esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Parametric Analysis  
Parametric analysis of the key parameters of the antenna is performed to investigate their effects on the performance of the antenna, as depicted in Figure 6. As shown in Figure 6a, when the length of the truncated corner was changed from 15.55 mm to 12.73 mm, all the resonances were shifted toward the higher side. An increment in bandwidth was observed for the third while the bandwidth of the first resonance decreases considerably, as depicted in Figure 6a. On the other hand,  
the dimension of \(L_{2}\), as depicted in Figure 6c. It is worthy to note that the second notch band and third resonance remain conserved for all variation in \(L_{1}\) while the first notch and first resonance remain conserved for all variation in \(L_{2}\). Moreover, from Figure 6d, it could also be observed that the gap between the slots (\(g\)) also has less impact of the performance of the antenna. Thus, the proposed antenna becomes a promising candidate for the application where independently controllable notch band antennas are required.
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
###### Abstract  
The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.  
V2X communication; Internet of Things; Internet of Vehicles; heterogeneous networking; software-defined networks; safety-critical vehicular applications +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 70; doi:10.3390/f11030070
## 3 Offline Payment Function after Types of Currency
### Offline Payment Function with E-Cash Proposed by [PERSON]  
[PERSON] et al. [72] divided the members participating in a transaction into the bank, the customer (payer), and the shop (payee) before presenting the transaction protocol of e-cash. Based on their involvement, the transaction is analyzed in three phases as drawn in Figure 2: 1. Withdrawal phase, 2. Payment phase, 3. Deposit phase. The three stages of the transaction are as follows:  
* Withdrawal phase: a transaction phase that involves a bank and a customer (payer);
* Payment phase: a transaction phase that involves a customer (payer) and a shop (payee);
* Deposit phase: a transaction phase that involves a shop (payee) and a bank.  
In online payments, undesired events are less likely to occur in the transaction process, since the three phases of a transaction occur continuously, monitoring or confirming continuously whether malicious actions occur during the processes or not. In contrast to online payment, the three phases occur separately in an offline payment function. Unlike online payment, the probability of malicious actions could increase during offline payment.  
To prevent malicious action, [PERSON] et al. [72] presented the requirements that the transaction members, the payer, and the payee, must satisfy in the offline payment function as follows:  
* The shop (payee): must be assured that the bank will accept the payment [72];
* The customer (payer): must be assured that the withdrawn money will later be accepted for payment and that the bank cannot claim that the money has already been spent. Furthermore, they may require that their privacy be protected [72].  
[PERSON] et al. [72] introduced a method to prevent fraudulent actions in the offline payment function of e-cash by using observers for preventing double spending through a trusted third party. This method begins when a trustee, who acts as an observer in a transaction, obtains a coin list of a payer that can be acquired from the withdrawal protocol if the payer acts with malicious intent, such as when a coin contained in the coin list is used more than once. The trustee intervenes in the offline payment function to prevent fraudulent actions.  
The anonymity of the information that the trustee can obtain through the withdrawal protocol must be guaranteed. Therefore, in the proposed offline payment function, the trustee must satisfy two requirements as follows:  
* The trustee can communicate only with the customer;
* It should be impossible for the bank to obtain and track payment information, even if the bank subsequently obtains information related to the customer through the trustee.  
Once these requirements for the trustee are satisfied, the transaction in the offline status will proceed safely. The solution for fraudulent actions in the offline payment through the trustee will be discussed in further detail in Section 4.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 2 Background
### Open RAN Architecture
#### 2.2.8 Vendor Lock-in Risk  
Another important consideration is the risk of vendor lock-in, which can limit the ability to adapt and innovate.  
The closed and proprietary nature of traditional RAN architectures, such as d-RAN, can expose operators to a higher risk of vendor lock-in as they become dependent on a single vendor for the entire network infrastructure.  
The Open RAN approach, with its emphasis on open interfaces and the potential for a more diverse ecosystem of vendors, can help mitigate the risk of vendor lock-in, as operators can more easily mix and match components from different suppliers.  
v-RAN and c-RAN, with their centralized management and virtualization capabilities, can also help reduce the risk of vendor lock-in, as network functions can be more easily migrated or replaced across different cloud platforms or data centers.  
Table 1 summarizes the key performance indicators and trade-offs for the different RAN architectures.
## 3 Data and Methods
### Model and Simulation Design  
To test the performance of the PBL parameterization schemes in simulating the winter-time lower troposphere in an urban area, we used Weather Research and Forecasting (WRF) model version 4.1.3 [18] in this study. We adopted four domains with two-way nesting, and the horizontal grid resolutions of the domains were 27, 9, 3, and 1 km (Figure 3). The model domain had 72 vertical layers and 43 vertical layers below \(\sim\)2 km from the ground to accurately simulate the lower troposphere. The vertical grid resolution below 1.5 km was -40 to \(\sim\)50 m. We used the 6-hourly National Centers for Environmental Prediction (NCEP) final analysis data with a horizontal grid resolution of 0.25\({}^{\circ}\) as the initial and boundary conditions. We ran four simulations with the YSU [19], Mellor-Yamada-Janjic (MYJ, ref. [20]), Mellor-Yamada-Nakanishi-Niino (MYNN, ref. [21]), and Asymmetric Convective Model version 2 (ACMZ, ref. [22]) PBL schemes. These four PBL schemes are the most widely used PBL schemes among the WRF model users. The YSU and ACM2 schemes are non-local schemes, and the MYJ and MYNN schemes are local schemes using a turbulence kinetic energy closure. In the four simulations, we used the surface-layer schemes relevant to the PBL schemes. We used the revised fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) surface-layer scheme for the YSU PBL scheme, the Eta similarity surface-layer scheme for the MYJ PBL scheme, the MYNN surface-layer scheme for the MYNN PBL scheme, and the Pleim-Xu surface-layer scheme for the ACM2 PBL scheme. For other physics schemes, we used the Dudhia shortwave radiation scheme [23], Rapid Radiative Transfer Model (RRTM) longwave radiation scheme [24], Kain-Fritsch cumulus convection scheme [25] only for the outermost two domains, and WRF single-moment 6-class cloud microphysics scheme [26] in the model. Additionally, we used a single-layer urban canopy model [27] to precisely reproduce urban meteorology. We integrated the model from 0300 LST on 16 January to 0300 LST on 19 January, and we used simulation data during the radiosonde observation period for the analysis.  
Figure 2: Satellite image showing radiosonde-launching site and AWS site in Seoul (NAVER map).
## 2 Principles of MDP and Reinforcement Learning
### Principles of RL
The implementation method includes the average entropy constraint to the original objective function and allows the entropy value to change in different states during training. The algorithm for training SAC RL agents for power flow control is given in Algorithm 1[23]. More details of the SAC algorithm can be found in [23] and are not repeated here due to space limitations. ```
1. Initialize the weights of neural networks, \(\theta\) and \(\phi\), for the policy \(\pi(s,a)\) and value function \(V(s)\), respectively; initialize weights \(\psi\) and \(\overline{\psi}\) for the two \(Q(s,a)\) functions; initialize replay buffer \(D\); set up training environment, env
2. for:\(k=1,2,\ldots\) (k is the counter of episodes for training)
3. for:\(t=1,2,\ldots\) (t stands for control iteration)
4. reset environment \(s\leftarrow\textit{env. reset}\)()
5. obtain states and actions \(a\sim\pi(\cdot|s_{t})\)
6. apply action a and obtain the next states \(s_{t+1}\), reward value \(r\) and termination signal done
7. store tuple \(<s_{t},a_{t},r_{t},s_{t+1},\ done>\) in \(D\)
8. \(s_{t}=s_{t+1}\)
9. if satisfying policy updating conditions, conduct
10. for a required number of policy updates, conduct
11. randomly sample from \(D\), \(<s_{t},\ a_{t},\ r,\ s_{t+1},\ done>\)
12. update Q function, \(Q(s,a)\): \(\theta_{i}\leftarrow\theta_{i}-\lambda_{Q}\
abla J_{Q}(\theta_{i})\)
13. update value function \(V(s):\ \psi\leftarrow\psi-\lambda_{V}\
abla J_{V}(\psi)\)
14. update policy network \(\pi(s,a):\ \phi\leftarrow\phi-\lambda_{\pi}\
abla J_{\pi}(\phi)\)
15. update target network \(\overline{\psi}\leftarrow\tau\psi+(1-\tau)\overline{\psi}\)
16. update temperature coefficient, \(\alpha\)
```  
**Algorithm 1**Algorithm for training the soft actor-critic (SAC) agent for power flow control.
## 2 System Model  
This paper considered the power beacon (PB)-aided wireless sensor-powered NOMA IoT networks as in Figure 1, where a base station \((S)\) harvests energy from a PB to transmit the information to two users (an IoT far user (FU) and an IoT near user (NU)) through a DF relay (R). In addition, we assumed that every node had a single antenna and that every node had complete knowledge of the CSI. \(g_{PS}\) and \(g_{PR}\) are the channel coefficients from PB to S and R, respectively; \(g_{SR}\), \(g_{RN}\), and \(g_{RF}\) are the channel coefficients from S to R, R to NU and FU, respectively. Furthermore, all channels follow Rayleigh fading. Thus, \(|g_{j}|^{2},j\in\{PS,PR,SR,RN,RF\}\) is an exponential random variable with the probability density function (PDF) and the cumulative distribution function (CDF) expressed, respectively, as  
\[f_{|g_{j}|}(x) =\frac{1}{\Omega_{j}}e^{-\frac{x}{\Omega_{j}}} \tag{1}\] \[\Gamma_{|g_{j}|}(x) =1-e^{-\frac{x}{\Omega_{j}}} \tag{2}\]  
where \(\Omega_{j}=d_{j}^{\ell}\) denotes the average channel gain, \(d_{j}\) denotes the distance, and \(\ell\) denotes the path-loss exponent.  
Furthermore, we considered the TS protocol for our proposed network as depicted in Figure 2. In particular, the PB transmits the energy to S and R during the period time of \(\beta T\) in the EH phase, where \(\beta\) denotes the TS factor, and \(T\) denotes the coherence time block. For the information transmission phase, the remaining time, \((1-\beta)T\), is divided evenly into two hops of transmission, where \(S\) and \(R\) employ the transmit power derived from the gathered energy for transmitting and relaying operations. Moreover, in the information phase, the channel cannot be perfectly estimated due to the complex channel intricacies. Thus, the channel for the information phase can be modeled as [50]  
\[g_{z}=\varepsilon\hat{g}_{z}+\sqrt{1-\varepsilon^{2}}\hat{g}_{z} \tag{3}\]  
where \(z\in\{SR,RN,RF\}\), \(\hat{h}_{z}\) denotes a circular symmetric complex Gaussian random variable with zero mean and the same variance of \(h_{z}\), and \(\varepsilon\) denotes the channel correlation factor that models the accuracy of the channel estimat  
Figure 1: The wireless sensor-powered NOMA IoT network.
# Multi-RAT Orchestration Method for Heterogeneous Wireless Networks
## 2 State-of-the-Art
It is necessary to gain user experience, which means ensuring a long  
Figure 1: The architecture of LTE networks with locations (marked in color), in which a decision to split the data stream can be made to/from the user terminal (UE). Figure 2: The architecture of LTE networks with locations in which a decision to split to 3 GPP and non-3 GPP access can be made to/from the user terminal (UE). battery lifetime, owing to the reduction of power consumption. From the 5G standardization perspective, ITU in [27] put the energy efficiency aspect as one of the minimum basic technical performance requirements for 5G networks. This requirement encompasses both: (a) Efficient data transmission in a loaded case; (b) Low energy consumption when there are no data. It is worth distinguishing that the total device energy savings can be achieved through the reduction of power consumption by either hardware solutions (e.g., proper CPU, screens) or adequate radio interface solutions, and their implementation. Referring to [27] below, a list of basic features, which are strictly dedicated to power saving aspects, may be indicated:  
* Power consumption reduction during the network access;
* Dynamic adaptation to traffic in different dimensions (carrier, bandwidth, and beamforming);
* Improvement of RRM measurements for low mobility UE. Furthermore, currently, mobile networks, both 4G/LTE and 5G/NR, widely use DRX (Discontinuous Reception) techniques, which allow for partial monitoring of the physical DL channels only in repetitive time slots defined by so-called time profiles. DRX capable UE receives RRC messages with the proper configuration, which allows for full synchronization with the base station. These solutions can reduce the total energy consumption in a macro timescale. Besides the RAT specific approach, mobile network deployments also impact directly on the UE energy consumption, what is caused by radiated power, distance to base station, indoor/outdoor reception, and network densification. In our research scenario, we implemented a new network element, i.e., a high-level E2E orchestrator placed outside radio access networks. The main goal is toggling transmission across different RATs based on instantaneous energy efficiency metrics, which depend directly on the proposed solution. This solution monitors the continuously changing radio network environment and makes decisions on current links usages depending on the present best energy efficiency of terminals during communication.
## 4 Experimental Results and Discussion
### Experimental Setup  
As mentioned previously, the proposed method was validated using actual turnout data from a subway. Comparative experiments were conducted to analyze the proposed method. All models were implemented on a computer with an NVIDIA RTX 3050 GPU and an Intel i5-12500H CPU. The proposed PL-MSMM hyperparameters included \(C\), \(\tau\), and \(p\). We conducted a detailed hyperparameter tuning process and determined the optimal combination of hyperparameter values by conducting experiments with different parameter  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Labels** & **Fault Phenomenon** & **Fault Cause** \\ \hline fault 0 & Normal state & Normal \\ fault 1 & The current is zero & Action circuit fault \\ fault 2 & The current is constant & Mechanical resistance \\ fault 3 & The current suddenly becomes zero & The point machine is not unlocked or in good contact \\ fault 4 & Small step current interruption & The contact of point machine is abnormal \\ fault 5 & The current increases & The point machine friction increases and internal jamming \\ fault 6 & No small step current during action & The indicate circuit is abnormal \\ fault 7 & Spikes in the current & Poor contact of the switch circuit controller \\ fault 8 & The current is large, the curve only maintains 0\(\sim\)1 s & Phase failure of the starting circuit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of different types of current curves of ZDJ9 turnouts.  
Figure 5: ZDJ9 turnout fault 0–8 current curves.  
combinations. Specifically, we chose \(C\) from \(\{1\times 10^{-3},2\times 10^{-3},5\times 10^{-3},1\times 10^{-2},...,1\times 10^{3}\}\) and manually adjusted \(\tau\) for each \(C\), selecting \(p\) from \(\{0.1,0.2,...,0.9,1\}\)  
To thoroughly evaluate the classifier's classification performance, we chose three evaluation metrics--that is, accuracy, precision, and F1-score. The F1-score can be calculated by precision and recall. Because the precision, recall, and F1-score are binary metrics, we adopted a macro-averaging approach to average the same measurements computed for each individual class. These evaluation metrics can be expressed as follows:  
\[\text{Accuracy}=\frac{\sum_{c=1}^{k}\text{TP}_{c}}{\text{Total}}, \tag{25}\]  
\[\text{Precision}=\frac{1}{k}\sum_{c=1}^{k}\frac{\text{TP}_{c}}{\text{TP}_{c}+ \text{FP}_{c}}, \tag{26}\]  
\[\text{Recall}=\frac{1}{k}\sum_{c=1}^{k}\frac{\text{TP}_{c}}{\text{TP}_{c}+ \text{FN}_{c}}, \tag{27}\]  
\[\text{F1-Score}=\frac{2}{\frac{1}{\text{Recall}+\frac{1}{\text{Precision}}}}, \tag{28}\]  
where \(\text{TP}_{c}\), \(\text{FP}_{c}\), \(\text{FN}_{c}\), and \(\text{TN}_{c}\) denote true positives, false positives, false negatives, and true negatives, respectively.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 7 Lectin, a Class of Protein Entangled with Glycoprotein, Affects Host-Pathogen Interactions
### Pathogen Lectins
BabA binds fucosylated residues of Lewis b antigen and H1 antigen, and LabA binds N,N'-diacetylallocastidine residues, these two residues are carried by the mucin MUC5 AC in human gastric surface epithelia [17; 33]. [PERSON] et al. demonstrated that \(\alpha\)(1,2)fucosyltransferase deficient mice resulted in the inhibition of the fucosylation of Lewis b, leading to the significant reduction of _H. pylori_ infection [205]. The pathogen lectins can also suppress the immunity of hosts. A notable example is the C-type lectin. C-type lectin is a class of animal-specific lectin that can possess the Ca\({}^{2+}\)-dependent sugar recognition activity on the cell surface. Several C-type lectins can be secreted by animal-parasitic nematodes (APNs): Tc-CTL-1 and Tc-CTL-4 secreted by _Toxocara canis_, CTL1/2 released by _Heligmosomoides polygyrus_ and _Nippostromyylus brasiliensis_[206; 207; 208]. These C-type lectins share the similar sequence to the immune cell lectins in their mammalian hosts, suggesting these proteins are involved in distinct roles in immune evasion [209]. Moreover, the pathogen-secreted lectins can mask the PAMP chitin from the cell wall by binding the oligosaccharidescharides to evade immune surveillance of hosts, and these lectins include Slp1 from _Magnaporthe oryzae_[21], Epc6 from _Cladosporium fulum_[34], and LysM1 from _Trichophyton rubrum_[35]. Alternatively, pathogen-secreted lectins might also suppress the host immune response by disturbing the sucrose signal. The lectin calreticulin [210; 211] Mi-CRT secreted by _Meloidogyrine incognita_[212] can inhibit the PAMP-triggered immunity (PTI) elicited by ELF18 that derives from the N-terminal of bacterial elongation factor-Tu. The C-type lectin Mg01965 secreted by _M. graminicola_ can suppress the PTI induced by flg22 which is a PAMP from the N-terminal of bacterial flagellin [131]. The above two lectins are unlikely to mask oligosaccharides, since carbohydrate residues are absent in ELF18 and flg22. In plants, endogenous sugar or sugar complex often involves in signal transduction. Indeed, local increases in certain sugar levels have been found under biotic stresses [213; 214; 215]. Shifts in apoplastic/cell sugar content may be sensed by plants and lead to the induction of immunity [215]. Therefore, the pathogen lectins may affect the signal transduction through binding endogenous sugar or sugar complex to suppress the immune responses [216; 217].
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results  
We simulate the base station (and the backend core network) using a computer and the user equipment using a MiniPC, both of which include USRP B210 software-defined radios for the radio frontend. We use _sysRAN v23.10_[38] for implementing the fake base station and the user equipment. To implement the backend 5G standalone core network, we use _Open5 GS v2.6.6_[39]. We modify the NAS- and RRC-layer source codes in _ssRAN v23.10_ at the base station and the user equipment to implement our attacks and defense mechanisms, respectively. Figure 5 shows our hardware setup for the implementation and experimentation.  
This section mainly focuses on our experimental results with the faulty vs. fake base stations, except as shown in Section 7.6. We verify that the connection works well with the legitimate base station (demonstrating the correctness of the 5G implementation) and that the fake base station can deprive the user equipment for an indefinitely long time. The faulty base station experiment in Section 7.3 informs the \(\tau_{T}\) and \(\tau_{N}\) selections. We vary and analyze the \(\tau_{T}\) and \(\tau_{N}\) parameters of our scheme for detecting and blacklisting a fake base station in Section 7.5; we also experimentally show how the use of both \(T\) and \(N\) measurements outperforms using either in our scheme.  
In addition to the faulty vs. fake base station experiment, we also implement link routing in the user equipment and experiment with a fake and a legitimate base station. In the experimentation, we set up a fake base station along with a legitimate base station to demonstrate a Sybil attack, where the fake base station changes its identity after being blacklisted by the user equipment. We show that link routing enables the user equipmentto recover from the fake base station and connect to the legitimate base station given that the fake base station still has the highest SNR compare with the legitimate base station. We analyze the performance of the link routing by measuring the time duration overhead in Section 7.6.