esa-sceva/satcom-chunk-collection huggingface dataset:
# Closest Farthest Widest
## 3 Convergence
### Convergence of Frank-Wolfe  
To prove that the algorithm map is closed for our version of Frank-Wolfe, suppose that \(df(\mathbf{x}_{n})\mathbf{y}_{n}\geq df(\mathbf{x}_{n})\mathbf{y}\) for every \(\mathbf{y}\in S\) and that \(\mathbf{x}_{n}\) and \(\mathbf{y}_{n}\) converge to \(\mathbf{x}_{\infty}\) and \(\mathbf{y}_{\infty}\). Then assuming \(df(\mathbf{x})\) is continuous, \(df(\mathbf{x}_{\infty})\mathbf{y}_{\infty}\geq df(\mathbf{x}_{\infty})\mathbf{y}\) for every \(\mathbf{y}\in S\). The ascent property is baked into Frank-Wolfe because it is a minorization-maximization algorithm. Finally, a stationary point \(\mathbf{x}\) satisfies \(df(\mathbf{x})\mathbf{v}\leq 0\) for all tangent vectors \(\mathbf{v}\). The set of tangent vectors \(\mathbf{v}\) is the closure of the set of points \(c(\mathbf{y}-\mathbf{x})\) with \(\mathbf{y}\in S\) and \(c>0\). This is where the convexity of \(S\) comes into play. Hence, \(\mathbf{x}\) is a stationary point if and only if \(df(\mathbf{x})\mathbf{x}\geq df(\mathbf{x})\mathbf{y}\) for all \(\mathbf{y}\in S\), which is equivalent to \(\mathbf{x}\in M(\mathbf{x})\). If \(\mathbf{x}\
otin M(\mathbf{x})\), then the objective strictly increases. Thus, [PERSON]'s theorem applies to [PERSON].  
These arguments shed light on the rate of convergence as measured by closeness to stationarity [24; 35]. Indeed, adding the inequality  
\[f(\mathbf{x}_{n+1})-f(\mathbf{x}_{n}) \geq df(\mathbf{x}_{n})(\mathbf{x}_{n+1}-\mathbf{x}_{n}) \geq df(\mathbf{x}_{n})(\mathbf{y}-\mathbf{x}_{n})\]  
for arbitrary \(\mathbf{y}\in S\) leads by telescoping to  
\[(n+1)\min_{0\leq k\leq n}\max_{\mathbf{y}\in S}df(\mathbf{x}_{k})(\mathbf{y}- \mathbf{x}_{k}) \leq f(\mathbf{x}_{n+1})-f(\mathbf{x}_{0}).\]  
This in turn implies  
\[\min_{0\leq k\leq n}\max_{\mathbf{y}\in S}df(\mathbf{x}_{k})(\mathbf{y}-\mathbf{x}_{k}) \leq \frac{1}{n+1}[\max_{\mathbf{x}\in S}f(\mathbf{x})-f(\mathbf{x}_{0})].\]  
Thus, the stationary condition \(\max_{\mathbf{y}\in S}df(\mathbf{x})(\mathbf{y}-\mathbf{x})\leq 0\) is reasonable to expect at a limit point \(\mathbf{x}\) of Frank-Wolfe.  
[PERSON] [36] demonstrates that Frank-Wolfe converges in a finite number of iterations when the objective \(f(\mathbf{x})\) is differentiable, convex, and bounded from above on a polyhedral set \(S\). This result is more or less obvious given that the number of extreme points of \(S\) is finite. [PERSON] and [PERSON] [37] show that the well-known convex-concaveprocedure (CCCP) [23] and its generalization are special cases of the Frank-Wolfe method with step-size selection. This insight quantifies the convergence rate of the CCCP.
Adsorption of Ethylenediaminetetraacetic Acid on a Gel-Type Ion-Exchange Resin for Purification of Liquid Waste Containing Cs Ions  
[PERSON]  
1 Organic and Optoelectronic Materials Laboratory, Department of Organic Materials Engineering, Chungnam National University, Daejeon 34134, Korea; [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (K.-W.L.)  
[PERSON]  
2 Decontamination and Decommissioning Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea; [EMAIL_ADDRESS] (C.W.P.); [EMAIL_ADDRESS] (K.-W.L.)  
[PERSON]  
The adsorption technique has been applied to separate EDTA from liquid waste using various adsorbents, such as metal oxide [14; 15; 16], goethite [17], mesoporous silica [18], and activated carbon [19]. The treatment of liquid wastes, containing EDTA by adsorption using activated carbon, is commonly used in the industrial processes, but presents several disadvantages, such as high hygroscopicity, a decrease in pore size during use, and combustion at high temperature after use. The use of zeolite-based adsorbents for the removal of organic contaminants from liquid waste has been reported [20; 21]; however, zeolites have confined micro-pores to separate large molecules dissolved in liquid waste. In addition, EDTA chelated with radioactive isotopes may be precipitated in RLWs, which might need filtration.  
Thus, ion-exchange resins (IERs), which are generally used in water treatment, could be a potential candidate for use in the separation and removal of EDTA in RLWs. IERs have been used as adsorbents for the purification or separation of biomaterials (e.g., proteins and amino acids) [22; 23], pharmaceuticals [24], organic species (e.g., acids and alcohols) [25; 26], CO\({}_{2}\)[27], and metal ions [28; 29]. However, EDTA removal, using IERs in an aqueous medium has been rarely reported, particularly for RLW purification. EDTA has four carboxylic acid groups, providing four negatively charged carboxylate groups in an aqueous solution. The four carboxylic acid groups can interact with positively charged functional groups on AERs, so that EDTA can be adsorbed on the AERs.  
In this work, two oppositely charged IERs, such as AERs and CERs were used to compare the removal efficiency of EDTA from an aqueous solution and from a mimic of RLWs, that contained EDTA with high concentration plus Cs ions. The adsorption behavior of EDTA on the IERs was investigated under various adsorption conditions, such as adsorption time, initial EDTA concentration, pH, and the quantity of adsorbents used to elucidate the adsorption mechanism.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Simulation Results  
#### 4.4.1 Evaluation for the Cell Selection of HCI Scheme  
The initial distribution of UE population in Macro BSs and pico BSs were 60% and 40%, respectively. To compare the HCI scheme with the SINR cell selection scheme, we let Macro BSs have higher load in order to assess whether pico BSs could offload the bandwidth requirements from UEs to Macro BSs. Figure 8 shows the population ratio of MUEs and PUEs of the two cell selection schemes. In the HCI scheme, more UEs could be offloaded to pico BSs from Macro BSs due to the consideration of the cell load. As a result of load balancing between Macro BSs and pico BSs, Figure 9 shows that the HCI scheme was able to achieve better QoS satisfactions from different aspects, in particular the QoS satisfaction of the whole system was increased by 3%. Notably, there was one exception, which was the PUE's QoS satisfaction. Clearly, since pico BSs were less loaded, the HCI scheme associated more UEs to pico BSs which resulted in less QoS satisfaction for PUEs as compared to the SINR scheme.  
Figure 8: Connection ratio of the Macro user equipment (MUE) and Pico user equipment (PUE).
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
During the encryption and decryption process, the Rivest-Shamir-Adleman-based Arnold map (RSA-AM) is utilised to generate the public key and the private key (H). Obstruction bloom breeding optimization associated with the key in encryption procedures is made more secure using this cryptographic approach. As long as the communal and private keys are fully protected, this type of encryption is very secure. The rest of this paper is structured as follows. The issues in medical picture security are discussed in Section 2, which also reviews the literature. Section 3 discusses the suggested image encryption and decryption, key generation, and optimization procedure. The experimental findings are presented in Section 4, and the algorithm's impacts are examined and tested in Section 5. Conclusions are provided in Section 6.
# Holographic 3D Display Using Depth Maps Generated by 2D-to-3D Rendering Approach
## 2 Generation of Depth Maps
### Close-Up Images  
If no convergence point is detected in the image, or multiple convergence points are detected, the image will be classified as a close-up image, rather than a perspective view image. For a close-up image, the CGDM can be found using occlusion [41]. Regions that contain fewer edges generally represent that they are farther away. The spatial relationship of a series of objects with multiple depths can be easily determined by counting the amount of edges. The local edge histogram [40] is employed to calculate the CGDM. As shown in Figure 2, the edges of the 2D image are first extracted by Canny algorithm. Then, the image of the edges is divided into 5 \(\times\) 5 blocks. The number of edges N\({}_{ij}\) in each block is counted. Blocks where N\({}_{ij}\) is larger than the average (N\({}_{av}\)) are defined as the main blocks. The total number of main blocks is \(M\), while the number of edges in each main block is denoted by \(N_{1}^{\prime}\), \(N_{2}^{\prime}\),..., \(N_{M}^{\prime}\). From a series of simulations, the reliable CGDM for each main block is proved to be a circle with a depth gradient. The center of the circle locates at the center of the corresponding block. The pixel values of the center and the circumference are assigned as 255 and 0, respectively. Meanwhile, the pixel value decreases evenly from the center to the circumference. The radius of the circle is obtained by traversal comparisons, and an optimized radius is the half the length (or width) of the image. The CGDM for the entire close-up image is obtained by fusing together the depth maps of the main blocks. If \(D_{i}\) is the CGDM of the \(l\)th main block, the fused CGDM \(D_{f}\) can be expressed as:  
\[D_{f}=\sum\limits_{i=1}^{M}\left(D_{i}\times\frac{N_{i}^{\prime}}{\sum\limits _{i=1}^{M}N_{i}^{\prime}}\right) \tag{6}\]  
Figure 2: Calculation of the CGDM for a close-up image.
## 3 Simulations
### Influence of Buoy-Ballast System on ROV Power  
The power of the ROV is calculated using the manufacturer data of the T200 brushless motors that equip the BlueRov. The manufacturer datasheets provide the motor power as a function of the motor speed, and the thrust as a function of the motor speed. The motors' speeds are controlled to achieve the desired vehicle speed. The total power is then calculated by summing the motor powers.  
The speed of the ROV is not affected by the presence of the compliant system since the simulated ROV is velocity-controlled. However, the total power of the ROV thrusters observed in simulation, represented in Figure 6, is 2% higher globally with the compliant system. This is due to the drag forces on the buoy-ballast system that the cable transmits to the ROV. The passive compliant system introduces slightly more tension in the cable throughout the movement of the ROV compared with a free cable with sufficient length.  
Figure 4: Distance between buoys and its variation when the ROV moves at constant speed.  
Figure 5: Distance between buoys versus ROV speed. Comparison between theoretical steady-state model and simulation using Vortex. Influence of \(T_{1c}\), the constant part of the cable tension from the left side.  
However, this increase in tension is not significant, and the compliant system on the cable prevents the cable from pulling strongly on the ROV.
## Appendix A Cellular Network Technologies
### Architecture
#### 3G Universal Mobile Telecommunications System (UMTs)  
The Universal Mobile Telecommunications System (UMTS), first released in 1999, is the third generation (3G) of cellular systems and represents the natural evolution of the second generation (2G) systems. It combines voice and data services, and it supports interworking with existing networks.
## 5 Performance Evaluation
### Scaling to Multiple Mobile Nodes  
SDMob lies in a spectrum in which one extreme is RPL with its scalability to thousands of nodes and the other extreme are the protocols specifically designed for mobile ad-hoc networks such as AODV. SDMob is designed for IoT applications that take advantage of the static multi-hop infrastructure and hence it can support a limited number of MNs.  
Figure 10 shows the comparison of the evaluation metrics for SDMob and RPL in a scenario with 25 randomly placed anchor nodes and a number of MNs moving with Truncated Levy Walk mobility pattern (for detail on mobility patterns see Section 5.4). From the results, we observe that although RPL shows an increasing pattern in packet delivery ratio (PDR), the proposed SDMob remarkably outperforms RPL. RPL performs better in terms of PDR for an increasing number of MNs since the Trickle algorithm resets its interval, which leads to more frequent DIO packet transmissions that congest the network. This also justifies the rise in average E2E delay for RPL. The congestion caused by the increasing MNs affects the accuracy of localization that happens due to the loss of a significant number of RSSI reports. However, the particle filter showed a good ability to converge again after not receiving RSSI reports, and the impact on localization accuracy was tolerable. The difference in the upper bound of handoff delay was negligible (100 milliseconds). Figure 11 shows SDMob's communication overhead in comparison with standard RPL for increasing number of mobile nodes.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 1 Introduction
### Background of FANET
Therefore, in our study, we mainly focus on the design criteria and architecture of DTN protocols in FANETs and call it _DTN-assisted FANET_.
# Aircraft Target Interpretation Based on SAR Images
## 3 Method  
Compared with traditional optical images, SAR images are complex, diverse, and contain background noise. The precision of detection and recognition is not satisfactory when using the YOLOv5s module directly, which always leads to the loss of small object targets. The following four improved methods were proposed based on the YOLOv5s module to solve this problem.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 4 Multi-Sensor Fusion Positioning Algorithm Based on BICI and IMM
### Simulation  
In order to evaluate the positioning accuracy of the BICI-IMM multi-sensor fusion positioning algorithm with uncertain noise variance, unknown covariance and motion model, we simulated the unmanned platform with constant velocity (CV) and constant turn (CT) models. The positioning accuracy evaluation is the same as in Section 3.3. The multi-sensor positioning system model is shown as  
\[x_{k+1}^{i}=F_{k}^{i}x_{k}^{i}+B_{k}^{i}w_{k}^{i},\quad i=1,2 \tag{60}\]  
\[z_{k}^{h}=C_{k}^{h}x_{k}+v_{k}^{h},\quad h=1,2,3\,. \tag{61}\]  
(1) The CV model  
It is assumed that the unmanned platform moves in a straight line with constant speed and the system state is \(x_{k}=\big{[}p_{x},v_{x},p_{y},v_{y}\big{]}^{T}\); then  
\[F_{\text{CV}}=\left[\begin{array}{cccc}1&T&0&0\\ 0&1&0&0\\ 0&0&1&T\\ 0&0&0&1\end{array}\right] \tag{62}\]  
\[B_{\text{CV}}=\left[\begin{array}{cccc}T^{2}/2&0\\ T&0\\ 0&T^{2}/2\\ 0&T\end{array}\right]\,. \tag{63}\]  
(2) The CT model  
It is assumed that unmanned platform moves in a turning motion at constant angular speed \(\psi\) and the system state is \(x_{k}=\big{[}p_{x},v_{x},p_{y},v_{y}\big{]}^{T}\), then \[F_{CT}=\left[\begin{array}{cccc}1&\frac{\sin\gamma T}{\psi}&0&-\frac{1-\cos\psi T} {\psi}\\ 0&\cos\psi T&0&-\sin\psi T\\ 0&\frac{1-\cos\psi T}{\psi}&1&\frac{\sin\psi T}{\psi}\\ 0&\sin\psi T&0&\cos\psi T\end{array}\right] \tag{64}\]  
\[B_{CT}=\left[\begin{array}{cccc}T^{2}/2&0\\ T&0\\ 0&T^{2}/2\\ 0&T\end{array}\right]. \tag{65}\]  
The trajectory of the unmanned platform is displayed in Figure 7, which contains CV and CT models in Table 2. The initial state is \(X_{0}=[10,5,10,0]^{T}\). In the simulation, we take \(Q=3I\), \(\tilde{Q}_{k}=0.8\), and the conservative noise variance upper bounds of the three sensors are 1 m, 2 m and 3 m, respectively. The actual positioning accuracies of the three sensors are 0.8 m, 1.2 m and 2 m, respectively. The correlation coefficients of sensor 1 and 2 are the same as in Section 3.3.  
Through 50 Monte Carlo simulations, the positioning results of BICI, SICI and BICI-IMM are exhibited in Figure 8. Figure 9 is the positioning error CDF of algorithms. It is indicated that BICI has a higher positioning accuracy compared with other single model algorithms. In contrast, the BICI-IMM has higher positioning accuracy. The algorithm based on CV model positioning accuracy is slightly higher than the model based on CT in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time (s)** & **Motion Model** \\ \hline \([0,50)\) & CV \\ \([50,70)\) & CT \\ \([70,120)\) & CV \\ \([120,140)\) & CT \\ \([140,190)\) & CV \\ \([190,210)\) & CT \\ \hline \hline \end{tabular}
\end{table}
Table 2: Motion model settings.  
Figure 7: The simulation trajectory of two-dimensional motion.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results  
### Experimental Setup  
For this paper, two different 24 h surveys were used. The first survey used GPS data from the International GNSS Service (IGS), captured using a geodetic-grade receiver in ESA's Malargue Satellite Tracking Station, in Argentina [20; 21]. The second survey was conducted at the GNSS Laboratory of Instituto de Telecomunicacoes, in Lisbon. Data collection for this survey was performed using two different types of receivers: two u-blox 6T receivers from u-blox (Thalwil, Switzerland) with NovAtel GPSAntenna Model 52I antennas from Novatel Inc. (Calgary, Canada) and an Ashtech ProFlex 500 receiver with an AT1675-7M antenna, both from Ashtec, now Magellan (San Dimas, California, U.S.A.). These receivers were connected to a laptop computer which provided both serial communication handling and internet protocol (IP) communication. The resulting surveys were saved in RINEX 3.02 format. The two u-blox receivers were also used for a DGPS test based on the initial results of this paper. In this setup, the antenna designated RF2 worked as base station, while the antenna RF6 worked as a rover (the naming scheme of the antennas is specific for the IT GNSS Laboratory and has no correlation with GNSS marker names). The data collection was performed in clear meteorological conditions to avoid signal degradation and to provide a good performance baseline. It should be noted that these conditions correspond to the expected real-world conditions for the expected uses of this methodology, so there is a good expectation of obtaining comparable results.  
For all the positioning methods, the ionospheric and clock modeling used is as defined in [22], and the tropospheric modeling follows that in [23].
# Resonant Airborne Acoustic Emission for Nondestructive Testing and Defect Imaging in Composites
## 6 Robotic AE Scanning Mode  
Unlike the above-studied RACE scanning of flat specimens, the use of robots combined with optical measurements of the surface profile brings forth imaging of the defects in complicated shape objects. In our experiments, the viability of robotic AE scanning is tested based on the six-axis robot, IRB 120 (ABB, Zurich, Switzerland). To employ the same ultrasound recorder, the robot control unit, IRC 5, was instructed to emulate the encoder signals over IO-card for virtual the y- and z-axes. To detect the AE signal, the microphone, in a special holder, is fastened to the robot flange (Figure 14).  
Figure 14: Experimental setup for robotic scanning.  
Figure 13: Mixed-frequency resonant AE spectrum for 19 kHz and 20 kHz primary wave interactions.  
To evade initial seeking for LDR frequency, the wideband, noisy excitation was used in the AE robotic mode. Figure 15b demonstrates the potential of robotic AE imaging for circular FBH in PMMA of equal radii \(R=10\) mm but of different back-wall thicknesses. Therefore, their LDR frequencies are significantly different (from 13,950 Hz to 21,300 Hz). However, the wideband mode enables activation of all of them and contributes to the image obtained by a single C-scan. As seen in Figure 15, similar vibrometry images are of superior quality, nonetheless, the AE images clearly identify the defects.  
As mentioned above, for cm-size defects the fundamental LDR frequencies are in the range of tens of kHz. In the excitation bandwidth employed (\(\sim\)100 kHz), multiple higher-order resonances are also activated in addition to the fundamental LDR. They cover the total defect area, and the superimposed multiple-order LDR readily reproduce the shape of the defects [11]. The effect is shown in Figure 16 for a set of four square (20 \(\times\) 20 mm\({}^{2}\)) subsurface inserts in a CFRP plate (5 \(\times\) 290 \(\times\) 330 mm\({}^{3}\)). The AE robotic mode validates a fair quality of imaging, clearly identifying the shapes, positions and the sizes of the defects.  
Figure 16: Noisy mode of robotic imaging of 4 square inserts in CFRP plate: AE imaging (**a**), wideband laser vibrometry (**b**).  
Figure 15: (**a**) 3 FBH in PMMA; (**b**) multiple AE images in a single robotic scan in noisy mode; (c) vibrometer image of FBH activated at their LDR frequencies.  
The noisy robotic scanning also works well to visualize the defects of appreciably more complex shapes. This is demonstrated in Figure 17a, where the noisy-mode AE image of the Teflon ring delamination (outer diameter 55 mm) in the CFRP plate (400 \(\times\) 400 \(\times\) 2 mm\({}^{3}\)) is compared with the laser vibrometry image obtained for (1-100 kHz) excitation Figure 17b. In the ring, the laser vibrometry unfolds a number of fractional LDR in the 10 kHz to 50 kHz range that visualize a particular part of the defect. Their superposition in the wideband AE mode reconstructs the whole picture of the ring delamination.
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
###### Abstract  
Shrouded bladed disks exhibit a nonlinear dynamic behavior due to the contact interfaces at shrouds between neighboring blades. As a result, reduced order models (ROMs) are mandatory to compute the response levels during the design phase for high cycle fatigue (HCF) life assessment. In this paper, two reduction strategies for shrouded bladed disk reduction are presented. Both approaches rely on: (i) the cyclic symmetry of the linear bladed disk with open shrouds to perform only single sector calculations, (ii) the Craig-Bampton (CB) method to reduce the number of physical degrees of freedom (dofs). The two approaches are applied to a set of test cases in order to evaluate and compare their accuracy and the associated computational effort. Although both approaches allow for generating accurate ROMs, it is found that the numerical efficiency of the two methods depends on the ratio of the number of nodes at the inter-sector interfaces over the number of inner nodes of the elementary sector model.  
Keywords:reduced order models; bladed disks; cyclic symmetry; shrouded blades; nonlinear forced response +
Footnote †: journal: Applied Sciences