esa-sceva/satcom-chunk-collection huggingface dataset:
# Black Loaded Columns--State of the Art Review
## 2 Experimental Testing  
There are no experiments on real scale specimens of bridge columns due to the experimental setup complexity and high costs, while the building columns are mainly conducted in full scale due to their maximum height of 2.5 to 3 m. Moreover, these types of experiments require special testing ground (usually military field ranges) and trained personnel for handling explosives. Even these special testing grounds have limitations regarding the maximum amount of explosives that can be used in one detonation. This also limits the scale of specimens. Research conducted in recent decades has shown that scale tests provide reliable results and the necessary knowledge to analyze the effects of blast load on full-scale structures ([PERSON] et al., 2018).  
If considering building and bridge columns, except specimen dimensions, there is a difference in their behaviors due to different levels of axial load capacity. Therefore, it is recommended to analyze bridge and building columns separately ([PERSON] et al., 2018). A list of conducted experimental research is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Author** & **Year** & **Structural Element** & **Experiment Type** & **Material** & **Scale** \\ \hline [PERSON] et al. (2018) & 2006 & Multicolumnents & Field & CFCSC & 1:4 \\ [PERSON] et al. (2018) & 2008 & Multicolumnents & Field & CFST & 1:4 \\ [PERSON] et al. (2018) & 2009 & Bridge column & Field & RC & S. s. + 1:2 \\ [PERSON] and [PERSON] (2018) & 2010 & Multicolumnents & Field & RC and RC SJ & 1:4 \\ [PERSON] et al. (2018) & 2011 & Bridge column & Field & RC & 1:2 \\ [PERSON] (2018) & 2013 & Building column & Field & RC + FRP + SJ & 1:1 \\ [PERSON] et al. (2018) & 2015 & Column & Shock tube & SFRC & 1:2 \\ [PERSON] et al. (2018) & 2015 & Building column & Field & CFST & 1:1 \\ [PERSON] et al. (2018) & 2015 & Building column & Shock tube & UHPFRC & 1:1 \\ [PERSON] et al. (2018) & 2016 & Building column & Field & RC & 1:1 \\ [PERSON] et al. (2018); [PERSON] et al. (2018) & 2016 & Building column & Field & RC, RC SJ, RC + & 1:1 \\ [PERSON] et al. (2018) & 2016 & Column & Field & UHPFRC + HRSC & 1:1 \\ [PERSON] et al. (2018) & 2016 & Bridge column & Field & CFFT + RC & 1:5 \\ [PERSON] et al. (2018) & 2016 & Multicolumnents & Field & RC MSJ & 1:4 \\ [PERSON] et al. (2018) & 2016 & Column & Filed & RPC-FST & 1:1 \\ [PERSON] et al. (2018) & 2016 & Column & Field & CFDST infilled with & 1:1 \\ [PERSON] et al. (2018) & 2017 & Column & Field & UHPC & 1:1 \\ [PERSON] et al. (2018) & 2017 & Column & Field & CFDST & 1:1 \\ [PERSON] et al. (2018) & 2017 & Building column & Field & RC + reinforced resin & 1:1 \\ [PERSON] et al. (2018) & 2017 & Bridge column & Field & RC & 1:3 \\ [PERSON] et al. (2018) & 2017 & Building column & Field & CFST & 1:1 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of experimental research.  
Figure 1: Percentages of terrorist attack types, based on data from ([PERSON] et al., 2018).
## 5 Discussion  
This study has conducted in-depth theoretical analysis and empirical research on the identification of bridge structural degradation and the updating of finite element models. Initially, a preprocessing method for bridge point cloud data was proposed, along with the determination of the key parameter value ranges. Subsequently, based on extensive point cloud data, methods for identifying bridge cracks and spatial morphological changes were explored and optimized, enhancing accuracy and efficiency compared to traditional manual crack inspection and two-dimensional image-based crack recognition. Furthermore, particle swarm optimization algorithms were selected to optimize two key parameters in crack detection, verifying the reliability and precision of the algorithms. Additionally, the study summarized deformation detection methods based on point cloud data, selected appropriate algorithms for engineering practice, and established a framework for bridge model updating. Finally, by integrating bridge crack and deformation detection results, and incorporating Bayesian model correction and adaptive nested sampling methods, the parameter setting process for bridge finite element models was further completed and applied to the analysis of point cloud data from the Hedong Bridge in 2017 and 2018.  
It is worth noting that the proposed approach offers significant advantages in terms of computational cost and efficiency compared to traditional methods. The use of AI algorithms for crack detection and deformation analysis eliminates the need for extensive manual inspections, saving both time and labor costs. In the case of Hedong Bridge, for instance, the crack and deformation identification program based on bridge point cloud data proposed in this study can complete the identification of deterioration within ten minutes for this six-span bridge, which is over 500 m long. Additionally, following the bridge point cloud data preprocessing process proposed in this study can reduce the time required to identify potential deterioration areas in the bridge by a factor of five. Additionally, the integration of point cloud data and advanced computing methods reduces the reliance on long-term sensor installations, further reducing maintenance and operational costs. The streamlined process of generating bridge diagnosis reports based on point cloud data significantly improves the efficiency and timeliness of decision making for bridge maintenance and rehabilitation.  
In conclusion, this study has made a significant contribution to the field of bridge engineering by proposing a comprehensive framework for the identification of bridge structural degradation and the updating of finite element models. The research results indicate that the use of large-scale point cloud data and advanced computing methods can improve the accuracy and reliability of bridge structure detection, and combining bridge diagnosis reports based on point cloud data for updating the finite element model of bridge structures can obtain more accurate, complete, and macroscopic update results.
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Collection and IoT
#### 3.1.1 Satellite Communications  
The use of receivers mounted on field equipment to acquire real-time geospatial information plays a catalytic role in PA. [PERSON] underscores the importance of global navigation satellite systems (GNSS) in field sampling and vehicle navigation as well as in optimizing the use of fertilizers and pesticides. He also highlights the advantages in accuracy and complexity of GNSS systems over older positioning technologies used in agriculture [18]. [PERSON] et al. refer to several artificial satellites (e.g., American Landsat satellites, European Sentinel 2 satellite system, GeoEye-1-system, WorldView) which provide multispectral imaging information. These artificial satellites can be very beneficial for PA (e.g., by providing information about soil and water cover, using the normalized difference vegetation index (NDVI)) [19]. NDVI is a vegetation index which utilizes visible and near-infrared spectrums to detect live vegetation. It can be used for various reasons (e.g., for assessing the impacts of forest/land fires and floods, predicting land deterioration, detecting occurrences of draught) [20].
## 2 Materials and Methods
### Machine Learning Procedure
#### 2.7.1 Feature Selection  
Once the data was prepared, we applied feature selection algorithms to remove both irrelevant and redundant attributes, and to identify the relevant variables for the classification task. In machine learning terms, a feature or variable is considered relevant or important if it is useful for the classification task. We also analyzed the importance of the variables using two different experiments: Recursive feature elimination (RFE) and the Boruta model.  
In the first experiment, we applied the RFE algorithm for the identification of relevant features using RF as the evaluator [19]. RFE is a recursive process that ranks features measuring their predictive power. Recursion is necessary because the relative importance of each feature can change substantially when they are evaluated on a different subset of features. Using the random forest classification method, uncorrelated and different predictions were made. Each tree uses a reduced sample of the training set. To improve the diversity among the trees, the algorithm works with randomness in the search of the attribute divisions. We assess the importance of each of the variables by means of the MDGI metric. The MDGI metric measures the proportion of incorrectly classified samples when the evaluated feature used to build the model is removed from the dataset [20].  
In the second experiment, we used the Boruta model [21], in order to analyze in more depth the subset of fifteen best variables according to RFE. Using a small number of features decreases the training time, reduces the complexity of the model, makes it easier to interpret, and improves the accuracy.  
Boruta is a heuristic procedure to find the characteristics with the greatest predictive power [21]. Boruta creates randomly some variables and compares its performance with the real variables using the Z-Score metric. The Z-Score measures the relationship of an element with the class (the group for this dataset). For example, when the Z-Score is zero it means that the element score is the same that the rest of the group.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Congestion Control Algorithms
#### 2.2.4 Weighted Vegas  
[PERSON] et al. [38] proposed weighted Vegas (wVegas) with the aim of fulfilling their proposed \"congestion equality principle\" rather than IETF's proposed MPTCP design goals [32]. They proposed a mechanism to monitor packet queueing delay and regarded it as a congestion signal. Then, based on their \"congestion equality principle,\" they obtained a fine-grained load balancing. However, as they solely focus on fulfilling their proposed \"congestion equality principle\", wVegas fails to comply with the MPTCP design goals. Moreover, wVegas shows tremendously lower throughput than SPTCP because of its highly aggressive fairer nature.
## 1 Introduction
[20] proposed an automated calibration approach for bonding parameters in discrete metamodels, particularly for adaptive matrix estimation in contexts where gradient noise is present. [PERSON] et al. [21] applied DEM to investigate the mechanical properties of ice and developed a bonded model for ice. [PERSON] et al. [22] combined discrete element modeling and mechanical experiments to establish a bonded model for CS, calibrating the adhesion parameters. In order to gain a deeper understanding of the crushing mechanism of ore particles in the crushing process, which is a challenging area of study due to the complex characteristics of the ore structure, it is essential to develop precise models. The accuracy of these models is contingent upon the accurate characterization of the model parameters. Aiming at the problem that it is difficult to characterize the parameters of the crushing process in the ore model, this study researched the characterization method of mineral particle bonding parameters. Based on the Discrete Element Method (DEM), simulation using EDEM [23] software (2018), BBD (Box-Behnken) design was combined with the response surface method (RSM) to characterize the bonding parameters such as unit area normal stiffness, unit area shear stiffness, critical normal stress, and critical shear stress, and the influence of each bonding parameter on compressive resistance was studied. Through simulation experiments, the validity of bonding parameters is verified. By accurately establishing a bonding model for ore particles, we provide a more specific methodology for studying bonding parameters that characterize the bonding properties of the ore. This approach offers more effective support for the subsequent analysis of the ore's behavior during roller compaction and crushing. The structure of this paper is organized as follows: Section 2 primarily addresses the development of the ore fragmentation particle bonding model. Section 3 elucidates the principles underlying the determination of the ore bonding parameters. Section 4 details the simulation experiments conducted and their subsequent validation. Lastly, Section 5 provides a comprehensive summary of the results and formulates a regression model to effectively characterize the bonding parameters.
## 2 Equivalent Range Model for GEO-LEO BiSAR
### Equivalent Range Model for GEO-LEO BiSAR
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{System Parameters} \\ \hline Carrier frequency & 5.4 GHz & Data take duration & 18 s \\ \hline Signal bandwidth & 240 MHz & PRF & 5000 Hz \\ \hline Pulse width & 45 \(\upmu\)s & Rotation distance & 1146 km \\ \hline Azimuth beam rotation rate & 0.37\({}^{\circ}\)/s & Receiver slant range & 990.4 km \\ \hline Receiver azimuth beamwidth & 0.58\({}^{\circ}\) & Bistatic angle & 32.4\({}^{\circ}\) \\ \hline \multicolumn{4}{c}{Orbit Parameters} \\ \hline Platform & GEO & LEO & \\ \hline Orbit altitude & 35,786 km & 754 km \\ \hline Eccentricity & 0.003 & 0.0009 \\ \hline Inclination & 20\({}^{\circ}\) & 98\({}^{\circ}\) \\ \hline Incidence angle & 43.9\({}^{\circ}\) & 41.2\({}^{\circ}\) \\ \hline Look angle & 6\({}^{\circ}\) & 36\({}^{\circ}\) \\ \hline Location & (8874, \(-\)24, 113, 26,908) km & (609, 235, 745) km \\ \hline Velocity & (\(-\)2661, \(-\)979, \(-\)0) km & (0, 7400, 1071) m \\ \hline Scene center & (0, 0, 0) m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters of sliding-spotlight GEO-LEO BiSAR. Figure 4: Bistatic range and phase errors of BiCoT-ESRM. Figure 3: Bistatic range errors of the competing range models. time, while RMOSE is based on the straight-line motion assumption of the receiving platform. It can be seen from Figure 3 that the range error of 'non-stop-and-go' RMOSE has an offset of approximately 0.17 m even at the center of the azimuth aperture. This range error offset is caused by the ESRM used for LEO receiver without considering the circular orbit trajectory, which will introduce considerable error during the derivation of 'non-stop-and-go' delay for RMOSE. Finally, the influence of second-order terms of \(\tau_{d}\) is considered during the derivation of'stop-and-go' assumption error, which further enhances the precision of BiCoT-ESRM compared with NSGRM.
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 2 Related Works
### Analysis of Methodological Constraints in Related Works
#### 2.3.4. Tire Model-Based Estimation Approaches  
In [14], the vertical force acting on the tire is estimated based on the tire contact angle (\(\theta_{r}\)) and internal tire pressure. The vertical load is expressed as a function of the contact angle through the following equation:  
\[F_{z}=2 Rp_{0}b\sin\theta_{r}+2R^{2}(k_{w}+k_{v})(\sin\theta_{r}-\theta_{r}\cos \theta_{r}) \tag{18}\]  
where \(R\) is the tire radius, \(p_{0}\) is the internal tire pressure, \(b\) is the tire width, and \(k_{w}\) and \(k_{v}\) represent the tire stiffness coefficients. The contact angle (\(\theta_{r}\)) is determined using intelligent tire sensors, which utilize dynamic tire data to calculate the force acting on the tire-road interface. This approach aims to model the dynamic load transfer effects by accurately capturing variations in the contact angle and tire pressure.  
However, the method is subject to certain limitations. The tire stiffness coefficients are assumed to be constant, whereas, in practice, these values can vary under different operating conditions. This simplification can lead to inaccuracies in force estimation, particularly during highly dynamic scenarios. Additionally, minor sensor errors in measuring the contact angle can result in significant deviations in the estimated vertical force. The model further neglects suspension dynamics and unsprung mass effects, which limits its accuracy on rough or inclined road surfaces where additional dynamic interactions occur.
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 1 Introduction
Researchers have used Kinect technology to develop different applications in different sectors such as physical rehabilitation, medical operation, healthcare monitoring, 3D reconstruction, augmented reality, video games, robotics, and object recognition [23]. Older people are in favor of Kinect technology. They find that using this technology can give them a greater sense of security by respecting their private life and guaranteeing the continuation of their lifestyles. Furthermore, Kinect-based fall detection systems effectively detect human falls from other life activities. In this paper, we are primarily interested in healthcare monitoring for the elderly. For several reasons, we focus mainly on fall detection applications using Kinect:  
* The technical advantages of Kinect for the multi-sensor type include depth, RGB, infrared, and voice, producing a precise system for detecting falls. * Kinect's Time Of Flight (TOF) principle makes it insensitive to lighting issues that reduce system reliability [24]. * Kinect generates 3D data with a single camera, whereas a stereoscopic system is needed to attain the same objective in the RGB domain. * The acceptable price of Kinect compared to other motion-tracking cameras encourages researchers to invest in the field of computer vision for health applications. * Kinect fully respects the privacy of the person, and provides skeleton and depth data independently of RGB data. * The release of the new Kinect Azure motivated us to write a survey on the different fall detection approaches that use Kinect V1 or V2. This survey provides guidelines to researchers interested in developing a healthcare monitoring system for fall detection for the elderly using Kinect technology. The remaining part of this document is organized as follows. Section 2 is devoted to the presentation of related work and the highlighting of our contributions. In Section 3, we describe the principal features of Microsoft Kinect. In Section 4, we discuss the different modules in the life cycle of the Kinect-based fall detection approach. Section 5 presents the various metrics used to measure performance. Section 6 describes public Kinect datasets for fall detection. Finally, our remarks are discussed in Section 7 before concluding and advising on future works in Section 8.
## 3 Preliminaries  
This article utilizes two frames: an inertial reference frame \(\{I\}\) and a body-fixed frame \(\{B\}\)[39]. We address several definitions in rigid-body dynamics [39].  
The origin of \(\{I\}\) is a point with three axes pointing North, East, and Down, respectively. We use the _virtual agent_ for drone controls. The virtual agent is a virtual drone located at the center of the grid formation. \(\{B\}\) is fixed to the virtual agent, such that the origin of \(\{B\}\) is at the virtual agent's center.  
The virtual agent changes its yaw and pitch while not rotating its body. In rigid-body dynamics [39], \(\theta\) and \(\psi\) define _pitch_ and _yaw_, respectively. For convenience, let \(c(\eta)\) define \(\cos(\eta)\). In addition, let \(s(\eta)\) define \(\sin(\eta)\). Let \(t(\eta)\) define \(\tan(\eta)\).  
The rotation matrix indicating the counterclockwise (CC) rotation of an angle \(\psi\) centered at the \(z\)-axis in \(\{B\}\) is  
\[\mathbf{M}_{R}(\psi)=\left(\begin{array}{ccc}c(\psi)&-s(\psi)&0\\ s(\psi)&c(\psi)&0\\ 0&0&1\end{array}\right). \tag{1}\]  
The rotation matrix representing the CC rotation of an angle \(\theta\) centered at the \(y\)-axis in \(\{B\}\) is  
\[\mathbf{M}_{R}(\theta)=\left(\begin{array}{ccc}c(\theta)&0&s(\theta)\\ 0&1&0\\ -s(\theta)&0&c(\theta)\end{array}\right). \tag{2}\]  
The combined rotation matrix is built by multiplying Equations (1) and (2) to obtain  
\[\mathbf{M}_{R}(\psi,\theta)=\mathbf{M}_{R}(\psi)\mathbf{M}_{R}(\theta). \tag{3}\]
## 3 Results
### TDC Measurement Accuracy  
The time measured by TDC are shown in Figure 6. TDC channel 1 specifically measured the optical trigger time \(t_{optical\_trigger}\), resulting in an average value of 861.1 ns, with a standard deviation of 0.70 ns. This indicates optical trigger signal has a time jitter of \(\sigma_{1}=0.70\) ns; TDC channel 2 measured the streak camera trigger time \(t_{streak\_camera\_trigger}\), yielding an average value of 39,600.4 ns with a measurement accuracy of \(\sigma_{2}=48\) ps. This high precision at the picosecond level demonstrates that the TDC is capable of achieving high-precision time measurements for pulse signals. Additionally, the measurement accuracy of 48 ps further underscores the control unit's capability to generate accurate trigger pulses, which is essential for the STIL system. This precision and stability are particularly crucial in future dynamic scanning measurement applications, ensuring more reliable detection performance.  
By combining the two TDC channels, the \(\Delta t_{trigger}\) was calculated as 38,739.3 ns for the 6 km target, reflecting the 5810 m distance information of the target. Above all, the standard deviation of the measured pulse time was 0.70 ns, with the time measurement accuracy primarily affected by the time jitter of the optical trigger signal. This jitter introduces errors in the final TDC measurement results. The ranging delay \(\tau\) was approximately 2 us. But the precise value of this delay requires further calibration and verification. The absolute measurement value requires further calibration and verification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Extreme** & **Threshold** & **Gaussian** & **Centeroid** \\  & **Value** & **Averaging** & **Fitting** & **Centeroid** \\ \hline Time Complexity & O(n) & O(n) & O(n\(\log n\))–O(n\({}^{2}\)) & O(n) \\ Standard deviation (pixels) & 2.92 & 1.64 & 1.42 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Waveform time dimension feature point extraction results.  
Figure 5: Streak images captured by the STIL system: (**a**) raw streak image (512 \(\times\) 1024 pixels) of the target in 6 km; (**b**) ROI (100 \(\times\) 100 pixels) of the laser spot: the dashed line indicates the row of the ROI used for range accuracy verification, with red dots marking the centroid feature point extracted.
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 5 Imaging Algorithm  
The key aspect of the imaging process of high-resolution spaceborne SAR is an accurate description of the range history. It has been noted that the range deviation between the actual range history and the equivalent squint range model (ESRM) becomes significant with increasing integration time. To acquire images with an azimuth resolution of 0.25 m, the modified equivalent squint range model (MESRM), which takes the equivalent radar acceleration into consideration, is used in this paper [19]. Since the sequence of pulse intervals is inconsistent, the raw signal recorded by a CVPRF system is inherently nonuniformly sampled. In principle, by focusing each pixel independently in the time domain (e.g., using the BP algorithm), uniform sampling would not be a strict requirement for SAR imaging. However, to reduce the computational cost and apply the conventional SAR processing in the frequency domain, the raw data of the CVPRF system should be resampled to a uniform grid.  
Figure 14: Performance prediction of the RASR for the design example. Ten strong point-like targets are distributed in the ambiguous area.The RASR of uniform PRF system is represented by the red line, and the RASR of CVPRF system is represented by the blue line.  
Figure 13: Geometry of the RASR for the uniform PRF (**a**) and the CVPRF (**b**). The red point \(A^{\prime}\) is a strong point-like target, and both the blue and yellow points are normal targets.