esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results
### Cluster Analysis
Therefore, the durability of mixes similar to this group is questionable. Finally, physical parameters, air void content (V\({}_{\rm m}\)), absorption (N\({}_{\rm w}\)) and density (\(\rho_{\rm MCAS}\)), were considered in the analysis of the recycled mixtures population. The results are shown in Figure 6.  
Analysis of the results of grouping the specimens of MCAS recycled mixtures indicates that the average value of air void content was the lowest in Group 3, Vm = 8.9%. The low level of this parameter was probably due to the high content of free bitumen in the recycled mixtures. In the other groups (1 and 2), the average air void content was about 11%, with high asymmetry observed in Group 2 in the distribution of air void content population with at least 25% of the results being above 15%. This result correlated with the low ITS performance. The mixtures in Group 2 had the lowest density, which may be a sign of low cohesion in the mix specimens. A certain singularity in achieving high mix density was observed in Group 1. This cluster grouped the mixes with the highest density but with the air void content was comparable with Group 2. This observation can be explained by the presence of recycled mixtures with high density steel slag. To summarize the discussion so far, we can state that the specimens of recycled mixtures in the first group were characterized by high air void content and low amount of bitumen. The viscoelastic rheological character of mixtures indicates excessively stiff response. Parameter ITS suggests moderate cohesion. The resistance to climatic conditions was at a satisfactory level. The second group, representing low cohesion mixtures containing recycled aggregates (RC) and up to 50% of RAP material in the virgin aggregate, was characterized by low ITS, minimal resistance to climatic factors and low stiffness, indicating potentially low durability. In contrast, the third group represented the mixtures with high moisture resistance, high indirect tensile strength, high stiffness modulus and noticeably more viscous behavior at 5 \({}^{\circ}\)C, attained owing to the adequate amount of bitumen and  
Figure 6: Grouping by (**a**) air void content (V\({}_{\rm m}\)), absorption (N\({}_{\rm w}\)); (**b**) density (\(\rho_{\rm MCAS}\)). higher amounts of RAP in the mixtures. The mixtures similar to those in Group 3 in the current dataset may be most recommended for use.
# An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities
## 3 Methods and Materials
### BLE Range Test Definition and Use Case Scenarios  
As antenna gain parameters can have a substantial impact on maximizing distance, it was decided to carry out measurement using +2.5 dBi (Figure 11) and +9 dBi (Figure 12) external antennas, connected to a IBLio module used with Gateway.  
The maximum Tx power from the module was +3 dBm [49], but a loss of 2 dB on the connections was considered, so that the target had the maximum allowable transmit power on the external antenna at +13 dBm, as indicated for use in Europe.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Direction** & \begin{tabular}{c} **Antenna** \\ **Attenuation (dB)** \\ \end{tabular} & \begin{tabular}{c} **Link Budget** \\ **(dB)** \\ \end{tabular} & \
\begin{tabular}{c} **Range** \\ **Calculated (m)** \\ \end{tabular} &
\begin{tabular}{c} **Range Tested** \\ **(m)** \\ \end{tabular} \\ \hline Front & \(-3\) & 100 & 470 & 450 \\ Back & \(-7\) & 92 & 300 & 300 \\ Side & \(-5\) & 96 & 370 & 340 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranges for BLE121 LR tested.  
Figure 10: Antenna height effect on BLE range.  
The hardware units used for range test are as follows:  
* iBLio BLE/USB G03 unit with either 2 dBi or 9 dBi antenna connected to laptop USB port.
* RedBear nano-BLE tag with CRC2032 battery.
* Android Mobile phone to test and validate signal level. The test procedure is as follows:
* High/low antenna gain connection to BLE module.
* Configuration of BLE Tx power level and custom device name.
* Confirmation that custom device name is properly transmitted.
* Activation of BLE tag device and arrangement of it to be in observer mode. This ensures that data can be received by the test device.
* Separation of the two devices until BLE nanomode can be marginally modified.
* Recording of the previous mentioned location.
* Use of geographical information software (in this case Google Earth), to measure the range of the connection.
* Repetition of the experiment by using different antenna heights. The iBLio BLE module sustained connectivity with each device to varying ranges before the connection was lost.
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
###### Abstract  
To separate the noise and important signal features of the indoor carbon dioxide (CO\({}_{2}\)) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold \(\zeta\) and B-spline order \(n\), were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (\(CCKur\)) constituted the objective function. Initially, the objective function \(CCKur\) was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely \(CC\), \(Kur\), MI, EE, EEM, and \(CCKur\), revealed that the synthetic measurement index \(CCKur\) and objective function _fitness_ were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method.  
CO\({}_{2}\) concentration signal; signal noise cancellation; time-varying filtering-based empirical mode decomposition (TVF-EMD); Bayesian parameter optimization (BO); correlation coefficient for kurtosis index (CCKur)
# Stability of Halloysite, Imogolite, and Boron Nitride Nanotubes in Solvent Media
## 3 Imogolite  
Concerning imogolite nanotubes, although their formation mechanism has been extensively investigated and described in recent literature [66], their stability in aqueous suspension was not deeply investigated. On the other hand, [PERSON] et al. [9] firstly observed that INTs show a liquid-crystal phase, columnar in particular, at low concentrations (\(\approx\)0.3%) with low visco-elasticity and that is aligned under an electric field.  
As expected, INT suspensions form a nematic phase at lower concentrations [67]. Contrarily, the columnar phase, which can be seen in suspensions of other rod-like particles, was only observed at large volume fractions (10-70%) [68; 69; 70]. Meanwhile the columnar phase of INTs is presented at concentrations that are two orders of magnitude lower (Figure 3) [9].  
Figure 3: Schemes of (**a**) isotropic, (**b**) nematic, (**c**) columnar, and (**d**) smectic phases in imogolite suspensions. Photographs of (**e**) both isotropic and nematic phases in sedimented spindle-shaped nematic droplets; and (**f**, **g**) the columnar phase, obtained with single-walled Si–imogolite nanotube (INT) suspensions. Scale bar, 200 mm. Adapted from [9].  
Such a difference is most likely due to the high aspect ratio of imogolite, which favors the nematic phase [67]. The large intensity of the electrostatic repulsions between charged linear objects is strongly evidenced by the ordered positions of the charged INT, which stabilizes the columnar phase at very low concentrations [9].  
These results could present important implications for the physics of suspensions of charged rod-shaped nanoparticles, and could be used for the preparation of ordered nanocomposites, as well for biophysics, in order to understand the behavior of rod-like biopolymers suspensions.  
Moreover, it was observed that imogolite forms sediments at an alkaline pH, even if the net charge is highly negative, and this leads to fibrous particle aggregation to form thick bundles [71]. To clarify this aspect, [PERSON] and [PERSON] [72] measured the charge features of INT by calculating the intensity of the electric field at the external surface, considering the model of imogolite structure and the Gauss' law.  
It was calculated that the field intensity, due to the charge at the external surface of INTs, is half that at the internal surface. It was impossible to explain the flocculation, because the cation exchange capability (CEC) of imogolite was high under alkaline conditions. For instance, assuming cation distribution inside the tube, or inner cation exchange, the measured CEC would correspond to the negative charge at the inner surface, and the electric field intensity at the external surface would become half that at the internal surface [72]. This means that the electric field, due to the negative charges, would be balanced by the counter-ions that entered the tube, and the external surface would be neutral, thus explaining that imogolite flocculates at alkaline pH and its dispersed at the point of zero net charge, or at pH = 6.0 [72].
## 4 STATCOM Performance and Results
### Cummulative Loads  
Figure 5 shows that the nominal reactive power value starts at 0 and time reaches to 0.1 s. The reference reactive power variation is moved to 35 MVAR within 0.15 s (i.e., 0.1 s to 0.25 s). Again, the cumulative reactive power 40 MVAR is added at 0.25 s, then the actual reactive power (\(Q_{S}\)) flows in the system without any major overshoots until 0.4 s. Now, the total cumulative inductive load (\(Q_{S}^{*}\)) is set to 75 MVAR, then the DC-Link voltage starts to decrease. Thus, the AC converter output voltage starts to decrease, then STATCOM will absorb the reactive power (\(Q_{S}^{*}\)) and, again, maintain the nominal reactive power value of 0 within 0.1 s, (i.e., 0.4 s to 0.5 s). The capacitive load starts at the time 0.5 s in the system with the reference reactive power moved to \(-\)35 MVAR and the time reaches 0.65 s. Again, the cumulative reactive power 40 MVAR is added at 0.65 s, then the actual reactive power (\(Q_{S}\)) flows in the system without any major overshoots until 0.8 s. Now, the total cumulative capacitive load (\(Q_{S}^{*}\)) is set to \(-\)75 MVAR, then the DC-Link voltage will start to increase. Thus, the AC converter output voltage starts to increase, then STATCOM will deliver the reactive power (\(Q_{S}^{*}\)) and, again, maintain the nominal reactive power value of 0 from 0.8 s to 0.9 s.  
Figure 6 shows STATCOM currents' harmonic spectra during cumulative loads of inductive and capacitive operations. The THD of the STATCOM currents is found to be 3.74% during the inductive operation when '\(Q_{S}^{*}\)' is set to 75 MVAR, as well as 5.01% during capacitive operation when '\(Q_{S}^{*}\)' is set to \(-\)75 MVAR.  
Figure 4: STATCOM currents harmonic spectra during continuous loads of inductive and capacitive operations.
## 1 Introduction
Additionally, when a ship is not new (e.g., over 15 years old), a significant percentage of the installed sensors is already obsolete, and many of them are out of stock [24]. The innovative approach of this research is focused on the design, manufacturing, and characterization of a new, state-of-the-art 3D printed strain sensor intended for use in the shipbuilding industry. The manufacturing process is simple, requiring only one 3D printer onboard. Building materials (filaments) are commercially available, while the sensing elements can be directly connected to the macroworld, overcoming the necessity of wire bonding. Note that employing available Commercial-Off-The-Shelf building materials and printer has a significant impact of the required manufacturing cost which is approximately equal to USD 0.21 per sensor (considering the cost of the building materials) which is notably lower than that of a typical commercial resistive strain gauge sensor. The above factors contribute significantly to the high availability (on-demand remote printing), ease of replacement in the case of defects, simplicity, and reliability. In addition, since the developed sensing element alters its initial geometry (passively, following the strain of the component on which it is applied), which in turn has a significant impact on its physical properties, it can be considered a device based on 4D printing technology [25]. More specifically, the printed strain sensor's resistance changes due to mechanical deformation, which introduces a dynamic element to its behavior beyond its static 3D printed form. We should note that the proposed measuring device can potentially be employed in real-time health monitoring systems on vessels with composite hulls by integrating it into the hull during fabrication, opening the way for numerous potential applications. With the prospect of additive manufacturing expanding in spare parts and machinery hardware in the near future (as in the reported case of Wartsila [6; 7; 8]), these benefits can become significantly important and have a substantial effect on the operational costs and performance of vessels. Furthermore, an additional advantage that should be highlighted when employing additive manufacturing for marine spare parts is waste minimization, as the most common manufacturing procedure, Computer Numerical Control (CNC) machining, involves waste production of 40-70% while additive manufacturing utilizing layer-by-layer material deposition can lead to a reduction in waste production by 95%, vastly affecting production cost and sustainability. In the following sections of the paper, a description of the developed sensing device along with the experimental setup employed will be presented. The measured characteristics of the sensor will be detailed, including the investigation of a necessary step of thermal treatment necessary to enhance sensor functionality. The obtained results will be presented and discussed, drawing the necessary conclusions regarding the specific research work.
## 4 Experiment Results
### D-Textureless Dataset Experiment  
The first experiment engages the publicly-available D-Textureless dataset by the creators of BOLD [23]. It contains 9 texture-less models, accompanied by 54 scenes with clutter and occlusions. Besides being texture-less, this dataset challenges algorithms on properties such as translation, rotation, and up to about 50% scale and occlusion. As mentioned above, 252 templates in the first stage are used to match the test image, generating a set of possible object locations. In the second stage, according to the scale and rotation of each possible object location, the appropriate template and the adjacent scale and rotation total of 9 templates are selected to detect the object and estimate its position, scale and rotation. We consolidate all participating algorithms to obtain the ROC plot as shown in Figure 10a.  
Figure 8: Experiments for object detection with different \(r^{c}\) initial values.  
Figure 9: Experiments for object detection with different \(r^{w}\) initial values.  
Upon analysis, it can be observed that texture based like SIFT clearly is inferior to the others in the texture-less objects detection. It is found that BORDER achieves an impressive object recognition rate, with OCM-DRW able to slightly edge out BORDER to claim top spot. Figure 10 presents some of OCM-DRW's, BORDER's and BOLD's recognition results in the D-Textureless dataset. Based on the compressing map which exploits all the encoded orientations in a neighborhood and the discriminative regional weight which can effectively distinguish different texture-less objects, OCM-DRW performs better than BORDER and BOLD, it can detect the rotated target in the cluttered background as shown in Figure 11.
# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
## 2 Methods  
### Algorithmic Process  
As shown in Figure 1, the first step is to calculate the point cloud resolution of both the source and target point clouds. Then, the average raster density is calculated based on the adaptive raster width, and adaptive raster filtering is conducted to complete the preprocessing of the point cloud. During the feature point extraction process, the calculated point cloud resolution is utilized as the radius for the adaptive spherical neighborhood. A neighborhood search is conducted for each point, followed by calculating the covariance matrix between the point and its neighborhood. The three corresponding major eigenvalues are determined through principal component analysis (PCA) [20]. The loadings for the largest and smallest eigenvalues are calculated, and the average of these loadings serves as the threshold value for extracting the feature points from the point cloud's surface contours. Then, the neighboring loadings of the three major eigenvalues are computed, and the matched pairs are filtered using the feature point similarity formula proposed in this study. Erroneous and duplicate matched pairs are eliminated using an angle thresholding method. The optimized matched pairs are coarsely aligned based on the sampling consistency initial registration [21]. Finally, the fine registration is completed using an ICP algorithm accelerated by KD-tree lookup.
Sensors_**2015**, _15_, 19507-19540; doi:10.3390/s150819507  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Scalable Context-Aware Objective Function (SCAOF) of Routing Protocol for Agricultural Low-Power and Lossy Networks (RPAL)  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 2, [PERSON] 2 and [PERSON] 1**  
1 IRSTEA - Centre de Clermont-FD, TSCF, 63173 Aubiere, France;  
E-Mails: [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (G.S.)  
2 Laboratoire LIMOS UMR 6158 CNRS, 63173 Aubiere, France;  
E-Mails: [EMAIL_ADDRESS] (K.-M.H.); [EMAIL_ADDRESS] (H.S.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-4-7344-0678.  
Academic Editor: [PERSON]  
_Received: 4 May 2015 / Accepted: 31 July 2015 / Published: 10 August 2015_  
**Abstract:** In recent years, IoT (Internet of Things) technologies have seen great advances, particularly, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL), which provides a powerful and flexible routing framework that can be applied in a variety of application scenarios. In this context, as an important role of IoT, Wireless Sensor Networks (WSNs) can utilize RPL to design efficient routing protocols for a specific application to increase the ubiquity of networks with resource-constrained WSN nodes that are low-cost and easy to deploy. In this article, our work starts with the description of Agricultural Low-power and Lossy Networks (A-LLNs) complying with the LLN framework, and to clarify the requirements of this application-oriented routing solution. After a brief review of existing optimization techniques for RPL, our contribution is dedicated to a Scalable Context-Aware Objective Function (SCAOF) that can adapt RPL to the environmental monitoring of A-LLNs, through combining energy-aware, reliability-aware, robustness-aware and resource-aware contexts according to the composite routing metrics approach. The correct behavior of this enhanced RPL version (RPAL) was verified by performance evaluations on both simulation and field tests. The obtained experimental results confirm that SCAOF can deliver the desired advantages on network lifetime extension, and high reliability and efficiency in different simulation scenarios and hardware testbeds.  
keywords: WSN; RPL routing protocol; 6 LoWPAN; protocol evaluation; Internet of Things +
Footnote †: journal: Journal of Computer Science
## 3 Results  
### Morphology  
Figure 1 shows high resolution micrographs of the different types of the graphene nanoplatelets used in this work. It can be clearly seen that the thickest platelets are GNP4 and the thinnest ones are GNP1, according to the datasheets of the manufacturer (Table 1). Despite not showing a cross-section of GNP1, the fact that the thickness of GNP1 is the lowest of the four types is attributed to the lack of different contrast between the nanoplatelet and the background shown in Figure 1a, elucidating the low number of layers. The average lateral dimension is difficult to determine, but it is possible to observe that GNP2 nanoplatelets are smaller, while the largest ones are GNP4. Figure 2 shows the schematic morphology of each type of graphene nanoplatelet used in the study.  
Figure 1: TEM micrographs of (**a**) GNP1; (**b**) GNP2; (**c**) GNP3; (**d**) GNP4 and SEM micrographs of (**e**) GNP2 and (**f**) GNP4.  
The aspect ratio of the GNPs, as well as dispersion and morphology, has a strong influence on resulting properties of nanocomposites. For that reason, it is necessary to analyze the morphology of the composites in order to determine the dispersion degree, which is the relative position of the GNPs through the epoxy matrix, in addition to the spatial distribution, referring to the possible exfoliation of graphite nanosheets or the appearance of wrinkling or stretching phenomena [15]. In a previous study carried out by X-Ray diffraction [13], it was confirmed that the exfoliation phenomenon does not occur with the applied dispersion technique.  
Figure 3 shows micrographs of the studied composites captured by FEG-SEM. These micrographs confirm a suitable dispersion degree for all the studied samples (Figure 3a,b) but also that high shear stirring could induce a wrinkling effect of the nanosheets [13]. This phenomenon seems to be more noticeable for composites reinforced with lower thickness GNPs (Figure 3c,d), while GNPs with higher thickness remain mostly stretched (Figure 3e,f). It is worthy to note that the percentage of GNPs added was the same one for all the samples. However, making a comparison between the micrographs (Figure 3a,b), it is possible to observe that the concentration of nanoplatelets, determined as the number of GNPs per observed area, is highest in the epoxy resin reinforced with GNP1. This concentration seems to decrease as the size of the nanoplatelets does. This is due to the different aspect ratios of the GNPs. GNP1 GNPs have the highest specific surface area and lowest thickness whereas GNP4 GNPs have specific surface area several orders of magnitude lower than GNP1 ones and higher thickness.  
Figure 3: FEG-SEM micrographs of epoxy composites reinforced with GNP1 (**a,c**); GNP 2 (**d**), GNP3 (**e**) and GNP4 (**b,f**).  
Figure 2: Scheme of graphene nanoplatelets: GNP1, GNP2, GNP3 and GNP4. These schemes are only comparatives. The thickness of nanoplatelets (\(\sim\)nm) is much lower than the lateral dimensions (\(\sim\)μm).