, that is, the vertical stress of soil to pile changes linearly in the conical head zone. The comparative analysis indicates that a pile with a conical head can effectively reduce negative friction, which is in agreement with the conclusion of [PERSON] et al [14] on a model test. A pile with a conical head can effectively improve the position of the neutral point, reduce negative friction, and improve bearing capacity.
## 3 Results  
### Device Accuracy of Laboratory Tests  
The preliminary testing of the system performance showed that the antennae worked at a range of 950 m, and that even with misalignment, the system worked with RTK corrections out to 450 m. At the initial (non-RTK) GNSS lock, the errors were of the order of 5 to 10 m. The results improved to approximately 2 to 3 metres as differential GNSS became available (Figure 7a). The initial RTK lock is relatively difficult to acquire for moving sensors but can be improved by starting with remembering the last measured location, and a full RTK lock was only achieved after the placement on the stands (Figure 7b). The regularity of the sensor plots (Figure 7b,c) indicates that for the areas with an unobstructed LOS, the relative errors drop below 0.1 m. As the grid frame was walked (by researchers) into an area of tree blockage (Figure 7d), and some loss of the GNSS signal at the rovers occurred, the regularity of the array decreased showing errors around 1 m. That level of error was also shown when the experimenters' bodies blocked the GNSS signal to the satellites or the correction signal from the base station. The signal blockage was less of a problem during the farm trial. The GNSS antennas for the rovers were fitted on the back of the sheep and when the sheep were standing, the rover had a hemispheric sky view. Some blockage of the GNSS signal occurred when the sheep lay down, when the harness slipped to one side of the sheep, and from corner sections of the field fence line where solid metal railings were installed. The correction signal from the base station to the rover was minimally degraded during the test days, however, was blocked when people or vehicles were in the field during the preliminary validation stages.  
The RTK rover errors were of the order of 0.1 m when LOS was available, and the system still showed significant enhancement over standard GNSS in areas with tree coverage and limited LOS to the base station. An advantage of the system is that the recorded data stream contains estimates of the quality of the location estimate, which identify areas where the location may have problems, and should thus be excluded from the analysis.  
Figure 6: Position of the camera outside of the paddock (the north corner) to record the animals’ movements during parts of the experiment to match the animal position with locational plots and social network graphs (see Section 3.3). Note: The dot points in the plot show the individuals within a 30 s time-period beginning at 12:32:30 p.m.
## 2 Materials and Methods
### BIGV Monitoring  
To monitor the BIGV around the mining area, vibrometers (iSV-420, Sichuan TDEC MEASUREMENT & CONTROL Co., Ltd., China) were deployed. The iSV-420 vibrometer integrates three-axis vibration sensors and digital measurement, storage, and wireless transmission. Low frequency response can be lowered to 0.01 Hz, with a built-in GPS function and automatic positioning function supporting distributed synchronous measurement. It can be used for shock and vibration monitoring and safety evaluation in blasting engineering. According to the \"Code for Seismic Design of Buildings\" (GB50011-2010) [28], the location of blasting vibration monitoring should be easy for operators to approach, and the ground should be flat without accumulated water. The plan should be developed through multiple on-site investigations of villages, roads, factories, etc. that may be affected by blasting operations. Based on an on-site investigation and the distribution of a large number of residential areas and office buildings around the ore body, in order to monitor the impact of surface vibrations caused by underground blasting operations on residential areas and office buildings on the surface, four monitoring points were arranged around the ore body. As shown in Figure 3, the settlement M1 was located to the north of the ore body, settlement M2 was west of the ore body, settlement M3 was located directly above the ore body, and settlement M4 was south of the ore body. The refraction and transmission of the blasting vibration wave is shown in Figure S1.
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
###### Abstract  
To allow fast communication--at several Gb/s--of multimedia content among processors and memories in a multi-processor system-on-chip, a new approach is emerging in literature: Wireless Network-on-Chip (WiNoC). With reference to this scenario, this paper presents the design of the key element of the WiNoC: the antenna. Specifically, a bow-tie antenna is proposed, which operates at mm-waves and can be implemented on-chip using the top metal layer of a conventional silicon CMOS (Complementary Metal Oxide Semiconductor) technology. The antenna performance is discussed in the paper and is compared to the state-of-the-art, including the zig-zag antenna topology that is typically used in literature as a reference for WiNoC. The proposed bow-tie antenna design for WiNoC stands out for its good trade-off among bandwidth, gain, size and beamwidth vs. the state-of-the-art.  
new trends in Network-on-Chip (NoC) architectures; on-chip antenna; networks-on-chip; wireless on-chip communications; multimedia communications +
Footnote †: journal: Low Power Electronics and Applications