esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework
### High-Frequency Components Fusion  
#### 3.2.1 Solution Discussion  
The traditional dictionary learning methods use the pixel value to select informative image block. According to the common pixel format, the pixel as the byte image is stored as an 8-bit integer giving a range of possible values from 0 to 255. Zero and 255 are taken to be black and white respectively. Values in between 0 and 255 make up the different shades of gray. Two hundred and fifty five is usually used as a basis to evaluate the pixel value. So the pixel selection may not be accurate. For instance, one pixel and its neighboring pixels may have significant differences. One pixel is bright, but its neighbors are dark. No matter whether a bright or dark pixel is selected, both informative image block selection and the accuracy of learned dictionary are affected.  
The gradient-based patch selection approach uses the eigenvalue of edge information to represent source images. Gradient ridges describe positions and shapes of singular structures. Since the local image structure is far more complicated than the parametric representation, it is difficult to model the gradient distribution with a few parameters. Due to the incorrect gradient estimation and no  
Figure 3: Detailed Comparison of Non-decomposition and Decomposition, (**a**) is the fused result of non-decomposition; and (**b**) is the fused result of decomposition.  
Figure 2: The Source Image Pair of Non-decomposition and Decomposition, (**a**) is the CT source image; and (**b**) is the MRI source image.  
gradient constraint, the reconstructed images are usually over-sharped, suffer from false artifacts, or have a blurring effect.  
There is a large set of source image blocks. For dictionary learning, it is difficult to select distinctive image blocks for dictionary construction. The entropy-based patch selection approach evaluates the gray-level probability of each image block, and identifies and selects the most informative image blocks based on the entropy rank (or threshold). Each image block is regarded as relative image information. The entropy calculation process does not depend on a single basis. The pixel numbers of each gray-level image block decide the entropy of each image block. Based on the criterion, the informative image blocks, as a small proportion of source image blocks, are selected.
## 2 Related Works
As in [9], the main drawback of this solution is the computational complexity which is equal to \(O(\frac{4}{3}n^{6})\). In [11], the authors proposed the Differentiated Deployment Algorithm (_DDA_), which is inspired from image processing and 3D modeling, namely mesh representation. Generally, mesh representation allows convenient modeling of arbitrary surfaces, where meshes serve as basic primitives to approximate a surface. For sensor deployment, the authors used an unstructured triangle mesh representation of the target area. Basically, the meshes nodes represent sensor positions and each arc is the Euclidean distance between the sensors. The main idea of algorithm is to permit meshes division as long as the required detection probability is not reached or the mesh division is not possible. During the mesh division process, each mesh division consists to deploy a new sensor. Unfortunately, the _DDA_ suffers form high computational complexity, equal to \(O(n^{6})\). In [12] and [13], the authors derive a sufficient conditions to guarantee a full coverage and the wireless communication network connectivity. The conditions are dependent on the communication and sensing ranges. For example in [12], the authors prove that if \(R_{c}\geq\sqrt{3}R_{max}\) (where \(R_{max}\) is the sensing range) and the area is full covered then the wireless communication network graph is connected. The authors have even considered the \(K\)-connectivity case. Unfortunately a binary event detection model was supposed in this work.
## 4 Simulation Verification
### Simulation of Continuous-Exposure Holographic Interferometry  
Figure 7 outlines the continuous-exposure workflow:  
Procedure:  
(1) Model construction: The object geometry is defined and the parameters input.  
Laser incidence and reflection angles (determining vibration phase sensitivity): \(\pi/2\) and \(\pi/3\), respectively. Maximum amplitude on object surface: 3.16 \(\upmu\)m. Vibration angular frequency: 5 rad/s. Number of sampling points per vibration cycle: 50. Initial vibration phase: \(\pi\) rad.  
(2) Static hologram: The hologram of the non-vibrating object is generated via T-FFT.  
(3) Vibration assignment: The vibration amplitudes are assigned using peak functions. The vibration parameters, sampling rates, and beam angles are adjusted.  
(4) Time-averaged hologram: The instantaneous diffraction patterns during vibration are computed, superimposing multiple frames.  
(5) Reconstruction and comparison:  
a. Static and time-averaged holograms are reconstructed using S-FFT.  
b. The reconstructed intensities are compared using zeroth-order Bessel function modulation.  
Figure 7a: Diffraction pattern of object wave before vibration. This is the Fresnel diffraction field of a flat rectangular object at distance zo, showing central bright rectangle + clear Fresnel diffraction rings.  
Figure 7b: Hologram before vibration. Thereentference between reference wave and static object wave; fringe orientation controlled by alpha, beta, showing uniform tilted straight fringes.  
Figure 7c: Time-averaged hologram. Vibration causes a time-averaging effect (fringe contrast decreases with increasing amplitude), showing fringe blurring/contrast reduction in the central region.  
Figure 7d: Static reconstructed image, showing a clear rectangular image + uniform background.  
Figure 7e: Time-averaged reconstructed image. The vibration amplitude distribution is modulated by the zero-order Bessel function. Bright rings: maxima (amplitude nodes); dark rings: zeros (amplitude extrema).  
Figure 7f: Bessel modulation distribution, showing ring fringes perfectly matching the reconstructed image.  
Theoretical analysis and simulation verification results demonstrate that the single-exposure method is constrained by data acquisition frequency and computational speed, limiting its capability for rapid measurement. In the continuous-exposure method, the intensity ratio between the reference wave and the object wave significantly impacts the contrast of real-time holographic interference fringes. Conversely, the interference fringes obtained via the double-exposure method can be altered by varying the exposure sequence and the position of the reference light source. This approach not only enables the quantitative measurement of deformation magnitude through the number of fringe displacements but also facilitates the qualitative determination of deformation direction based on the movement direction of the interference fringes. Consequently, the double-exposure method was employed for stress detection in the experimental phase.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products  
Seven LAI products, i.e., Sentinel-2, MODIS, GEOV2, GEOV3, EPS, GLASS and C3S, were validated in this study. All the LAI products covering the whole growing cycle of maize were validated and the poor-quality data were discarded by consulting quality control layers. Table 1 lists the multi-resolution LAI products validated in this study.
# Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
## 4 Results and Discussion
### Chaotic Characteristic Analysis
#### 4.2.2 Maximum Lyapunov Exponent Analysis  
In order to verify the above-mentioned analysis results, the maximum Lyapunov exponent, \(\lambda_{1}\), was chosen to further analyze the chaotic characteristics of the SRG with different opening sizes. Using the above-selected optimal embedding parameters, the maximum Lyapunov exponent, \(\lambda_{1}\), was calculated using small datasets. Figure 15 presents a calculation diagram of measuring point 7 in the \(z\)-direction. It can be seen that the separation factor remains stable following the linear growth activity. The least squares  
Figure 14: Saturation correlation dimension of 5 points in different directions under three working conditions.  
fitting of the line close to growing segment presents the slope as the maximum Lyapunov index, \(\lambda_{1}\). The reconstructed attractor is bounded and the latter half is stable and invariant. Thus, the separation factor, \(y(j)\), does not exceed the attractor range. The distribution of \(\lambda_{1}\) at different measuring points under three working conditions is shown in Figure 16.  
As shown in Figure 16:  
1. Under three working conditions, the maximum Lyapunov index value of each measuring point ranges from 0.0246 to 0.0681, which is greater than 0. It shows that the vibration responses of the main beams and supporting arm have obvious chaotic characteristics during the water discharge process.
2. In general, except for a few measuring points, the \(\lambda_{1}\) value of each measuring point under working condition 1 is the highest. The chaotic characteristics of the gate vibration gradually weakens with the decrease in the gate opening sizes, which is consistent with the chaotic characteristics derived from the saturation correlation dimension.
3. The \(\lambda_{1}\) value of point 3 in the \(x\)-direction on the lower main beam presents the highest magnitude, followed by its results along the \(y\)- and \(z\)-directions. The upper main beam obeys no obvious rule. The \(\lambda_{1}\) value of point 3 along the \(x\)-direction is obviously higher than that of point 3 along the \(x\)-direction, indicating that the lower main beam is more affected by the flow pattern under the gate bottom edge and exhibits chaotic characteristics.  
Figure 16: The maximum Lyapunov exponent of 5 points in different directions under three working conditions.  
Figure 15: The maximum Lyapunov exponent, \(\lambda_{1}\), of point 7 in the \(z\)-direction.  
(4) The lateral vibration, \(\lambda_{1}\), values of the three measuring points on the supporting arm are greater than the radial and vertical values on the supporting arm.
ISPRS Int. J. Geo-Inf._**2015**, \(4\), 754-782; doi:10.3390/ijgi4020754  
DENS/KGR/ESS  
ISPRS International Journal of _Geo-Information_  
**ISSN 2220-9964**  
www.mdpi.com/journal/ijgi/  
_Article_  
**HybVOR: A Voronoi-Based 3D GIS Approach for**  
**Camera Surveillance Network Placement**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Geomatics Department, Faculty of Environmental Designs, King AbdulAziz University,  
Jeddah 21589, Kingdom of Saudi Arabia; E-Mails: [EMAIL_ADDRESS] (M.E.Y.);  
[EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (W.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +966-12-640-0000 (ext. 68460); Fax: +966-12-695-2756.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 6 November 2014 / Accepted: 24 April 2015 / Published: 5 May 2015_  
**Abstract:** As a consequence of increasing safety concerns, camera surveillance has been widely adopted as a way to monitor public spaces. One of the major challenges of camera surveillance is to design an optimal method for camera network placement in order to ensure the greater possible coverage. In addition, this method must consider the landscape of the monitored environment to take into account the existing objects that may influence the deployment of such a network. In this paper, a new Voronoi-based 3D GIS oriented approach named \"HybVOR\" is proposed for surveillance camera network placement. The \"HybVOR\" approach aims to achieve a coverage near 100% through three main phases. First, a Voronoi Diagram from buildings' footprints is generated and cameras are placed on the Voronoi Edges. Second, the level of coverage is assessed by calculating a viewshed based on a raster Digital Surface Model of the region of interest. Finally, the visibility of the main buildings' entrances is evaluated based on a 3D vector model that contains these features. The effectiveness of the \"HybVOR\" approach is demonstrated through a case study that corresponds to an area of interest in Jeddah Seaport in the Kingdom of Saudi Arabia.  
**Keywords:** camera surveillance placement; voronoi diagram; coverage; 3D GIS; viewshed analysis
# OTFS-Based Handover Triggering in UAV Networks
## 7 Numerical Analysis
### Against HST Speed  
In this part of numerical analysis, we study the effect of the HST speed on the performance of the schemes involved in the comparison. Figures 12-14 show the performances of the compared schemes against different HST speeds in Km/Hr. In these simulations, six deployed UAVs with \(n^{\prime}=12\) and the worst case of \(P_{th}=0\) dB are used. Also, perfect DD channel estimation, i.e., \(MSE_{\mathcal{H}}=0\) in (38) is considered. These figures validate the hypothesis that the proposed OTFS-based handover-triggering mechanism delivers superior and sustainable handover performance, independent of the HST speed. This resilience is attributed to the DD channel's unique ability to resolve channel gains in the DD domain, resulting in gains that remain constant over each OTFS symbol duration, even at high HST speeds. In contrast, the handover performance of the other two candidate mechanisms, particularly the RSS-based approach, is considerably affected by variations in HST speed. The RSS-based handover triggering is especially sensitive to speed-induced fluctuations because it encapsulates ISI and cumulative Doppler effects across all frequencies, leading to rapid signal strength variations.  
In OFDM, the Doppler shift impacts each subcarrier according to its specific frequency, creating lower FT channel fluctuations than RSS, mainly due to ICI. As a result, the OFDM-based handover mechanism achieves higher stability than RSS-triggered handover, as reflected by these figures. However, the performance of OFDM-based handover remains substantially inferior to that of OTFS, which provides a more consistent and high-performing handover strategy under high-speed conditions. As a numerical example, when the HST speed is equal to 100 Km/Hr, the proposed scheme achieves 0.0018 s, 14.3 Mbps, and 0.0007 for the handover overhead, the average throughput, and the ping  
Figure 11: Ping-pong ratio against the number of UAVs.  
Figure 12: Handover overhead against HST speed.  
pong ratio, respectively. However, the OFDM (RSS) scheme achieves 0.0084 (0.012) s, 3.7 (1.71) Mbps, and 0.0042 (0.44), respectively. When the HST speed is set to 1000 Km/Hr, the proposed scheme achieves the same handover performances as the case of 100 Km/Hr. However, the handover metrics are highly deteriorated for OFDM (RSS) scheme, which achieves 0.0175 (0.039) s, 1.98 (1.01) Mbps, and 0.0113 (1) for handover overhead, average throughput, and ping-pong ratio, respectively. These results reveal that at extremely high speeds, e.g., 1000 Km/Hr, the ping-pong ratio of the RSS-based handover triggering reaches one. This indicates that nearly all handovers initiated by the RSS scheme at this speed are unnecessary.
# Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks
## 2 The Job Shop with Two Jobs and Two Machines  
In this section we describe a polynomial algorithm for the job shop problem with two jobs and two machines; i.e., \(J2|n=2,s-prec|C_{\max}\). For notation simplicity, in this section we denote the two jobs as \(A\) and \(B\), consisting of the sequence of _tasks_, \(A=\{A_{1},A_{2},\ldots,A_{n_{A}}\}\) and \(B=\{B_{1},B_{2},\ldots,B_{n_{B}}\}\), respectively. Task \(A_{i}\) (\(B_{h}\)) requires processing time \(p_{i}^{A}\) (\(p_{h}^{B}\)) on machine \(M(A_{i})\) (\(M(B_{h})\)).  
Obviously, if two consecutive tasks of the same job, say, \(A_{i}\) and \(A_{i+1}\), require the same machine, then \(A_{i+1}\) has to wait for the completion of \(A_{i}\), but if the machines required by the two operations are different, i.e., \(M(A_{i+1})\
eq M(A_{i})\), then \(A_{i+1}\) can start even if \(A_{i}\) has not completed yet. So, unlike the classical job shop setting in which precedence relations are finish-start, in our model it may actually happen that \(A_{i+1}\) even _completes_ before \(A_{i}\) (the same of course applies to job \(B\)).  
Given a partial schedule, the first unscheduled tasks of the two jobs will be referred to as the _available tasks_. Suppose now that one of the two machines, say \(M^{\prime}\), has just completed a task, while the other machine, say \(M^{\prime\prime}\), is still busy. If both the available tasks require \(M^{\prime\prime}\), we say that machine \(M^{\prime}\) is _blocked_ and this certainly results in idle time on \(M^{\prime}\).  
We let \(A[i]\) and \(B[h]\) denote the first \(i\) tasks of \(A\) and the first \(h\) tasks of \(B\); i.e., \(A[i]=\{A_{1},A_{2},\ldots,A_{i}\}\) and \(B[h]=\{B_{1},B_{2},\ldots,B_{h}\}\).  
Given \(A\) and \(B\), consider any two task subsequences \(X\subseteq A\) and \(Y\subseteq B\). We want to characterize the schedules of \(X\cup Y\) such that each task starts right after the previous task on the same machine has completed. More formally, a schedule of \(X\cup Y\) is a _no-idle subschedule_ (NIS) if, across the span of such a subschedule, the only machine idle time occurs, on one machine, after all the tasks of \(X\cup Y\) have _started_. When \(X=A[i]\) and \(Y=B[h]\), for some \(1\leq i\leq n_{A}\) and \(1\leq h\leq n_{B}\), then we say that the NIS is an _initial no-idle subschedule_ (INIS).  
Consider Figure 1 and the task set \(A[2]\cup B[2]\). The subschedule in Figure 1a is _not_ an INIS for \(A[2]\cup B[2]\), since on \(M_{1}\) there is idle time before \(B_{2}\) starts. On the contrary, in the case depicted in Figure 1b, the subschedule of \(A[2]\cup B[2]\) is an INIS. Note that if we restrict our attention to the task set \(A[2]\cup B[1]\), then the subschedule in Figure 1a is an INIS.
## 2 Materials and Methods
### Antioxidant Activity of Biosynthesized AgNPs  
The antioxidant activity of biosynthesized AgNPs was determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay (RSA) according to the method of [PERSON] et al. [24] with some modification. Briefly, 1 mL of 0.1 mM DPPH (3.94 mg dissolved in 100 mL methanol) was added to 1 mL aliquot of different concentration (31.3, 62.5, 125, 250, 500, and 1000 \(\upmu\)g/mL dispersed in methanol) of biosynthesized AgNPs. The mixture was vortexed thoroughly and incubated in the dark for 30 min. The absorbance was recorded at 517 nm using a UV-Visible spectroscopy (Uviline 9400, Secomam, Ales, France) at the end of the incubation. DPPH without sample was used as the control, and methanol was used as the blank. The same procedure was carried out using ascorbic acid as a standard antioxidant. The percentage of DPPH scavenging activity was calculated by the following formula:  
\[RSA_{DPPH}=\frac{A_{control}-A_{test}}{A_{control}}\,\times\,100\]
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 4 The HO-FIM Architecture
### Overview of HO-FIM
#### 4.1.2 Handover Controller  
The handover controller, once activated, finds a handover sequence to reach a target system condition with minimal handovers. The past handover history also needs to be taken into account for a user not to suffer from repeated (or ping-pong) handovers; once a user is handed over, he will not be considered again as a candidate for handover for a certain duration. The algorithm designed for the handover controller's decision determines handovers iteratively in a greedy manner.
## 6 Conclusions  
In this paper, the EDMR protocol was proposed to enhance the DMR protocol. It introduces a new sleeping mechanism to reduce the overall energy consumption in WSNs. Clusters that either lack updated data or are located far from the target are deactivated and enter sleep mode. The EDMR protocol assumes that the network lifetime is divided into rounds. Accordingly, each round is divided into two main phases: the network architecture phase and the routing phase.  
The EDMR protocol supports two types of applications: monitoring applications and event-driven applications. The EDMR-S protocol is dedicated to monitoring applications, while the EDMR-D protocol is designed for event-driven applications. Both protocols were evaluated against the DMR, EDMHT-LEACH, DMHT-LEACH, and the traditional LEACH protocols. The simulation results revealed that both the EDMR-S and EDMR-D protocols improved the network performance compared to the other protocols, with a relative performance advantage observed for the EDMR-S protocol. Specifically, they prolonged the network lifetime, improved the network stability, and increased the network throughput.  
The EDMR protocol may be further extended by incorporating energy-harvesting mechanisms, which enable nodes to recharge their batteries from environmental sources such as solar or thermal energy. Recent research on ambient energy-harvesting in WSNs has proposed various techniques that utilize solar, thermal, and vibration energy resources, achieving considerable improvements in the network lifespan. For instance, the High-Coverage Energy-Harvesting with Uneven Clustering (HCEH-UC) [24] offers an approach to adapt clustering and sleep cycles based on harvested energy levels, which in turn enhances sustainability in long-term deployments. In addition, the EDMR protocol could be evaluated under alternative conditions, including the use of mobile nodes and the initialization of nodes with different initial energy levels.  
Conceptualization and methodology, E.A.; software and validation, E.A. and M.A.; formal analysis, E.A.; investigation, E.A.; resources and data curation, E.A. and M.A.; writing--original draft preparation, E.A.; writing--review and editing, M.A.; visualization, E.A. and M.A.; supervision, E.A.; project administration, E.A. and M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and pseudocode for the phases of the proposed protocols are provided in this paper.  
The authors declare no conflicts of interest.