esa-sceva/satcom-chunk-collection huggingface dataset:
### Review on Groundwater Level Prediction Using Ensemble Learning
The objectives of the paper are as follows:  
* To obtain attributes using multi-collinearity by applying the variance inflation factor (VIF) value;
* To obtain the data approximation using the randomized low-rank approximation (RLRA) technique over the attribute selection;
* To combine random forest as a base learner using a stacking mechanism and double-edge bi-directional LSTM as a meta-classifier to obtain the deep ensemble Model (DEM);
* To find better classification accuracy, the DEM was applied to both approximated and non-approximated reduced datasets;
* To reduce the overfitting using the proposed DEM mechanism;
* To check for minimal or no information loss during data approximation to maintain data authenticity;
* To calculate the time taken to train and to test the data iteratively to assess the computation cost. The article is articulated as follows: the introduction discusses the need for groundwater and the existing groundwater level in the states of India, followed by the related research activities carried out in Section 1. Background fundamentals are discussed in Section 2. The proposed research methodology on groundwater level prediction is emphasized in Section 3 with feature selection and approximation processes, whereas Section 4 explains the experimental analysis according to the deep ensemble model. Section 5 explains the results and discussion. Section 6 describes the comparative analysis with existing research methods. Section 7 illustrates the managerial implication of the proposed groundwater prediction process. Section 8 follows with conclusions and future work.
## 4 Result and Discussion  
### Data Processing  
This section shows how to process the data at each stationary cross-section. The same as in the experiment, one analytical B-scope was obtained from the results of one cross-section. Figure 5 shows the RMS distribution at each generation point. It can be seen that even when there were no defects, there was signal attenuation due to diffusion as the propagation distance increased. In the following, a normalized RMS distribution was used to emphasize attenuation due to defects by dividing the calculated RMS distribution by the defect-free RMS distribution. Figure 11 shows the normalized RMS distributions for the B-scope and the extraction region for the \(y=0\) mm and \(-0.48\) mm cross-section. Although the results differed depending on the \(y\) position, the attenuation of the ultrasonic signal was observed similarly as in the experiment. Repeating the same operation for each y position provided a two-dimensional (\(x,y\)) normalized RMS value map, as shown in Figure 12. A three-color scale (green-yellow-red) was applied to the map according to the degree of relative RMS value. Points that decreased from the RMS value of the no-defect part were less than 1 (green), and those that increased were higher than 1 (red). This map shows the degree of attenuation of ultrasonic waves generated at each (\(x,y\)) point until they reached the detecting point (\(-8,y\)). The red boxes indicate the defect locations, and three \(xz\) cross-sections through the defects are also shown. Ultrasonic waves generated from the top or right side of the defect were expected to attenuate by the time they reached the detecting position on the left side, and the results vaguely showed this tendency.  
Figure 10: Correspondence between the traveling speed and measurement position.  
However, as shown by the rectangular black dotted line, there was a region of low attenuation, even though the cross-section passed almost through the center of the defect. This is because the ultrasonic waves reached the defect, diffracted on the \(+x\) side from the defect, and then re-met on the \(-x\) side to strengthen and reduce attenuation.  
Furthermore, in areas where defects existed near the surface, the normalized RMS value increased due to reflections from the defects, as shown by the black dotted circular line. The increasing area was on the negative \(x\)-axis, meaning on the left side of the figure, which caused the slope of the RMS distribution to be large and negative, as shown in the schematic diagram in Figure 13. This makes determining the defect easier. It was found that this method, which aimed to capture the attenuation, not only captured the attenuation but also a signal increase close to the detecting point when a defective reflected wave was observed.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 3 Methodology
### Data  
ArcGIS [27] as GIS software was used in this study to create spatial data, connect (join) the spatial and attribute data, identify the nearest social infrastructure facility from a residential block (RB), and for spatial query. First, the census shapes were processed to create RBs. Second, the population data were combined with the associated RB spatial feature. Third, the spatial data, including the coordinates of social infrastructure facilities, were created. Fourth, we obtained the travel time between the RBs and infrastructure facilities using the navigation API.
# Tracking and Registration Technology Based on Panoramic Cameras
## 5 Conclusions  
In this paper, we propose an unsupervised deep learning-based model for panoramic camera pose estimation. To address the issues of inefficiency, label scarcity, and complex relative motion in panoramic camera pose estimation, a lightweight architecture is designed, comprising a depth estimation sub-network and a camera pose estimation sub-network. The depth estimation sub-network incorporates several lightweight modules and opera  
Figure 6: The impact of image resolution on model efficiency. (**a**) Relationship between resolution and frame rate. (**b**) Relationship between resolution and the resolution-to-frame rate ratio.  
tions. Specifically, a multi-scale rectangular convolution module is employed to address distortion issues commonly found in panoramic images. A dilated convolution module is used to enlarge the receptive field of the network, while skip connections are introduced to fuse shallow detail features with deep semantic features. Additionally, multi-scale prediction is utilized to improve the accuracy of depth estimation. The camera pose estimation sub-network leverages a pose estimation module and an occlusion mask module to infer the relative camera motion and occlusion masks between adjacent image pairs from a shared encoder, without using a decoder, thereby further reducing the computational load and making the network more lightweight. To enable joint training of the two sub-networks, a spherical photometric consistency loss is designed based on panoramic view reconstruction. Experimental results demonstrate that the proposed method achieves a good balance between accuracy and efficiency on the synthetic panoramic sequence dataset.  
Conceptualization, C.X.; methodology, G.L.; software, G.L.; validation, Y.B. ([PERSON]); formal analysis, Y.B. ([PERSON]); investigation, C.H.; resources, Y.B. ([PERSON]); data curation, Z.C.; writing--original draft preparation, G.L.; writing--review and editing, Y.B. ([PERSON]) and C.H.; visualization, Z.C.; supervision, Y.B. ([PERSON]) and C.H.; project administration, C.X.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jilin of China, grant number 20250102241 JC.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from the authors of the paper \"Self-Supervised Learning of Depth and Camera Motion from 360 Videos\" ([PERSON] et al., ACCV 2018 [18]), and are available from the authors with the permission of the original authors.  
The authors declare no conflicts of interest.
## 4 5G and V2X Deployment Projects
### Vital-5g  
The VITAL-5G project aims to establish an open, virtualized, and flexible experimentation facility for the Transport and Logistics (T and L) sector, utilizing 5G connectivity. Its main focus is on developing, managing, and sharing innovative network applications specific to the T and L industry. The consortium recognizes several key challenges in the adoption of 5G for production services in the T and L sector, including the network-centric vision, the mismatched backgrounds between telecommunication companies and verticals, and the limited availability of vertical-specific network applications. To achieve its objectives, the project has set strategic goals. Firstly, it aims to develop and share novel vertical-specific and vertical-agnostic network applications tailored to the T and L sector. This involves supporting open-source tools and an open repository to facilitate the development and sharing of these applications. Secondly, the project aims to deliver an open, virtualized 5G-enabled testing and validation experimentation facility. This facility will provide a platform for relevant T and L stakeholders to deploy and benchmark their innovative network applications on a 5G network.  
Additionally, VITAL-5G aims to demonstrate the benefits of 5G connectivity for advanced multi-modal logistics services. By showcasing the added value of 5G in creating a functional value chain for highly automated freight transportation across European roads, seas, and rivers, the project aims to highlight the advantages of 5G in the T and L sector. Furthermore, the project aims to provide customized and virtualized access to network and T and L infrastructure. This will enable dynamic and tailor-made service provisioning to third parties, such as SMEs, allowing them to validate their applications over otherwise unavailable resources and boosting their confidence prior to actual deployment.  
VITAL-5G also focuses on the development of novel business models for integrated and cooperative services. By facilitating the development of open and cooperative services across multiple domains, the project aims to address specific T and L use cases and justify investments from key stakeholders. Moreover, the project aims to advance a T and L-centered ecosystem by driving the integration of 5G services into the T and L vertical. This involves fostering collaboration between key stakeholders, such as port authorities, road operators, mobile network operators, and SMEs developing cutting-edge technology and applications. To achieve these goals, VITAL-5G follows a methodology that includes activities such as developing a functional architecture, onboarding and upgrading existing 5G testbeds, designing T&L-related network applications, and conducting extensive technical and business validations through real-life trials. The project emphasizes collaboration with ongoing 5G-PPP projects and seeks to contribute to standardization organizations while disseminating its results widely [95].
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
###### Abstract  
This paper addresses the issue of onboard inference for AI-based routing algorithms in dynamic LEO (Low Earth Orbit) satellite networks. In dynamic LEO networks, it is essential to maintain communication performance across varying topologies while considering link disconnections and overcoming computational constraints for real-time inference on embedded boards. This paper proposes a GPU-based inference acceleration method to reduce the computation time required for real-time onboard inference of a Dueling DQN (Deep Q-Network)-based routing algorithm in dynamic LEO satellite networks. The approach is composed of memory management, low-level operations, and efficient indexing methods, which collectively enhance computational efficiency. As a result, the proposed method achieves approximately 2.4 times faster inference compared to conventional CPU-based approaches. Additionally, the kernel performance analysis reveals that the proposed method reaches 10% of the peak computational performance and 20% of the peak memory performance. This demonstrates the compatibility of the proposed method for integration with additional applications in the multitasking systems of LEO satellites.  
LEO satellite networks; routing; DQN; GPU; onboard computer; real-time inference +
Footnote †: journal:  
0000-0002-4011-11028
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 3 Methodology
### Empirical Method for Tunnel-Induced Vertical Displacements
Conversely, when \(\Delta\)x \(>\) 2:i\({}_{x}\), the total settlement profile exhibits two peaks corresponding to the tunnel axes, as shown in Figure 5c. To obtain the total settlement profile, a single value for both the volume loss V\({}_{\rm L}\) and the inflection point position i\({}_{x}\) can be used, provided that the excavation process is identical and the ground conditions are the same for both tunnels:  
\[\mathrm{w_{tot}(x)=0.31\cdot V_{L}\cdot D^{2}/i_{x}\cdot[exp[-x^{2}/(2\cdot i_{ x}^{2})]+exp[-(x+\Delta x)^{2}/(2\cdot i_{x}^{2})]]. \tag{5}\]  
Equation (5) gives a reliable estimate for the total settlement profile when tunnels are aligned side by side and their axes distance is greater than 2:D, such that the excavation process of the second tunnel does not influence the first.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the Electromagnetic Interference of Electric Grid with the Human Body  
This sub-circuit is specifically engineered to replicate the voltage that an individual could encounter due to nearby electrical circuits linked to the power grid. In this model, resistive components symbolize the electrical loads in close proximity to the person, while capacitive components represent the medium of air through which electromagnetic waves propagate towards the human body. This design essentially depicts the network as a source of electromagnetic waves, with the human subject serving as the recipient. The primary purpose of this sub-circuit is to simulate, rather than reduce, electromagnetic interference, hence improving the accuracy of EEG signals by defining patterns of interference. Figure 6 depicts the EEG sub-circuit that models the EMI in a 50 Hz electric grid.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Common Mode** & & & & \\
**Rejection Ratio** & **Circuit Bandwidth** & **Signal Quality** & **Common Mode Rejection Ratio (CMRR)** \\ (CMRR) & & & & \\ \hline \multirow{6}{*}{Greater than 90 dB} & \multirow{6}{*}{0.5 Hz to 20 kHz} & \multirow{6}{*}{Noise within 0.1 to 10 Hz: Below 500 nVpp root mean square noise} & \multirow{6}{*}{Cost-effective.} \\  & & & Specialized Focus: 4 Hz to 30 Hz & & Maintains high-quality \\ \cline{1-1}  & & & High Input Resistance: Max & & signal integrity. \\ \cline{1-1}  & & & current \textless{} 10 \(\mu\)V & & Simplifies system without \\ \cline{1-1}  & & & & complicated techniques such as \\ \cline{1-1}  & & & chopping. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirements and specifications in the novel proposed EEG.  
Figure 6: EEG sub-circuit modeling the EMI in 50 Hz electric grid.
# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## 3 Statistical Analysis of the Consumption Dynamics

Table 1: _Cont._width \(a\) and the bar heights are proportional to the relative frequency of the data represented in that interval or bin [52]. In our application, this simple technique offers a panoramic and ordered view of the estimated _pdf_ of the consumption in each node of the three networks to easily identify the trends, variability, and shape of the energy consumption distributions. In addition to the estimated averaged _pdf_, it is informative to define a CI for it. However, this calculation is not simple, because the underlying distributions are not known in practice. For this reason, a non-parametric method is required to estimate the density of the estimated statistics, which is required to be robust with respect to the underlying distribution shape. Bootstrap resampling is a statistical estimation method which has been widely applied in Probability Theory and in Statistical Inference for the construction and estimation of CI of a wide variety of statistical processes. This technique uses the data collected from a given process and the plug-in principle to randomly generate new simulated samples (resamples) of the same size as the original sample [49]. Bootstrap resampling techniques are widely used in practical problems; they are versatile and robust with respect to the underlying statistical distribution; and they can be applied to virtually any estimator that can be obtained computationally [53]. The simple autocorrelation function (SAF) is a statistical tool that determines the correlation or similarity between a signal and its out-phase replica as a function of delay time (commonly denoted as \(\tau\)) [54]. In the field of signal processing, the autocorrelation facilitates the analysis of seasonality of functions or time series in the time domain, especially when focused on applications related with recognition of repetitive statistical patterns, on detection of (near-)periodic signals masked by noise, or on the study of autoregressive moving average processes, among many others [55].
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 5 Multi-Operator Game for SF Assignment
### Game Formulation  
As such, we define a multi-player game \(\mathcal{G}_{SF}\) between the different present operators. The formulation of this game \(\mathcal{G}_{SF}=\langle\mathcal{N},\prod_{i}S_{i},U_{i}\rangle\) can be described, as follows:  
* A finite set of players \(i\in\mathcal{N}\), the set of operators.
* The action of a given player is the percentage of devices allocated any spreading factor \(s\), the strategy chosen by an operator \(i\) is then \(\mathbf{p}^{i}=(p_{1}^{i},...,p_{6}^{i})\), the percentage of its devices on each spreading factor, where, for example, \(p_{1}^{i}\) is the percentage of devices using SF7.
* For each player \(i\), the space of pure strategies is \(S_{i}\), such that \(S_{i}=\{\mathbf{p}^{i}\in[0,1]^{S}|\) (4b) and (4c), \(\forall s=1,...,S\}\).
* A set of utility functions (\(U_{i\in\mathcal{N}}\)) that quantify players' profit for a given strategy profile.
## 3 Sensitivity Analysis  
### Mesh Convergence and Mass Scaling Effect  
The mesh in the region close to the spudcan is refined to guarantee accuracy. Providing that the Coupled Eulerian-Lagrangian approach is also mesh-dependent, three cases of spudcan penetration in uniform sand are conducted with different mesh sizes close to the spudcan (0.05\(D\), 0.02\(D\), 0.005_D_), as shown in Figure 4. The red color in the figure represents zero filling of the element, while the blue color represents the full filling of the element with soil material.  
As shown in Figure 5, the estimated resistance decreases with the mesh size. In the case of \(0.02D\), the resistance diverges less than \(2\%\) to \(005D\). It is implied that the mesh size of \(0.02D\) is small enough to guarantee the prediction accuracy.  
It is known that the computation cost reduces with an increased penetration rate. However, the increased penetration rate may have a significant influence on the result if the material is rate dependent. Alternatively, scaling the mass has the same effect as increasing penetration velocity. In Figure 6, 3 cases of different scaling factors, 1000, 10,000, and 100,000, are compared to each other. A scaling factor of 1000 and 10,000 show good resemblance to each other, while a scaling factor of 100,000 shows a significant inertia effect.  
Figure 4: Mesh close to the spudcan with different mesh sizes: (**a**) \(0.05D\); (**b**) \(0.02D\); (**c**) \(0.005D\).  
Figure 5: Penetration resistance profiles for different mesh sizes.  
Figure 6: Penetration resistance profiles for different mass scaling factors.  
Based on the above analysis, a mesh size close to the spudcan of \(0.02D\) and a mass scaling factor of 10,000 are selected for the analysis.
## 3 Results and Discussion
### Ethylene Polymerization of Supported Metallocene Catalysts  
The metallocene-catalyst systems ((n-BuCp)\({}_{2}\)ZrCl\({}_{2}\)/MAO) were immobilized on the prepared PDVB-based POPs, then the supported (n-BuCp)\({}_{2}\)ZrCl\({}_{2}\)/MAO@POPs catalysts were evaluated for ethylene polymerization in a slurry process reactor. The supported catalysts were characterized for zirconium- and aluminium-loading content, and the results of Zr, Al/Zr (molar ratio) and ethylene polymerization were shown in Table 2. From Table 2, we can see that quite different Zr  
Figure 8: Scanning electron microscope images of functionalized POP particles. (**a**) The P(DVB) particles of Sample 2, (**b**) the P(st-co-DVB) particles of Sample 1, (**c,d**) the P(HEMA-co-DVB) particles of Sample 3, (**e,f**) the P(HEMA-co-VBC-co-DVB) particles of Sample 11, (**g,h**) the P(HEMA-co-VBC-co-DVB) particles of Sample 14.  
and Al/Zr values were obtained, and the Zr- and Al-loading content were highly dependent on the pore structure of the prepared POPs, when MAO can be effectively immobilized on the surface of the prepared functionalized POPs. High loading content of Zr and Al were obtained on highly porous POPs--Samples 4 and 14 with SSA of 430 and 380 m\({}^{2}\)/g, respectively, while low loading content Zr and Al were obtained on badly porous POPs--Samples 5 and 12 with SSA of 17.1 and 4.88 m\({}^{2}\)/g, respectively. The ethylene-polymerization results show that high polymerization activity and productivity were obtained on the immobilized catalysts with a high Zr loading content and Al/Zr molar ratio. Samples 4 and 14-supported catalysts obtained high polymerization of 8033 and 7152 kg PE/mol Zr h bar, respectively. Compared with Sample 4, Sample 5-supported catalysts obtained low polymerization activity and productivity due to low Al/Zr- and Zr-loading content, and the Sample 12-supported catalyst exhibited no polymerization activity due to near lack of Zr loading on its badly porous surface.  
The bulk density of the obtained PE could be explained by the replicating mechanism of the polyethylene on the support in the polymerization process proposed by [PERSON] and coworkers [PERSON] and coworkers [PERSON] and others (2014); [PERSON] and others (2015). The higher the bulk density of the prepared POPs is, the higher the bulk density of the obtained PE from them can be achieved. The PE from Sample 4 (0.24 g/cm\({}^{3}\)) obtained the highest bulk density of 0.30 g/cm\({}^{3}\), while the PE from Sample 5 (0.08 g/cm\({}^{3}\)) obtained the lowest bulk density of 0.11 g/cm\({}^{3}\).  
From the polymerization results, we can see that the support plays a significant role on the supported metallocene catalyst and the polyolefin product. Therefore, the design and synthesis also is critical to the supported catalysts and the polyolefin product, especially on how to control the pore structure and the bulk density by choosing suitable functional comonomer(s) on the PDVB backbone.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 4 Performance Analysis  
In this section, we provide the numerical analysis to evaluate the performance of the considered CSS signal in this paper. Recall that the performance evaluation of the pilot component was already studied in [11]. In this section, we focus on the performance of the data component and its relationship with the pilot component. To do so, we first provide in Section 4.1 the analysis of the achievable data rate for the two considered modulations (i.e., BOK- and FSK-CSS). Second, Section 4.2 studies the BER for the two considered modulations. For these two analyses, we consider different bandwidth and CN0 values. Furthermore, to link the analysis of the data component with the pilot component, we consider the sensitivity analysis given in the left plot of Figure 2 as a reference for the design of the chirp period and the corresponding MSI as computed in [11].