esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Evaluation
### Brief Summary  
In SC-Sched, a sink node decides the communication schedule and precisely controls the wake-up timing of each sensor node. This helps reduce the loss due to collisions in CSMA/CA and the idle listening time. Furthermore, a sink node includes the routing information in WuCs, which greatly reduces the overhead of updating routing information in the mobile sink scenario. Therefore, SC-Sched  
\begin{table}
\begin{tabular}{c|c|c|c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multirow{2}{*}{**Method**} & **Number of Data** & **Frame Delivery** & **Maximum** & **Network Lifetime** & **Improvement in Network Lifetime (\%)** \\ \cline{3-8}  & & **Collection Points** & **Ratio (\%)** & **Consumption (\%)** & **Consumption (\%)** & **(day)** & **Lifetime (\%)** \\ \hline \multirow{4}{*}{100} & \multirow{2}{*}{SC-Sched} & 1 & 99.6 & 184.2 & 2.39 & 4217.2 & \multirow{2}{*}{17.3} \\ \cline{3-8}  & & 4 & 99.8 & 184.2 & 2.02 & 4948.3 & \\ \cline{3-8}  & & 1 & 99.9 & 198.1 & 2.56 & 3926.5 & \\ \cline{3-8}  & \multirow{2}{*}{WuR-TDMA} & 4 & 99.9 & 198.2 & 2.18 & 4596.4 & \\ \cline{3-8}  & & 4 & 99.8 & 844.2 & 13.31 & 754.9 & \\ \cline{3-8}  & & MAC & 4 & 99.7 & 843.2 & 10.29 & 972.9 & \\ \hline \multirow{4}{*}{200} & \multirow{2}{*}{SC-Sched} & 1 & 99.6 & 368.0 & 2.57 & 3922.8 & \multirow{2}{*}{23.0} \\ \cline{3-8}  & & 4 & 99.8 & 368.1 & 2.07 & 4824.1 & \\ \cline{3-8}  & & 1 & 99.9 & 421.7 & 2.87 & 3500.9 & \\ \cline{3-8}  & & 4 & 99.9 & 421.8 & 2.36 & 4236.4 & \\ \cline{3-8}  & \multirow{2}{*}{Contiki} & 1 & 99.9 & 1987.6 & 20.39 & 495.7 & \\ \cline{3-8}  & & MAC & 4 & 99.9 & 1985.2 & 13.60 & 736.2 & \\ \hline \multirow{4}{*}{300} & \multirow{2}{*}{SC-Sched} & 1 & 99.6 & 552.0 & 2.75 & 3687.7 & \multirow{2}{*}{28.2} \\ \cline{3-8}  & & 4 & 99.9 & 552.0 & 2.12 & 4726.6 & \\ \cline{3-8}  & & 1 & 99.8 & 671.7 & 3.12 & 3226.2 & \\ \cline{3-8}  & & 4 & 99.9 & 671.7 & 2.55 & 3918.2 & \\ \cline{3-8}  & \multirow{2}{*}{Contiki} & 1 & 99.1 & 3262.8 & 24.59 & 410.8 & \\ \cline{3-8}  & & MAC & 4 & 99.2 & 3260.9 & 15.83 & 632.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation in the mobile sink scenario.  
Figure 10: Network lifetime in the three methods under different numbers of data collection points.  
can benefit from more data collection points with little overhead and improve network lifetime. By activating nodes only for actual communication without idle listening, SC-Sched reduces energy consumption by almost one order compared with ContikiMAC. However, its data collection time, after using two channels, is still longer than that of the other methods. The question of how to further reduce the data collection time is left to future work.
# Stochastic and Higher-Order Effects on Exploding Pulses
## 2 Influence of Additive and Multiplicative Noise on Exploding Dissipative Solitons
### Numerical Method  
The parameters we used are \(\beta=1+0.8\,\mathrm{i}\), \(\gamma=-0.1-0.6\,\mathrm{i}\), \(D=0.125+0.5\,\mathrm{i}\). The complex cubic-quintic Ginzburg-Landau equation has seven parameters. After scaling \(t\), \(x\) and \(A\), we can fix \(\beta_{r}=1\), \(\gamma_{r}=-0.1\) and \(D_{r}=0.125\). Nevertheless, explosions occur as a function of \(\mu\) only in a subset of the space \((\beta_{i},\gamma_{i},D_{i})\). According to our previous experience in explosions [26], we choose \(\beta_{i}=0.8\), \(\gamma_{i}=-0.6\) and \(D_{i}=0.5\).  
We considered periodic boundary conditions and by varying the box size we made sure that our box is sufficiently large so that none of the results presented in the following are sensitively dependent on the box size.  
The bifurcation parameter \(\mu\) is varied from \(-0.26\) until \(-0.16\). This range includes values of \(\mu\) where deterministically we find stationary solutions and oscillatory solutions (with one and two frequencies) and exploding solitons [28]. Initial conditions are deterministic solutions of the CQGLE.  
To numerically solve Equations (1) and (2), we used a pseudo--spectral split--step method, where the derivatives are computed in Fourier space using a fast Fourier transformation and the non-linear part is integrated in time by a fourth order Runge-Kutta method. To perform these operations, we write our equations in the following way  
\[\partial_{t}A=M[A]A+D\partial_{xx}A, \tag{3}\]  
where \(M[A]A\) represents the part of the equation containing the linear and non-linear terms with the exception of the double derivative in \(x\). If we split Equation (3) into two operators, we get \(\partial_{t}A_{L}=D\partial_{xx}A_{L}\) and \(\partial_{t}A_{M}=M[A]A_{M}\).  
We integrate the differential part over a small interval \(\Delta t\), by performing a Fourier transformation \(\partial_{t}\hat{A}_{L}=-Dk^{2}\hat{A}_{L}\), where \(k^{2}\) is the squared wave vector. After integrating the differential and nonlinear part separately, we obtain  
\[\hat{A}_{L}(t+\Delta t) = e^{-\Delta tDk^{2}}\hat{A}_{L}(t), \tag{4}\] \[A_{M}(t+\Delta t) = e^{\Delta tM[A]}A_{M}(t).\]  
The second integration is performed by a fourth order Runge-Kutta method which gives us a more numerically stable result. Finally, we get the solution for \(A\) integrated over a small time step \(\Delta t\)  
\[A(t+\Delta t)=e^{\Delta tM[A]}e^{\Delta tD\partial_{xx}}A(t)\,. \tag{5}\]  
This numerical method was implemented using a Python code with Fortran subroutines. For the numerical simulations, we used \(N=1024\) Fourier modes over a discretized grid of length \(L=50\) and a time step of size \(\Delta t=0.005\).
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 3 Results and Discussion
### Influence of the Running Speed  
The running speeds of 250 km/h, 300 km/h, and 350 km/h were selected to analyze their influence on the power flows and corresponding indices.  
The power flows of the track-bridge system under different running speeds are depicted in Figure 15. It can be observed that there were phase differences in the power flows of the rail and the slab track, and the phase differences in the rail and the slab track were more obvious than those in the bridge. Considering these phase differences, it can be observed that the power flows of the track-bridge system rise as the running speeds increase, on the whole.  
Figure 16 illustrates the track-bridge system's PFFRs. It can be observed from Figure 16 that the PFFRs were all smaller than 1.0, except for those from the slab track to the bridge at certain frequencies. Further, the PFFRs rose obviously when the running speeds increased at low frequency range, on the whole. Although the differences under different running speeds were small at the high frequency range, it also can be seen that the PFFRs basically increased with increasing running speed. Therefore, it can be said that the PFFRs of the track-bridge system increase with rising running speeds, on the whole.  
Figure 15: The power flows of (**a**) the rail, (**b**) the slab track, and (**c**) the bridge.  
From Figure 17, it can be observed that the AELVs of the track-bridge system all increased with the growth of the running speed; that is, the higher the running speed, the more vibration energy the track-bridge system stored. The increments of the bridge were a little smaller than those of the rail and the slab track. Finally, it can be concluded that the AELVs of the track-bridge system are all sensitive to variation in the running speed.
## 4 Proposed Solution
Generalized formulas of average service rate, the average time of flow remaining in the system, and per-flow average waiting time:  
\[\mu_{c}=\sum_{i=1}^{m}p(C_{i})\times\mu_{i} \tag{2}\]  
\[T_{q}=\frac{\frac{1}{\mu}}{1-p}=\frac{1}{\mu_{c}-\lambda_{c}} \tag{3}\]  
\[T_{w}=T_{q}\bigg{(}\frac{\lambda_{c}}{\mu_{c}}\bigg{)} \tag{4}\]  
Again, these formulas are repeated below with modifications according to the RA (random algorithm):  
\[\mu_{(}cr)=\frac{1}{2}\mu(h+1) \tag{5}\]  
\[T_{qr}=\frac{1}{\frac{1}{2}\mu(h+a)-\lambda_{c}} \tag{6}\]  
\[T_{wr}=T_{rq}\frac{\lambda_{c}}{\mu_{cr}}=\frac{\lambda_{c}}{\frac{1}{2}\mu(h +1)[\frac{1}{2}\mu(h+1)]-\lambda_{c}} \tag{7}\]Below are, again, these formulas according to the FCFS algorithm (first come first serve):  
\[\mu_{cf}=\sum_{i=1}^{m}P_{f}(C_{i})\mu_{i}=\frac{1}{3}\mu(2h+1) \tag{8}\]  
\[T_{uf}=\frac{1}{\frac{1}{3}\mu(2h+1)-\lambda_{c}} \tag{9}\]  
\[h=\left[\frac{\mu_{max}}{\mu_{min}}\right] \tag{1}\]0  
In the average service rate formula, the probability of the controller to be assessed is denoted by \(p(C_{i})\). In our model, as we discussed above, \(m\) heterogeneous controllers are divided into \(n\) groups based on their service rate, where each group consists of \(G_{n}(n=1,2,3,\ldots h)\) controllers. The service rate of each controller is represented by \(n\mu\), thereby changing the system of heterogeneous controllers into an approximate homogeneous system. Our model is based on M/M/1 queue discipline. To compute the average time of flow that remains in the system, Equation (3) is used.This equation is derived from the basic formulas of the M/M/1 queue model and the use of Little's law. The whole system incoming flow arrival rate is represented by \(\lambda\), and for individual controllers, it is \(\lambda_{c}=\lambda(1/m)\), where \(\lambda\) is a heterogeneous controller. For a system to remain in a stable state, the stability factor is \(p=\lambda_{c}/\mu_{c}\). In any situation we need, \(\lambda_{c}\leq\mu_{c}\) or \(0\leq p\geq 1\). The per-flow average waiting time is a product of the stability factor, and the average time of flow that remains in the system, as shown in Equation (4). After analyzing the whole system, we implement this framework to fill some of its gaps. The framework is not tested using controller bandwidth and network delay. We introduce the bandwidth parameter and analyze the result. The term controller bandwidth refers to the bandwidth of the links between the central controller and peer controllers. Finally, we apply a simple mechanism to the known available bandwidth of the link, and if the selected controller's available bandwidth is more than the threshold point, then another
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 2 Background and Related Work
### Stream Processing  
The term Stream Processing refers to a distributed computing paradigm that supports the gathering, processing, and analysis of high-volume, heterogeneous, continuous data streams to extract insights and actionable results in real-time [14]. The paradigm is well suited to data that are received at some rate in an unbounded fashion, with no set start or end, requiring extraction of knowledge as the data arrive. This exists within the world of Big Data, as a response to processing challenges of earlier batch-based paradigms, such as MapReduce [15], which are unable to handle this on their own. A number of Stream Processing systems have been proposed and developed, both in industrial contexts and academic research, such as Apache Storm [16; 17], Apache Flink [18], Apache Spark [19], and Kafka Streams [20].
###### Abstract  
The ultra-wide band (UWB) technology has many advantages in positioning and measuring systems; however, powers of UWB signals rapidly reduce while traveling in propagation environments, hence detecting UWB signals are difficult. Various modulation techniques are applied for UWB signals to increase the ability for detecting the reflected signal from transmission mediums, such as pulse amplitude modulation (PAM), pulse position modulation (PPM), and so on. In this paper, we propose an ultra-wide band pulse position modulation technique with optimized additional time shift (UWB-PPM-ATS) to enhance the accuracy in locating buried object in nondestructive environments. Moreover, the Levenberg-Marquardt Fletcher algorithm (LMTA) is applied to determine the medium parameters and buried object location simultaneously. The influences of proposed modulation technique on determining system's parameters, such as a propagation time, distance, and properties of the medium are analyzed. Calculation results indicate that the proposed UWB-PPM-ATS gives higher accuracy than the conventional one such as UWB-OOK and UWB-PPM in both homogeneous and heterogeneous environments. Furthermore, the LMTA with the proposed UWB-PPM-ATS outperforms the LMTA with the traditional modulation method, especially for unknown propagation environment.  
UWB-PPM; UWB-OOK; buried objects; nondestructive environment; Levenberg-Marquardt method +
Footnote †: journal: Applied Physics Letters  
_Article_
## 2 Architecture of Cooperative Visual Augmentation Algorithm  
Because dedicated short range communications (DSRC) can support safety applications in high data rates [20,21], video information can be transmitted between nearby vehicles in real time. By fusing the image information from neighbored vehicles, we can possibly enhance the host vehicle driver's or auto-driving system's visual field. As shown in Figure 1, the view of the host vehicle (Vehicle B in Figure 1) is blocked by other vehicles. Vehicle B's visual perception can be augmented by combing the visual images from leading vehicles (Vehicle A in Figure 1). The fusing algorithm of inter-vehicle images is based on the 3D inter-vehicle projection model and new deep-affine transformation. Similar to [PERSON]'s ability, our visual augmentation method can make occluded objects visible so as to eliminate blind spots, and thus potential traffic accidents will be decreased sharply. The locations of two cooperative vehicles and occluded objects can be more flexible. The vehicles can drive in the same lane (Figure 1a) or in different lanes (Figure 1b).  
An overview of the cooperative augmentation procedure is shown in Figure 2. A connected vehicle environment is considered so that sensor data (images) from forward vehicles are available for acquirement. The algorithm process has been divided into two main phrases: (1) geometric projection based on deep-affine and (2) object based fusion. The first phase features two images, \(f_{a}\) and \(f_{b}\), which are extracted separately. Matching feature pairs \((\widetilde{P}_{A},\widetilde{P}_{B})\) are selected based on two feature maps and those mismatches are eliminated. Based on those filtered matching feature pairs \((P_{A},P_{B})\), the parameters of projection matrix \(H\) are computed. Our method adopts affine transformation as the geometry projective transformation and the parameters of the matrix \(H\) are automatically  
Figure 1: Cooperative visual augmentation method of intelligent vehicle (host vehicle): (**a**) in the same lane and (**b**) in different lanes.  
optimized by integrating with the depth information. We name this optimized affine the deep-affine transformation with new matrix \(H_{new}\). The optimizing part is described in Section 3.4. The second phase is the fusion part which applies the deep-affine matrix \(H_{new}\) to improve the results of the visual augmentation. The fusion region is decided by merging results from the object detection module. This step is detailed in Section 3.5.
## 2 Characterization of the Study Area  
The project area is situated in the Szaldere village on the northern shore of the Gulf of Saros in the Aegean Sea (Figure 1). The Gulf of Saros is 30 km wide and 55 km long with an east-west trending wedge-shaped morphology [32]. The depth of the Gulf of Saros increases towards the west. The seabed topography of the Gulf of Saros is asymmetrical, with the topography of the northern and southern parts differing from each other [33]. The slope of the shelf becomes steeper from where the water depth is deeper than 100 m. The coastline and the shoreline overlap, thereby the beach could not develop within the narrow shore in the project area. Rock slopes and shorelines lie following the end of the forest and vegetation cover (Figure 2). At a distance of 150 m from the shore, the water depth is 7 m and the slope of the seabed is less than 5%. Further from this section, the water depth at the end of the project site is 30 m and the slope of the seabed varies between 10% and 15%.  
Figure 3 presents the geological map of the close vicinity of the project area. In the northern part of the Gulf of Saros, sedimentary and volcanic rocks aged from the middle Eocene to the Miocene are observed. Quaternary and Miocene sedimentary sequences are separated from each other by an angular uncomformity in this region [34]. The main geologic unit in the project site is the Korudag Formation composed of the alternation of the upper Eocene-aged sandstone and claystone.  
The evolution of the basin structure of the Gulf of Saros is mostly controlled by two tectonic regimes: the shearing activity of the North Anatolian Fault (NAF) System and the extension along the north-south direction of West Anatolia [32; 35; 36]. In this region, the NAF formed as a southward concave arc in the late-middle Miocene and divides into many northeast-southwest trending branches [37]. One of these branches, the Ganos Fault (GF), is the closest active fault to the project area (Figure 4). GF lies between the western part of the Marmara Sea and the North Aegean Trench. GF is defined as a dextral strike-slip fault on the land while it has an oblique component in the sea [38; 39; 40; 41]. The most detrimental earthquake generated by the GF during the instrumental period was the Sarkoy-Murefte Earthquake (9 August 1912). A total of 216 people lost their lives in this earthquake with a  
Figure 1: Location map of the project area.  
moment magnitude (Mw) of 7.4. The largest horizontal peak ground acceleration (PGA) of the study area was determined from the interactive web application of Turkiye Earthquake Hazard Maps prepared by AFAD [42]. Accordingly, the peak ground acceleration for the rock environment obtained as 0.619 g and 1.051 g for the earthquake ground motion level with a probability of exceeding 50 years is 10% (recurrence period is 475 years) and 2% (recurrence period is 2475 years), respectively.  
Figure 3: Geological map of the project area (modified after [43]).  
Figure 2: Views from (**a**) west to east of the project area, (**b**) the coastal area, (**c**) the infill construction.
## 4 Discussion
### The Segmentation Algorithm Performance  
On the validation set, the algorithm demonstrated robust detection under various drawing conditions (varying the number of strokes, hand position, and drawing speed). Among the five trials where the number of brushstrokes was incorrectly identified, the algorithm was off by only one brushstroke in all five trials. In all instances of erroneous segmentation, the common tracing behavior was that the number of brushstrokes was purposely minimized. The speed and hand position seemed to have a smaller but non-negligible effect on segmentation. In all cases of segmentation error, unique data points were not recorded as the image was traced due to intermittent server connection, creating an inflated mid-stroke pause which led to the incorrect decomposition of a single stroke as multiple brushstrokes. Perhaps the prevalence of pen-contact samples in these trials, which necessitated longer periods of continuous data streaming, increased the risk of the observed hardware fault. The algorithm also demonstrated robust performance with the clinical dataset where it only misidentified two trials and was off by one and two brushstrokes each.
## 1 Introduction
Thus, the objective of this work is exactly to propose a solution based on blockchain technology and based on the concepts of the Internet of Things (IoT), which ensures the security of data transferred from the field (sensors and meters) to the measurement bulletins (final stage). They become dynamic, fully automated, independent from human intervention-subjected operations, and highly reliable. Blockchain is a distributed ratio technology that incorporates a decentralized network, where each transaction is immutably recorded, verified, and stored as encrypted information [6]. [PERSON] et al. [7] mention that this potential makes this technology open to the new IoT-based era (Internet of Things), which can offer greater transparency, traceability, and security throughout data transfer. It gained popularity with the emergence of cryptocurrency and bitcoin due to its applicability in finance [8]. However, the authenticity features of this technology paved the way for wider use in other non-financial business areas [9]. Blockchain is essentially different from most of the most popular programming software architectures because it consists of four distinct features: decentralization, visibility, traceability, and smart execution [10]. Thus, this work has been organized as follows: Section 2 describes the typical structure of a measurement system of a production unit and addresses the main technological aspects of the blockchain. Section 3 details the research method considered. Section 4 describes the main components of the proposed solution. Section 5 summarizes the conclusions, limitations, and possible need for further studies. The proposed solution focuses on the use of blockchain technology in the measurement chain, from the field sensors to the regulatory agencies, which are directly responsible for the management of the concession contracts. Once this technology deals with public resources, security in measurement operations is critical because it is directly linked to the payment of royalties. Thus, blockchain is a viable alternative to promote robustness in this process.