esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Paper Outline  
This article is organised as follows: Section 2 illustrates the basics of wireless communication system, i.e., coverage definitions, coverage issues, coverage monitoring, and coverage requirements. Section 3 focuses on the coverage enhancement technologies such as small cells, CA, D2D, NOMA, MIMO, and optimization in 5G networks. In this section, each coverage enhancement technique implementations and their future challenges are explored. In Section 4, 5G key parameter enhancement techniques, their advantages, limitation and future challenges are illustrated. 6G and its future challenges are explored in Section 5. Section 6 concludes the article and provides the future scope. The structure of this article is shown in Figure 1. Table 3 provides the list of abbreviations used in this article.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Abbreviation** & **Full Form** & **Abbreviation** & **Full Form** \\ \hline
3 GPP & Third Generation Partnership Project & LTE-A & Long Term Evaluation Advanced \\
5G & Fifth Generation & ML & Machine Learning \\ AGMA & Arithmetic Geometric Mean Algorithm & MIC & Multiple Interference Cancellation \\ ANN & Artificial Neural Networks & MIMO & Multiple input Multiple Output \\ BS & Base Station & mmWave & Multi-After Wave \\ CA & Carrier Aggregation & NOMA & Non Orthogonal Multiple Access \\ CC & Carrier Component & OC & Operator Control \\ CDNOMA & Code Division NOMA & OFDMA & Orthogonal Frequency Division Multiple Access \\ CDMA & Code Division Multiple Access & OMA & Orthogonal Multiple Access \\ CCP & Complementary Geometric Programming & PA & Power Allocation \\ CR & Cognitive Radio & PDNOMA & Power Domain NOMA \\ CS-NOMA & Compressing NOMA & PCC & Primary Carrier Component \\ CSI & Channel State Information & QoE & Quality of Service \\ CU & Cellular User & RAN & Random Access Network \\ CUE & Cellular User Equipment & RB & Resource Block \\ D2D & Device to Device & RD & Random Distribution \\ DL & Down Link & RF & Radio Frequency \\ DR & Device Relaying & RL & Reinforcement Learning \\ DC & Direct D2D Communication & SINR & Signal 0 Interference Noise Ratio \\ DU & D2D User & SIC & Successive Interference Cancellation \\ DUE & D2D User Equipment & SOP & Security Outage Probability \\ eMBB & Enhanced Mobile Broad Band & SCC & Secondary Carrier Component \\ FDD & Frequency Division Duplexing & SU & Secondary User \\ FDMA & Frequency Division Multiple Access & TDD & Time Division Duplexing \\ FRI & Frequency Range 1 & UE & User Equipment \\ FR2 & Frequency Range 2 & UL & Up Link \\ IC & Interference Cancellation & URLLC & Ultra Reliable Low Latency Communication \\ IM & International Mobile Telecommunication & UAV & Unmanned Aerial Survey \\ IoT & Internet of Things & V2V & Vehicle to Vehicle \\ ITU & International Telecommunication Union & V2X & Vehicle to Anything \\ LTE & Long Term Evaluation & WRC & Worlds Radio-communication Conference \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of Abbreviations.
## 1 Introduction
### Contributions and Paper Structure  
Against the above backcloth, there are some problems to solve here.  
1. To improve QoS of EUs, EUs ask help from D2D pairs. EUs intend to share spectrum resource with D2D pairs in exchange of D2D pairs' relaying data for them. However, how to allocate the spectrum resource between EUs and D2D pairs in overlay mode to achieve Win-Win?
2. Compared to the underlay spectrum-sharing scheme, overlay spectrum-sharing scheme does not consider interference among users but reduces spectrum efficiency. Hence, how to improve spectrum efficiency in overlay spectrum-sharing scheme?  
To solve the above problems, this paper proposes a reciprocal-selection-based 'Win-Win' overlay spectrum-sharing scheme (RSWW-OSSS) for the D2D-enabled cellular network supporting overlay spectrum sharing scheme and cooperative transmission among multiple EUs and multiple D2D devices. More explicitly, we developed the following contributions:  
1. A RSWW-OSSS is designed for a D2D-enabled cellular network in order to provide a better connective experience for EUs with the aid of cooperative communication. More explicitly, according to the proposed RSWW-OSSS, EUs are capable of achieving an increased rate with the aid of cooperation assistance provided by the D2D devices. Furthermore, in order to perform D2D transmission, the D2D devices intend to forward data for the EUs in exchange for accessing the part of cellular spectrum resource which is not used by the EUs.
2. In order to improve the spectral efficiency, DTs employ the NOMA technology to simultaneously convey EUs' and their own data at different transmit power. SIC is invoked at both the BS and D2D receivers (DRs) for separating the EUs' and DTs' data. As we know, NOMA technology is not introduced in overlay D2D cooperation communications in [19; 20; 21].
3. Aiming for maximizing the utilities of both EU and D2D device, their behaviours are modelled as a two-stage Stackelberg game [31; 32; 33]. More explicitly, as the leader, EUs calculate the optimal time allocation coefficient in order to maximize its transmit rate. Furthermore, DTs are modelled as the followers which determine the optimal transmit powers for the cooperative transmission and D2D communication, in order to improve the achievable rate of D2D communication and simultaneously reduce the energy consumed for forwarding EUs' data. The backward induction [34] is invoked for analysing the game and deriving the optimal strategies of EUs and DTs.
4. For the sake of achieving a stable matching between multiple EUs and multiple D2D devices, we analyse the algorithmic stability of the proposed RSWW-OSSS with the aid of matching theory [35].  
The rest of this paper is organized as follows. The system model is introduced in Section 2. Section 3 describes the proposed RSWW-OSSS. In Section 4, the attainable performance of our scheme is evaluated. Finally, we conclude in Section 5.
## 4 Aggregate Analysis
### Income Group  
In addition to analyzing the overall mode share, we thought it would be interesting to look into the mode share shift by household income and examine whether this shift happened evenly across income groups. Table 3 shows the mode share across income groups in 1994 and 2011. The growth rate of the transit mode share was higher for the poverty and low-income groups than for the middle- and high-income groups. For example, the mode share of bus increased from 4.81% to 19.15% for the poverty group, but only increased from 3.26 to 4.58% for the high-income group. This would seem to indicate that the poverty and low-income commuters were taking advantage of the transit investment, probably more than the middle- and high-income groups.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & **1994** & **2011** & **\%** & **1994** & **2011** & **\%** & **1994** & **2011** & **\%** \\  & **All Trips** & **All Trips** & **Change** & **CBD Trips** & **CBD Trips** & **Change** & **Trips** & **Trips** & **Change** \\ \hline Driving & 86.9 & 71.7 & \(-\)17.5 & 55.8 & 36.5 & \(-\)34.55 & 90.6 & 76.6 & \(-\)15.4 \\ Passenger & 2.8 & 5.1 & 77.8 & 5.2 & 4.0 & \(-\)24.39 & 2.6 & 5.2 & 103.3 \\ Bus & 4.2 & 6.9 & 63.3 & 24.1 & 25.4 & 5.55 & 1.9 & 4.3 & 127.4 \\ Rail & 0.8 & 4.7 & 466.1 & 4.1 & 19.1 & 362.18 & 0.5 & 2.7 & 503.8 \\ Walk & 3.9 & 5.8 & 48.1 & 8.3 & 5.1 & \(-\)38.15 & 3.4 & 5.9 & 73.9 \\ Bike & 1.3 & 5.9 & 345.5 & 2.5 & 9.9 & 301.58 & 1.2 & 5.3 & 348.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mode share for HBW trips by mode and area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Income Level**} & **MODE** & **1994** & **2011** \\  & **Mode Share** & **Mode Share** \\ \hline  & PASSENGER & 5.38 & 6.65 \\  & BIKE & 5.69 & 2.51 \\  & BUS & 4.81 & 19.15 \\  & DRIVING & 75.94 & 53.29 \\  & RAIL & 0.00 & 7.89 \\  & WALK & 8.18 & 10.50 \\ \hline  & PASSENGER & 4.80 & 6.81 \\  & BIKE & 2.26 & 6.92 \\  & BUS & 6.64 & 7.40 \\  & DRIVING & 76.60 & 54.80 \\  & RAIL & 1.12 & 9.02 \\  & WALK & 8.58 & 15.06 \\ \hline  & PASSENGER & 2.71 & 5.64 \\  & BIKE & 0.99 & 5.40 \\ Mid income & BUS & 3.91 & 7.83 \\  & DRIVING & 88.99 & 72.70 \\  & RAIL & 0.83 & 3.73 \\  & WALK & 2.57 & 4.70 \\ \hline  & PASSENGER & 1.81 & 3.93 \\  & BIKE & 0.81 & 6.26 \\  & BUS & 3.26 & 4.58 \\  & DRIVING & 90.84 & 79.50 \\  & RAIL & 0.77 & 2.84 \\  & WALK & 2.51 & 2.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mode share for HBW trips by household income group.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 5 Conclusions  
This paper has proposed a new algorithm for detecting onsets, offsets, and the transitions between notes in singing. The algorithm can work in both offline and real-time environments. In the case of real-time, a 57-millisecond delay is needed to have adequate information for calculating the events. Compared to other well-known algorithms, the algorithm shows an improvement of between 2% and 36%, especially when the window size for calculating the F-measure is equal to or greater than 150 ms.  
Conceptualization, B.F.; methodology, B.F. and S.C.; software, B.F.; validation, B.F.; formal analysis, B.F.; investigation, B.F. and S.C.; resources, B.F. and S.C.; data curation, B.F. and S.C.; writing--original draft preparation, B.F., A.Y., S.C.; writing--review and editing, B.F., J.T., A.Y., S.C.; visualization, B.F.; supervision, J.T.; project administration, B.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable.  
All the relevant files to this study, such as an implementation of the proposed algorithm and the dataset generated by all the algorithms mentioned in this paper, are available online at [[https://github.com/BehnamFaghihMusicTech/Onset-Detection](https://github.com/BehnamFaghihMusicTech/Onset-Detection)]([https://github.com/BehnamFaghihMusicTech/Onset-Detection](https://github.com/BehnamFaghihMusicTech/Onset-Detection)), accessed on 15 July 2022. Thanks to Maynooth University and the Higher Education Authority in the Department of Further and Higher Education, Research, Innovation and Science in Ireland for their support. The authors declare no conflict of interest. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Recent Improvements in the Development of Soft Grippers
## 3 Dexterous Manipulation  
Dexterous manipulation has been defined in several ways in the literature. The two general approaches are anthropomorphic and object-centric manipulation. The former considers that having a shape or capabilities like the human hand on a gripper is the origin of dexterous manipulation. The latter considers that dexterous manipulation is  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Dexterity** & **Hybridization Strategy** & **Performance** \\ \hline
[54] & DM & Use of pneumatic & Bending \\  & & actuators & stiffness \\  & & surrounded with & increased by \\  & & braided nylon & more than \\  & & (pneumatic muscles) & 150\% \\ \hline
[30] & DM & Use of LMPA tubes & Capable of \\  & & to stiffen a cable- & preventing \\  & & driven arm with the & vertical \\  & & rigid phase & deflection with \\  & & & pulling force: \\  & & & 2N \\ \hline
[51] & No DM & Use of LMPA & Stiffness \\  & & to stiffen an FEA & increased \\  & & actuator with the & 35 times \\  & & rigid phase & 7 possible \\  & & & shapes \\ \hline
[11] & No DM & Use of SMP & Maximum \\  & & to stiffen an FEA & lifting \\  & & actuator with the & load: 500 g \\  & & rigid phase & 7 possible \\  & & & shapes \\ \hline
[21] & DM & Use of a translated & Maximum \\  & & rod that varies & bending \\  & & the bending point and & angle: 90\({}^{\circ}\) \\  & & the stiffness of & Exerts up to \\  & & the finger & 650 mN in \\  & & & less than 3 s \\ \hline
[55] & DM & Use of programmable & Twisting angle: \\  & & ligaments (elastomers) & 18\({}^{\circ}\) \\  & & whose stiffness varies & Complete \\  & & & twisting process \\  & & & in less than \\  & & & 15 s \\ \hline
[50] & No DM & Use of particle & Stiffness \\  & & jamming; stiffening & increased \\  & & of an FEA actuator & 10 times and \\  & & & gripping force \\  & & & from 0.18 \\  & & & to 1.8 N \\ \hline \end{tabular}
\end{table}
Table 1: Functional properties resulting from different hybridization strategies. (DM stands for dexterous manipulations.).  
arbitrarily manipulating an object in space without considering the gripper's configuration or the strategy used to perform the movement [56]. These two approaches also define the gripper's architecture. Considering the diversity of forms and technologies that grippers can have, the object-centric approach is considered the most adequate since it facilitates a holistic view of the grippers and the manipulation itself. Thus, dexterous manipulation is a function carried out by animals and robots allowing the spatial manipulation of an object around its 6 degrees of freedom (DOFs) in an arbitrary way, starting and ending in stable grasping positions [56; 57].
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
###### Abstract  
This work studies the hypothesis of whether the UDP DoS attack affects voice and video flows in a VoIP network. It is a continuation of a previous work that studied the same hypothesis, but the VoIP server was under different types of TCP DoS attacks. The studied VoIP platform is the Asterisk FreePBX. A simple IP network model was developed for the purpose of the study. The used platform for the modeling of IP networks is GNS3. The study is conventionally divided into two parts: in the first part, only voice streams are exchanged in the network, and the server is subjected to a UDP DoS attack, and in the second part, only video streams are exchanged in the network, and again, Asterisk is subjected to a UDP DoS attack. The obtained results confirm the results of the previous study--the performance of the Asterisk FreePBX is not affected by the UDP DoS attack. Although the server is flooded with UDP packets, it works and is not blocked, and different types of VoIP calls are realized without problems. The UDP DoS attack does not affect the parameters of voice and video VoIP streams.  
Asterisk FreePBX; DoS attack; GNS3; IP network modeling; Kali Linux; network monitoring; UDP DoS attack; VoIP streams Article
## 3 Methodology
### Anomaly Detection for the Target Domain  
The above pre-trained model is less effective in segmenting the target domain due to the data distribution bias between the source and the target domains. Therefore, the process of anomaly detection for the target domain is introduced into our AD-DAF framework to further remove outliers (i.e., non-buildings) in the building layer. In this paper, the isolation forest approach [38] is selected to perform anomaly detection for the target domain. It is an unsupervised anomaly detection approach with less execution time, memory requirements, and a high precision [39]. The isolation forest approach isolates each anomaly data by constructing binary trees. In general, the binary trees are constructed from the random properties of the dataset and anomaly data are those nodes that have the shortest average path lengths on the binary trees [40].  
In this paper, the color feature, texture feature, morphological building index, and morphological shadow index are selected to construct the feature space as the random properties for anomaly detection [41]. The specific processes are shown below. First, the mean-shift algorithm [42] is used to segment the building layer into different objects. Then, the feature vectors of each segmented object are computed using the above features. Next, binary trees are constructed from the feature vectors of the segmented objects and non-building objects are detected based on the shortest average path lengths on the binary trees. Finally, a refined building layer is obtained for the local region's reclassification.  
In practice, the above anomaly detection approach can also be used in the non-building layer in order to reduce the possibility of buildings in the non-building layer and to add anomaly-free objects to the mixed layer.
# Encryption of 3D or Higher-Dimensional Images: A Review
## 5 Contributions and Discussion
Furthermore, a new encryption scheme was proposed with the application of the memristive neuron, in which the simulation results show that the scheme passes all tests in the NIST (National Institute of Standards and Technology) test suite (version 800-22) and presents superiority in relation to other algorithms existing in the literature. As future work, we propose extending the concept of the MNN and investigating its application to 3D or higher-dimensional space. Searching the literature for combinations of techniques aimed at digital image security, it is clear that there is still a vast field to be explored, as is the case with the combination of techniques such as encryption and steganography or encryption and watermarking. The method proposed by [PERSON] et al. [46] brings a research perspective to 3D light field cryptography, applying monocular depth rendering, since the real-time performance of light-field 3D encryption technology based on the integral imaging principle is restricted by the acquisition speed and the data of the EIA. [PERSON] and [PERSON] [52] proposed a novel security algorithm, named CIEST (Combined Image Encryption and Steganography Technique), which combines the concepts of encryption and steganography, encrypting the secret image that is hidden in the cover image to ensure secure transmission of information. [PERSON] et al. [53] proposed an optical encryption scheme based on computational ghost imaging and watermarking. This combination of techniques was restricted to application in two-dimensional images. As a future direction, the combination of these techniques with chaotic maps in light field or point cloud images could be investigated. Autoencoder (AE) is an unsupervised deep learning neural network algorithm that compresses the input vector into a lower-dimensional vector [54]. Such a feature can be used to compress images, and encryption is applied to the compressed data. [PERSON] and [PERSON] [55] proposed a novel deep joint compression-encryption model, that integrates encryption algorithms with a deep learning compression network, with AE as the backbone. The aforementioned combination is scarce in two-dimensional images and is lacking when extended to three-dimensional or higher-dimensional images, representing a challenge to be explored in the literature. Although [PERSON] et al. [56] did not propose an encryption method, we found a contribution to the literature, such as the pioneering construction of a database of perceptually encrypted LF images, which were named by the authors as PE-LFID; they aim to protect only a part of the content. The image is not completely encrypted in this database, and there is an intentional degradation of the quality of the encrypted image, so that during decryption the data that remain intact do not need access to the secret key. Furthermore, they proposed a new benchmark visual security measurement method for perceptually encrypted LF images.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 4 Experiments Results and Discussion
### Feature Point Extraction and Matching  
In the segmentation area, feature points are extracted and matched, which effectively reduces the matching error and operation time. The experimental comparison methods include the SIFT, SURF, and ORB algorithms as well as our proprietary algorithm. Experimental results are presented in Table 3.  
The performance of our algorithm surpasses that of the SIFT and SURF algorithms in terms of time consumption and matching points, as evidenced by the results presented in Table 3. Furthermore, our algorithm outperforms the ORB algorithm in terms of the number of matching points.
## 2 Material and Methods
### Finite Element Analysis and Design Regions Decomposition  
Numerical simulations are then conducted on the six L-systems structures via COMSOL Multiphysics(r) 4.3a software. A beam elements formulation is used for the calculations in this step of the proposed method. Similar to the 2D rough geometry, the L-systems' structures are all clamped at their lower boundary and subjected to different loading types. For example, CL-1 and CL-2 are subjected to a compressive load of 60 N. Structures SH-1 and SH-2 are subjected to a shear load of 60 N, and structures CL-SL-1 and CL-SL-2 are subjected to a compressive load of 60 N and a shear load of 60 N (see Figure 6).  
In order to reach the objectives of light weight and increasing stiffness by emulating natural aspects, it was necessary to decompose the structures into two regions (see Figure 7). The first region represents the tree's trunk and the rectangular contour (in red), while the second region represents the tree branches (in blue). Beam elements with rectangular sections are used in these simulations. These sections present a thickness in the z-axis of 12.7 mm for both regions and are equal to the thickness used in the 2D rough design static and stationary simulation. Their thickness in the y axis is variable according to the region decomposition and presents three levels for each region. The thicknesses used for the first region vary between 1.5, 2, and 2.5 mm (denoted \(\mathrm{t_{FR1}}\), \(\mathrm{t_{FR2}}\), and \(\mathrm{t_{FR3}}\), respectively) while those used for the second region vary between 1, 1.25, and 1.25 mm (denoted \(\mathrm{t_{SR1}}\), \(\mathrm{t_{SR2}}\), and \(\mathrm{t_{SR3}}\), respectively).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Description** & **Specific L-Systems in the Study** \\ \hline Axiom & Starting string for first recursion & F \\ Rule & Production rule that generates the L-systems & F = F[+F][\(-\)F][F+F][\(-\)F][F+F][F][F+F][\(-\)F] \\ \(n\) & Number of recursions & 2 \\ \(l\) & Branch length & Adapted to the PSLs length \\ \(\alpha\) & Branch angle & Adapted to the tilt of PSLs \\ Start Point & Specify the point where the L-systems’ generation starts & An extremity of the PSL on the design space boundary \\ Direction & Specify the plane and the vector to indicate the & Adapted to the direction of the PSLs \\  & L-systems’ generation direction & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grammar of the L-systems used in the biomimetic design method.  
Figure 5: Flowchart describing the L-systems’ generation along PSLs’ directions within a 2D design space.