esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Energy Performance of Iridium Satellite IoT Technology
## 4 Current Consumption Model for Iridium Satellite IoT
\begin{array}{c}E[I_{Act}]=I_{OK}\cdot(1-P)+\frac{I_{OK}+I_{KO}}{2}\cdot P\cdot( 1-P)+\frac{I_{OK}+2\cdot I_{KO}}{3}\cdot P^{2}\cdot(1-P)\\ +\cdot\cdot\cdot+\frac{I_{OK}+(n-1)\cdot I_{KO}}{n}\cdot p^{n-1}\cdot(1-P)\end{array}  \tag{2}\]  
The above equation can be expressed as follows:  
\[E[I_{Act}]=\sum_{i=1}^{n}\frac{I_{OK}+(i-1)\cdot I_{KO}}{i}\cdot p^{i-1}\cdot( 1-P) \tag{3}\]  
We next determined \(E[T_{Act}]\), under the considered assumptions. \(E[T_{Act}]\) can be determined as follows:  
\[ \begin{array}{c}E[T_{Act}]=T_{Retry}\cdot(1-P)+2\cdot T_{Retry}\cdot P\cdot (1-P)+3\cdot T_{Retry}\cdot P^{2}\cdot(1-P)+\\ \cdot\cdot\cdot+n\cdot T_{Retry}\cdot p^{n-1}\cdot(1-P)\end{array}  \tag{4}\]  
The above equation can be expressed as shown next:  
\[E[T_{Act}]=T_{Retry}\cdot(1-P)\sum_{i=1}^{n}i\cdot p^{i-1} \tag{5}\]  
The next term from (1) that we determined is \(E[T_{Slap}]\), which can be easily found as follows:  
\[E\Big{[}T_{Sleep}\Big{]}=T-E[T_{Act}] \tag{6}\]  
Finally, \(E[T_{Sleep}]\) can be determined empirically. This value decreases with \(T\), and it tends asymptotically to 80.7 uA. Therefore, \(I_{agg}\) can be calculated based on the presented Equations ((1)-(6)) and values. Assuming that the Iridium IoT device runs on a battery of capacity \(C_{batt}\), the theoretical lifetime of the device can be calculated as follows:  
\[Lifetime=\frac{C_{batt}}{I_{Avg}} \tag{7}\]  
Note that the theoretical device lifetime calculated as per (7) assumes a battery of ideal characteristics. A real battery will typically exhibit non-ideal features (e.g., a certain degree of self-discharge) that will lead to a lower actual device lifetime. Therefore, the theoretical device lifetime must be considered as an upper bound of the actual device lifetime. The last performance metric th we considered was the energy efficiency of data transmission when using the Iridium device. This metric can be calculated in terms of the energy cost per delivered user data bit, \(EC_{db}\), as shown next, where \(V\) indicates the battery voltage:  
\[EC_{db}=\frac{I_{Avg}\cdot V\cdot T}{L} \tag{8}\]
# Research on Grouting Dynamic Monitoring Based on Borehole-Tunnel Joint Resistivity Method
## 2 The Background of the Study Area
### Grouting Design Parameters  
Borehole layout: Three grouting holes (see Figure 1) were vertically positioned approximately 30 m below the coal seam working face tunnels.  
Grout composition: The grout mixture primarily consisted of cement (15-30%) and fly ash (70-85%), with a water-cement ratio of 2:1.  
Operational details: Drilling orientations and grout volumes are summarized in Table 2.  
Figure 1: Schematic diagram of the drilling and grouting layout for faults in a coal mining face.  
To evaluate grouting efficacy, real-time monitoring of grout diffusion patterns was conducted during the drilling-grouting process, ensuring precise control over fracture sealing and hydraulic isolation.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
#### 1.1.3 Phase 3  
In the third phase, we further expanded the implementation from Phase 2 by integrating our MLS ROS software onto the ScanEagle UAV VM and the Collaborative Autonomous Systems for Standoff Maritime Inspection and Response (CASSMIR) USV. The test environment consisted of (A) one Ubuntu VM serving as ground control for ScanEagle, (B) the ScanEagle UAV VM and (C) the CASSMIR USV. Control commands were sent while simultaneously sharing data with the CASSMIR USV using MLS over an ad-hoc Silvus military-grade wireless mesh radio network. The UAS VM was only utilized instead of the physical UAS device for reasons of flight permissions and ease of transport, given the size of the UAS. The VM was a high-quality simulation and was deemed equivalent to a physical ground test in lieu of flight permissions in the test locality.
# Wind-Induced Response Control of High-Rise Buildings Using Inerter-Based Vibration Absorbers
A few earlier studies also suggested the use of TID [55] and TMDI [36; 56] to suppress wind-induced vibration in high-rise buildings. [PERSON] and [PERSON] [36] investigated wind-induced vibration mitigation of a 74-story benchmark building equipped with TMDI using a frequency-domain stochastic approach, based on empirical power spectral density (PSD) matrix of across-wind aerodynamic force [57]. Their results indicated that the TMDI reduced the peak top-floor acceleration more effectively than the TMD but employing smaller attached-mass values, especially for some selected topologies of installation. Additionally, they showed that the inclusion of the inerter dramatically reduced the TMD stroke. In this regard, it is worth noting that across-wind aerodynamic forces in high-rise buildings are induced by vortex shedding which highly rely on the actual cross section of the building [57]. Moreover, the empirical PSD of across-wind aerodynamic force adopted in [36] is not applicable when surrounding buildings exist, such that wind directions are not consistent with coordinates of structures. This research work falls into the same research line as the previous papers, but it uses a different wind-excitation model underlying an alternative time-domain analysis perspective. More specifically, in this paper synchronous multi-point pressure measurements from wind tunnel tests of a scaled high-rise building of height 340 m are carried out. This allows the definition of a more appropriate set of aerodynamic forces that are consistent with the actual cross-section of the benchmark building and with the existing surrounding conditions. The time histories of aerodynamic forces at each story are determined along 24 different wind directions from 0\({}^{\circ}\) to 345\({}^{\circ}\) at an interval of 15\({}^{\circ}\). Performance-orientedoptimization of parameters of both the TMDI and the TID are carried out to find out the optimal parameters of corresponding vibration mitigation device to suppress wind-induced responses with an eye for practical aspects like frequency ratio, damping ratio and placement of TID/TMDI. Combining aerodynamic forces from wind tunnel tests, structural dynamic characteristics (mass and stiffness of the building) extracted from the finite element model of the primary structure and parameters of optimally-designed TMDI and TID, a time-domain mathematical model of the benchmark building is used in this study for analyzing the wind-induced responses under the assumption of linear elastic behaviors. The effects of TMDI and TID on wind-induced displacement and acceleration responses and on ESWLs are studied and compared with results of the benchmark building equipped with classical TMD that shares the same physical mass as the former two inerter-based vibration absorbers (the physical mass denotes the attached mass in the TMD case, and the sum of mass of inerter devices and attached mass in the inerter-based vibration absorbers).
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 3 Modeling and Performance Analysis
### Rural Environment  
Now, let us consider state transition diagram for rural environment as shown in Figure 3.  
The UE registering in cell 1 (state S\({}_{1}\)) can enter cell 2 or cell 3 (state S\({}_{2}\)) with a probability of \(qm\) or enter any one of the remaining four cells with a probability of \((1-q)m/4\). If it enters one of the two neighboring cells that are not in the current TAL, it will register its location (state S\({}_{1}\)).  
If a call occurs to/from the UE, before the UE registering in cell 1 (state S\({}_{1}\)) enters a neighboring cell (state S\({}_{2}\) or state S\({}_{3}\)), the state of the UE becomes state S\({}_{0}\). Such a state transition occurs with a probability of \(1-m=P[T_{c}\leq T_{m}]\). Note that in this case, cell is not changed. However, a new TAL is set up (by implicit registration) and the cell becomes cell 1 in Figure 1.  
Similar to an urban environment, we can obtain transition probability matrix \(P_{rural}\) for a rural environment as follows:  
\[P_{rural}=\left[\begin{array}{cccccc}1-m^{\prime}&\frac{1-q}{2}m^{\prime} &qm^{\prime}&\frac{1-q}{2}m^{\prime}&0&0\\ 1-m&\frac{1-q}{2}m&qm&\frac{1-q}{2}m&0&0\\ 1-m&0&\frac{1}{6}m&\frac{1}{3}m&\frac{1}{3}m&\frac{1}{6}m\\ 1-m&\frac{1}{2}m&\frac{1}{6}m&0&\frac{1}{6}m&\frac{1}{6}m\\ 1-m&\frac{1}{3}m&\frac{1}{3}m&\frac{1}{3}m&0&0\\ 1-m&\frac{1}{2}m&\frac{1}{6}m&\frac{1}{3}m&0&0\end{array}\right] \tag{6}\]
## 5 Results and Discussions
### System 1: The IEEE 33-Bus
#### 5.1.2 Case 2: Simultaneous Minimization of APL and VD  
In this case, the minimization of both APL (OF\({}_{1}\)) and VD (OF\({}_{2}\)) is simultaneously considered. Therefore, in order to provide different solutions regarding the studied problem, the weighting factors in Equation (18) are taken as \(\omega_{1}=0.5=\omega_{2}=0.5,\omega_{3}=0\).  
The QODELFA is applied for three different values of power factor: Unity, 0.95 and 0.866 lag. The results obtained by applying the proposed QODELFA are given in Table 4 and compared to those from two other methods. It might be noticed that, with unity power factor DGs (Case 2.1), the APL is reduced to 78.308 kW by using QODELFA, which is less than that of 92.50 kW from SIMBO-Q [28] and 88.90 kW from QOSIMBO-Q [28].  
Similarly, when the power factor is set to 0.95 lag (Case 2.2), the APL reduced by QODELFA (29.231 kW) is lower than that of 32.20 kW from SIMBO-Q [28] and 31.10 kW from QOSIMBO-Q [28].  
Since the VD values (0.0055 and 0.0007 p.u.) obtained by QODELFA are slightly higher than those obtained from both algorithms in [28], and the APL values (in kW) gained by QODELFA are much lower than those from the algorithms in [28] for cases 2.1 and 2.2, the solutions for both APL and VD of the proposed algorithm are regarded as better solutions because they have a better compromise than those of the methods from [28].  
When the power factor is set to 0.866 lag (Case 2.3), the APL and VD reach 15.502 kW and 0.0003 p.u., respectively. This explains the power factor's effect again on the optimal operation of DGs as well as its importance to achieve better values of the studied objective functions. Moreover, as pointed out in Table 4, the very small SD of results in all cases obtained by the proposed algorithm also proves its robustness.  
As noticed from Figure 8, which shows the convergence characteristics of Case 2 as a verification of effectiveness, the QODELFA can rapidly converge to the optimal solutions.  
Figure 7: Convergence characteristics for Case 1 of the IEEE 33-bus system using QODELFA.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 5 Conclusions  
Route determination is instrumental for successful implementation of various urban applications. Noise prediction application requires extraction of routes in outdoor environment through which the noises or acoustic pressure waves primary propagate to generate a noise level for a place. GIS-based applications determine the routes primarily in 2D; however, the acoustic pressure waves transmit in 3D, requiring extraction of routes in 3D. Authors understood the need to establish a solution in this direction. Further, for noise propagation modelling, the available applications primarily consider the noise sources as linear source, which is further averaged over a long duration to normalize fluctuation of noise levels over time. The averaging significantly reduces the prediction accuracies specially for a short period or instantaneous scale. Authors realized the challenge and came up with the scheme of point-to-point prediction, considering noise originating from a point and propagating to many surrounding points. Novel cutting plane technique essentially tried to accurately determine all the possible routes between every pair of noise source and destination. It helped to compute the extent of sound pressure level (dB) reaching a destination point after originating from a source. This point-to-point computational technique was extended for all the noise sources (be it point source or line source or area source) and noise receiving location pairs to generate a noise map for an area. The authors developed a robust approach where it tried to determine all the principal routes in stages, which are independently dedicated to route over top, route around sides, reflected routes, etc., when direct propagation of noise is not possible. The challenge of handling huge unlabeled point cloud data is managed through extraction of edges of the buildings (and other obstructions), along with various terrain points such as building corner, reflection point, diffraction point or the transmission point (as the case may be). This approach in comparison to the existing technology is significantly more efficient in determining and analyzing the terrain points and terrain parameters. With the above computation, authors focus on determining the principal routes that are required to be ascertained for determining the noise level without considering a large number of non-principal routes, saving time for computation, and avoiding compromising the accuracy. The proposed routing technique is further used for determining the optimal location of cell phone towers for adequate signal strength.
## 3 Results and Discussion
### Reuse
The increase of time required for a single secured write operation, for reuse purposes, can be computed as the ratio between the number of instructions A needed for password write and the number of instructions B required for a simple write operation, which is equal to 1:4. This limits the maximum number of tags that can be written over time. If a company produces 1000 standard tags per day, the maximum rewritable tag number, for the same company, will be 714. Based on this consideration, an annual cost prospect is followed (Figure 7), with different percentages of returned tags. It can be noticed that, in this configuration, the annual costs for a company that adopts the reuse process is lower as the returned tags are more than 30% of the total amount of tags. Moreover, the annual amount of programmed tags corresponds to 365 K for a standard process and to 260 K for a reuse-enabled process, since the maximum number of programmable tags per day is reduced. If each tag is sold at a price three times greater than the self-adhesive paper tag purchase price, for both standard and reuse processes, the cost-to-earnings ratio is increased up to 112.3% for a 50% of returned tags and up to 140.7% for a 60% of returned tags. It is important to underline that this analysis does not take into account two important factors, which have not quantified at this stage: The first is represented by the benefit that the producer could earn with a \"green image\" of the company, which is associated with the reuse process. The second is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substrate Type** & **Technology** & **Size** & **Cost 1 [g/Unit]** \\ \hline Self-adhesive paper & Alien H3 & \(100\times 25\) mm & 0.05–0.06 \\ Pet flexible, transparent & Alien H3 & \(73\times 23\) mm & 0.09–0.12 \\ Waterproof self-adhesive & Alien H3/H4 & \(100\times 25\) mm & 0.10–0.15 \\ Textile washable & Alien H3 & \(85\times 27\) mm & 0.02–0.15 \\ PET flexible, anti-metal & Monza MR6-P & \(40\times 25\)mm & 0.25–0.45 \\ Flexible silicone & Alien H3 & \(55\times 12\times 2.5\) mm & 0.35–0.7 \\ \hline \hline \end{tabular} \({}^{1}\) Prices vary according to requested tag quantity.  
\end{table} 
Table 6: Comparison of UHF RFID passive tags with different material substrates. Figure 7: Annual cost projections for standard, non-reusable tags versus reused tag with different percentages. The blue bars represent the standard tags while the orange bars indicate the reuse tag process. linked to the EPR, since the total amount of e-waste that derives for RFID tags is significantly reduced, therefore the cost associated with the producer responsibility of waste production is potentially lowered.
# S-Band Doppler Wave Radar System
## 6 Conclusions  
This paper introduces the MORSE system, which is capable of monitoring oceans with the coherent method. By directly measuring the radial velocity of water scatterer, MORSE can obtain the wave height spectra via the relation between the velocity spectrum and wave height spectrum. Then, the ocean dynamic parameters can be derived from the wave height spectra.  
Comparisons of the non-directional wave height spectra, significant wave height and average wave period are presented in this paper. The data were acquired in an experiment conducted in March, 2015. Both the amplitude and the shape of the non-directional wave height spectra are consistent with those measured by the buoy. The accuracy of the significant wave height measured by MORSE is validated by a high correlation coefficient of 0.9. However, the results of the average wave period, especially under a low sea state, are not satisfying. Further research will be performed to assess additional factors that could affect the measurement, such as wind. The comparisons presented herein show that MORSE is capable of consistently and accurately monitoring an area of ocean.  
This work was supported in part by the National Natural Science Foundation of China under Grant No.41376182 and 41506201; in part by National Key Research and Development Plan No.2016 YFC1400504 and No.2017 YF0206404; and in part by the Project of Hubei Province Science and Technology Support Program under Grant No.2014 BEC057; in part by the Public Science and Technology Research Funds Projects of Ocean under Grant No.201205032.  
[PERSON] designed the MORSE system. [PERSON] participated in the module design and development of MORSE and wrote the manuscript. [PERSON] supervised the preparation of the manuscript and coordinated revisions. [PERSON], [PERSON] and [PERSON] participated in the experiment and data processing.  
The authors declare no conflict of interest.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods
### Time Reversal Reconstruction Algorithm  
The pressure received at the transducers can be represented as \(g(y,t)\), with \(y\)\(\in\)\(\gamma\). The goal of the reconstruction problem is to reconstruct the tissue parameters, \(\mu_{a}\) in the optical excitation or \(\sigma\) in the RF excitation case, given \(g(y,t)\). One way of recovering the parameters is to try and find the initial pressure \(f(x)\) from the pressure measurements \(g(y,t)\), and then divide the pressure by the assumed known energy distribution to recover the parameters. One of the more common methods to recover the initial pressure is using the time-reversal technique. For the time-reversal to rigorously reconstruct \(f(x)\), requires [PERSON]'s principle to be valid. Unfortunately, [PERSON]'s principle does not hold when the speed of sound is not constant, and more problematically, it does not hold in two dimensions [38]. Therefore, in most domains of interest the time-reversal method recovers an approximation of the initial pressure \(f(x)\).  
As the name suggests, time reversal entails taking the received pressure \(g(y,t)\), and using it as a source on \(\gamma\), with time moving in reverse. If the forward simulation was run until a stoppage time \(T\), then when simulated using the time reversal method \(p(x,T)\approx f(x)\). There are several other methods that can be used to recover \(f(x)\) besides time-reversal, but there is no universally accepted reconstruction algorithm that works in all cases of interest. Most of these methods require pressure information at the sensors as functions of time. For this reason, the computationally expensive time-domain wave equation was used for solving the pressure wave equation instead of a Helmholtz equation similar to Equation (5). Since the initial pressure acts as a delta function, the received signal is inherently broadband. A time domain simulation is more efficient for measuring broadband response, and provides a better representation of what would be measured during a physical experiment.
## 1 Motivation and Introduction
Therefore, a potential improvement of the steam oxidation resistance can only be expected if the Cr-content is significantly increased. A group of 22 wt. % steels (Crofer\({}^{\circledR}\) 22) were originally developed for applications in high temperature fuel cell stacks [17]. Due to the addition of the alloying elements W, Nb, and Si, a favorable combination of creep strength and steam oxidation resistance is provided [17; 18]. This strengthening effect is achieved by the precipitation of the intermetallic (Fe,Si,Cr)\({}_{2}\)(W,Nb) Laves phase at the grain boundaries as well as inside the grain [19]. In addition to the Laves phase, the formation of the undesirable brittle \(\sigma\)-phase (Fe,Cr) is characteristic of high-chromium steels. The reduction of the Cr-content to 17-18 wt. % leads to a required suppression of the \(\sigma\)-phase formation in the operation temperature range from 620 to 650 \({}^{\circ}\)C [19]. Simultaneously, this Cr-content of 17-18 wt. % is sufficient to improve steam oxidation resistance even after the precipitating of the Laves phase. The increased contents of W (3.6 wt. %) and of Nb (1.0 wt. %) for further improved mechanical properties, especially thermomechanical fatigue resistance, make the Fe17 Cr2.6W and Fe17 Cr3.6W steel designed in this work a competitor even for austenitic steel grades [20]. The present work focusses on the deformation-induced precipitation of the Laves phase as part of a new developed thermomechanical processing route and deals with the impact of tungsten on the precipitation behavior. The new thermomechanical processing route uses the rapidly occurring precipitation of the Laves phase during a hot deformation process. This enables a significantly shortened process route because hot forming, heat treatment and precipitation annealing are integrated in a one-step process. Consequently, the conventional two-step tempering, taking hours to precipitate Laves phase particles, can be shortened, resulting in precipitation within minutes. The conventional thermal process route as well as the developed \"integrated\" thermomechanical process route are schematically shown in Figure 1. The conventional tempering treatment for Laves phase precipitation consists of two separate steps. The first step at 520-550 \({}^{\circ}\)C serves to nucleate the particles. The desired growth and coarsening of the particles are provided by the second tempering step at 60-650 \({}^{\circ}\)C. The integrated thermomechanical process contains two deformation steps (forging I and forging II). Forging I represents a partial deformation followed by recrystallization. The final one-step deformation, forging II, guarantees the precipitation. The present work specifically deals with the final deformation for Laves phase precipitation (forging II). The alloying element tungsten represents the strongest Laves phase-forming element in the high chromium Laves phase-strengthened stainless steels.