esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 1 Introduction
### Related Work
The work in Reference [26] developed an ESDP relaxation method and designed a distributed algorithm relying on the alternating direction method of multipliers (ADMM), of which the convergence property was guaranteed and proven in Reference [27]. Then, in Reference [28], the authors proposed a distributed method based on ADMM in the presence of harsh nonconvexities. In oder to guarantee that the solution converges to the optimal point, the authors established the mathematic model to choose penalty parameters. The work in Reference [20] proposed a tighter convex problem via projection relaxation and put this problem into the distributed Nesterov framework. A hybrid solution was presented in Reference [29] by combining the convex relaxations of Reference [20] and the distributed method of Reference [28]. These distributed approaches are effective and bring a controlled computational burden. Convergence performance is generally guaranteed. However, the aforementioned distributed algorithms work only in line-of-sight (LOS) scenarios. In some practical situations, such as in forests, cities, and indoor places, most connections between sensors are non-line-of-sight (NLOS) because of the obstacles in the direct paths of signal propagation. This phenomenon can severely degrade localization accuracy if the NLOS impact is not taken into consideration. In general, two approaches are applied to alleviate NLOS propagation in localization. The first approach distinguishes LOS and NLOS connections via prior information [30; 31; 32]. This approach needs other measurement techniques to provide NLOS information, such as Direction of Arrival (DOA). Hence, its estimation model is less suitable for cooperative localization methods based on range-based techniques. The second approach modifies the original model by weighting and adding constraints of range-measurement errors [33; 34]. This kind of approach can be applied to more general scenarios. Some representative work is listed as follows. The work in Reference [35] is \"the first one to address NLOS node localization in WSN\". According to three different scenarios, the authors provided three modified models of the ML estimator by adding the upper and lower bounds of range measurements. These models were solved in standard SDP problems with heuristic estimation. The work in Reference [34] proposed an ESDP method and added constraints to improve robustness. In Reference [36], the authors introduced NLOS bias parameters to the estimation model for the source-node locations and turned the model into a SDP problem. In Reference [37], the authors proposed a three-block ADMM algorithm based on the model in Reference [36]. However, this method does not decrease the size of the original problem, and the authors also mention that \"it is hard to distribute the calculation\" because it involves matrix projection. The work in Reference [38] proposed a distributed algorithm based on the Huber estimation of Reference [39], but it did not provide theoretical proof for the convergence property.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
###### Abstract  
In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications.  
underwater sensor networks; receiver-initiated MAC; adaptive data polling; traffic estimation 2018 18 3895 doi:10.3390/s18113895
## 2 Sensor Design
### Curing Process  
The FBG, embedded in the patch material, can additionally be used to monitor the patch curing process. What happens during the curing process in terms of residual stress and exothermal reaction is extremely important for the configuration of the FBG inside the patch. It is well known that the gfrp material shrinks significantly during curing, up to 2%, depending on the resin system used. The resin curing process has been discussed in detail in [4].  
The characteristic curing cycling can be observed with FBG sensors during embedment into the patch carrier, as shown in Figure 3.  
Figure 2: Sensor patch applied to cfrp specimen.  
The total volume shrinkage (1-5) can be expressed as a change in the Bragg wavelength of the FBG. During heating, volume expansion (1-2) can be observed. Although the final curing temperature is not yet reached (2), the reaction shrinkage begins (2-3), which is observed in a significant drop in Bragg wavelength. After isothermal hardening (3-4), volume contraction due to cooling (4-5) is observed.  
The embedded FBG was not temperature-compensated. The effects observed here cannot therefore clearly be separated into temperature load and mechanical forces acting at the sensor. For a description of the curing process, and for the use of these sensors for cure monitoring, it is necessary to develop a specially adapted sensor system based on fundamental work in literature [5, 6, 7]. Additionally, interferences on spectral phenomena have to be investigated, e.g. transverse forces and residual stresses.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Comparative Analysis  
For performance comparison of the proposed MIDS, an existing LEACH-based IDS is chosen. Another technique is that MS-LEACH is chosen for comparative analysis. The LEACH-based IDS mechanism adapts a physical division-wise clustering approach and detects forwarding attacks. MS-LEACH adapts a key-based cryptographic approach. Both these techniques outperform their ancestor techniques in terms of overall  
* Network energy consumption.
* Network throughput.
* Network lifetime.  
The proposed MIDS is also compared with the chosen techniques based on these three parameters. The decision to keep energy consumption, network lifetime, and throughput aligns with the similar simulation parameters used by the LEACH-based IDS and MS-LEACH techniques for comparison. By maintaining consistent parameters, we ensure a fair and accurate evaluation of performance across different algorithms. This approach  
Figure 8: Throughput with rounds.  
Figure 9: Residual energy during communication phase.  
allows us to directly compare metrics such as energy consumption, network lifetime, and throughput without introducing variability that could arise from changing the number of sensor nodes. It provides a controlled environment to validate the effectiveness and efficiency of the MIDS algorithm relative to the established techniques. The detail of the results is given below.
## 3 Results  
The vector field in Figure 6b shows the spatially varying fiber orientation as reconstructed by the ECT method described in this work, which we herein refer to as an \"rc-scan\". A spatial resolution of 1 mm in the \(x\) direction and 1 mm in the \(y\) direction was used with an angular resolution of 15 degrees. At each location in the raster grid, the principal direction and magnitude, such as shown in Figure 6a, are generated. Upon close inspection of the quiver plot, one can see the area of waviness by the suddenly changing fiber orientation. A more intuitive method of visualizing this data uses flow lines, which we accomplish here with MATLAB's streamline function. The resulting flow plot in Figure 9 shows the area of waviness as a representation of the fiber paths themselves.  
To obtain the quantitative error analysis, we compare the ECT results with the results from the CT scanning and subsequent image processing over the same region of the fabricated part. Figure 10a shows both direction fields displayed as quiver plots on the same axes. Figure 10b displays the streamlines from ECT testing overlaying the top-down mean X-ray CT data to show the alignment of the two testing methods.  
Figure 8: Energy of UT c-scan over waviness region.  
It is worth noting that the regions of higher error are associated with variations in the CT reference data from the normal trend of the waviness. Furthermore, the total variation of the CT reference data is higher than the variation of the ECT data. We recognize that there are difficulties in determining the \"ground truth\" fiber direction; even our CT approach contains some errors due to the limitations of image processing. As such, the actual error of our method could be different than what is given in Table 1. Figure 11 illustrates the width calculation and shows the detected waviness region plotted over the CT and ECT streamlines.  
\begin{table}
\begin{tabular}{l c c c} \hline  & \(\theta_{CT}\) & \(\theta_{ect}\) & \(\theta_{err}\)=\(\theta_{CT}\)-\(\theta_{ECT}\) \\ \hline Average max angle, \(\theta_{max}\) & 12.68\({}^{\circ}\) & 10.40\({}^{\circ}\) & - \\ Average width 1  & 24.22 mm & 26.80 mm & - \\ Total variance & 6\({}^{\circ}\) & 4.4\({}^{\circ}\) & 3\({}^{\circ}\) \\ Total RMSE & - & - & 3\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Error and variance results.  
Figure 10: (**a**) Comparison of direction fields from CT and ECT testing; (**b**) ECT streamlines overlayed with CT scan to show alignment.  
Figure 9: ECT rc-scan testing results for fiber orientation, streamline plots and heatmap.
## 1 Introduction
### Clustering with Outliers  
In practice, data sets include some noise measurements which do not reflect a real part of the data. These are called _outliers_, and even a single outlier may completely change the optimal solution that is obtained without this outlier. One option to handle outliers is to change the distance function to a function that is more robust to outliers, such as \(M\)-estimators, e.g., where the distance \(\operatorname{dist}(p,x)\) between a pair of points is replaced by \(\min\left\{\operatorname{dist}(p,x),c\right\}\) for some fixed \(c>0\) that may depend on the scaling or spread of the data. Another option is to compute the set of \(k\) centers that minimizes the objective function, excluding the farthest \(m\) points from the candidate \(k\) centers. Here, \(m\geq 1\) is a given parameter for the number of outliers. Of course, given the optimal \(k\) centers for this problem, the \(m\) outliers are simply the farthest \(m\) input points, and given these \(m\) outliers the optimal solution is the \(k\)-means for the rest of the points. However, the main challenge is to approximate the global optimum, i.e., compute the optimal centers and outliers simultaneously. Guessing \(m\) is less challenging since it is an integer between \(1\) and \(n\), and the cost function is monotonic in \(m\), which enables the usage of binary search.  
As explained in [40], detecting the outliers themselves is also an NP-hard problem [41]. Intensive research has been done on this problem, as explained in [42], since the problem of outliers is known in numerous applications [43; 44]). In the context of data mining, [PERSON]. [45] proposed a definition of distance-based outlier, which is free of any distributional assumptions and it can be generalized to multidimensional datasets. Following [45], further variations have been proposed [46; 47]. Consequently, [PERSON]. [48] introduced paradigm of local outlier factor (LOF). This paradigm has been extended in [43; 49] in different directions.  
As explained in [50], and following the discussion in [51; 52] provided an algorithm based on Lagrange-relaxation technique. Several algorithms [51; 53; 54] were also developed. The work of [55] gives a factor of \(O(1)\) and a running time of \(O(n^{m})\) for the problem of \(k\)-means with \(m\) outliers. Another heuristic was developed by [56]. Finally, [50] provided an \(O(1)\)-approximation in \(O(k^{2}(k+m)^{2}n^{3}\log n)\) time. In the context of \(k\)-means, Ref. [57] provided several algorithms of such constant factor approximation. However, the number of the points which approximate the outliers is much greater than \(m\), and is dependent on the data, as well as the algorithm running time.
## 5 Discussion and Conclusions
### Practical Implications  
This study analyzes the determinants of collaborative robot innovation adopted by SMEs in three aspects: technology, organization, and environment. It is proved that the five elements of innovation within the technology aspect, top management support and organizational readiness within the organization aspect, and vendor support within the environment aspect have significant impacts on the adoption of collaborative robot innovation by SMEs. With the continuous spread and popularization of collaborative robot innovation, it is necessary to provide some practical suggestions for managers.  
First and foremost, SME managers need to weigh the potential risks and benefits before adopting collaborative robot innovation. It is necessary to not only consider the economic benefits brought by collaborative robot innovation but also avoid excessive adoption based on the actual situation of the enterprise.  
Second, managers of SMEs need to broaden their horizons, understand the cutting-edge technologies of industry development, and constantly deepen their understanding of innovation. Besides, it is suggested to actively raise funds and recruit talents to prepare for the implementation of innovation.  
Third, collaborative robot vendors need to continuously publicize the comparative advantages of innovation. In the early stage of the adoption of collaborative robot innovation, it is recommended to use the means of mass communication for dissemination, such as live streaming. At the same time, continuous technical improvements are made to improve the usability of collaborative robots, such as the development of a collaborative robot AI voice system to achieve real-time conversation-based human-machine collaboration. In addition, it is suggested to develop high-quality benchmark customers in various regions, take full advantage of the role of opinion leaders in innovation diffusion, and facilitate the display of innovation achievements to intended customers. At the same time, free trials can also be provided to intended customers to facilitate transactions.  
Finally, government deciders need to fully understand there is a lack of support for innovation policies for SMEs and need to formulate independent innovation policies for SMEs, such as formulating a series of laws and regulations applicable to the needs of SMEs at different stages, and creating innovation support funds, etc.
# Experimental Investigation on the Dynamic Characteristics of Bubble-in-Chain Near a Vertical Wall
## 2 Materials and Methods  
### Experimental Setup  
The experimental equipment layout is depicted in Figure 1. The experiment was conducted in a rectangular plexiglass tank measuring 300 mm in length, 300 mm in width, and 650 mm in height. A stainless-steel needle with an outer diameter of 1.48 mm and an inner diameter of 1.12 mm was fixed at the center of the tank's bottom. The vertical wall was constructed using plexiglass material, with dimensions of 580 mm (length), 200 mm (width), and a thickness of 20 mm. The distance between the wall and the needle was precisely controlled by a lead screw mechanism with an adjustment precision reaching up to 0.1 mm. The \(z\)-axis is aligned with the direction of bubble ascent, while the \(x\)-axis is perpendicular to the wall, and the \(y\)-axis is orthogonal to the \(xz\) plane.  
The physical parameters of the glycerol aqueous solution are presented in Table 1, considering it as a continuous phase. Additionally, the liquid level within the tank is maintained at 550 mm. A constant pressure flow is provided by the air pump, while the bubble generation frequency is regulated through a micro-regulating valve. The specific bubble generation frequencies employed in the experiment are presented in Table 2. The bubble behavior is captured by two orthogonal CCD cameras at a frequency of 180 Hz, with an image resolution of 2048 \(\times\) 1280 pixels. The test area is illuminated by two LEDs emitting wavelengths of 320-350 nm and 380-420 nm, respectively. Corresponding filters are installed in front of the camera to mitigate interference from ambient light. To achieve homogeneous illumination, sulfuric acid paper is positioned as a light diffuser between the light source and the water tank.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution
### Detailed Design of Composite Solution  
One typical application scenario for islanding is loss of main grid connection resulting from a system fault. The circuit breaker in the incoming feeder is disconnected to clear the fault and then the DG unit is isolated. The power system status is described as transitions between the following states (1) Healthy \(\rightarrow\) (2) Faulted \(\rightarrow\) (3) Islanding. If an obvious power imbalance exists in the islanded power system, the voltage phase angles in status (1) and (3) will be different. So, the voltage phase angle and system frequency just before the fault inception shall be recorded. The recorded voltage phase angles in status (1) shall be continuously compared with subsequent voltages angles calculated each power cycle, to detect the voltage angle changes in the same direction and then to initiate the calculation of accumulated phase angle drift.  
The voltage phase angles are calculated from a one cycle Discrete Fourier Transformer (DFT) based on phase to phase voltages, which is more stable under earth fault conditions in an isolated system. The real-time frequency tracking is applied and the frequency measurement \(F(n)\) is calculated per power cycle. The voltage phase angles \(Ang_{A}(n)\), \(Ang_{B}(n)\), \(Ang_{C}(n)\) are calculated per power cycle.
# Potential Synergistic Inhibition of _Enterococcus faecalis_ by Essential Oils and Antibiotics
## 3 Results  
### Antimicrobial Effects of Cinnamon and Cove EOs  
The antimicrobial efficacies of the cinnamon (CN10%, CN5%, CN2.5%, and CN1.25%) and clove (CL50% and CL25%) EO solutions were quantitated using the Kirby-Bauer disk diffusion test. The CN and CL EOs showed a concentration-dependent growth inhibition of _E. faecalis_ at all tested concentrations compared to the control (F (6, 24) = 227.8, \(p\) < 0.0001, Figure 2A). At the lowest concentration of CN1.25%, the zone of inhibition (ZOI) was 4.67 \(\pm\) 0.21 mm. For the CN2.5% and CN5%, the ZOIs were 7.50 \(\pm\) 0.22 mm and 9.00 \(\pm\) 0.40 mm, respectively. At the highest concentration of CN10%, the ZOI was 11.67 \(\pm\) 0.33 mm. Across all concentrations of CN EO examined, there was a significant increase in ZOI by 36% \(\pm\) 1.2% as the concentration of CN EO doubled. For CL EO, the ZOIs for CL25% and CL50% were 6.00 \(\pm\) 0.00 mm and 8.33 \(\pm\) 0.33 mm, respectively. Similar to CN EO, the CL EO also showed a significant increase of about 38% in ZOI as the concentration increased by two-fold from 25% to 50%. When comparing the antimicrobial effects between the CN and CL EOs, the CN5% solution produced much stronger antimicrobial effect than those observed in CL 50% solution at about a 10-fold lower concentration. The diffusion disks immersed in control DMSO solution did not produce any inhibitory growth responses on _E. faecalis_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment Groups** & **Ampicillin 10 mg/mL** & **Kanamycin 5 mg/mL** & **Gentamycin 3 mg/mL** & **Streptomycin 5 mg/mL** \\ \hline
**Cinnamon 10\% EO** & Ampi5 + CN5\% & Kana2.5 + CN5\% & Genta1.5 + CN5\% & Strep2.5 + CN5\% \\ \hline
**Cinnamon 5\% EO** & Ampi5 + CN2.5\% & Kana2.5 + CN2.5\% & Genta1.5 + CN2.5\% & Strep2.5 + CN2.5\% \\ \hline
**Cinamon 2.5\% EO** & Ampi5 + CN1.25\% & Kana2.5 + CN1.25\% & Genta1.5 + CN1.25\% & Strep2.5 + CN1.25\% \\ \hline
**Clove 100\% EO** & Ampi5 + CL50\% & Kana2.5 + CL50\% & Genta1.5 + CL50\% & Strep2.5 + CL50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Treatment groups by combining antibiotics and EOs.  
Figure 1: An image of BEA agar plate inoculated with _E. faecalis_ showing the zones of inhibition of 5% gentamicin (1.5 mg/mL) combined with cinnamon 2.5% EO. The plate was incubated at 37 \({}^{\circ}\)C for 24 h and the size of zone of inhibition was measured using a ruler. BEA, Bile Esculin Azide; EO, essential oils.  
The Tukey post-hoc test showed significant paired differences between the cinnamon EOs as the concentration increased by two-fold (Figure 2A; \(p<0.005\)). A similar statistical response was observed between the clove EOs where the CL50% had significantly larger ZOI than the CL25% (Figure 2A; \(p<0.001\)).
## 3 Problem Formulation and Proposed Solution
### Complexity Analysis  
Without regarding the complexity of the convex optimization tools, the complexity of Algorithm 1 is analyzed in this section. The process of initialization needs \(\mathcal{O}(m\mathcal{K})+\mathcal{O}(m\mathcal{K})=\mathcal{O}(m\mathcal{K})\) operations, where \(m\) is the size of the particle swarm and \(\mathcal{K}\) is the number of elements in the assignment matrix \(\mathbf{F}\). The process of generating the initial particle and the initial velocity need the same operations \(\mathcal{O}(m\mathcal{K})\). We need \(m\) operations to calculate the historically optimal fitness for every particle, and meanwhile, we need the same number of operations to select the historically optimal location of the particle swarm. The process of updating the position and velocity of the particle swarm needs \(\mathcal{O}(m\mathcal{K}T)\) operations at most, where \(T\) represents the maximum number of iterations. It is required to cost \(\mathcal{O}(mT)\) and \(\mathcal{O}(mT)\) operations at most to calculate the historically optimal fitness for every particle and select the historically optimal position of the particle swarm according to the updated particles, respectively. Hence, the computational complexity in the worst-case scenario of the whole updating process is \(\mathcal{O}(m\mathcal{K}T)\). Consequently, the integral computational complexity of Algorithm 1 is \(\mathcal{O}(m\mathcal{K})+\mathcal{O}(m\mathcal{K}T)=\mathcal{O}(m\mathcal{K}T)\).