esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
###### Abstract  
In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods.  
unmanned aerial vehicles; object tracking; object proposal voting +
Footnote †: journal: Applied Sciences
## 2 Theoretical Approach  
### Acoustelasticity  
The acoustoelastic effect is identified in tensile tests monitored with ultrasound ([PERSON], 1966). It studies the variation in the velocity of elastic waves when the body is subjected to stress ([PERSON], 1966; [PERSON], 1966). When this effect is considered in ultrasonic measurements, one must assume that there is a nonlinear relation between the speed of elastic waves and the stress field in the solid media. Compared with other ultrasonic waves, longitudinal waves have the highest sensitivity for applications under mechanical stress ([PERSON], 1966). However, longitudinal waves are relatively small and decay very fast, making the acoustoelastic effect a small effect ([PERSON], 1966; [PERSON], 1966).  
For example, in metals, mechanical stress causes a variation in the ultrasonic velocity of only 3%, which is considered a small variation in the measurement but may generate large errors in measurements based on time-of-flight (TOF) techniques. Another issue is that other external factors, such as temperature, coupling conditions, anisotropic properties of materials, and micro-textures, among other factors, also influence ultrasound waves. Of those listed, temperature and coupling conditions have been the main factors affecting the measurement of mechanical stress ([PERSON], 1966; [PERSON], 1966).  
[PERSON] and [PERSON] (1966) initially developed the theory of acoustoelasticity and its modeling. Previously, the effect was associated only with perfectly elastic solids. The deformation was a function of mechanical stress and temperature, and the model was based on infinitesimal deformations. The modern theory of acoustoelasticity is completed with the general theory of finite and nonlinear deformations presented by [PERSON] (1966). For this effect to be associated only with elastic linear strains, the model is based on Hooke'slaw, which is the linear relationship between stress and strain [37]. However, this model fails to describe the relationship between mechanical stress and acoustic waves observed in practice [32].  
Practical observations clarify that when acoustic waves propagate in metal, they cause the displacement of particles that superpose the existing internal tensions. Considering the linear elastic relationship methodology, the solution would be just overlapping waves without one influencing the other. After crossing, they would continue traveling without affecting one another [32]. However, this is not what happens in the acoustoelastic case. Mechanical stress influences the acoustic wave, and the experimental model can only be explained mathematically by introducing a nonlinear elastic model of finite deformations [32; 37], i.e., the stress-strain relationship can be linear. However, it becomes necessary to use nonlinear strain and stress related to linearity, an equivalent stiffness tensor for the dynamic deformation of waves [38], making the formulation require second- and third-order coefficients [35; 39].
## 2 Key Steps in a FRB Search Pipeline
### Dedispersion
#### 2.2.1 Brute Force  
The direct dedispersion algorithm sums the intensities of a dynamic spectrum over the entire bandwidth, along the quadratic sweep corresponding to the trial dispersion measure. The algorithm calculates a dedispersed time series \(t_{\text{D},t,\
u}\) from an input dataset \(\mathcal{I}\) where the subscripts indicate a given DM \(D\), time \(t\), and frequency \(\
u\). The algorithm is run over a number of frequency channels \(N_{\
u}\), number of time samples \(N_{\text{t}}\), and a number of DM trials \(N_{\text{D}}\). Typically, on modern heterogeneous architectures, the brute force dedispersion algorithm is parallelised over \(N_{\text{t}}\) and \(N_{\text{D}}\), with the summation over \(N_{\text{t}}\) happening serially. One drawback of the brute force method is that the complexity of this algorithm is very high, \(\mathcal{O}(N_{DM},N_{t},N_{v})\). The other issue with this algorithm is that it requires a large memory bandwidth in the compute hardware and has a very low arithmetic density once the data are loaded to memory. While these challenges appear daunting, three methods are combined in practice to remedy both issues (see, e.g., [7]), allowing us to dedisperse data in real-time. For the first two, an error budget is estimated once, at the start of a survey, based on the intra-channel dispersion that is unavoidable for incoherent dedispersion at higher DMs, and this is encoded in a look-up table. Then, first, for higher DMs, the input data are downsampled such that they remain below this error, reducing \(N_{\text{t}}\) without loss. Second, dedispersion takes place using a two-step sub-banding algorithm--i.e., first dividing the entire bandwidth over multiple sub-bands, next dedispersing within these, and finally combining all sub-bands--effectively reducing \(N_{\
u}\) (cf. [19]). Finally, memory access is tuned such that the required data can be very rapidly retrieved from the cache (\"data re-use\" [20]).
# Adaptive Emergency Call Service for Disaster Management
## 3 System Model
### Informational Model  
Each device \(m\in M\) is characterized by the following data:  
* Current battery level (residual energy), SoC\({}_{m}\in[0,100]\);
* Coverage status by at least one gNB, IC\({}_{m}\in\{0,1\}\): The coverage status indicates if the UE has at least one direct communication link with a station \(n\in N\), allowing it to communicate directly with the core network. When a UE is out of coverage, it no longer has a direct connection link with the gNBs.  
Figure 3: The proposed emergency call service architecture. Emergency UEs in the disaster area forward their calls in the direction of the functioning gNBs via relay UEs in the neighborhood. Distinct emergency calls generated by each emergency UE are represented by the color of the arrow. Solid lines indicate the D2D communication mode, while dashed lines indicate the classical communication between gNB and UE.  
GPS localization accuracy, \(\text{LocAcc}_{m}\in[0.001,1]\): Location information is obtained using a global positioning system (GPS). It is assumed that the error in such a GPS measurement is represented by the distance between the actual position and the estimated position of \(m\), \(\delta_{m}\in[0..\infty]\) measured in meters. Hence, \(\text{LocAcc}_{m}\) is modeled as (1), \[\text{LocAcc}_{m}=\frac{1}{\delta_{m}+1}m^{-1}.\] (1)
* Received signal strength indicator (RSSI) power that is detected, \(\text{RSSI}_{m}\): The broadcast physical layer synchronization signals (PBCH) periodically transmitted by the operational gNBs enable the UEs to establish quality indicators, including RSSI, reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR) and channel quality indicator (CQI) up to 75 km from the gNB [35; 36]. These measured indicators are useful in the selection of the best paths toward the nearest operational gNB. The RSSI indicator [37] is measured by each UE to estimate the strongest signal received from the surrounding operational stations \(g\in N\) as shown in (2). The RSSI value depends on device location and more specifically on the signal path loss [37] between the gNBs and the device. Signals' path loss, namely \(P_{l}(d,f)\), is varied due to the propagation parameters such as the distance between gNB and receiver UEs, height and location of UEs, transmission frequencies, terrain contours, environment. \[\text{RSSI}=\max_{n\in N}\left(\mathbb{E}\bigg{(}\sum_{r\in RB}P_{l}(n)-P_{l} (d,f)\bigg{)}\right),\] (2) where \(P_{l}(n)\) is the power of the transmitted signal by a given station \(n\). \(RB\) represents the set of radio resource blocks used to transmit the physical broadcast channel (PBCH) signals. \(RB\) corresponds to the set of OFDM symbols and sub-carriers on which the PBCH is transmitted. For each operational station, the user equipment (UE) computes the average received signal power [38] over all the symbols and sub-carriers, \(f\) of \(RB\). Finally, the UE only considers the highest computed average.
## 2 The Working Principle of CFST Ductile Column Energy Dissipation Device (CFST Dampers)
### Lateral Stiffness of CFST Dampers  
#### 2.1.1 Yield Stage of CFST Ductile Columns  
Analysis of the stress mechanism of the frame structure with CFST dampers shows that the supports and the energy-dissipating columns are first connected in series, and then connected in parallel with the frame columns at both ends, so the overall initial stiffness is described by Formula (1)  
\[K_{1}=K_{sc21}+2K_{c1} \tag{1}\]  
where \(K_{c1}\) is the initial stiffness of a single column of frame column, which refers to reference [19]; \(K_{sc21}\) is the initial stiffness of \(n\) CFST ductile columns when it is connected in series with the supports at both ends.  
\[K_{sc21}=\frac{K_{c21}\times nK_{sc1}}{K_{c21}+2 nK_{sc1}} \tag{2}\]  
where \(K_{c21}\) is the initial stiffness of the support; \(K_{sc1}\) is the initial stiffness of a single CFST ductile column.  
The details of Formulas (3) and (4) are specified in reference [20].  
\[K_{z1}=\frac{1}{\frac{1.2h_{1}}{\frac{1}{C_{z}}A_{z}}+\frac{h_{1}^{3}}{3E_{z} I_{z}}} \tag{3}\]  
\[K_{sc1}=\frac{12E_{sc}I_{sc}}{h^{3}} \tag{4}\]  
where \(h_{1}\) is the height of the support; \(h\) is the height of the CFST ductile column; \(E_{z}\) is the elastic modulus of the support; \(G_{z}\) is the shear elastic modulus of the support; \(E_{sc}\) is the elastic modulus of the CFST ductile column; \(I_{sc}\) is the inertia moment of the CFST ductile column; \(A_{z}\) is the cross-sectional area of the support; \(I_{z}\) is the inertia moment of the support.  
According to the relationship between force, displacement, and stiffness, the value of the yield strength can be obtained by Formula (5).  
\[F_{1y}=K_{1}\Delta_{1y}=K_{1}\frac{nV_{scy}}{mK_{sc1}} \tag{5}\]  
where \(\Delta_{1y}\) is the overall lateral displacement of the frame when a single CFST ductile column yields; \(V_{scy}\) is the yielding shear of a single CFST ductile column; \(n\) is the number of CFST ductile columns; \(m\) is the height ratio of the CFST ductile column to the frame; and \(m=h/H\); \(H\) is the height of the frame.  
From reference [18], \(V_{scy}\) can be calculated according to Formula (6).  
\[V_{scy}=\frac{1.15M_{y}}{\frac{h}{2}+(2.6-50a_{b})\frac{N}{k_{a}}} \tag{6}\]  
where \(k_{a}\) is the elastic half-column stiffness of the CFST ductile column; \(a_{b}\) is the stiffness reduction coefficient of the CFST ductile column after entering the strengthened stage; \(M_{y}\) is the bending moment corresponding to the yielding of the CFST ductile column; \(N\) is the axial force borne by a single CFST ductile column.
# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Training Setup and Strategy  
All of the networks are trained using \(256\times 256\) images with a batch size of 8 due to the machine memory. The optimization is performed employing the Adam optimizer [49] with \(\beta_{1}=0\) and \(\beta_{2}=0.9\), which is a stochastic optimization method with adaptive estimation of moments. For the experiments, the hyper-parameters are set to \(\lambda_{fm,1}=\lambda_{fm,2}=10.0\), \(\lambda_{con}=1.0\), \(\lambda_{pix,2}=\lambda_{pix,3}=1.0\), \(\lambda_{perc}=1.0\) and \(\lambda_{adv,1}=\lambda_{adv,2}=\lambda_{adv,3}=0.001\). In the training processing, the stability is very important. In the edge and color model, the parameters of \(\lambda_{fm,1}\) and \(\lambda_{fm,2}\) should be set as having larger values than others, since the feature matching loss stabilizes the training processing. In the image completion network, its training process is more stable because it has more information including edge and color priors guiding the training. Thus, the \(\lambda_{perc}\) should be set 1.0. All of the adversarial parameters should be set as having very small values because values that are too large tend to lead to instability in the early stage. Firstly, generators \(G_{1}\), \(G_{2}\) and \(G_{3}\) are trained separately with learning rates \(10^{-4}\) until convergence. Then, the networks are fine-tuned by removing \(D_{1}\) and \(D_{2}\) with learning rate \(10^{-5}\) until convergence. Corresponding discriminators are trained with a learning rate one tenth of the generators. During each training iteration, the discriminator is trained \(k\) steps and the generator is updated once. \(k\) is set to 3 in all of the experiments. If \(k<3\), the discriminators cannot distinguish the real and fake images; if \(k>3\), the generators cannot get enough gradients from the discriminators. In order to stabilize the model during training, in the early stage, very small adversarial loss weights compared to the other ones are adopted, i.e., \(\lambda_{adv,1}=\lambda_{adv,2}=\lambda_{adv,3}=0.001\). After that, the adversarial loss weights should be gradually increased until \(\lambda_{adv,1}=\lambda_{adv,2}=\lambda_{adv,3}=1.0\), similar to the curriculum learning. The larger the adversarial parameters, the more details are generated. Thus, the adversarial parameters should be gradually increased to generate more details and keep the training process stable.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 3 PS-OCT Applications
### Other Applications of PS-OCT  
PS-OCT also found applications in biomedical fields other than those discussed in the previous sections as well as in non-medical fields. One exciting use of PS-OCT is in imaging applications relying on small particles as exogenous contrast agents. As such, plasmon-resonant nanoparticles like gold nanostars are popular contrast agents in biophotonic imaging. In order to increase detection sensitivity for single particles, their polarization-sensitive scattering signal in the near infrared can be modulated by an external oscillating magnetic field [231]. PS-OCT can be used to detect these dynamic scattering signals and was demonstrated for depth-resolved viscosity measurements based on the diffusion of gold nanorods [232]. Based on temporal changes in polarization contrast parameters, a method for differentiating light scatterers such as cells and gold nanorods was developed [233]. Recently, PS-OCT based detection of gold nanorods was proposed for detecting nanotopological changes in 3D tissue models of mammary extracellular matrix and pulmonary mucus (Figure 13d) [234]. Having a non-invasive imaging technique like PS-OCT for studies of such tissue models may help to interpret biophysical changes associated with disease progression.  
Its sensitivity for microstructural changes affecting sample polarization properties along with its 3D imaging capabilities made PS-OCT also an interesting modality for materials science and nondestructive testing applications [236]. Using translucent glass-epoxy composite phantoms, the spatial distribution of mechanical stress was mapped by PS-OCT [237]. Strain mapping by also exploiting the optic axis orientation in PS-OCT images was demonstrated as a method for charting the directionality of strained sample areas (Figure 13a-c) [235]. Material dynamics were optically  
Figure 13: Nonmedical applications of PS-OCT. (**a–c**) Transversal ultrahigh resolution PS-OCT images of the resist-wafer interface of a photoresist mold for a micromechanical wheel (adapted with permission from [235], Optical Society of America, 2006). (**a**) Intensity image; (**b**) PS-OCT retardation image. The arrows indicate highly strained areas at the teeth of the wheel; (**c**) Orientation of the slow optic axis (color-coded and displayed as vector-field scaled by the magnitude of the retardation); (**d**) Diffusion of gold nanorods (GNRs) in in vitro extracellular matrix models (adapted with permission from [234], [PERSON]). Representative PS-OCT B-scan images showing cross-polarized signal of fibroblasts in collagen I: Matrigel versus cell seed density and incubation time show how GNRs provide positive contrast within the matrix between cells, whereas cells appear dark.  
investigated by translating measured phase retardation into stress images [238]. Thereby, dynamics could be studied from the elastic regime over the deformation phase up to fracture.
## 3 Results
When this drone was flying without the protector, only two of the participants (2/24) could hear the drone indistinctly at the horizontal distance of 150 m and only one participant could hear indistinctly the sound at a horizontal distance of 250 m (1/24). None of the participants could hear when the drone was 250 m or farther.
## 3 Design of CP-ABE
### Preliminaries Works
#### 3.1.3. Linear Secret Sharing Schemes (LSSS)  
The LSSS is used to express access policy in access structure (\(A\), \(\rho\)), where \(A\) is a policy matrix and \(\rho\) is a mapping of each row \(A_{i}\) of the matrix \(A\) to an attribute [37]. In this scheme, the presence of an attribute universe was denoted as \(AU\), which has n categories of attributes.  
**Definition 1 (Lsss)**.: _Let \(AU\) = (\(Att_{1}\), \(Att_{2}\), \(Att_{3}\), \(\ldots\), \(Att_{n}\)). Each attribute \(Att_{n}\) contains two-part which are attribute name and attribute values. Possible values of the attribute value, \(AVx\) = {\(\xi_{x,1}\), \(\xi_{x,2}\), \(\xi_{x,3}\), \(\ldots\), \(\xi_{x,nx}\)}. Meanwhile, \(\mathbf{A}\) = \(\frac{l_{X}}{2\rho}\) refers to a share-generating matrix, while each row in \(A\) is a map to an attribute name index, and that mapping was denoted as \(\rho\). LSSS consisting of the two following algorithms:_  
1. _Secret share: The secret shared,_ \(s\
i\mathbb{Z}p\) _and the value_ \(\lambda_{x}\) _is computed for each row_ \(A_{x}\) _of_ \(\mathbf{A}\)_, where_ \(V=(s,\ y_{2},y_{3},\ldots,y_{n})-\ \in_{R}\mathbb{Z}_{p}^{n}\) _and_ \(y_{2},y_{3},\ \ldots\)_,_ \(y_{n}\) _are chosen randomly from_ \(\mathbb{Z}p\)_. Hence, the secret share value is given_ \(\lambda_{x}=A_{x}\times\) _v._
2. _Secret Construction: This algorithm takes in the secret share {\(\lambda_{x}\)} and set \(P\) which contains the authorized attribute name index. Then it sets_ \(I=\{x|\rho(x)\in P\}\subseteq\{1\)_,_ \(2\)_,_ \(\ldots\)_,_ \(l\}\) _and computes the constant_ \(\{\omega_{x}\}_{x\in I}\) _such that_ \(\sum_{x\in I}\omega_{x}A_{x}=(1,\ 0,\ 0,\ \ldots,\ 0)\)_. Then the secret_ \(s\) _is reconstructed by_ \(s=\sum_{x\in I}\omega_{x}\lambda_{x}\)_._  
Similar to [23], we construct the LSSS matrices over \(\mathbb{Z}_{N}\). In our proposed scheme, we denoted the user's attribute as \(S=(B_{s},\ I_{s})\), where \(B_{s}\subseteq\mathbb{Z}_{N}\) is the attribute name index, and \(J_{s}=\{l_{x},\ i\}_{x}\subseteq\ B_{s}\) is the attribute value set. Denote \(\mathbb{A}=(\mathbf{A},\ \rho,\ \mathcal{T})\) as access policy and \(\mathcal{T}\) is the attribute value for each row of \(A\) where \(\mathcal{T}=\Big{(}\ t_{\ \rho(1)},t_{\ \rho(2)},\ t_{\ \rho(3)},\ldots,\ t_{\ \rho(l)}\Big{)} \Big{(}t_{\ \rho(x)}\in AV_{\rho(x)}\Big{)}\). \(S\) satisfies \(\mathbb{A}\) means that there exits \(I\subseteq\{1,\ 2,\ \ldots,\ l\}\) satisfying \((A,\ \rho)\), \(\{\rho(x)|x\in I\}\subseteq\ B_{s}\) and \(l_{\ \rho(x)}=t_{\ \rho(x)}\forall x\in I\).
## 3 Proposed Design
### Space-to-Ground Atmospheric Model  
The Gamma-Gamma channel distribution model used for the proposed design is given as [18; 23]:  
\[f_{h_{a}}(h_{a})=\frac{2{(st)}^{(\frac{s+t}{2})}}{\Gamma(s)\Gamma(t)}.h_{a}(^{ \frac{s+t}{2})-1}K_{s-t}\Big{[}2{(sth_{a})}^{\frac{1}{2}}\Big{]} \tag{2}\]  
where \(h_{a}\) means atmospheric turbulence, \(\Gamma(.)\) is the Gamma function, \(K_{s-t}\) is the modified Bessel function with order \((s-t)\), and \(s\) and \(t\) are number of large- and small-scale irradiance fluctuations which can be defined as [18; 23]:  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Component** & \multicolumn{2}{c}{**Parameters**} & **Value** & **Unit** \\ \hline CW laser & Wavelength & 1550, 1550.8, 1551.6, 1552.4 & nm \\  & Power & 0 & dB \\  & Linewidth & 0.1 & MHz \\  & Azimuth & 45 & deg \\  & Reference wavelength & 1550 & nm \\  & Range & 21,000–30,000 & km \\  & Tx and Rx aperture diameter & 15 & cm \\  & Tx and Rx optics efficiency & 0.8 & \\  & Free space path loss & Yes & \\  & Geometric gain & Yes & \\  & Tx and Rx pointing error & 0.1 & \(\mu\)rad \\  & Additional losses & 0 & dB \\  & Wavelength & 1550, 1550.8, 1551.6, 1552.4 & nm \\  & Range & 5–30 & m \\  & Geometrical loss and gain & Yes & A/W \\  & Scintillation model & Gamma–Gamma & \\  & Aperture diameter & 15 & cm \\  & Beam divergence & 2 & mrad \\  & Transmitter loss & 0.5 & dB \\  & Optics efficiency & 0.9 & \\ Spatial PD & PD & PIN & \\  & Responsivity & 1 & A/W \\  & Dark current & 9 & nA \\ Low pass filter & Cut off frequency & 0.75 \(\times\) Bit rate & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components’ parameters values [18].  
Figure 2: Generated OAM modes’ 2D and 3D view.  
\[s=\frac{1}{\left[exp\left\{\frac{0.49k_{0}^{2}}{\left(1+0.18d^{2}+0.56k_{0}^{ \frac{12}{6}}\right)^{\frac{2}{6}}}\right\}-1\right]} \tag{3}\]  
and  
\[t=\frac{1}{\left[exp\left\{\frac{0.51k_{0}^{2}\left(1+0.18d^{2}+0.56k_{0}^{ \frac{12}{6}}\right)^{\frac{2}{6}}}{\left(1+0.90d^{2}+0.62d^{2}k_{0}^{\frac{12} {6}}\right)^{\frac{2}{6}}}\right\}-1\right]} \tag{4}\]  
where \(k_{0}^{2}\) is the Rytov variance and \(d\) is the spherical wave diameter. Again, to evaluate the strength of the atmospheric turbulence, the refraction structure parameter, \(C_{n}^{2}\), is used, and is defined as (in m\({}^{-2/3}\)) [18; 23]:  
\[C_{n}^{2}=C_{T}^{2}\times\left(79\times\frac{P}{10^{6}T^{2}}\right)^{2} \tag{5}\]  
where \(C_{T}^{2}\) is the atmospheric temperature structure parameter, \(P\) is atmospheric pressure, and \(T\) is the average temperature. Also, the Rytov variance can be used for atmospheric turbulence classification, as [18; 23]:  
\[k_{0}^{2}=0.5C_{n}^{2}\left(2\pi/\lambda\right)^{\frac{7}{6}}L^{\frac{11}{6}} \tag{6}\]  
where \(\lambda\) is operating wavelength and \(L\) is transmission length.