esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge Computing Approach for Vessel Monitoring System
## 1 Introduction
Due to high communication costs, the current transmission rate is only one position every two hours, which compromises the identification of the most important phases of the fishing operations. However, in the blue box proposed in this research, these registers occur every 10 min, and it can be programmed to increase this frequency. The MONICAPTM Terminal (Figure 1) supports a wide range of supply voltages covering the needs of different vessels, supports multiple communication media (allows the automatic selection of the most appropriate means of communication or the lowest cost) and has the capacity to transmit to more than one control center--if the rules so require. It can store the last 65,535 records (from 455 days, once every 10 min), and transmit the last 1000 records stored. It is possible to perform parameterization both locally and remotely, which can be done for all zones, by groups of zones, or by individual zones: setting transmission parameters (e.g., 24/24 h transmission when at port); configuring events that require transmissions (e.g., transmits whenever the vessel enters a restricted zone within a certain period); set the \"wake up\" frequency of the mobile unit (e.g., when in battery mode it can wake up every 2 h if in port). It also has an extended alarm management system, which can be used to generate alarms in real-time regarding internal or external abnormalities to alert the control center. There are 34 different alarms, such as: (1) attempt to tamper the terminal; (2) external power failure/cut-off; (3) antenna blockage; (4) hardware problem; (5) distress request (SOS); and (6) hardware change. The MONICAPTM Terminal installed onboard records a set of data (position, date/time, integrity, status records) by using an integrated GPS+SBAS (EGNOS) system [8], guaranteeing precise position information.
## 3 Proposed Scheme: RSP-Based Message Authentication for VANET
### Obtaining New Registration with \(ORG_{Vi}\)  
At \(T_{1}\) or the first time (\(T_{t}\)), a vehicle enters the coverage of an \(\mathrm{RSU}\), and requests the new registration set from that \(\mathrm{RSU}\) (\(Ra\)). The steps are as follows.  
1. \(V_{i}\) randomly chooses one of its \(IDS_{V_{i}}\) in \(ORG_{V_{i}}\), \(ID_{V_{i}}\), to form the information of its anonymous identity and selects \(N_{V}\) new anonymous \(\mathrm{ID_{S}}\) (\(IDS^{\prime}{}_{V_{i}}\)), before sending the request to \(R_{a}\). \[V_{i}\to R_{a}\colon(ID_{TA},\,T_{0},\,ID_{V_{i}},\,DS_{V_{i}}),(\, IDS^{\prime}{}_{V_{i}})\]
2. After receiving the request, \(R_{a}\) compares \(DS_{V_{i}}\) with \(DS_{Ra}\) in \(ORG_{Ra}\), and checks that if any \(d_{V_{i}x}{}^{\prime}\) in \(DS_{V_{i}}\) equals \(d_{Ray}{}^{\prime}\) in \(DS_{Ra}\).
3. If the equality holds, then \(R_{a}\) randomly chooses a subset of (\(I\)-\(R_{R}\)) to be the new \(DS^{\prime}{}_{V_{i}}\); sets new \(PRS^{\prime}{}_{V_{i}}\); makes the pairing value \(P_{Ra,V_{i}}\); returns these message to \(V_{i}\), and records information that includes the anonymous identity and new registration information.  
\[R_{a}\!\!\rightarrow\!V_{i}\colon(ID_{TA},\,T_{0},\,ID_{Ra}),\,(DS^{\prime}{} _{V_{i}}),(d_{V_{i}x}{}^{\prime})
## 6 Conclusions  
This paper addresses the probability of resource collisions in NR-V2X Mode 2 communication, despite the use of autonomous resource selection and probability reselection mechanisms. To mitigate the impact of collisions on the communication process, we proposed utilizing NOMA's serial interference cancellation mechanism. Additionally, we employed the MPDQN algorithm to dynamically adjust the transmission interval and transmission power of vehicles to reduce the average information age and energy consumption in the system. Firstly, we established communication models for NR-V2X and NOMA and then constructed a reinforcement learning framework based on MPDQN. In the learning framework, we modified the action space to enable simultaneous scheduling of discrete and continuous actions and finally optimized the joint problem of information age and energy consumption. Through simulation analysis, we demonstrated the advantages of NR-V2X over LTE-V2X, the improvement effect of NOMA on the information age performance in NR-V2X scenarios, and the effectiveness of MPDQN in reducing the information age and energy consumption in NR-V2X scenarios. Some potential challenges in this direction are noted: Future vehicles may use both LTE-V2X and NR-V2X for communication, so considering the coexistence and integration of these two technologies is a challenge [38]. In addition, fairness is often considered a key factor in NOMA-related scenarios [39]. Therefore, our future research will focus on performance optimization in scenarios where LTE-V2X and NR-V2X coexist and fairness in resource allocation in relevant scenarios with NOMA. In addition, MPDQN is a combination of DQN and DDPG, so the optimal performance can be improved by improving them intuitively.  
Conceptualization, S.S., Z.Z. and Q.W.; Methodology, S.S., Z.Z. and Q.W.; Software, S.S. and Z.Z.; Writing--Original Draft Preparation, Z.Z.; Writing--Review and Editing, P.F. and Q.F. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Average energy consumption with message size under different algorithms.  
Figure 9: Average AoI with message size under different algorithms.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant No. 61701197, in part by the National Key Research and Development Program of China under Grant No. 2021 YFA1000500(4), and in part by the 111 project under Grant No. B23008.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Qualcomm. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
## 3 Algorithm Description
signal from each interference base station can be obtained and it is used as the reference signal by the time-domain cancellation method to suppress the corresponding interference signal. 4. Time-domain interference cancellation:  
Based on the reference signals obtained in the previous step, the ECA-B algorithm proposed in [13] is used to eliminate the interference signals from each CCBS. 5. Spatial beamforming in the surveillance region:  
The beamforming technique based on the worst-case performance optimization is exploited again to generate a beam directing to the surveillance region of interest to resolve the target echo. At the same time, this operation can further suppress the interference residual. 6. Evaluation of interference suppression effect:  
In this step, we use the CAC method proposed in our previous work [10] to estimate the interference to signal noise (INR) in the remaining signal. The INR is used to evaluate the level of interference residual. If the interference residual is still strong, the cascaded cancellation loop continues to further suppress the interference. It can be seen from the preceding cancellation procedure, the core step is the direction finding of the interference at each cancellation loop. In the following, we explain the direction-finding method in more detail. Direction Finding of the Interference based on the Cyclostationary Characteristics of the OFDM Signal and the Sparse Reconstruction Technique  
It can be seen from the signal model described by Equation (1) that the surveillance signal consists of many co-channel interference signals. It is not easy to use the traditional spatial spectrum estimation method to estimate the DOAs of the signal sources. In this section we propose a method that utilizes the cyclostationary characteristics of the OFDM signal and the sparse reconstruction technique to estimate the DOAs of the interference signals. By doing so, only a small sized antenna array is necessary to get an adequate DOA estimation. The flow-chart of the estimation algorithm is shown in Figure 4.
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 3 Experiment Setup
### FS-BOTDR Setup for Measuring BFS  
A FS-BOTDR for measuring BFS change arising from temperature was built to verify the influence of the laser linewidth on BFS measurement accuracy, as shown in Figure 7.  
The continuous light from the laser was divided into two beams through a 90:10 coupler. The 10% beam as reference light beats with the Brillouin backscattering for coherent heterodyne detection. The polarization scrambler (PS) was for eliminating the polarization noise. The 90% beam was attenuated to the proper power via a variable optical attenuator (VOA) and injected into a pulse modulator for generating probe pulses. The peak power of the probe pulse was amplified by a pulse erbium-doped fiber amplifier (Pulse EDFA) and filtered by a dense wavelength division multiplexer (DWDM1) to remove the amplified spontaneous emission (ASE) noise. Then, the probe pulse was launched into the sensing fiber through an optical circulator (OC). The Brillouin backscattering was amplified by the low-noise EDFA (LN-EDFA), filtered by the DWDM2, and then beats with the reference light in a 13.5-GHz photodetector (PD) through a 50:50 coupler. The beating signal was amplified by an electronic low-noise amplifier (LNA) and connected to a microwave heterodyne system.  
In the microwave heterodyne system, the beating signal was mixed with a tunable microwave source to perform frequency scanning, the output frequency of which increased from 11.2 GHz to 11.4 GHz with a fixed frequency internal. The mixed signal was filtered by a BPF with the center frequency of 600 MHz and bandwidth of 87 MHz. The time-domain power trace along the sensing fiber at every scanned frequency obtained by a logarithmic detector and acquired by the data acquisition (DAQ) digitalizer. Every power trace was averaged 213 times. Eventually, the power points obtained at all scanned frequencies were fitted to the Lorentzian profile in an industrial personal computer (IPC), for calculating the BFS distribution along the fiber.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 3 Factors in the Synthesis of a Mesoporous Material
### Co-Surfactants  
_Co-_surfactant materials, such as alcohols, not only significantly affect the ductility and size of the pores, but also, as their concentrations are increased, diminish the percentage of spherical shapes present in the respective mesoporous materials. Hence, they control the shape and size of the particles, and in their absence, amorphous particles will be formed having different and disordered sizes [72; 73]. Surfactant materials, such as the anionic/cationic surfactant, ionic liquid, solvents, and so forth, that are used in the fabrication of mesoporous materials, can also be used in _co-_surfactant roles with changes in their morphology. For instance, [PERSON] et al. [74] suggested an ionic liquid/copolymer system for the synthesis of zirconium loaded inside the pores of mesoporous-microporous silica nanoparticles. The interactions between NTA (\(N\)-tritbutylamine acetate (ionic liquid)) and P-123 (co-polymer) and a change in the pH resulted in diverse structures with spherical and branch morphologies. Interestingly, the amount of alcohol serving as a _co-_surfactant can determine the final structure of a _meso_-silica. [PERSON] et al. [75] used butanol as _co-_surfactant for the syntheses of hexagonal SBA-16 and an array of hexagonal SBA-15 mesopores and studied its effects. Adding it to the reactions increased the sizes of the SBA-16 and SBA-15 pores, thus improving their catalytic and drug delivery performances. [PERSON] et al. [76] showed that in acidic conditions, pore size, thermal stability, cavity volume, and surface area are enhanced by an appropriate molar ratio of a _non_-ionic (P123)/ionic (SDS: sodium decylsulphate) _co-_surfactant system. Therefore, co-surfactant can act as a complementary role in the manufacture of mesopore nanoparticles in terms of controlling the morphology and size of particles.
## 3 Model Formulation
### Observation  
Because of the limitations of ILI equipment and methods, the actual state of the pipeline can be hard to accurately observe. The POMDP observation values are what is reported as a reading from the ILI. The observation, however, can be related to the actual states through a probability distribution. This can be determined empirically or by a numerical analysis of the main contributors to these discrepancies.  
[PERSON] and [PERSON] (2018) illustrated four sources of ILI tool errors: detection errors, sizing errors, false call errors, and reporting threshold errors. Detection errors occur when the ILI equipment fails to detect existing corrosion. This error increases as the size of the corrosion decrease since small-size corrosion defects are easy to miss. The probability of detection (\(PoD\)) is defined as a function of defect depth (\(d>0\)) and tool detection capacity (\(k_{d}\)) ([PERSON] and [PERSON], 2018). The tool detection capacity can be replaced by mean detection thresholds (defined as \(1/k_{d}\)), which are information supplied by the manufacturers.  
\[PoD=1-e^{-dk_{d}} \tag{6}\]  
where  
\[d\ =\ \text{defect depth};\ k_{d}\ =\ \text{tool detection capacity}\]  
Random measurement errors (\(\epsilon\)) are generated due to the inherent nature of the tool. Thus, they cannot be avoided. This error can be formulated as an independent, normally  
Figure 1: Initial transition probability matrix.  
distributed function [16]. The actual corrosion defect depth \(\mathrm{d}\) is the sum of the depth reading of the equipment (\(d_{r}\)) and the measuring error (\(\epsilon\)).  
False calls are created when ILI equipment reports a defect that does not exist in the pipeline. According to [PERSON] and [PERSON] [23], the cause for this error can be attributed to noises in the reading process or the misinterpretation of readings. This error depends on the ILI tool's state (quality) and the corrosion defect's size. In this case, the probability of committing an error increases as the size of the defect increases. Therefore, the probability of making a true call (\(PoTC\)) in the ILI reading is defined as a function of the equipment working condition factor (\(k_{w}\)) and the depth of the defect (\(d\)).  
\[PoTC=1-e^{-dk_{w}} \tag{7}\]  
where  
\[d\ =\ \text{defect depth};\ k_{w}\ =\ \text{tool detection capacity}\]  
Reporting threshold errors arise from the limitations of the ILI equipment. Most manufacturers label their equipment's threshold level, meaning any corrosion defects below this level will not be detected. All sources of error identified above refer to defects above this threshold level.
## 7 Results: Example Filtration of the Selected Signal  
To illustrate the operation of the network, filtration of the rectangular signal with additional noise was performed for the best obtained mapping of the reference frequency response (Figure 2). The SNR value of the input signals equaled 11.6037. In Figure 6, the comparison between two cases--the network with time-invariant parameters and the network with time-varying parameters--is presented.  
The introduction of the time-varying parameters to the network improved results, as the duration of the transient state was reduced.  
Afterward, the operation of the non-stationary network was tested for removal of undesirable signal elements. Figure 7 presents an input signal which is the sum of the rectangular signal, sine wave from the bandwidth range of the example, and noise signal. Figure 8 shows the result of conducted filtration.  
Figure 6: Comparison of system response to rectangular signal for both stationary and non-stationary networks.  
Figure 7: Input signal.  
As shown above, the network effectively attenuated the sine wave, leaving the noise signal. The output signal also had no problem following the changes of the rectangular signal shape, which was the result of varying the network parameters.
## 3 Results and Discussions
### Reactivity Studies by DSC Analyses
The addition of spruce bark powder also led to an increase in reactivity, but it was lower compared to that of HC systems, with the enthalpy of reaction reaching 300 J\(\cdot\)g\({}^{-1}\) for the mixture with 30 wt% SB. This high increase in curing reaction enthalpy of ELO/DDSA in the presence of HC and SB, corroborated with the decreased onset of temperature reactions, shows a catalytic effect of both fillers, and moreover, perhaps a sign of their chemical bonding may be due to the presence of several reactant particles that have the energy to combine and increase reaction rates, such as the formation of new chemical bonds by the interaction of residual -OH groups with the epoxy groups. The higher reaction enthalpy of HC-filled systems compared to those containing SB can be explained probably by a combined chemical and physical effect [39; 40]. As we can observe from elemental analysis, the HTC process was mild, with the elemental content of HC still being rich in oxygen  
Figure 2: Evolution of the enthalpy of reaction (\(\Delta\)H; black line) and the maximum temperature of reaction (\(T_{\text{peak}}\); red line) as a function of filler ratio. (21.11%), and hydrogen (5.20%), a sign of the presence of oxygenated functional groups. The reactivity of these functional groups present on HC is probably also higher than those on SB due to the HC's enlarged specific surface. This result is highlighted in Figure 2, where we can clearly observe that the increase of the fillers' content produces a high increase in reactions' enthalpy at the same time as the decreases of peak temperature and reaction onset, as previously discussed. Consequently, based on DSC analyses, it was found that both HC and SB substantially influence the crosslinking parameters of the systems, confirming the chemical contribution of the filler as a catalyst in the copolymerization process.
###### Abstract  
Background: To evaluate different adhesive hitting procedures on coronal dentin bond-strength of Cerasmart CAD-CAM blocks with uTBS test. Methods: 36 molar crowns were flattened in order to expose sound dentin and a standardized smear layer was created with 600 grit paper. Specimens were divided into six groups according to the luting cement employed (n = 12 each): G1: Panavia V5 (Kuraray, Japan); G2: Bifix QM (Voco, Germany); G3: Esteem (Tokuyama, Japan). CAD-CAM blocks (Cerasmart, GC), shade AZLT, size 14, were sectioned with a diamond saw to obtain 4 mm high specimens, which were then luted on the coronal dentin, following the manufacturer instructions. Specimens were serially sectioned to obtain 1 mm thick beams in accordance with the uTBS test technique. Half of the beams were stressed to failure after 24 h (t = 0), while the other half were stored in artificial saliva for 12 months, at 37 \({}^{\circ}\)C, for ageing before stressing to failure (t = 12). Results: two-way ANOVA test showed significant difference for the factor \"luting cement\" (\(p\) = 0.0002), while the factor \"time of storage\" (\(p\) = 0.0991) had no significant effect on uTBS. Conclusions: PanaviaV5 seems to have better uTBS values at T0 than QM and ES and 1 year aging doesn't seem to affect the bonding strength of tested systems.  
CAD-CAM material; dual-curing cement; bond strength; aging +
Footnote †: journal: Applied Sci.
## 3 Flight Controller Design
### Attitude Controller Design
 \tag{44}\]  
Substituting the control law (43) into (44), it yields  
\[J(\Theta)\dot{s}_{a}=-c_{a}s_{a}-\varepsilon_{a}\operatorname{sgn}(s_{a})+e \tag{45}\]  
Consider a candidate Lyapunov function as  
\[V_{a}=\frac{1}{2}s_{a}^{T}s_{a} \tag{46}\]  
the derivative of the Lyapunov function along (45) satisfies  
\[\dot{V}_{a} =s_{a}^{T}\dot{s}_{a} \tag{47}\] \[=s_{a}^{T}J(\Theta)^{-1}[-c_{a}s_{a}-\varepsilon_{a} \operatorname{sgn}(s_{a})+e] \tag{48}\]  
As \(\varepsilon_{a}>D_{e}^{*}\), we have  
\[\dot{V}_{a}<-s_{a}^{T}J(\Theta)^{-1}c_{a}s_{a}\leq 0 \tag{49}\]It indicates that the sliding mode surface \(s_{a}\) can converge to the equilibrium point asymptotically under the proposed control law (43). As the reachability condition of sliding mode surface is satisfied, the attitude control subsystem (38) is asymptotically stable when \(k_{a}x_{1}+x_{2}=0\). **Remark 2**: _Due to the application of the disturbance observer, the estimation error \(e\) is much smaller than the upper bound of \(D_{e}^{*}\). As for the switch gain \(\varepsilon_{a}\), it can be set much smaller than the conventional SMC approach. As a result, the chattering is alleviated and robustness can be improved without sacrificing the nominal control performance._
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 5 Application Example
### Contact Bandwidth  
In order to accurately characterize the hunting movement of wheelset, the horizontal ordinate variation range of wheel profile contact points at one side when a wheelset moves to the positive/negative direction under a certain lateral displacement is defined as a contact bandwidth. The ratio of the contact bandwidth to the lateral wheelset displacement range is adopted as the contact bandwidth ratio. The contact bandwidth and its ratio indexes reflect the variation of contact points along the axial direction of the wheelset.  
Figure 18 shows the contact bandwidth and contact bandwidth ratio when the two types of wheel profiles match a CHN60 rail. It can be seen that when no flange contact occurs, both the contact bandwidths and contact bandwidth ratios of LMa and optimized wheels increase with the increase of the lateral wheelset displacement and the contact bandwidth and contact bandwidth ratio of the optimized wheel are less than those of the LMa profile, indicating that the vehicle runs most stably when the optimized wheel profile matches the CHN60 rail, however the wheels and rail are liable to fatigue and damage due to regular contact at the same position.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 3 The Methodology
### Local Motion Controller Based on IA3C
#### 3.4.2 A3C Method
```
1://\(\theta_{a},\theta_{b}\): global shared parameters of actor-network and critic-network
2://\(\theta^{\prime}_{a},\theta^{\prime}_{b}\): thread-specific parameters of actor-network and critic-network
3://\(t\): thread step counter
4://\(t_{max}\): max value for \(t\)
5://\(T\): global shared counter
6://\(T_{max}\): max value for \(T\)
7://env: an instance of the environment
8:\(T\gets 0\)
9:Repeat
10:\(\theta^{\prime}_{a}\leftarrow\theta_{a}\)
11:\(\theta^{\prime}_{b}\leftarrow\theta_{v}\)
12:\(t\gets 0\)
13:\(s_{l}\) = env.reset()
14:While True
15:\(a_{t}=\Pi(a_{l}|s_{l};\theta^{\prime}_{a})\)
16:\(r_{t}\), \(s_{t+1}\) = env.step(\(a_{t}\))
17:\(t\gets t+1\)
18:\(T\gets T+1\)
19:If terminal \(s_{l}\) or \(t\) % \(t_{max}=0\)
20: upload the gradients of \(\theta^{\prime}_{a},\theta^{\prime}_{b}\) to \(\theta_{a},\theta_{v}\)
21:update \(\theta_{a},\theta_{v}\)
22:download the parameters of \(\theta_{a},\theta_{v}\) to \(\theta^{\prime}_{a},\theta^{\prime}_{v}\)
23:If terminal \(s_{t}\)
24:break
25:until (\(T>T_{max}\))
```  
**Algorithm 1**Pseudo-code of each actor-learner in Asynchronous Advantage Actor-Critic (A3C).