esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[18] compared FGCBA and coal fly ash regarding their effect on ion permeability resistance and found that the FGCBA displayed superior performance. [PERSON] and [PERSON] [19] substituted fly ash, coal bottom ash, and two types of slag powder, each with distinct fineness properties, for silica fume in UHPC, assessing slump flow, compressive strength, porosity, setting time, and drying shrinkage and conducting a thermogravimetric analysis. The results demonstrated that coal fly ash and coal bottom ash could effectively replace silica fume without significant workability and strength development loss; setting time tests showed no notable difference when adding coal bottom ash compared to silica fume, and the strength loss caused by the addition of coal bottom ash was not significant. The thermogravimetric analysis suggested that the coal bottom ash had a higher hydration level than silica fume but lower strength, likely due to weaker hydration products. In another study by [PERSON] [20], coal bottom ash sieved through No. 40 mesh was added to high-strength mortar, which showed higher pozzolanic reactivity and similar compressive strength at 3 and 28 days compared to high-strength mortar containing only cement and fly ash. In our study, finely ground coal-fired bottom ash is used instead of fly ash in cement to understand its impact on the performance of freshly mixed concrete in terms of hardening and durability. The use of FGCBA and FA as replacements for Portland cement was tested at replacement rates of 0%, 20%, 40%, and 60%. Activity index tests were conducted on both raw materials. In terms of fresh properties, workability, unit weight, air content, and setting time were measured. For hardened properties, compressive strength and drying shrinkage were assessed. As for the determination of durability, a rapid chloride ion penetration test and a hardened concrete porosity test were employed.
# Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming
## 5 Conclusions  
Used in very diverse areas of applications, classical data interpolation by a general spline with free knots is formulated as a linear programming problem to minimize spline \(l_{\infty}\)-norm (max norm) of the derivative of order \(r\), for reduced complexity, and the problem is efficiently solved by using linear programming solvers. Theoretical convergence to the true optimal splines is proven, indicating that the interpolatory data points obtained by solving the LP problem are arbitrarily close to the points on the true optimal splines. Testing results based on cubic interpolatory splines provide good approximations to optimal splines within a fast computation time. The main benefit of the new method is the significant reduction in complexity to obtain near-optimal splines with near-optimal curvature. Another advantage is that the method has a potential to be generalizable for 2D dimensional (and higher) cases, whereby \"interpolatory surfaces\" can be obtained by using 2D finite differences. Moreover, as indicated in the Introduction section, since the splines are expected to recover local behavior with good quantifiable accuracy, the proposed method is suitable for finding local as well as global minima of the implied underlying function quickly. The complexity involved in finding a global optimum of the resulting spline based on \"merge sort\" is \"subquadratic\" \(O(n\cdot log(n))\). A limitation of this study is that the near-optimal position of knots cannot be efficiently obtained in online implementations since the method only outputs points belonging to the near-optimal splines, although near-optimal positions of knots can be estimated offline based on the points where the third derivative (for cubic splines) changes its sign. Future research will be to obtain an exact functional representation of the optimal splines as well as the exact positions of the knots.  
Conceptualization, L.S.T.; methodology, M.A.B.; validation, M.A.B.; investigation, L.S.T. and M.A.B.; writing-original draft preparation, L.S.T. and M.A.B.; software, M.A.B.; writing-review and editing, L.S.T. and M.A.B. All authors have read and agreed to the published version of the manuscript.  
School of Business Summer Research grant, University of Connecticut.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Discussion  
Firstly, image segmentation is employed in our approach. The accuracy of depth information of measured objects may be influenced by the result of image segmentation. It is not easy to separate the different types of objects measured from an image with a complex background using the same parameter. The depth accuracy would be reduced greatly if the result of segmentation is wrong. Therefore, how to extract the depth information of the target objects with complex background is one of our next works.  
Secondly, our algorithm is time consuming, because image segmentation, SIFT feature point detection and matching are employed in our algorithm. The configuration of the computer which used to the experiment is as follows: memory 16 GB, CPU i7-4810 MQ, 2.80 GHz, 4 cores. The codes were run in Matlab 2012b. It took about 139.6 s to extract the depth information from an image (resolution: 1280 \(\times\) 1024 pixels) by our method. Of these, about 95 s were used for image segmentation, about 43.5 s were used for the SIFT feature point detection and feature point matching, and about 1.1 s were used for the rest. Therefore, we will focus on how to reduce the computational complexity in our next research.  
Lastly, in our method, the images of the same object in the images \(A\) and \(B\) were matched by using SIFT feature points, and a pair of straight line segments, which were used to compute the depth of objects, were chosen according to the algorithm described in Section 4.3. When the length of the straight line segment was less than 150 pixels, shorter lengths of straight line segment correspond to lower accuracy of computed depth. Thus, our method has a low accuracy for very small objects. In addition, the depth cannot be measured unless there are no less than 4 SIFT feature points being detected on the target object. Therefore, our next research will focus on how to improve the depth measurement accuracy of small objects and how to measure the depth of target object whose number of SIFT feature points is less than 4.  
Figure 9: Comparison the depth of the objects measured by our method with the ground truth.
## 3 Proposed Methodology  
Experiments are conducted on different benchmark datasets. The performance of WR-SVM is compared with F-SVM, which was the recently proposed radius margin technique. WR-SVM learns classifiers such as \(w\) & \(b\) and then weighted mean of the class simultaneously. The classifier attributes are first used to classify the data in the case of the linear kernel. In contrast, in the case of RBF, kernel data are transformed into feature space, and the classification is applied to that extended space [44].  
In the first stage, after loading the data into the simulation environment, the linear separability of the data is checked to reduce execution time. For this purpose, SVM with the linear kernel is used by applying one vs. all techniques for all experiments. The linear separability allows us to apply either soft margin SVM or hard margin SVM.  
In the first stage of experiments, SVM is applied to check the linear separability of the data [45]. Based on the separability check result, either soft margin SVM or hard margin SVM is applied.  
There are multiple classes in single feature space in a multi-class problem, due to which samples of one class may overlap another class [46]. Therefore, there is a need to separate these classes in a way that the maximum prediction accuracy will be achieved. The multi-class problem can be solved using the one-vs-all technique. It is not easy to find out a mathematical equation that separates all classes from one another. In many cases, it is not possible to find an optimal solution. In the One-Against-All technique, firstly proposed by [PERSON] in 1995 [16], one class is classified from all other classes. First, we computer \(w\) and \(b\) and then draw a hyperplane between one and all other classes using these weights and bias as shown in Figure 2(a-c).  
Figure 3: One Against All technique.  
After finding these hyperplanes between classes, we set all hyperplane in one plot to show separability between all classes as shown in Figure 4.  
Next, each class sample should be bounded in an enclosing ball. To do the task, initial mean is computed, and the radius of each class is calculated to draw MEB.  
According to [40], the upper bound of the radius-margin is bounded with \(max_{k}\mu_{k}R_{k}^{2}\leqslant R_{\mu}^{2}\leqslant\sum_{k}\mu_{k}R_{k}^{2}\) where \(R_{k}\) is the radius of dimension \(k\). Ref. [42] proposed a tighter bound for the radius-margin, where  
\[R_{o}\leqslant R_{\mu}\leqslant((1+\sqrt{3})/2)R_{o} \tag{17}\]  
However, the experiments are restricted to only linear feature space. The kernelized features space is not used to compute the radius. While the proposed technique first transforms the data into kernelized feature space, and then the radius of the MEB kernelized feature space is transformed into weighted kernelized feature space.
## 6 Conclusions and Future Works  
The rapid development of IoT technologies has led to a new dimension in the healthcare field. Remote monitoring of patients' health conditions using IoT is a promising approach that may result  
Figure 10: Cumulative distribution function of user quality of service (QoS) in the OCC, BLE, and hybrid schemes.  
Figure 9: OCC-to-BLE handover probability versus LED-to-camera distance for various \(\partial_{\text{AOV}}\) and \(\xi\).  
in various convenient solutions to nursing assistants. In this paper, we proposed a wearable ECG monitoring system based on a hybrid OCC/BLE architecture. A patch circuit was suggested where an LED array and BLE transmitter chip are integrated. The patch collects the ECG data from the sensing network and transmits it through a hybrid infrastructure. Depending on the patient's health condition, a NAR is generated in the patch circuit, and the appropriate network (OCC or BLE) is decided by a network-selection algorithm. The selection mechanism in multiple-camera scenarios is assisted by fuzzy logic. To ensure the safety of both mobile and stationary patients, network-handover mechanisms were proposed. The healthcare information is transmitted to the gateway, where it is stored in an eHealth database before being sent to a remote monitor. The network selection and outage probabilities of each network were mathematically formulated. As confirmed in simulation studies, the hybrid network significantly improved the performance of the data transmission system. However, the performance was evaluated only in an idealized indoor scenario. When multiple patients in a large room are monitored by the same camera, a multiple-input multiple-output setup might be required. In future research, we will test the optimality of the proposed hybrid system over other optical-RF hybrid infrastructures. Other implementation complexities, such as the size and weight of the patch, will also be considered in our future work.  
All the authors contributed to this paper. [PERSON] and [PERSON] proposed the idea; [PERSON] wrote the paper; [PERSON] and [PERSON] performed the simulations; [PERSON] analyzed and verified the simulation data; and [PERSON] supervised the work and provided funding support.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the IITRC (Information Technology Research Center) support program (IITP-2018-0-01396) supervised by the IITP (Institute for Information & Communications Technology Promotion).  
The authors declare no conflicts of interest.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Aluminum Oxide Films  
Aluminum oxide showed some excellent properties. including friction resistance, heat resistance, and corrosion resistance. The aluminum oxide or aluminum (III) oxide (Al\({}_{2}\)O\({}_{3}\)) films were deposited on aluminum substrates at room temperature in the experiment [177]. The obtained films showed hydrophobic properties with contact angles of 117\({}^{\circ}\). SEM studies revealed non-uniform morphology and rougher surface.
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 4 Results  
In order to investigate the performance of the architecture and the protocol that we propose, we have conducted a simulation in order to verify the time required to obtain an entanglement between two GSs using a quantum satellite backbone. In order to develop the software, we used the _skyfield_ Python package to operate on the Two Line Elements set (TLE) [52,53] data in order to calculate the inter distance matrices, and the [PERSON]'s algorithm included in the _scipy_ package. In the scenario that we have simulated, we used the Iridium-NEXT constellation, composed of 75 satellites. We have considered the activation of two controllers on the path selected by the MCS, and we performed a simulation considering a reference time interval of 1 h, capturing a sample every second. The GSs were located at a distance of 20,000 km from each other. During the simulation, the number of satellites that composed the best path varied from 4 to 6; then, we derived a different distribution for each case. As can be seen from the graph depicted in Figure 6, with an increase in the number of satellites involved, the time required to generate a remote entanglement decreases.  
This fact confirms in part what has been claimed in [22; 45]; i.e., that a larger constellation allows better results to be obtained in terms of the entanglement rate, defined as the number of transmitted entangled states per second, which is measured as Bell pairs per seconds [22].  
From the graph in Figure 6, it appears that, considering the same number of controllers within the path, performance improves as the number of satellites increases. This is because, given the shorter distance, the probability of success in order to obtain an entanglement increases. Since the time needed to obtain the entanglement strongly depends on this probability, the time needed to obtain it decreases.  
This provides a first indication that as the number of satellites that compose the path changes, the performance of the protocol is not degraded, but the beneficial effects of the distance between the satellites that compose the domains can be seen. Further studies on scalability could provide useful indications for the realization of an intelligent architecture in which the MCS allocates an appropriate number of controllers inside the path in order to achieve effective quantum communications.
## 2 Statement of the Problem  
Let us consider a spatially fed planar array, namely a reflectarray or transmitarray, comprised of \(N\) elements with a regular periodicity of \(p_{x}\times p_{y}\). The goal is to calculate the near field radiated by the array in a volume discretized in \(M\) points in front of the antenna and to shape the near field such that it complies with certain specifications for a given application. To that end, Figure 1 shows a sketch of the near field model for the two considered arrays. From the point of view of the analysis of the array, both spatially fed arrays are analysed in the same way, considering their own local coordinate system (ACS, array coordinate system) defined by \((\hat{x}_{a},\hat{y}_{a},\hat{z}_{a})\). The location (i.e., centre) of the \(i\)-th array element is given by \(\vec{r}_{i}\,^{\prime}=(x_{i}^{\prime},y_{i}^{\prime},z_{i}^{\prime}=0)\), which is a point in the surface of the planar array, and \(\vec{r}=(x,y,z)\) is a point in space in front of the array, i.e., with \(z>0\). Both \(\vec{r}_{i}\,^{\prime}\) and \(\vec{r}\) are defined in the ACS, and \(\vec{R}=\vec{r}-\vec{r}_{i}\,^{\prime}\).  
On the other hand, the radiated field is referred to a rotated--and translated--coordinate system (NFCS, near field coordinate system) noted as \((\hat{x}_{nf},\hat{y}_{nf},\hat{z}_{nf})\). The relation between the ACS and NFCS is given by matrix \(T\), which is derived in Appendix A. Since the analysis of the array is carried out in the ACS, the calculated radiated field will have to be transformed so that it is referenced to the NFCS.  
In the following section, two near field models will be developed. One is based on the exact radiated field equations, while the other is based on the superposition of the far field radiated by each array element. In both cases, the radiation element is modelled as a small rectangular aperture with constant field on it.  
Figure 1: Sketch of the (**a**) reflectarray and (**b**) transmitarray scenarios. The goal is to obtain the near field radiated by the arrays in a volume in front of the antenna. The analysis of the arrays is carried out in the array coordinate system (ACS) defined by \((\hat{x}_{a},\hat{y}_{a},\hat{z}_{a})\), while the radiated field will be obtained in the near field coordinate system (NFCS) defined by \((\hat{x}_{nf},\hat{y}_{nf},\hat{z}_{nf})\). The transformation between ACS and NFCS may be consulted in Appendix A.
## A Comprehensive WSN-Based Approach to Efficiently Manage a Smart Grid  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Department of Information and Communication Technologies, Universidad Politecnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain; E-Mails: [EMAIL_ADDRESS] (R.M.-S.); [EMAIL_ADDRESS] (F.G.-S.); [EMAIL_ADDRESS] (J.G.-H.)
2 School of Engineering & Physical Sciences Mountbatten Building Edinburgh, Heriot-Watt University, Edinburgh, EH14 4 AS Scotland, UK; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-968-326-538; Fax: +34-968-325-973.  
External Editor: [PERSON]  
_Received: 25 July 2014; in revised form: 6 September 2014 / Accepted: 26 September 2014 / Published: 10 October 2014_  
**Abstract:** The Smart Grid (SG) is conceived as the evolution of the current electrical grid representing a big leap in terms of efficiency, reliability and flexibility compared to today's electrical network. To achieve this goal, the Wireless Sensor Networks (WSNs) are considered by the scientific/engineering community to be one of the most suitable technologies to apply SG technology to due to their low-cost, collaborative and long-standing nature. However, the SG has posed significant challenges to utility operators--mainly very harsh radio propagation conditions and the lack of appropriate systems to empower WSN devices--making most of the commercial widespread solutions inadequate. In this context, and as a main contribution, we have designed a comprehensive ad-hoc WSN-based solution for the Smart Grid (SENSED-SG) that focuses on specific implementations of the MAC, the network and the application layers to attain maximum performance and to successfully deal with any arising hurdles. Our approach has been exhaustively evaluated by computer simulations and mathematical analysis, as well as validation within real test-beds deployed in controlled environments. In particular, these test-beds cover two of the main scenarios found in a SG; on one hand, an indoor electrical substation environment, implemented in a High Voltage AC/DC laboratory, and, on the other hand, an outdoor case, deployed in the Transmission and Distribution segment of a power grid. The results obtained show that SENSED-SG performs better and is more suitable for the Smart Grid than the popular ZigBee WSN approach.  
s smart grid; wireless sensor network; power asset; IEEE 802.15.4; MicaZ
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 4 Discussion
### Time Domain Characteristics  
The spatiotemporal correlation coefficient was used to characterize the interdependence of different spatial points at different time fluctuating pressures, reflecting that large-scale vortices kept their scales flowing downstream in a certain spatial range.  
Figure 13 shows how the autocorrelation coefficients of various shapes were compared with fresh water and sediment-laden flow. The variation rules of fluctuating pressure correlation curves were similar for different overflow shapes. The autocorrelation curves all showed a downward trend from 1 to 0, and the autocorrelation coefficient decreased with the increase in the time interval. At time intervals of 0-5 s, the autocorrelation coefficient decreased rapidly. With an increase in time, the autocorrelation coefficient slowly decreased and gradually approached zero. As the contraction ratio decreased, the autocorrelation coefficient distribution dropped rapidly. With an increased sediment concentration, the effect of shape change on the scale of vortex was reduced. The autocorrelation coefficient decreased more slowly with the increase in sediment concentration in a given overflow shape. When the sediment concentration was 250 kg/m\({}^{3}\), the autocorrelation coefficient decreased most obviously as the outlet size shrank. These results correspond well with those in [35; 36]; the longitudinal sizes of the macroscale eddies increase with the increase in sediment concentration and are mainly decided by the low-frequency, large eddies.  
Due to the change in turbulent structure and surface tension with increased sediment concentrations, the scale of the vortex enlarged. The scale of vortex in the hyper-concentrated flow was larger than that of fresh water. This was also the result of the combined effects of gravity caused by sediment-laden water and air entrainment caused by overflow weir shape changes.  
Figure 13: Profile of autocorrelation coefficient for various shapes in fresh water and sediment-laden flow at 2.20 m upstream with different sediment concentrations.