esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Application to the Image Restoration Problem
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Images** & **k** & **OAURN1** & **OAURN2** & **ADIA Alg. 3.3** & **AOM Alg 3.1** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Size = 350 \(\times\) 600 \\ \end{tabular} } & \multirow{4}{*}{300} & SNR & SNR & SNR \\  & & 23.8342 & 23.5978 & 20.8637 & 11.7830 \\  & & 600 & 23.3478 & 23.04771 & 20.9999 & 11.9876 \\  & & 1600 & 224.9893 & 24.5673 & 22.6738 & 16.3562 \\  & & 2000 & 24.7839 & 24.3435 & 22.8246 & 16.8673 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Hand X-ray \\ Size = 520 \(\times\) 750 \\ \end{tabular} } & \multirow{4}{*}{300} & 23.9984 & 23.6374 & 21.2243 & 11.7803 \\  & & 600 & 23.9999 & 23.8563 & 21.6754 & 11.8587 \\ \cline{1-1}  & & 1600 & 24.9738 & 24.8973 & 22.7437 & 16.3478 \\ \cline{1-1}  & & 2500 & 24.99989 & 24.94555 & 22.5467 & 17.8495 \\ \hline \end{tabular}
\end{table} 
Table 1: Numerical comparison for the methods OAURN1, OAURN2, ADIA Alg. 3.3, and AOM Alg. 3.1. Figure 1: Comparison of restored images via various methods when the number of iterations is 2000 for the apple image. Figure 2: _Cont_. Figure 3: Graphs of SNR for the methods OAURN1, OAURN2, ADIA Alg. 3.3, and AOM Alg 3.1 for the apple image. Figure 2: Comparison of restored images via various methods when the number of iterations is 2500 for the hand X-ray image. **Remark 4**. : _From Figures 1-4 and Table 1, one can see that the qualities of the recovered images are better with higher SNR values. Thus, it is evident that Algorithms 1 and 2 are more efficient than the other compared algorithms._
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 3 Performance Analysis
### BER Performance Analysis of the Desired Position
So,  
\[P_{i}=\frac{\frac{1}{2}log_{2}(\frac{N_{s}}{N_{T}})\frac{N_{s}}{N_{T}}}{(\frac {N_{s}}{N_{T}}-1)log_{2}(\frac{N_{s}}{N_{T}})}=\frac{\frac{N_{s}}{N_{T}}}{2( \frac{N_{s}}{N_{T}}-1)}. \tag{26}\]  
As for \(P_{\mathbb{Q}1}\) and \(P_{\mathbb{Q}2}\), after a brief analysis, we could get  
\[P_{\mathbb{Q}1}=\frac{1}{2}, \tag{27}\]  
and  
\[P_{\mathbb{Q}2}=P_{QAM}, \tag{28}\]  
where \(P_{QAM}\) denotes the BER of classical QAM modulation. Hence, substituting (25)-(28) into (22) and (23) we could obtain \(n_{i}\) and \(n_{\mathbb{Q}}\). Finally, \(P_{s}\) could be derived by substituting (21)-(23) into (20). In terms of PSS Plus RP, it did not include the index demodulation and its BER performance was similar to the classical OFDM system in the Gaussian channel, which could be represented as  
\[P=P_{QAM}. \tag{29}\]  
From (28) and (29) we could see that \(P_{s}\) was equal to \(P\) when the greedy detection was always right, i.e., \(P_{i}=0\), which was in line with the analysis. In terms of Eve, it is worth pointing out that, when employing an RSCS-IM scheme,the bits transmitted by te index could be demodulated correctly when SNR was high enough i.e.,  
\[P_{e1}=\frac{\frac{1}{2}log_{2}M}{log2(\frac{N_{s}}{N_{T}})+log_{2}M}, \tag{30}\]  
where \(P_{e1}\) represents the BER of Eve when SNR was high enough for RSCS-IM. While for PSS Plus RP, assuming \(P_{e2}\) represents the BER of Eve, regardless of the value of SNR,  
\[P_{e2}=\frac{1}{2} \tag{31}\]  
since it did not include the index modulation.
## 3 Research Results and Discussion
### Radiogenic Heating
Aqueous solutions, exposed to ionizing radiation, can decompose into oxidizing and reducing components (OH, H\({}_{2}\)O\({}_{2}\), -O\({}_{2}\), -e\({}^{-}\), H\(\cdot\), H\({}_{2}\)[138]) near the steel reinforcement and in pores. As a result, a protective passivation layer of steel reinforcement is exposed to the effect of radiolysis products with an increase in the rate of solubility of the latter, which directly affects the acceleration of corrosion [139]. In this case, a reduction in concrete strength described in this paper may be directly related to the radiolysis of residual water [73]. Since at temperatures around 100 \({}^{\circ}\)C, physically or chemically bound water is lost due to effects of radiation [131]. However, as [PERSON] et al. prove in their study [77], deterioration of mechanical properties is greater for dry concrete specimens than for those in the humid state. Hence, they conclude that water radiolysis may not play a leading role in degradation at low doses of gamma radiation. When corrosion-triggered degradation of reinforcement in a reinforced concrete structure is analyzed, it becomes obvious that radiolysis will be one of the leading processes. Thus, authors of a study [80] assume that the direct effect of ionizing radiation on corrosion is generally insignificant (in an oxygen-free/oxygen-free aqueous environment). The majority of radiation-triggered cases of damage and corrosion processes occur due to the reaction of materials with water radiolysis products.
## 3 Methodology
### Activation Function (AF)  
The activation function transforms the weighted summed input from the node into an output value, which is then fed to the next hidden layer or used as output. It is the activation function that decides whether or not a neuron should be activated. The activation function determines whether a neuron should be activated, i.e., whether the neuron's input to the network is important in predicting the future. In the absence of an activation function, neurons simply perform linear transformations on inputs using weights and biases. Moreover, a neuron's activation function adds nonlinearity to its output, enabling it to solve complex problems. Different activation functions may be used in different portions of a model, and they affect the capabilities and performances of the neural network. A neural network is usually trained with a backpropagation algorithm that requires the derivative of prediction error to update the weights of the model, which requires differentiable activation functions. There are a variety of activation functions available in the literature, including sigmoid, tanh functions, ReLUs, ELU, swish, softmax, etc. [51; 52].  
In addition to predefined functions, [PERSON] et al. introduced adaptive activation functions by integrating a trainable hyper-parameter that accelerates PINN convergence. In their study, they showed that the adaptive activation function could be used to solve a range of forward and inverse problems more quickly and accurately [53]. For layer-wise and neuron-wise activation functions, they introduced an activation slope-based slope recovery term in the loss function to further reduce the training cost [54]. Researchers also employed physical activation functions (PAFs) derived from physical laws governing the phenomena under study [55]. They validated the performance of PAFs integrated with neurons of hidden layers in combination with other AFs by solving harmonic oscillations equation, Burger's equation, Advection-Convection equation, etc.  
[PERSON] et al., 2022 proposed a Rowdy-Net with Rowdy activation functions based on Kronecker neural networks (KNNs) [56]. In KNN, [PERSON]'s product made the network wide while keeping the number of trainable parameters low, thereby enabling faster convergence whereas the Rowdy activation removed the saturation zone from every layer in the network, allowing it to explore more and learn faster.  
In this study, we chose the tanh activation function. In addition, we used adaptive activation, following the approach presented in [53].
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 3 Proposal of Multilayered Network Model for Mobile Network Infrastructure Disruption
### MANET Layer
#### 3.3.1 Cognitive Radio in MANET Layer  
In [26] we introduce the Adaptive Routing for CR-MANET (AR-CRM) based on Fuzzy logic. This routing method is based on functional blocks that can provide the functionalities of MANET nodes to sense spectrum, provide intelligent management of Wi-Fi channels and routing communication. In the MNM it is possible to implement methods for spectrum sensing and intelligent method for channel management, which can result in lower interference between MANET nodes that uses Wi-Fi communication interfaces. Spectrum sensing provides input data for Fuzzy logic based on SIR (Signal-to Interference Ratio) calculated from RSSI (Received Signal Strength Indicator) and Traffic. The output of precisely adjusted membership functions of Fuzzy logic provides the set of the best optimal channels for each device.  
With this method the manage Wi-Fi channels according to the WSN channels is also possible. If the WSN layer uses the standard IEEE 802.15.4 ZigBee, the interference among MANET nodes can occur. The authors in [6] describes standard IEEE 802.11b/g/n/ax (Wi-Fi) channels from 1 to 13 in the range of 2401 MHz to 2495 MHz. The Zigbee standard IEEE 802.15.4 uses 16 frequency channels (from '11' to '26') each of 2 MHz. Wi-Fi and ZigBee channels depiction can be seen in Figure 2.  
With the assumption of existing sensing methods for discovery of Zigbee channels, it is possible to arrange non-interfere MANET channels for each ZigBee channel based on the fuzzy logic model introduced with AR-CRM. Therefore, it would be possible to set MANET channels among MANET nodes according to nearby WSN channels to avoid interference. This paper does not describe the method of interference avoidance between the MANET and WSN nodes. The purpose of this section is to show the possible way to accomplish this problem. However, the AR-CRM is still possible to use as a protocol in the MNM MANET layer for interference avoidance among MANET nodes.
## 3 Results and Discussion
### Bioactivity of pDNA
#### 3.3.3 p53 Mediated Apoptosis in HeLa Cells  
The cellular apoptosis mediated by the p53 protein was evaluated in HeLa cancer cells. Figure 7 shows the results of apoptosis concerning the pDNA products purified by the three different chromatographic matrices. The results reflect an apoptosis rate of approximately 6%, 3%, and 5% for cells transfected with pDNA purified by arginine-macroporous support, histidine-agarose, and histidine-monolith, respectively, when compared to the control group (K-). Comparing the results of p53 protein expression (Figure 6) with the apoptosis (Figure 7), it is possible to observe a pattern, because the conditions that allowed a higher p53 expression also resulted in higher rates of apoptosis mediated by the therapeutic gene, following the trend in the arginine-macroporous, histidine-monolith, and histidine-agarose samples. If we take into consideration the protein expression levels that were achieved, we could expect higher apoptosis rates, following transgene levels, but actually we observe relatively low levels of apoptosis. Although the literature reports that p53 is the major tumor suppressor gene, there are also other proteins involved in the pathway of apoptosis and the inactivity or reduced activity of any of these proteins could also compromise the apoptotic cycle. Although these were relatively low apoptotic values, it a higher apoptotic rate in cells transfected with sc pDNA purified from arginine-macroporous support was obtained, which also presented high expression levels of the therapeutic gene (Figure 6). However, if we consider the complexity of cancer, we understand that not only one, but several genes in the genome can be affected by different types of mutations and in the same way promoter sequences of these genes may be inactivated or even reduce the expression of a particular protein. There are known cases, such as colon, breast, brain, or pancreas cancers, which average 33 to 66 different types of genetic mutations and in other cases up to 200 types of genetic material damage are detected [47]. Based on this information, we may consider that other genes involved in the cascade  
Figure 6: Evaluation of the p53 protein expression through ELISA kit in HeLa cancer cells after transfection with different p53-pDNA lipoplexes. Non-transfected cells were used as controls. Data are represented as mean \(\pm\) SD, n = 3.  
that triggers apoptosis may also be inactivated. In addition, it is recognized that a cancer cell is in constant mutation, aiming at greater propagative advantage, guaranteeing not only suppression of any mechanism of programmed cell death but also the specialization of growth genes [48]. In a study that reveals the intricate genetic structure after successive mutational events occurring in HeLa cells almost 70 years after the extraction of the cells from the patient [PERSON], researchers have shown that genetic aberrations during these years may have unknown implications in biological experiments, since these cells have a genomic instability typical of tumors [49; 50].
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 4 Conventional Traffic Signal Control Strategies
### Fixed Time Traffic Signal Control  
The Fixed Time Signal Control (FTSC) strategy, also known as open-loop control, relies on historical data. Obtained signal programs have predefined more or less optimal phase sequences based on historical data, which corresponds to daily traffic volume fluctuations. However, when relying on historical data, optimal signal programs mean the plans are optimal only for traffic patterns that are derived from historical data. The traffic patterns tend to change over time, thus, the signal programs will become obsolete or at least suboptimal at some point. Examples of FTSC strategies are [PERSON]'s method [23] and its extensions, SIGSET [24], SIGCAP [25] for isolated intersection control together with MAXBAND [26] and its extensions, TRANSYT [27], and MULTIBAND [28] for coordinated intersection control.  
Calculation simplicity is the advantage of FTSC, but predefined signal programs can not alleviate occasional events such as traffic accidents, roadworks, special events (e.g., concerts, social events), etc. Furthermore, traffic volume fluctuations differ from day to day, which affects the effectiveness of predefined signal programs since signal programs may not be optimal due to constant fluctuations in traffic volume. To overcome the drawbacks of FTSC strategies, first actuated and then Adaptive Traffic Signal Control (ATSC) strategies were introduced.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 2 Dynamic Measuring Principle and Device of Atom Gravimeter
### Dynamic Measurement Principle of Atom Gravimeter  
In a dynamic environment, the gravity value varies with the movement of the carrier position. If the gravity value is calculated by changing \(T\) to obtain the center of the fringe as in the static state, the data rate will inevitably be greatly reduced. For example, ZJUT takes the results of fringe fitting as a comparison item in the dynamic experiment, and the data rate is only \(1/59\) Hz [38]. In addition, the atom gravimeter measures the acceleration of atoms relative to the mirror; the carrier movement causes the mirror itself to generate vibration acceleration \(a_{\mathrm{vib}}\). And \(a_{\mathrm{vib}}\) is often greater than the dynamic range corresponding to a fringe period, which leads to the gravity information being submerged in vibration noise, and it is almost impossible to obtain the gravity value through fringe fitting. Most research teams use an accelerometer (or seismometer) and an atom gravimeter for combined measurement and adopt different schemes to take advantage of the two to solve the above problems. At present, there are the following data processing schemes.  
Figure 3: Interference fringe and fringe center.
# AC Microgrids Protection: A Digital Coordinated Adaptive Scheme
In this regard, a fast and reliable protection scheme for MGs system identify, and isolate fault conditions in a coordinated manner is required. In this paper, a comprehensive and coordinated protection scheme based on digital PDs to efficiently satisfy protection coordination requirements in both the grid connected and islanded modes of operation of AC microgrids is proposed. The proposed scheme covers PCC, feeders, lines, and DCU protection requirements for various type of fault. Its coordination settings are set adaptively, according to the MGs and DGU features. To do so, in an offline procedure, coordinated time delays for various type of employed relays are defined; then, an online adaptive protection algorithm is developed so that switch the PD setting automatically based on the information received by the system, and meanwhile, the algorithm calculates the optimal pickup current and TMS setting in a real-time progress. It means that an offline (calculated and stored) as well an online (measured and calculated in real-time) data are used for reliable, coordinated, and fast fault detection. Briefly, the main contributions of the proposed protection scheme are:  
* A coordinated adaptive digital protection scheme is derived. The proposed scheme contains various protective modules such as DOCR, DOCR-NSC, DOCR-THD, DFRs and UVRs, which facilitate detecting various faults. * Coordinated protection modules for different modes of an MG results in reliable, selective, and coordinated protection, which is appropriate to detect all types of faults. * In addition to coordinate the DOCRs, coordination of the NSC, THD, and UVR modules have been conducted through appropriate time delays tuned by an adaptive algorithm. * The coordination is performed through an adaptive scheme, which includes offline and online steps. In the offline step, as the first step of the proposed adaptive scheme, all the settings are adopted for a selective and coordinated scheme among proposed protective modules. Then, in the online procedure, after detection any change in the system topology, a new set of setting for proposed modules will be performed to adapt the settings accordingly. * The proposed method is appropriate not only for the grid-connected mode, but also for the islanded-mode of the operation. The rest of the paper is organized as follows. Section 2 proposes protective modules for each region of an MG, with the implementation of them. Section 3 presents the proposed coordination algorithm. Simulations have been performed to validate the merits of the proposed protection scheme in Section 4. Furthermore, lastly, the conclusion and potential directions are discussed in Section 5.
## 3 Experimental Approach  
Most of the experiments in this manuscript were conducted using the small induction wind tunnel at the Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA) unless otherwise noted. The test section of this wind tunnel is 100 mm in width, 400 mm in height and 700 mm in length. The turbulence intensity in the center is identified to be approximately 0.08% at flow speed of 6.6 m/s. All the airfoils used in this wind tunnel experiment have a chord length of 100 mm. More details of the experiments and the facility have been previously reported [13]. Reynolds numbers based on the chord length is 63,000, corresponding to the freestream velocity of 10 m/s. Additional experiments were conducted to evaluate the scaling effect (higher speeds and/or a larger model scale) using the large wind tunnel owned by a private company [19].  
The DBD plasma actuator attached to the airfoil consists of two copper electrodes (Cu-35C tape; 3M Japan Ltd.) (Tokyo, Japan) with two polyimide dielectric layers (Teraoka Seisakusho, 650S, and material thickness 50 \(\upmu\)m) (Tokyo, Japan) between them. Electrodes of 70 \(\upmu\)m in thickness and two polyimide dielectric layers of 80 \(\upmu\)m in thickness are used. The width of the exposed electrode is 2 mm and the width of the insulated electrode is 6 mm. The upper and lower dielectrics are 30 and 25 mm in width, respectively. The overlap between the exposed electrode and the insulated electrode is 1 mm. The rear edge of the exposed electrode is located at 5.0% of the chord from the leading edge.
## 3 Thermal Side-Channel Attack Using CNN  
### Hardware & Software Setup for Experiments  
We also chose Odroid XU4 [25] board to execute the attack in order to verify the affect of thermal side-channel exploitation. Odroid XU4 employs the Samsung Exynos 5422 [34] MPSoC, which is popularly used in Samsung mobile devices, especially Samsung Galaxy S5. The Odroid XU4 is a representational development board of Galaxy S5 smartphone. Exynos 5422 MPSoC contains clusters of big (4 Cortex A-15) and LITTLE cores (4 Cortex A-7). This MPSoC provides DVFS feature per cluster, where the big core cluster has 19 frequency scaling levels, ranging from 200 MHz to 2000 MHz with each step of 100 MHz and the LITTLE cluster has 13 frequency scaling levels, ranging from 200 MHz to 1400 MHz, with each step of 100 MHz.  
Figure 2: Focus area of ResNet50 network on a representative graph of password: passw0 rd.  
The Odroid XU4 was running on UbuntuMate version 14.04 (Linux Odroid Kernel: 3.10.105). During the time of performing the attack, the average ambient temperature of the room was 21 \({}^{\circ}\)C. When we executed the attack, we changed the governor [26] between conservative, ondemand, performance, interactive, and powersaver to study which Linux governor is more vulnerable to such attack.  
Brief description of the different types of governors are as follows:  
* **ondemand**: Sets the operating frequency of the CPU depending on the CPU utilization. In this, the operating frequency is set to maximum whenever there is any load on the CPU.
* **conservative**: Is a fork of ondemand governor and sets the operating frequency of the CPU depending on the CPU utilization. It differs from ondemand by increasing or decreasing the operating frequency of the CPU gradually based on the CPU utilization.
* **performance**: Sets the operating frequency of the CPU to the highest frequency within the borders of user specified minimum frequency and maximum frequency.
* **powersaver**: Compared to performance, this governor sets the operating frequency of the CPU to the lowest frequency within the borders of user specified minimum frequency and maximum frequency.
* **interactive**: Dynamically scales CPU operating frequency in response to the CPU utilization. Interactive is significantly more responsive than ondemand because it scales the operation frequency over the course of time to max frequency based on the CPU utilization.