esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Attacks in Smart Healthcare Systems
### Man-in-the-Middle (Sniffing, Eavesdropping, Data Integrity) Attacks  
MiTM attacks, where a preparatory (attacker) interferes with the conversation between two legitimate users. The preparatory (attacker) either intercepts (eavesdrop) the communication or uses impersonation (using address resolution protocol (ARP) spoofing) to jeopardize the communication between two parties. The sole purpose of this attack is to passively listen to ongoing communication and steal personal information for their own benefit. In a smart healthcare system perspective, the MiTM attacker hijacks the communication between healthcare providers, wearables, or patients and doctors, whereby they unethically alter or copy healthcare data. Further, with MiTM attacks, the attacker inserts malicious payloads that disrupt the patient's monitoring system. The smart healthcare system consists of different portable sensor nodes, which require short-range communication (with low bandwidth) to relay their information to other sensors. Formally, they use conventional Bluetooth technology (with low energy) to exchange data with local servers; however, intruders launch MiTM attacks by leveraging the precarious communication link between sensors and local servers [70]. The attackers either uses publicly available tools, such as Hetty, Bettercap, MiTMproxy, and Proxy,py, or they develop their own sophisticated MiTM tool that the first line of defense (e.g., firewalls) cannot detect. Using the tools mentioned above, an attacker can successfully intercept (sniff) the communication (both HTTP and HTTPS) between sensors and local servers; then, it conducts traffic analysis to find the sensitive information. As a result, the attacker can insert the healthcare emergency or abnormal medical data into normal data and send it back to the local servers. For example, in [71], a security expert from McAfee shows a MiTM attack on insulin pumps, where a modified antenna and software are designed to wirelessly control the device. They show how an attacker can remotely control the insulin dosage of a diabetic patient. Further, two researchers, [PERSON] and [PERSON] from QED security, improved the detection of attacks by determining the radio frequencies between the insulin pump and its controller known as \"boluses\". They stated that the communication between implant device and controller is entirely unencrypted and that it is relatively easy for an attacker to perform a coordinated MiTM attack [72]. Furthermore, the authors of [73] considered a smart healthcare communication environment along with an elliptic curve-based secure and preserve protocol to maintain the data authentication and integrity during the wireless communication between patient and healthcare experts.
# Study on Seismic Reduction Measures of a Diaphrag Wall--Underground Structure System
## 3 Combined Measures of the Sectional Interlayer and Column Top Sliding Support
### The Effect of Combined Measures
#### 3.2.3 Internal Force Analysis of Key Sections  
The changes of the maximum bending moment, axial force and shear force of the key structural members in the station structure are shown in Figures 28-30 by adopting the combined measures of the segmented isolation layer, the sliding bearing on the column top and the sliding bearing on the column top of the segmented isolation layer. DW, SIL, SB and CM represent the diaphragm wall, segmented isolation layer, sliding bearing and combined measures in the figures, respectively.  
Figure 28: Bending moment \(M\) of the key section of the structure.  
Figure 27: Cloud diagram of the compression damage of the structure: (**a**) the diaphragm wall; (**b**) the segmented separation layer; (**c**) the sliding bearing; (**d**) the combined measures.  
Figure 30: Shearing force \(Q\) of the key section of the structure.  
Figure 29: Axial force \(N\) of the key section of the structure.  
It can be seen from Figures 28-30 that, compared with the case of only the diaphragm wall, the sliding bearing generally reduces the internal force at the middle column, side wall and the connection between the middle plate and the side wall of the station structure, but slightly increases the internal force at the connection between the top plate and the side wall. Additionally, when compared to the case with only the diaphragm wall, the combined measures of segmented isolation layer and sliding bearing generally decrease the internal forces on the middle column, side wall and the joints between the top and middle plates and the side wall of the station structure. The combined measures of segmented isolation layer and sliding bearing have a noticeable effect on reducing the internal forces of the main structure compared to the installation of sliding bearings alone.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### Copula Theory
#### 2.1.3 Selection of the Best-Fitting Copula  
The copula parameter \(\theta\) in the copula function describes the correlation between variables. Different types of copula functions have different characteristics in describing the correlation between variables. It is necessary to select the most suitable copula function when modeling the correlation between variables [39]. The best-fitting copula can be selected based on the minimum value of the Akaike information criterion (AIC) or the Bayesian information criterion (BIC) [40]. The AIC and BIC are defined as follows:  
\[\text{AIC}=-2\ln(\text{L}(\theta))+2n, \tag{4}\]  
\[\text{BIC}=-2\ln(\text{L}(\theta))+n\ln N. \tag{5}\]  
where \(\text{L}(\theta)\) represents the likelihood function value of the copula function value with \(N\) samples, \(n\) represents the number of parameters in the copula function, and \(N\) is the number of samples.
## 5 Conclusions  
Stray light nonuniform background elimination is not only a requirement of space-based surveillance, but is also an essential prerequisite for subsequent target detection and tracking. To overcome the insufficiency of current methods in accurate stray light nonuniform background elimination, we proposed a robust and accurate elimination method based on image block self-adaptive gray-scale morphology (IBSGM).  
In this study, we first analyzed the formation and elimination principles of stray light nonuniform backgrounds. Then, we defined two structural operators with different sizes and domains, which can make full use of the difference information between the target region and surrounding background region. Finally, we blocked the original surveillance image to obtain the optimal sizes of the structural operators suitable for performing morphological operations on each image block. The experimental results on simulated and real image datasets show that, compared with other methods, IBSGM has higher precision in eliminating nonuniform backgrounds with nearly no target losses.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA17010205.  
Not applicable.  
The study did not report any data.  
The authors are grateful for the anonymous reviewers' critical comments and constructive suggestions.  
The authors declare no conflict of interest.
## 5 Simulations and Performance Analyses  
In this section, some examples with simulated data are used to verify the suppression performance of the proposed joint optimization algorithm. In the simulation, the transmitted digital video broadcasting signal is approximated by the Gaussian random signal [8]. The important parameters of the simulated scenarios are given in Table 1. The reference channel receives the DP signal and three MP signals. The normalized Doppler frequency of the DP signal is 0.5, and the DP signal to noise ratio is set to 60 dB where the noise power is normalized. The relative (relative to DP signal) time bins-relative (relative to DP signal) normalized Doppler frequencies-MP signal to DP signal ratios (MDRs) of three MP signals are (3, 0.23, \(-\)18 dB), (4, \(-\)0.08, \(-\)18 dB), and (9, \(-\)0.22, \(-\)25 dB), respectively. The velocity standard deviations of ICM-CP fluctuation and ICM-MP fluctuation are \(\sigma_{1,v}=0.5\) and \(\sigma_{2,v}=0.7\), respectively. A total of 20 training samples are used in global and local searches to select atoms. Further, 200 training samples are used to estimate the covariance matrix. In addition, we set \(Q=11\), local threshold \(\delta_{1}=0.00001\) and a search threshold \(\delta_{2}=0.1\) for the proposed joint optimization algorithm.
## 2 Biogas Production in AnMBRs
### Process Yield
#### 2.2.2 Impact of Temperature
More specifically, for temperatures of 25 \({}^{\circ}\)C; 20 \({}^{\circ}\)C; and 15 \({}^{\circ}\)C the recorded values of the methane yield were equal to 0.244 L/g COD, 0.234 L/g COD and 0.205 L/g COD, respectively. In turn, [PERSON] et al. [76] have demonstrated that in a pilot-scale submerged AnMBR used for municipal wastewater treatment, decreasing the temperature below 15 \({}^{\circ}\)C resulted in negligible biogas production. What becomes apparent from the discussed studies is that, generally, increasing temperature leads to an increase in the biogas and methane yield. Analysis of data collected from the literature (Figure 5) shows the slight impact of the temperature on the average methane yield. It can be observed that the highest process performance (methane yield: 0.312 L/g COD and 0.333 L/g COD) was obtained under the highest values of temperature (33 \({}^{\circ}\)C and 35 \({}^{\circ}\)C) applied. This finding can be attributed to the fact that increasing temperature allows for an increase in the reactivity and kinetics of the biological and chemical processes of biogas production. On the other hand, it has to be emphasized that since heating the reactors requires energy, it leads to an increase in the AnMBRs technology cost. Based on the findings presented above, it can be indicated that there is a lack of feasibility studies on biogas production in AnMBRs via the treatment of municipal and domestic wastewater under thermophilic conditions. Although mesophilic conditions are more stable, thermophilic ones demonstrate several benefits, such as earlier organic material degradation and thus shorter hydraulic retention times and greater efficiency [16; 39]. Hence, it would be meaningful to investigate biogas production in AnMBRs under thermophilic conditions.
## 6 Future Research Directions and Discussion
Therefore, the use of 5G NB-IoT NTN is recommended for remote areas and sub-urban IoT applications. This is an emerging IoT solution that will help to address the problem of poor availability of cellular networks in these locations. Hence the 5G NB-IoT NTN will enable ubiquitous IoT connectivity, especially in rural developing countries that lack numerous IoT applications due to poor availability or out-of-reach of cellular networks. Above all, a reliable 5G and LPWAN-IoT architecture for improved smart cities should support the following:  
* Diversified backhaul connectivity;
* QoS/QoE support;
* Adequate integration with emerging technologies;
* Adequate interference management strategies;
* Endogenous security defense mechanism support;
* Ubiquitous or global connectivity support. Hence, future research should be directed toward the aforementioned considerations for improved smart cities.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 2 Related Works
### Reinforcement Learning in SDN
In contrast to prior research, our emphasis is on redressing the imbalance in recent studies, which prioritized network performance but overlooked vital aspects such as privacy, security, and real-time communication delay. We introduce an enhanced PPO algorithm tailored for optimizing multi-path routing in SDN, with a significant focus on security, network delay, and variations in multi-path delays.
## 4 Methodology
### Route Discovery Security  
The proposed protocol concentrated on data protection for the selected beam based on heuristics routing in the second stage. To guarantee connectivity solutions with the least propagation delay, it also provides a route maintenance plan [6]. The proposed protocol uses a CEM that allows nodes to simultaneously encrypt data packets with randomization [38]. Three elements are required to start the CEM encoding method: a data package (\(D_{i}\)), a private key (\(K\)), and counter bits (\(C\)). To create the distinctive pattern of the counter block \(B_{i}\) shown in Equation (4), the node \(N_{i}\) and counter bits are combined together and then sent through an encoding function \(E_{f}\) using key \(K\).  
\[B_{i}=E_{f}(K(N_{i}+C)) \tag{4}\]  
An encoder enables the encoding and decoding of the packet header, and a key generator is used to carry out the learning process. The network's parameters were optimized through a number of experiments, enabling the system to encode and decode data packets and \(B_{i}\) very quickly. The activation function used in this experiment is given by Equation (5).  
\[f(x)=\frac{1}{1+e^{-x}} \tag{5}\]  
where \(f(x)\) is the sigmoid function. The originating nodes \(n_{i}\) and \(E_{f}\) are used to create the new counter block \(B_{i+1}\) for the data packets \(D_{i+1}\) after being concatenated with \(N_{i}\) and \(C\). The ciphertext sequences \(c_{i},\ldots,c_{n-1},c_{n}\) nodes for \(n_{i},\ldots,n_{n-1},n_{n}\) are consequently obtained in the BS, indicating that the decoding function \(D_{f}\) with the same key is used to obtain authentic data packets \(D_{i}\) where \(i=1,\ldots,n\) applying Equation (6).  
\[D_{i}=\left[D_{f}(K(N_{i}+B_{i}))\right]\oplus c_{i} \tag{6}\]
## 2 Reconfigurable Holographic Surface  
### Physical Structure  
Holographic surface is a type of leaky-wave antenna, which mainly consists of four parts, including feeds, waveguides, metamaterial radiation elements, and radiation amplitude controllers. The feeds are embedded in the bottom layer of the RHS to generate reference waves carrying useful information propagating along the RHS. Such a structure makes the RHS thinner than traditional antenna arrays. The waveguide serves as the transmission medium for the reference wave and guides it to propagate on the surface. Metamaterial radiation elements are used to generate leaky waves. When it is excited by the reference wave, it will radiate an electromagnetic wave, which is called a leaky wave. Radiation amplitude controllers are implemented to regulate the amplitude characteristics of each component. By manipulating the magnetic or electric field bias, the radiation characteristics of each element can be adjusted, enabling the implementation of beamforming [14].
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
###### Abstract
For the first system, the electricity generated by the wind turbines travels a short distance to the electrolyzer platform, where hydrogen is produced, compressed, and transported to shore in a pipeline. On the other hand, for the second system, the electricity is transmitted to shore by a traditional cable, where a choice can be made: sell the electricity directly to the grid or produce hydrogen. This is known as a hybrid system, where the operator can control the amount of power being sold to the grid or fed into the electrolyzer, even being able to buy electricity from the grid to produce hydrogen during periods of extremely low electricity prices, which provides load flexibility to the grid operator as well. Since the source of the electricity powering the electrolyzer is wind farms, no carbon is emitted during the production of hydrogen. This paper is concerned with hydrogen production using electricity coming from offshore wind farms, i.e., green hydrogen production. The paper offers an overview of the current situation on the subject by highlighting the main features of the technologies used by the different components of the hydrogen production system as well as an outline of the system configuration options (offshore vs. onshore electrolyzer location) and potential uses of hydrogen. Moreover, the paper reviews the main recent research topics related to the subject through a thorough literature review, including state-of-the-art reports and journal papers. The aim is to point out directions of future developments in green hydrogen production from wind power and other renewables. The remainder of the paper is organized as follows. In Section 2, a discussion of the system components used for hydrogen production from offshore wind power is offered. The system configurations, i.e., the option between the offshore and onshore location of the electrolyzer, are discussed in Section 3. A summary of the main hydrogen uses is presented in Section 4. Section 5 contains a literature review of the main subject areas being investigated, including a comparison regarding the Levelized Cost of Hydrogen (LCOH) produced from different renewable energy sources. Finally, in the last section, the main conclusions of the work performed are drawn.
# On the Performance of LDPC-Coded Massive MIMO Schemes with Power-Ordered NOMA Techniques
## 1 Introduction
Two receivers are used in this study: the Zero Forcing (ZF) receiver and the Maximum Ratio Combiner (MRC) receiver. It is shown that the use of LDPC-coded NOMA with m-MIMO, when combined with conventional or cooperative NOMA, can be utilized with a low level of complexity when the MRC receiver is adopted, whose performances in different scenarios is very close to the ZF receiver (which has much higher computational requirements). This article is organized as follows: Section 2 describes the system characterization for m-MIMO using SC-FDE transmissions; Section 3 deals with the NOMA concept; Section 4 analyzes the performance results; and Section 5 concludes the article. Figure 1: The three groups of 5G use cases.
## 6 Conclusions  
The iterative virtual events method, when used to suppress internal multiples, involves first using the traditional virtual events method to build the initial multiple models, and then iterating the initial multiple models to complete the suppression of internal multiples. This approach not only simplifies the process of multiple suppression and improves computational efficiency and accuracy, but also obtains more abundant multiple information. From the model test, field data processing, and profile and spectrum analysis before and after processing, we can conclude the following:  
1. The internal multiples in the post-stack data are suppressed using the iterative virtual event internal multiples suppression method, significantly reducing the number of events close to the primary shape.
2. After suppressing the internal multiples, the spectrum is significantly broadened, the overall energy is raised, and the resolution is improved.
3. The phenomenon of multiples' energy and frequency varying greatly in the horizontal direction is weakened. The obtained seismic profile is more conforming to the actual geologic structure.  
Figure 11: Spectrum analysis before and after the internal multiple suppression. (**a**) Partial stacked sections before the internal multiple suppression; (**b**) partial stack section after the internal multiple suppression; (**c**) frequency distribution curve before and after the internal multiple suppression.  
These results indicate that this method can effectively suppress the internal multiples in seismic data and can be widely used in the internal multiples suppression of field data.  
Conceptualization, M.X. and W.W.; methodology, J.X. and W.L.; software, J.S.; validation, W.L., J.S. and B.J.; formal analysis, Y.Z. and W.L.; investigation, T.Z. and Y.Z.; resources, Y.W. and B.J.; data curation, J.X., Y.W. and T.Z.; writing--original draft preparation, M.X. and Y.W.; writing--review and editing, W.W. and J.X.; visualization, B.J. and M.X.; supervision, W.W. and W.L.; project administration, Y.Z.; funding acquisition, T.Z. and W.W. All authors have read and agreed to the published version of the manuscript.  
We thank the prospective basic projects of CNPC under grant no. 2021 DJ-0304 and the National Natural Science Foundation of China under grant nos. 41974116 and 4227040468 for supporting this work.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Micromodulus
### Influence Function  
The long-range force effect can be characterized by the influence function [16]. In order to improve the applicability of numerical micromodulus \(c_{2}\), the influence function introduced into \(c_{2}\) can be expressed in Equation (17).  
\[c=c_{2}g(\xi,\delta) \tag{17}\]  
where \(g(\xi,\delta)\) is the influence function, which should hold the following requirements [22]:  
\[\left\{\begin{array}{l}\begin{aligned} & g(\xi,\delta)\geq 0\\ &\lim_{\xi\to 0}g(\xi,\delta)=\max\;\;g\\ &\lim_{\xi\to\delta}(\xi,\delta)=0\\ &\delta(\xi,\delta)=g(-\xi,\delta)\\ &\int_{-\infty}^{\infty}\lim_{\delta\to 0}g(\xi,\delta)=\int_{-\infty}^{ \infty}\Delta(\xi)dx=1\end{aligned}\end{aligned}\right. \tag{18}\]  
in which \(\Delta(\xi)\) is the Dirac delta function. \(g(\xi,\delta)=1\) means that the bond force is constant and will not change with the bond length. Eight types of influence functions published in previous studies are listed in Table 3. For convenience, each influence function has a symbol, \(g_{i}\;,\;\;i=1,2,3\ldots 8\), which will be used in the follow-up paper.  
As can be depicted from Table 3, these influence functions are based on physical parameters (\(\delta\) and \(\xi\)), effectively expressing the physical properties of long-range force. Moreover, the spatial strength for the long-range force depends on the bond length. Except for \(g_{1}\), the rest reflect the gradual weakening of the long-range force spatial strength distribution as the bond length increases.  
\begin{table}
\begin{tabular}{c c} \hline
**Horizon \(\delta\)** & **Numerical Micromodulus \(c_{2}\)** \\ \hline
2\(\Delta\)x & 0.405186768\(\frac{E}{\Delta x^{4}}\) \\
3\(\Delta\)x & 0.108285522\(\frac{E}{\Delta x^{4}}\) \\
4\(\Delta\)x & 0.047962844\(\frac{E}{\Delta x^{4}}\) \\
5\(\Delta\)x & 0.025026356\(\frac{E}{\Delta x^{4}}\) \\ \hline \end{tabular}
\end{table}
Table 2: Numerical micromodulus \(c_{2}\).