esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 3 Proposed Secure Communication, Multi-User Cooperation, and User Clustering Strategy in PD-NOMA
### Proposed Solution for Secure PD-NOMA Communication  
The proposed system shown in Figure 3 assumes a total of five users (\(R_{1}\), \(R_{2}\), \(R_{3}\), \(R_{4}\), and \(E_{1}\)). Users \(R_{1}\), \(R_{2}\), \(R_{3}\), and \(R_{4}\) are considered registered users of the system, whereas \(E_{1}\) is an unregistered user. In this case, the strong user is \(R_{1}\), which can decode the data of all the weak users, including \(R_{2}\), \(R_{3}\), and \(R_{4}\). Similarly, \(R_{2}\) can decode both \(R_{3}\) and \(R_{4}\) data, but \(R_{3}\) can only decode \(R_{4}\) data. The proposed technique uses unique short spreading codes that are assigned to gNodeB and registered users. These spreading codes may be orthogonal or non-orthogonal, such as Walsh, pseudorandom sequence (PN), etc. These short codes are used for signal spreading and de-spreading at gNodeB and by registered users. Due to these codes, the registered user and the gNodeB are able to correctly decode the signals from the spread composite signal. The assigned codes may be single-level or multi-level based on the requirements of the system. The proposed system uses single-level codes for securing the users' data from eavesdroppers. However, multi-level codes can also be used to protect complicated systems where eavesdroppers are able to break the security of single-level codes. To validate the proposed technique, we consider the multi-user downlink PD-NOMA scenario where the composite signal at the gNodeB is multiplied with a short secret code and then transmitted over a channel. At the receiving end, each user first de-spread the received composite signal by multiplying it with the same code. The post-processing, such as SIC and demodulation, has been implemented upon successful decoding of the composite signal. The eavesdropper \(E_{1}\), which compromises the ordinary PD-NOMA system security, has no such codes and is not capable of correctly decoding the composite signal. In doing so, the PD-NOMA system's privacy is preserved. In both the uplink and the downlink scenarios, this method works well to keep unintended users from accessing the data. The proposed method helps a lot to protect PD-NOMA communication,  
Figure 3: Block diagram of downlink PD-NOMA with registered and unregistered users.  
especially in wireless sensor networks (WSN), from unregistered users of the system who might try to break its security.
## 3 Results and Discussion
### Biological Characterization of the Scaffolds
#### 3.7.4 Collagen Production by the MC3T3-E1 Pre-Osteoblastic Cells  
A hallmark of osteoblast differentiation both in vivo and in vitro is the formation of an extracellular matrix (ECM) [44]. Type I collagen accounts for about 95% of the organic matrix proteins in bone. In order to examine the potential of the MC3T3-E1 pre-osteoblasts cultured on the Alg/SiO\({}_{2}\) scaffolds to differentiate into mature osteoblasts in vitro, we investigated the effect of collagen production as a predominant protein during  
Figure 7: Cytotoxicity assessment of the five scaffold compositions. Cell metabolic activity of MC3T3-E1 pre-osteoblasts is expressed as absorbance units. The values represent means \(\pm\) standard deviations of triplicates (\(n=3\)), and the asterisks indicate statistical significance compared to the Alg/MC/TMC control surface at each experimental time point (\({}^{*}\): \(p<0.05\), \({}^{**}\): \(p<0.01\)).  
ECM formation, by quantifying the levels of total collagen secreted by the pre-osteoblastic cells. Figure 10 demonstrates that all modified Alg/SiO\({}_{2}\) scaffolds significantly enhanced collagen production on day 7 compared to the Alg/MC/TMC control surface (\(p<0.01\)) and thus supported ECM formation. Another study applying alginate/chitosan gel-like porous scaffolds loaded with dental pulp stem cells (DPSCs) reported increased ECM formation and a significant increase in the gene expression of fibrocartilaginous markers including collagen types I and X, aggrecan, SOX9, and cartilage oligomeric matrix protein after three weeks in culture, suggesting the ability of the biomaterials to promote temporomandibular (TMJ) joint disc regeneration [42]. In the same report, dynamic thermomechanical analysis revealed that alginate/chitosan scaffolds loaded with DPSCs significantly increased the storage modulus and elastic response compared to cell-free scaffolds, indicating similar values to those of native TMJ discs.  
Figure 8: Scanning electron micrographs illustrating MC3T3-E1 cell adhesion and growth on Alg/MC/TMC, Alg/0 Si_5, Alg/0 Si_10, Alg100 Si_5, and Alg100 Si_10 during 4 and 10 days of cell culture. Original magnifications are \(\times\)500.  
Figure 10: Levels of total collagen produced by MC3T3-E1 pre-osteoblastic cells cultured on the five scaffolds for 4 and 7 days. The values represent means \(\pm\) standard deviations of triplicates (\(n=3\)), and the asterisks indicate statistical significance compared to the Alg/MC/TMC control surface at each experimental time point (**: \(p<0.01\), ****: \(p<0.0001\)).  
Figure 9: Alkaline phosphatase activity of MC3T3-E1 pre-osteoblastic cells on the five scaffold compositions after 3 and 7 days in culture. The values represent means \(\pm\) standard deviations of triplicates (\(n=3\)). Statistical analysis revealed no significant differences among the five scaffold compositions at each time point.
# Defense Against Adversarial Attacks in Deep Learning
## 3 Deep Denoising Network Based on U-Net: UDDN
### U-Net  
U-Net is a variant of convolutional neural network and is an improved algorithm based on Fully Convolutional Networks (FCN). The U-Net's entire network consists of two parts: a contracting path and an expanding path. The contracting path is used to capture the context information in the image by gradually reducing the spatial dimension of the pooling layer, while the expanding path is to accurately locate the segmented image by expanding spatial dimensions and gradually recovering the details. To reconstruct the details of the target, the U-Net combines the high-pixel features extracted from the contracting path with the new feature map during the upsampling process. This feature fusion method can preserve important feature information in the process of downsampling. The basic module of a U-Net is shown in Figure 2.  
In the contracting path, every two 3 \(\times\) 3 of the unpadded convolutional layers are followed by a 2 \(\times\) 2 maximum pooling layer with a step size of 2. Each convolutional layer is followed by a ReLU  
Figure 2: U-Net block. The U-Net consists of a contracting path and an expanding path. The horizontal connection combines the features of the upsampling and downsampling processes.  
layer, which is used to downsample the original image. In addition, each downsampling doubles the number of feature channels. In the deconvolution of the expending path, each block consists of a 2 \(\times\) 2 convolution layer and two 3\(\times\)3 convolution layers. The feature map of each block in the expanding path is added to by the output of corresponding block in contracting path. An important feature of U-Net is that it can basically convolve images of any size, especially arbitrarily large ones.
###### Abstract  
(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D'Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (\(n=137\)), 95.4% for intermediate risk (\(n=570\)), and 86.4% for high risk (\(n=102\)). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set); the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D'Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT.  
biochemical recurrence; CyberKnife; nomogram; prostate cancer; risk classification; stereotactic body radiation therapy; predictive analytics
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 3 Measurement Principles and Characteristics
Therefore, the main component of the mass spectrum is ribosomal protein, which is well conserved in each bacterial species, and different mass spectra can be obtained depending on the bacterial species. According to a study using _E. coli_, about half of the peaks detected in the range of 4000 to 20,000 Da are ribosomal proteins (including some with post-translational modifications), and the others are DNA-binding proteins, cold-shock proteins, etc. [32]. The MALDI mass spectrum differs depending on the type of bacteria. The MALDI-TOF MS is a pattern-matching method that matches the mass spectra to those of strains in the database [33; 34; 35]. Candidate strains are displayed in order of increasing score. A score of 2.0 or higher indicates high reliability at the species level (green), and a score of 1.7 or higher but less than 2.0 indicates a match at the genus level (yellow). Scores below that are considered indicative of unidentified isolates (red). The same bacterial species with multiple strains registered is considered identifiable even if there is pattern diversity among the strains. We have cited several references and compiled our own table of the features and advantages of microbial identification by mass spectrometry (Table 1) [8; 26; 36; 37; 38; 39; 40; 41; 42; 43; 44]. The mass spectral patterns of molecules, mainly ribosomal proteins, unique to microorganisms obtained by MALDI-TOF MS are searched and matched with a library of known standard strains to identify the target bacterial species.
## 4 Results and Discussions
### Electrical and Photovoltaic Properties
Under standard illumination of AM1.5G irradiation, the fabricated solar cell showed a significant PV effect, with an open circuit voltage of \(V_{\alpha c}\) = 440 mV, and a short circuit current density of \(J_{sc}\) = 16.7 mA/cm\({}^{2}\) for the best prepared sample. The PV energy conversion efficiency was limited to 2.7% and the Fill Factor (FF) to 36.5%. Finally, we observed a cross-over voltage effect at \(V_{bias}=488\) mV (indicated as a dashed oval), which is an intrinsic property of thin film solar cells, wherein the under-light J-V curve intersects wtih the dark J-V curve. As the final step of the characterization tests, the EQE spectral response of the CuIC solar cells samples was measured. The results of the EQE measurements of the two samples which were fabricated from thin films annealed at 850 \({}^{\circ}\)C and 900 \({}^{\circ}\)C are shown Figure 12. According to this figure, these solar cells only had relatively good response in the green band of the spectrum; beyond 650 nm, their response especially in the red region of the spectrum was very low, which limited \(J_{sc}\) @ 550 nm to only 16 mA-cm\({}^{-2}\). Therefore, the photo-generation in this device was restricted to a narrow band of the whole spectrum, reducing the overall \(J_{sc}\). Additionally, the measured energy band gap (\(E_{g}\)) was 1.304 eV, while the typical \(E_{g}\) of poly-Si solar cells made by aluminum-induced crystallization (AIC) is lower than 1.2 eV [35]. Figure 11: (a): the p-n junction rectification ratio and cell dynamic resistance, (b) the J-V curve of the best sample of a micro-Si solar cell fabricated by MS and CuIC. The structure of the designed solar cell and two fabricated samples are shown in the inset pictures.
## 5 Feature Fusion Based on Skewness Factor  
Based on the above study, it can be seen that even with 150 samples (the largest number of samples), the BP neural network model still needs 365 iterations to converge smoothly so as to obtain 100% correct classification of the verification set, and the calculation time required is 11.43 s. As offline calculation of the script, this calculation time is completely acceptable, but to apply the model to online monitoring procedures for real-time updates on the model, the calculation speed needs to be improved.  
Based on the sigmoid function activation function, which turns linear regression into a classification problem in order to improve the fitting ability of the neural network, we need to improve the nonlinearity of variables in linear regression. This paper starts with a study of adding nonlinear fusion features.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 2 Segmented Telescope Imaging Theory
### Derivation of WFE from Sub-Mirror Position Error
#### 2.2.1 Influence of X, Y Decenter Error on Phase \(\phi_{\rm j}\)  
If the ideal coordinates of a point on the sub-mirror are \(\left(x_{0},y_{0},z_{0}\right)\), and the coordinates after the decenter transformation are \(\left(x_{0}{{}^{\prime}},y_{0}{{}^{\prime}},z_{0}{{}^{\prime}}\right)\), its decenter along the X and Y axis \(\rm dx\), \(\rm dy\) and the coordinates satisfy the following relationship:  
\[{x_{0}}^{\prime}=x_{0}+\rm dx\] \[{y_{0}}^{\prime}=y_{0}+\rm dy \tag{5}\]  
The WFE caused by the sub-mirror decenter is the difference between the vector heights \(\rm Sag\) before and after the change, and in the practical case \(\rm dx\), \(\rm dy\) are small, \(\rm dx^{2}\ll x_{0}dx\), \(\rm dy^{2}\ll y_{0}dy\), can be simplified as:  
\[\phi_{\rm j-Decenter}=\frac{2\pi}{\lambda}\left({z_{0}}^{\prime}-z_{0}\right) \approx\frac{2\pi}{\lambda}\left(\frac{\rm dx}{r_{0}}x_{0}+\frac{\rm dy}{r_{ 0}}y_{0}\right) \tag{6}\]  
The decenter error of the sub-mirrors can be equated to the tilt error, and the tilt factor is the ratio of the decenter to the radius of curvature of the vertex, but because of the \(\rm dx\), \(\rm dy\ll r_{0}\), the decenter error does not have a significant effect on the WFE of the system.
# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 3 Algorithm and Sample Extraction  
### Anomalies of Electromagnetic Signals before the Earthquake  
The analyzed cases were selected from earthquakes (M. \(>\) 6.0) in the target area (22\({}^{\circ}\) N-34\({}^{\circ}\) N, 98\({}^{\circ}\) E-107\({}^{\circ}\) E) in recent years:  
1. On 8 August 2017, a magnitude 7.0 earthquake occurred in Jizhaigou County, Aba Prefecture, Northern Sichuan Province. Figure 3 shows the changes of the electromagnetic disturbance signal of the AETA stations near the epicenter;
2. On 17 June 2019, a magnitude 6.0 earthquake occurred in Changing County, Yibin City, Sichuan Province. Figure 4 shows the changes in electromagnetic disturbance signals of the AETA stations near the epicenter.  
The red line at the top of the figure indicates the main shock and the aftershocks that followed.  
It can be seen from Figures 3 and 4 that there are some signal anomalies before the earthquakes, and we argue that the abnormal change is related to the occurrence of earthquakes. It is difficult to quantify such anomalies directly from manual observations, and there may be other undetectable changes in addition to this obvious information. These subtle anomalies mixed with abnormal disturbances are difficult to detect with the naked  
Figure 4: Electromagnetic signals from three stations near the Yibin earthquake.  
Figure 3: Electromagnetic signals from three stations near the Jizhaigou earthquake.  
eye. Therefore, a lot of potentially abnormal information will be lost directly through simple observation. In order to discover the hidden information, we chose the LSTM autoencoder model to capture anomalies.
# Synthetic Experiences for Accelerating DQN Performance in Discrete Non-Deterministic Environments +
## 2 Background
### Deep Q-Learning  
The DQN algorithm is the combination of the classic Q-Learning [12; 13] with neural networks and was introduced in [2; 14]. The authors showed that their algorithm is able to play Atari 2600 games on a professional human level utilizing the same architecture,algorithm and hyperparameters for every single game. As DQN is a derivative of classical Q-Learning, it approximates the optimal action-value function:  
\[Q^{*}(s,a)=\max_{\pi}\mathbb{E}\big{[}r_{t}+\gamma r_{t+1}+\gamma^{2}r_{t+2}+ \ldots|s_{t}=s,a_{t}=a,\pi\big{]}. \tag{1}\]  
However, DQN employs a neural network instead of a table to parameterize the Q-function. Equation (1) displays the maximum sum of rewards \(r_{t}\) discounted by \(\gamma\) at each time-step \(t\), which is achievable by a behaviour policy \(\pi=P(a|s)\), after making an observation \(s\) and taking an action \(a\). DQN performs a Q-Learning update at every time step that uses the temporal-difference error defined as follows:  
\[\delta_{t}=r_{t}+\gamma\max_{a^{\prime}}Q(s_{t+1},a^{\prime})-Q(s_{t},a_{t}). \tag{2}\]  
[PERSON] et al. [5] showed that a nonlinear function approximator used in combination with temporal-difference learning, such as Q-Learning, can lead to unstable learning or even divergence of the Q-Function.  
As a neural network is a nonlinear function approximator, several problems arise:  
1. the correlations present in the sequence of observations;
2. the fact that small updates to Q may significantly change the policy and, therefore, impact the data distribution; and
3. the correlations between the action-values \(Q(s_{t},a_{t})\) and the target values \(r+\gamma\max_{a^{\prime}}Q(s_{t+1},a^{\prime})\) present in the td-error shown in Equation (2).  
The last point is crucial because an update to \(Q\) will change the values of both the action-values and the target values. This change could lead to oscillations or even divergence of the policy. To counteract these issues, two concrete actions have been proposed:  
1. The use of an ER solves, as stated above, the two first points. Training is performed each step on minibatches of experiences \((s,a,r,s^{\prime})\sim U(D)\), which are drawn uniformly at random from the ER.
2. To remove the correlations between the action-values and the target values, a second neural network is introduced that is basically a copy of the network used to predict the action-values. The _target_-network is either frozen for a certain interval \(C\) before it is updated again or \"soft\" updated by slowly tracking the learned networks weights utilizing a factor \(\tau\). This network is responsible for the computation of the target action-values. [2; 15]  
We use the target network performing \"soft\" updates as presented above and extend the classic ER with a component to create synthetic experiences.
## 7 Case Study
### Effectiveness of the Proposed Sensor Deployment Scheme  
To verify the effectiveness of our sensor deployment scheme, we use the Monte Carlo method to simulate the lifetime of the WSN under a random sensor deployment scheme in order to provide comparisons. The lifetime of a hexagonal deployed WSN is also simulated to analyze the simulation error. Our Monte Carlo simulation was implemented in MATLAB, and the general simulation procedure is described as follows.  
* Step 1. Generate the sensor locations according to the deployment scheme (_i.e._, the random topology or the hexagonal topology);
* Step 2. Assign the corresponding attributes to each sensor, including initial energy, packet length of sensing data, number of packet sensed in a data gathering cycle, sensing range, transmission range, _etc._;
* Step 3. Execute the following steps at each data gathering time until the WSN life ends;
* Determine the active sensors according to the sleep/wake-up protocol (_i.e._, PEAS protocol),  
Figure 8: Optimal number of sensors deployed under different \(R_{a}\).  
Figure 9: Residual energy in WSN under different \(R_{a}\).  
* Generate sensing data at all alive sensors,
* Transmit the sensing data to the base station hop-by-hop based on the routing protocol (_i.e._, GPSR protocol). For each DATA packet transmission between two hops, implement the following procedures,
* (i) Transmit the DATA to its receiver, and determine whether it is received successfully according to the DATA retransmission rate;
* (ii) If the DATA is received by the receiver, transmit an ACK back to the transmitter and determine whether the ACK is received successfully according to the ACK retransmission rate, else go to step (iii);
* (iii) If the transmitter fails to receive the ACK and the maximum number of retry times is not achieved, go to step (i) to retransmit the DATA, else go to step (iv);
* (iv) Calculate the energy consumption of each DATA or ACK transmission and reception. If the energy of a sensor is exhausted, remove it from the WSN.
* Evaluate the coverage rate using pixel method proposed in [10]. If the coverage rate cannot satisfy the coverage requirement, terminate the simulation, and record the time as the lifetime for this simulation.
* Step 4. Count the number of DATA that is successfully received by the base station, and evaluate the success transmission rate;
* Step 5. Go to Step 1 until the number of simulation runs is achieved. Otherwise, go to Step 6;
* Step 6. Calculate the average lifetime and success transmission rate.
# Features Engineering to Differentiate between Malware and Legitimate Software
## 2 Malware Detection Methods  
Malware analysis explains how to examine a malware file in order to comprehend its behaviour and look at the many components of harmful software to identify and minimise the harm inflicted by attackers. Static and dynamic approaches can be used to analyse malware. It is not necessary to run the malware for simple static analysis. Instead, this analysis looks for indicators of harmful intent in the file. Static analysis can be used to gather a variety of static data, such as the data extracted from PE header [10,11], which is derived as string-based entropy. Static analysis has the advantages of being quick and not requiring an isolated environment to run malicious code. However, these advantages are thwarted by obfuscation methods, such as encryption and packing techniques used to hide the dangerous payload and render the malware unrecognisable [12,13].  
On the other hand, dynamic analysis requires that the malware file be executed in isolated settings to observe the malware's behaviour during runtime and then collect the dynamic features needed [14]. Using a dynamic analysis methodology, several types of data have been gathered. Malicious behaviours can be exhibited by maintaining memory images throughout the runtime, such as the memory image of Opcodes [15]. Additionally, the behaviour of the malware can be represented by gathering the triggered API calls [16,17], file information [18,19], machine operations [20,21], network data, and registry [22,23].  
Even though malware that has been obfuscated cannot hide its behaviour from dynamic analysis, more research is still needed to extract characteristics that will meet all malicious conditions and allow for the exploration of all execution paths.