esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.7 Position-Based  
FANETs frequently modify their network structure as a result of the high mobility of drones, which has led to the widespread adoption of position-based routing protocols in these networks. Reactive, greedy, and predictive are the broad categories used to describe these protocols.  
The reactive routing protocol RGR [57] utilizes location information in routing. Upon packet transmission, the protocol initiates a broadcast of a route request message to nodes within communication range. Upon receiving the message, the UAV node selects the node in the routing table that is closest to its own location as the next hop. If the node is not found, then the protocol selects the node that is the next furthest away as the next hop. In the 2000s, GPSR [58] was a classical greedy routing protocol that relied on location information to make forwarding decisions. For example, if a packet cannot reach the destination node in the next hop, the protocol selects the node closest to the destination node's location as the next-hop address in a greedy manner. [PERSON] [59], [PERSON] [60], and [PERSON] et al. [61] later proposed further improved protocols based on GPSR. [PERSON] et al. [62] proposed a routing protocol that dynamically adjusts the frequency of beacons sent by predicting the location of UAVs. The prediction-based location routing protocol can reduce communication delays and overheads by anticipating UAV locations.  
Despite the results of research into position-based routing protocols, there are still a number of pressing issues that need to be addressed. For example, existing protocols struggle to support large-scale UAV networks with high mobility in order to guarantee delays or delivery rates. Secondly, GPSR does not consider display cases such as link failures and collisions in order to reduce the number of hops. We believe that future research on position-based routing protocols can be developed in the following directions: developing more stable routing protocols to improve the communication stability of UAV networks through methods such as multi-path or redundant paths; designing high-performance routing protocols for large-scale FANETs to meet the growing demand for UAV applications; and combining technologies such as deep learning to investigate intelligent routing decision methods so that routing protocols can cope with dynamic topological networks.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 2 Recent Research Direction for D2D Communications
### Network Architectures and Standardization  
D2D communication modes have been included in the 3 rd generation partnership project (3 GPP) by defining the proximity services (ProSe) into the standard [2]. Later, ProSe network architecture has been modified to incorporate mmWaves D2D applications. Towards that, a message exchange entity was added to the ProSe architecture to manage the operations of mmWave links discovery, establishment, and maintenance. In addition, a new paradigm of mmWave D2D networks was introduced in [6] based on the interworking between LTE, unlicensed uW and mmWave bands, where wide coverage uW band, e.g., Wi-Fi, was used to overcome the shortcoming of the mmWave transmissions and assists the construction/management of the mmWave D2D links.
## 1 Introduction
Compared with the traditional optical encryption method, computational ghost imaging encryption is more suitable to be used because of its small amount of ciphertext data, simple experimental setup and good encryption effect. We embed the encrypted watermarked image into the carrier image by cellular automata transformation (CAT) and test the imperceptibility of the embedded watermarked image with peak signal-to-noise ratio (PSNR). The watermark is extracted using the inverse transform of the cellular automaton, which extracts the encrypted image. We associate the extracted image to be decrypted with the scattered field intensity in all iterations one by one and add up each consecutive frame with a suitable weight factor to reproduce the initially embedded watermarked image. The simulation results indicate that the embedded information has good imperceptibility when the PSNR of the embedded watermark reaches above 38 dB [26], the quality of the reproduced image is good and it can resist attacks such as noise, filtering and compression, which demonstrates robustness. The results show that it is a general method for copyright protection of 3D light-field images. The main contributions of this scheme to the copyright protection of images are as follows:  
1. Ghost imaging optical encryption generates ciphertext through different light intensities. By controlling the initial phase, it saves key space and improves the security performance of the system. 2. Ghost imaging generates a key through different light intensities, and the speckle is embedded into the original image through meta-cellular automata watermark. The embedding effect of the speckle key is better than that of traditional watermark embedding. 3. Ghost image encryption embeds the original image through the speckle key. The extracted speckle still has irregular light intensity distribution. Without the key, the correct watermark cannot be restored, which strengthens the security of the system. 4. The key is embedded into the original image through meta-cellular automata, and the algorithm has strong confidentiality. In extracting the watermark, the same sort as that in embedding should be used, otherwise it will be extracted incorrectly, which makes the encryption of the scheme have a double guarantee.
## 3 Experiments and Discussion
### Frequency Response
#### 3.1.1 Method  
Our experimental device, as shown in Figure 5(a), includes a transmitter, a proposed sensor which consists of a laser diode, a Mach-Zehnder EO modulator (10 Gb/s intensity modulator, made by JDS Uniphase Corporation, Milpitas, CA, USA) and a photodetector. Moreover, a scope meter (FLUKE 196C) was used for displaying the receiving signal. Additionally, to simulate the actual application of BSN, the transmitter and the scope meter were powered by a battery module. On the other hand, a 27 year-old male with 80 kg weight and 182 cm height was chosen as the subject. Figure 5(b) shows the  
Figure 4: The structure of receiving circuit.  
torso-arm (70 cm) and leg-arm (180 cm). In contrast, it has comparatively greater effect on the signal attenuations of the signal transmission based on an electronic sensor. As shown in Figure 6(a), the results corresponding to the (20 cm) arm path with respect to the Mach-Zehnder EO sensor almost remain invariant within the frequency range of 2 MHz-30 MHz, while the maximum deviation of the results is only 2.85 dB. However, the corresponding results with respect to the electronic sensor have comparatively greater variation, while the maximum deviation is up to 20.70 dB. Additionally, even though both the signal attenuation curves shown in Figure 6(a) decrease gradually with the increase of signal frequency from 500 kHz to 2 MHz, the results referring to the Mach-Zehnder EO sensor are far less than the corresponding results referring to the electronic sensor. On the other hand, as shown in Figure 6(b,c,d), a similar phenomenon can also be found from the results corresponding to the paths of arm-right arm, torso-arm and leg-arm, which also indicate that the results referring to the Mach-Zehnder EO sensor have less variance in the frequency range of 2 MHz-30 MHz, while the results referring to the electronic sensor have comparatively greater variation. This phenomenon indicates that compared with the electrical sensor-based signal transmission, the proposed Mach-Zehnder EO sensor has a comparatively steady frequency response in the frequency range of 2 MHz-30 MHz. Meanwhile, the above phenomenon can be explained by the influences of the extremely high input impedance of the EO sensor as well as the electrically isolation between the ground electrode of the EO sensor and the receiving circuit [7,9]. Finally, if the signal frequency or the carrier frequency is set in this range, high quality of the signal transmission based on the human body medium will be expected.  
Figure 6: The experiment results of the frequency response corresponding to the signal transmission paths of (**a**) arm (20 cm), (**b**) left arm-right arm, (**c**) torso-arm and (**d**) leg-arm.
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 3 Simulation Tests
### University Test  
In this scenario, the receiver is in a static state first, and it receives the real signal. This process lasts about 80 s. Then, the signal transponder is turned on. The receiver is held by hand and approaches the signal transponder's transmitting antenna slowly. This process is repeated twice and lasts about 100 s. We adopt the algorithm proposed in this paper and the traditional least squares method to analyze the collected data; the result is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Scenario Type** & **Receiver State** & **Signal Type** \\ \hline No. 1 (University Test) & meaconing attack & static and dynamic & real and spoofing \\ No. 2 (Beidou Open Laboratory Test) & simplistic attack & static & real and spoofing \\ No. 3 (The Texas Spoofing Test Battery) & real & static & real \\ No. 4 (The Texas Spoofing Test Battery) & real & dynamic & real \\ No. 5 (The Texas Spoofing Test Battery) & intermediate attack & static & real and spoofing \\ No. 6 (The Texas Spoofing Test Battery) & intermediate attack & dynamic & real and spoofing \\ No. 7 (The Texas Spoofing Test Battery) & intermediate attack & dynamic & spoofing \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of different experiments.  
Figure 3: Pseudorange double-difference model establishment and solution.  
From Figure 4, we can see that the position deviations are smaller than the threshold value when the signal is real. When the signal transponder is turned on, the receiver starts to receive the replayed signal, and the position deviations start to increase and become larger than the threshold value. Based on these differences, we can determine the existence of the spoofing signal. The false alarm rate when the signal is real is 0%, and the missed detection rate when the signal is spoofing is 18%. The missed detections mainly occur within the short period after the signal is switched from real to spoofing. In this period, the position deviations caused by the spoofing signal are smaller than the threshold value.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 4 Comparison of Deep Learning Results between Signal-Processed Data and No Signal-Processed Data  
### CNN (Convolution Neural Network) Algorithm  
During deep learning, raw data (Time-domain) was compared with the pre-processed data through signal processing using the CNN algorithm. CNN is a convolution neural network, an algorithm first proposed by [PERSON] in 1989. The biggest feature is the convolution layer as indicated in the name, and the second feature is the pooling layer, which is also called sub-sampling [27]. Figure 25 shows the basic structure of CNN.  
In this paper, the LeNet model was used among CNN algorithms. It was modified to improve performance and speed in the existing architecture, applying modified pooling and activation function. First, the pooling method was changed from the existing average pooling to max pooling. Second, the activation function was changed from the existing Tanh to Leaky ReLu. Finally, Batch Normalization was added. The existing architecture of LeNet-5 is shown in Figure 26 adapted from ref. [28]. In the application of learning, the  
Figure 24: Difference between with noise signals and without noise signals in STFT [**(a)**: with noise, **(b)**: without noise].  
Figure 25: Architecture of CNN.  
dataset is usually composed of the ratio of training data, validation data, and test data as 6:2:2 [29]. Therefore, the dataset used in this paper was organized as shown in Table 6, and the test data was composed of 648 normal data and 504 anomaly data.  
Next, a sample of the spectrogram image of normal and anomaly data used for deep learning is shown in Figure 27. The levels of learning were classified into normal and abnormal according to the safety standards for domestic urban railroad vehicles.  
As shown in the diagram in Figure 28, the spectrogram image used for learning was resized to 32 \(\times\) 32 so that it could be fitted in the LeNet model after cropping the image in units of one wheel.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **EA** & **Ratio [\%]** \\ \hline Train Data & 2988 & 60 \\ Validation Data & 837 & 17 \\ Test Data & 1152 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dataset is of Training data, Validation data, and Test Data.  
Figure 27: Samples of normal and abnormal data.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 3 Methodology
### Doppler Shift Computation  
The Doppler shift of a GNSS signal is predominantly influenced by the relative velocity between the transmitter satellite and the receiver, along with a common offset that is proportional to the error in the receiver clock's frequency. However, as demonstrated in [24; 25], various ionospheric effects, such as changes in the redistribution and densityof electrons in the ionosphere, lead to frequency variations in the electromagnetic waves emitted by a stable transmitter. These variations are manifested as the Doppler shift and can be quantified as the time derivative of the phase path of the signal. When considering only the ionospheric delay term in the carrier phase observation model [26], the residual phase path expressed in units of cycles can be given by:  
\[\phi=\frac{\Delta_{p_{\mathrm{i}m}}}{\lambda} \tag{5}\]  
where \(\lambda\) is the wavelength of the GPS L1 frequency (0.1905 m). As the Doppler shift \((f_{d})\) of a given signal corresponds to the rate of change of its carrier phase over time, it can be computed using the following equation:  
\[f_{d}=\frac{d\phi}{dt} \tag{6}\]
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 4 Grinding Experiments Validation
### The Application of BEMD and Hilbert Transform to Experimental Chatter Signals  
As previously mentioned, BEMD demonstrates its powerful capability in terms of processing the two-dimensional simulation chatter signal and extracting true IMFs from it. Moreover, the simulation results well illustrate the phase information and synchronization between real and imaginary parts of the IMFs, which is of significant benefit to applying the BEMD method to real-world chatter. Thus, the BEMD is applicable for decomposing the experimental signal above, which sets 16 projection directions (according to the selection of the ideal projection direction number, Section 3.4) and sets 10 iterations. Then, the extraction criterion based on the correlation coefficient can be applied to the extracted IMFs. The correlation coefficients of each IMF are shown in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Sensor** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\
**Number** & & & & & & & & \\ \hline Sensitivity & 9.9 & 10.6 & 10.4 & 10.1 & 10.4 & 10.5 & 10.2 & 10.1 \\ (mv/g) & & & & & & & & \\ Position & Column Z & Column X & Spindle Z & Spindle Y & Motor Z & Motor X & Motor Y & Column Y \\ \hline \hline \end{tabular}
\end{table}
Table 4: The position of sensors and the corresponding sensitivities.  
Figure 12: Newly reconstructed complex-valued signal from experimental data.  
From Table 5, it is clearly seen that the first three IMFs, which have relatively high correlation with the original signals, should be reserved. The other four IMFs have to be removed and classified as a part of the residual. So, the first three IMFs can be recognized as the true IMFs, which contain the main frequency components of the signal. The eventual decomposition result is shown in Figure 13.  
Then, the Hilbert transform is performed on each true IMF to obtain the marginal spectrum, as shown in Figure 14.  
In Figure 13, the marginal spectrum of both \(x\)- and \(z\)-directions shows the same frequency components, of about 300, 580, and 1200 Hz. Yet the \(z\)-direction IMFs have larger amplitudes relative to the \(x\)-direction, as signals tend to vibrate more in the \(z\)-direction according to previous studies.  
Figure 14: Marginal spectrum of \(x\)-direction IMFs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**IMF No.** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** \\ \hline Correlation coefficient & 0.9694 & 0.2355 & 0.0835 & 0.0242 & 0.0351 & 0.0144 & 0.0264 \\ \hline \end{tabular}
\end{table}
Table 5: Correlation coefficients of each experimental IMF.  
Figure 13: The true IMFs of experimental chatter signal.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacteriocins in Cancer Therapy
#### 3.1.2. Colicins  
Colicins are a family generally characterized as antibacterial cytotoxins synthesized by _E. coli_ and other _Enterobacteriaceae._ Colicins are proteins of high molecular mass ranging from 40 to 80 kDa and released into the environment during the SOS response. Colicins use outer membrane proteins to bind to the target cell, and their lethal effect depends on their contact with the plasma membrane of sensitive cells.  
Colicins affect sensitive bacteria cells via specific receptors in their walls to mediate their specific antibacterial effects. These receptors include porins (such as OmpF, PepA, TolQ, TolR, TolA, TolB, and Pal) located in the outer membrane proteins that have a natural role in small metabolite transport. In addition to their inhibitory effect on a sensitive bacterial cell wall, they have inhibitory effects on wall-less (L-form) cells, which have only the plasma membrane on their surface. This effect demonstrates that colicins could also inhibit eukaryotic cells. When colicin is transferred into the cytoplasm, the cytotoxic domain of colicin can kill the target cell by different kinds of effects such as depolarization of the cytoplasmic membrane (colicins A, B, E1, Ia, Ib, K, L, N, U, 5, and 10), a nonspecific DNase activity (colicins E2, E7, E8, and E9), a highly specific RNase activity (colicins E3, E4, E6, E5, and D), or by inhibition of murein synthesis (colicins M and pesticin). Another study reported the cytotoxic effects of colicin members on human solid tumor cell lines, and also colicin E1 and A are cytotoxic, inhibiting the breast carcinoma cell line including MCF7, ZR75, BT549, BT474, MDA-MB-231, SKBR3, and T47D [44,46].
## 3 Simulation and Experimental Results
### Simulation  
The simulation was conducted assuming that the MZM has an ideal transfer curve and that LiNbO\({}_{3}\) has exactly three times the refractive index difference. The relative optical power vs. BER graph of the proposed technique was then compared to that of MPAM. Each graph was composed of a modulation method with the same spectral efficiency, for example, OOK-OOK and PAM4, and PAM4-OOK and PAM8. It was confirmed that the entire linear region was fully used for both the proposed technique and MPAM. In addition, it was assumed that the \(3V_{\pi}\) range was used to maximize performance. In Figure 5, the blue line represents the BER graph of MPAM, and the orange line depicts the BER graph of the proposed technique. The relative optical power is the relative power of light, assuming that the noise power is fixed. As shown, for all cases, the BER performance of the proposed technique was better than that of the MPAM technique. In addition, as the modulation order increased, the difference between the proposed technique and MPAM increased. As the total modulation order increases, the MPAM modulation order also increases along the \(X\)-axis. This makes the eye per bit smaller on the \(X\)-axis. As the simulation was conducted assuming the optimal condition in Figure 4, more power should be allocated to the \(X\)-axis to make the eye per bit of each axis equal. Accordingly, the power that should be allocated to the \(Y\)-axis decreases as the order increases. Because the power allocated in the \(Y\)-axis is 1 bit, less power is required to increase it by 1 bit as the order increases.
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 3 Essential Oils as Food Preservatives: Current Status and Challenges
### Challenges in the Use of EOs as Food Antimicrobials
coli O157:H7_ (MIC: 0.625 ul/mL); \\  & Caused by menthone, menthol, menthol, menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ menthol/ ment/ menthol/ ment/ ment/hol/ ment/ ment/ ment// ment// ment/ ment// ment// ment// ment// ment// ment// ment// ment// ment/// ment/// ment/// ment/// ment/// ment/// ment/// ment/// ment/// ment/// ment/// ment//// ment/// ment/// ment/// ment/// ment/// ment//// ment//// ment/// ment//// ment/// ment/// ment//// ment//// ment/// ment/// ment/// ment//// ment//// ment//// ment//// ment//// ment/// ment//// ment//// ment//// ment//// ment/// ment//// ment/// ment//// ment//// ment//// ment///// ment/////The main challenges of using EOs as food-preserving agents can be overcome to some extent by using nanoencapsulation, rather than applying the EOs directly to food products as free antimicrobials. The incorporation of EOs into nanosized encapsulation systems can significantly improve their physical properties, such as dispersibility, dispersion stability, turbidity, and viscosity, and thus promote their functional activities, in comparison to free EOs [44]. Moreover, nanoencapsulation can also be used to protect EOs against oxygen, light, pH, moisture, and degradation during processing and storage; to improve the solubility of lipophilic compounds in aqueous media; to mask unpleasant taste and aroma; and to release them in the desired location at an appropriate rate through efficient design of the capsules and proper selection of wall materials [45].
# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 5 Simulation and Numerical Results
In Figure 7b, we can observe that the increasing value of the initial reputation will have a more obvious influence in terms of the task coverage rate. This is because our RBUR and RCUR approach take the cover ability of the user as a vital indicator of the instantaneous reputation so that the platform will relatively select the user who may have the better cover ability to complete the task. Figure 8 illustrates that the values of the user sensing radius have a significant impact on the task completion rate and task coverage rate. The user sensing radius represents the user's cover ability, which means that the user is able to provide a guarantee for task coverage. From Figure 8a, we can clearly see that when the value of the user sensing radius becomes bigger, mobile users have more powerful sensing and cover ability, so the four algorithms in MCS have an improvement in terms of the task completion rate. Furthermore, similar to Figure 7, the results have proven that user sensing radius is a contributing factor to the task coverage rate in Figure 8b. When the user sensing radius is  
Figure 6: Performances under various numbers of tasks. (**a**) Task Completion Rate; (**b**) Task Coverage Rate. Figure 7: Performances under various values of reputation. (**a**) Task Completion Rate; (**b**) Task Coverage Rate. equal to five, the limited cover ability cannot satisfy the task's constraints, so all algorithms attain a relatively low task coverage rate. With the user sensing radius increasing, in some cases, the tasks may be completely covered by the mobile users. Meanwhile, due to the collaborative sensing by the mobile users and edge nodes, our RCUR can achieve a better performance compared with other algorithms.