esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Star Topology  
The first assessed scenario was the star topology. Consider an application that is monitoring the temperature of four boilers. A sensor node is installed in each boiler as described in Figure 14. Within this context, it is assumed that the application fails if at least one field device fails.  
Figure 14: A star topology composed of four field devices.  
Figure 15: Reliability evaluation for a star topology.  
It is intuitive to realize that the network reliability will increase as long as more reliable devices are used. This behavior is shown in Figure 15. If there is no redundancy, the scenario that uses sensor nodes with failure rate of 1E-4 (MTTF--1 year) presents a network reliability lower than all the other scenarios using more reliable field devices.  
In what concerns dependability requirements, design decisions are usually related to the selection of more reliable and more expensive devices _vs._ less reliable and inexpensive devices. In general, there is a global policy whose goal is to improve the reliability of the applications. The result presented in Figure 15 can be used for that purpose.  
On the other hand, in some cases the application requires an increase of the network reliability, but the acquisition of more robust devices is not an option. A possible solution for this problem is the use of redundancy. For instance, consider the topology described in Figure 14, composed of sensor nodes with failure rate 1E-4. Depending on the number of spare devices that can be used, the network reliability can reach levels comparable with those achieved when more reliable devices are used. According to the results shown in Figure 15, when just one spare device is used (\(1r\)) in each field device, during 2000 h the WSN achieves reliability levels comparable to the case where field devices five times more reliable are used. If two spare devices are used (\(2r\)) in each field device, the network reliability during 3500 h presents levels comparable with a scenario where devices ten times more reliable are used. On the other hand, during 6000 h the network reliability has a performance level that is comparable to the case where devices five times more reliable have been used. This result is three times better than the result found when using just single redundancy (1r).  
Another way to evaluate the influence of redundancy is through a MTTF analysis, which is dependent on the network failure conditions. We assume four types of failure conditions: case I, at least one field device fails; case II, at least two field devices fail; case III, at least three field devices fail; case IV, all field devices fail. The results are summarized in Figure 16. For example, if it is considered that the network fails if at least one field device fails, then the use of single redundancy (1r) may increase the network MTTF by 125%, whereas the double redundancy (2r) may increase it by 220%. This MTTF-based analysis can be useful to find the desired application requirements.  
Figure 16: Influence of failure conditions and redundancy levels to the network MTTF.
## 3 Flow Field Calculation
### Equation Discretization and Boundary Conditions  
The numerical simulation of flow fields was implemented on the CFX platform (ANSYS 15.0 2013, Canonsburg, PA, USA), and the finite volume method (FVM) was applied to discretize the governing equations. A high-order solution was used for the convective term and diffusion term, and pressure gradients were expressed by finite element functions. Fully-implicit multi-grid coupling was utilized for the solution of flow fields by coupling momentum equations and the continuity equation [21; 25]. In view of the gravity effects on the overall flow fields, the pressure inlet and outlet along water depths and different operating conditions were set up by the CFX expression language (CEL). Adiabatic and no-slip boundary conditions were adopted for the solid walls. The junction of the impeller fluid domain and solid impeller was defined as the FSI boundary. The transient frozen rotor method was conducted to transmit information between dynamic and static regions with a time step of 0.001 s; i.e., a blade rotates 1.5\({}^{\circ}\) in each time step, and there are 240 time steps in each cycle. Considering the computational economic efficiency and reliability, eight complete cycles were applied to the overall stimulation, and then the total physical time was 1.92 s.
## Appendix A.3.3 Physical-Layer Cell Identifiers  
LTE networks are designed to employ both single and multiple-frequency cell deployment techniques. For single-frequency deployment, all neighboring cells and sectors use the same channel, and interference is a consequence. In a similar fashion to UMTS, the physical-layer cell identifier in LTE includes two attributes:  
* Evolved absolute radio frequency channel number (EARFCN), a 16-bit code that represents the channel number and is bound to the used frequency by a formula;
* Physical cell identifier (PCI), a 9-bit code which is composed of the physical group ID and the physical cell ID. A typical LTE deployment scenario is a three-sector installation on same EARFCN, where each sector has a sequential PCI. The standard defines 168 cell identity groups, each having three identities; thus, there are \(168\times 3=504\) PCI values available.
# Scheduling Non-Preemptible Jobs to Minimize Peak Demand
## 3 Algorithms
### An Approximation Algorithm
#### 3.2.3 Continuous Timescales
\text{ (since }Z_{t}=hY_{t},\forall t) \tag{5}\]  
We use a Chernoff-type bound [14] to bound the deviation of \(Y_{t}\) above \(\frac{H^{\text{opt}}}{h}\). Note that \(E[Y_{t}]\leq\frac{H^{\text{opt}}}{h}\) by (3). Since \(X_{i,j}\) are independent Bernoulli trials, for any given \(t\), with \(E[X_{i,j}]=p_{i,j}\) and \(0<\frac{h_{j}}{h}\leq 1\) for all jobs, the following bound exists for all \(\delta>0\) (Theorem 1 in [15] proves that \(\Pr[X>(1+\delta)\mu]<\big{[}\frac{e^{\delta}}{(1+\delta)^{(1+\delta)}}\big{]}^ {n}\) for \(\mu=E[X]\), but it is easy to see that this holds for all \(\mu\geq E[X]\)):  
\[\Pr[Y_{t}>(1+\delta)\frac{H^{\text{opt}}}{h}]<\bigg{[}\frac{e^{\delta}}{(1+ \delta)^{(1+\delta)}}\bigg{]}^{\frac{H^{\text{opt}}}{h}}\]  
Substituting \(\alpha=1+\delta\) and gathering terms on the right hand of the expression into one exponential results in,  
\[ \begin{array}{rl}\Pr[Y_{t}>\alpha\frac{H^{\text{opt}}}{h}]&<\exp\left[-( \frac{H^{\text{opt}}}{h})(\alpha\ln\alpha-\alpha+1)\right]\\ &\leq\exp\left[-\alpha\ln\alpha+\alpha-1\right]\text{ (since }\frac{H^{\text{opt}}}{h}\geq 1 \end{array}  \tag{6}\]  
for all \(\alpha>1\). Next, choose \(\alpha=\frac{4\log n}{\log\log n}\) and consider the value of \(\ln\alpha-1\):  
\[ \begin{array}{rl}\ln\alpha-1&=\ln\left(\frac{4\log n}{\log\log n}\right)-1 \geq\ln\log n-\ln\log\log n\\ &=\frac{\log\log n}{\log\log n}-\frac{\log\log\log n}{\log\epsilon}\text{ (change of base)}\\ &\geq\frac{1}{\log\epsilon}(\log\log n-\frac{1}{2}\log\log n)=\frac{\log \log n}{2\log\epsilon}.\end{array}  \tag{7}\]Plugging (7) and \(\alpha=\frac{4\log n}{\log\log n}\) into (6) yields  
\[ \begin{split}\Pr\left[Y_{t}>\frac{4\log n}{\log\log n}\frac{H^{ \text{opt}}}{h}\right]&<\exp\left[\frac{-4\log n}{\log\log n} \frac{\log\log n}{2\log\epsilon}\right]\\ &=\exp\left[\frac{-4\ln n\log\epsilon}{\log\log n}\frac{\log\log n }{2\log\epsilon}\right]\\ &=\exp\left[-2\ln n\right]=1/n^{2}.\end{split}  \tag{8}\]  
Finally, we use (5), (8), (4), and Lemma 3 to give a probabilistic bound on the height of rounded schedule, \(H^{\text{mrd}}\):  
\[ \begin{split}\Pr\left[H^{\text{mrd}}>\frac{4\log n}{\log\log n}H^ {\text{opt}}\right]&\leq\sum_{t\in L}Pr\left[Z_{t}>\frac{4\log n }{\log\log n}H^{\text{opt}}\right]\\ &=\sum_{t\in L}\Pr\left[Y_{t}>\frac{4\log n}{\log\log n}\frac{H^ {\text{opt}}}{h}\right]\\ &<\sum_{t\in L}1/n^{2}\\ &\leq\left|L\right|/n^{2}\\ &\leq 2n/n^{2}=2/n\in O(1/n).\end{split} \]  
So, with probability \(1-O(1/n)\), \(H^{\text{mrd}}\leq\frac{4\log n}{\log\log n}H^{\text{opt}}\).
## 2 Materials and Methods
* Time to Alert (TA): The maximum allowable time elapsed from the onset of the navigation system being out of tolerance until the equipment enunciates the alert. * Integrity Risk (IR): The probability that, at any moment, the position error exceeds the Alert Limit. * Protection Level (PL): Statistical bound error computed so as to guarantee that the probability of the absolute position error exceeding said number is smaller than or equal to the target integrity risk. The horizontal protection level provides a bound on the horizontal positioning error with a probability derived from the integrity requirement. Based on IR and PL values, MI can be estimated. There are two integrity monitoring methods: Receiver Autonomous Integrity Monitoring (RAIM) and receiver assisted by ground monitoring stations, namely Satellite-Based Augmentation Systems (SBAS) or Ground-Based Augmentation Systems (GBAS). We will use in the following a RAIM system to estimate the integrity indicators. The camera aiding algorithm proposed in this paper is as follows:  
* The GNSS pseudoranges and pseudorange rates are computed with the software receiver GNSS Environment Monitoring Station (GEMS) [7] using the Multi-Correlator (MC) tracking loop with the multipath detection activated. * These measurements are fed into the EKF prediction step and the associated Receiver Autonomous Integrity Monitoring (RAIM) can thus exclude faulty satellites. * For each satellite left, the elevation and the azimuth are computed and projected onto the camera reference frame using the vehicle heading computed from the reference trajectory. In an operational mode, the heading would be computed from the GNSS velocity. For our experiments, it is computed from the reference trajectory, so there is no error on the projection of the satellites on the camera reference frame and then the best possible contribution of the camera aiding can be assessed. * A three-levels discretization of LoS probability map has been considered:
* LoS level: When the probability to be in LoS (pLoS) is between 0.75 and 1, the received signal is considered as the LoS, and the measurements are kept unchanged. * Doubt level: When the pLoS is between 0.25 and 0.75 (most likely related to building edges or vegetation), there is doubt on the quality of the received signal, so an overweighting is applied on the measurements as: *  
We present in the following the principal materials and methods used in our research, following the steps of the camera aiding algorithm already presented.
## 2 State-of-the-Art
### The Potential of 5G in Manufacturing  
The deployment of 5G wireless technology comes at a time when many industries are undergoing transformations due to increased automation, thanks to technologies like the IoT, machine vision, machine learning, and robotics. 5G connections are expected to enable the intelligent IoT, in addition to human communication needs. The next generation of telecommunication technologies (NextGen) will be adopted by a broader range of industries and sectors, with digital manufacturing being benefited. Next-generation wireless control of highly sophisticated mobile machines is becoming possible, allowing machines to take  
Figure 6: Three major application scenarios for 5G in 2015 defined by ITU [13].  
advantage of the massive computing power and cloud storage options, without being tethered by physical wires. To this end, factory [34] and machine shop [35] monitoring, in which sensors are installed in the shop-floor level to monitor equipment conditions and ensure that everything is working as it should. Additionally, with 5G technology real-time production monitoring will be possible, knowing the near real-time state of equipment or the expected manufacturing time after an order has been placed, with proper access from anywhere around the globe. As such, the various factors that have to be considered to define the business model and the pricing of a 5G industrial network are discussed in [36]. Next, in terms of predictive maintenance, the identification of potential issues before they occur, is an early area of interest for 5G in the IIoT [37]. Moving information and network functions to the network's edge can help reduce latency, which will be necessary for applications that require ultra-low latency. When dealing with lengthy distances between nodes, applications that must span end-to-end, such as remote machinery control, will require 5G at the very least. 5G latency can be divided into three network topologies, assuming a roundtrip ping scenario, as presented in Figure 8.  
* Public cloud to device: 50 ms to 100 ms
* Telco cloud to device: 20 ms to 50 ms
* Telco edge to device: 1 ms to 2 ms
## 4 Numerical Modeling
### Sliding Mesh Model  
In the sliding mesh model, shown in Figure 5, the computational domain of the tank is divided into two regions that are treated separately: the region of the impeller, and the region of the tank which includes flow, the surrounding walls and the baffles. The mesh region, which contains the impeller, rotates with constant velocity while the other region (computational domain) is fixed. This method, in general, is able to predict flow evolution over time, but it has a high computational cost. The mesh with an independent solution for this model (following recommendations from the literature [11; 29]) has 586,483 cells, which can be seen in Figure 6.  
Figure 5: Computational domain scheme of the sliding mesh model. (**a**) Side view of the tank. (**b**) Top view of the tank.
## 3 Materials and Methods
### Sensor Node Architecture and Implementation  
The master node is a so-called base station that includes a computer, router, and microcontroller. Other slave sensor nodes are linked to the master node. As illustrated in Figure 5, each slave sensor node comprises a sensor, microcontroller, and power unit. The sensor collects data from the environment and provides it to the microcontroller for processing. The slave node sends the data read from a sensor to the master node for additional processing and storage for real-time monitoring. Finally, the data received by the master node are saved on a personal computer for further study and timely action. The power unit could compromise the energy harvester to collect electromagnetic radiation for charging the battery. Experiments were conducted by employing four sensor nodes, one of which served as a master node and others are slave nodes.  
Figure 4: Energy-harvesting antenna.
## 2 Materials and Methods
### Treatment and Monitoring System
In the experiment, treatment laser exposures for the creation of RPE lesions were correlated to signal variations in SD-OCT M-scans, consisting of 256 sequentially recorded A-scans (at 7.76 ms total acquisition time per M-Scan). For each lesion, seven consecutive M-scans were recorded, which leads to time-resolved records of 37.24 ms duration. The treatment laser was triggered to emit at the first third of the M-scan time period. The OCT beam waist with a diameter of 12 \(\upmu\)m was adjusted to the center of the treatment spot.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 3 Network Model, Q-Learning, and Fuzzy Logic  
### Network Model and Problem Definition  
Figure 1 shows the considered network model, in which a WRSN monitors several targets. The network has three main components: an MC, sensor nodes, and a base station. The MC is a robot that can move and carry a wireless power charger. The sensor nodes can receive charged energy from the MC via a wireless medium. The base station is static and responsible for gathering sensing information. We assume that there are \(n\) sensors \(S_{j}\) (\(j=1,\ldots,n\)) and \(m\) targets \(T_{k}\) (\(k=1,\ldots,m\)). We call a sensor a _target-covering_ sensor if it covers at least one target. Moreover, if there exists an alive routing path between a sensor and the base station, it is _connected_ to the base station. The target is defined as to be _monitored_ when at least one sensor connected to the base station covers it.  
A sensor node that has its remaining energy below \(E_{th}\) (i.e., a predefined threshold) will send a charging request to the base station. The base station then uses one-hop routing to transfer the request to the MC. We assume that the MC can interact with the base station over a long-range communication. We target a non-preemptive charging schedule, in which charging requests from sensors are queued at the MC. We assume that there are \(k\) charging locations denoted by \(D_{1},\ldots,D_{k}\) in the network. When the MC completes its tasks  
Figure 1: The network model.  
at a charging location, it runs our proposed algorithm to select the next optimal charging location from \(D_{1},\ldots,D_{k}\). Moreover, the MC also determines the optimal charging time at that charging location. When the energy of the MC goes below a threshold, it returns to the depot to recharge itself. Besides gathering the sensing information, the base station is also responsible for collecting information about the remaining energy sensors. Based on that, the MC estimates every sensor's energy consumption rate using the weighted averaging method. Given all sensors and the targets' locations, our on-demand charging algorithm aims to maximize the network lifetime.
## 1 Introduction
[PERSON] and [PERSON] suggested a simplified pseudo-dynamic method that switches the account of the time effect for seismicity while it ignores the significant variables involving boundary state and medium damping [30; 31]. The modified pseudo-dynamic method put forward by [PERSON], which was developed from earlier approaches, has been extensively employed in the seismic safety assessment of geotechnical engineering [32; 33; 34]. Thus, this approach is also utilized in this study. According to related research, altering the geometry of slopes can also significantly enhance the aseismatic performance of slopes while being more cost-effective than providing additional reinforcement. Thus, the aseismatic effectiveness of coupling the prestressed anchor cable [35] with the two-stage slope, that is, adding a step platform on the slope surface, is investigated in the present article. For the studies of slope anchorage based on the LA method, [PERSON] et al. investigated the stability of 2D slopes reinforced with a single-row anchor using the rotating failure mechanism and discovered that there is a risk of local instability of the slope when the anchor is close to the toe of the slope [36]. And [PERSON] et al. proposed a method for assessing the stability of 2D slopes under seismic conditions based on the modified pseudo-dynamic method, which considers the dynamic changes in the axial force of the anchor cable [37]. Based on the foregoing literature, this paper expands the 2D slope condition to 3D. In this paper, an analytical solution for the seismic safety of the 3D two-stage rock slope reinforced with prestressed anchor cables governed by the nonlinear H-B criterion is presented, in which the analyses of seismicity are performed by the latest modified pseudo-dynamic method. And a 3D horn-shaped rotating failure model is utilized to investigate and determine the external forces and internal energy dissipation. With the prerequisite, FS can be compiled as an explicit function with regard to the five variables. To confirm the correctness for analytical solutions, the results are contrasted with those of the pertinent literature and other approaches, and the errors were all within the acceptable range. Eventually, the sensitivity analyses and coupling effects of the seismic pseudo-dynamic parameters and prestressing anchorage parameters were carried out.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 7 Conclusions  
This paper proposes a IoT-over-satellite-based localization framework using both AoA and Doppler shift frequency patterns. A likelihood function was derived from the measurements based on the Gaussian approximation of the Kent distribution for AoA and the Gaussian distribution for Doppler. To estimate the location of a ground IoT device, a stochastic optimizer was utilized to maximize the likelihood function, or, in other words, to minimize the negative log likelihood function. An experimental satellite ground station was utilized to obtain realistic Doppler measurements of NOAA LEO satellites in order to support the Gaussian model of Doppler error. From the simulation results, it is observed that the achievable median localization error is less than 50 m when using the best configuration (highest number of satellites and measurement points and lowest number of measurement deviations). Furthermore, it is shown that the combination of AoA and Doppler measurements improves the localization performance significantly. In the future work, we suggest the satellite's attitude errors to be included in the localization estimation to accommodate for a wider range of scenarios.  
Conceptualization, I.S.M.H. and A.A.-H.; Methodology, I.S.M.H. and A.A.-H.; Software, I.S.M.H.; Formal analysis, I.S.M.H.; Investigation, I.S.M.H. and A.A.-H.; Writing--original draft, I.S.M.H.; Writing--review & editing, A.A.-H.; Visualization, I.S.M.H.; Supervision, A.A.-H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Government Research Training Program.  
Figure 21: The localization error using Doppler, AoA and joint Doppler-AoA measurements for Doppler standard deviation, \(\sigma_{\text{d}}=5\) Hz and azimuth, \(\sigma_{\Phi}=0.01^{\circ}\) and elevation, and \(\sigma_{\Theta}=0.01^{\circ}\) deviation using six satellites and 75 s of measurements; the results are based on 1600 runs.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to a very large simulation data size.  
Conflicts of Interest: The authors declare no conflict of interest.
## 2 Fundamentals and Motivation
### Motivating Scenario  
The following motivating scenario is used throughout the paper to explain our variability modeling concepts and to show technical feasibility. We choose here a very simple scenario consisting only of two components that form the entire application to be deployed since already this simple scenario shows the drawbacks and difficulties if variants are managed in the form of individual deployment models. The motivating scenario is a simple composite application that consists of two components, (i) a _web component_ and (ii) a _database_. This application can be deployed in different variants. Requirements, such as expected workload, costs, availability, development simplicity, and compliance, influence the decision on which variant to use.  
During development, the deployment of the entire application should be cost efficient and fast. Therefore, often all components are simply deployed on a single virtual machine that runs on a hypervisor such as a private OpenStack as shown on the left in Figure 1. In our scenario, the web component is a NodeJS14 application that runs on a local NodeJS14 runtime that connects to a local SQLite3 database.  
However, if the application has to serve production workloads all components should be hosted on managed elastic cloud service offerings. In our scenario, we select GCP as provider as shown on the right in Figure 1: The web component is hosted inside Google App Engine [5] and Google Cloud SQL [6] is used to provide a MySQL5 database. If GCP is not a suitable provider, of course also Amazon Web Services (AWS) or others could be used.  
Thus, in general, any combination of suitable technologies, cloud providers, and cloud service types can be used to deploy this composite application. If many different deployment variants of a single application are required, it is infeasible to model each variant as a separate deployment model. Managing all these deployment models would be a complex, time-consuming, and error-prone task. For example, if a new version of the web component is released, the respective component must be updated in each deployment model.