esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Influence of CNN Network Hyperparameters on Modulation Recognition Rate
#### 5.2.1 Influence of the Number of Network Layers on Recognition Results  
We use the model shown in Figure 8. Keep the original hyperparameters unchanged and change the number of convolution layers with 256 convolution cores. We set up four sets of experiments. The number of convolution layers of each group is 1, 2, 3, 4 and 5. Comparison of corresponding recognition accuracy rates on different SNRs is observed, as shown in Figure 11. It can be seen that under the low SNR (less than \(-8\) dB), the number of convolution layers has little influence on the recognition rate. The recognition rate of different convolution layers is almost the same. However, when the SNR is greater than \(-8\) dB, the recognition rate is the best when the convolution layer number is 3. When the number of convolutional layers is less than or equal to 3, the recognition rate increases with the increase of the number of convolutional layers; when the number of convolutional layers is greater than 3, the recognition rate decreases with the increase of the number of convolutional layers. The recognition rate of convolution layer 1, 2, 4 and 5 is not as good as that of convolution layer 3. This indicates that too few or too many convolution layers will affect the overall performance of recognition. When the number of convolutional layers is too small, the range of local perceptual field is small and the learning ability of the network is very low, which leads to low recognition rate. However, that does not mean that the larger the number of convolution layers, the better. There are also negative effects when the number of convolution layers is too deep. When the number of convolution layers is too large, the overfitting phenomenon easily occurs, resulting in gradient attenuation and it is difficult to find the optimal solution of parameters. This can degrade network performance. That leads to bad results. Therefore, the appropriate number of convolution layers should be selected when modulation recognition of radio is carried out.
# A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System
## 1 Introduction  
Optical 3D measurement technology is used in many applications, such as reverse engineering, quality inspection, restoration of cultural properties, face recognition, and dental computer aided design/computer aided manufacturing (CAD/CAM), because of its high measurement speed and precision [1; 2; 3; 4; 5]. Many passive stereo vision technologies have been proposed [6; 7], but active stereo vision using structured light is one of the most popular optical 3D measurement techniques. It has high 3D scan data accuracy and fast data processing speed, but it has difficulties in expressing fine surface features. On the other hand, photometric stereo--another optical 3D measurement technique--can achieve fine surface feature but provides less accurate 3D scan data and requires multiple light sources. The output of active stereo vision is point data, and normal vector is calculated based on this point data, while the output of photometric stereo is normal vector, and point data is calculated by integrating this normal vector. Therefore, point data obtained by active stereo vision is more precise than that of photometric stereo, and normal vector obtained by photometric stereo is more detailed than that of active stereo vision. In this paper, we aimed to acquire more precise and detailed 3D scan data by combining active stereo vision and photometric stereo without external light sources in an active stereo vision hardware setup. The final result was verified by comparing distance deviation of point data and angular deviation of normal vector acquired with ground truth.
## 3 Active Bearing Concept of the FSP
### FSP and Its Action Mechanism
This numerical simulation is simplified according to the stress state of the surrounding rock mass and steel pipe in the actual project. In the initial state, through the balanced geo-stress analysis step, the stress of the soil mass along the longitudinal driving direction of the steel pipe at the locking feet increases continuously, which is used to reflect the loose degree and stress release of the surrounding rock mass. Before the steel pipe is loaded laterally, there is a longitudinal dead weight stress, which is used to reflect the initial stress caused by the friction between the steel pipe and the soil during the driving process. Through the above analysis, it can be determined that the constraints of each component in the numerical simulation are as follows: the symmetry plane of the entire assembly only restricts the displacement in the x direction; the side of the cylindrical soil mass can produce longitudinal horizontal displacement but cannot produce transverse deformation; the soil bottom directly restricts the displacement in x, y, z directions; there is no displacement constraint on the top surface of soil mass; and the end of the steel pipe bears the vertical load, and there is friction contact with the side and bottom of the hollow soil component. The tangential friction coefficient is 0.39, and the normal direction adopts \"hard\" contact, and it is allowed to separate after contact. Both loess and steel pipe are simulated by 3D solid element C3D8. The loess parameters are determined by combining geotechnical experiments with on-site steel pipe lateral bearing tests. The joint development of loess is not considered, and the loess soil material is assumed to be isotropic without tectonic stress. The formation of the supporting force depends on the bending deformation of the steel pipe end (Figure 9): In the bearing limit range, the greater the bending deformation, the greater the supporting force, and vice versa. When the deformation exceeds the plastic limit, the formed supporting force provides slow growth or no more growth.
## 1 Introduction
It can be reasoned that the evaporation of water particles in the fuel is delayed, due to the relatively low ambient temperatures and the pressure under low load conditions. Thus, the possibility arises that the atomization and evaporation characteristics of diesel fuel will be inferior when compared to the spraying characteristics of emulsion fuels alone [14]. As a study on emulsion fuels, [PERSON] et al. [15] produced emulsion fuels containing water and low diesel, and compared the exhaust gas emissions. [PERSON] [15] reported a 41% reduction in PM emissions and a 35% reduction in PM. [PERSON] et al. [16] reported a 30% reduction in NOx and a 59% reduction at full load, when compared to diesel, at a water mixing ratio of 25%. In addition, [PERSON] [19; 20] confirmed that NOx and 35% smoke decreased when the water content of emulsion fuel was 15% in the full load region. As in the case of using water, it is possible to expect NOx reduction at the same time, due to the reduction of soot from the micro-explosion phenomenon, caused by the intense evaporation of water, and the effect of promoting combustion through free radicals, and the decrease of the combustion chamber temperature due to the latent heat of evaporation of water [21]. As shown in Figure 1[22], in the micro-explosion phenomenon, the difference in the boiling point between diesel and water upon fuel injection in a combustion chamber results in the evaporation of water droplets and a more thorough splitting of fuel droplets. As a result, secondary atomization contributes to enhancing the air-fuel mixture and reducing the incomplete combustion. Here, the characteristics of nitrogen oxide and black carbon reduction, versus the water content of the emulsion fuel, were studied using the numerical analysis method, and specifically, exhaust gas and fuel consumption rates as a result of adjusting the moisture content.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
###### Abstract
Owing to the robustness and indiscernibility of the watermarking systems in the frequency domain, many researchers proposed several watermarking strategies in this domain. [PERSON] et al. [15] proposed an image watermarking method for copyright protection. The color host image is divided into blocks, which are modified using (2D-DCT). The color logo image is embedded in the chosen intermediate coefficients of these selected blocks. The scheme is robust to most popular image processing attacks. [PERSON] et al. [16] suggested a new image watermarking method based on discrete Fourier transform (DFT) of a fractional quaternion type. The watermark bits are concealed in the intermediate frequency band of the transformed RGB components by adjusting the least significant bits of the quantified magnitudes. Many authors appealed to utilize the wavelet transformations in many watermarking schemes, in which it is a powerful tool that has been widely used in signal and image processing [17]. However, at the same time, it still has significant disadvantages such as (i) shift sensibility, any transitions in the input signal lead to unexpected changes in wavelet coefficients; (ii) inadequate directionality, since wavelet coefficients disclose only three spatial orientations; (iii) non-stationary signal, in view of the lake of phase information; (iv) computational complexity, especially for wavelet tree and numerous decomposition; and (v) complex accurate compilation of the mother wavelet and sub-bands mainly when applied in complicated fields. Our proposed method tried to overcome these significant problems using a hybrid technique by utilizing FWHT and MfrLFMs benefits to release a new, robust watermarking methodology. Many researchers have touched upon the concept of perceiving the color image copyrights efficiently by using an advanced hybrid combination of the spatial and frequency domains techniques to achieve fast computation features of the spatial domain and at the same time take advantage of the durability of the frequency domain techniques. [PERSON] et al. [18] introduced a blind watermarking method based on a combination of spatial and frequency domains techniques. It was achieved by applying quantization steps on the DCT and discrete Hartley transformation (DHT) to embed a color logo image into the standard direct current frequencies blocks of the discrete cosine transform and DHT components. The watermark is extracted in a blind extraction manner by applying an inverse embedding process. The scheme is highly robust to many attacks. The remnant of this paper is structured as follows. Section 2 handles diverse related works. Section 3 includes the essential preliminaries that have been used in the proposed method. Section 4 illustrates the proposed watermarking methodology in a detailed manner. Section 5 documents the experiment's numerical results with discussion, followed by the conclusion in Section 6.
## 5 AEDG: The Proposed Scheme
### Data Collection Phase  
In the data transmission phase, nodes first sense data and then forward them to the next node by using the SPT. Finally, the data are received at the GN, which transmits them to the passing AUV. As a large amount of data is relayed by the GN, it depletes its energy more quickly, thereby reducing the network lifetime. In order to increase the lifetime of the network, the idea of rotating the GNs is introduced. Each node that is within direct communication range of the AUV is eligible to become the GN if its residual energy is greater than the rest of the nodes. The GN periodically checks the residual energy after a specific interval. If the residual energy reaches its threshold, the GN broadcasts the GN leaving message. After listening to this message, the nodes share their information with each other, and the node with the highest residual energy is selected as the GN. As the network evolves, the GNs are rotated, and the member nodes attached associate with their respective the GNs by using the SPT algorithm. Hence, in the next cycles, the AUV communicates with the next selected GN. Figure 9 shows the formation of the SPT and the data transmission of the member nodes to the GNs. The member nodes sense, as well as forward the aggregated data to the next node, and then, the data are destined for the GNs. Finally, these GNs transmit the aggregated data to the passing AUV.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 4 Cognitive Interference Cancellation  
The CW tone interference is one of the most popular interferences in wireless communication. CW tone interference can be generated by two different sources: Malicious transmitters (especially, a jammer), and inter-modulation and total harmonic distortion of nonlinear elements in an RF/analog transceiver. In this work, we focus on the single tone interference detection and cancellation in a satellite with a digital channelizer.  
Tone interference detection includes both the decision on the existence, and frequency estimation of the CW tone interference (e.g., finding a subchannel corrupted by the interference). For cognitive interference cancellation of the CW tone interference, however, the exact estimation of frequency, amplitude and phase is crucial. For example, inaccurate estimation of the interference frequency can crease another interference source. To avoid the complex process of interference estimation and cancellation, interference cancellation based on a digital channelizer is proposed. In detail, we divide the received wideband signal into a large number of subchannels, i.e., 1024 subchannels, and identify the subchannel with CW tone interference and remove it. As expected, as the number of subchannels increases, the interference cancellation performance gain increases as well as this requires higher implementation complexity from a digital channelizer. Hence, a reasonable number of subcarriers can be determined based on the maximum operating signal-to-noise ratio (SNR) of a satellite link. For example, if the maximum operating SNR is given by about 20 dB, an interference signal with power -30 dB less than the desired wideband signal is ignored, i.e., the performance degradation caused by this interference is negligible. As one of the typical satellite communication standards, digital video broadcasting-second generation (DVB-S2) requires 16.05 dB SNR for 32 amplitude phase shift keying (APSK) modulation and 9/10 low density parity check (LDPC) coding rate in ideal AWGN channels [25]. Assuming a 4 dB implementation margin considering the practical impairments such as carrier frequency offset, timing offset, phase noise, nonlinear power amplifier and so on, the maximum operating SNR is higher than 20 dB, and -30 dB interference less than the desired signal is ignored because such low interference does not degrade the system performance.  
When the interference power is -30 dB lower than the desired signal, we do not need to detect and cancel the interference. Additionally, when the interference is falsely detected and one of subchannels are nullified, the distortion of the wideband signal should be less than -30 dB. Therefore, we set the number of subchannels to 1024, to cancel the interference of which power is -30 dB less than the desired signal power. Based on this requirement, we can determine the number of subchannels in a digital channelizer as 1024. In the following subsections, we introduce the two interference detection methods based on the digital channelizer.
## 8 Conclusions  
This research focused on two research goals:  
* The students' perceived usability of the biofeedback system (RG1).
* The students' technology readiness and acceptance (the personality traits that reflect the corresponding technology beliefs of the technology readiness index model) are related to perceptions of usefulness, ease of use and behavioral intention regarding the biofeedback system (RG2).
* According to the results derived and analyzed from both case studies, the following conclusions can be reached per research goal:
* RG1: The evaluation results showed that a sufficient number of users were quite satisfied with the usability of the system, whilst there is a considerable concentration of users whose score is almost equal to the average usability satisfaction. These two aforementioned categories make up 53.6% of the participants. Therefore, we could support that the usability level of the presented biofeedback system was quite satisfactory.
* RG2: The second research question was answered by the final path model, which is based on TRAM. Optimism, a TR dimension, seems to be crucial, since it has direct and indirect positive effects on TAM dimensions. Thus, optimistic people are more open to use and adopt the biofeedback system. Moreover, a positive attitude towards using the examined device seems to be the most crucial factor in predicting the users' behavioral intention and actual usage of this system.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### First Scenario
The amount of information transmitted is the same for the FP and PA protocols. This is explained because both protocols use the maximum power to reach the CH in one hop since the node closest to the jammer cannot reduce its transmit power. Therefore, in both strategies, this node will exhaust its battery first. For the cooperation strategies, the amount of information transmitted and residual energy is slightly better for the PAC protocol than CP. In these strategies, the nodes selected as relays are under more demand. Therefore, the relay node that cooperates with a larger number of nodes will exhaust its battery faster than a node that does not cooperate. Consequently, the relay nodes in the first stage of the re-routing path, which provide the connectivity to the isolated nodes, present a higher load. Finally, our proposed FCPA strategy outperforms the performance of the other analyzed strategies in terms of residual energy and the amount of information transmitted. Its residual energy is less than other strategies, but the information transmitted is larger. Figure 8: The FCPA strategy increases energy efficiency through combined power allocation and cooperation strategies. Consequently, the CH of each cluster is the first node that exhausts its batteries. We use arrows with markers to show the preference routes by the algorithm. Arrows with more markers indicate more used routes. Figure 9: Evaluation of the different strategies according to the information transmitted (kb) and the residual energy (J).
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 4 Cloud Security Challenges and Proposed Solutions
### Technical Security Threat  
#### 4.2.1 Insecure API  
In cloud computing, secure API threats relate to vulnerabilities and dangers associated with Application Programming Interfaces (APIs) used to communicate with cloud services. APIs serve as a link between distinct software components, allowing for communication and data sharing. Here are a few examples of prevalent insecure API risks in cloud computing [24, 39, 41].  
* Unauthorized access and authentication bypass
* Injection attacks
* Insecure Data Transmission  
**Mitigating API insecurity:**  
Implement strong authentication and authorization systems, such as strong passwords, multi-factor authentication (MFA), or token-based authentication. To guarantee that only authorized users have access to certain API functions and resources, employ fine-grained authorization rules.  
API monitoring and logging: Put in place strong monitoring and logging systems to record and analyze API activity. Examine API logs for unusual or suspicious activity that might signal a security breach or unauthorized access attempt.
## 2 Theory
### Traditional Cam-Shift Algorithm
#### 2.2.2 Mean-Shift Iteration  
The Mean-Shift iteration is a robust non-parametric method for climbing density gradients to find the peak of probability distributions. Its major steps are as follows:  
1. Initialize the center and the size of the search window.
2. Calculate the center of mass of the search window: Calculate the zero moment \(M_{00}\) and first moments \(M_{10}\) and \(M_{01}\): \[\left\{\begin{array}{l}M_{00}=\sum\limits_{x}\sum\limits_{y}I(x,y)\\ M_{10}=\sum\limits_{x}\sum\limits_{y}xI(x,y)\\ M_{01}=\sum\limits_{x}\sum\limits_{y}yI(x,y)\end{array}\right.\] (1) where \(I(x,y)\) represents the probability value of point \((x,y)\) in the back-projection image. Then, the center of mass \((x_{c},y_{c})\) can be obtained: \[x_{c}=\frac{M_{10}}{M_{00}},y_{c}=\frac{M_{01}}{M_{00}}\] (2) Move the center of search window to the center of mass.
3. Exit the program when the moving distance of search window is less than a given threshold or reaches the maximum number of iterations; otherwise, return to step 2.  
Figure 4: (**a**) Original image, (**b**) histogram of the H component of the target LED signal source, and (**c**) back-projection image, respectively.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 5 Simulations
### Probability of Coverage  
Figure 8 shows the probability of coverage \(p_{cov}\) versus frequency \(f\) with different values of spread factor \(k\) and wind factor \(w\). We can see that the probability of coverage varies with different acoustic frequencies. In particular, \(p_{cov}\) increases with the increased frequency when frequency is relatively low. After \(p_{cov}\) reaches the peak, \(p_{cov}\) starts to decrease with the increased frequency. Moreover, the peak values of \(p_{cov}\) decrease with the increased factors of \(k\) and \(w\).  
Figure 9 shows the probability of coverage \(p_{cov}\) versus frequency \(f\) with different values of intensity of PUs \(\lambda_{p}\), and Figure 10 shows the probability of coverage \(p_{cov}\) versus frequency \(f\) with different transmission power of PUs \(P_{p}\). It can be seen from Figures 9 and 10 that \(p_{cov}\) decreases with  
Figure 8: Coverage \(p_{cov}\) versus frequency \(f\) with different spreading factor \(k\) and wind speed \(w\). System parameters are set as \(R_{d}=12\) km, \(P_{p}=110\) dB, \(P_{s}=100\) dB, \(\lambda_{p}=0.003\), \(s=1\), \(\delta_{s}=20\) dB, and \(\delta_{d}=20\) dB.  
Figure 7: Probability of connection \(p_{con}\) versus distance \(r\) with different transmission power of PUs \(P_{p}\). System parameters are set as \(k=1\), \(w=10\) m/s, \(\lambda_{p}=0.005\), \(f=30\) kHz, \(P_{s}=100\) dB, \(s=1\), \(\delta_{s}=20\) dB, and \(\delta_{d}=20\) dB.  
a higher \(\lambda_{p}\) or \(P_{p}\), implying that the probability of coverage of SUs are significantly affected by PUs. Moreover, the peak value of \(p_{cov}\) also varies with different values of \(\lambda_{p}\) and \(P_{p}\).