esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.2.1 Horizontal (H) and Vertical (V) Radar Reflectivity (0 th Doppler Moment)  
As mentioned in Section 2.2, a high-range-resolution reflectivity value is coded as long as  
\[10\ Log\left(\frac{S+N_{u}}{N_{e}}\right)\geq 4\ \mathrm{dB}, \tag{1}\]  
where the numerator represents the power (I\({}^{2}\) + Q\({}^{2}\)) of the Signal plus the (unknown) Noise of the current bin, and the denominator is an estimate (performed every 2.5 min) of the Noise performed at high Elevation angles (40\({}^{\circ}\) and 35\({}^{\circ}\), see Figure 2 in Reference [16]). In other words, the measured power (Signal plus the unknown Noise) will be at least 2.512 times the estimated Noise that will be recorded as a quantitative value. If not, the radar bin is assumed to be without any (detectable) precipitation and DN is set to 0 (flag value). Let us suppose that the estimate of the Noise is close to the actually measured value: this means that the net Signal should be at least 1.512 times the Noise. As far as quantization is concerned, a value of 0.5 dBZ has been chosen by MeteoSwiss. The linear conversion from DN to Log-transformed radar reflectivity is the following  
\[Z_{dBZ}=(\mathrm{DN}-64)/2, \tag{2}\]  
where the radar reflectivity units, \(Z\), are \([Z]=\mathrm{mm}^{6}/\mathrm{m}^{3}\), while \(Z_{1}\) (\(Z_{1}=1\ \mathrm{mm}^{6}/\mathrm{m}^{3}\)) is the normalization factor necessary to Log-transform \(Z\) into the so-called dBZ unit, which, in formula form, is:  
\[Z_{dBZ}=10\ Log\left(\frac{Z}{Z_{1}}\right). \tag{3}\]  
In practice, the maximum recorded value for the Monte Lema radar very rarely exceeds 85 dBZ (DN = 234), while DN = 14 (\(-\)25 dBZ) can, in principle, only be observed within a range of 3 km or closer (already at the edge of the T-R limiter effects).
# Roadmapping of Nanoelectronics for the New Electronics Industry
## 14 More Than Moore and Packaging Integration  
For smart system applications, heterogeneous integration of digital, analog and mixed-signal technologies is needed. This More than Moore (MtM) domain was firstly introduced in the framework of ITRS in 2005, then it was fully integrated in the NEEEID and IRDS Roadmaps in the last decade. At the same time, the Roadmaps became mostly application driven instead of only technology driven. This new paradigm is associated with the need of multidisciplinary expertise along the complete innovation chain to develop generic technology modules, which can be made available on open technology platforms.  
MtM includes innovative functionalities, in addition to logic and memory, such as RF communication [23], sensing [24], energy harvesting and management, power devices, flexible electronics. Complex embedded software needs also to be integrated into SoCs and SiPs to optimize performance scaling.
## 5 Conclusions  
Based on the non-uniform input-output dataset, an auxiliary model-based stochastic gradient (AM-SG) algorithm is developed in this paper to estimate the parameters of Hammerstein systems. To improve the identification performance of the AM-SG algorithm, an auxiliary model-based multi-innovation stochastic gradient (AM-MISG) algorithm is proposed by introducing an innovation length \(p\). The simulation results illustrate that the AM-MISG algorithm with a larger \(p\) can provide more accurate parameter estimates and a faster convergence rate. In addition, the proposed algorithm can be extended to identify more complex nonlinear systems with non-uniform sampling.  
This paper is supported by the National Natural Science Foundation of China (No. 61403166), the Natural Science Foundation of Jiangsu Province (China; BK20140164) and Fundamental Research Funds for the Central Universities (USRP11561, JUSRP51510).  
[PERSON] derived the identification algorithm and was in charge of the overall research. [PERSON] helped with preparing the manuscript.  
Figure 4: The predicted outputs and the measured outputs. (**a**) For the noise variance \(\sigma^{2}=0.10^{2}\). (**b**) For the noise variance \(\sigma^{2}=0.50^{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \(\mathbf{p=1}\) & \(\mathbf{p=5}\) & \(\mathbf{p=12}\) & **True Values** \\ \hline \(a_{01}\) & \(-0.4613\pm 0.0261\) & \(-0.7870\pm 0.0217\) & \(-0.7921\pm 0.0249\) & \(-0.80860\) \\ \(a_{02}\) & \(0.0186\pm 0.0168\) & \(0.2275\pm 0.0173\) & \(0.2376\pm 0.0268\) & \(0.25514\) \\ \(b_{00}\) & \(0.7931\pm 0.0161\) & \(1.0017\pm 0.0170\) & \(1.0071\pm 0.0231\) & \(1.00000\) \\ \(b_{01}\) & \(-0.0777\pm 0.0191\) & \(-0.5491\pm 0.0217\) & \(-0.5954\pm 0.0350\) & \(-0.61623\) \\ \(b_{02}\) & \(0.3489\pm 0.0178\) & \(0.4452\pm 0.0203\) & \(0.5015\pm 0.0307\) & \(0.50931\) \\ \(a_{11}\) & \(-0.5009\pm 0.0225\) & \(-0.8320\pm 0.0203\) & \(-0.9036\pm 0.0269\) & \(-0.94779\) \\ \(a_{12}\) & \(0.0951\pm 0.0195\) & \(0.4552\pm 0.0203\) & \(0.5323\pm 0.0260\) & \(0.57794\) \\ \(b_{10}\) & \(0.7872\pm 0.0197\) & \(0.9938\pm 0.0203\) & \(1.0007\pm 0.0295\) & \(1.00000\) \\ \(b_{11}\) & \(0.1037\pm 0.0168\) & \(-0.3375\pm 0.0244\) & \(-0.4518\pm 0.0327\) & \(-0.49522\) \\ \(b_{12}\) & \(0.2375\pm 0.0171\) & \(0.5744\pm 0.0256\) & \(0.7177\pm 0.0312\) & \(0.75553\) \\ \(c_{2}\) & \(0.6130\pm 0.0121\) & \(0.4961\pm 0.0141\) & \(0.4949\pm 0.0207\) & \(0.50000\) \\ \(c_{3}\) & \(0.4446\pm 0.0185\) & \(0.2510\pm 0.0138\) & \(0.2462\pm 0.0166\) & \(0.25000\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The AM-MISG parameter estimates with standard deviations (\(\sigma^{2}=0.50^{2}\)).
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 5 Proposed Model
### Proposed Q-Learning Model  
In our Q-learning model, since the continuous space is too large and not practical, the target environment is divided into small square cells with equal sizes. The DBS is the agent obviously, who performs the actions while the user moves around the cells.  
The components of the model are defined as follows.  
State Space: The state represented by \(S=[\mathcal{L}_{d},\mathcal{L}_{u}]\), includes the 2D location of DBS \(\mathcal{L}_{d}\) and the 2D location of the user \(\mathcal{L}_{u}\). In this case, the 2D location of the user or the DBS is the center of a cell. Given \(C\) small cells in the environment, the size of the state space would be \(C\times C\).  
Action Space: The DBS action space in each state contains eight different moving directions towards a cell that leads to updating the state of the problem. Clearly, the drone cannot perform some of the actions in certain states, due to the environment border. A penalty is then considered for taking these actions.  
Reward: The agent needs a reward function that encourages the drone to perform actions reaching a higher performance for the model, as well as avoiding the performance of invalid actions. The main objective is to enhance two-hop communication capacity, therefore, in this model the reward reflects the two-hop communication performance corresponding to the current state of the problem. Note that the state of the problem composed of the location of user and the location of the drone. The DBS selects a direction and moves during the next time step to reach the next state. We choose the two-hop capacity presented in the Equation (5) as a reward to encourage the drone. On the other hand, the agent should be penalized in case of taking certain actions. Hence, the reward function \(R\) is defined as follows:  
\[R(s,a)=\begin{cases}-10,&\text{if }a\
otin valid-actions\\ \min(\mathcal{C}_{d,u},\mathcal{C}_{m,d})&\text{otherwise.}\end{cases} \tag{10}\]  
Figure 2: Deep Q-learning model.  
The Q-values are then updated according to the Equation (7), and the learning process repeats until converging to a Q-table. Finally, the drone performs actions following the learned Q-table during its mission. Duration of the missions depends on the battery level of the DBS. Given the equal time steps and constant drone speed, the battery depletes after a particular number of transitions of drones between states.  
We will apply the decaying epsilon greedy in our Q-learning model where the epsilon starts with 1, and its value reduces gradually according to the following equation, until reaching to zero.  
\[\epsilon=e^{(-1\mathit{isdecay\_rate})}, \tag{11}\]  
where \(i\) increases by each learning step.
## 3 Proposed Approach
### The HFuzzNDA Approach
```
1: Denote the current set of clusters belonging to the considered class as \(CurSet\)
2:for\(p\)-th cluster belonging to the considered class, \(p=\overline{1,k_{class}}\)do
3:for\(q\)-th cluster belonging to the considered class, \(q=\overline{1,k_{class}}\)do
4:if\(p\
eq q\)then
5: Calculate fuzzy similarities \(FS_{pq}\) between \(p\)-th and \(q\)-th clusters
6:endif
7:endfor
8:endfor
9: Find the \(maxFS=\max\{FS_{pq},p=\overline{1,k_{class}},q=\overline{1,k_{class}}\}\)
10: Determine centres of the most similar clusters \(cntr_{1}\) and \(cntr_{2}\) corresponding to the \(maxFS\) value
11:if\(maxFS>0\)then
12:\(knew_{class}=k_{class}-1\)
13: Create a new (empty) set \(TempSet\), which can contain \(knew_{class}\) clusters
14: Create new cluster with centre \(cntr\) by merging two the most similar clusters
15:\(cntr=0.5\times(cntr_{1}+cntr_{2})\)
16: Fill set \(TempSet\) with newly created cluster and all clusters from \(CurSet\) except two the most similar
17: Execute the PFCM algorithm with new \(knew_{class}\) clusters from \(TempSet\) to update them
18: Choose \(N_{b}\) instances belonging to the considered class
19: For chosen instances and both sets \(TempSet\) and \(CurSet\) calculate the \(f_{merge}\) values
20: Choose the set of clusters with better \(f_{merge}\) value
21:endif
```  
**Algorithm 3** The merging procedure  
Finally, during the novelty detection phase, instances stored in the short memory are divided into clusters using the NL-SHADE-RSP algorithm. The maximum possible number of short-memory clusters is set to the current value of \(c_{max}\). After that, membership and typicality matrices are determined. Then, the standard steps of the novelty detection procedure introduced for the PFuzzND approach are conducted. The general scheme of the proposed approach is demonstrated in Figure 1.
## 4 Biological Activities of Hop Compounds
### Effects on Specific Diseases  
#### Anti-Inflammatory Activity  
Inflammation is a biological response of the organism to injury or infection [12]. However, according to the authors, an excessive inflammatory reaction is associated with certain diseases such as cancer or ischemic heart disease, among others. Bitter acids and polyphenic compounds from hops have been identified as promising molecules to inhibit inflammatory processes. Both molecules act on the nuclear factor kappa B (NF-kB) that is involved in the expression of pro-inflammatory genes such as tumor necrosis factor alpha (TNF-\(\alpha\)), inducible nitric oxide synthase (iNOS), kinases, cyclooxygenase 1 and 2 (COX-1 and COX-2), and an amount interleukins [12; 64]. Several in vitro and in vivo studies evidenced that the anti-inflammatory effects of hops are primarily attributed to humulone, xanthohumol, and 8-prenylnaringenin [12; 64]. For instance, [PERSON] et al. [65] reported that the topical application of humulone inhibited 12-O-tetradecanoylphorbol-13 acetate (TPA)-induced COX-2 expression through the regulation of nuclear factor-kB. Humulone also reduces the expression of various kinases related to the inflammatory response [66]. Xanthohumol and 8-prenylnaringenin also showed a strong anti-inflammatory effect via the inactivation of NF-kc in microglial cell lines [67]. It has also been studied that the isoxanthohumol reduced inflammatory factors including tumor necrosis factor alpha (TNF-\(\alpha\)) and nuclear factor kappa B in human aortic smooth muscle cells and human umbilical vein endothelial cells [68]. In both animal models and human intervention trials, isohumulones have been effective in the treatment of inflammatory arthritis [69].
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 5 Experiments
### Setup
#### 5.2.4 Models  
Considering the algorithms/models used in related works (Table 1) and the available built-in models on Concrete ML, we considered tree-based models and neural networks. Thus, we chose Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) among tree-based models and Fully Connected (FCNN) among neural networks.
# Advanced Technologies in Data and Information Security
## 2 Blockchain Technology  
Blockchain technology is one of the latest security technologies that has attracted the increasing attention of various actors in many fields, including the educational sector and the Internet of Vehicles.  
[PERSON] et al. [1] proposed HEDU-ledger, a secure architecture for attestation, verification and traceability of higher education degrees based on a private permissioned blockchain network, and in particular the Hyperledger Fabric. The HEDU-Ledger architecture is a complete decentralised solution that first endorses attestation records, and then validates the degree and maintains the secure chain between the stakeholder peer nodes. The stakeholders and other connecting parties are initially identified and authenticated by the Higher Education Commission (HEC) using digital signatures. Moreover, the authors presented the entire mechanism of the proposed architecture, including the HEC policy, the attestation and verification criteria, and the other elements involved in the procedures. Finally, HEDU-Ledger also records more details about the stored ledger in an IPFS (InterPlanetary File System) data storage structure in protected and immutable form.  
[PERSON] et al. [2] conducted a systematic literature review providing an overview of the current state-of-the-art related to blockchain applications in education.  
The systematic review was based only on articles published in peer-reviewed journals, that were identified by searching eleven scientific databases. The analysed papers answered research questions about the current state of blockchain applications in education, the features of blockchain that could benefit this sector, and the challenges that need to be addressed. According to the authors' findings, there are no widely used blockchain solutions in education, but only models, pilots and proofs of concept. However, the progress is continuous and the initiatives are becoming more and more interesting, as they prove that blockchain can be a key technology in future educational scenarios.  
In another direction, [PERSON] et al. [3] performed a literature review to identify proposed solutions for different sectors of Internet of Vehicles (IoV), which use blockchain technology, while also attempting to protect the privacy of involved parties. The privacy protection concerns users' identity, vehicles' location and exchange data between vehicles and infrastructures. In this context, the authors analysed the main characteristics and properties of existing solutions to provide a comprehensive and critical overview and identified their contribution to the field. Moreover, this review provides researchers with suggestions for future work in the field of privacy-preserving blockchain-enabled solutions for vehicular networks.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 1 Introduction
[26] introduced the micro-computed tomography (CT) technique in detail. [PERSON] et al. [27; 28] conducted ambient vibration tests (AVTs) and finite element analysis of a cable-stayed bridge, and successfully identified the significant mode frequency of a long-span cable-stayed bridge below 1.0 Hz. [PERSON] et al. [29] proposed an improved method for determining load rating based on field static load tests and finite element analysis. Compared with traditional methods, the improved method can provide more accurate load rating results for all limit states. [PERSON] et al. [30] presented a performance evaluation method for steel-concrete composite beam bridge superstructures based on dynamic loading tests, and analyzed lateral load distribution, internal force redistribution, and dynamic load allowance. [PERSON] et al. [31] carried out an experimental study on Qingzhou Bridge, and presented the results of static field loading tests and numerical analyses on deck displacement, tower displacement, and stresses of the bridge deck. The results showed that the bridge possessed an adequate load-carrying capacity. A significant number of studies have been devoted to the performance evaluation of complex bridge structures based on field load tests. Previous studies have made some contributions to the loading testing of long-span bridges, but there are still some disadvantages. Due to the complexity of cable-stayed bridges' structure, most studies only focus on either static load testing or dynamic load testing. Moreover, the advanced level of instruments and equipment used in loading tests is not enough, leading to greater labor costs, and possibly causing large test error. This study proposed a field-loading-test-based non-destructive performance evaluation method for long-span stayed-cable bridges, and carried out the experimental study of mode, displacement, strain, and cable force. The load test scheme and non-load test scheme were introduced in detail. Then, cable-stayed bridge in Guangxi with the largest span (Pingnan Xiangszhou Bridge) was selected as a case study to illustrate the applicability of the proposed method. This proposed method can simultaneously analyze the static and dynamic characteristics of the complex long-span cable-stayed bridge, comprehensively test the key performance indices, and accurately evaluate the safety performance of the bridge system. The reasonable test method and specific test scheme, as well as advanced, high-precision testing instruments, were adopted in order to obtain reliable test results. The findings of this study can provide reference for the non-destructive testing research of long-span cable-stayed bridges.
## 2 Materials and Methods
### Semantic Segmentation Using Deep Learning  
Semantic segmentation is the process of linking each pixel in an image to a class label. Applications of semantic segmentation include autonomous driving, robotics, medical research, space imaging, etc. Various methods have been proposed to perform this task, with the most common methods recently shifting to deep learning algorithms. [PERSON] et al. [3] proposed a fully convolutional network (FCN), trained end-to-end for image segmentation tasks. It is a conventional classification neural network in which all fully connected layersare replaced by convolutional layers. An additional advantage of replacing fully connected layers is that the modified network is not limited to fixed-size input dimensions and thus can be applied to input images of any size. Because high-level activation maps are small in size, the final maps cannot represent an object's details very well. Instead, a remedy in form of an up-sampling layer is used to generate a final map with the same size as the input. DeconvNet is a novel semantic segmentation algorithm presented by [PERSON] et al. [17], which is composed of two separate parts: encoder and decoder. The encoder is a convolutional network with a VGG16 architecture generating a vector of features, while the decoder is a deconvolutional network taking the vector of features as input and generating a probability map of each pixel. The deconvolution network expands the dimensions of feature maps while keeping the class information-dense, whereas the unpooling layers preserve the information's location in the feature maps. [PERSON] et al. [2] suggested an extended version of FCN for biological microscopy applications called U-Net. Similar to DeconvNet, U-Net is composed of encoding and decoding parts. During the encoding process the number of features increases while the dimensionality decreases. Conversely, the decoding part increases dimensionality and decreases the number of features. To keep the pattern information, U-Net includes \"skip connections\" after each block, copying feature maps from the encoder to the decoder part. DeepLabv1, proposed by [18], uses the concept of atrous convolution to explicitly control the resolution of feature maps within deep convolutional neural networks. DeepLabv3 [19] is a newer variant of the first network using spatial pyramid pooling to segment objects at multiple scales with filters at multiple sampling rates and effective field-of-views. Atrous convolution captures multiple scales of an input image. Without the max-pooling layer, the resolution of the final output does not decrease, but the number of weights remains the same. Atrous Spatial Pyramid Pooling (ASPP) is a set of several atrous convolutions with different rates that are applied to the same input. It processes feature maps into separate branches and then concatenates them.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### RQ3: What Are the Security Mechanisms or Protocols Required by Smart Health Systems Based on 5G?
#### 3.3.3 Coding  
Finally, encryption is another of the mechanisms used to guarantee and protect the confidentiality of smart health systems from attacks that seek to steal information for impersonation or invasion of privacy. Only 2.27% (\(n=1\)) of the clinical applications used this mechanism.  
[PERSON] et al. [29] employed physical network layer (PCN) encryption in telesurgery systems, because the use of cryptographic algorithms requires more resources due to their complexity and could reduce system efficiency. This technique is applied to protect wireless network information and to achieve high-speed, low-latency communications. It uses some PCN techniques, such as polar coding, low-density-parity check coding, and lattice coding. It uses the properties of wireless channels (noise, fading, and interference) to deteriorate a signal received by malicious users who try make use of the system. It allows secure, keyless transmission through the design and signal processing mechanisms. Unlike cryptography, this technique is not based on data encryption and decryption, so it can realize flexible security configurations for processing.