esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The rock-embedded piles and enlarged base piles of the pre-drilled piles primarily introduce an additional load-bearing system at the pile base or increase the cross-sectional area of the pile base to enhance the load-bearing capacity and stability of the pile foundation. [PERSON] et al. [25] conducted experimental studies on the uplift behavior of enlarged base piles in layered strata using a semi-infinite small-scale 1 g model and Particle Image Velocimetry (PIV) technology. Anchoring the pile base into deeper and stronger strata is beneficial for enhancing the pile's uplift capacity. [PERSON] et al. [26] utilized the Material Point Method (MPM) to simulate centrifuge uplift tests of enlarged base piles in sand soil. The results indicate that piles with expanded bases exhibit good compressive and uplift load-bearing capacities. The aforementioned methods effectively enhance the pile'sload-bearing capacity; however, the randomness of boulder distribution in the construction site was not considered during pile installation. To further mitigate the impact of isolated stones on construction, targeted pre-treatment based on preliminary detection results is employed in conjunction with pile planting technology by first creating a hole and then inserting high-strength concrete pre-fabricated piles; additionally, drilling is performed in the core area of the isolated stone, integrating it into the bearing system so that the isolated stone and the planted pile function collectively as a unified support, thus forming a novel pre-drilled composite planted pile suited for isolated stone strata. In summary, this research addresses the gap in the study of load-bearing capacity and load transfer mechanisms of a new type of pre-drilled composite pile, which combines the advantages of expanded base piles and composite piles suitable for isolated rock formations. This study conducts static pullout tests based on transparent soil technology, comparing the pullout resistance characteristics of planted piles and planted piles with isolated stone at the pile tip. Furthermore, the load transfer mechanism, deformation displacements, side friction resistance of the planted piles, and interaction between the piles and surrounding soil are analyzed. Additionally, a discrete-continuous coupled three-dimensional numerical model is also established, allowing for comparative analysis with the uplift static load tests of the planted piles to clarify the detailed load characteristics of the strata and structures. This study could provide valuable insights for managing large isolated stones in fill stone strata.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Localization  
RF-based positioning technology has become increasingly mature, leading to numerous studies on RF-based UAV positioning. RF sensors are the only technology that can locate both the UAV and the pilot. On the other hand, acoustic-based UAV positioning technology is feasible for short-distance detection [22]. Although acoustic-based UAV localization technology started later than other methods, it has achieved remarkable results in recent years. In contrast, vision-based UAV tracking technology has its advantages, but due to the rapid movement of UAVs, the effect of visual positioning technology is not ideal. This section focuses on reviewing the RF and acoustic-based UAV localization technologies.
## 2 Related Research
### Metaverse Network for Virtual Concert  
To design the network structure constituting the virtual concert, an analysis of requirements specialized for various content and services must first be conducted. In particular, for the communication of interaction data between performers and the audience, network designers need to perform tasks to derive requirements for interactions and network functions. We assume an environment capable of constituting a virtual concert world where thousands can participate simultaneously, and ensuring free interaction between performers and users within the concert hall. As shown in Table 1, network functions for interactions can be derived.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Requirements** & **Network Function** \\ \hline Pre-stored user motion information & \\ (joint variations) & Selection of interactions at each step and definition of data structures are necessary. \\ Emotional expressions through emoticons & \\ Text message (chatting) & Joint variation information in 3D vector form is delivered through data streaming. \\ Voice & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network function for interaction data based on requirements [16].  
Figure 5: (**a**) Sequence diagram of MCF and MIF in the XDN system based on WebXR (WebRTC) and (**b**) sequence diagram of MCF and MDF in the XDN system based on WebXR (WebRTC) [17].  
Before designing a network for the communication of interaction data, the network for the overall virtual concert must first be designed. The metaverse virtual concert platform allows subscribers to access the platform and participate in their desired virtual concert service channel according to role distinction, managed through subscriber management and concert information management. To provide scalable server operations and variable load balancing features, the virtual concert platform is loaded onto a cloud-based server. This allows for an increase in the number of physical subscriber servers as the number of simultaneous users increases. As shown in Figure 6, pre-defined fixed stage-related assets in the planning process for the virtual concert hall and stage equipment are downloaded before the real-time concert, and during the real-time concert, only real-time data provided from the performers are streamed.  
A virtual concert takes the form of users connecting to a virtual channel, and this virtual channel can vary dynamically. The session manager manages and controls the physical channels within the virtual channel, and the producer takes on various roles according to events that occur asynchronously during the concert. The interaction data discussed in this paper are created and delivered bidirectionally between the performer and the audience. The virtual concert network can be configured to selectively provide only real-time media streaming for user devices that have difficulty generating motion data.
## 3 Data and Methods
### Correlation Analysis of Spatiotemporal Dynamics  
For the analysis of the spatiotemporal dynamics, we first performed a segmentation of the dataset using the Orfeo Toolbox (OTB, Version 8) to reduce variabilities inherent to the pixel level at such a high resolution. The following steps were carried out for the segmentation: (i) Stacking of the following bands: yearly E-W DInSAR displacement, yearly U-D DInSAR displacement as well as absolute seasonality of all available orbits; (ii) conversion of stacked bands to a common dynamic range cutting of the 0.5% extreme values; and (iii) execution of the segmentation (spatial radius: 5, range radius: 10, convergence threshold: 0.1, max iterations 100, min region size: 50). For each segment, median yearly (i) E-W displacement (\(d_{E\_W}\)), (ii) elevation change (\(d_{z}\)), (iii) direction of movement (\(\tau_{dir}\)), (iv) absolute seasonality (\(seasonality_{abs}\)) and (v) relative seasonality (\(seasonality_{rel}\)) are calculated. All five parameters were tested against the local elevation and slope of the respective segment to identify potential drivers for differences. To avoid local furrows and ridges affecting the slope analysis and to ensure that the slope of the bedrock was captured, we first ran a mean window on the local elevation of 50 by 50 m before calculating the slope parameter. We further assessed possible correlations of the movement parameters and compared absolute and relative seasonality of movement against horizontal movement, elevation change and direction of movement.  
Moreover, the start of seasonal increase was identified for each year separately using Equation (8):  
\[season\_start(seg)=\begin{cases}\text{first }DOY&,\text{where }(disp(seg,DOY)-disp( stable,DOY))>(med_{\text{\it jam\_jul}}(seg)+1)\text{ (two times)}\\ na&,\text{otherwise}\end{cases}, \tag{8}\]  
In this Equation, the 6d-displacement of each segment is corrected by the mean of a chosen stable area to ensure that the reference is not affected by errors. Second, for each acquisition starting from 1 st July onwards, we test whether the corrected 6d displacement of the segment exceeds its preseason median by more than one millimeter. If this is the case for two consecutive acquisitions, the first of these two dates is defined as the start of the seasonal increase for the respective segment. As the snow cover and availability of water from snowmelt is said to be a crucial factor for rock glacier dynamics, we additionally calculated the date when snow cover ended (end of snow cover--EOS) for all years using the SAR intensity time series of ascending orbit 15. This methodology is based on the approach of [PERSON] et al., 2022 [85].  
The assessment of the seasonal movement was limited to segments, which are located within a buffer zone of 50 m around the investigated landforms and show at least 5 cm annual movement in either horizontal or vertical direction. Further, areas affected by phase unwrapping errors were also excluded. The areas used in the correlation analysis are shown in Appendix A Figure 11.
# Patent Analysis Using Bayesian Data Analysis and Network Modeling
###### Abstract  
Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology.  
Bayesian additive regression trees; keyword visualization; management of technology; multiple linear regression; technology analysis Article
## 2 Testing of Dynamic and Static Elastic Parameters  
The mechanical parameters of rock (Poisson's ratio and elastic modulus, etc.) are the basis of petroleum engineering exploration and development design. At present, there are two approaches to determining the mechanical parameters. One is the dynamic rock mechanical parameter (i.e., dynamic elastic modulus), which can be acquired by downhole tests, such as wave logging and seismic data. The other is the static rock mechanical parameter measured by the uniaxial and triaxial experiments in the laboratory (i.e., static elastic modulus). Static elastic modulus is an important parameter in drilling, completion, and fracturing. According to the characteristics of underground engineering, the static elastic parameters of rock should be used in practical engineering. It is necessary to convert the dynamic elastic parameters of log calculation into static elastic parameters for the convenience of the application. However, the static elastic parameters of the rock can only be obtained by extracting the core from the ground and testing it in the laboratory, which is time consuming and expensive. To obtain the real static elastic parameters under the reservoir conditions, it is necessary to simulate the temperature and pressure conditions under the reservoir, which is more expensive and often requires a lot of core experimental data to accurately describe the mechanical characteristics of the reservoir. Therefore, the best method is to establish the transformation relationship between static and dynamic parameters. We can use acoustic tools such as acoustic logging, seismic data, and indoor ultrasonic testing to predict the static parameters of rock mechanics. It provides a way to obtain the mechanical property parameters of rock.
# Effect of Using Plastic Waste Bottles on Soil Response above Buried Pipes under Static Loads
## 3 Results and Discussion
### Bottles-Reinforcement Width (b/B) Effect  
Figure 10a-c presents the vertical applied pressure-surface settlement of footing graphs with different relative densities for the unreinforced and plastic bottle-reinforced beds at various plastic bottle reinforcement widths (b/B). The bed-reinforced width (b/B) of PET bottles was tested at a single u/B (at the optimum u/B derived from earlier test series) for three ratios. The height ratios (H/B) for all tests are 1.4. The unreinforced case was also plotted for comparison. The ultimate bearing capacity of the soil increases with increasing reinforcement width. In contrast to reinforcement placement depth, ultimate bearing capacity slightly increases with reinforcement width, probably because all widths of bottle reinforcement mattresses prohibit the shear stress from being transmitted on the underneath soil. So, the bearing capacity is not enhanced by increasing the bottle reinforcement width. However, when b/B increases, the rate of BCR vales increases, leading to a value of 1.71 (71% increase) at u/B = 0.5 due to the increased soil surface settlement at later stages of loading. The footing surface settlement decreases with increasing reinforcement width. Figure 11a-c illustrates the variation in BCR and PRS with a change in reinforcement width at various relative densities. The BCR values increase as the reinforcement width ratio increases. However, the behavior is the same for PRS.  
The present results are compatible with the findings of [20,22,34]. [PERSON] and [PERSON] [20] concluded that bearing capacity increases with the increase in the width of plastic water bottle reinforcement. In addition, [PERSON] et al. [22] mentions the improvement in the ultimate bearing capacity of soil due to the presence of plastic bottles, and that the reinforcement zone decreases as the depth of placement increases and width decreases. [PERSON] et al. [34] found that increasing soil shear strength reduces pipe  
Figure 9: Variation of BCR and PRS with different placement depths reported at (**a**) DR = 30%, (**b**) DR = 60%, and (**c**) DR = 80%.  
deflection under pressure due to an increase in soil strength and the relative stiffness of the pipe-soil system.
# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 4 Experiment and Results  
### Experimental Setup and Dataset  
Our method was implemented using Microsoft Visual Studio 2013 Ultimate 2013 ([PERSON], WA, USA) + emgu.cv3.1 (SourceForge, San Francisco, CA, USA)+ Microsoft Kinect sensor v1.0 2013 (Redmond, WA, USA)on a PC using an Intel Core i7-4790 3.60-GHz processor, 8 GB RAM clocked at 1333 MHz. Since Kinect is a newly emerging sensor and the feature, the torso angle, is first raised and imported for fall detection, the existing depth action datasets cannot provide the necessary information we need. For example, Cornell Activity Datasets CAD-60/120 [36], the most commonly-used RGB-D  
Figure 7: The data changing curve of the torso angle and centroid height. (**a**) The torso angle data changing curve; (**b**) the centroid height data changing curve.  
dataset for action recognition, contains 12 activities, such as rinsing mouth, brushing teeth, wearing contact lens, etc., which do not provide falling action samples. Therefore, we built a dataset by ourselves for experiments.  
This dataset was collected using Microsoft Kinect sensor v1.0, which was installed 1.4 m high from the ground. The people involved in the self-collected dataset are aged between 20 and 36, with different heights (1.70-1.81 m) and genders (four male and one female volunteers). The distance from the monitored person to the Kinect sensor is between 3 and 4.5 m. The actions performed by the single volunteer were separated into two categories: ADL (activity of daily living, including walking, controlled lying down, bending and crouching) and fall (four directions of falls, including forward, backward, left and right). For safety and realistic performance considerations, subjects performed the fall actions on a 15 cm-thick cushion. Each activity is repeated five times by each subject involved. There are in total 100 fall videos (each fall direction contains 25 videos) and 100 ADL videos (each ADL contains 25 videos). In these experiments, the five volunteers were asked to perform in slow motion to imitate the behavior of an elderly person at least one time in different kinds of activities. The joints positions in the 3D coordinates and joint heights were recorded frame by frame in an XML file. The fall samples contain 55-290 frames, and the ADL samples contain 65-317 frames in each video. All of the samples start by standing postures, which last 1-8 s, containing 30-240 frames.
## 3 Modelling, Parameter Adjustment, and Results
### Boundary Conditions and Initial Conditions  
The initial and boundary conditions are described below, some being implicitly set by the FDS software, others being set by the user.  
The following initial conditions, applied for CALIB1 case:  
* the initial temperature at all points in the range was considered to be equal to +20 \({}^{\circ}\)C;
* the initial air speed at all points in the range was equal to 0 m/s;
* the initial oxygen concentration was equal to 0.208 mol\({}_{\text{Q2}}\)/mol\({}_{\text{air}}\);
* the initial carbon dioxide concentration was equal to 0.000387 mol\({}_{\text{CO2}}\)/mol\({}_{\text{air}}\);
* the initial visibility, in all points, was equal to 30 m.  
The following boundary conditions, applied during the CALIB1 case:  
* the air temperature at the burner surface was imposed, depending on the amount of heat with a linear evolution of the HRPPUA;
* air speed in contact with the inner face of the booth walls is equal to 0 m/s;
* air speed at the air outlet interior surface was equal to 0 m/s;
* material characteristics of the envelope do not change during the 760 s period.  
The definition of the numerical model reunites the following characteristics: the geometrical shape of the analyzed air and walls domains, burner modelling (by means of its main characteristics (dimensions, location, the SQ, and HRRPUA variation)), walls thermal conductivity, fan operation, mesh dimension, boundary conditions, and initial conditions.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 1 Introduction
### Organization and Notations  
The remaining sections in this paper are structured as follows: a system model and specific signal representation for multi-user sequencing are presented in Section 2; Section 3 analyzes the OP and throughput of the AmBC-NOMA systems; the EE performance of AmBC-NOMA systems is studied in Section 4; the analysis and verification of the simulation results are in Section 5; Section 6 provides conclusions. The specific derivations of the formulae are included in the Appendixes A and B.  
The main notations used in this paper are shown as follows: \(\mathbb{E}\{\cdot\}\) denotes expectation operation; \(f_{\text{X}}(\cdot)\) and \(F_{\text{X}}(\cdot)\) denote the probability density function (PDF) and the cumulative distribution function (CDF) of a random variable \(X\), respectively.
## 3 Field Trialed System
### Remote Controlled Tractor  
In the field test, a tractor was remotely controlled over mobile networks. The objective was to drive the tractor remotely to the field using a fertilizer work machine. After that, the work machine was able to automatically fertilize the field together with the tractor. Remote control was then used to monitor and guide the tractor. To do this, the remote control cabin had a steering wheel, pedals, and other controls needed for operating the tractor (Figure 3). Three screens were attached to display video feeds from the tractor.  
Figure 3: Elements of the remote-controlled tractor: the remote control cabin on the left, the remote-controlled tractor on the middle, and the computer for CAN tunneling over mobile networks on the right.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**Technology Capabilities in Trialed Private Networks**} \\ \cline{2-4}
**Characteristics** & **StandAlone LTE/Cow Shed Bubble** & **StandAlone LTE van + Field Bubbles** & **Satellite \& Wi-Fi** \\ \hline Coverage avg & 1000 m & 500–1500 m & Global \\ Capacity uplink avg/max & 2 Mbps/8 Mbps & N/A/10 & 9.8 Mbps \\ Capacity downlink avg/max & 7 Mbps/108 Mbps & N/A/50 & 34.0 Mbps \\ Latency (RTT median) & 50–70 ms & 30-85 ms & 700 ms (geostationary) \\ Security & 3 GPP [46] & 3 GPP [46] & WPA [47] \\ Frequency & 2350 MHz & 2310 MHz & 26.5–40 GHz \\ Band & 40 & 40 & K\({}_{a}\) \\ Bandwidth & 20 MHz & 20 MHz & N/A \\ Transmit power & 5 W & 1 W & N/A \\ Ant. height & 5 m & 10–21 m & 0.75 m \\ Rx antenna gain & Omni 2 dBi & Omni 2 dBi & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing technologies for tactical bubbles.  
Figure 4 shows an overview of the network and devices for remotely controlling the tractor. All network traffic went over a virtual private network (VPN) connection, and from the device's perspective, they were all in the same local area network. A router [48] from Goodmill Systems connected the tractor to the tactical network using various mobile networks. By default, the router uses all these connections simultaneously and switches between them to provide the best possible service. For the trial, the router was configured to use the networks one at a time for testing purposes.