esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Data Hiding Based on Block Truncation Coding
## 3 Survey of Related Works
### Non-Reversible Data Hiding
#### 3.1.3 Difference Expansion (DE)  
[PERSON] et al. [4] introduced an algorithm that hides several bits in the difference expansion (DE) of vectors of adjacent pixels. In 2017, [PERSON] et al. [38] proposed a DH method exploiting the pixel difference of two quantization levels using DE and the embedding bit rate of this method is \(log_{2}T\)-bits. To improve image quality, a way of adjusting the difference between two quantization levels is introduced.  
Let \(\delta_{i}\) be the difference between two quantization levels \(\mu_{1}^{i}\) and \(\mu_{0}^{i}\), i.e., \(\delta_{i}=\mu_{1}^{i}-\mu_{0}^{i}\). Here, let \(D\) be the decimal value of secret bits. The following Equations (6) and (7) allow to revise quantization levels to carry secret bits:  
\[\mu_{0}^{\prime}=\begin{cases}\mu_{0}+\left\lfloor\frac{(\delta_{i}-D)}{2} \right\rfloor,\;\;\text{if}\;(\delta_{i}\leq D\leq T),\\ \mu_{0}+\left\lfloor\frac{(\delta_{i}-D+T)}{2}\right\rfloor,\;\;\text{ otherwise},\end{cases} \tag{6}\]  
\[\mu_{1}^{\prime}=\begin{cases}\mu_{1}-\left\lceil\frac{(\delta_{i}-D)}{2} \right\rceil,\;\;\text{if}\;(\delta_{i}\leq D\leq T),\\ \mu_{1}-\left\lceil\frac{(\delta_{i}-D+T)}{2}\right\rceil,\;\;\text{ otherwise}.\end{cases} \tag{7}\]  
**Example 4**.: _Suppose \(\mu_{0}=146,\mu_{1}=149,T=4\), and the bits to be embedded are \(10_{2}\). Because \(\delta=\mu_{1}-\mu_{0}=3<T\), the block is smooth, and we have \(D=10_{2}=2\). According to Equation (6), we have \(\mu_{0}^{\prime}=\mu_{0}+\left\lfloor(\delta-D)/2\right\rfloor=146+\left\lfloor (3-2)/2\right\rfloor=146\), \(\mu_{1}^{\prime}=\mu_{1}^{\prime}-\left\lceil(\delta-D)\right\rceil/2=149- \left\lceil(3-2)\right\rceil/2=148\). To extract the embedded bits, we calculate \(\delta^{\prime}=\mu_{1}^{\prime}-\mu_{0}^{\prime}=2\). The embedded bits are \(10_{2}\)._
## 2 Materials and Methods
### Feature Extraction
#### 2.2.3 Radon Barcodes  
The Radon Barcodes (RBC) operator is based on the Radon transform, which is having an increasing interest in image processing, since it is extremely robust to noise and presents scale and rotation invariance [52; 53]. Moreover, it has been used for years to process medical images, and is the basis of current computerized tomography. As mentioned before, facial expression features are based on facial deformations and involve changes in shape, texture, and motion. As Radon transform presents valuable features regarding image translation, scaling, and rotation, its application to facial recognition of emotions has been considered in this work.  
Figure 3: Graphic example of the LTP descriptor.  
Essentially, Radon transform consists of an integral transform which projects all pixels from different orientations to a single vector. Consequently, RBCs are basically the sum (integral) of the values along lines constituted by different angles. Thus, Radon transform is first applied to any input image, and then projections are performed. Finally, all the projections are thresholded individually to generate code sections, which are concatenated to build the Radon Barcode. A simple way for thresholding the projection is to calculate a typical value using the median operator applied on all non-zero values of each projection [53]. Algorithm 1 shows how RBC works [53] and in Figure 4 a graphic example is shown.  
Until now, the main application of Radon Barcodes comes from medical image retrieval, where it has given high accuracy. As in the recognition of facial expressions robustness in orientation, illumination, and scale changes are needed, we consider that the RBC descriptor can be a good technique to provide a reliable classification of pain/non-pain in infants using facial images, being the first time that RBC are used in these kinds of applications.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 5 Evaluation  
### Network Convergence Time in Case of Node Replacement  
During a disaster, network nodes are usually damaged and need to be replaced. Network convergence time after replacement should be as fast as possible to re-enable emergency communication through the network. Hence, we evaluated our system network convergence time by comparing between replacing NDN edge nodes and IP edge nodes. Both NDN routers and IP routers are deployed as containers over KubeEdge. We chose the EIGRP routing protocol [34] for IP networks because it has been proven to be the fastest convergence IP routing protocol by several studies [35; 36]. Network convergence time is calculated from the moment when the replaced node joins the KubeEdge cluster until the routing advertisement process is completed. The equation for calculation is:  
\[\mathrm{T_{NC}}=\mathrm{T_{CJ}}+\mathrm{T_{RD}}+\mathrm{T_{A}} \tag{1}\]  
where \(\mathrm{T_{NC}}\) is network convergence time, \(\mathrm{T_{CJ}}\) is cluster joining time, \(\mathrm{T_{RD}}\) is containerized router deployment time and \(\mathrm{T_{A}}\) is advertisement time. We gradually increased the amount  
Figure 11: KubeEdge extension to support NDN device management.  
Figure 12: New NDN device management sequence diagram.  
of replaced nodes at the same time to evaluate the performance. The network convergence time of our system is shown in Figure 13 and the comparison between it and the IP system is shown in Figure 14.  
Figure 13 illustrates that our system achieved a very fast convergence time even when multiple nodes needed to be replaced simultaneously. The network can recover to a normal state after replacement in less than 2 s and the impact of increasing the amount of replaced nodes simultaneously is trivial (only 0.1 s per node). Compared with the IP network, Figure 14 shows that the NDN network with NLSR routing protocol converges much faster than the IP network with EIGRP routing protocol. The NDN network only requires 1.8 s to converge while the IP network requires 10.5 s when the amount of replaced nodes is 7. Moreover, when the number of replaced nodes at the same time increases, the convergence time for the NDN network increases slightly while it increases significantly for the IP network. The difference is caused by NLSR faster advertisement time thanks to its multipath routing calculation feature [37].  
Figure 14: The comparison of network convergence time between NDN and IP network.  
Figure 13: Our system network convergence time.
## 3 Dynamic Window Approach
### Velocity Sampling  
As a result of the restrictions in the mobile robot's hardware and environmental factors, there exists a range of limits in the speed sampling space of the mobile robot.  
1. Linear and angular velocity constraints for robots. \[V_{s}=\{(v,\omega)|v_{\min}\leq v\leq v_{\max},\omega_{\min}\leq\omega\leq \omega_{\max}\}\] (8) where \(v_{\min}\) is the minimum linear velocity, \(v_{\max}\) indicates the maximum possible linear velocity, \(\omega_{\min}\) represents the minimum possible angular velocity, and \(\omega_{\max}\) denotes the maximum angular velocity. 2. Motor performance effects and acceleration constraints. \[V_{d}=\left\{(v,\omega)\big{|}v_{c}-\dot{v}_{b}\Delta t\leq v\leq v_{c}+\dot{ v}_{a}\Delta t,\;\omega_{c}-\dot{\omega}_{b}\Delta t\leq\omega\leq\omega_{c}+ \dot{\omega}_{a}\Delta t\right\}\] (9) Here, \(v_{c}\) represents the robot's linear speed, \(\omega_{c}\) refers to its angular speed, while \(\dot{v}_{a}\) indicates the maximum linear acceleration, and \(\dot{\omega}_{a}\) denotes the maximum angular acceleration. 3. Impact of obstacles. \[V_{a}=\left\{(v,\omega)\Big{|}v\leq\sqrt{2 dist(v,\omega)\dot{v}_{b}},\;\omega \leq\sqrt{2 dist(v,\omega)\dot{\omega}_{b}}\right\}\] (10) The maximum linear deceleration is represented by \(\dot{v}_{b}\), \(\dot{\omega}_{b}\) denotes the maximum angular deceleration, and \(dist(v,\omega)\) indicates the distance from the robot's current velocity to the closest obstacle in its path.  
By combining the three types of velocity constraints mentioned above, the final velocity sampling space of the robot exists at the convergence of the three velocity spaces.  
\[V_{r}=V_{s}\cap V_{d}\cap V_{a} \tag{11}\]
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Functional Characteristics of the Tumor Vascular System  
The cancer microvasculature exhibits unique functional features, such as angiogenesis, hyperangiogenesis, irregular blood flow, extensor vascular permeability, and abnormal lymphatic drainage, which collectively describe the enhanced permeability and retention (EPR) phenomenon [85]. Factors such as cancer type, stage, and location affect blood vessel flow and pressure, endothelial leakage rate, vascular permeability, and molecular retention, contributing to tumor heterogeneity [72].
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
###### Abstract  
The dynamic range (DR) index lacking of an official definition leads to ambiguities in performance evaluation. The existing measurement methods of DR do not always match with the various actual application conditions, and some detailed distortion behavior of the device under test (DUT) is not extracted. In this paper, a new index for evaluating the DR performance of audio systems is proposed and validated, herein referred to as the audio distortion dynamic range (ADDR). It reduces the uncertainty of measurement conditions by an explicit definition and unifies the signal-to-noise ratio (SNR) and the signal-to-noise-and-distortion ratio (SINAD) indexes if under the same measurement condition. Moreover, to comprehensively reflect the impact of harmonic, spurious, and noise components on the DUT, the definitions of the traditional indexes based on classification of distorted components are replaced by the variable thresholds in the ADDR definition. Subsequently, the detailed steps of ADDR and the critical factors influencing its accuracy, are analyzed and then the optimized measurement conditions are given. Experiments based on simulated DUTs show the ADDR index can distinguish performance difference that the traditional indexes cannot distinguish, which proves it is an effective supplementary to the existing indexes in some real applications.  
dynamic range; SNR; SINAD; audio system; performance evaluation; audio distortion dynamic range (ADDR) +
Footnote †: journal: Applied Sciences
## Appendix C Sensitivity Calculations of HOE Variables  
The calculation procedure of sensitivity for the three main HOE variables are described below.  
Note:D = difference in absolute terms between values at 95%and at 5% cumulative probability dr = difference in relative terms between values at 95%and at 5% cumulative probability Challenging fire scenario 'not comply with the instruction'  
\[D_{95-5}=0.70-0.21=0.49\text{ in absolute terms}\]  
\[d_{95-5}=\frac{0.49}{0.46}=1.07\text{ relative to the mean value}\]  
'deficient training'  
\[D_{95-5}=0.41-0.10=0.31\text{ in absolute terms}\]  
\[d_{95-5}=\frac{0.31}{0.25}=1.24\text{ relative to the mean value}\]  
'inefficient emergency plan'  
\[D_{95-5}=0.21-0.03=0.18\text{ in absolute terms}\]  
\[d_{95-5}=\frac{0.18}{0.11}=1.64\text{ relative to the mean value}\]  
'ERL'  
\[D_{95-5}=5.88*10^{-6}-5.59*10^{-6}=2.90*10^{-7}\text{ in absolute terms}\]  
\[d_{95-5}=\frac{2.90*10^{-7}}{5.77*10^{-6}}=0.05\text{ relative to the mean value}\]  
Sensitivity  
\[S(not\_comply\_with\_instr)=\frac{0.05}{1.07}=0.05\]  
\[S(deficient\_training)=\frac{0.05}{1.24}=0.04\]  
\[S(ineffective\_emergency\_plan)=\frac{0.05}{1.64}=0.03\]  
As expected, the HOE variable 'not complying with the instructions' has the greatest influence on the outcome with a sensitivity of 5% followed by 'deficient training at 4% and 'inefficient emergency plan' at 3%  
Robustness Check scenario  
'operator fails'  
\[D_{95-5}=0.037839-0.0257955=0.012\text{ in absolute terms}\]  
\[d_{95-5}=\frac{0.012}{0.03157}=0.38\text{ in relationships to the average value}\]  
ERL  
\[D_{95-5}=7.11*10^{-4}-6.36*10^{-4}=0.75\times 10^{-4}\text{ in absolute terms}\]  
\[d_{95-5}=\frac{0.75\times 10^{-4}}{6.68\times 10^{-4}}=0.1123\]  
in relationship to the average value  
Sensitivity  
\[S(operator\_fails)=\frac{0.1123}{0.38}=0.29\]
# A Multi-Coupling 3D Frequency-Selective Surface with High Selectivity and Wide Dual Passband
## 2 Design Principle
### Simulation and Analysis
In addition to demonstrating stability with respect to the incidence angle and polarization, a functional FSS for practical applications must also possess strong frequency selectivity and adequate bandwidth across both passbands.
## 2 Study Area
### Site 2  
A stretch of riprap with a length of 140 m was removed from Site 2 (local toponym: Ile des Cailloux) two years after the first site, in September 2019, and trees were cut but not stumped [37]. This riprap was built much more recently to protect a gravel mining extraction platform installed between 1974 and 1977. The gravel extraction present until the beginning of the 1990s was located just upstream of the mining extraction platform and its protected riverbank. Channel enlargement, still present today, is evidence of this human in-channel activity. Before the restoration works, the active channel width was 43 m (measured on the IGN orthophotograph of 2019), and an incision of 1.44 m was recorded between 1935 and 2020 [33]. The water surface slope was 0.0015 m/m in 2020. The site is part of a reach with lateral dynamics upstream and downstream of the restored bank without marl outcrops in the channel (Figure 1c). The closest gauging station of Limons, located 10 km downstream of Site 2, registered an interannual discharge of 69 m\({}^{3}\)/s and a two-year flood of 396 m\({}^{3}\)/s (\"[[https://hydro.eaufrance.fr](https://hydro.eaufrance.fr)]([https://hydro.eaufrance.fr](https://hydro.eaufrance.fr)) (accessed on 15 February 2023)\").  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Site 1** & **Site 2** \\ \hline Name of nearest gauging station & Vic-le-Comte & Limons \\ Active channel width (m) & 44 & 43 \\ Water slope (m/m) & 0.0015 & 0.0015 \\ Incision 1935-2020 (m) & \(-1\) & \(-1.44\) \\ Interannual discharge (m\({}^{3}\)/s) & 60.5 & 69 \\ Two-year flood (m\({}^{3}\)/s) & 436 & 396 \\ Specific stream power (W/m\({}^{2}\)) & 146 & 135 \\ Barfull stage (m\({}^{3}\)/s) & / & 156 \\ Bankfull stage (m\({}^{3}\)/s) & / & 789 \\ Floodplain land use & Alluvial forest & Grazing \\ Riprap protection linear & Important & Moderate \\ Channel bed features & Marl outcrops & Alluvial channel bed \\ D\({}_{50}\) (mm) & 47 & 30 \\ D\({}_{90}\) (mm) & 102 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information and main geomorphological and hydrological characteristics of the two study sites.  
On the basis of the width of the active channel before the works, the water slope in 2020, and the Q\({}_{2}\) discharge, the specific stream power was 135 W/m\({}^{2}\). Additionally, from the topbathymetric surveys carried out at Site 2, it was possible to calculate the barfull and bankfull stages from the rating curves (\"[[http://www.bedloadweb.com](http://www.bedloadweb.com)]([http://www.bedloadweb.com](http://www.bedloadweb.com)) (accessed on 15 February 2023)\"), which corresponded to 156 m\({}^{3}\)/s and 789 m\({}^{3}\)/s, respectively (Table 2).
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision
### Structured Light Camera  
A structured light camera is an active 3D imaging device that projects specific light patterns such as dot patterns, stripes, and encoded graphics onto the surface of a target object. It then captures the deformed patterns using a camera and calculates the depth information of the object based on the principle of triangulation to reconstruct its 3D shape. The camera consists of one or more monocular cameras and a projector. It projects a series of known patterns into the scene and uses the correspondence between the projection frames and the captured frames to obtain depth information based on the degree of pattern deformation. Compared to stereo cameras, it can achieve good results even for objects with weak textures, and the depth accuracy is higher by calculating the deformation of encoded light spots or stripes. Structured light cameras also have some drawbacks, such as interference from strong light sources, and their cost is generally higher than that of monocular cameras and stereo vision cameras [35,36].  
In [37], a structured light projection system was defined, with structured light projected from the top and sides to cover the entire plant. This addresses the issue of insufficient surface texture on plants, improving matching accuracy and enabling effective segmentation of plants from the background even in low-light environments. Ref. [38] utilizes embedded projectors and industrial cameras to build a structured light camera system, dynamically adjusting the width of the stripes to scale each layer proportionally. Thisensures point cloud density and improves small hole detection capabilities. Ref. [39] uses low-cost structured light cameras to perform multi-angle scanning, data calibration and performance verification, and parameter extraction to measure the three-dimensional body of cows. This enables low-cost, non-contact monitoring of cow growth.
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 5 Trajectory Objectives
### Energy Consumption
\begin{array}{ll}\mathbb{C}_{e}\left(n,n^{\prime}\right)=&\left(\frac{C_{d }\delta_{air}A}{2\eta_{T}}\left|v_{vw}\right|^{3}+P_{c}\right)\cdot\frac{o \cdot d\left(n\prime\right)}{E_{c}\left|v_{vw}\right|+v_{w}\right|}\\ &\qquad\qquad+\frac{\triangle\delta_{air}\left(n,n^{\prime}\right)V_{v} \triangle\rho\left(n,n^{\prime}\right)}{E_{c}\delta_{air}\left(n\right)\eta_ {pump}}\end{array}  \tag{11}\]  
The energy objective cost function (11) is a non-linear function with respect to \(v_{vw,\parallel}\), which makes solving for the vehicle velocity for minimum energy, \(v_{ox}\), difficult. It can be solved using non-linear optimization or numerical methods, but this was found to be too computationally expensive for real-time planning. For these simulations, \(v_{ox}\) was solved directly assuming that \(\eta_{T}\) is constant and,  
\[v_{vw}^{3}\approx v_{vw,\parallel}^{3}+v_{vw,\perp}^{3} \tag{12}\]  
Although the trajectory planner finds the optimal path based on the energy and time priorities, the vehicle's velocity along the path also has a significant impact. When time and energy priorities have conflicting vehicle speeds, a weighted sum of both optimal speeds is used to select the speed according to,\[v_{vw,\parallel}\left(n^{\prime}\right)= \begin{cases}\frac{W_{v}}{W_{t}+W_{e}} \cdot v_{ot}\left(n^{\prime}\right)+\frac{W_{v}}{W_{t}+W_{e}}\cdot v_{oe}\left(n ^{\prime}\right),&v_{oe}<v_{ot}\\ v_{oe},&v_{oe}\geq v_{ot}\end{cases}  \tag{13}\]  
while satisfying the vehicle's velocity limits of \(0<v_{vw}\left(n^{\prime}\right)<v_{v,\max}\). If the vehicle speed for minimum energy is faster than the one for arriving on time, the vehicle speed for minimum energy is used.
# Efficacy of Maximally Expansion with Clear Aligner in the Mixed Dentition: A Systematic Review
## 4 Discussion
### First and Second Interdeciduous Molar Width  
First interdeciduous molar width (linear distance between the vestibular cusp tips of the first deciduous molars) and second intercediduous molar width (linear distance between the sulcus of the second deciduous molars) were assessed in five [7; 15; 18; 26; 27] of the fourteen articles considered. The studies by [PERSON] et al. [27], [PERSON] et al. [7], and [PERSON] et al. [18] demonstrated that Invisalign(r) First system determined a significant increase (\(p<0.05\)) of first and second intercediduous molar width between T0 and T1. The articles by [PERSON] et al. [15; 26] reported an increase in these parameters both in IF group and RME group, but they do not specify whether this difference is statistically significant.
## 1 Introduction
In this paper, we present a low cost platform for the monitoring and helping of decision making about different areas in a neighboring community, for efficient management and maintenance, using information and communication technologies and proposing television as an interaction device, called SmartResidents. The systems available in the market are intended, for administration, to focus exclusively on developments to preserve security and restrict/prevent access to suspicious or undesirable presences, as well as to prevent attempted theft. Our proposal also contributes to safety. In addition, it is responsible for reporting and monitoring various parameters that will help make services work efficiently. The main novelty of this paper is the development of a framework that integrates not only a wireless sensor network and an IoT platform but also a real front-end application for Interactive Television (iTV). In this work, the deployment of sensors and their online access has been successfully addressed, which has not been without challenges [4]. On the other hand, iTV (representing the evolution of traditional television) is a new platform on which new applications, utilities, and services for the end user are being developed. In this case, we provide a new ability to control the residential environment from the comfort of the home chair. iTV has the challenge of finding standards that support the new capabilities it can offer [5], and guides and tools for the development of new applications, as it is the case that competes us. This article is organized as follows. In Section 2, the technologies involved are introduced into the platform. In Section 3, the proposed architecture for SmartResidents is specified. In Section 4, we detail the deployment scenario of the built prototype. Section 5 concludes with the work done, as well as the proposals for future work.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
###### Abstract  
A novel communication and navigation fusion system (CNFS) based on cellular communication system is developed to realize high-accuracy localization. Because of the small signal coverage of every transmitter and the fluctuation of received signal and noise, the CNFS positioning receiver requires fast processing of signal and stable performance. As the first operation performed by receiver, signal acquisition with fast speed and stable detection performance should be realized. This paper proposed a novel fast acquisition method with fine detection performance using search space reduction (SSR) and DD-MAX/TC-CACFAR techniques. SSR method is utilized to reduce the size and number of search space in the cross ambiguity function (CAF) evaluation stage. DD-MAX/TC-CACFAR method is employed to realize fine and stable detection performance in signal detection stage. The expressions of acquisition time are then derived considering the size and number of search space and the detection performance. Theoretical performance analysis and Monte Carlo simulation, which contain the comparison with other acquisition method, are presented to demonstrate the effectiveness of proposed method. Simulation and analysis results show that the proposed method can realize fast acquisition with fine and stable detection performance.  
communication and navigation fusion system; acquisition time; search space; constant false alarm +
Footnote †: journal: Applied sciences