esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Ice Load Prediction Model  
The ship-ice interaction process is known as a complex phenomenon which highly depends on ice properties, hull geometry and relative speed [9]. [PERSON] [10] divided this interaction process into ice breaking, ice rotation, ice sliding, and ice clearing based on the main phenomena in breaking the level ice [11]. This break-displace process and ice forces are idealized in Figure 1.  
Figure 1: Idealized time histories of ice forces.  
The ship-ice interaction begins with a localized crushing of a free ice edge at the contact zone. When the ship keeps moving forward, both the contact area and the crushing force increase. The ship advance motion causes the ice sheet to deflect down along the hull surface, thus bending stresses are build up in ice sheet until it breaks off into small pieces, as shown in Figure 2. The ice flexural failure occurs at a distance, expressed as the ice breaking length, \(l_{b}\) from the crushing region. It is mainly determined by the ice thickness and the ship speed among all other parameters. The final force to break the ice sheet is denoted as the ice breaking force \(P_{b}\).  
The numerical model developed in this paper aims to provide an efficient way to compute ice-hull impact force with two primarily ice failure features. Ice is regarded as an isotropic material with flexural strength, \(\sigma_{f}\) which is used as the failure criterion. The process is assumed to be a repetition of the following steps: (1) initial contact and crushing, (2) cracking due to ice bending, (3) more cracks are triggered and both radial and circumferential cracks are generated. A detailed process of the periodic ice impact load prediction model and calculation procedure are well described herein.
## 3 Results
### Test 2  
The goal of the second test was to measure data rate as a function of the distance between two transmitters. The ground station with one pMDLL2450 data link module remained stationary while the other pMDLL2450 data link module and the Raspberry Pi 3 computer in charge of sending data were moving. The tests were carried out until a 275-m distance was reached. The data rate readings were taken every 25 m. The complete system was 1.5 m in height. This was an outdoor test, so all the equipment was powered using batteries. The  
Figure 7: Code snippet for the server side (**left**) and for the client side (**right**).  
Raspberry Pi 3 was powered by a 4.8-V battery with a 2300-mAh capacity, and the data link was powered by a 9.6-V battery with a 1900-mAh capacity. The pMDLL2450 module setup was the same as the setup from Test 1 except for the Tx/Rx power. For this test, Tx/Rx power was set to 30 dbm (1 W). The results are shown in Figure 8.  
The throughput stagnates and even rises with an increase in distance (Figure 8), which is the opposite to the expected results. The reason for this result is the small distance between the transmitters. Moreover, optical visibility between the transmitting and receiving ends was always achieved.
## 4 Results
### Saltwater Intrusion  
According to the authors of [69], the electrical resistivity values of the saturated zone in alluvial coastal aquifers range from 10 to 100 Ohm\(\cdot\)m, depending on the total dissolved solids (TDS) concentration. The TDS content in groundwater is an indication of its salinity and the electrical resistivity decreases progressively with an increase in the levels of ionic concentrations or salinity in groundwater. The presence of a low resistivity zone in ERT sections (lower than 10 Ohm\(\cdot\)m) can be interpreted as indicating the presence of a seawater intrusion [70; 71; 72] because freshwater typically has a resistivity of between 50 and 100 Ohm\(\cdot\)m [73], with a resistivity of 10 to 50 Ohm\(\cdot\)m corresponding to the transition, or brackish water zone [32]. In terms of the study area, we have selected values smaller  
Figure 5: Examples of electrical resistivity tomography (ERT) cross-section results obtained at Port de la Selva site. (**a**) ERT cross-section P8; (**b**) ERT cross-section P1. B: bedrock (aquiclude unit); SZ: Saturated shallow aquifer; black dashed line represents the inferred hydrogeological contact.  
than 10 Ohm\(\cdot\)m for mapping the saltwater intrusion and in the range of 10-50 Ohm\(\cdot\)m to infer a transition zone using implicit modeling tools.  
The 3D implicit model obtained shows a general resistivity increasing trend towards the hills. An inward feature wedge-shaped with low resistivity values (below 10 Ohm\(\cdot\)m) is identified at the seaside--until 310 m inland--which highlights possible marine intrusion (Figure 7). Resistivities in the range of 10-100 Ohm\(\cdot\)m are also mainly located close to the coastline, reach 400 m inland, and should include a brackish or mixed water zone. The model also showed a low resistivity anomaly 1-100 \(\Omega\)-m beneath and towards the location of the municipality water-supply well reflecting a low resistivity anomaly as a result of a rising deep saline water effect (upcoming) from intensive water pumping.  
Figure 6: Shallow aquifer base depth model inferred from ERT and borehole data (Port de la Selva, Spain).
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 3 Preliminaries
### Bagging  
Bagging is an ensemble technique that is used to improve the stability and accuracy of machine learning algorithms by combining the prediction of multiple weak classifiers [69]. Bagging works better for unstable learning algorithms where a little change in the training set results in large changes in predictions (i.e., ANN, DT). Bagging predicts an outcome several times from different training sets that are combined either by voting or with uniform averaging [70]. To describe the bagging algorithm, consider a dataset with N instances and a binary class label. The following steps summarize the Bagging algorithm:  
1. Generate a random training set of size N with replacement from the data.
2. Train the random training set using any classification technique.
3. Assign a class to each node.
4. Repeat steps 1 to 3 many times.
5. Use voting to predict the class label.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
###### Abstract  
Long-Term Evolution (LTE) and its improvement, Long-Term Evolution-Advanced (LTE-A), are attractive choices for Machine-to-Machine (M2M) communication due to their ubiquitous coverage and high bandwidth. However, the focus of LTE design was high performance connection-based communications between human-operated devices (also known as human-to-human, or H2H traffic), which was initially established over the Physical Random Access Channel (PRACH). On the other hand, M2M traffic is mostly based on contention-based transmission of short messages and does not need connection establishment. As a result, M2M traffic transmitted over LTE PRACH has to use the inefficient four-way handshake and compete for resources with H2H traffic. When a large number of M2M devices attempts to access the PRACH, an outage condition may occur; furthermore, traffic prioritization is regulated only through age-based power ramping, which drives the network even faster towards the outage condition. In this article, we describe an overlay network that allows a massive number of M2M devices to coexist with H2H traffic and access the network without going through the full LTE handshake. The overlay network is patterned after IEEE 802.15.6 to support multiple priority classes of M2M traffic. We analyse the performance of the joint M2M and H2H system and investigate the trade-offs needed to keep satisfactory performance and reliability for M2M traffic in the presence of H2H traffic of known intensity. Our results confirm the validity of this approach for applications in crowd sensing, monitoring and others utilized in smart city development.  
LTE; RACH; PRACH; IEEE 802.15.4; IEEE 802.15.6; non-saturation operating regime; backoff error; smart city +
Footnote †: journal: Sensors. Actuator Netw. **2018**, \(7\), 27; doi:10.3390/jsan7030027
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 5 Linear Programming-Based Mathematical Formulation
### Feasible Region Energy Minimization  
For minimizing the energy consumption, we have defined the objective function in Equation (5).  
\[Minimum\Sigma_{r=1}^{r_{max}}E_{consumption}(r)\quad\forall r\in r_{max}. \tag{5}\]  
The linear constraints for energy minimization are given in Equations (5a)-(5c).  
\[E_{trans},E_{rcv}\geq E_{re} \tag{5a}\] \[E_{trans},E_{rcv}\leq E_{init}\] (5b) \[T_{rn}\leq T_{max}. \tag{5c}\]  
Equation (5a) shows restriction on transmission and receiving energy, which must not exceed the residual energy \(E_{re}\) of the node; while transmission and receiving energy are restricted through Equation (5b) using initial energy \(E_{init}\) of the node. Equation (5c) shows the restriction that for receiving a good quality signal, the sensed information should be transmitted within its given transmission range; where \(T_{rn}\) is the transmission range of the node and \(T_{max}\) is the maximum transmission range of the node; whereas, \(E_{consumption}\) is the total energy consumed in data communication, i.e.,  
\[\Sigma_{r=1}^{r_{max}}E_{consumption}(r)=E_{trans}+E_{rcv}\quad\forall r\in r_{ max}. \tag{6}\]  
where,  
\[E_{trans}=P_{trans}\left(\frac{Packet\_size}{Data\_rate}\right), \tag{7}\]  
\(E_{trans}\) is the transmission energy, and \(P_{trans}\) is the transmission power.  
\[E_{rcv}=P_{rcv}\left(\frac{Packet\_size}{Data\_rate}\right). \tag{8}\]  
\(E_{rcv}\) is the receiving energy, and \(P_{rcv}\) is the receiving power.
## 2 Materials and Methods
### Satellite Imagery Selection  
Archive imagery from the NASA/USGS Landsat program ([[https://landsat.gsfc.nasa.gov/](https://landsat.gsfc.nasa.gov/)]([https://landsat.gsfc.nasa.gov/](https://landsat.gsfc.nasa.gov/)), accessed on 12 March 2020) was reviewed to identify potentially suitable images from the Thematic Mapper (TM) sensor covering some or all of the study site area in the time period of 1989 through 2013 (Orbroich) or 2015 (Wahnbachtal). Given the extended length of this analysis, imagery from Landsat 4, 5, 7, and 8 were all considered as potential sources, with images from Landsat 5, 7, and 8 selected. TM image data are composed of seven core spectral bands starting with Landsat 4 (although more bands were added to subsequent satellites) ranging from visible blue (0.45-0.52 um) to short-wave infrared (2.08-2.35 um) at a 30 m ground resolution. These data also include a thermal band (10.40-12.50 um) at a 60 m resolution that was not used in our analysis.  
The presence of clouds during image acquisition impedes the usefulness for optical image analysis as clouds and haze (and their associated shadows) can drastically affect the reflection of some electromagnetic wave energy from the surface of the earth. Images were divided into quadrants and filtered based on a ruleset where images exceeding a threshold of cloud cover were removed from consideration during the review process. In total, 166 Level-1 images (i.e., raw digital numbers (DNs) and not atmospherically corrected) were selected and downloaded for the Orbroich site and 266 images for the Wahnbachtal site. The number of images available across years and seasons varied (Table S3). A detailed assessment of image quality was performed, including georeferencing accuracy/image-to-image registration, a more detailed cloud cover assessment, and overall spectral data quality. Details of this assessment are provided in Supplementary Materials.
# Application of a DC Distribution System in Korea: A Case Study of the LVDC Project
## 4 Construction of Actual DC Distribution Line
### DC Distribution Equipment  
The significant equipment of the DC distribution system connected to the actual grid in Gwangju are primarily composed of the transformer, converter system, protection system, and monitoring system. The specifications of each device are shown in Table 3. Single-phase AC/DC converter and single-phase DC/AC inverter switching devices use SiC for efficiency improvement [14; 15].  
The detailed system configuration is shown in Figure 10. In case of a problem with the DC distribution system, the customer can switch to the existing AC system in order to prepare for the long-term power outage. The Cut-Out Switch (COS) shown in Figure 10 and the pole transformer on the load side can supply AC. To supply DC voltage, open the COS (1) for the two lines and put COS (2) to use. On the contrary, to supply AC voltage, put COS (1) to use for the two lines and open the COS (2). The power supply and the load side AC/DC Converter are separated from the transformer to ensure stability. A low-voltage line was constructed below the neutral line in consideration of the induction voltage of the high-voltage line. Also, to build a non-grounding (IT) system, the grounding of the enclosure through the three-class grounding was enabled, and the IMD for monitoring the grounding fault of the line was installed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Pole Transformer** & **Converter (AC/DC)** & **Inverter (DC/AC)** \\ \hline Input voltage & 1\(\varnothing\) 13.2 kVac & 1\(\varnothing\) 220 Vac & 750 V\({}_{\text{dc}}\) \\ \hline Capacity & 30 kVA & 30 kW & 30 kW \\ \hline Rated voltage & - & 750 V\({}_{\text{dc}}\) & 220 Vac \(\pm\) 5\% \\ \hline THD & - & \textless{5\%} & \textless{5\%} \\ \hline Efficiency & - & \textgreater{97\%} & \textgreater{97\%} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of DC distribution equipment.
## 3 Results and Discussion
### Anttimicrobial Activity  
Overall, the results of antimicrobial activity revealed differential potential against pathogenic and beneficial microorganisms associated with the gastrointestinal tract (Table 2). Interestingly, the hydroalcoholic extract of _L. indigo_ and _C. nuda_ showed strong anti-_H. pylori_ activity (MIC! 160 \(\mu\)g mL\({}^{-1}\)), and these results should be considered as a valuable background to perform further exploration for elucidating putative bioactive compounds and their mechanisms of antimicrobial action. Contrarily, the extract from the six edible mushrooms showed weak or negligible activity on the assayed strain of _E. coli_ (>300 \(\mu\)g mL\({}^{-1}\)). Similar results were observed for _S. cerevisiae, L. reuteri, L. acidophilus_, and _B. bifidum_ where the hydroalcoholic extracts had no significant toxic effects. This evidence strongly suggests that consuming these sources could probably be safe for some probiotics associated with the human gut lumen. On the other hand, the extracts of _L. indigo, C. nuda, C. subclavipes, R. delica_, and _A. jacksonii_ produced a noticeable inhibitory effect on _E. faecalis_ whereas those of _L. indigo, C. nuda_, and _A. jacksonii_ were highly effective against _S. typhi_. Previous works focused on evaluating ethanolic or hydroalcoholic extracts from edible mushrooms suggest the possible use of these foods as a source of natural inhibitors of pathogenic bacteria as well as agents to promote the proliferation of beneficial probiotics [38; 39].
# Aggregatable Subvector Commitment with Efficient Updates
## 2 Preliminaries
### Bilinear Pairing  
We denote the parameters associated with the pairing by using \((p,e,\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T},g_{1},g_{2})\). In particular, \(\mathbb{G}_{1},\mathbb{G}_{2}\) and \(\mathbb{G}_{T}\) are all groups of order prime \(p\), where \(g_{i}\) is the generatorof \(\mathbb{G}_{i}\). Define \(e\) as \(\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{T}\) to be a bilinear pairing that satisfies the following properties [22; 23; 24; 25]:  
* Bilinearity: \(\forall u\in\mathbb{G}_{1},w\in\mathbb{G}_{2}\) and \(a,b\in\mathbb{Z}_{p}\) it is \(e(u^{a},w^{b})=e(u,w)^{ab}\).
* Non-degeneracy: \(e(g_{1},g_{2})\
eq 1\).
* Computable: For all \(u\in\mathbb{G}_{1},w\in\mathbb{G}_{2}\), we have an efficient algorithm to compute \(e(u,w)\).  
To simplify the description of the protocol, we consider \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\) as the same group \(\mathbb{G}\) and use the same generating element \(g\).
## 2 Experimental Work
### Thermal Evolution  
The heat caused by the internal friction between microstructures under cyclic loading is dissipated through weld metal. The temperature increment \(\theta\) with respect to initial temperature could be obtained from the thermal-graphic measurement. The thermal evolution in whole life cycles under stress amplitudes 300 MPa and 260 MPa are shown in Figures 7 and 8, respectively. Before the temperature increment comes into the stable stage II, as shown in Figure 7, the temperature of the material surface increases continuously, due to cyclic straining, until a relative equilibrium state between the internal heat production and external heat exchange is reached [19; 20; 21; 22; 23]. Being different from pure metal materials, the temperature increment of this welded joint linearly increases during the stable stage II both in high stress amplitude and low stress amplitude. Thus, the method for calculating accumulated intrinsic dissipation and predicting lifespan has to be developed.  
Figure 6: Temperature distribution contour measured by IR camera (unit: \({}^{\circ}\)C).  
Figure 7: The thermal evolution in whole life cycles under stress amplitude 300 MPa.
# Optimal Design of Aperiodic Reconfigurable Antenna Array Suitable for Broadcasting Applications
## 6 Conclusions  
An aperiodic reconfigurable antenna array that satisfies multiple requirements essential for DVB-T broadcasting, main lobe tilting, high forward gain and null filling inside the service area, low SLL outside the service area and low SWR at the input of all the array elements has been designed. The optimization problem has a significant complexity since 26 parameters have been optimized in order to best satisfy 13 goals. All the goals have been completely satisfied. The results show that the PSOvm is a robust optimization method which can be used to efficiently solve antenna optimization problems of high complexity while saving significant computational time. Also, among the optimizers found in CST, TRF proves to be not only the most efficient of them but also a very competitive one. However, the PSOvm achieves the best result in the least possible computational time compared to those derived by all the other optimizers. Since all the results in this study were obtained by using full wave analysis with CST, they reflect realistic conditions quite well. By applying on-off switches in the feeding network, the antenna array becomes a reconfigurable one, thus providing different shapes of radiation pattern according to the particular requirements that apply to a certain application.  
Conceptualization, Z.D.Z.; Data Curation, I.P.G. and Z.D.Z.; Methodology, Z.D.Z.; Project Administration, Z.D.Z. and T.D.X.; Software, P.I.L., N.V.K. and T.D.X.; Supervision, Z.D.Z., P.I.L., T.V.Y., N.V.K., C.S.A. and T.D.X.; Validation, I.P.G. and Z.D.Z.; Visualization, I.P.G. and Z.D.Z.; Writing--Original Draft, I.P.G.; writing--review and editing, I.P.G., Z.D.Z., P.I.L., T.V.Y., N.V.K., C.S.A., I.P.C. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union [European Social Fund (ESF)] through the Operational Program \"Human Resources Development, Education and Lifelong Learning\" in the context of the project \"Strengthening Human Resources Research Potential via Doctorate Research\" under Grant MIS-5000432, implemented by the State Scholarships Foundation (IKY).  
The authors declare no conflict of interest.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Proposed Localization Technique  
Given that the noisy range measurements \(\mathbf{\Lambda}=\{\tilde{d}_{ij}\}_{i,j=1,i\
eq j}^{m+n}\) are available from (4) and (11), we define our problem as finding the configuration of \(m+n\) nodes in lower dimensional space from \(\tilde{d}_{ij}\) such that it is well approximated by the \(d_{ij}\). The error function for the pairwise noisy distances is defined as  
\[\min_{\mathbf{L}}\sum_{i<j}v_{ij}\left(\tilde{d}_{ij}-d_{ij}(\mathbf{L})\right)^{2}, \tag{12}\]  
where \((\mathbf{L})=\{I_{1},I_{2},...,I_{m+n}\}\) are the two-dimensional coordinates of all the nodes and \(v_{ij}\) are the weighting coefficients. A number of different techniques are available in the literature to solve this optimization problem, but all of them consider a single observations approach. In this paper, we propose a dimensionality reduction technique with multiple input observations. The objective function for the proposed technique is defined  
\[\min_{\Phi^{(u)},\mathbf{L}}\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}\sum_{i<j}v_{ij}\left( \tilde{d}_{ij}-d_{ij}(\mathbf{L})\right)^{2}, \tag{13}\]  
where \(\Phi^{(u)}\) is the importance of \(u\)-th observation such that \(\sum_{u=1}^{R}\Phi^{(u)}=1\), \(\gamma\) is the controlling parameter and \(R\) is the total number of observations. The controlling parameter for weights (\(\gamma>1\)) determine the distribution of the multiple observations. The weight of each observation is added to the error function in (13). The logic behind using multiple observations is that, if \(\Phi^{(u)}\) is used directly, then the observation view with small error function has \(\Phi^{(u)}=1\), and the rest of the observation views have \(\Phi^{(u)}=0\). This behavior is not optimal as only one view is selected while all other views are ignored. Therefore, the proposed adaptive weight learning paradigm is an intuitive solution that considers a combination of weights for each observation view. The optimization problem in (13) is solved by decomposing it into two sub-problems.