esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Geofencing in Indoor Navigation  
Geofencing uses positioning technology like GPS to create a virtual geographic boundary, enabling software to trigger a response when a mobile device enters or leaves a particular area [16]. Geofencing is a mature technology in LBS commonly used for location-based notification, wayfinding, and navigation. It is also utilized in research like behavioral studies [30]. In the indoor environment, as GPS is generally unavailable, Wi-Fi/Bluetooth beacons and RFID tags are often used for positioning. Common use cases include proximity marketing [31], behavior monitoring [32], and enhanced user experience. For example, ExhibitXplorer, developed by [PERSON] [33], uses a combination of geofencing, artificial intelligence (AI), and microservices to provide dynamic, personalized content to museum visitors. However, these implementations usually depend on infrastructures like Wi-Fi and/or beacons for positioning, and their requirement for location accuracy is much lower than an AR application. This is because the purpose of geofencing in our proposed framework differs from roughly detecting enter/leave behaviors. For instance, geofences triggering direction display updates at waypoints may require accuracy under one meter to avoid malfunction or misunderstanding. Furthermore, these geofences are triggered by the virtual location of the direction arrow displayed in the AR interface instead of the smartphone's actual location for a more appropriate user experience. Thus, the strategies of geofence placement, sizes, and error tolerance can be different from those using different positioning technologies.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 4 Experiments and Discussion  
In this section, simulated multi-channel BS-SAR data are used to assess the performance of the proposed method to estimate the DRR error, CDC errors, and the phase inconsistency errors. For convenience, the phase inconsistency errors are called PI errors for short in Tables 2 and 3.
## 2 Study Area and Input Data
### Water Volumes from WaterGAP  
In order to analyze the surface water volume that can be monitored by satellite altimetry in the MRB, the WaterGap Global Hydrology Model WGHM [2] is used. The model simulates water resources with a focus on anthropogenic inventions due to human water use and man-made reservoirs [22]. WGHM uses a spatial resolution of 0.5\({}^{\circ}\), and the temporal resolution of the model output used within this paper is monthly. A comprehensive description of the current model--version 2.2d--used within the context of this study is given by [PERSON] et al. [22].  
Water bodies are represented within the model as area fractions of the 0.5\({}^{\circ}\) grid cells. The GLWD (Section 2.4) and the GRanD database [19; 23] are used for the definition of water bodies within the model. Each water body is defined as lake, regulated lake, reservoir or wetland. Furthermore, the model distinguishes between local and global water bodies: lakes are implemented as global if their area is larger than 100 km\({}^{2}\), and for regulated lakes and reservoirs, a threshold of 0.5 km\({}^{3}\) storage capacity or 100 km\({}^{2}\) minimum area is applied [22]. In this study, global lakes, regulated lakes and reservoirs are used (no local water bodies). As bathymetry and the initial volume of the water bodies are unknown to the model, the WGHM water volumes for global water bodies are treated as anomalies. For reservoirs and regulated lakes, the maximum storage capacity is applied as an upper threshold [22]. The commissioning year (GranD database) is used to start filling reservoirs and, if applicable, to change the type of the water body from a lake to regulated lake [22].
## 3 Multi-Scale Coupled Vibration Analysis of the TTB System
### TTB System Analysis Model
With the knowledge of \(\mathbf{X}_{\mathrm{b}}\), dynamic stresses of both the primary members and structural details can be computed simultaneously using the following formula as:  
\[\mathbf{S}_{\mathrm{b}}=\mathbf{E}\mathbf{B}\mathbf{X}_{\mathrm{b}} \tag{2}\]  
where \(\mathbf{S}_{\mathrm{b}}\) is the vector of element stresses; \(\mathbf{E}\) is the elastic matrix determined by element material properties; and \(\mathbf{B}\) is the displacement-strain relationship matrix evaluated through derivative operations of the element shape function. In this study, the multi-scale vibration analysis of the TTB system (i.e., solving Equations (1) and (2)) is performed by combining the MATLAB [30] procedure and ANSYS procedure. Firstly, the equation of motion of the train subsystem is solved using the program developed in MATLAB. It is assumed that the initial motion of the bridge is null. Accordingly, the excitations to the train subsystem are track irregularities of which the numerical samples are generated. Secondly, the wheel-rail interaction forces obtained from the previous iterative loop (or the first step) are applied to the multi-scale bridge FE model to compute updated time-histories of the bridge movement with the transient analysis module in ANSYS. Thirdly, using the updated bridge movement data and track irregularity samples as the excitations, the vehicle equation is solved and updated wheel-rail interaction forces are yielded. Finally, time-histories of wheel-rail interaction forces obtained in the third step and the previous iterative loop (or the first step) are compared to calculate the differences. If the differences are small enough, the computation can be considered as completed. Otherwise, the solution process continues from the second step until a satisfactory convergence is achieved. Specifically, convergence is checked in the present analysis by using the following formula as:  
\[\left|F_{\mathrm{v}}^{\mathrm{I}}(t)-F_{\mathrm{v}}^{\mathrm{I-1}}(t)\right| \leq\mathrm{Tol} \tag{3}\]  
where \(F_{\mathrm{v}}^{\mathrm{I}}(t)\) and \(F_{\mathrm{v}}^{\mathrm{I-1}}(t)\) are the vertical, lateral, or torsional wheel-rail interactions of each wheelset associated with time \(t\) obtained by solving the vehicle equation of motion for the \(l\)th iterative loop and (\(l\)-1)th iterative loop, respectively; and Tol represents the allowable tolerance. In this study, the values of Tol are set as 100 N, 10 N, 10 N m for the vertical, lateral, and torsional wheel-rail interaction forces, respectively. With the abovementioned scheme, stress responses of the bridge can be obtained in the post-processing module of the ANSYS software.
## 2 Background
### Information Gain (IG)  
Information gain is an entropy-based feature evaluation method, widely used in the field of machine learning [56]. Information gain calculates the reduction in entropy from the transformation of a dataset. The equation for calculating information gain is as follows:  
\[IG(T,\ a)=H(T)-H(T|a) \tag{1}\]  
where \(T\) is a random variable, and \(H(T|a)\) is the entropy of \(T\) given the attribute \(a\)[57]. It can be used for feature selection by evaluating the information gain of each variable in the context of the target variable.
## 7 Conclusions
The available data transfer standard developed for IoT networks is not compatible. Therefore, lots of work must be conducted to enable intercommunication between the sensor nodes using different communication protocols while operating under low power. Another area the researchers or stakeholders must focus on is developing efficient storage techniques and low-power hardware design to reduce operational costs. Heterogenous networks for individual applications should be processed in one giant smart city network, and 5G can play a vital role in future smart city concepts. AI also has an enormous possibility for future work, including developing data fusion techniques for making heterogeneous data sources more accessible and intelligent data reduction for ensuring that surplus or 'uninteresting' data are not part of the AI development pipeline. In summary, the current communication technologies are inadequate in providing uninterrupted connectivity in smart cities as they were initially created to accommodate a restricted number of devices and possess limited communication capabilities. Thus, there is a pressing need to develop intelligent and standardized protocols, such as the Internet of Things enabled by 5G technology, to cater to the needs of future smart cities effectively. The current review paper has not thoroughly examined the ethical, social, and political ramifications of integrating AI with IoT in smart cities, including its potential impact on privacy, security, and social justice. Additionally, it lacks a comprehensive critical analysis of the advantages and drawbacks of this integration, leaving unanswered questions regarding the long-term sustainability, scalability, and efficacy of such approaches in smart cities. Conceptualization, M.E.E.A. and A.S.; methodology, M.E.E.A. and A.S.; software, M.E.E.A. and A.S.; validation, M.E.E.A. and A.S.; formal analysis, M.E.E.A. ; investigation, M.E.E.A., A.S., F.W.T., A.N. and S.C.M. ; resources, M.E.E.A., A.S., F.W.T. and A.N. ; data curation, M.E.E.A. and A.S.; writing--original draft preparation, M.E.E.A. and A.S.; writing--review and editing, M.E.E.A., A.S., F.W.T., A.N., W.K., K.S. and S.C.M. ; visualization, M.E.E.A. ; supervision, M.E.E.A., W.K., K.S. and S.C.M. ; project administration, M.E.E.A., F.W.T. and A.N. ; funding acquisition, M.E.E.A. All authors have read and agreed to the published version of the manuscript. This research was funded by Personal Research Funding, Walailak University (grant no. WU66260 and WDU66249). Not applicable.
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Minimizing the Effects of Frequent Link Disconnections to Improve Routing  
Frequent link disconnections and network partitions can increase route discovery and maintenance to reestablish routes as well as increase packet retransmission [133]. Consequently, network performance degrades, which can be unacceptable in critical circumstances, such as disaster relief and rescue operations [104, 134]. There are two main challenges in improving network connectivity: a) high dynamicity (X.1) of network topology and b) high cost (X.2) due to increased routing overhead, energy consumption, and computational cost. Addressing this open issue helps to achieve higher QoS performance (P.1), lower energy consumption (P.2), and higher route setup success rate (P.4). Further investigation could be pursued to reduce link disconnections and network partitions; for instance, the next geographical location of a UAV can be predicted and used to maintain a route or to select intermediate nodes of a route.
## 1 Introduction
These plants were selected as the phytoremediation agents in this study because of their rapid growth rate and tolerance to various environmental conditions. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Pollutant** & **Type of Plant** & **Removal Rate (\%)** & **Duration** \\ \hline [PERSON] et al. [4] & COD, TSS & _Eichhorria cassipes_ & 83, 92 & 28 days \\ \hline [PERSON] et al. [17] & NH\({}_{3}\)-N, TSS & _Salvinia molesta_ & 96, 88 & 7 days \\  & & _Pistia stratites_ & 83, 95 & \\ \hline [PERSON] and [PERSON] [18] & COD, NH\({}_{3}\)-N & _Lemma minor_ & 93.7, 66.4 & 6 days \\  & & _Azolla filiculoides_ & 94.2, 52.7 & \\ \hline [PERSON] et al. [19] & NH\({}_{3}\)-N, TSS & _Centella asatica_ & 98, 90 & 14 days \\  & & _Ipomea aquatic_ & 73, 73 & \\  & & _Salvinia molesta_ & 63.9, 89.3 & \\  & & _Eichhorria cassipes_ & 74, 96 & \\  & & _Pistia stratites_ & 78, 98 & \\ \hline [PERSON] et al. [20] & NH\({}_{3}\)-N & _Eichhorria cassipes_ & 86 & 49 days \\ \hline \end{tabular}
\end{table} 
Table 1: The removal of total suspended solids (TSS), ammoniacal nitrogen (NH\({}_{3}\)-N) and chemical oxygen demand (COD) by phytoremediation treatment based on previous studies in Malaysia.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results  
### Evaluation of the Experimental Set Up  
#### 3.1.1 Sensor Noise and Signal-to-Noise Ratio  
The estimated type B uncertainty was found to be between 0.722 and 1.776 kPa. Trial measurements for both devices showed that the recorded airflow pressure had maximal magnitudes of 1.600 V (15.6 kPa). Thus, the range of uncertainty for the airflow pressure was between 0.725 and 0.889 kPa (5.433 to 6.669 mmHg).  
The average noise level of the sensor was detected to be 0.166 kPa with a range from 0.139 to 0.168 kPa (1.25 mmHg, from 1.041 to 1.258 mmHg); airflow pressure less than the maximum noise level could be distinguished from the sensor noise.  
The signal to noise ratio during the maximum airflow pressure was 17.6 dB (V = 1.445 kPa, SD = 191.20 Pa). To minimise the noise of the sensor readings, the raw data were smoothed by eliminating the Fourier components, with higher frequencies than the calculated cut-off frequency using five consecutive data points as the analysis interval. This improved the signal to noise ratio by 57.0 dB (SD = 2.020 Pa).
## 1 Introduction
However, with an additional independent component analysis (ICA), the diagnosis system became unintelligent and complicated. [PERSON] et al. [38] used DWT to select features as well as denoise the signals in the first step, and put the features into artificial neural network (ANN) to do classification. Wavelet Daubechies-6 was selected for processing 49 signal samples per condition in their work. It is important to select a proper wavelet mother function which should be highly similar to impulses generated by a bearing or gear defect, and to determine a wavelet decomposition level/depth for retaining the resonant frequency band excited by the defect. In general, the performance of DWT processing relies on the selection of wavelet mother function and the depth of wavelet decomposition to obtain the representative features. On the other hand, a proper classifier construction is also needed for the extracted DWT features. The current study proposes a deep-learning-based fault diagnosis method. Accordingly, a deep fault recognizer based on SDAE is used to deal with the random noises of the original signals and extract features to simultaneously perform fault pattern recognition. The SDAE is an integral model, in which the weights of the feature selection part are updated based on the final output every iteration. The proposed method has been empirically shown to avoid obtaining poor convergence, which is typically reached with random initialization [39]. In addition, the experiment manifests that the SDAE method obtains superior diagnosis performance compared to the DBN methods, particularly in the existing situation of noises. The remainder of this paper is organized as follows. Section 2 details the methodology of the traditional SDAE. Section 3 provides details of the structure and the algorithm of the proposed SDAE based diagnosis system. Section 4 validates the effectiveness of the SDAE method on rolling bearing datasets and gearbox dataset. Moreover, this section further tested the advantage of the proposed method through a comparative study between DBN and SDAE on both original datasets, as well as the dataset mixed with artificially white noises. Finally, Section 5 presents the conclusion.
# Biomechanical Analysis of Different Pacifiers and Their Effects on the Upper Jaw and Tongue
###### Abstract  
Aim: Pacifiers play a critical role in the early stages of craniofacial and palate development during infancy. While they provide comfort and aid in soothing, their use can also have significant impacts on the growth and function of the oral cavity. This study aimed to simulate and predict the behavior of six different types of pacifiers and their functional interaction with the tongue and palate, with the goal of understanding their potential effects on orofacial growth and development. Materials and Methods: Biomechanical analysis using Finite Element Analysis (FEA) mathematical models was employed to evaluate the behavior of six different commercial pacifiers in contact with the palate and tongue. Three-dimensional solid models of the palate and tongue were based on the mathematical framework from a 2007 publication. This allowed for a detailed investigation into how various pacifier designs interact with soft and hard oral tissues, particularly the implications on dental and skeletal development. Results: The findings of this study demonstrate that pacifiers exhibit different interactions with the oral cavity depending on their geometry. Anatomical-functional pacifiers, for instance, tend to exert lateral compressions near the palatine vault, which can influence the hard palate and contribute to changes in craniofacial growth. In contrast, other pacifiers apply compressive forces primarily in the anterior region of the palate, particularly in the premaximilla area. Furthermore, the deformation of the tongue varied significantly across different pacifier types: while some pacifiers caused the tongue to flatten, others allowed it to adapt more favorably by assuming a concave shape. These variations highlight the importance of selecting a pacifier that aligns with the natural development of both soft and hard oral tissues. Conclusions: The results of this study underscore the crucial role of pacifier geometry in shaping both the palate and the tongue. These findings suggest that pacifiers have a significant influence not only on facial bone growth but also on the stimulation of oral functions such as suction and feeding. The geometry of the pacifier affects the soft tissues (tongue and muscles) and hard tissues (palate and jaw) differently, which emphasizes the need for careful selection of pacifiers during infancy. Choosing the right pacifier is essential to avoid potential negative effects on craniofacial development and to ensure that the benefits of proper oral function are maintained. Therefore, healthcare professionals and parents should consider these biomechanical factors when introducing pacifiers to newborns.  
finite element analysis; malocclusion; non-nutritive sucking; pacifier; palate; suction; cupping 2020
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 6 Conclusions  
In this paper, we proposed a traffic prediction method, called FPK-Net, which utilizes fused prior knowledge; namely, the self-similarity properties of network traffic. To increase the interpretability of the deep learning model, the temporal features of traffic sequences are extracted by FPK-Net through the combination of a one-dimensional convolutional neural network and an attention mechanism. In addition, the traffic self-similarity and attention  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Step** & **MAE** & **MSE** & **RMSE** & \(\mathbf{R}_{\textit{adapted}}^{2}\) \\ \hline
**50** & 0.390 & 0.284 & 0.533 & 0.753 \\
**60** & 0.388 & 0.282 & 0.531 & 0.758 \\
**70** & 0.384 & 0.275 & 0.525 & 0.763 \\
**80** & 0.382 & 0.274 & 0.524 & 0.765 \\
**90** & 0.382 & 0.274 & 0.524 & 0.765 \\
**100** & 0.372 & 0.269 & 0.519 & 0.768 \\
**130** & 0.369 & 0.259 & 0.509 & 0.769 \\
**150** & 0.373 & 0.261 & 0.510 & 0.764 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FPK-Net model prediction measures under different sliding window lengths.  
Figure 9: Schematic diagram of interpretability analysis.  
based long-short memory neural network are adapted to guide the prediction under various traffic sizes in future time periods. The accumulation of prior knowledge during training meaningfully guides the network traffic prediction, thus significantly improving the performance of the training model. In terms of the absolute coefficient of correction, the proposed model achieved values at least 10% higher than those of traditional statistical models. Thus, the reliability and superiority of the proposed model were illustrated in the article while the results demonstrated that the model is also interpretable.  
Conceptualization, Y.W.; methodology, Y.W.; software, Y.W.; resources, Y.W.; Writing--Original draft preparation, Y.W.; Writing--Review and editing, J.S., H.S.; supervision, R.C.; project administration, C.P.; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grants No. 61931004).  
Not applicable.  
In this paper, the transit links of the WIDE network (AS2500) in Japan since February 2013 are selected as the data set for the experiment. The IP addresses shown in the dataset are anonymized using the prefix preservation method. In this paper, a total of 52,493 data were sampled from this dataset between 1 January 2020 and 31 December 2020, with a sampling interval of 10 min. The data download link is as follows: [[https://mawi.wide.ad.jp/~agurim/](https://mawi.wide.ad.jp/~agurim/)]([https://mawi.wide.ad.jp/~agurim/](https://mawi.wide.ad.jp/~agurim/)) (accessed on 1 February 2013).  
The authors wish to thank the editor and the anonymous reviewers for their valuable suggestions on improving this paper. This work is supported in part by the National Natural Science Foundation of China (No. 61931004).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Optimal Surfactant Solution Composition  
Figure 3 provides detailed information on the variation of contact angle at the oil/water interface with different surfactant mixture concentrations.  
As can be seen from the data in Figure 3, the contact angle at the oil/water interface reaches its lowest at about \(55^{\circ}\) when the mixing ratio of cationic and nonionic surfactants reaches 2.5:1. This result indicates that at this particular ratio, the surfactant mixture was able to significantly reduce the wettability of the oil/water interface, transforming it from a slightly oil-wet state to a slightly water-wet state. Further increase in concentration beyond 2.8 mg/L did not result in a significant change in contact angle, indicating that the wetting properties of the system were stabilized above this concentration.  
Figure 4 demonstrates the variation of interfacial tension with different surfactant mixture concentrations. At a cationic/nonionic surfactant mixture concentration ratio of 2.5:1, a concentration of 2.8 mg/L was able to minimize the interfacial tension to approximately 0.3 mN/m. This low interfacial tension contributes to the formation of a stable microemulsion system, i.e., the Winsor type III, which contains a mixture of the liquid phase of oil, water, and surfactant. The formation of this microemulsion demonstrated the efficiency of the surfactant mixture in reducing the interfacial tension between oil and water.  
Figure 3: Measurement of contact angle of different surfactant solutions.  
Based on the results of contact angle measurements and interfacial tension experiments, it can be concluded that the optimal surfactant solution composition is a cationic/nonionic mixture of 2.8 mg/L with a mixing ratio of 2.5:1. This specific composition can effectively regulate the wettability of the oil/water interface and reduce the interfacial tension, which provides a theoretical basis and experimental support for enhancing the oil-water separation efficiency and improving the recovery of crude oil during the oil extraction process.