esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
###### Abstract  
Vehicular Ad hoc Networks (VANETs) are one of the pillars of the Internet of Vehicles, they provide plenty of applications ranging from safety to entertainment. Safety applications largely depend on reliable and authentic traffic-related data. However, ensuring the data reliability and authenticity is facing many challenges due mainly to the scalability of VANETs such as the high speed, the long roads, and the open nature of VANETs. This paper addresses these challenges by proposing a decentralized Blockchain-based trust management framework (BC-TMF) aiming to compute trust metrics for vehicles. These trust metrics rely on the authenticity of the messages. Each vehicle assesses the authenticity of the received messages in real-time, calculates a local trust metric for the originator of such messages, then shares it with a miner. Periodically each miner aggregates the received trust metrics into global trust metrics, then packs them in a block. To investigate the efficiency and consistency of the proposed framework, extensive simulations are conducted. The obtained results show that the proposed BC-TMF has an excellent capability in computing accurate trust metrics for vehicles. Besides, it outperforms the existing ones in terms of the accuracy of computed trust metrics, particularly for malicious vehicles.  
VANETs; trust; vehicle; malicious; blockchain Article
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 7 Contributions to the Use of USVs for Disaster Applications  
This paper is inserted in the context of a research call organized by two Brazilian institutions, named CAPES (Coordination for the Improvement of Higher Education Personnel) and CEMADEN (National Center for Natural Disaster Monitoring and Alerts). The goal of this call is to propose new tools and methods to help CEMADEN to improve the preparedness for natural disasters in Brazil. In the context of this project (The project webpage is at [[http://disaster-robotics-proalertas.github.io/](http://disaster-robotics-proalertas.github.io/)]([http://disaster-robotics-proalertas.github.io/](http://disaster-robotics-proalertas.github.io/))), the authors of this survey have developed some robotic tools to simplify the information gathering for disaster prevention and to be used for disaster mitigation and response. Two main robotic platforms are being used in the project: USVs for flooding prevention and response, and UAVs for mapping areas prone to landslides. However, description of the later is out of the scope of this paper.  
This section presents the current and near future contributions of the authors in the field of USVs, with focus to disaster applications. These contributions are organized as: boat prototypes (Section 7.1), new payload (Section 7.2), realistic robotic simulation for flooding scenarios (Section 7.3), and USV software (Section 7.4).
# The Effect of Space Objects on Ionospheric Observations: Perspective of SYISR
## 2 Space Objects in the SYISR Ionospheric Observations
The corresponding power spectra can be derived from the ACF through the Fourier transform. If the space object pollution is not eliminated properly in the original data, it will affect the ACF and power spectra calculation. As an example, Figure 2 shows the ACF and power spectra profiles derived from SYISR measurements on 16 April 2022 at 11:23:15. In this case, the space objects appear at altitudes of approximately 381 km and 488 km. As indicated, the space object occurrence results in a strong amplitude in both ACF and power spectra at the corresponding altitude and makes the results unreliable and unusable. Its range extension is approximately 72 km in the LP signal with a pulse length of 480 \(\upmu\)s. We use the 16-bit AC signal with the same pulse length as the LP signal. The range extension after decoding reduces to approximately 4.5 km, which is equal to one baud length. The range resolution using this AC signal is 16 times shorter than the LP.
## 3 Methodology
### Metrics of Evaluation  
The evaluation metrics used in this study are accuracy, recall, and precision.  
The _accuracy_ is defined as  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{1}\]  
where _TP_ and _TN_ are the numbers of True Positives and Negatives, respectively, _FP_ and _FN_ are the numbers of False Positives and Negatives, respectively.  
The _precision_ is defined by the following equation:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**CNN Parameter** & **Value** \\ \hline Width, filter size, and number of filters of the 1 st convolutional layer & 16, 16, 32 \\ \hline Width, filter size, and number of filters of the 2 nd convolutional layer & 8, 8, 16 \\ \hline
1 st pooling layer & Max pooling (4,4) with Stride (2,2) \\ \hline
2 nd pooling layer & Max pooling (2,2) with Stride (2,2) \\ \hline Activation functions & REctified Linear Unit (RELU) \\ \hline Number of heads in the attention layer & 8 \\ \hline Number of channels for keys and queries in the attention layer & 64 \\ \hline Maximum number of epochs & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the CNN parameters used in this study.  
Figure 2: Architecture of the Convolutional Neural Network.  
\[Precision=\frac{TP}{TP+FP}. \tag{2}\]  
The _recall_ is defined by the following equation:  
\[Recall=\frac{TP}{TP+FN}. \tag{3}\]  
_F1 score_ is defined by the following equation:  
\[F1 score=2\times\frac{Precision\times Recall}{Precision+Recall}. \tag{4}\]  
Confusion matrices are also used a classification metrics. This paper adopts the convention that column of the matrix represents the instances in an actual class while each row represents the instances in a predicted class,.
## 3 Results
### LiDAR Biomass and Change at the Plot Scale  
The areas of plots 1-4 were 0.11, 0.10, 0.15, and 0.16 ha, as was defined by the positions of trees monitored for growth and after removing the wetland areas. However, this area included the tree and trunk buffer regions (0.75 m), which were removed for understory analysis (described in Methods). In May 2018 (pre-fire), the mean plot biomass estimated with LiDAR ranged from 2.87 to 3.34 Mg ha\({}^{-1}\), while in November 2018 (five months post-fire), the mean plot biomass changed to 2.40 to 2.75 Mg ha\({}^{-1}\), or a 12.7% reduction (Table 1). The field sampling method estimated a \(\sim\)16% reduction in biomass five months post-fire and was much more variable than the estimates from LiDAR (Figure 7). The power analysis using estimated biomass from LiDAR indicated that the field sampling method would have required 59 1 \(\times\) 1 m sample subplots to detect the estimated 12.7% reduction in biomass.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**LiDAR Flight** & **Plot** & **N** & **Min** & **Max** & **Mean** & **Stdev** \\ \hline Before fire & 1 & 4635 & 1.00 & 6.31 & 3.01 & 0.87 \\  & 2 & 3441 & 0.86 & 17.13 & 3.13 & 0.92 \\  & 3 & 6185 & 1.03 & 17.13 & 2.87 & 0.97 \\  & 4 & 6262 & 0.94 & 17.15 & 3.34 & 1.39 \\ \hline After fire & 1 & 4633 & 0.94 & 7.93 & 2.40 & 0.74 \\  & 2 & 3441 & 0.83 & 17.01 & 2.74 & 0.92 \\  & 3 & 6186 & 0.92 & 17.15 & 2.63 & 0.94 \\  & 4 & 6329 & 0.80 & 17.15 & 2.75 & 1.29 \\ \hline Change (after-before) & 1 & - & - & - & \(-\)0.61 & - \\  & 2 & - & - & - & \(-\)0.39 & - \\  & 3 & - & - & - & \(-\)0.24 & - \\  & 4 & - & - & - & \(-\)0.59 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of 0.5 \(\times\) 0.5 m pixels (N), and the biomass (Mg ha\({}^{-1}\)) minimum (Min), maximum (Max), Mean, standard deviation (Stdev), and change for each plot and flight. The before fire UAV LiDAR flight occurred on the 3/5/2018, and after the fire, it occurred on the 3/11/2018. Here, the dashed line (-) indicates that the change was not estimated.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
## 1 Introduction
### State-of-the-Art
#### 1.1.3 Existing Solutions for High Availability of Distributed Services  
Load balancing must be addressed, when aiming to transform a request processing service into a highly available one, because simply distributing the service among more nodes does not guarantee that it will also balance the load of the involved nodes, in order to yield high availability. A classical and wide spread, but not so flexible solution, for load balancing at the network or at MAC/data link level is the Virtual Router Redundancy Protocol (VRRP), which is a solution to provide redundancy in a network, in the same virtual entity. The VRRP technique's drawback is that it is not so flexible, this being the reason why self organizing peer-to-peer networks, including DHTs, seem more appealing to the current study.  
Thus, the existing load balancing techniques that could be used are DHT-based techniques [9; 11; 12; 33], but also the VRRP protocol [34], which has an active copy and a backup copy of the running service, or components of the DNS (Domain \(N\)ame System), such as the _DNS NAPTR_ records [35] combined with the _DNS SRV_ records [36]. These techniques are based on the existence of more physical machines behind a DNS name. In case any of these machines experiences heavy load, its task can be taken over by another machine. Even if these techniques offer a means for load balancing and availability increase, they are _static_. This is why the described DNS method used only by itself is _not scalable_ and, just by itself, cannot be used to process requests in a highly available manner, within large scale distributed systems.  
Distributed Hash Tables (DHTs) on the other hand, are by their nature extremely scalable and resilient to failure. This is something that should be adapted and used, in order to make an existing distributed request processing service to be highly available, with no changes of its external interface towards its clients.
# Versatile Video Coding-Post Processing Feature Fusion:
## 4 Experimental Evaluations and Discussion
### Quality Metric  
The final video quality was evaluated using two assessment methods: peak signal-to-noise ratio (PSNR) and the Bjontegaard Delta Rate (BD-Rate) [43] measurements. PSNR, a widely accepted quality metric in the video compression community, was used to assess video quality. Additionally, the performance of the proposed approach was compared to the VVC codec using the BD-Rate, which measures the bitrate savings of the new approach compared to the reference codec.  
* PSNR  
The proposed method was evaluated by comparing the quality of its output with that of the decoded frames produced by the VVenC v1.10.0 software, using PSNR as the evaluation metric. The PSNR calculation is given by Equation (7), which quantifies the difference between the original and reconstructed frames.  
\[\mathbb{P}=10*log10\left(\frac{\left(255\ll\left(\beta-8\right)\right)^{2}}{ \mathcal{M}}\right). \tag{7}\]  
In this equation, \(\beta\) specifies the number of bits used to encode each pixel color data in an image. \(\mathcal{M}\) calculates the average of the squared differences between actual and predicted values. The left-shift operator (\(<<\)) is applied to compute the maximum possible value based on the bit depth.  
BD-Rate  
BD-Rate is a crucial metric in video coding that measures the compression efficiency of a video codec by comparing its bitrate to a reference codec or technique. It is calculated as the average percentage difference in bitrate across various quality levels or quantization parameters. A lower BD-Rate indicates better compression performance. Typically expressed as a percentage, the BD-Rate provides a concise summary of the efficiency of coding performance, making it a valuable tool for the video coding community to evaluate and compare the performance of different codecs and techniques.
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 2 Related Work
### Traditional Panoramic Imaging Model Based Method  
The panoramic imaging model-based method, which departs from the feature-based method, requires accurate inner orientation elements of each camera and the relative orientation parameters between cameras to develop an accurate projection transformation relationship from the coordinate system of a perspective camera to that of the virtual panoramic camera.  
Traditionally, the ideal panoramic spherical and cylindrical imaging model are formulated by disregarding the eccentric error between the center of the virtual panorama camera and that of an actual camera [26; 27]. The rigorous panoramic imaging model is further proposed by incorporating the eccentric vectors among the optical centers of the cameras into the panoramic imaging model. The rigorous model is theoretically more accurate than the ideal model [25]. However, both the ideal and rigorous panoramic imaging models are developed without consideration of the varied object-distance information in a scene. [PERSON] [30] tries to estimate a unified cylinder projection radius based on a loss function to minimize the projection error of the whole scene. Due to difficulties in fitting a regular cylinder surface for a real environment, stitching errors may still appear where the projection radius departs from the object-distance information. In addition, traditional panoramic models describe the transformation relationship from perspective imaging to panoramic imaging, which can easily lead to pixel aggregation or empty pixels in panoramic images.  
Aiming at accurate and seamless panoramic imaging with a multi-camera system, an inverse panoramic imaging method is proposed by estimation of object-distance information of the surrounding scene, construction of the inverse transformation model and introduction of the eccentric errors.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 2 Principles of Light Polarization and PS-OCT
### Polarization Effects
Figure 3: Depolarization measurement by PS-OCT. (**a**) Stokes vectors are plotted in Poincaré spheres for polarization preserving tissue (left, pink window) and for depolarizing tissue (right, blue window). Different Stokes vectors (red) correspond to the polarization states in adjacent image locations. In order to assess polarization scrambling within the evaluation kernels represented by the pink and blue rectangles overlaid on the OCT reflectivity image (center) of a rodent retina, the length of the average Stokes vectors is computed; (**b**) The length of the average Stokes vector (shown in blue in the Poincaré spheres) is decreased in case of depolarization. The DOPU image (center) assigns a color to the DOPU value of each pixel. The color map plots values from DOPU = 0 (blue, completely depolarized) to DOPU = 1 (red, uniform polarization). Fixels with low reflectivity (typically up to several decibels above the noise level) are masked in black. Note that the resolution in the DOPU image is slightly reduced by convolution with the evaluation kernel. In the DOPU image, most retinal structures appear to be polarization preserving, while pigmented structures such as the retinal pigment epithelium and the choroid scramble the polarization.
## 3 Results and Discussion
### Baseline Characteristics  
Characteristics and concentrations of biochemical parameters at baseline for the control and test groups are shown in Table 2 and the results of the FFQ are shown in Figure 1.  
The participants in the study had normal BMI. Most of the women, particularly in the test group, were physically active and consumed dietary supplements and alcohol (\(p\) = 0.041). This can be explained by the fact that many women probably exercise and take supplements to achieve the socially prescribed ideal body image [46]. Most of the biochemical parameters did not differ at baseline. The only statistically significant difference was observed in the higher concentration of HDL cholesterol in the test group (\(p\) = 0.029), which could be due to their higher consumption of fish and marine products compared to the control group (Figure 1). However, the concentrations for both groups were within the reference interval, so this difference was not considered clinically relevant.  
Figure 1: Box-Whisker plot of food frequency questionnaire by the control and test group (median with 25 th and 75 th percentile (Box value) and non-outlier range (Whisker value); markers \({}^{\circ}\) denote outliers and \({}^{\star}\) extreme).  
Food-item consumption frequencies are on a 1 to 5 Likert scale (5--every day; 4--three to five times a week; 3--once a week; 2--one to two times a month; 1--never).  
The diet of participants contained items from each food group. The consumption of biscuits/cake/milk chocolate (including some extremes) was higher while the consumption of fish and marine products was lower in the control group than in the test group.
## 1 Introduction  
Optical turbulence can have significant effects on laser beam propagation, impacting the performance of directed energy weapons and free-space optical communication systems. Various models have been developed and numerous experiments conducted to characterize optical turbulence over the ocean and over land [1, 2]. A particularly challenging environment to model is a near-coastal region, where turbulence can be impacted by the changing topography, heating of both the land and ocean surface, and the presence of on-shore or off-shore air flow [3, 4].  
The navy vertical surface layer model (NAVSLAM) was developed by the Naval Postgraduate School (NPS) Meteorology Department to model optical turbulence over the ocean. It produces profiles of the refractive index structure parameter, \(C_{n}^{2}\), versus height in the surface layer (up to \(\sim\)100 m) above the ocean surface. This model has been validated by comparisons to experiments [5, 6]. A two-level version of NAVSLAM was recently developed to model turbulence over land. Both versions of NAVSLAM are based upon Monin-Obukhov similarity theory (MOST), which assumes that conditions are horizontally homogenous and stationary. It is unclear whether such a model is valid in a near-coastal environment, where surface and atmospheric conditions can change rapidly in both time and space. Further details on MOST and its application for modeling vertical profiles of \(C_{n}^{2}\) can be found in [PERSON] et al. [1].  
The surface layer and other vertical profile models of turbulence can, in principle, be tested against radiosonde measurements. Radiosondes can measure turbulence profiles that extend many kilometers in altitude [7, 8, 9]; NAVSLAM, on the other hand, is applicable only in the first tens of meters above the surface. Thus, to test the validity of the two-level version of NAVSLaM in a coastal environment, the NPS Physics Department recently conducted an experiment along the shore of the Monterey Bay using point measurements of turbulence just a few meters apart at two heights above the surface. This approach provided a near-continuous stream of data at a fixed location and probing heights very close to the surface, where the assumptions of NAVSLaM are presumably more plausible. Over approximately one month, we took measurements to obtain values of \(C_{n}^{2}\) and other meteorological parameters, including air temperature, wind speed, and humidity, at two different heights. The meteorological data was then fed into the two-level version of NAVSLaM to predict \(C_{n}^{2}\) values at the two heights, which were then compared to the measured values.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Perturbation Theory
#### 4.5.3 Higher Order TD Perturbation Using the TD-DE  
[PERSON] et al. proposed a higher order TD perturbation model for absorbing perturbation by introducing a probability distribution function and defining TD mixed-pathlength moments (mixed-moments) [94; 95]. The fundamental equation is valid for both the TD-RTE and TD-DE, but analytical solutions for the TD-RTE are not available while those of the TD-DE are available. Also, calculations of the mixed-moments are not easy, but because the contributions of the mixed-moments are negligibly small compared with those of the self-moments, calculations using the self-moments only is found to provide good results. The fourth order perturbation using the Green's functions for the TD-DE and the Pade approximants have shown excellent results for large-volume inclusions with large absorption contrasts to the background.  
[PERSON] [92] derived general formulations of a higher order TD perturbation model for absorbing perturbations, and the author discussed that higher order perturbation is effective when the characteristic parameter, \(\Lambda\), is smaller than 3, i.e., \(\Lambda=\mid\delta\mu_{a}\mid R_{inc}{}^{2}/D_{0}<3\) where \(R_{inc}\) is the radius of a spherical inclusion. The paper provided calculation results of the second and third perturbations of the TR transmittance from a 60-mm thick slab having an absorbing inclusion with a diameter of 10 mm and a contrast of \(\delta\mu_{a}/\mu_{a0}\) about unity, and it summarized that the second perturbation is sufficient for accurate predictions.  
[PERSON] et al. reported explicit formula of higher order perturbations similar to those derived by [PERSON] [92,93], not only for absorbing perturbations but also for scattering perturbations up to the third order corrections although calculation results for scattering perturbations were not shown. The authors also refer to the characteristic parameter, \(\Lambda\), as Wassermann for the criterion for convergence of the Born series for absorbing perturbations [92].  
Most of the above reports for higher order perturbations are applicable to absorbing perturbations only, and the development of higher order perturbation models for scattering perturbations is desirable.