esa-sceva/satcom-chunk-collection huggingface dataset:
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 2 Preliminary and Related Work
### Specifications of IEEE 802.15.6
#### 2.1.2 Security Scheme of WBAN  
The IEEE 802.15.6 standard specifies three security levels for all communications in WBAN, Level 0, Level 1, and Level 2, respectively. Level 0 is unsecured communications, where neither authentication nor encryption are required. Public information, such as time stamps, which is neither critical nor private, could be transmitted at this security level. Meanwhile, the communication of security Level 1 contains private, but not critical data, such as names, ages, and locations. These data are not significant for the physical conditions of the patients; however, they would still not want to release it to the public. In these cases, authentication is required, while encryption is not involved. In the case of the most critical data, such as blood pressure, heart rate, and parameters for a pacemaker, it is a requirement for them to be transmitted at security Level 2. Both authentication and encryption are mandatory for Level 2 communication.  
In terms of the methods implementing authentication and encryption, the standard specifies the certificate validation as the authentication method and Elliptic-Curve Cryptography (ECC) as  
Figure 4: The frame structure of a general MAC frame defined in IEEE 802.15.6. FCS, Frame Check Sequence.  
the encryption method. Based on the specifications, multiple security schemes have been proposed to implement and even improve the security protection of WBAN systems. Precisely, lightweight data authentication schemes have been proposed in [13; 14], which achieved much lower power consumption than conventional WBAN security schemes. Moreover, the work in [15; 16] proposed data authentication and encryption methods by utilizing the data collected from the sensors to generate dynamic keys, which simplifies the system complexity of the security scheme while increasing the security level. Furthermore, an ASIC implementation of security scheme for WBAN has been proposed [8]. In this design, besides validating the certificate, there is a second phase for the authentication procedure called the challenge-response phase to increase the security protection of the communications in WBAN.
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 3 Bit Error Probability
### Error Probability for Nakagami-m Channels  
We first consider that a random variable \(\alpha\sim\text{Gamma}(k,\theta)\). Then, we relate the Nakagami-m parameter to this distribution such that \(k=m\) and \(\theta=\Omega/m\). In the case of Nakagami-\(m\) channels, the correlation output at the demodulator is given as  
\[\sum_{n=0}^{2^{SF}-1}r_{k}(nT)\cdot\omega_{i}^{*}(nT)=\begin{cases}\sqrt{ \alpha E_{s}}+\phi_{i},&i=k,\\ \phi_{i},&i\
eq k\end{cases} \tag{16}\]  
where \(\sqrt{\alpha}\) follows a Nakagami-m distribution. Accordingly, (6) can be modified to include the Nakagami-\(m\) random variable as  
\[P_{e|\alpha}=Pr\left[\rho>|\sqrt{\alpha E_{s}}+\phi_{i}|\right]. \tag{17}\]  
Following similar rationale as in (9), the bit error probability for Nakagami-\(m\) can now be expressed as  
\[P_{e|\alpha}=\int_{0}^{\infty}\int_{0}^{\infty}\left[1-\left[1-e^{-\frac{y^{2 }}{2\sigma^{2}}}\right]^{2^{SF}-1}\right]\Psi_{m}(y,\alpha)dyd\alpha, \tag{18}\]  
where  
\[\Psi_{m}(y,\alpha)=f_{\beta_{k}|\alpha}(y)\frac{m^{m}}{\Gamma(m)}\alpha^{m-1}e ^{-m\alpha}, \tag{19}\]  
and  
\[f_{\beta_{k}|\alpha}(y)=\frac{y}{\sigma^{2}}\exp\left[-\frac{(y^{2}+\alpha E_ {s})}{2\sigma^{2}}\right]I_{0}\left(\frac{y\sqrt{\alpha E_{s}}}{\sigma^{2}} \right). \tag{20}\]  
Note that \(\Omega=1\) in the term \(\frac{m^{m}}{\Gamma(m)}x^{m-1}e^{-mx}\) is the normalized channel power for Nakagami-\(m\) distribution. The expression in (18) is the average bit error rate probability for Nakagami-\(m\) channelsin the integral form. A closed-form expression of (18) can be obtained following the same rationale in the steps of (10)-(13), so that  
\[P_{e|\alpha}=\sum_{q=1}^{2^{SF}-1}\int_{0}^{\infty}\int_{0}^{\infty}\binom{2^{ SF}-1}{q}(-1)^{q+1}e^{-\frac{qq^{2}}{2^{SF}}}\times\Psi_{m}(y,\alpha)dyd\alpha, \tag{21}\]  
In the sequel, we must apply a change of variable of the form \(z=y^{2}\) and perform the integration in order to get  
\[P_{e|\alpha}=\sum_{q=1}^{2^{SF}-1}\int_{0}^{\infty}\frac{\binom{2^{ SF}-1}{q}(-1)^{q+1}e^{-\frac{2q^{2 SF}}{q+1}}}{(q+1)}\frac{m^{m}a^{m-1}e^{-m \alpha}}{\Gamma(m)}d\alpha \tag{22}\]  
finally, integrating with respect to the \(x\) variable, we can get the following expression  
\[P_{e|\alpha}=\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{q+1}\binom{2^{ SF}-1}{q}\left[1+\frac{q}{m(q+1)}\gamma\cdot 2^{SF}\right]^{-m} \tag{23}\]  
where \(\gamma\) was defined in (4). Note that  
\[\lim_{m\to\infty}\left[1+\frac{q}{m(q+1)}\gamma\cdot 2^{SF}\right]^{-m}= \exp\left(-\frac{q}{q+1}\gamma 2^{SF}\right) \tag{24}\]  
and for this case (23) coincides exactly with (14), that is, the Gaussian case is the limit when the Nakagami-\(m\) parameter tends to infinity.  
Again, according to [12], we finally find the average bit error rate for Nakagami-m such that  
\[P_{b}=\frac{2^{SF-1}}{2^{SF}-1}\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{q+1} \binom{2^{SF}-1}{q}\left[1+\frac{q}{m(q+1)}\gamma\cdot 2^{SF}\right]^{-m} \tag{25}\]
## 3 Results
### Colorimetry of the Three Wood Species Using CIEL*a*b* Space  
#### 3.3.1 The Color of Paulownia Wood before and after Planning  
Immediately after cutting the log into lumber, the paulownia wood darkened in color to a light gray. This coloring was observed on a small thickness of about 0.5-0.7 mm, due to the oxidation of wood sap when it came into contact with atmospheric air. This color was kept as long as the wood was not processed via planning. The dark color disappeared when the timber was planned to a thickness of at least 0.7 mm, obtaining the real beautiful color of paulownia wood surface (Table 4).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Wood Species**} & \multicolumn{8}{c}{**Density at Different Moisture Content, \(\rho_{\mathbf{u}}\), kg/m\({}^{3}\)**} \\ \cline{2-10}  & **0\%** & **12\%** & **30\%** & **60\%** & **90\%** & **120\%** & **150\%** & **MCmax** \\ \hline Linden & 461 & 489 & 526 & 578 & 619 & 672 & 681 & 681 (MC\({}_{\text{max}}=150\%\)) \\ \hline Paulownia & 304 & 328 & 362 & 411 & 454 & 490 & 522 & 612 (MC\({}_{\text{max}}=262\%\)) \\ \hline Balsa & 118 & 130 & 148 & 176 & 202 & 227 & 250 & 540 (MC\({}_{\text{max}}=780\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Density of wood species at different moisture content.  
Figure 9: Empirical cumulative distribution function (CDF) of wood density.  
Figure 10 shows the degree of black and white of the paulownnia timber before and after planning, clearly demonstrating an increase in color from white to black (from 5.41, to 61.79 units).  
Figures 11 and 12 show the green-red and blue-yellow degrees of paulownia timber before and after planning. There was a decrease in the degree of green-red from \(-8.8\) to \(-22.9\) units (14.1 units) and a large increase in the degree of blue-yellow from \(-9.9\) to \(21.1\) units (an increase of 31 units).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \
## 1 Introduction
The single-stage network uses the regression network to obtain the ear target classification and detection box. The representative networks are the SSD and YOLO series. In 2021, [PERSON]. [19] proposed an ear recognition system based on a single-stage network SSD and lightweight network MobileNetv1, and the ear recognition rate reached 98% on the USTB dataset. [PERSON]. [20] proposed a dynamic human ear recognition method based on YOLOv3, and the recognition accuracy in the CCU-DE human ear database has exceeded 90%. Through the literature research, we found that the above methods can perform ear recognition, but compared with traditional ear recognition algorithms, the ear recognition algorithm based on deep learning has certain advantages [21]. In deep learning, the single-stage network is faster than the two-stage network. Combining with the auxiliary network can improve the recognition accuracy, but it will also increase the amount of computation, the number of parameters, and the size of the model, resulting in unsatisfactory real-time performance of the network. A key factor in the practical application of human ear recognition is to improve the real-time performance of the method while ensuring recognition accuracy. Therefore, in this paper based on the single-stage deep neural network YOLOv5s, we propose a human ear recognition method based on the lightweight network MobileNetV3 and Ghostnet. This method can simultaneously make the feature extraction of the backbone network and the feature fusion of the neck network of YOLOv5s lightweight. We use four performance indicators (mAP, model size, GFLOPs, and params) to test the YOLOv5s-MG method proposed in this paper on three datasets (CCU-DE, USTB, and EarVN1.0) and compare it with other lightweight methods. The simulation results show that the method has very low computational complexity, parameter quantity, and model size while ensuring accuracy and improving the real-time performance of the method. The main contribution of this paper is to propose a lightweight ear recognition method named YOLOv5s-MG. In this method, we replaced the backbone network of YOLOv5s with MobileNetV3 lightweight network so that feature extraction is also lightweight. Simultaneously, by using the idea of the Ghostnet network, we replaced the C3 module and Conv module of the neck network of YOLOv5s with the C3 Ghost module and GhostConv module so that feature fusion is lightweight. Therefore, the method proposed in this paper can not only maximize real-time performance but also ensure the accuracy of human ear recognition. The rest of the paper is arranged as follows. YOLOv5s and three kinds of lightweight networks are introduced in Section 2. The improved YOLOv5s-MG method is proposed in Section 3. Experiments and results analysis are presented in Section 4. The paper is summarized in Section 5.
## Abbreviations  
List of Abbreviations  
Subcarrier Index Modulation OFDM  
Visible Light Communication  
Light Fidelity  
Intensity Modulation/Direct Detection  
Pulse Amplitude-Modulated Digital Multi-Tone  
Asymmetrically clipped Optical OFDM  
ACO-OFDM  
DC-Biased Optical OFDM  
U-OFDM  
Unipolar OFDM  
Asymmetrically clipped DC-biased optical OFDM  
ADO-OFDM  
Layered ACO-OFDM  
OFDM with Index Modulation  
OFDM-IM  
Multiple-Input Multiple-Output  
MIMO
Dual-Mode index modulation-aided OFDM  
DM-OFDM  
time-domain sample index modulation  
TIM
indexed dimming  
iDim  
Spectral Efficiency  
S.E  
Carrier-to-Noise Ratio  
CNR  
Signal-to-Noise Ration  
SNR  
Spread Spectrum-aided SIM-OFDM  
NS-SIM-OFDM  
Index Modulation  
IM
Bit Error Rate  
BER  
Symbol Error Rate  
Quadrature Amplitude Modulation  
Digital Pulse Interval Modulation  
DPIM  
Double-sided Pulse Interval Modulation
## 2 Materials and Methods
### Phantom Design  
A solid phantom mimicking the optical properties of murine brain tissue at the probing and detection wavelengths (690, 785, 720, and 835 nm) was employed in the study. The bulk of the phantom was designed as a mixture of 2.2 g of agar and 75 ml of water, which was heated up to 70 \({}^{\circ}\)C at the first stage. At the next step, the solution was cooled down to 36 \({}^{\circ}\)C and was mixed up with 40 ml of 20% Lipofundin MST/LST (Braun, Germany) and 0.5 \(\upmu\)L of black ink (Koh-i-Noor, Czech Republic). The agar mixture was poured into the special cuvette with three transparent plastic tubes with an internal diameter of 400 \(\upmu\)m (Figure 4), and cooled to the room temperature until solid. The cylinder-shaped cuvette was 20 mm in diameter and 20 mm in the Z-direction. After the mixture hardening, the three tubes within the phantom were filled, correspondingly, with DMSO solution of IRDye800 (LI-COR, USA) at a concentration of 178 \(\upmu\)mol/L, DMSO solution of BPD (USP, USA) at a concentration of 634 \(\upmu\)mol/L, and pure DMSO (Paneco Ltd., Russia), used as a reference. According to [23; 24] the selected concentrations of BPD and IRDye800 were below the toxic threshold in animals.  
Figure 4: Photography of the tubes containing the phantom filled with water (**a**) and agar mixture (**b**).  
Figure 3: The schematic of the optoacoustic (OA) setup and a phantom with three embedded tubes.  
The tube embedding depth in the phantom was about 1 mm. To localize the position of the tubes with respect to the entire phantom, bimodal imaging of the tubes filled with solutions of IRDye800, BPD, and DMSO was also performed prior to agar mixture pouring. Figure 3 shows the photos of the phantom filled with water (Figure 4a) and with agar mixture (Figure 4b). Preparation of tissue mimicking phantoms, their characterization, and comparison with real biotissues are described in details in previous papers [8; 25; 26]. Optical properties of the employed agar mixture and their comparison to the values for murine brain ex vivo are summarized in Table 1. Here, \(\mu_{\text{a}}\) is the absorption coefficient and \(\mu_{\text{s}}{{}^{\prime}}=\mu_{\text{s}}(1-g)\) is the reduced scattering coefficient, where \(\mu_{\text{s}}\) is the scattering coefficient and \(g\) is the scattering anisotropy factor.
# Flexible Null Broadening Robust Beamforming Based on JADE
## 3 Proposed Algorithm
### DOA and the Actual Interference Steering Vector with Gain-Phase Errors Estimated  
In order to obtain the actual interference steering vector with gain-phase errors, the correlation coefficient method is used to match the steering vector in the estimated array flow matrix. The DOA of the interference is estimated first.  
The correlation coefficient between \(\mathbf{x},\mathbf{y}\) is defined by  
\[cor(\mathbf{x},\mathbf{y})=\frac{|\mathbf{x}^{\mathrm{H}}\mathbf{y}|}{\|\mathbf{x}\|\|\mathbf{y}\|} \tag{14}\]  
where \([\cdot]^{\mathrm{H}}\) denotes conjugate transpose operation, \(|\cdot|\) denotes modular operation and \(\|\cdot\|\) denotes the L2 norm of vector. Then, the DOA of the \(i\)-th interference can be estimated by the following formula  
\[\hat{\theta}_{i}=\underset{\theta}{\mathrm{argmax}}cor\left(\overline{\mathbf{b}} _{i},\mathbf{a}(\theta)\right),\theta\in\left(-\frac{\pi}{2},\frac{\pi}{2}\right) \tag{15}\]Then, the actual interference steering vector with gain-phase errors can be selected by the correlation coefficient with the nominal steering vector constructed by the estimated DOA, i.e.,  
\[\hat{a}_{\Gamma}(\theta_{i})=\overline{b}_{i} \tag{16}\]  
In addition, with combined (7) and (16), we can estimate the gain-phase errors  
\[\widetilde{\Gamma}=\frac{1}{L}\sum_{i=1}^{L}\text{diag}\big{[}\hat{b}_{i} \odot a^{*}(\hat{\theta}_{i})\big{]} \tag{17}\]  
where \(\odot\) and \((\cdot)^{*}\) denotes the Hadamard product and conjugate operation, respectively.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### The Parameters for Calculating the Capabilities of the Ground Station
#### 4.2.3 Free Space Path Loss  
The free space path loss represents the signal loss over the distance between the satellite and the ground station. The distance between the ground station and the satellite can be calculated using Equation (5), where \(D\) is the distance in meters, \(R_{e}\) is the Earth's radius of 6,378,000 m, \(H\) is the altitude of the satellite's orbit from the ground in meters, and \(\varepsilon_{0}\) is the elevation angle [20].  
The free space path loss can be determined using Equation (6). Here, \(L_{p}\) is the free space path loss that occurs over the distance from the satellite to the ground station, measured in dB; \(D\) is the distance calculated from Equation (5), measured in meters; \(f\) is the frequency of communication (Hz); and \(c\) is the speed of light in a vacuum (m/s).  
\[D=R_{e}\left[\sqrt{\left(\frac{H+R_{e}}{R_{e}}\right)^{2}-\cos^{2}(\varepsilon_ {0})}-\sin(\varepsilon_{0})\right] \tag{5}\]  
\[L_{p}=20 log(D)+20 log(f)+20 log\left(\frac{4\pi}{c}\right) \tag{6}\]
###### Abstract
In the proposed protocol, we also use a threshold probability of preP but it is not a non-decreasing function of time due to the aging property of the delivery predictability of the PRoPHET protocol and thus, controls the message disseminations efficiently. The basic idea of using both delivery predictability and the previous delivery predictability of the sending node of the proposed protocol is similar to our preliminary work presented as an extended abstract in [9]. However, the proposed work was extended significantly from the previous work from the following aspects:  
* The proposed protocol is described in more detail by completing the flowchart of the proposed protocol and using an example of a message delivery to illustrate the proposed protocol. * The performance of the proposed protocol is newly analyzed thoroughly using simulation and compared with four other protocols in terms of the delivery ratio, overhead ratio, and delivery latency. * Related works are significantly extended in this paper. The remaining part of this paper is organized as follows: In Section 2, we survey related works. In Section 3, we describe the algorithm of the proposed protocol in detail using examples. In Section 4, we analyze the performance of the proposed protocol extensively using simulations. Finally, we conclude this work in Section 5.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS with Advanced Sensors
#### 3.2.2 FAS Based on Smartphones
[184] introduced Aurora Guard (AG), leveraging light reflection to extract depth and material features and adding a light CAPTCHA (i.e., the random light parameters sequence) for enhancing security. [PERSON] et al. [185] developed a LiDAR-based FAS algorithm that resisted light changes and replayed attacks by providing 3D depth data and using random light patterns as a security mechanism. FAS based on sensors: [PERSON] et al. [186] leveraged the penetrability, material sensitivity, and fine-grained sensing capability of millimeter wave (mmWave) to propose a FAS algorithm named mmFace. By capturing facial biometric features and structural characteristics from mmWave signals reflected off the face, mask attacks can be detected. Later, [PERSON] et al. [187] extracted both the 3D geometry and inner biomaterial features of faces using a cost radio frequency identification(RFID) tag array, which could effectively identify 2D print/replay and 3D mask attacks with high accuracy. On top of this, [PERSON] et al. [188] designed GazeGuard, which captured a user's gaze responses and corresponding periocular features using random dots to ensure accurate face authentication. [PERSON] et al. [189] introduced Auto-FAS, based on neural architecture search (NAS), to find lightweight networks suitable for mobile-level FAS and real-time applications. [PERSON] et al. [190] proposed M3 FAS, a multi-modal mobile system that integrated visual and auditory modalities with a dual-branch neural network and multi-head training strategy, improving accuracy, generalization, and robustness. Table 15 compares FAS on smartphones in terms of spoofing clues, advantages, disadvantages, attack types, and hardware. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Ref.** & **Clues** & **Advantages** & **Disadvantages** & **Attacks** & **Special Hardware** \\ \hline \multirow{8}{*}{FAS based on acoustic} & [176] & 3D geometric structure & Acoustic-based FAS can effectively detect spoofing attacks using readily available & The effectiveness of acoustic signals may be affected by environmental noise or interference. & 2D,adv & No \\  & [177] & 3D geometric structure & Acoustic-based FAS can effectively detect spoofing attacks using readily available & The effectiveness of acoustic signals may be affected by environmental noise or interference. & 2D,3D & No \\  & [178] & and 3D geometric structure & smartphone sensors without & & 2D,3D & No \\  & [179] & 3D geometric structure & additional hardware. & & 2D,3D & No \\  & [180] & 3D geometric structure & & & 2D,3D & No \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Overview of FAS on smartphones: 2D represents print and replay attacks, while 3D represents 3D mask attacks and Adv represents physical adversarial attacks.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 5 Array Optimization Methodology  
Reducing the physical array aperture while maintaining usable imaging resolution requires balancing size, microphone number, spatial sampling, and angular coverage. Simply downscaling a regular grid array would significantly increase grating lobes (Figures 4 and 9). We instead utilize array signal processing optimization procedures that allow unconventional configurations with microphone numbers and positions tailored to imaging requirements. Irregular layouts are synthesized based on maximizing acoustic power focused toward directions of interest and minimizing ghost images. Key concepts are briefly introduced below (Figure 9), with formulations adapted from [8; 18; 35].
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 3 The Proposed Model and Algorithm  
### The Proposed Model  
[PERSON] [2] pointed out that the oscillatory components should use a weaker norm to describe, Equations (3), (5) and (7) are not suitable for image decomposition, and [PERSON] gave the following model  
\[\min_{u,v}\int_{\Omega}|\
abla u|dx+\tau\|v\|_{G}\quad,\quad s.t.\quad f=u+v \tag{9}\]  
where \(\|\cdot\|_{G}\) represents the dual \(G\) norms (see details in [2]).  
Because Equation (9) is hard to compute its numerical solution, [PERSON] and [PERSON] [3] gave an approximation model of (9) as follows.  
\[\min_{u,g}\int_{\Omega}|\
abla u|dx+\frac{\beta}{2}\int_{\Omega}\left|f-(u+ \text{div}g)\right|^{2}dx+\frac{\mu}{2}\left[\int_{\Omega}\left(\sqrt{g_{1}^{ 2}+g_{2}^{2}}\right)^{p}dx\right]^{\frac{1}{p}} \tag{10}\]  
where \(\beta,\ \mu\) are positive tuning parameters, \(p>0,\ g=(g_{1},g_{2}).\text{div}g=\text{div}(g_{1},g_{2})\) represents the texture component and \(u\) represents the cartoon component.  
With the Hodge decomposition of \(g\) in [4], then \(g=\
abla P+Q\), where \(P\) is a single-valued function and \(Q\) is a divergence-free vector field. Letting \(v=\text{div}g\), we have \(P=\Delta^{-1}v\), \(g=\
abla\big{(}\Delta^{-1}v\big{)}\) by ignoring \(Q\), where \(\Delta=\text{div}(\
abla)\) is the Laplace operator, \(\Delta^{-1}\) its inverse operator. Setting \(p=2\), then Equation (10) can be turned into the following model:  
\[\min_{u,v}\int_{\Omega}|\
abla u|dx+\frac{\beta}{2}\int_{\Omega}\left|f-(u+v) \right|^{2}dx+\frac{\mu}{2}\left(\int_{\Omega}\left|\
abla\big{(}\Delta^{-1} \big{)}v\right|^{2}dx\right)^{\frac{1}{  
where \(h(\omega)\) is defined as Equation (1), \(\mu,\ \beta,\ \lambda(0<\lambda<1)\) are the positive parameters, \(f\) is an observed image, \(u\) is the cartoon image, and \(v\) is the texture image or noise. Similar to the definition of [11; 39; 40], the norm of \(\omega\) is called the pseudo edge function.  
The first term in (12) contains an edge indicator function \(h(\omega)\) and when \(\omega\to\infty\), \(h\to 0\), it can preserve edges; when \(\omega\to 0\), then \(h\to 1\), it can remove some small details. The second term in (12) is the \(H^{-1}\) norm for describing the texture. The third term in (12) is a residual term. Compared with [11; 39; 40], mathematically, the proposed model is more suitable for image decomposition.