esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Template-Based Watermarking for DIBR 3D Images
## 3 Proposed Method
### Proposed Watermark Embedding Method
#### 3.2.1 Block Separation  
If the template and the message watermark are inserted at the same position, the signals interfere with each other, and the robustness is degraded. To avoid overlapped insertion, the image is spatially divided into \(M\times N\) blocks, as shown in Figure 6. The set of blocks is defined as \(B\), and the template and message are inserted into the different blocks.  
A random binary code is generated using the template key, and this code is then used to generate a 2D binary matrix, \(K\), of size \(M\times N\), which determines whether each block is a template or message watermark block in an image divided into blocks. To match the number of template blocks with the number of message watermarks, the average (\(K\)) should be 0.5. The template key is only used to  
Figure 4: Watermarking process overview. (**a**) Embedding process; (**b**) extraction process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Template** & **Message Watermark** \\ \hline Domain & Curvelet & 1D-DCT \\ \hline Geometric & \multirow{2}{*}{Recoverable} & \multirow{2}{*}{Vulnerable} \\ Distortion & & \\ \hline \multirow{2}{*}{DIBR} & Robust, & \\  & but unrecoverable & \\ \hline Payload & - & More than 1 bit per block \\ \hline \hline \end{tabular}
\end{table}
Table 3: The roles of templates and message watermarks.  
determine the position of the template blocks, independent of the watermark key to be used later. Therefore, the template key has no effect on the message watermark.  
The following rules distinguish the roles of the blocks.  
\[B(x,y)=\left\{\begin{array}{rl}&\text{Template block, if }K(x,y)=1\\ &\text{Watermark block, if }K(x,y)=0\end{array}\right. \tag{5}\]  
where \(x\) and \(y\) are the horizontal and vertical coordinates of \(B\) and \(K\), \(0\leq x<M\) and \(0\leq y<N\).
## 4 Conclusions  
In this study, we analyzed the impact of the distance and the semi angle on the SNR. As can be seen from mathematical formulas, with lower power LEDs under light interference, the semi angle reduction has affected the SNR more than the distance reduction. Moreover, the measured results demonstrate the correctness of this conclusion. From these results, the peak-to-trough SNR difference shows a small variation when compared to the intersymbol interference. Next, we proposed a novel VLC receiver design based on canceling the average voltage. Our circuit design eliminates the effect of the ambient light and amplifies the original signal from the transmitter by greater than 200 times. Several experiments were conducted under different low-SNR conditions to prove that our proposed receiver can operate with a high performance up to 1 Mbps and at an SNR of \(-2.7\) dB being error-free. Thus, we recommend the use of the average-voltage tracking circuit for its advantages over the HPF circuit in terms of performance.  
This work was supported by a Research Grant of Pukyong National University (2017 year).  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed materials and analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interests.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 3 Requirements for Implementation of the Virtual Coordinate System Algorithm
* Vectors \(\mathbf{s}_{n+1,1},\mathbf{s}_{n+1,2},\ldots,\mathbf{s}_{n+1,N-1}\) remain unchanged while we minimize the function \(F_{n+1,N}(\mathbf{s}_{N})=F(\mathbf{s}_{n+1,1},\mathbf{s}_{n+1,2},\ldots, \mathbf{s}_{n+1,N-1},\mathbf{s}_{N})\) by the vector \(\mathbf{s}_{N}\). We obtain the approximation \((\mathbf{s}_{n+1,1},\mathbf{s}_{n+1,2},\ldots,\mathbf{s}_{n+1,N-1},\mathbf{s}_ {n+1,N})\), where \(\mathbf{s}_{n+1,N}\)-- is the optimal value of the vector \(\mathbf{s}_{N}\). After \(N\) steps, where each step corresponds to vector \(\mathbf{s}_{k}\), \(k=1,2,\ldots,N\), the \((n+1)\)-th cycle is finished. The process is repeated for \(n=1,2,\ldots,\) until the stopping condition is satisfied either by the execution time or by an achieved accuracy. As a result of solving the localization problem for each vertex \(i\in\mathcal{V}\), we obtain the values \(\mathbf{s}_{i}\) of virtual coordinates, that form the desired set of virtual coordinates \(\sigma=(\mathbf{s}_{1},\ldots,\mathbf{s}_{N})\) for UAVs in the swarm. Note that the representation \(\sigma\) allows us to work with it by standard routing methods. In Section 5, we define the performance metrics and in Section 6 evaluate the performance of the proposed UVCS approach in comparison with Greedy Forwarding Geographic Routing (Geo) algorithm, Gradient Assisted Routing (GAR) algorithm [19], and Dijkstra algorithm as a benchmark.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Performance Comparison between the HSN and FC-AE-1553  
#### 5.2.1 Simulation  
In this section, the main focus is to compare the overall performance of the existing FC-AE-1553 network and the new hybrid network. The number of nodes in the backbone network is four, and the number of nodes in each access network is four. The transmission direction of each node is random, with transmission buffers ranging from 512 Kb to 512 Mb. In the hybrid network, the number of wireless connections is four times the number of access network nodes.
## 4 Proposed Scheme
### Detection and Mitigation of DDoS Attack at Home Router
#### 4.3.1 Filtration  
Before detecting abnormal network traffic, it is important to filter the network traffic. For example, if a device is uploading a huge amount of traffic after each time frame, then it is necessary to check whether this traffic is legitimate or not. A video camera might send video traffic that is, legitimately, heavier in size for a particular amount of time. Therefore, we cannot classify video traffic as abnormal traffic. Thus, the IoT devices connected to the network generate dynamic traffic, and therefore, it is not easy to classify them. Similarly, in the recent literature, we have seen many IDS techniques using signature and anomaly-based filtration. However, such techniques may perform inadequately in the case of overhead traffic. In normal circumstances, most of the traffic generated from IoT devices is benign; therefore, it is better to filter such traffic in advance. One of the reasons for such filtration is to reduce target traffic for the detection phase. Traffic filtration is the first step in ensuring the effectiveness of the system. In the proposed filtration mechanism, we used the list-based mechanism, i.e., a blacklist and a whitelist, to classify the traffic as normal and abnormal.  
Blacklist-Based Filtration  
We will first create a blacklist of all the IP addresses that were previously involved in malicious activities. If any incoming traffic from the IoT devices is matched with the blacklisted IP addresses, the packets will be handled according to the predefined network security rules. These rules will be updated over time to ensure the unblocking of traffic if, in the future, a blacklisted IP address becomes a whitelisted IP address.  
Whitelist-Based Filtration  
The traffic from whitelisted IP addresses can go directly to the corresponding server. Such filtration helps in reducing the burden of checking each packet generated from every IoT device on the network. Further, the creation of a whitelist in our scheme is performed and managed by the network administrator.  
In addition to the filtration discussed above, it is necessary to sample the packets to reduce the number of packets that will pass to the detection phase. In the literature, two  
Figure 2: Attack Scenario.  
schemes are widely used to sample the network traffic, (1) packet-based and (2) flow-based. In the case of the proposed scheme, we will use packet-based sampling techniques. From our experience, flow-based techniques are better where hundreds and thousands of nodes are connected to the network. The packet-based sampling will further help in reducing the number of packets to be scanned by the proposed detection technique.
## 8 Challenges of AI in Cognitive Sensing and the Way Forward
### The Way Forward  
To mitigate some of the identified challenges, the following concepts can be explored by the research community for their potential application in CS solutions:  
1. _Data Warehousing_: This concept describes the use of a central database to integrate data from multiple heterogeneous sources to support FIoT operations. Historical weather data, threat/attack data, user data, and other applicable IoT data are required by AI algorithms but sometimes these are not available due to new installations having insufficient data or outright data loss. As a result, keeping these datasets in locations where they can be accessed on demand by FIoT nodes when needed for their operation becomes a valuable approach.
2. _Computational offloading_: This is a technique that can help alleviate the resource constrain problem in IoT nodes by transferring complex computations to more resourceful devices and receiving the results back from these devices [184]. It has been used in mobile device clouds and mobile edge for task execution but yet to be fully exploited for IoT operations.
3. _Energy Neutral Operation_: This is a mode of operation of an IoT device where the energy consumed during operation is always less or equal to the energy harvested from the environment. In this mode, devices can theoretically operate infinitely without energy constraint. Hence, this concept is worth further exploration of its usage in the FIoT.
4. _Lightweight AI Algorithms_: Due to the computationally intense nature of some algorithms which make them difficult for use in IoT devices, resource-efficient AI algorithms that can run seamlessly on FIoT nodes are needed from the research community.
5. _Effective Data Management Policies_: Data privacy describes the interwoven relationship between data collection, transmission, usage, user's privacy, and the legal issues surrounding them. Hence, if not managed well, data privacy poses a threat to data availability for AI algorithms. Furthermore,data abuse or leaks could result in unwanted scandals that negatively affect individuals, businesses and organizational processes [185]. Some of the existing data management solutions or policies like the general data protection regulation [186], the databox project [187], and the IBM data privacy protection framework [188], could be leveraged or extended to provide customized solutions for the IoT domain.
4. _AI-assisted Data Collection_: Over the years, AI/ML systems have proven to be far more accurate than other systems at a variety of tasks such as automation, diagnostics, analytics, etc. hence, using AI to assist the data collection process is a potential research direction for the future. This, however, is likely to raise the issue of its effect on human rights and ethics. For example, how can such a system understand the human right to data consent, access, protection, privacy or fair processing? Will it understand the thin line between private and public data?
# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 6 Discussion
### Comparison between the Proposed Method and Conventional Methods for Identifying Customers' Needs
#### 6.2.1 Sample Size and Sample Bias  
In terms of sample size, the proposed framework is much stronger than conventional methods. The number of social media users is huge, so it is easy to gather various opinions on social media. With respect to sample bias, the proposed framework also has advantages and disadvantages. First, because the number of social media users is huge, there is little bias towards the small sample size. However, social media users may be biased toward age or sex. For example, it is not easy to get the opinions of older people with online social media. This bias is difficult to control for researchers. Therefore, it is a good idea to select proper subjects to utilize social media data.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 5 IoT in 5G Enabling Technologies
### Duplexing  
This technological group includes in-band full duplexing (FD) and dynamic time division duplexing (TDD).  
**In-band FD** or simultaneous data transmission and reception will provide a 1000-fold increase in throughput [131; 240], double spectral efficiency [132; 240], and reduce the air interface delay [240]. The central research problem for the practical implementation of an in-band full-duplex radio is the attenuation of the self-interference signal by an adequate amount [132]. For the practical implementation of an in-band full-duplex radio, many aspects of network design and management need to be restructured, where terminals antenna and circuit design and the development of theoretical foundation are in focus [55]. One practical implementation of the full-duplex radio is shown in [133]. Authors in [134] commented that for the design of a full-duplex radio, it is necessary to unify researches from three domains, i.e., RF circuit and system design, digital signal processing and networking.  
**Dynamic TDD** is the predecessor of FD transmission technology and a candidate for 5G [133; 135]. It represents a scheduling technique in which every base station (BS) is free to choose its own uplink/downlink (UL/DL) split [136; 137; 138; 139]. This technique is used to adapt the allocation of network resources to variable traffic requirements [139; 140], often found in ultra-densely deployed networks [141]. The dynamic TDD can significantly increase bandwidth efficiency [142] and provide higher throughput and low latency [141]. However, it is characterized by the severe co-channel interference (CCI) [143; 141; 142]. Dynamic TDD could be used in combination with D2D communication and in the self-backhauling scenario, as described in [55].
## 2 Methodology
### Datasets  
The CR dataset [35] (Figure 2) consists of 165 RGB images of colorectal tissue obtained from 16 representative sections of colorectal cancer at stages T3 or T4. The samples are divided between benign (74 images) and malignant tumors (91 images). Image acquisition was performed by digitally photographing histological sections with a Zeiss MIRAX MIDI slide scanner. The pixel resolution was 0.620 \(\upmu\)m, corresponding to a 20\(\times\) magnification. The images have different sizes, ranging from 567 \(\times\) 430 to 775 \(\times\) 522 pixels.  
The LA and LG datasets [36] (Figures 3 and 4) comprise RGB images of liver tissue obtained from mice. The LG consists of 265 images obtained from male (150) and female (115) mice subjected to calorie-restricted diets. The LA dataset is composed of 529 images divided into four classes, each representing a different age group of female mice on ad libitum diets: 1 month (100), 6 months (115), 16 months (162), and 24 months (152) of age. The samples were obtained using a Carl Zeiss Axiovert 200 microscope and a 40\(\times\) objective. All images have a resolution of 417 \(\times\) 312 pixels. Both datasets were available through the Atlas of Gene Expression in Mouse Aging Project (AGEMAP).  
The UCSB dataset [37] (Figure 5) is a critical case of image scarcity. It consists of 58 RGB images of breast tissue divided into two groups: benign breast cancer (32) and malignant breast cancer (26). The samples provided by the Center of Bio-Image Informatics at the University of California at Santa Barbara have a quantization rate of 24 bits and a size of \(768\times 896\) pixels.
# Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload
## 4 Case Studies and Result Analysis
The longest time taken is 339.11 s by the 3G network to deliver the file; this time is reduced to only 0.49 s if this flow is offloaded to the WiFi network. Figure 4: Packet Delivery Analysis for Various Types of Flows. Figure 5: Total Flow Duration Analysis for Offloading and No Offloading. Figure 6 presents the analysis of delay sum and jitter sum for all the case studies. The delay sum is the addition of all delays for each packet for the full duration of the flow, whereas the jitter sum is the addition of all jitter for every packet for any particular flow. For the global average, the delay sum is calculated as 598.50 s. For other case studies, such as 5G, 4G-LTE, 4G, and 3G, it is computed as 390.32, 806.39, 1749.86, and 8466.05, respectively, for the non-offloaded flows. If compared with the delay sum obtained by the offloaded flows, we can see a drastic reduction. The delay sums obtained for the offloaded flows are 45.34, 16.95, 16.75, 16.42, and 16.42 for the global average, 5G, 4G-LTE, 4G and 3G. Jitter sum is also improved in the case of offloading, whereas it is high if no offloading is performed. The jitter sum of 1.51 s is reduced to 0.53 s for the global average case study if offloading is performed. It is reduced to 0.17 s from 3.16 for 5G, 0.23 s from 1.41 s for 4G-LTE, 0.28 s from 7.98 s for 4G, and an impressive 0.28 s from 45.63 s for the 3G case study if offloading is performed. The enhanced packet delivery ratio, along with improved delivery time, reduced delay sum and reduced jitter sum, enhance the overall quality of the experience provided by the MNOs to their subscribers.
## 2 Methods
### Reachable Workspace Relative Surface Area (RSA)  
The ideal upper limb reachable workspace is a part of spherical enclosure with a radius of the arm expansion length. The reachable workspace is divided into four quadrants (I, II, III, and IV) in which each quadrant corresponds to a quarter of a sphere. The shoulder joint is defined as the coordinate origin, the horizontal is the direction in which the arm extends horizontally, and the vertical direction is parallel to the direction in which the subject stands. The avatar is mirror controlled by the subject, so the coordinate system and four quadrants are shown mirrored in Figure 6.  
The right hand is selected as an example to analyze the reachable workspace, and the left hand can be calculated in the same way. The collected 3D upper extremity motion trajectory is analyzed using the methods described previously [21; 28], with some improvements.  
Briefly, according to the trajectory of the upper extremity, the sphere is fitted by the least squares method to determine the parameters of the working space sphere. Then, the trajectory data are projected into the spherical coordinate system, and the maximum boundary of the trajectory is determined by the \(\alpha\)-shape geometry. Catmull-Rom splines are applied to smooth the boundary. Next, all information is projected back into Cartesian coordinates to extract the corresponding accessible surface patches. The application of a triangular mesh avoids the problem of jagged edges, making the surface boundary smoother than before.  
The quadrants and the total accessible surface area are calculated. The relative surface area is the ratio of the actual surface area to the total surface area, which can be calculated by Equation (6) to facilitate the comparison between different subjects. Therefore, the value of RSA is between 0.0 and 1.0, where 1.0 represents the area of the entire sphere.  
\[\text{RSA}=\frac{\text{Reachable Surface Area}}{\text{Total Surface Area}} \tag{6}\]
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling
### Connector Modeling
a scalar power-loss factor \(l_{p}(r_{0},z_{0})\) for a given pair of misalignments [51]. A more flexible approach is to calculate the proportion of light that on exiting the radiating fiber at a given angle of interest in free-space (thus, an external angle) \(\theta_{i,ext}\), is able to reach the receiving fiber. This is equivalent to assuming that each point of the fiber radiates light in a very narrow angular range centered at that angle. Then, instead of a circular pattern, its projection onto a perpendicular plane will be a very narrow ring. The equations to calculate this angular loss function are:  
\[R(\theta_{i,ext},r,z) =2\pi z\tan(\theta_{i,ext})\ a^{2}\mathrm{H}^{-1}\bigg{(}\frac{J _{1}(2\pi a\rho)}{a\rho}\cdot J_{0}(2\pi z\tan(\theta_{i,ext})\rho)\bigg{)} \tag{20a}\] \[l_{p}(\theta_{i,ext},r_{0},z_{0}) =\left\{ \begin{array}{c}\frac{2\int\limits_{0}^{r_{0}+a}\cos \mathrm{-i}\bigg{(}\frac{\partial_{1}^{2}r^{2}-2^{2}}{2r_{0}^{2}}\bigg{)}R( \theta_{i,ext},r,z_{0})ndr}{2\pi\int\limits_{0}^{\infty}R(\theta_{i,ext},r,z_{ 0})ndr},\ \text{if }r_{0}\geq a\\ \frac{2\pi\int\limits_{0}^{\infty}R(\theta_{i,ext},r,z_{0})ndr+2\int\limits_{ 0}^{\infty}\cos\mathrm{-i}\bigg{(}\frac{\partial_{1}^{2}r^{2}-2^{2}}{2r_{0}^{ 2}}\bigg{)}R(\theta_{i,ext},r,z_{0})ndr}{2\pi\int\limits_{0}^{\infty}R(\theta_{ i,ext},r,z_{0})ndr},\ \text{if }r_{0}<a\end{array} \right. \tag{20b}\]  
In the simplest case, we can model the connector using just the analytical model for power loss due to lateral and longitudinal misalignments. However, this will not capture the effects of imperfections in the fiber end facets, nor rapid power diffusion during re-injection of optical power into the second fiber [30]. Thus, the complete model, as described in [50; 51], combines both the basic connector matrix, which is used to model the fiber-fiber connection in the absence of any lateral and longitudinal misalignments, along with the analytical misalignment model:  
\[\mathbf{P_{out}}=\mathbf{M_{misalign}}\cdot\mathbf{M_{conn}}\cdot\mathbf{P_{in}} \tag{21}\]  
where \(\mathbf{M_{misalign}}\) is a diagonal matrix whose diagonal elements are calculated from Equation (20b) after conversion from external to internal angles:  
\[m_{misalign}(\theta_{i})=l_{p}(\theta_{i},r_{0},z_{0}) \tag{22}\]  
Figure 4: The radiated pattern \(R(r_{0},z_{0})\) is integrated over the receiving fiber end surface to obtain misalignment loss. Equation (21) can be written either in the temporal or in the frequency domain by only introducing the variables of the input and output intensity vectors. The complete connector matrix \(\mathbf{M_{CC}}\) is the product of the misalignment matrix \(\mathbf{M_{misalign}}\) and the basic connector matrix \(\mathbf{M_{conn}}\), and is the same for the temporal and frequency approaches. Figure 5 shows the connector matrices for several misalignment pairs. The images show how the connector introduces not only attenuation but also diffusion. Misalignments do not introduce further diffusion but can considerably increase attenuation.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Mechanisms of Rubber Toughening
#### 2.1.2 Cavity Theory  
In the 1980s, [PERSON] et al. [27] proposed that the formation of cavities, or voids, at the interface between rubber elastic particles and the matrix is a primary mechanism for toughening epoxy resin systems. When subjected to external forces, dispersed rubber particles create stress concentration effects, which generate three-dimensional tensile stresses in the surrounding matrix and induce cavitation in the rubber particles, leading to the formation of voids or holes around them. This cavitation process causes debonding at the particle-matrix interface, allowing the rubber particles to release their inherent elastic strain energy and thus contribute to toughening. Additionally, cavitation can promote shear yielding in the material, suppress crack propagation, and dissipate substantial amounts of energy, resulting in increased toughness [28].