esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Simple Radiance Assimilation Algorithm
Table 1 summarizes the similarities and the differences between the 1 DVAR retrieval, the simple radiance assimilation scheme used in this study, and the operational 3 DVAR radiance assimilation process. OpenMP [41] is also implemented for parallel processing in the radiance assimilation procedure used here. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**MiRS**} & \multicolumn{2}{c}{**Simple Radiance**} & **Radiance Assimilation** \\  & **1 DVAR Retrieval** & **Assimilation 1 DVAR FGAT** & **3 DVAR FGAT** \\ \hline \multicolumn{2}{c}{**Satellite radiance**} & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \multicolumn{2}{c}{**Other observations**} & & & \(\surd\) \\ \hline \multicolumn{2}{c}{**First guess**} & & May be different from background & Same as background & Same as background \\ \hline \multirow{3}{*}{**Background**} & \(1\)D & \(\surd\) & \\ \cline{2-4}  & \(3\)D & & With spatial \& temporal & With spatial \& temporal \\ \cline{2-4}  & Background & \(\surd\) & interpolation & interpolation \\ \hline \multirow{3}{*}{**Error covariance**} & Observation & \(\surd\) & \(\surd\) & \(\surd\) \\ \cline{2-4}  & Observation & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \multirow{3}{*}{**Iteration**} & Outer loop & \begin{tabular}{c} RT model called, observation error \\ matrix is changed if O-B is large \\ \end{tabular} & \begin{tabular}{c} Background changed and RT model called \\ model called \\ \end{tabular} & \begin{tabular}{c} Background changed and RT model called \\ \end{tabular} \\ \cline{2-4}  & Inner loop & & \(\surd\) \\ \hline \multicolumn{2}{c}{**Data thinning**} & & & \(\surd\) \\ \hline \multirow{3}{*}{**Cost function**} & \(1\)D & \(\surd\) & \(\surd\) \\ \cline{2-4}  & \(3\)D & & \(\surd\) \\ \hline \multirow{3}{*}{**Output**} & \
\begin{tabular}{c} Analysis grid at \\ analysis time \\ \end{tabular} & & Increments propagated to & Increments propagated to \\ \cline{2-4}  &
\begin{tabular}{c} Analysis time \\ analysis time \\ \end{tabular} & & grids at analysis time & grids at analysis time \\ \cline{2-4}  & Orbit & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of 1 DVAR retrieval, the simple radiance assimilation scheme used in this study, and operational radiance assimilation.
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method  
This section explains the calculation method of the propagation time and the attenuation rate considering the effect of the refraction at the shear layer. Here, the Amiet method [5,12], which is a de facto standard method calculating the refraction in geometric acoustics, is employed, and the effects of the refraction on the acoustic waves are considered.  
As mentioned in the problem setting of the present study, we consider the propagation time and attenuation rate from one microphone to grid points in the sound source localization area. The propagation path from the microphone to the source grid is equivalent, even if the sound source and microphone are exchanged in their places. Thus, the propagation time and attenuation rate can be calculated by considering those of the acoustic wave that is generated from a virtual sound source located at a given grid point. Figure 1(a)-c show a schematic image of the propagation path when considering the acoustic wave generated from one point on the sound source grid and its refraction. The acoustic propagation actually occurs in a three-dimensional space, but the refraction can be considered to be two-dimensional because the flow velocity in the \(Y_{n}\) and \(Z_{n}\) axes inside the wind tunnel is  
Figure 2: Schematic image of acoustic propagation.  
zero and the air density does not change through the shear layer. Therefore, the refraction occurs in the \(X_{n}-Y_{n}\) plane, but not in the \(Y_{n}-Z_{n}\) plane. The propagation, including refraction, can be assumed to only occur in the \(X_{n}^{\prime}-Y_{n}^{\prime}\) plane, as shown in Figure 2a. While [PERSON] derived the refraction correction formulas in the plane perpendicular to the shear layer [5; 12], the present study simply extends this Amiet formula to outside the plane perpendicular to the shear layer with the assumption of the same sound speed inside and outside the test section as each other. An additional assumption simplifies the calculation. The deviation of the extended formula is presented, as follows.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-I(Key Recovery System Using Signcryption)
#### 3.2.2 Objects of Proposed Scheme-I  
The system objects of the key recovery system using signcryption is shown in Figure 2. In addition, M-KRA additionally exists, and its roles are as follows:  
Figure 2: Proposed scheme-I.  
* Participants: \(Part\) represents all participants (\(Dev\), \(Med\), \(KRC\), \(M\)-\(KRA\)) who use the encrypted communication provided by \(KGC\). \(Part\) can perform encrypted communication only by using \(params\) provided by \(KGC\).
* Key Recovery Center (\(KRC\)): \(KRC\) is an organization in charge of key recovery and plays a central role in key recovery. The key recovery process is performed according to \(Med\)'s request for key recovery, and \(KRF\) is converted into a form that can be recovered using \(KRC\)'s private key. In this model, to reduce the burden of \(KRC\)'s key recovery operation, the help of \(M\)-\(KRA\) is needed.
* Multi-Key Recovery Agents (\(M\)-\(KRA\)): \(M\)-\(KRA\) is the agent that helps some operations of key recovery by reducing the burden on \(KRC\). The \(KRA\) included in the \(M\)-\(KRA\) determines whether the \(KRF\) is suitable for recovery to prevent abuse of the \(KRF\)'s authority. When receiving a key recovery request from \(KRC\), \(M\)-\(KRA\) perform the \(KRF\) recovery process using their private key. Furthermore, \(M\)-\(KRA\) send the obtained session key pieces to \(KRC\).
## 1 Introduction
In our previous study [17], we proposed a cluster head selection method for energy-aware routing problems in UASNs and investigated how expected available energy affects cluster head selection while accounting for the stochastic energy harvesting techniques of individual sensors. We tackle the same problem and propose a novel reinforcement learning-based algorithm for determining cluster heads (CHs), which involves sensor nodes' positions and remaining energy in addition to estimated harvested energy [18]. Numerical results validate that our introduced approach significantly increases harvested energy and, hence, extends the network's operational lifetime considerably, proving that energy harvesting awareness improves the operational lifetime of underwater sensors. In this study, we introduce a time slot scheduling policy and improve the medium access control (MAC) protocol designed specifically for energy harvesting underwater acoustic sensor networks (EH-UASNs). The main goal of this policy is to optimize the network's lifespan while enhancing throughput. We utilize a clustered topology, leveraging its duty cycling and data aggregation nature, to enhance the efficiency of network energy utilization. We investigate the time division multiple access (TDMA) scheme for intra-cluster communication. TDMA optimizes energy usage by enabling inactive nodes to transition into sleep or idle modes until their allocated time slot for communication arrives. Our main goal is to enhance the efficiency of energy harvesting within clusters of the network by leveraging the allocation of time slots. We introduce a novel cooperative multi-agent reinforcement learning algorithm to schedule time slots of the TDMA MAC protocol for EH-UASNs to maximize harvested energy and enhance throughput through adaptive time slot allocation in intra-cluster nodes. Our main contributions can be listed as follows:  
* We propose a novel method for TDMA time slot allocation where nodes independently learn from their ambient energy resources and harvesting opportunities in order to autonomously decide their communication slots in their cluster. In our method, we model the TDMA time slot allocation task as a multi-armed bandit problem on individual nodes. We combine multi-armed bandit problem with a cooperative and independent Q-learning algorithm to maximize harvested energy in the network and mitigate the continuous scheduling duty on the cluster heads. * To analyze the efficiency of our proposed method in realistic channel conditions, we optimized the power consumption of the sensor node to maintain connectivity in clusters via optimal carrier frequency selection according to the channel parameters and distance to the receiver. The remainder of this paper is organized as follows: Section 2 presents the related work. Section 3 gives the system model; then Section 4 presents the problem statement. Section 5 proposes our novel approach. Section 6 evaluates the performance of the proposed algorithms numerically. Section 7 further investigates the performance of our approach. Section 8 concludes our work; Section 9 gives future work.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Energy Consumption  
No matter whether in a terrestrial wireless sensor network or an underwater acoustic sensor network, the energy consumption is always the first important factor, since low energy consumption means long network lifetime. Many energy consumption models have been proposed in recent years. In [29], the authors proposed a realistic power consumption model for WSN devices by incorporating the characteristics of typical low power transceivers. In [30], an energy model that is specifically built for online accounting is presented. This model only considers two states of the microcontroller and radio chip. The energy model in [31] uses a set of finite state machines to represent the states and transitions of a sensor node's hardware. There are also many energy consumption models that  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Underwater Environment** & **Terrestrial Environment** \\  & **(Acoustic Wave)** & **(RF Wave)** \\ \hline Propagation speed & Low (1200 m/s to 1400 m/s) & High (3 \(\times\) 10\({}^{8}\) m/s) \\ Energy consumption & High & Low \\ Propagation delay & High & Low \\ Bandwidth & Low & High \\ Data rate & Low & High \\ Noise and interference & High & Low \\ Dynamics & High & Low \\ Reliability & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between the underwater environment and the terrestrial environment.  
can be found in [32; 33; 34; 35]. For instance, in [35], the authors propose an energy consumption model for underwater wireless sensor network in which both shallow water conditions and deep water conditions are taken into account. Even through these energy consumption models are all proposed for terrestrial wireless sensor networks and cannot be applied directly in underwater environments, the review of these models can provide some guidance for underwater wireless sensor network energy consumption analysis.
# 5G V2X System-Level Architecture of 5 GCAR Project
## 4 Detailed Analysis of Selected Features
### Evolution of Infrastructure-Based Communication for Localised V2X Traffic
For instance, the SMF that could be used for the establishment of the local e2e paths could also trigger the appropriate event and support the collection of charging required charging information [28]. Localized communication through the Uu interface requires the introduction of a data routing/forward function at the BS (e.g., gNB) that transmits the data packets, e.g., among vehicles in a fast and guaranteed way without traversing any core network entity (i.e., user plane). This routing table in the BS maps and connects the uplink (UL) and downlink (DL) radio bearers of different UEs for the formation of the local radio paths and consequently the faster forwarding of localized V2X traffic (user plane latency reduction). According to the type of the traffic, the routing table at the BS undertakes to forward the data packet to one or more UEs in the same of neighboring cells (e.g., multicast and unicast transmissions). In case UEs are attached to different cells, a possible solution that requires further investigations is to use Xn interface for path establishment and for data packet exchange. Figure 9 provides an overview of the involved entities and interfaces. A UE requests the establishment (or update) of the local cellular V2V paths using RRC, NAS protocols, for localized V2X traffic and to transmit/receive data packets over a local e2e path. The type of the service and the identifiers of other involved UEs in the corresponding V2V service are information that the initiating UE should provide and is used for the establishment of the paths as well as for the configuration of the routing tables. RRC and NAS protocols need an extension to support establishment, update, and release of local cellular V2V paths between the UEs over the gNB(s) as well as to update  
Figure 9: Overview of vehicle-to-vehicle (V2V) paths via the cellular interface. and configure the routing table needed for the forwarding of localized data traffic. Based on these RRC or NAS messages, core AMF and SMF can control the establishment, modification, and release of this new type of link (i.e., local cellular V2V paths) as well as to update and configure the routing tables that are introduced at the BSs in order to form V2V paths for localized V2X traffic over the Uu interface. In general, the low latency and high reliability of multicast and unicast transmissions via the local e2e paths solution allows the realization of V2V services that more demanding and have more complex interactions (e.g., cooperative maneuvers, sensor data exchange, cooperative perception, and see-through).
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### Probability Distributions for the Statistical Characterization of Supraharmonics
#### 2.3.4. Burr Distribution  
The Burr distribution includes multiple distribution subfamily types and can fit a wider range of sampled data. It has three parameters, and its PDF is:  
\[f_{Burr}(x|\alpha,\beta,\gamma)=\frac{\gamma\beta}{\alpha}\Big{(}\frac{x}{ \alpha}\Big{)}^{\beta-1}\bigg{[}1+\Big{(}\frac{x}{\alpha}\Big{)}^{\beta} \bigg{]}^{-\gamma-1}, \tag{11}\]  
where \(x>0\), \(\alpha\) is a scale parameter and \(\beta,\gamma\) are shape parameters. The MLE of the parameters on a dataset \(\mathbf{x}=\{x_{1},\dots,x_{N}\}\) of observed samples has no explicit formulation; thus, the implicit optimization problem (to be solved numerically) is reported:  
\[\hat{\alpha},\hat{\beta},\hat{\gamma}=\underset{\alpha,\beta,\gamma}{ \operatorname{argmax}}\prod_{n=1}^{N}\frac{\gamma\beta}{\alpha}\big{(}\frac{ x_{n}}{\alpha}\big{)}^{\beta-1}\Big{[}1+\big{(}\frac{x_{n}}{\alpha}\big{)}^{ \beta}\Big{]}^{-\gamma-1} \tag{12}\]
## 3 System and Problem Definition for Scheduling Problem
### Jobs and Value Curves  
Each job \(j\) is modeled as an tuple \(J=(T;A)\), where \(T\) is the arrival time of the job and \(A\) is the application to be executed in order to complete the job. Each job \(j\) also has its own value curve function \(VC_{j}\) which converts a completion time of its execution to the value of the job to its user. These functions are typically monotonically-decreasing until reaching zero at a certain threshold of time, as shown in Figure 2. It is assumed that the value curves are pre-designed for each job and accurately reflect the economic importance to the user as the economic model is out of the scope of this paper.  
The PARSEC benchmark suite was used as the set of applications to queue in the system. This is because the benchmark applications were designed to have a range of multi-threading and other resource requirements. The focus on emerging workloads means the jobs are representative of potential future workloads in all situations including but not exclusive to HPC systems. A number of applications from the suite were selected and value-curves were designed for each application based on testing the execution time across different numbers of cores. In particular, we considered PARSEC applications listed in Table 1. The table also represents the value-curve for each application in terms of execution time and the value achieved if job is executed by that time. The jobs are generated by selecting a random application from this list and assigning an arrival time to it. Short and long periods of no jobs arriving are created to realize periods of no users (such as nights and weekends); these allow the scheduler to \"catch-up\" on remaining jobs in the queue.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Application** & \multicolumn{3}{c}{**Execution Time (s)**} & \multicolumn{3}{c}{**Value (Currency)**} \\ \hline blackscholes & 4 & 6 & 9 & 12 & 100 & 80 & 50 & 0 \\ bodytrack & 6 & 8 & 11 & 14 & 100 & 80 & 50 & 0 \\ dedup & 5 & 7 & 9 & 12 & 100 & 80 & 50 & 0 \\ facesim & 8 & 15 & 18 & 22 & 100 & 80 & 50 & 0 \\ ferret & 22 & 26 & 29 & 31 & 100 & 80 & 50 & 0 \\ fluidanimate & 7 & 9 & 11 & 14 & 100 & 80 & 50 & 0 \\ freqmine & 9 & 15 & 17 & 20 & 100 & 80 & 50 & 0 \\ streamcluster & 22 & 28 & 35 & 42 & 100 & 80 & 50 & 0 \\ vips & 9 & 11 & 13 & 16 & 100 & 80 & 50 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value at different execution times for PARSEC benchmark applications.  
The value curves create a natural \"soft deadline\" which implies an ideal time for a job to finish, but also that violation of the deadline does not mean the job completion was irrelevant [38]. Instead, the value of the job is reduced depending on the extra time the user has had to wait for completion [15; 39]. Major violations of the deadline will create no value for the user and therefore the energy consumed by the computation was wasted.
## 2 Materials and Methods
### Evaluation  
Model performance was assessed on each test cohort, both globally and per-dataset, using the following metrics: Sensitivity (Recall), Specificity, Positive Predictive Value (PPV or Precision), Negative Predictive Value (NPV), and F1-score.  
We also calculated the confusion matrices and generated the ROC (receiver operating characteristic) and PR (precision-recall) curves, along with calculating the area under the curve (ROC-AUC or AUROC, and PR-AUC or AP, average precision) in order to visualize performance at different classification thresholds. PR-AUC is useful given the imbalanced scenario of pneumothorax in the different datasets [70].  
We estimated 95% confidence intervals for all performance metrics by nonparametric bootstrapping with 1000 resamples.
## 3 Corrosion Modelling
### Non-Uniform Corrosion  
It was suggested by [PERSON] and [PERSON] [31] that general corrosion causes degradation on two levels: the reduction in the mean thickness and local irregularities on the surfaces of corroded elements. The local irregularities can be further divided into global non-uniformity and micro non-uniformity. The former can be captured by a very dense measurement grid, but the latter cannot be captured because their size is smaller than the sizes of measuring probes.  
In the present study, the local irregularities on the mesh size level are considered. The mesh size for hot-spot stress analyses is around 2 mm. More localized irregularities are  
Figure 6: Modelling of (**a**) uniform corrosion and (**b**) grooving corrosion.  
Figure 7: FE model of (**a**) uniform corrosion and (**b**) grooving corrosion.  
ignored. The uniform corroded tubular joint is modelled at first. Then the nodes around the weld are moved upward or downward along the direction perpendicular to the surface. The moving distance and direction of these nodes follow a sample of a normally distributed random variable with a mean value of zero. The standard deviation of the random variable is derived from the roughness parameter \(Ra\). The definition of \(Ra\) is as follows:  
\[Ra=\frac{1}{\mathrm{l}_{r}}\int_{0}^{\mathrm{l}_{r}}|z(\mathrm{x})|\mathrm{d}x \tag{1}\]  
where \(l_{r}\) is the evaluation length, and \(z\) is the vertical coordinates of the corroded surface concerning the mean surface. Assuming \(z\) is a normally distributed random variable with a mean value of zero, the absolute value of \(z\) follows a half-normal distribution. The mean value of the half-normal distribution, which equals \(Ra\), is as follows [32]:  
\[Ra=\sigma\sqrt{2/\pi} \tag{2}\]  
where \(\sigma\) is the standard deviation of \(z\). If the roughness of a corroded surface is known, \(\sigma\) can be derived from Equation (2). A sample can be generated based on the normal distribution of \(\mathrm{N}\left(0,\sigma\right)\). The non-uniform corroded FE model is obtained by moving the nodes around the weld. An example of the non-uniform corroded FE model is shown in Figure 8.  
The relationship between the roughness and corrosion time is approximated using measured data. The Ra values of steel plates for 2 years and 5 years of exposure time in a real marine environment were measured by [PERSON] et al. [33] and [PERSON] et al. [34]. [PERSON] et al. [34] obtained Ra values from three samples exposed to an underwater environment for 5 years. The average of the three Ra values and the Ra value for 2 years of exposure time obtained by [PERSON] [33] are used for the regression. The uncertainty of Ra is not considered in the present study. A linear relationship between Ra and the corrosion time is assumed, and a straight line is fitted based on the data as shown in Figure 9.  
Figure 8: Non-uniform corroded FE model.