esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 2 Background Review  
VANET congestion control is an important and active area of research, and has received significant attention in recent years, for both safety and comfort applications [23; 24; 25]. Integrating 5G technology with VANET communications for future applications has received significant research attention [26], including issues related to security and privacy [27; 28]. In regard to safety applications, the two types of safety messages are event-driven messages and periodic messages, or basic safety messages (BSMs). Event-driven messages are sent whenever certain events, such as traffic accidents or road hazards, are detected, while BSMs are sent at regular intervals by each vehicle in the network, regardless of the road conditions. This means that as the vehicle density increases, the total number of BSMs being transmitted within the network increases proportionally. According to Ref. [21], the allotted channel bandwidth can quickly become congested from increases in vehicle density, even in simple vehicle networks. In Ref. [29], the authors aim to alleviate congestion by finding the best distribution over EDCA access categories. In Ref. [30], the authors propose congestion control for vehicular safety applications that considers different quality of service needs based on vehicle context, such as the relative position and speed of surrounding vehicles. In Ref. [31], the authors investigate how different parameters for congestion control impact the stability of platooning systems. The work in Ref. [32] provides a comparative study of the different dynamic congestion control (DCC) approaches in terms of CBR, position error, and awareness range.  
In this section, we will first briefly review some important techniques that use message rate or transmission power to control congestion, including hybrid approaches, which use a combination of these parameters to reduce the channel load. Then, we will focus on approaches that adjust the data rate to reduce congestion, both by itself and in combination with other parameters.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 3 Methodology  
### NILM Framework for Adapting to Diverse New Appliances  
The proposed framework is shown in Figure 1. As the preparation for continual learning, a lightweight model with dual output branches is first built using depthwise separable convolutions, as shown in the left part of Figure 1. In the model, the feature extraction module transforms appliance waveforms into informative features. The load identification branch maps the features to appliance types. The novelty detection branch encloses the original data by constructing hyperspheres in the feature space. To enhance the feature representation and facilitate adaptation to new appliances, a pretraining strategy based on supervised contrastive learning (SCL) is proposed, as shown in the middle of Figure 1. When applied to streaming appliance data, the model first freezes all parameters except for the output layers. It then iteratively performs novelty detection and continual learning, as shown in the right part of Figure 1. When new data arrive, the novelty detection branch first determines whether the appliances are learned. If so, the appliance types are estimated by the load identification branch. If not, the appliance types of the unknown waveforms need to be labeled, and continual learning is then triggered. During continual learning, the load identification branch first expands its output layer according to the number of appliance types. Then, the parameters of the two output branches are recursively updated through analytic continual learning: on the one hand, data from new appliances are integrated into the waveform-to-type mapping of the original appliances; on the other hand, features of the new appliances are adjusted within the original hypersphere.
## 3 The Electric Fields in the Medium
### Loss Tangent of Medium  
In the previous chapter, the attenuation constant \(\alpha\) is determined for the Lossy medium and how conductivity effects the strength of the electric and magnetic fields was defined.  
The electromagnetic wave energy propagate through a conductive medium is consumed as heat due to resistance, which can cause power loss in the CPT system. Equation (7b) indicates that the magnetic field consists of the conduction current density component \(J_{\sigma}=\sigma\hat{E}_{x}\) and the displacement current density component \(J_{d}=j\omega\varepsilon_{0}\varepsilon_{r}\hat{E}_{x}\). By plotting the two current density components on the complex plane, the phase relationship between the two current density magnitudes can be expressed as follows [12]:  
\[\frac{J_{\sigma}}{J_{d}}=\frac{\sigma}{\omega\varepsilon_{0}\varepsilon_{r}}= \text{tan}\delta \tag{10}\]  
where tan\(\delta\) is the loss tangent. \(\delta\) can be the represented angle by which the displacement current density leads the total current density and indicates that the loss is caused by the material through which electromagnetic waves are transmitted. According to Equation (10), the loss tangent is determined by the dielectric constant and conductivity of the medium and the frequency of the electromagnetic wave. In general, a tan\(\delta\ll 1\) medium can be evaluated as a good dielectric, and a tan\(\delta\gg 1\) medium can be evaluated as a good conductor [13]. A medium with a low loss tangent has a small loss, and it can be recommended that an insulator with low conductivity is more suitable as a medium to be used in a CPT system.  
In many applications, air is considered nearly lossless and satisfies the good dielectric condition of tan\(\delta\ll 1\) less than the GHz band [14]. Since the loss tangent of most media that can be considered to replace air in a CPT system cannot be better than air, the improvement in coupling capacitance due to high permittivity must be designed to within a limited distance considering field attenuation according to the propagation distance. As a result, if the amount of loss in the medium due to conductivity is greater than the gain due to high permittivity, the transfer efficiency can be lower than that of free space under the same CPT system condition.  
Currently, several kHz to several MHz operating frequencies are adopted in existing CPT systems research [15], and many studies have considered water medium instead of air used for the gap of transmitter and receiver [16; 17; 18] due to its high relative permittivity of 81. In the next chapter, the loss tangent for four types of water is calculated and evaluated whether it is effective in improving the performance of the CPT system.
## 5 Conclusions  
This paper presents quantitative measurement through an experimental test of 640 Gbps 16-QAM CO-OFDM over 16 \(\times\) 50 km optical fiber link with a mid-link OPC, which is implemented using FWM via HNLF. For a given 16-QAM CO-OFDM signal, the design of OPC needs to focus on the selection of HNLF length and determine signal power and pump signal power at the input of OPC, which are highly related to the characteristics of HNLF. There are four different types of HNLFs investigated. The numerical results show that for a given 16-QAM CO-OFDM signal at 193.1 THz (1552.52 nm), the optimum length for HNLF A-D is 550, 500, 750, and 800 m, while the optimum signal power into OPC is \(-2.5\), \(-3.3\), \(-2.4\), and 1.7 dBm and the optimum pump power is 23, 23, 23, and 21 dBm, respectively. The performance evaluation is presented in terms of OPC conversion efficiency, Q-factor improvement, received signal constellation, and BER at the receiver end. It is important because 16-QAM CO-OFDM signal status is heterogeneous and the mitigation of transmission impairment to the signal is still unclear. The study of received 16-QAM signal constellation diagrams shows that OPC plays a better role for compensating fiber dispersion and nonlinearity effect comparing to the case of using DCF. The numerical results show that mid-link OPC using 550 m HNLF A provides Q-factor of 9.8 dB and BER of \(6.1\times 10^{-4}\), which has the Q-factor improvement of 3 dB and nearly two orders magnitude improvement in BER comparing to the case of no OPC. The evaluation methodology presented in this paper provides useful information for OPC design.  
Data curation, J.W.; formal analysis, C.L., Z.L.; methodology, Y.D.; project administration, J.W.; validation, C.L., Z.L.; writing--original draft, Y.D.; writing--review and editing, J.W., Y.D., and J.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Inner Wheel Lifting Phenomena
### Summary of This Section and Setting Specifications for Further Study  
* Lateral displacement \(LD\) is proportional to steering angle \(MA\) and the square of vehicle speed \(v\), and inversely proportional to the square of steering input frequency \(f\).
* Increase of steering input frequency \(f\) itself is disadvantageous for front inner wheel lifting. Increase of input steering wheel angle \(MA\) to maintain lateral displacement \(LD\) on increase of steering input frequency \(f\) causes further severe conditions of front inner wheel lifting.
* Increase of vehicle speed \(v\) requires quicker steering input frequency \(f\), which is disadvantageous for front inner wheel lifting.
* Inner wheel lifting phenomenon is only that vehicle roll is unavailable to follow target roll angle \(TRA\) due to roll moment of inertia of sprung mass \(Ixx^{*}\) and is almost dominated by the roll dynamics of PMVs. Smaller \(Ixx^{*}\) and larger \(K\varphi\), i.e., higher roll resonance frequency _f\(\varphi\)_ improves front inner wheel lifting phenomena.
* Unstable roll phenomena without intentional steering input related to front inner wheel lifting phenomena is suppressed by decrease of I gain of PID tracking control to active roll angle. In order to study realistic roll moment of inertia \(Ixx^{*}\) and roll stiffness \(K\varphi\), PID control parameters in further study are set as Equation (5).  
Figure 16: Equivalent roll dynamics gives same wheel lifting phenomena.  
Figure 15: Roll resonance frequency is about 1.67 Hz (I = 100).  
\[\mathrm{P}\;=\;4000,\;\mathrm{I}\;=\;50,\;\mathrm{D}\;=\;0 \tag{5}\]
## 4 POD and the Atomic Clock's Frequency Stability Performance
In Figure 10, the two auxiliary dotted lines, \(1/\tau\) and \(1/\sqrt{\tau}\), represent white noise and random walk noise, respectively [34]. The results about clock frequency stability of GPS, GLONASS, Galileo and BD2 satellites are also shown in the figure, and the experimental results indicate that the frequency stability of BD3 is a little better than that of the GPS Block IIF (RAFS) satellites, but worse than that of the Galileo FOC satellites (PHM). The performance of the BDS clock frequency stability at averaging time intervals of 10000 s and 86400 s is listed in Table 9. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PRN** & **MEAN** & **STD** & **RMS** & **NP\#** & **NP(U)** & **Utilization\%** \\ \hline C20 & 0.96 & 5.03 & 5.12 & 927 & 924 & 99.7 \\ C21 & 4.08 & 4.96 & 6.42 & 954 & 943 & 98.9 \\ C29 & \(-\)0.83 & 4.57 & 4.65 & 1034 & 1020 & 98.7 \\ C30 & \(-\)2.03 & 3.82 & 4.32 & 964 & 951 & 98.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The satellite laser ranging (SLR) validation accuracy (MEAN, STD, RMS) of the BD3 MEO satellite’s orbit (units: cm).
## 3 System Model for Dynamic Multiplexing and Orthogonal Slicing  
Let \(\mathcal{U}_{eMBB}\) and \(\mathcal{U}_{URLLC}\) denote a set of eMBB and URLLC AVs, respectively. In this study, we considered a downlink system in which the AVs in \(\mathcal{U}_{eMBB}\) and \(\mathcal{U}_{URLLC}\) share a single CBW. Let \(\Phi\) and \(\phi_{u}\) denote the number of RBs in the CBW and the number of RBs allocated to the AV \(u\), respectively. The common BWP size for eMBB AVs is the same as that for CBW, i.e., \(\Phi=\sum_{u\in\mathcal{U}_{eMBB}}\phi_{u}\). We assumed a full buffer model for eMBB AVs, implying that eMBB AVs always have data to transmit. Further, we assumed a non-full buffer model for URLLC AVs, in which packets of a fixed size \(P\) (bits) arrive with a Poisson distribution with an average rate of \(\lambda\) (the number of packets/ms/user). The average arrival rate \(\lambda\) was assumed to change only at each eMBB slot boundary and can be known in advance. Then, the traffic load of the URLLC load, which is denoted as \(D\), can be defined as the total number of bits arriving during each eMBB slot. For a given TTI parameter \(\mu\), it is given as  
\[D=\left|\mathcal{U}_{URLLC}\right|\cdot\lambda\cdot P\cdot\left(1/2\right)^{\mu} \tag{1}\]  
One eMBB slot is divided into \(L=2^{\mu}\) URLLC slots. The eMBB AV transmits one TB with \(N_{CBC}\) CBGs in each eMBB slot of \(\phi_{u}\) RBs. \(N_{CBC}\) CBGs are encoded using the maximum distance separable (MDS) code with a code rate \(r=k/n\). Low-density parity check (LDPC) code can be constructed based on the MDS code [15].  
Figure 2 illustrates how URLLC traffic is multiplexed over the ongoing eMBB traffic for DM and OS-based coexistence within an eMBB slot. The eMBB AV \(u\) is scheduled in units of eMBB slots in the time domain and \(\phi_{u}\) consecutive RBs in the frequency domain. First, in DM-based URLLC multiplexing, the URLLC traffic should be scheduled as soon as the traffic arrives. In addition, we assumed that URLLC AVs have a BWP size equal to the CBW, which minimizes the effect of puncturing of eMBB traffic. In a given BWP, URLLC traffic is assumed to be scheduled in a uniform distribution over its allocated BWP preempting the eMBB resource as shown in Figure 2a. As URLLC AVs cannot be informed about the ongoing eMBB traffic, the uniform preemption of URLLC traffic is most acceptable for immediate scheduling.  
Figure 1: Grouping of CBG: Example for \(C=7\) and \(N=4\).  
In OS-based URLLC multiplexing, the resources for eMBB and URLLC AVs are orthogonally sliced, as shown in Figure 2b. Therefore, URLLC AVs are allocated with an exact size of BWP to support a load of \(D\) with a part of the eMBB slot, while the eMBB BWP occupies the remaining resources within the CBW. It is expected that the larger the URLLC load, the lower the eMBB throughput performance in both DM and OS, with an obvious performance tradeoff between them. In the subsequent section, the throughput performance of eMBB traffic subject to URLLC multiplexing is analyzed in terms of the various parameters involved in DM and OS, while discussing the size of URLLC BWP.
# SAM-PAY: A Location-Based Authentication Method for Mobile Environments +
## 4 Design of the SAM-PAY Authentication Method  
As stated in the introduction, to defend themselves from attackers who falsely identify themselves as legitimate users, many security services and applications have started to implement stronger user-authentication methods based on more authentication factors or behavioral characteristics, especially when executing critical operations, like commercial transactions. For example, SwipePass [62] authenticates a smartphone user by examining the distinct physiological and behavioral characteristics embedded in the user's pattern-lock process. SwipePass is a two-factor authentication system that can sense the entire unlocking process and extract discriminative features to authenticate the user from the signal variations associated with hand dynamics.  
In this work, we have used multiple authentication elements in our mechanism: the UT device, which can be used as a localization and security device, and the position of the UT, which is certified by the LE ground component. In addition, the third authentication factor used in our approach is the \"classical\" one-time code (OTC), referred to in some contexts also as a one-time password (OTP). Remote authentication with one-time codes is based on the idea that both the prover (the entity whose identity is verified) and the verifier share a secret: the client either presents the secret to the server as is (in this case, the shared secret is the OTC), or in a form derived according to some algorithm, like, for example, the OTC generated with an RSA SecurID authenticator. Typically, the OTC has a limited validity lifetime, e.g., 60 s for the codes generated by an RSA SecurID, because time is used for OTC generation. Moreover, to protect from replay attacks, the prover can use an OTC to authenticate themself to the verifier only once, i.e., an OTC cannot be re-used a second time.  
The OTC can be generated independently by the user (for example, with an RSA SecurID token), or it can be generated by the verifier and sent to the user (if some previous relationship has been established between the user and the verifier). The latter method is used by several banks to offer advanced services, such as mobile banking or fund transfers to non-registered third-party accounts. In some security products, like in the Clavister MFA [63], the users authenticate with a mobile app Clavister Authenticator and an SMS-based One Time Password (OTP) service.
# Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning
## 4 Evaluation
### Part 1: Discounted Return and States Discovered  
We use the following environments: deep sea [4; 52], taxi driver [59], and four novel benchmark gridworlds. All the environments have sparse discounted rewards, and each has peculiar characteristics making it challenging to solve.  
**Evaluation Criteria.** For each environment, we evaluate the algorithms on varying two different conditions: optimistic vs. zero initialization of the behavior Q-tables, and short vs. long horizon, for a total of four scenarios. For each of them, we report the expected discounted return of \(\pi(a|s)\), which is greedy over \(Q^{\pi}(s,a)\), and the number of visited states over training samples. In all environments, the initial state is fixed.  
Optimistic initialization [25] consists of initializing the Q-tables to a large value. After visiting a state-action pair, either the agent experiences a high reward confirming their belief about its quality, or the agent experiences a low reward and learns that the action was not optimal and will not execute it again in that state. By initializing the Q-tables to the upper bound \(r_{\max}/(1-\gamma)\) we are guaranteed to explore all the state-action pairs even with just an \(\epsilon\)-greedy policy. However, this requires prior knowledge of the reward bounds, and in the case of continuous states and function approximation the notion of a universally \"optimistic prior\" does not carry over from the tabular setting [4]. Thus, it is important that an algorithm is able to explore and learn even with a simple zero or random initialization.  
The length of the horizon is also an important factor for exploration. If the agent can perform a limited number of actions during an episode, it has to carefully choose them in order to explore as efficiently as possible. Prominent examples in this regard are real-world tasks such as videogames, where the player has limited time to fulfill the objective before the game is over, or robotics, where the autonomy of the robot is limited by the hardware. Thus, it is important that an algorithm is able to explore and learn even when the training episode horizon is short. In the \"short horizon\" scenario, we allow the agent to explore for a number of steps slightly higher than the ones needed to find the highest reward. For example, if the highest reward is eight steps away from the initial position, the \"short horizon\" is ten steps. The \"long horizon\" scenario is instead always twice as long.  
Results are first presented in plots showing the number of states discovered and discounted return against training steps averaged over 20 seeds. Due to the large number of evaluated algorithms, confidence intervals are not reported in plots. At the end of the section, a recap table summarizes the results and reports the success of an algorithm, i.e., the number of seeds the algorithm learned the optimal policy, and the percentage of states visited at the end of the learning, both with 95% confidence interval.
###### Abstract  
Recent studies have demonstrated the potential of bacterial cellulose (BC) as a substrate for the design of bio-based ion exchange membranes with an excellent combination of conductive and mechanical properties for application in devices entailing functional ion conducting elements. In this context, the present study aims at fabricating polyelectrolyte nanocomposite membranes based on poly(_bis_[2-(methacryloyloxy)ethyl] phosphate) [P(_bis_MEP)] and BC via the in-situ free radical polymerization of _bis_[2-(methacryloyloxy)ethyl] phosphate (_bis_MEP) inside the BC three-dimensional network under eco-friendly reaction conditions. The resulting polyelectrolyte nanocomposites exhibit thermal stability up to 200 \({}^{\circ}\)C, good mechanical performance (Young's modulus > 2 GPa), water-uptake ability (79-155%) and ion exchange capacity ([H\({}^{+}\)] = 1.1-3.0 mmol g\({}^{-1}\)). Furthermore, a maximum protonic conductivity of ca. 0.03 S cm\({}^{-1}\) was observed for the membrane with P(_bis_MEP)/BC of 1:1 in weight, at 80 \({}^{\circ}\)C and 98% relative humidity. The use of a bifunctional monomer that obviates the need of using a cross-linker to retain the polyelectrolyte inside the BC network is the main contribution of this study, thus opening alternative routes for the development of bio-based polyelectrolyte membranes for application in e.g., fuel cells and other devices based on proton separators.  
Keywords:bacterial cellulose; proton exchange membrane; nanocomposites; poly(_bis_[2 -(methacryloyloxy)ethyl] phosphate); protonic conductivity +
Footnote †: journal: Applied Physics Letters
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 3 Model and Theory Fundamental  
In the previous section, we introduced some typical NRS architectures and several neighbor cooperation approaches in distributed systems. In this paper, we focus on LNMRS, which has the novel characteristic of deterministic latency name resolution and can meet the capability requirements of NRS under 5G/B5G scenarios. Considering that the current LNMRS cannot fully exploit the advantages of local NRS, we will design a neighbor structure approach of name resolution nodes based on LNMRS, including the discovery, usage, and maintenance of nodes' neighbor relationship, expecting that higher name resolution success rates can be achieved while preserving the deterministic latency characteristics.  
Before presenting our proposed approach in the next section, the related knowledge is presented in this section. We first model the LNMRS architecture, describe its structural characteristics, and then introduce the concepts related to adjacency information entropy, used in our proposed method. For brevity of presentation, Table 1 summarizes the meanings of the symbolic identifiers used in this paper.