esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Tests on the Swedish Leaf Dataset  
#### 3.2.1 Introduction to Swedish Leaf Dataset  
The first dataset selected for the experiment was the Swedish leaf dataset [28], which originated at Linkoping University in Sweden and is widely used to evaluate the performance of recognition methods. The dataset has 15 categories, each with 75 images. Figure 9a-c, separately, show some original sample images, input images obtained by direct compression, and input images obtained by MEQ standardization. It can be found that, compared with the direct compression of leaf samples, the MEQ method can retain the basic information of these samples more completely and make the shape and texture characteristics of the leaves more normalized. In addition, it is obvious that the leaf samples in this dataset all have similar colors, and some samples have little difference in shape and texture, such as Class 7 and Class 15.
# Not All Seeds Are Important: Fuzzing Guided by Untouched Edges
## 3 Motivating Example  
We use a program's control flow graph in Figure 2 to illustrate our motivation. The initial seed A is considered to be a quality seed, i.e., the seed is capable of executing the main logic codes of the program. Figure 2a shows the execution path of seed A, with red circles denoting the basic blocks covered by the seed. When the coverage-guided greybox fuzzer mutates seed A, it can easily produce seed B and seed C. Both of these seeds cover edges associated with validation checks at the control flow graph level, i.e., BB1\(\rightarrow\)BB10 and BB2\(\rightarrow\)BB10.  
The appearance of these two new edges is interpreted by the fuzzer as an increase in edge coverage, leading to their inclusion in the seed queue. Furthermore, according to the previous description in Section 2.1.3, AFL marks both seed B and seed C as favored seeds during seed scheduling since they cover new edges. However, when attempting to mutate these seeds, they encounter challenges in producing descendant seeds that explore deeper code areas.  
We further assume that after mutating seed A, the fuzzer generates not only seed B and seed C but also seed D. Seed D's execution path is shown in Figure 2d. The difference between seed A and seed D lies in the fact that seed D explores basic block BB6, an unexplored area of seed A's execution path. In this case, both two seeds are effective for the exploration of basic block BB5. However, if seed E, which covers basic block BB5 is produced later, seed A and seed D become ineffective as they make limited contributions to exploring basic blocks BB8 and BB9. Nevertheless, AFL still regards seed A and seed D as favored because they both cover new edges.  
As discussed in Section 2.1.3, AFL's seed scheduling algorithm selects favored seeds based on covered edges. However, it lacks tracking information for unexplored areas at the CFG level. AFL's perspective hinges on the assumption that if a seed covers new edges, it is more likely to explore unexplored regions. This perspective relies on a crucial precondition: the new edges must be adjacent to unexplored areas; otherwise, these edges are likely related to validation checks or have already been explored by other seeds. Particularly in the context of expansive and complex programs, AFL expends substantial fuzzing resources on ineffective seeds, impeding the prioritization of genuinely effective seeds and slowing down the convergence of the fuzzing process. Therefore, this paper addresses the issue in AFL's seed scheduling algorithm by introducing a new mechanism to track unexplored regions.
## Nomenclature  
\begin{tabular}{l l} \(\sigma_{\text{v}}\) & vertical stress \\ v & displacement speed \\ s & cumulated displacement \\ \(\text{c}_{\text{react}}\) & viscous constant of soil at reactivation \\ c & viscous constant of soil under residual steady condition \\ \(\beta\), \(\delta\) & constants of Equation (4) \\ \(\tau_{0}\) & reference strength at zero displacement speed \\ \(\text{t}_{\text{age}}\) & time of quiescence under \(\tau_{0}\) condition \\ \(\tau_{\text{react}}\) & maximum strength at reactivation for given \(\sigma_{\text{v}}\) and v \\ \(\tau_{\text{r}}(\text{v})\) & steady strength at constant displacement speed \\ \(\Delta\tau_{1}=\tau_{\text{react}}-\tau_{0}\) & maximum shear strength increase during reactivation \\ \(\Delta\tau_{2}=\tau_{\text{r}}(\text{v})-\tau_{0}\) & shear strength increase under steady condition \\ \(\varphi_{0}\) & secant strength angle associated with \(\tau_{0}\) and \(\sigma_{\text{v}}\) stress state \\ \(\varphi_{\text{r}}\) & secant strength angle associated with \(\tau_{\text{r}}(\text{v})\) and \(\sigma_{\text{v}}\) stress state \\ \(\varphi_{\text{react}}\) & secant strength angle associated with \(\tau_{\text{react}}\) and \(\sigma_{\text{v}}\) stress state \\ \(\Delta\varphi_{1}\) & increase in secant strength angle corresponding to \(\Delta\tau_{1}\) \\ \(\Delta\varphi_{2}\) & increase in secant strength angle corresponding to \(\Delta\tau_{2}\) \\ \(\frac{\text{(CF+PI)}}{\text{w}\text{r}}\) & comparison parameter \\ PI & plastic index \\ OCR & overconsolidation ratio \\ FF & fine fraction \\ CF & clay fraction \\ AI & activity index \\ w\({}_{\text{P}}\) & plastic limit \\ w\({}_{\text{L}}\) & liquid limit \\ \end{tabular}
## 4 Conclusions  
A study similar to the present study may be performed, for any plate, to determine whether the azimuthal variation in surface-wave velocities can be due to seismic anisotropy or to lateral heterogeneity. In the present study, this azimuthal variation is not due to seismic anisotropy but to lateral heterogeneity, which is compatible with anisotropic earth models of hexagonal symmetry (six models of SH and SV-velocity versus depth, from 0 to 260 km). From these models, the parameter \(\xi\) is calculated for each region, observing seismic anisotropy only from 10 to 100 km depth, and the following conclusions are obtained from the values determined for \(\xi\):  
Depth range: 0-30 km. For regions 3 and 4, the higher values of \(\xi\) can be related to the lithospheric heel that exists beneath the Mesopotamian Plain and to the vertical alignment of minerals in the crust due to its horizontal shortening, which is caused by the collision between the Arabian and Eurasian plates from \(\sim\)14 Ma. For region 5, the higher values of \(\xi\) can be related to the shape-induced anisotropy that can occur in the Oman folded zone. This shape-induced anisotropy can exist for the crust and the subcrustal lithosphere.  
Depth range: 30-100 km. For region 1, the higher values of \(\xi\) can be related to the plume material propagation in the Arabian shield from the Afar plume and to the Yemeni Oligocene Trapps. Also, these higher values of \(\xi\) can be related to the post-12 Ma volcanism in western Arabia. For region 2, the higher values of \(\xi\) can be related to mantle plume material from the Jordan plume. Also, these higher values of \(\xi\) can be related to the existence of a lithospheric heel observed beneath the Arabian platform and the Taurus-Zagros folded zone (TZFZ) in previous studies.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app14125142/s1](https://www.mdpi.com/article/10.3390/app14125142/s1)]([https://www.mdpi.com/article/10.3390/app14125142/s1](https://www.mdpi.com/article/10.3390/app14125142/s1)).  
This research received no external funding.  
Not applicable.  
Data Availability Statement:Datasets for this research are available in the National Geophysical Data Center (from the web server at [[https://maps.ngdc.noaa.gov/viewers/grid-extract/index.html](https://maps.ngdc.noaa.gov/viewers/grid-extract/index.html)]([https://maps.ngdc.noaa.gov/viewers/grid-extract/index.html](https://maps.ngdc.noaa.gov/viewers/grid-extract/index.html)) (accessed on 12 June 2024)) and in the Incorporated Research Institutions for Seismology (from the web server at [[http://www.iris.washington.edu/wilber3/find_event](http://www.iris.washington.edu/wilber3/find_event)]([http://www.iris.washington.edu/wilber3/find_event](http://www.iris.washington.edu/wilber3/find_event)) (accessed on 12 June 2024)).  
The National Geophysical Data Center (NGDC) and the Incorporated Research Institutions for Seismology (IRIS), have provided the elevation and seismic data, respectively.  
The author declares no conflict of interest.
## 4 Current Control Strategy  
Figure 12 displays the proposed current-balancing control strategy. The duty cycle of the gate-driving signal \(v_{g1}\) for the first phase after the first pulse-width modulation (PWM) generator is tuned to get the wanted output voltage \(V_{o}\), based on the voltage loop with the controller \(G_{c1}(z)\) having the voltage command reference \(V_{\textit{ref}}\) and the digital sensed voltage \(V^{\prime}_{o}\), which is obtained from the  
Figure 11: Current path in mode 8.  
Figure 10: Current path in mode 7.  
Figure 9: Current path in mode 6.  
sensed voltage \(v^{\prime}_{o}\) by the voltage divider with a gain \(k\) and the first analog-to-digital converter (ADC1). Meanwhile, the sensed current of the first phase, named \(i^{\prime}_{L1}\), is sent to ADC2, to get the digital sensed current \(I^{\prime}_{L1}\), which will be used as the current command reference for the current-balancing loop with the controller \(G_{c2}(z)\) having the digital sensed current \(I^{\prime}_{L2}\) from the sensed current of the second phase, named \(i^{\prime}_{L2}\), after ADC3, so that the duty cycle of the gate-driving signal \(v_{g2}\) for the second phase after the second PWM generator is automatically tuned to force the input current to be evenly distributed between the two phases and hence the load current balancing between the two phases can be realized.  
Afterwards, the current-balancing behavior is described below. First, by assuming that the output power (\(P_{o}\)) is identical to the input power (\(P_{i}\)), the output current (\(I_{o}\)) is identical to the product of the DC inductor current (\(I_{L}\)) and one minus duty cycle (1-\(D\)). Therefore, if \(I_{L}=I_{L1}+I_{L2}\) and \(I_{o}=I_{o1}+I_{o2}\), then under \(I_{L1}=I_{L2}=0.5I_{L}\), \(I_{o}=0.5I_{L1}\) (1-\(D_{a}\)) + \(0.5I_{L2}\) (1-\(D_{b}\)), where \(D_{a}\) is the duty cycle of \(v_{g1}\) generated from the controller \(G_{c1}\) (\(z\)) and \(D_{b}\) is the duty cycle of \(v_{g2}\) generated from the controller \(G_{c2}\) (\(z\)). For example, if \(I_{o1}\) is larger than \(I_{o2}\), then \(D_{a}\) will be increased and \(D_{b}\) will be decreased, thereby causing \(I_{o1}\) to go down and \(I_{o2}\) to go up. Eventually, both \(I_{o1}\) and \(I_{o2}\) are equal to 0.5 \(I_{o}\), making the current balancing realized.
# Optical Trapping, Sizing, and Probing Acoustic Modes of a Small Virus
## 3 Results  
Polystyrene nanospheres and PhiX174 bacteriophage were separately optically trapped, all in the same DNH aperture in a gold film. Figure 2a shows the laser transmission signal as measured at the APD during a trapping event, with a characteristic jump in voltage, as well as an increase in the noise level. The autocorrelation of the signal after the trapping event was computed and fitted to a double exponential to extract the characteristic time, as shown in Figure 2b. The averaged characteristic time for each of the sample types is plotted against the known radius of the particles in Figure 2c, showing a good linear fit. This justifies the assumptions used in the Stokes coefficient calculation, i.e., spherical particles, far from the aperture walls.  
For the EAR study, polystyrene spheres were used as a control, with results matching previous EAR studies [9; 10; 11; 22]. We also applied this technique to another bacteriophage, MS2, but it is not reported here. For each sample type, consistent peaks were observed across multiple trapping events. The EAR spectrum of the PhiX174 bacteriophage from eight separate measurements taken over two days is shown in Figure 2d, with a peak at 32 GHz. This differs slightly from the predicted value from elastic theory of 30 GHz. This difference may be partly attributable to the simplification in the model of the icosahedral geometry to that of a sphere or to the estimation of values of the wave speeds in the virus structure.
# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 3 Materials and Methods
### Methods
#### 3.1.5 Gated Recurrent Unit Networks  
Gated recurrent unit (GRU) networks [54] seem to be one of the most promising alternatives to LSTM. Being recently developed, their architecture does not provide a memory cell but includes two gates called the update gate and reset gate, which govern the flow of information. In fact, GRU represents a simplified version of LSTM and allows avoiding the gradient vanishing using a lower number of parameters. Using the previously adopted notation, the number of weights in a GRU layer with \(q\) units equal to \(3\times q\times(d+q+1)\) is lower than an LSTM layer.  
Let \(W^{(p)}\in\mathbb{R}^{q\times d}\), \(U^{(p)}\in\mathbb{R}^{q\times q}\), and \(\boldsymbol{w}_{0}^{(p)}\in\mathbb{R}^{q}\) be the weights indexed by \(p\in\{o,r,c\}\). The output of the \(q\)-dimensional GRU layer can be formalised as follows:  
Figure 1: A pictorial representation of the LSTM unit (**left**) and GRU unit (**right**).  
\[\mathbf{o}^{(t)} = \sigma\bigg{(}\mathbf{w}_{0}^{(o)}+W^{(o)}\mathbf{x}^{(t)}+U^{(o)}\mathbf{z}^{(t- 1)}\bigg{)} \tag{9}\] \[\mathbf{r}^{(t)} = \sigma\bigg{(}\mathbf{w}_{0}^{(r)}+W^{(r)}\mathbf{x}^{(t)}+U^{(r)}\mathbf{z}^{( t-1)}\bigg{)}\] (10) \[\mathbf{c}^{(t)} = \tanh\bigg{(}\mathbf{w}_{0}^{(c)}+W^{(c)}\mathbf{x}^{(t)}+U^{(r)}(\mathbf{r}^ {(t)}\odot\mathbf{z}^{(t-1)})\bigg{)}\] (11) \[\mathbf{z}^{(t)}(\mathbf{x}) = (1-\mathbf{o}^{(t)})\odot\mathbf{z}^{(t-1)}+\mathbf{o}^{(t)}\odot\mathbf{c}^{(t)} \tag{12}\]  
The reset gate (Equation (10)), using the sigmoid activation function, determines when the hidden state has to ignore the previous hidden state and uses the input data (Equation (11)). The update gate (Equation (9)) helps the RNN store long-term information, determining how much of the previous memory should be kept. In fact, it realises a task similar to that performed by the memory cell in the LSTM network. Finally, the output of the GRU cell is computed by combining the outputs of both gates (Equation (12)).  
Generally, a GRU layer contains several units, each having separate reset and update gates. This mechanism is used to learn the dependencies over different time scales. Some units will tend to have reset gates that are frequently active and therefore learning short-term dependencies, while other units will have most active update gates, and so they will be able to capture longer-term dependencies.  
The number of weights in a GRU layer with \(q\) units equal to \(3\times q\times(d+q+1)\) is lower compared with the number of parameters in an LSTM layer with the same number of units. A graphical representation of the GRU cell is shown on the right side of Figure 1.
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
###### Abstract  
In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of \(1\times 10^{-4}\) in 16-QAM for a signal-to-noise ratio, \(E_{b}/N_{0}\), of 20 dB is achieved.  
generalized frequency division multiplexing; cooperative unmanned aerial vehicle; massive machine type communication; physical layer security; T-transformation spreading codes; out-of-band; signal-to-interference-plus-noise ratio; gyre precoding +
Footnote †: journal: 25  
_Acticle_  
Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm
### Crossover  
The crossover operation is performed through the classic roulette wheel method, which is based on the selection of the best fit solutions. Thus, the higher the _Fit1_ value of the chromosome, the higher the probability is that its superior gene will be inherited by the next generation. Because this paper adopts an integer-coded chromosome with variable length, traditional GA single point crossover and double point crossover may lead to the failure of crossover operation. Therefore, a segmented single point stitching crossover method is specifically designed with the following specific operation steps:  
* Select two chromosomes to be crossed from the parent population: _parent A_ and _parent B_. Divide _parent A_ and _parent B_ into \(K\) segments according to the third row of the chromosome, with each segment represents a routing scheme for a UAV. For example, the _k_th segment of _parent A_, denoted by \(A_{k}\), represents the routing scheme of UAV \(k\) in _parent A_.
* Stitch the chromosomes' segments with the same index in _parent A_ and _parent B_, denoted as \(A_{k}\) and \(B_{k}\), respectively. The stitching rules are as follows: (i) if there is only one shared target index, \(g\), in \(A_{k}\) and \(B_{k}\), then the genes behind \(g\) are exchanged to obtain the new segments \(C_{k}\) and \(D_{k}\); (ii) if there is more than one index in \(A_{k}\) and \(B_{k}\), randomly select one index, \(g\), and then exchange the genes behind \(g\) to obtain the new segments \(C_{k}\) and \(D_{k}\); (iii) if there is no shared index \(g\) in \(A_{k}\) and \(B_{k}\), then stitch \(B_{k}\) to the end of \(A_{k}\) to obtain a new segment \(C_{k}\), and stitch \(A_{k}\) to the end of \(B_{k}\) to obtain a new segment \(D_{k}\). A stitching example is illustrated in Figure 5.
* Repeat step 2 according to the number of UAVs, \(K\), until all segments in _parent A_ and _parent B_ are stitched. Then, child chromosomes called _child C_ and _child D_ are obtained by merging all \(C_{k}\) and \(D_{k}\), respectively.
* Repeat steps 1-3 according to the preset population size, \(N_{P}\), until each chromosome of the parent population has completed the stitching operation and all the child chromosomes constitute a child population.  
Figure 4: Sample diagram of two chromosomes with the same expected profit but different total flight times.  
The single point stitching crossover method can preserve the superior genes of the chromosome as much as possible and increase the diversity of the population, which is helpful to find a new solution. It is not necessary that all chromosomes of the child population after crossover operation satisfy all constraints of the MURARP model. Therefore, constraint checks should be performed on the child population and the chromosomes that do not satisfy the constraints must be adjusted. See Section 3.1.3 for details. After constraint checking and adjustment, the chromosomes are a feasible routing scheme of the MURARP.
# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 2 Literature Review
Their work emphasizes the need for multimodal fusion strategies, integrating diverse data sources to enhance detection precision. [PERSON] et al. [47] applied deep learning combined with multimodal decision fusion to improve the identification of vulnerabilities, setting a benchmark for integrated approaches. In addition, in recent years, the frequent use of deep learning algorithms in vulnerability detection has been seen in the literature [3, 45, 48, 49, 50, 51]. The field of quantum computing has gained significant attention for leveraging quantum mechanics to execute computations at speeds beyond the reach of classical computers [52]. QML combines quantum computing with ML algorithms, potentially offering exponential speedups for certain computational tasks [20]. [PERSON] and [PERSON] [24] discussed how QML could revolutionize data processing by handling large datasets more efficiently. In the context of blockchain security, [PERSON] et al. [53] examined quantum computing's potential to both threaten and enhance blockchain systems. While quantum attacks could compromise cryptographic algorithms used in blockchain, quantum solutions could also fortify security measures. Although specific studies applying QML directly to smart contract vulnerability detection are sparse, the potential to leverage quantum-enhanced feature extraction and classification aligns with the increasing complexity of blockchain systems. Future research may build on these foundational works to develop QML-enabled tools. HQCDNNs have emerged to utilize the advantages of quantum computing in ML tasks. By incorporating quantum computing elements into classical neural network architectures, these approaches seek to leverage quantum computational capabilities while ensuring compatibility with existing classical systems. [PERSON] and [PERSON] [22] proposed a systematic methodology for the design of quantum layers in HQCDNNs, achieving significant computational advantages over pure classical architectures. In financial time series forecasting, [PERSON] et al. [23] designed a mixed quantum-classical framework utilizing quantum-assisted LSTM networks and showcased enhanced effectiveness over traditional models. In cybersecurity, [PERSON] and [PERSON] [54] evaluated an HQCDNN model for botnet detection and achieved high performance in certain situations. [PERSON] et al. [19] introduced an integrated quantum-based approach for identifying deepfake content. They obtained remarkable results in the study. When these studies are evaluated, they emphasize the potential of HQCDNNs in vulnerability detection by combining quantum computing capabilities with classical neural network models.