esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 6 Evaluation
### Throughput and Delay vs. Packet Size  
Figures 9 and 10 present the throughput and delay when the packet size is varied, respectively. We generate 10 CBR sources randomly chosen from the nodes. This simulation is quite similar to the one in Figure 3 but is performed in a more general network with more nodes. As expected, we can see in Figure 9 that the throughput of min-hop metric is higher than MTM when the packet size is small, and vice versa. Figure 10 shows that min-
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 5 Experiments
### Quantized Load Adaptation  
The proportional control of the loads might not always be feasible in practical cases because appliances often can only be switched on or off. To overcome this problem, we propose an alternative control scheme, based on _statistical_ decisions about the activation/de-activation of the loads. To meet the DSO constraint, in case the power constraint \(P_{DSO}\) is higher than the estimated power demand \(P=p\cdot N\cdot n\) of the Overgrid, users could randomly switch off each appliance with probability:  
\[Pr\{off\}=1-\frac{P_{DSO}}{p\cdot n\cdot N}. \tag{2}\]  
If the number of nodes (and appliances) is large enough, the effect of such random decisions will make the total power demand close to \(P_{DSO}\). Figure 10 shows again the DSO constraint over time (blue curve), together with the total power demand of a network with \(N=100\) nodes and \(D=20\), with (red curve) or without (green curve) activating the DR scheme based on this statistical load control. The figure shows that the adaptation (red curve) closely follows the DSO requests variation, although the two curves do not perfectly match because of the random decisions and discrete values of the consumption loads. To quantify the mismatch between the desired \(P_{DSO}\) power demand and the one achieved by PP-Overgrid, Figure 11 shows the Cumulative Distributed Function (CDF) of the absolute error \(|P-P_{DSO}|\). From the figure, it is evident that the adaptation error is lower than 5 kWh in more than 90% of the cases. The figure also shows the CDF of the corresponding Binomial function,  
Figure 9: Detail of the PP-Overgrid convergence adaptation with \(N=100\) nodes, subnodes \(n=\{1,10\}\) and graph degree \(D=\{5,20\}\).  
showing that the errors follow the random process of touring the loads off, with approximately the same distribution. This means that the error can be controlled and minimized by increasing the number of nodes in the network, as well as reducing the size (quantization) of the loads.
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 4 Discussion
### Gps
* GPS provides the largest quantity of tracked satellites of all GNSSs, even though it is not the GNSS with the most operational satellites in orbit. Therefore, although BeiDou provides a similar number of signals (despite those from its IGSO satellites arriving with lower power), the receiver acquires more from GPS satellites. The reason for this could be a higher robustness of GPS signals or a better adaptation of the receiver to them due to GPS legacy.
* The average signal power of GPS is the smallest under optimal conditions, but this is due to the fact that a higher number of satellites are tracked, even those with low SNR values. In fact, when attenuation increases, its signal power resists better than those of the other GNSSs, surpassing the values of GLONASS and Galileo at 4 dB, and those of BeiDou at 8 dB.
* GPS presents the lowest TTFF results for attenuation levels above 6 dB. In addition, GPS provides the most accurate horizontal position for all attenuation values.
## 3 Results and Discussion  
This section presents and discusses the simulation findings from the lower inclination orbit proposal over the near-equatorial region, including the link budget, and also the real-time data collected from the satellite using the designed IoT terminal.
# Development of Simultaneous Dual-Resolution Digital Holography System
## 5 Conclusions  
A multiplexed holographic system for simultaneous dual-spatial-resolution measurements of deformation is presented in this paper. The main innovation of our research is we are the first to enable the digital holographic system to take simultaneous measurements with both the large and small fields of view and to observe whole-field deformations as well as local information, such as in the areas of strain concentration. The developed system is beneficial for deformation and strain measurements as well as for non-destructive testing, especially if the loading is not repeatable. The polarization technique is used to obtain two polarized holographic setups with two different-sized fields of view, large and small, by changing the combined focus length. The phase distributions of two holograms have been successfully extracted from one image containing two holograms recorded by a single camera through the spatial carrier fringes' phase shift method. The optical setup is relatively simple and the spectrums of two holograms can be separated easily by adjusting the orientation of the carrier fringes. This along with the good measurement results obtained from the setup increase its utility for practical applications. The special advantage of the system developed is its capability to perform simultaneous and dual-resolution measurements. This is particularly meaningful for some experiments which cannot be repeated; additionally, this can also lead to reduced labor and time costs associated with testing. The experiments conducted have demonstrated that the developed system is an effective tool to measure both whole and local information, especially in cases in which more detailed information is required for areas with a strain concentration and in cases in which smaller defects need to be found.  
Conceptualization, L.Y. and X.Z.; methodology, X.Z. and L.Y.; validation, X.Z., B.G. and S.F.; formal analysis, X.Z.; investigation, X.Z. and B.G.; resources, S.F. and B.S.; writing--original draft preparation, X.Z.; writing--review and editing, L.Y. and B.S.; supervision, L.Y.; project administration, X.Z. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.  
Figure 9: (**a**) Intensity image of an interferogram with carrier fringes, the area of the rectangle is magnified to show the carrier fringe. (**b**) Distribution of the spectrum.
# Slot Waveguide Enhanced Bloch Surface Waves
###### Abstract  
The paper presents a novel concept for the on-chip integration of a multilayer platform sustaining Bloch surface waves further enhanced by the so-called slot waveguide effect. Through simulations, we demonstrate that a carefully designed polymer waveguide arrangement coated with a subwavelength dielectric multilayer can be efficiently used to first excite a Bloch surface wave at the surface of the multilayer and second to enhance this wave and allow longer propagation of the surface mode by constructive superimposition of two evanescent tails inside a narrow gap.  
surface wave; polymer waveguide; slot waveguide; field localization Article
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism
### System Model
Similarly, \(X_{n+1}=X_{n}\), if the user has no data to transmit and _Process 2_ and _Process 3_ are not called. Observe that the DTMC is very similar to the discrete time birth and death Markov chain, as shown in Figure 6. Therefore, the balanced equations of \(X_{n}\) are:  
\[\pi_{0}*\gamma=\pi_{1}*p_{2} \tag{3}\]  
where \(\pi_{i}\) is the frequency of being in a certain state. Similarly, according to Figure 6:  
\[\pi_{1}=\pi_{0}*\gamma+\pi_{1}*p_{3}+\pi_{2}*p_{2} \tag{4}\]  
We can further generalize the equations for state \(n\), where \(n\geq 2\), as follows:  
\[\pi_{n}=\pi_{n-1}*p_{1}+\pi_{n}*p_{3}+\pi_{n+1}*p_{2} \tag{5}\]  
\[\pi_{n}=\sum_{m=0}^{x}(p_{i,n}\pi_{m}) \tag{6}\]  
The collision probability is plotted in Figure 7a. The X-axis shows the probability of a node having data to transmit, i.e., \(\gamma\). The Y-axis shows the resource collision probability. In addition, the degree of uncertainty is analyzed at a confidence interval level of 95% to show the range in which the values of & = 0.2, & = 0.5, and & = 0.7 fall. The plotted graph shows the collision probability for different values of \(\delta\). Observe that for higher values of \(\delta\) and \(\gamma\), the collision probability increases rapidly. The behavior of the DTMC suggests that the design of the ARS mechanism should be adaptive towards the vehicle density and/or traffic intensity to reduce resource collisions. In the following section, we present the TARS mechanism, which not only employs a novel technique for autonomous resource selection, but also is adaptive towards the vehicle densities. Figure 6: Birth and death Markov chain.
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup
### Nodes Deployment, Position Control, and Path  
The application layer used to control the HROV position was first presented in [118], where the controller drove the underwater vehicle along the desired trajectory by sending absolute movement commands in the form of subsequent way-points to be covered and the speed that the HROV should use to reach that way-point. The y-x displacement of the resulting path when all way-points were reached is presented in Figure 6a, while the depth position changed between 9 m and 11 m. The three acoustic relays are represented with red crosses and were deployed 3000 m apart; the first relay was depicted 3000 m from the control station, that was deployed at the origin of the axes and represented with a red circle. Both the relays and the control station were deployed at a depth of 5 m.  
To better visualize the path characteristics in different sections, we depict two zoomed-in parts in Figure 6b,c. The former presents the path in the proximity of the control station, and the latter presents the path from a distance of 200 m up to 1450 m far from the control station. The joystick control hwas simulated by sending several way-points close to each other. The section of the path depicted in Figure 6b from the coordinates (0, \(-\)10) and (20, 10) is the desired trajectory of the joystick control and was taken from the real motion of the CNR INM ROV described in [119]. During the dive performed in Biograd Na Moru [120,121], the ROV moved at an average speed of 0.2 m/s.  
The remaining part of the path was controlled through way-point transmissions: the speed reaching a way-point varied from 0.5 m/s in zones where there were many details to inspect and precise maneuvers to be performed (e.g., the area depicted in Figure 6b) up to a speed of 1.5 m/s when the path was straight forward or did not require precise maneuvering (e.g., Figure 6c and the area that surround the relays in Figure 6a).  
To this aim, two application layers were used at the control station:  
1. JOY_TX, used to transmit the joystick control packet;
2. WP_TX, used to transmit the way-point control packets.
# Simulation of Rock Failure Cone Development Using a Modified Load-Transferring Anchor Design
## 3 Results and Discussion
### Model Validation
The findings from [34; 61] reinforce the validity of the presented FEM model and demonstrate its applicability across different rock types. The role of the friction coefficient between the anchor head and the rock medium is crucial in shaping the extent and geometry of the failure zone. As shown in our previous study [60], increasing the value of the Coulomb friction coefficient results in a significant reduction in the range of the failure cone measured on the rock's free surface. A higher friction coefficient leads to an increased initial fracture angle (\(\alpha_{0}\)), thus narrowing the failure zone and modifying the trajectory of crack propagation. These effects were also observed in complementary analyses using different anchor designs [14], including XFEM-based simulations of breakout anchors subjected to radial expansion. In the present study, a friction coefficient \(\mu\) = 0.2 was selected based on averaged literature data and experimental consistency for dry sandstone contact conditions. However, additional parametric studies (e.g., \(\mu\) = 0.0-1.5) presented in [60; 93] confirmed the critical impact of this parameter on the initiation and evolution of rock damage.
## Error and Congestion Resilient Video Streaming over Broadband Wireless  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,* and [PERSON] 1**  
1 School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3 SQ, UK; E-Mails: [EMAIL_ADDRESS] (L.A.-J.); [EMAIL_ADDRESS] (M.G.)  
2 Department of Electrical and Computer Engineering, University of Duhok, 1006 AJ Duhok, Iraq; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-1206-872-770; Fax: +44-1206-872-900.  
Academic Editor: [PERSON]  
_Received: 13 January 2015 / Accepted: 14 April 2015 / Published: 21 April 2015_  
**Abstract:** In this paper, error resilience is achieved by adaptive, application-layer rateless channel coding, which is used to protect H.264/Advanced Video Coding (AVC) codec data-partitioned videos. A packetization strategy is an effective tool to control error rates and, in the paper, source-coded data partitioning serves to allocate smaller packets to more important compressed video data. The scheme for doing this is applied to real-time streaming across a broadband wireless link. The advantages of rateless code rate adaptivity are then demonstrated in the paper. Because the data partitions of a video slice are each assigned to different network packets, in congestion-prone wireless networks the increased number of packets per slice and their size disparity may increase the packet loss rate from buffer overflows. As a form of congestion resilience, this paper recommends packet-size dependent scheduling as a relatively simple way of alleviating the buffer-overflow problem arising from data-partitioned packets. The paper also contributes an analysis of data partitioning and packet sizes as a prelude to considering scheduling regimes. The combination of adaptive channel coding and prioritized packetization for error resilience with packet-size dependent packet scheduling results in a robust streaming scheme specialized for broadband wireless and real-time streaming applications such as video conferencing, video telephony, and telemedicine.  
keywords: broadband wireless; access network congestion; data partitioning; multimedia networking; packet scheduling +
Footnote †: journal: Journal of Network Science
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 3 Results and Discussion
### Total Polyphenol Content (TPC)
Moreover, acidified aqueous ethanol and ChCit extracts were significantly correlated (Pearson's coefficient of 0.792 and 0.950; \(p<0.01\), for seeds and skin, respectively); due to similar acidity and polarity of the solvents.
###### Abstract  
Crest cracking is one of the most common damage types for high earth-rockfill dams. Cracking risk of dam crest is closely related to the duration of abnormal deformation state. In this paper, a methodology for dynamic risk evaluation and early warning of crest cracking for high earth-rockfill dams is proposed and mainly consists of: (a) The discrimination of abnormal deformation state related to crest cracking, which is implemented by comparing the crest settlement inclination with the threshold value. (b) Computation of crest cracking probability and estimation of cracking time. The exponential distribution is adopted to represent the probability distribution of the duration \(T_{AS}\) of abnormal state before crest cracking. Then the crest cracking probability in a given time can be computed by integration with respect to \(T_{AS}\). Inversely, the cracking time corresponding to a given probability can be estimated. (c) Determination of the values of probability adopted to early warn crest cracking, which are suggested to be selected by statistical analysis of the calculated probabilities at the observed cracking times. (d) Bayesian estimation and updating of probability distribution of the parameter \(\lambda\) in the PDF of \(T_{AS}\), according to observed durations of abnormal state before crest cracking. The methodology is illustrated and verified by the case study for an actual earth-rockfill dam, of which crest cracking and recracking events were observed during the periods of high reservoir level. According to the observed values of \(T_{AS}\), the probability distribution for \(\lambda\) is progressively updated and the dispersion of the distributions of \(\lambda\) gradually decreases. The crest cracking probability increases with the duration of abnormal state and the width of confidence interval of the estimated cracking probability progressively contracts with the updating of the distribution for \(\lambda\). Finally, the early warning of crest cracking for the dam is investigated by estimating the lower limit of cracking time. It is shown that early warning of crest cracking can be issued from at least 20 days ahead of the occurrence of crest cracking event. The idea of using duration of abnormal state of crest settlement to evaluate crest cracking risk of the earth-rockfill dam in this paper may be applicable to other dams.  
**Keywords:** earth-rockfill dams; cracking; risk; early warning; Bayesian parameter updating
## 2 System Model  
### Network Model  
Since the Panda-Hunter model was put forward in Reference [6] to study the source location privacy problem of WSNs, it has been widely used by researchers [6; 7; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 25]. In this paper, similar to the existing researches, we also use the Panda-Hunter model as our network model, which is a deterministic deployment. For the sake of understanding, we make the following assumptions about the network model:  
(1) There is only one sink node in the network, whose location is static and fixed in the network center. And the resources of the sink node, such as computing power, storage capacity and energy are not limited [26].  
(2) The network is evenly divided into small grids. The sensor nodes in each grid are all fully connected. The whole network is fully connected through multi-hop communications [4].  
(3) The sensor nodes in the network are deployed prior to the initialization phase. After being deployed, each node has the knowledge of its own basic information, such as node ID number, grid number, etc. The sink node knows about the basic information of each node in the entire network.  
(4) When the panda appears, the node closest to the panda becomes the source node, that is, there is only one source node in the whole network. The source node will generate and send encrypted packets to the sink node through a multi-hop routing. The panda will stay for a period of time before leaving, and our schemes are applied during the panda's stay period.  
(5) To facilitate latter theoretical analysis and simulation, we assume that all sensor nodes in the network are the same type and any two nodes can communicate via multi-hop fashion.  
(6) In order to ensure the universality of the scheme, for example, the scheme can be applied even in the resource-constrained wireless networks, the node does not have the positioning and mobility capabilities. Therefore, it cannot obtain its own precise coordinates and cannot directly calculate the actual distance between two nodes. Therefore, the hop count is used as the index to measure the distance. The distance between two nodes within the communication radius of each other is called one hop.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 3 Proposed FH Sequence Recovery Method
### Network Transfer Learning  
For different frequency-hopping communication systems, due to different communication bandwidths, the number of frequency points in the frequency set may be different. When the number of frequency points changes, the corresponding frequency-hopping sequence should also change, and the network trained will no longer be directly applicable to the system. In order to train a new network with better performance under a small number of datasets, we adopted the method of transfer learning (TL), as shown in Figure 6.  
For the trained network, it was able to perform correct feature extraction on the input data. The basic characteristics of artificial jamming on the time-frequency diagram will not change due to the change in the frequency band, so for the frequency-hopping system with different frequency-hopping sets, the input data of the network have similar characteristics to a certain extent. When the number of frequency points of the communication system changes, only the last fully connected layer of the network needs to be changed. The size of its output dimension corresponds to the number of frequency points of the current frequency-hopping system. The network architecture of other layers remains unchanged, and the trained parameters are used as initial values. On this basis, parameter fine-tuning through training is more effective than training the network directly.  
Figure 6: Schematic diagram of transfer learning.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs  
Many studies on protocol design in WSN, MANET, and IoT have been conducted. Each study typically seeks to provide improvement and a more optimal result than previous research or compared to the existing standard protocols. The improvement results are usually in term of energy efficiency, the network lifetime, as well as the network QoS parameters such as packet delivery fraction, delay and throughput. The protocol design objects also varies, including the Media Access (MAC) layer, the clustering and routing in the network layer, and cross layer.