esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 4 The Proposed System  
### An Overview  
The UWSN in the proposed work consists of a set of sensor nodes and sea buoys that are replaced in a specified area (Figure 1). In this non-static network where node mobility is presented due to the continuous water currents, sensor nodes can move far from their sea buoy (cell centroid). This consequently can lead to the loss of connection between the sensor node and its sea buoy, which requires a successful handover process to help the sensor node to safely attach to a new sea buoy. According to Figure 2, the classifier initiates a training phase based on historical data (i.e., from the Korea Hydrographic and Oceanographic Agency dataset). The proposed ML model considers the sensor's location, the received signal strength from neighboring buoys, and the weather forecast to predict the suitable buoy to complete the handover process successfully. Finally, in the test phase, the proposed model assists the sensor node in making adequate handover decisions. The structure of the cells is illustrated in Figures 3 and 4.  
Figure 2: Overview of the proposed system.  
Figure 4: The proposed simulated UWSN with 61 base stations.  
Figure 5: Block diagram of the proposed system.  
Figure 3: An example for a cell with its six neighboring cells.  
Figure 5 depicts a block diagram of the proposed UWSN handover prediction system. The proposed system consists of two phases: the data collection phase and the machine learning phase. The data collection phase consists of collecting the handover occurrence data. That is, when a handover occurs in the network, the sensor sends environmental data (e.g., sensor location and signal strength of neighbor cells) of the last collected data before the handover. The second phase (the machine learning phase) is responsible for training, validating the machine learning models using handover data, and starting the deployment of the UWSN handover prediction system.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 1 Introduction  
After the industrial revolution, the increasing consumption of fossil fuels caused numerous environmental and socioeconomic problems. In this context, it is considered a prime objective to replace fossil fuel technologies in many sectors with more sustainable technologies in order to reduce the amount of carbon dioxide released into the atmosphere. Increasing environmental and economic concerns and the decisions taken by states to reduce our dependence on fossil fuels have led to an acceleration of research and development activities in this area. This also holds for the logistics sector, where new regulations are introduced in order to reduce environmental problems. One of the most recent regulations in this field is the decision approved by the European Parliament on 18 February 2019, stating that the emissions of new trucks will need to be 30% lower in 2030 compared to 2019 emissions data [1].  
In this context, sustainable city logistics emerged as a concept for reducing the negative impact of urban transportation activities on society, the environment and the economy. Sustainable city logistics is characterized by using environmentally friendly vehicles for freight distribution and designing multi-tier transportation structures to eliminate problems caused by freight vehicles operating in cities. A growing number of logistics ande-commerce companies make use of environmentally friendly electric vehicles for distribution in urban areas due to their low noise and zero exhaust emissions; see, for example, [2]. Despite these advantages, the en-route charging necessity of electric vehicles due to a limited driving range produces new difficulties for the planning and managing of logistics activities. In fact, the ability to derive optimal charging plans for electric vehicles taking into account the total time requirements and route distances is essential.  
In this study, the issue of sustainable city logistics is addressed by means of a new variant of the classical vehicle routing problem: the two-echelon electric vehicle routing problem with time windows (2E-EVRP-TW). In two-echelon distribution networks, large trucks transport products from central warehouses to satellites in the surrounding areas of cities. Subsequently, smaller vehicles distribute goods from these satellites to customers located in the cities. Electric vehicles are preferably used in the second echelon of such a distribution network, as they are less noisy and have no exhaust emissions. In other words, two-echelon distribution networks are advantageous for preventing large trucks entering the cities and, in this way, reducing urban traffic jams, noise, and pollution. In addition to these features, our 2E-EVRP-TW problem also considers time window (TW) constraints for the delivery of goods to customers. Note that time windows can be used to control the visiting times of the customers, which might be regulated by local jurisdictions, but also by the customers themselves.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 2 Crowd-Sensing-Based Crack Detection Method  
After detecting cracks on a bridge, it is necessary to reposition them for timely repair. Therefore, the location information of bridge cracks is crucial for maintenance work. Due to the sparse distribution and vast coverage of bridge structures, crack positioning presents significant challenges. The traditional positioning method involves manually recording on papers during field inspection, and then manually inputting the data into a computer after the field inspection process. This process has low digitization and automation, and with an increase in the number of cracks, manual inputting can lead to recording errors. Moreover, due to the lack of a uniform coordinate system, the recording of crack locationsis subjectively categorized by inspectors based on bridge direction or pier numbers. Such methods have low standardization and poor readability, which makes the process of crack data recording and transmission difficult. In this section, based on image acquisition using crowd sensing, a crack positioning method based on the GPS system was proposed.  
The GPS is a satellite-based navigation and positioning system designed by [PERSON] and his team in the United States after 20 years of effort. It is capable of accurately positioning most areas on the Earth's surface [44; 45]. The GPS has already been applied in civil engineering for bridge displacement monitoring, building structure deformation, dam displacement, etc. [46; 47].  
The crowd sensing crack collection system requires participants to collect data on their mobile devices and then gather the information on the cloud for analysis and processing. The design concept is shown in Figure 1. The hardware of the system consists of four parts, including the application installed on the participants' mobile phones, the transmission network represented by signal towers and broadband networks, the cloud server for data storage and analysis, and the management unit's terminal computers.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion  
### Prohibited Zone with Omnidirectional Antenna  
The prohibited zones encompass both the horizontal and vertical dimensions. The horizontal prohibited zones are defined by points in the _XOY_ (horizontal) plane that exceed the interference threshold. The vertical prohibited zones are defined by points in the _XOZ_ (vertical) plane that exceed the interference threshold. We consider these prohibited zones for both the omnidirectional antenna and the 16 \(\times\) 16 beamforming antenna configurations.  
Initially, we assume a flight altitude of 200 ft for the aircraft and subsequently analyze the scenarios at 1000 ft and 2000 ft. In Table 2, category 2 altimeters are exclusively applicable for altitudes below 2000 ft and are most susceptible to interference. Hence, only interference scenarios below 2000 ft are considered. As for the category 1 altimeters in Table 2, the change in interference threshold relative to 2000 ft is inconspicuous at 5000 ft. However, path loss significantly increases at 5000 ft. Therefore, it is evident that the prohibited zone at 5000 ft is smaller than at 2000 ft. Consequently, conducting analyses for altitudes of 200 ft, 1000 ft, and 2000 ft effectively encompasses typical application scenarios.
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 2 Wind Vector Fields  
Wind can be modelled as the vectored sum of two components: a steady ambient component and a stochastic (gust) component [33]. The gust component can be estimated using the Dryden model [34]. The Dryden model consists of passing white noise through the a filter with generalized parameters based on flight conditions such as altitude and severity of turbulence as defined in MIL-F-8785C [35].  
The steady ambient wind is typically expressed in the Earth inertial reference frame and can be modelled based on weather information for a given area. For example, wind vector fields are available in 6-h increments with a resolution of ten nautical miles for multiple areas across North America [36] and will likely get more accurate, timely and widespread as weather sensing technology improves. Figure 2 shows a typical wind vector field provided by the National Weather Service in the U.S.  
Figure 2: A typical 2D wind vector field [36].  
As an alternative to the publicly available weather information, CFD software is capable of estimating 3D spatial WVF over a DEM given sparse wind monitoring stations. CFD software is especially useful where real-time weather information for an area is not available or for periods when communication with the UAV is lost. It can also provide finer resolution fields capable of capturing local weather patterns such as wind currents around buildings and other surface features such as variations in terrain [37].
# Ion Beam Nanopatterning of Biomaterial Surfaces
## 3 Ion Beam-Nanopatterned Surfaces in Contact with Biomolecules and Cells
### Biomolecular Adsorption
#### 3.1.1 Proteins and Peptides
negatively charged protein BSA, where different degrees of denaturation could be verified on the flat and rippled SiO\({}_{\rm x}\)/Si surfaces. In a follow-up study that employed TiO\({}_{\rm x}\)/Ti films with different surface morphologies including those on nanorippled Si substrates, we could further show that the adsorption of these three proteins is governed by different surface statistical parameters [102]. In particular, we found that BSA adsorption is governed mostly by root-mean-square (RMS) surface roughness, while TGL adsorption is more sensitive toward surface skewness and kurtosis. This demonstrates the importance of a precise tailoring of surface morphology at the nanometer level to enable the rational control of biological response to biomaterial surfaces. Ion beam nanopatterning appears to be a viable and versatile tool in this regard. Finally, in a recent work from our lab, the effect of such 30 nm ripple patterns on SiO\({}_{\rm x}\)/Si surfaces on the surface-assisted aggregation of a small peptide hormone called human islet amyloid polypeptide (hIAPP) was investigated [108]. The aggregation of hIAPP into cytotoxic amyloid fibrils is a key intermediate step in the development of type 2 diabetes mellitus [109]. Quantitative characterization of the aggregate morphologies at the flat and nanorippled surfaces by time-lapse AFM revealed that the presence of the nanopattern affected the pathway of hIAPP aggregation. In particular, hIAPP fibrillization at the nanopatterned surface was retarded compared to the flat surface. Instead, peptide aggregation was dominated by the formation of large amorphous aggregates. This demonstrates that surface nanopatterning not only affects protein adsorption and denaturation but may also influence protein-protein interactions.
### Noise's Influence on the Higher Derivatives of the Measured Signal  
Generally, the signal to be measured \(f(t)\) is continuous in the time domain. In the neighborhood of \(t_{0}\), it can be approximated using a Taylor series as in expression (1).  
\[\begin{array}{c}f(t)=f(t_{0})+f^{\prime}(t_{0})(t-t_{0})+\frac{1}{2!}f^{ \prime\prime}\left  
becomes larger and larger. Subsequently, the \(k\)th order derivative of the measured signal \(C(t)\) also becomes larger and larger. When \(\varepsilon_{t}\) is large enough or \((t_{t}-t_{0})\) is small enough, the \(k\)th order derivative of noise \(\varepsilon(t)\) at \(t_{0}\) will be large enough, and the \(k\)th order derivative of measured signal \(C(t)\) at \(t_{0}\) will deviate seriously from the corresponding derivative of the signal \(f(t)\).  
The above analysis shows that even if the Taylor series expansion of the noise has only the \(k\)th order term, provided the \(k\)th order derivative is large enough, the \(k\)th order derivative of the measured signal can be seriously interfered with. Additionally, the \(k\)th order derivative of the measured signal will deviate substantially from the \(k\)th order derivative of the real physical process. Therefore, it is necessary to reduce the noise disturbance to obtain the higher derivatives of the measured data conforming to the real physical trend.
###### Abstract  
The mechanical response of steel auxetic structures manufactured using laser-powder bed fusion was explored. The level of control exerted by the key design parameters of vertical strut length (H), re-entrant strut length (L), strut thickness (t) and re-entrant angle (\(\theta\)) on the mechanical response was examined through a design of experiment approach with ANOVA statistical analysis methods applied. The elastic modulus in directions normal to (E\({}_{x}\)) and parallel to (E\({}_{y}\)) the vertical strut was found to be primarily dependent upon t and L, respectively, whereas yield strength in both test directions (\(\sigma_{x}\) and \(\sigma_{y}\)) was strongly dependent on t and L. A large variation in modulus was found between the two test directions (E\({}_{x}\) / E\({}_{y}\) - 1.02 \(\pm\) 0.07 GPa/4.4 \(\pm\) 0.1 GPa), whereas, yield strength showed little anisotropy (\(\sigma_{x}\) / \(\sigma_{y}\)-45 \(\pm\) 6 MPa/45 \(\pm\) 9 MPa). Poisson's ratio parallel to the vertical strut varied considerably with geometry but not in a direction normal to the vertical strut. Deformation mechanisms were found to be different of compression in the x and y directions, being a combination of stretching of the vertical strut; compression, bending and hinging of the re-entrant strut (x); and vertical strut compression and re-entrant strut stretching and bending (y).  
additive manufacture; auxetic; mechanical; manufacturing; accuracy +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source of Variation** & **Sum of Squares** & **Degrees of Freedom** & **Mean Squares** & **Calculated F** \\ \hline \(A\) & \(SSA\) & \(a-1\) & \(MSA=\frac{SSA}{DSA}\) & \(F=\frac{MSA}{MSA}\) \\ \(B\) & \(SSB\) & \(b-1\) & \(MSB=\frac{SSA}{DSA}\) & \(F=\frac{MSA}{MSA}\) \\ \(C\) & \(SSC\) & \(c-1\) & \(MSC=\frac{SSA}{DSA}\) & \(F=\frac{MSA}{MSA}\) \\ \(AB\) & \(SSAB\) & \((a-1)(b-1)\) & \(MSAB=\frac{SSA}{DSA}\) & \(F=\frac{MSA}{MSA}\) \\ \(AC\) & \(SSAC\) & \((a-1)(c-1)\) & \(MSAC=\frac{SSAC}{DSAC}\) & \(F=\frac{MSA}{MSAC}\) \\ \(BC\) & \(SSBC\) & \((b-1)(c-1)\) & \(MSBC=\frac{SSAC}{DSAC}\) & \(F=\frac{MSA}{MSAC}\) \\ \(ABC\) & \(SSABC\) & \((a-1)(b-1)(c-1)\) & \(MSABC=\frac{SSABC}{DSABC}\) & \(F=\frac{MSA}{MSAC}\) \\ Residual & \(SSR\) & \(abc(n-1)\) & \(MSR=\frac{SSA}{DTRA}\) & \\ Total & \(SST\) & \(abcn-1\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of variance for the general case of the three-factor model with fixed effects. \[AC =\frac{1}{4n}\;[(1)-a+b-ab-c+ac-bc+abc\;], \tag{7}\] \[BC =\frac{1}{4n}\;[(1)+a-b-ab-c-ac+bc+abc\;],\] (8) \[ABC =\frac{1}{4n}\;[abc-bc-ac+c-ab+b+a-(1)], \tag{9}\]  
On the other hand, in a \(2^{3}\) design, each effect has a corresponding contrast with a single degree of freedom, while if there are \(n\) replicates, the sum of squares for any effect may be determined as follows:  
\[SS=\frac{specific\;contrast}{8n}, \tag{10}\]  
Although, in a factorial design of this type, the impact of the factors and their interactions (the model terms) on the total sum of squares is usually more precisely verified using the ANOVA tool, it is sometimes also important to obtain a rough value of it, as defined by the following Equation (11):  
\[\textit{Percent contribution of the term relative to the total }SS=\frac{SS\;of\;the\;term}{\textit{Total }\;SS}, \tag{11}\]  
Another important formulation in terms of a design of this type is the regression equation. This usually only includes the model terms that have been proved to be significant in terms of their \(p\)-values in the ANOVA results. The following Equation (12) shows an example of a regression equation for the specific case of a \(2^{3}\) design, such as the one described further in this paper; however, using a similar analogy may allow the experimenter to modify this equation and make it fit any other design type. \[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\beta_{3}x_{3}+\beta_{12}x_{1}x_{2 }+\beta_{13}x_{1}x_{3}+\beta_{23}x_{2}x_{3}+\beta_{123}x_{1}x_{2}x_{3}\;+\varepsilon \tag{12}\]  
Last but not least, in this experimental design, there is a need to formulate the hypotheses for the experiment (see Table 4). In the case of a \(2^{3}\) design type such as the one addressed in this paper, the hypotheses to be defined are shown below. These hypotheses can be evaluated or tested using any of the different means for such purposes, e.g., the F\({}_{0}\) statistic from the ANOVA results, as presented in Table 3, or the \(p\)-values (see [20]).
## 5 DBF Performance
### Beam Synthesis Performance and Steering Tests
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Polarization**} & \multicolumn{1}{c}{**H**} & \multicolumn{1}{c}{**V**} & \multicolumn{1}{c}{**HV**} \\
**Beam steering** & & & & \\ \hline \(-20\,^{\circ}\) & 24.6 \({}^{\circ}\) & 24.2 \({}^{\circ}\) & 24.8 \({}^{\circ}\) \\ \(-15\,^{\circ}\) & 25.0 \({}^{\circ}\) & 24.8 \({}^{\circ}\) & 25.5 \({}^{\circ}\) \\ \(-10\,^{\circ}\) & 24.1 \({}^{\circ}\) & 24.0 \({}^{Figure 17(a) shows the DBF results in dB for the 0 \({}^{\circ}\) steered beam and H polarization for the polar and cross-polar beams scanning on the non-steerable domain (\(\varphi\)). In this case the SLL is \(-25\) dB and the cross-polar level is \(-30\) dB, which fulfills another design requirement (\(<\)25 dB). Figure 16b shows the steerable beam which is slightly worse than the previous one but still has a good performance. The SLL is \(-17.5\) dB and the cross-polar level is \(-25\) dB. Figure 18(a) shows the DBF results in dB for the 0 \({}^{\circ}\) steered beam and V polarization, and cross-polar beams scanning on the non-steerable domain (\(\varphi\)). In this case, the SLL is \(-20\) dB and the cross-polar level is \(-27\) dB. Figure 18(b) shows the steerable beam which is again slightly worse than the previous one but still has a good performance. The SLL is \(-17.5\) dB and the cross-polar level is \(-27\) dB. Figure 17: PAU-RAD DBF results in dB for the 0 \({}^{\circ}\) steered beam and H polarization, **(a)** polar and cross-polar beams scanning on the \(\varphi\) domain (non-steerable), **(b)** polar and cross-polar beams scanning on the \(\theta\) domain (steerable). Figure 18: PAU-RAD DBF results in dB for the 0 \({}^{\circ}\) steered beam and V polarization, **(a)** polar and cross-polar beams scanning on the \(\varphi\) domain (non-steerable), **(b)** polar and cross-polar beams scanning on the \(\theta\) domain (steerable). Figure 19 shows the \(\theta=0\,^{\circ}\) and \(\varphi=0\,^{\circ}\) cuts of the HV polarization. This case is different from the two previous cases, because the third Stokes parameter is collected through the real part of this antenna pattern product and the fourth one from the imaginary part. So that, amplitude and phase of the cross-correlation beam have to be presented to show the instrument performance in this full polarimetric mode. Figure 19(a,c) shows the modulus in dB of the DBF results for the \(0\,^{\circ}\) steered beam and HV polarization for the \(\varphi\) and \(\theta\) domains, respectively. In both
## 4 Mobile Localization Algorithms
### Centralized Localization Algorithms
#### 4.1.3 Motion-Aware Self-Localization (MASL) Scheme  
In wireless communication process, in order to avoid excessive collisions occurring over a shared channel, the gathering of ranging information actually occurs over a short time epoch \(T\). The problem is that mobility causes the node positions to change significantly during epoch \(T\). Therefore,  
in [40], the authors proposed a Motion-Aware Self-Localization (MASL) scheme for mobile UWSNs. During MASL, nodes first perform ranging with their neighbors to get all the distance estimates in the localization epoch. Then, all distance estimates are sent to a central station and processed offline. An iteration algorithm is started to obtain the positions. At each iteration, the algorithm refines position distributions by dividing the area of operation into smaller grids, selecting the area in which the node resides with high probability and using it in the next iteration.  
MASL is a centralized anchor-free localization algorithm, which reduces unknown nodes' computational burden and can be used to localize large scale UWSNs. The problem of MASL is that the iterative algorithm cannot provides real-time location information. Real applications always need UWSNs to do online monitoring and provide real-time location information. Simulation results show that, compared with a robust self-localization algorithm, named Multi-dimensional Scaling (MDS) [41], MASL localizes 70% of nodes with error lower than that of MDS.
# Differentiation of Fresh and Processed Fruit Juices Using Volatile Composition
The main goal of this study was to establish and compare the volatile signature of fresh and processed fruit juices, obtained from the same batch of fruits, by means of headspace solid-phase microextraction (HS-SPME), combined with gas chromatography-quadrupole mass spectrometry (GC-qMS). In addition, the impact of processing on the volatile signature of juices from grapes (_Vitis vinifera_ L.), wild red fruits (_Fragaria x ananassa Duch., Vaccinium cryombyum_ L., _Rubus ideaus_ L.), orange (_Citrus sinensis_ L.), pear (_Pyrus communis_ L.), and apple (_Malus domestica Bordh._), as well as the corresponding processed juices (from the same batch), was investigated. These comprehensive data will allow the evaluation of the impact of processing on the volatile signature of fruit juices from the same batch. As far as we know, this is the first time that different fruit juices corresponding to different fruit species were analyzed simultaneously using the same analytical technique, therefore enabling us to describe both the volatile profile in the juice and the variability in the volatile profile between fresh and processed fruit juices. The obtained results may help to enlarge the database on the volatile and aroma composition of fresh and processed fruit juices, as well as provide a useful platform to improve the organoleptic characteristics of the processed juices and consequently contribute to their economic valorization and improve their acceptance by consumers.