esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
###### Abstract  
Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.  
m 2021
###### Abstract  
The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2 Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.  
deep learning; convolutional neural networks; bathymetry; Sentinel-2; wave kinematics; coastal physics Article
## 3 Results
Farmers were less likely (probability of 41.6%) compared to employees (probability of 58.9%) to quit smoking, and 75% of people with an SSC-level education or above were likely to quit SLT use compared to 53% of people with no education (Table 6). \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Intentions about Quitting Smoking Items** & \multicolumn{2}{c}{**Tobacco Smoker, \(n\) = 62**} & \multicolumn{2}{c}{
\begin{tabular}{c} **SLT User,** \\ \(n\) = 87** \\ \end{tabular} } \\ \hline  & & \(n\) & \% & \(n\) & \% \\ \hline \multirow{4}{*}{Thinking about quitting smoking} & Not at all & 0 & 0 & 5 & 5.7 \\ \cline{2-5}  & A little & 16 & 25.8 & 30 & 34.5 \\ \cline{2-5}  & A lot & 43 & 69.4 & 51 & 58.6 \\ \cline{2-5}  & Enormously & 3 & 4.8 & 1 & 1.1 \\ \hline \multirow{4}{*}{Feel unhappy} & Never & 7 & 11.3 & 24 & 27.6 \\ \cline{2-5}  & Sometime & 38 & 61.3 & 31 & 35.6 \\ \cline{2-5}  & Often & 17 & 27.4 & 32 & 36.8 \\ \hline \multirow{4}{*}{Six-month plan} & Smoking as much & 3 & 4.8 & 9 & 10.3 \\ \cline{2-5}  & Cut down a little & 40 & 64.5 & 48 & 55.2 \\ \cline{2-5}  & Cut down a lot & 15 & 24.2 & 21 & 24.1 \\ \cline{2-5}  & Stop smoking & 4 & 6.5 & 9 & 10.3 \\ \hline \multirow{4}{*}{Stopping smoking will increase self-image} & Not at all & 5 & 8.1 & 2 & 2.3 \\ \cline{2-5}  & A little & 23 & 37.1 & 39 & 44.8 \\ \cline{2-5}  & A lot & 32 & 51.6 & 44 & 50.6 \\ \cline{2-5}  & Enormously & 2 & 3.2 & 2 & 2.3 \\ \hline \multirow{4}{*}{Stop smoking at the moment} & Not at all & 8 & 12.9 & 8 & 9.2 \\ \cline{2-5}  & A little & 35 & 56.5 & 47 & 54.0 \\ \cline{2-5}  & A lot & 18 & 29.0 & 31 & 35.6 \\ \cline{2-5}  & Enormously & 1 & 1.6 & 1 & 1.1 \\ \hline \multirow{4}{*}{Next four weeks smoking cessation plan} & Smoking as much & 4 & 6.5 & 7 & 8.0 \\ \cline{2-5}  & Cut down a little & 39 & 62.9 & 59 & 67.8 \\ \cline{2-5}  & Cut down a lot & 12 & 19.4 & 9 & 10.3 \\ \cline{2-5}  & Stop smoking & 7 & 11.3 & 12 & 13.8 \\ \hline \multirow{4}{*}{Tried to stop smoking} & Never & 17 & 27.4 & 35 & 40.2 \\ \cline{2-5}  & Once & 15 & 24.2 & 29 & 33.3 \\ \cline{2-5}  & Several times & 30 & 48.4 & 23 & 26.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Intention to quit smoking of 62 tobacco smokers and 87 SLT users (participants with high blood pressure). Smokers who could not mention any diseases caused by smoking were less likely to quit smoking (44% vs. 54%, \(p=0.14\)) than those who could name two diseases. The tobacco smokers were asked to mention the names of two diseases that are associated with tobacco smoking.
## 4 Presumptions and Network Structuring  
### Presumptions  
Let us consider \(S\) to be a multi-hop sensor network that consists of \(n\) sensor nodes and a solitary sink node. Let us consider the sensor network consists of cheap and small size sensors that have low power and memory. The nodes located in the network are invariable and kindred in nature. These nodes have distinctive \(d\) to discriminate them from other sensor nodes present in the network. The base station is provided with adequate hardware, ample memory, and abundant power. The source nodes are responsible for sensing different kinds of weather information such as voltage, light, humidity, temperature, etc. Here, sensing the data is performed by the source nodes. The aggregation task is completedby the aggregator nodes. The timing scheme is intermittent which means every source node at time slot \(j\) senses a new data measurement after time \(T\) node forms a vector of data measurement which is considered to be its weight function (\(wt\)).
# Evaluation and Comparison of Pear Flower Aroma Characteristics of Seven Cultivars
## 3 Results
### Odor Types, Description, and OAVs  
The OAVs showed only a limited number of volatiles that could be detected at concentrations high enough to be perceived (OAV \(\geq\) 1), which were considered as contributors responsible for the overall flower odor (Table 3). Due to the OAVs, 1-nonanal, 1-octanal, 1-decanal, phenethyl acetate, methyl 2-hydroxy-3-methyl pentanoate, and hexyl acetate were the key active odorants to the aroma of Anli flower; 1-octanal, 1-nonanal, 1-decanal, (\(E\))-2-nonenal, 1-decanol were the most active odorants in Bayuesu and Xizilu flower; phenethyl acetate, 1-nonanal, 1-octanal, eugenol were the odor-active compounds to the aroma of Golden flower; 1-nonanal, phenethyl acetate, and phenyl acetaldehyde were the key contributors to Brown peel flower; 1-octanal, 1-nonanal, phenethyl acetate were the key contributors to KorlaXiangli flower; and 1-nonanal, phenethyl acetate, (\(E\))-\(\upbeta\)-damascone were the active odorants in Lyubaoshi flower. To sum up, volatile aldehydes, particularly 1-octanal, 1-nonanal, 1 decanal, and (\(E\))-2-nonenal were present in higher amounts than their threshold and were considered the key factors to form the odor of pear flower. However, most esters and alcohols showed less importance due to their amounts being below the threshold.  
Figure 2: The amount of different volatile chemical classes in flowers of seven pear cultivars. The error bars represent the standard deviation (\(n\) = 3). Different letters above the column indicate statistical differences between cultivars (\(p\) < 0.05), the same as below.  
Based on odor description, eight main odor types were grouped (Table 3), described as fruity, aldehyde, green, floral, citrus, fatty, waxy, and pungent. Remarkably, several compounds had multiple flavors benefiting the flower's fragrance. Total amounts of different odor types suggested, the floral, fruity, pungent, and green types showed most abundance in Anli, Golden, and Brown peel flowers; the aldehyde, waxy and floral types showed higher values in Bayuesu and Xizilu flowers; aldehyde type was present higher in Lyubaoshi, but all types were significantly lower in KorlaXiangli flowers (Figure 3). This finding suggested Bayuesu and Xizilu flowers had stronger aroma intensity of aldehyde and waxy, but the odor strength of KorlaXiangli was much weaker than the other cultivars.
## 3 Results and Discussion
### Lean Construction Tools
#### 3.1.2 Validation of Lean Tools in the Building Design Process
These differences in ratings may be influenced by the specific application context and the experts' prior experience in using each tool within the building design process. The results of the Kruskal-Wallis test indicated statistically significant differences in the evaluations of the various tools (H = 41.39, \(p<0.001\)), confirming that not all tools were assessed equally by the experts. This global significance suggests a differentiated perception regarding the applicability or value of certain tools within the design process. While subsequent pairwise comparisons using the Mann-Whitney U test with Bonferroni correction did not yield statistically significant results after adjustment, consistent trends were observed. Tools such as Building Information Modeling (BIM) and Visual Management (VM) received systematically higher ratings among participants, frequently contrasting with lower-rated tools such as Value Stream Mapping (VSM) or Design Structure Matrix (DSM). These patterns suggest that, although pairwise differences did not reach corrected significance thresholds, BIM and VM may be perceived as more mature or valuable tools in current design practices.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 3 Routing Protocols in Connected CubeSat network  
Given a connected CubeSat network, the routing protocols can be categorised based on the network topology over one or more standard satellite orbits into two groups, namely: (i) single-layer routing protocols, where the nodes are clustered and communicate within the same level of orbit [20; 21; 22; 23; 24; 25; 26]; and, (ii) multi-layer routing protocols where nodes communicate with other nodes from another level [27; 28; 29; 30; 31; 32; 33; 34]. These protocols, which were originally proposed for Mobile Satellite Services (MSS) and Satellite Personal Communication Networks (S-PCN), include constellations of conventional satellites to provide voice and data mobile satellite services, such as Iridium and Inmarsat. Protocols that were proposed for these two categories are based on TCP/IP technology and will be comprehensively discussed in the following section with an analysis of each routing protocol.
# Improvement of Buildings' Air Quality and Energy Consumption Using Air Purifying Paints
## 3 Results
### Real-Scale Photocatalytic Performance of the Photo-Paints in Air Pollutant Degradation  
The depollution efficiency of both organic and inorganic paint under the real-scale application was calculated by the difference in the concentration levels of NO and toluene in the two rooms of the Demo Houses: the \"green\" and the \"reference\" rooms (Table 3). As it was expected from previous studies [50; 51; 52], real-scale applications of photo-paints show lower photocatalytic performance in terms of air pollutant elimination than in the lab-scale tests of photo-powder. The mixture of photo-powder in the paint matrix and the controlled environment of the laboratory versus the more complex and polyparametric environment of a real-scale application are the main reasons for the lower values observed for the photocatalytic parameters of the paints. However, they provide a more realistic approach of the photocatalytic efficiency of the photo-paints to improve IAQ through in situ air pollutant photocatalytic degradation.  
Based on the above-described methodology (2.5) and equations (3.2), it was calculated that by activating the photocatalytic building material (turn on the Vis light), the pollution level in the 'green room' was reduced up to 61.7% for the organic paint and 36.8% for the inorganic paint, while in the conventional one, it was up to 24.6%. The corresponding values for toluene were significantly lower (up to 5.8% for the organic paint while for the inorganic one, the elimination was negligible). All side effects (adsorption on walls, photolysis, photocatalysis, etc.) were also estimated. In the scenario that the windows were not covered and sun light (UV light) irradiated the room, the reductions in NO were even higher, up to 70.1% (Figure 12) for the organic paint.  
In order to normalize the initial concentrations of the experiments (as there were differences), the ln(Ct/Co), where Ct is the initial concentration and Co is the average concentration of the NO before the lamps were turned on for the experiments, was calculated. Figure 13 presents the ln(Ct/Co) vs. time during the conventional tests (IC, 2C, 3C) and the photocatalytic (4P, 5P, 6P) experiments. The significant reduction in NO during photocatalytic tests is clearly presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Real Scale—Organic Photo-Paint**} & \multirow{2}{*}{**Real Scale—Inorganic Photo-Paint**} \\  & & **under Vis Light** & & **under Vis Light** \\ \hline  & **NO** & **Toluene** & **NO** & **Toluene** \\ \hline \% \(\eta\) & 61.7 & 5.8 & 36.8 & 2.3 \\ rphoto (\(\upmu\)g/m\({}^{2}\)s) & 0.10 & 0.01 & 0.06 & 0.004 \\ Vd (m/s) & 0.03 & 0.001 & 0.01 & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Photocatalytic removal of NO in the Demo Houses due to VISIONS paints.
## 3 Methods
### Wind Field Simulation and Symmetry Index  
In reality, no storm's wind field is perfectly symmetric. Thus, the storm symmetry is actually the degree of approximation to a symmetric wind field. In this work, we specified the Pearson correlation coefficient between retrieved and simulated wind fields for the symmetry index (\(SI\)) using Equation (3).  
\[SI=\frac{\text{cov}\left(U_{10}^{R},U_{10}^{S}\right)}{\text{std}\left(U_{10} ^{R}\right)\text{std}\left(U_{10}^{S}\right)} \tag{3}\]  
where \(U_{10}^{R}\) is the SAR-retrieved wind field, and \(U_{10}^{S}\) is the simulated wind field. \(\text{cov}\left(U_{10}^{R},U_{10}^{S}\right)\) stands for the covariance of them. \(\text{std}\left(U_{10}^{R}\right)\) and \(\text{std}\left(U_{10}^{S}\right)\) are the standard deviations of \(U_{10}^{R}\) and \(U_{10}^{S}\), respectively. Based on Equation (3), a TC is more symmetric if the \(SI\) value is  
Figure 5: Radial wind distributions of (**a**) Typhoon Jebi; and (**b**) Hurricane Gaston. The black curve stands for the fitting function of the relationship between wind retrieval and the radius, ranging from \(r_{\text{m}}\) to 200 km.  
high. The symmetric wind fields, which have same point numbers with retrievals, were simulated according to the wind field model proposed in our previous study [18]:  
\[U_{10}(r)=\left\{\begin{array}{l}\frac{U_{\rm m}}{r_{\rm m}}r,\ r<r_{\rm m}\\ \par a^{b},\ r\geq r_{\rm m}\end{array}\right., \tag{4}\]  
\[a=\frac{U_{\rm m}}{r_{\rm m}}, \tag{5}\]  
\[b=\log_{1-TCF}\frac{U_{\rm m}}{17}, \tag{6}\]  
\[TCF=0.166U_{\rm m}\,^{0.403}, \tag{7}\]  
Based on the combination of wind-TCF relationship and the Rankine vortex model, this model can simulate symmetric wind field (\(r\leq 200\) km) with the inputs of maximum wind (\(U_{\rm m}\)) and \(r_{\rm m}\). The \(U_{\rm m}\) and \(r_{\rm m}\) of the simulated wind field are same as those in retrieved wind field. Completed evaluation suggested that this model has a better performance than pure or modified Rankine vortex models [18]. Figure 6 illustrates the simulations of the two storm examples at a 1 km spatial resolution. The straight borders of storms are the same as those in SAR images.
## 3 Results and Discussions
### Variation in pH with Crack Width  
The test results showed that pH was not sensitive to the maximum crack widths at different depths, as shown in Figure 9. Therefore, it is assumed that crack widths have little effect on the variations in pH.  
Figure 8: Variation in pH with concrete compressive strength: (**a**) uncracked samples with minor corrosion; (**b**) uncracked samples with moderate corrosion; (**c**) cracked samples with severe corrosion.  
Figure 9: Variation in pH with maximum crack width of cracked samples.  
The variation in carbonation depth with maximum crack widths is shown in Figure 10. It can be seen that the carbonation depth increases linearly with increases in maximum crack width. This is because, with the increase in crack widths, the diffusion rate of carbon dioxide in the concrete increases, which results in deeper and rapid penetration of carbonation. This is in agreement with previous test results [24; 42; 43].
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 2 Materials and Equipment
### 3D Printer  
A frame-type 3D printer was designed and built using an open-source 3D printing control motherboard with a mechanical accuracy of 0.1 mm in three alignments of X, Y, and Z axes. The printer was operated at a maximum movement speed of 100 mm/s in the XY plane, with a maximum printing size of 60 cm \(\times\) 60 cm \(\times\) 1500 cm. The resolution of the motion system was 0.1 mm, acceptable for the concrete printing operations. The control unit of the printer was based on the RepRap open-source project, ensuring precise motion positioning and stable execution for a smooth printing process. A round nozzle with a diameter of 10 mm was developed to achieve good geometry and relatively high printing accuracy. The extrusion bin was fed locally by hand instead of remote pump feeding. The developed extruder with a two-segment screw is shown in Figure 2. The large screw blade is for material feeding, and the small screw is for extrusion.
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
## 5 Conclusions  
In this work, we present a framework based on message stream simulation to evaluate solutions of classification. The use of the queueing theory in the modelling of the user profiles contributed a bigger formalism to the proposed solution. The incorporation of randomness in some of the events of the simulation allowed approximating the generation of the message stream to a real scenario. The evaluation module provides the ability to apply the evaluation measures to analyse the behaviour of the classifiers under the same input conditions. The case study demonstrates an evaluation design that allows identifying the factors present in real contexts that most influence the quality of classification solutions, and thus knowing how to adapt the solution. For the evaluated solution, the factors that most influenced the quality were message obfuscation and latency. Future work is planned to perform a robustness or sensitivity analysis to better understand how changes in simulation parameters affect the reliability of the proposed framework; to develop new test cases and/or scenarios in contexts such as news analysis, unanswered message identification, social media message classification, and others that demonstrate greater versatility and applicability of the proposed framework to a wider range of message classification challenges; to use the proposed framework to evaluate and compare solutions from the literature for semi-supervised text classification in intermediate and extreme latency scenarios; and to make social modelling of message delivery sources to identifysocial and proactive behaviours, which can then be implemented using an intelligent multi-agent approach.  
Conceptualization, W.H.-M. and M.M.-E.; methodology, F.M.-P. and J.V.B.M.; validation, M.M.-E., F.M.-P. and J.V.B.M.; formal analysis, W.H.-M., F.M.-P. and M.M.-E; writing--original draft preparation, W.H.-M.; writing--review and editing, IL.F. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in [[http://mlkd.csd.auth](http://mlkd.csd.auth)]([http://mlkd.csd.auth](http://mlkd.csd.auth)). gr/datasets.html.  
The authors declare no conflicts of interest.
## 2 Literature Review  
In WBAN, routing is a challenging task because of the limited available resources like limited memory, computational power, and energy source [18]. Energy consumption is greatly affected by the Radio Frequency (RF) at the physical layer of sensor nodes in WBAN. Besides, the residual energy of sensor nodes can be increased by the MAC protocols by controlling the duty cycle of the RF part. Regardless of this fact that physical and MAC layer protocols improve network communication, still, some other requirements, including end-to-end packet delivery, addressing techniques, and route determination methods, are not discussed. Here, the network layer is the optimal option to address these issues, and consideration of network layer routing protocols is very significant in terms of energy conservation [19]. WBAN is a subset of WSN; however, the characteristics and specifications of WBAN are different from WSN. Therefore, the designing of an efficient WBAN routing protocol needs to be considered carefully than WSN. Various important factors are required to be considered for the WBAN routing protocols, i.e., heterogeneous network nature (due to specific physiological data), energy consumption (due to higher transmission rate), temperature-rise (due to a high number of transmissions), coverage area (due to low transmission dimension), mobility (due to postural movements) and quality of service (due to priority-based data) [20]. Among all stated characteristics, the most crucial factor is related to the temperature-rise of implanted sensors that adversely affects the performance of WBAN. The temperature of a sensor node rises due to radiation absorbed and energy consumption by the antenna that causes the damage to the heat-sensitive areas of the body [21] and can affect body tissue surrounding the sensor nodes [22]. Therefore, careful research is needed to design a routing algorithm for WBAN to prevent damage of body tissue due to the temperature-rise of a node. The second important consideration for WBAN routing is to deal with energy consumption issues as the replacement of sensor nodes (particularly implanted sensor nodes) is not feasible. Energy consumed during sensing and monitoring of data is less than the energy used during communication between the devices [23]. Moreover, a limited communication range for data transmission prevents the energy consumption, and the proposed routing algorithm is required to be capable of choosing the optimal path among the multiple available paths. Hence, intelligent routing (considering temperature-rise and residual energy) can enhance the life-span of a network.