esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
###### Abstract  
In the first part of the paper, we modeled and characterized the underwater radio channel in shallow waters. In the second part, we analyze the application requirements for an underwater wireless sensor network (U-WSN) operating in the same environment and perform detailed simulations. We consider two localization applications, namely self-localization and navigation aid, and propose algorithms that work well under the specific constraints associated with U-WSN, namely low connectivity, low data rates and high packet loss probability. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network using a low number of anchor nodes and distance measurements from the underwater channel. Once the network has been self-located, we consider a node estimating its position for underwater navigation communicating with neighboring nodes. We also propose a communication system and simulate the whole electromagnetic U-WSN in the Castalia simulator to evaluate the network performance, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power), hardware limitations (e.g., clock drift, transmission buffer) and complete MAC and routing protocols. We also explain the changes that have to be done to Castalia in order to perform the simulations. In addition, we propose a parametric model of the communication channel that matches well with the results from the first part of this paper. Finally, we provide simulation results for some illustrative scenarios.  
underwater communications; ad hoc networks; radio-frequency; localization; Castalia 2016 162176; doi:10.3390/s16122176
## 1 Introduction
Experimental verification was performed by using a pressure sensor at the corner region for a 10 mm thick laminate compacted on a 12 mm radius female mold, in which a significant 40% compaction pressure drop was observed in the corner region, as shown in Figure 1. Furthermore, inter-ply friction prevented conducive shape conformation, which leads to corner thickness variation [34; 38]. However, the information on the effects of various bagging techniques using different curing profiles and laminate spring effect after releasing from the mold are not reported. The studies showed that residual stresses are inevitably built up mainly due to different thermal expansion coefficients between matrix and fibers during prepreg processing [39]. An increase or decrease in the enclosed angles of the angled section is referred to as spring effect [40]; either spring back or spring forward. Spring back is defined as a positive angular difference due to the widening of the angle between the formed composite laminate and the mold geometry while spring forward is described as a negative angular difference. The spring effect in the final product creates problems in the  
Figure 1: Pressure profile for a concave-corned laminated composite produced using VBO [34]. final assembly due to poor fit with the mating structures, which is usually dealt with some compensation factors during designing. Certain characteristics of the materials, such as anisotropic mechanical behavior during the curing process is also a reason for the spring effect [41]. Several parameters are suspected to have an influence on the spring effect, which are characterized as intrinsic or extrinsic parameters. Intrinsic parameters are the physical characteristics of the laminated composites, whereas extrinsic parameters are related to manufacturing process parameters and tooling [42]. The spring effect of composite laminates due to the rise of residual stresses and distortions is also influenced by the tool-part interaction. Metallic molds expand more than the laminates during curing, causing laminate tension [43]. To avoid assembly problems, spring effect in composite parts must be fully understood and controlled through optimization. In this paper, the aim is to investigate the thickness variation and spring effect of 45\({}^{\circ}\) angled composite laminates using various bagging techniques (SVB, MSVB and DVB), curing profiles (MRCC, EMRCC and DC) and composite structures (concave and convex corners). The goal is to examine and understand the effect of these processing parameters on the shape conformation i.e., corner thickness variation and spring effect, on the laminated composites with complex shapes.
###### Abstract  
Although structural topology optimization has been developing for decades, it still plays a leading role within the area of engineering design. Solving contemporary design problems coming from industry requires the implementation of efficient methods and approaches. This stimulates research progress in the development of novel and versatile topology optimization algorithms. To follow these modern trends, an original topology generator has been elaborated and finally built as a Cellular Automaton with original update rules. The motivation for building the algorithm in this way came from the idea of utilizing the benefits of local compliances sorting. This is conducted on two levels: on the global level, the monotonic function mapping local compliances distribution is defined based on their sorted values; on the local level, for each cell, the compliances are sorted within the cell neighborhood. The three largest absolute values are selected, and these are the basis from which to formulate Cellular Automata update rules. These original rules can efficiently control the generation of structural topologies. This technique is somewhat inspired by the grey wolf optimizer strategy, wherein the process of updating design variables refers to the positions of the three best fitted wolves. It is proposed that we refer to the topology algorithm that benefits from the adaptation of sorted compliances optimization as TABASCO. The developed algorithm is a modified version of the flexible Cellular Automata one presented previously. The implemented extension, regarding the local level cell sorting, allows us to improve the resulting compliance values. The advantages of the algorithm, both from numerical and practical engineering points of view, as compared to the others developed within the field, may be gathered as follows: the algorithm works based on simple update rules, i.e., its numerical implementation is not complicated; it does not require gradient computations; filtering techniques are not needed; and it can easily be combined with professional structural analysis programs which allow engineering applications. The developed topology generator has been linked with ANSYS to show that it can be incorporated into a commercial structural analysis package. This is especially important with respect to the engineering implementations.  
topology optimization; heuristic methods; compliance minimization; cellular automata method; grey wolf optimizer; hybrid methods +
Footnote †: journal: Applied  
doi: 10.3390/app131910595
## 2 Bees as Chemical Biosensors in the Laboratory and in the Field
### Whole Colonies Used in Environmental Monitoring Studies  
Local and landscape-scale biomonitoring has been conducted using _passive detection_, which capitalizes on natural behaviors of organisms. Beginning in 1974 up until 2000, we conducted numerous landscape-level surveys and mapping of pollutant dispersal over thousands of hectares. Forager bees from colonies placed in areas of interest bring back numerous environmental chemicals from air, soil, water, and vegetation that then accumulate in their hives. We published a seminal paper of our methodology in Science in 1985 [36]. Beyond using bees as passive biological monitors, that research pioneered the application of Kriging to biological survey data. Kriging or Gaussian process regression is a method of interpolation for which the interpolated values are modeled by prior covariances. Kriging allowed us to construct chemical-specific, risk-exposure isopleth maps [36].  
Other investigators, especially in Europe, also began using bees for bioenvironmental monitoring. A 2003 review of bees and chemical monitoring [37] details numerous examples of applications of honey bees as biomonitors. This type of investigation continues to this day. Recent uses of bees as passive detectors or monitors include periodic analysis of honey collected by bees near eight airports [38] in Germany and monitoring chemical pollution in the Kurdistan Province of Iran [39].  
In contrast to passive detection where bees free roam, _active detection_ is directed search. This can be accomplished in a variety of ways, ranging from modifying the natural foraging and search behaviors of an animal to driving the animal by some form of technological interface. For example, DARPA investigated controlling cockroach movement using an electronic neural interface mounted on the back of the insect [40].  
DARPA's exploration of the potential for training insects to find materials of interest to the US military led to our own development of bee-training protocols (operant conditioning). During this decade-long research we tested and validated a large number of chemical and biological agents of interest to the military. Our field-based bee conditioning systems [16] are autonomous devices for: (1) directing thousands of bees to search for unique chemical signals such as those leaking from landmines [15], drug laboratories, dead bodies, or chemical signals produced by diseased plants and animals; (2) focusing bee foraging on specific crops; and (3) chemical signal detection requiring canvassing of large areas.  
Just as Inscentinel's instrumentation hardly resembled the straws and index cards initially used to constrainer bees for PERs testing, our solar-powered, micro-processor-controlled, wireless-networked bee-trainers (Figure 2) [16] bear little resemblance to primitive odor-delivery systems used by the Russians in the 1930s. However, the underlying operant-conditioning concepts and methods are similar.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 3 Fault Diagnosis of the High-Speed Train Traction System and Brake System  
### Fault Diagnosis of Train Traction System  
As the heart of the whole high-speed train, the traction system of the train can generate traction power, which mainly consists of a traction transformer, traction power supply (rectifier, DC link, and inverter), and traction motor. Early failures of traction systems are often caused by unavoidable factors, such as the degradation of winding insulation and the aging of mechanical and electronic components, which in turn affect the safety of trains. Therefore, these early faults need to be detected in time, and their main diagnostic techniques are presented in Figure 7.  
The traction system of high-speed trains will deteriorate under long-term operation, leading to various early failures. If these early failures are not successfully detected, they evolve into safety incidents. To detect early faults in the traction system, [PERSON] et al. [73] introduced a method based on Kullback Leibler divergence and independent component analysis (ICA), and the experimental results showed that the method could diagnose three early faults in the train traction system with higher computational efficiency. To effectively diagnose early faults in train traction systems, [PERSON] et al. [74] designed an improved relevant vector machine (RVM) approach based on a Bayesian framework and demonstrated the effectiveness of the method with practical cases. To improve the accuracy of fault diagnosis of train traction motors under external disturbances, [PERSON] et al. [75] developed a method based on interference isolation with an unknown input observer. To identify early faults in the traction system of trains more accurately, [PERSON] et al. [76] advanced a method based on improved total measurable fault information residual (ToMFIR) and validated the effectiveness of the proposed method by simulation models. Insulated-gate bipolar transistor (IGBT) is the main power supply component of traction systems in high-speed trains, and to improve the accuracy of fault diagnosis of IGBTs, [PERSON] et al. [77]  
Figure 6: Fault diagnosis method of wheel-rail system: (**a**) adaptive multiscale morphological filtering (AMMF) [66]; (**b**) adaptive noise canceling (ANC) [69].  
presented a method based on wavelet transform and SVM. In order to diagnose the typical faults of traction systems effectively, both simulation-driven and data-driven methods are effective [78; 79]. [PERSON] et al. [80] proposed a method based on statistical indicators and fuzzy clustering, which was verified by experimental data from the train traction control system. By constructing a nonlinear fault dynamics model for high-speed trains, [PERSON] et al. [81] proposed a method for fault-tolerant control based on state observation.  
Therefore, the fault diagnosis of the train's traction system is mainly made by pattern recognition, and the main models used include ICA, relevant vector machine, ToMFIR, SVM, etc.
###### Abstract  
Chromatographic development for the determination of pharmaceuticals in environmental water samples is particularly challenging when the analytes have significantly different physico-chemical properties (solubility, polarity, pKa) often requiring multiple chromatographic methods for each active component. This paper presents a method for the simultaneous determination of azithromycin, erythromycinromycin (antibiotics), fluoxetine (anti-depressant) and sotalol (b-blocker) in surface waters by ultra-high-performance liquid chromatography coupled with ultra-high-resolution time-of-flight mass spectrometry. These pharmaceuticals--presenting a broad spectrum of polarity (0.24 \(\leq\) log K\({}_{\mathrm{ow}}\)\(\leq\) 4.05)--were separated on a C-18 analytical column, after a simple filtration step for freshwater samples or after a liquid-liquid extraction with Methyl-tertio-butyl ether (MTBE) for seawater samples. The optimized separation method (in terms of nature of column and eluent, elution gradient, and of mass spectrometric parameters), enable one to reach limits of detection ranging between 2 and 7 ng L\({}^{-1}\) and limits of quantification between 7 and 23 ng L\({}^{-1}\) for the four targeted molecules, within a three minute run. This method was validated using samples collected from three different surface waters in Lebanon (freshwater and seawater) and analytical results were compared with those obtained in surface waters sampled in a French river, equivalent in terms of human activities. Using this method, we report the highest concentration of pharmaceuticals found in surface water (up to 377 ng L\({}^{-1}\) and 268 ng L\({}^{-1}\), respectively, for azithromycin and erythromycincin, in the Litani river, Lebanon).  
emerging compounds; UHPLC-MS; pharmaceutically active compounds; occurrence; freshwater; seawater +
Footnote †: journal: Applied Sciences
# Sensors and Communication Simulation for Unmanned Traffic Management
###### Abstract
As an alternative, we suggest the use of simulation techniques to perform reproducible, scalable, and exhaustive testing while reducing costs and evaluation time. We propose an agent-based simulation architecture (simulator, from now on) that allows reproducing the set of input signals required to excite an UTM system, i.e., authorization requests, telemetry messages, incoming tracks (continuous trace that by means of a unique identifier represents the consecutive positions of an aircraft) from sensors or surveillance systems, etc. Agent-based simulation is a subtype of discrete event simulation in which a series of entities, called agents, interact autonomously based on a series of rules. This simulation technique is useful to model complex systems that can be explained as an aggregate of these actors and is especially useful for the detection and assessment of emergent behavior. The simulator is able to reproduce user-defined complex scenarios encompassing the actions of the main actors: pilots, drones, GCSs, surveillance and communications network; and the complete transmission chain followed by the information to the UTM system. This simulation is also linked to other external elements (e.g., weather, terrain morphology) that might affect the behavior of the previous actors constituting a common simulation environment for the architecture itself and for the UTM system. Finally, the proposed simulator is capable of simulating actions that can cause a change in the default or expected behavior of an actor including system failures, environment variations, or pilot's decisions. These actions are known as contingencies and can occur both naturally during the simulation and by simulator user definition (as checking the reaction of UTM system to contingencies occurrence would be critical to assess its safety). By using these simulated inputs, it is possible to evaluate both pre-flight and in-flight stages by analyzing the output information provided by the UTM system and the actual actors' simulated state. The proposed simulator is evaluation-agnostic in the sense that it does not define which metrics or tests must be performed to assess UTM performance. Alternatively, it provides a set of tools that enable their implementation by accessing information from the simulator and the UTM system. The information flow between the proposed simulator, the evaluated UTM system, and the evaluation tools is depicted in Figure 1.
## 5 Evaluation and Results
### Real Test-Bed Implementation of SWIFTNET  
The efficacy of SWIFTNET is tested with air temperature data, using our in-house deployment testbed of 15 sensor nodes powered by batteries, shown in Figure 7 for our application scenario. The nodes are organized in a star topology, each node acquire its data and runs the SWIFTNET algorithm. For every sample interval, a node decides to transmit or compress the data depending on the threshold value that is set. Although, nodes are multi-modal, meaning they could sense other parameters such as humidity, wind speed, _etc._, we prefer to concentrate on the air temperature, as it offers easy testing on the reactiveness of our testbed. We implemented SWIFTNET algorithm on the WISPES W24 TH nodes, and the pseudo-code is given in Algorithm 2. Each leaf node runs this algorithm. Upon startup, a node initializes its parameters on Lines 2 and 3, and it starts sensing the phenomenon. The sensed data is acquired at every data collection round in Line 4. Then, the algorithm intelligently switches between CS and adaptive prediction at every data collection round, and the alarm condition is checked for violation. If the sensed data is up to a dangerous level predefined by the user on Line 5, it calls the prediction algorithm on Line 6, which is built on an adaptive sampling strategy in Equation (15), and it triggers an immediate reporting of the phenomenon depending on the rate of change of the air temperature, in this case, a wildfire application. From our experiments, on average the reporting fluctuates between 12 to 30 s. When the air temperature drops below the threat level, the node calls the CS algorithm shown in Lines 9 to 24, and thus proceeds with data compression depending on the size of the history table. When the history table is full, the data is transmitted in one or a few packets. This way the radio usage is significantly minimized, since it only turns \"ON\" when transmitting. For example the \(comp_{(vector)}\) is transmitted to the sink node, which uses Algorithm 1 for the decompression and online distributed inference.
# Numerical Analysis and Experimental Test for the Development of a Small Shaped Charge
## 3 Subject and Scope of the Tests  
The numerical model of the stand for the penetration test with the use of the shaped charge was developed based on the solid model of the analyzed system. During the experimental tests, a charge consisting of a casing in which the explosive was compressed, a sleeve regulating the distance between the target and the charge and a shaped charge liner were used. The view of the analyzed system for extreme variants of the distance between the load and the target is shown in Figure 3.  
The distance between the face of the casing with the explosive and the liner insert was adjusted with the use of a solution based on a system of five serrations enabling insertion and locking of the sleeve within them. The solution allows for a six-step adjustment of the distance of the load from the target, with the minimum distance in step 1 being 7.5 mm and changing every 12.5 mm up to the maximum distance of 70 mm.  
Based on the solid model, an axially symmetric shell model was developed, consisting of a steel block, housing, charge and a shaped charge liner placed in the air domain. The axisymmetric model covered half of the whole system due to the applied axial symmetry.  
The discretization of the geometry made it possible to freely change the distance between the load and the target. In the initial phase of the analysis, two models were built for the two extreme distances as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\rho\) [Kg/m3] & E [GPa] & \(\
u\) [–] & **SIGY [MPa]** \\
1040 & 2.2 & 0.35 & 34.17 \\ \hline EPS1 [\%] & EPS2 [\%] & EPS3 [\%] & EPS4 [\%] \\
0 & 0.8 & 1.4 & 2.8 \\ \hline EPS5 [\%] & EPS6 [\%] & EPS7 [\%] & EPS8 [\%] \\
5.7 & 6.5 & 7.1 & 7.7 \\ \hline ES1 [MPa] & ES2 [MPa] & ES3 [MPa] & ES4 [MPa] \\
34.17 & 34.52 & 34.72 & 35.13 \\ \hline ES5 [MPa] & ES6 [MPa] & ES7 [MPa] & ES8 [MPa] \\
35.49 & 35.53 & 35.54 & 35.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Acrylonitrile Butadiene Styrene (ABS) material data [25].  
Figure 3: View of two variants of shaped charges: (**a**) charge closest to the target and (**b**) charge farthest from the target.  
The ALE procedure requires that the finite element mesh be constructed from the smallest possible elements to properly implement the explosion phenomenon. For this reason, the models consist of many elements, which translates into a time-consuming calculation. The analyzed models consisted of finite elements with the dimensions of 0.25 mm \(\times\) 0.25 mm--158,455 for the model with the load closest to the target and 199,351 for the load farthest from the target.  
The calculations were performed at time t = 0.5 ms. For numerical calculations, an explicit type of algorithm was used to solve the structure dynamics equations in the non-linear range.