esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis Results and Discussion
The different deflection patterns between the experiments and simulations shown in Figure 12a,b,d are attributable to the different initial imperfection shapes; however, a comparison of the maximum deflection ranges, i.e., the difference between the maximum and minimum deformation for each case, indicates consistency between the simulation and experimental results, as shown in Figure 13a. Figure 13b shows the distribution of error rates for each case using a Box-Whisker plot. The mean and median of the error rates are approximately 22%, and the 75 th percentile is 32%. However, all cases with a large error rate are the cases where the absolute value of deflection range is small, i.e., the error is less than 4 mm. It can be seen that the error rate is very low for relatively large deflection range. The effect of the initial deformation is considered to be larger when the amount of deformation is small due to the low drop height. Figure 12: Comparison of relative plastic deflections between experiment and simulation. Better predictions via simulations for slamming impact events are expected when the precise initial imperfection measurement and air pocket are considered.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 3 Forward Algorithm  
### FD Representation for 2D Magnetotelluric Problem  
Before we can solve Equations (5) and (6), it is necessary to split the computational area into a mesh with \((N_{zb}+N_{za})\times N_{y}\) cells, where \(N_{y}\) is the number of cells in the horizontal direction and \(N_{zb}\) and \(N_{za}\) denote the number of cells in the vertical direction for the subsurface and the air, respectively. A resistivity value is assigned to each cell or each node.  
Figure 1: A sketch of computational domain for modeling 2D magnetotelluric responses.  
Therefore, substituting Equations (13) and (14) into Equation (6), the difference equation for TE-mode can be written as  
\[\frac{\frac{u_{i+1,j}-u_{i,j}}{\frac{\Delta y_{i}+\Delta y_{i+1}}{2}}-\frac{u_{i,j }-u_{i-1,j}}{\frac{\Delta y_{i-1}+\Delta y_{i}}{2}}}{\frac{\Delta y_{i}+\Delta y _{i+1}}{2}}+\frac{\frac{u_{i,j+1}-u_{i,j}}{\frac{\Delta z_{j}+\Delta z_{j+1}}{ 2}}-\frac{u_{i,j}-u_{i,j-1}}{\frac{\Delta z_{j-1}+\Delta z_{j}}{2}}}{\frac{ \Delta y_{i}+\Delta y_{i+1}}{2}}+\frac{\frac{u_{i,j+1}-u_{i,j}}{\frac{\Delta z _{j}+\Delta z_{j+1}}{2}}-\frac{u_{i,j}-u_{i,j-1}}{\frac{\Delta y_{i}+\Delta y _{i+1}}{2}}}{\frac{\Delta z_{j}+\Delta z_{j+1}}{2}}+\mathrm{i}\omega\mu\sigma _{i,j}u_{i,j}=0 \tag{15}\]  
The solution vector \(\mathbf{u}\), which represents the components \(H_{x}\) or \(E_{x}\) for the different mesh nodes, is rewritten by  
\[\mathbf{u}=\left(\begin{array}{c}u_{1}=u_{1,1}\\ u_{2}=u_{1,2}\\ \vdots\\ u_{(i-1)\times(Ny+1)+j}=u_{i,j}\\ \vdots\\ u_{Nz\times(Ny+1)+Ny}=u_{Nz+1,Ny}\\ u_{(Nz+1)\times(Ny+1)}=u_{Nz+1,Ny+1}\end{array}\right), \tag{16}\]  
Considering boundary conditions of the 2D magnetotelluric forward problem, the resulting linear system of equations in the form  
\[\mathbf{Ku}=\mathbf{s}. \tag{17}\]  
gives a numerical solution of Equations (5) and (6). Where \(\mathbf{K}\) is the system matrix containing electrical parameters \(\sigma\), and the right-side column vector \(\mathbf{s}\) contains information related to boundary conditions. Equation (17) can be solved by the direct solver for a sparse matrix in MATLAB. Finally, the magnetotelluric responses including impedance, apparent resistivity and phase at each site for each frequency are calculated by Equations (7)-(9).  
The subroutines described here for calculating magnetotelluric responses, given in Appendices A and B, were developed in MATLAB.
## 2 Materials and Methods
### Geological Formation  
Sites for CRs and GNSS installations should be located in areas with a stable geological background, free of land deformations, landslides, or other hazardous geological phenomena such as karstic features. Landslide refers to the destabilization of mass layers on a slope and their movement downward and outward [39]. The main causes of landslide creation are (a) geological (e.g., type of lithology), (b) morphological (e.g., tectonic uplift), and (c) human causes (e.g., deforestation) [40].  
Figure 11: Sigma 0 images overlayed on ALEV DSM. (**a**) The subset of the ascending pass; (**b**) The subset of the descending pass. The sigma 0 most favourable values are depicted from purple to blue colour.  
One of the most hazardous geological phenomena is that of karstic features because they, amongst others, are difficult to detect. The formation of surface and subsurface cavities (e.g., sinkholes) results from karstic processes. The triggering factor of the karstic features is mainly the water and occurs in areas formatted by carbonate rocks (limestone, dolomite and marble), sulphate rocks (gypsum and anhydrites), and halite (salt) sediments [41]. Sedimentation is another hazard that occurs in Cyprus. This phenomenon takes place in areas with expanding soils (e.g., marks and clays), and alluvial soil (i.e., sand, silts, or gravel) areas [42].  
Since all the aforementioned types of lithology are problematic in terms of GNSS and CR monumentation, they were excluded from the suitability analysis. The AOI of ALEV is formatted by Sheeted Dykes (Diabase) ophiolite complex of Troodos mountain [43]. Diabase could describe mantle rocks' partial fusion or its fractional crystallization [44]; therefore, regarding monumentation, such material is very convenient.  
DLS provided the boundaries of all the areas of interest after requesting permits to install permanent GPS/GNSS stations and Corner Reflectors. The vector data of the boundaries were used to reduce the suitability analysis results inside the AOI while excluding the unnecessary information--outside of the governmental parcel--acquired from UAV missions, satellite images and GSD geological data.
# M-Emu: A Platform for Multicast Emulation
## 1 Introduction
Thus, C++ enables the data plane of NS4 to deploy arbitrary complex multicast protocols and algorithms. In order to improve the functional deficiencies, based on the Service-Forwarding Architecture, M-Emu integrates a Multicast-Emulation Framework. The Multicast-Emulation Framework has a complete multicast tree construction and maintenance mechanism, which significantly reduces the emulation complexity of multicast technology. In addition, we deploy statistical tools for common multicast performance metrics. We take the emulation of Protocol-Independent Multicast Sparse Mode (PIM-SM) as an example to explain in detail how the various parts of M-Emu cooperate to complete the multicast emulation, and we prove that M-Emu is efficient in CPU and memory consumption, etc., through a large number of experiments. The notations used in this article are shown in Table 1.  
The contributions of this paper are as follows:  
1. M-Emu presents a Service-Forwarding Architecture, which enables the data plane to process packet scheduling tasks and to deploy arbitrary complex multicast protocols and algorithms. 2. Based on the Service-Forwarding Architecture, M-Emu integrates a Multicast-Emulation Framework. The Multicast-Emulation Framework has a complete multicast tree construction and maintenance mechanism, which can reduce the workload emulations. 3. We deploy statistical tools for common multicast performance metrics. 4. The effectiveness and efficiency of M-Emu is analyzed theoretically and experimentally. This paper is organized as follows. In Section 2, we present research related to our work. In Section 3, we introduce the implementation of M-Emu. In Section 4, we explore the effectiveness and efficiency of M-Emu theoretically and experimentally. In Section 5, we conclude the paper.
## 2 Study Area and Materials
### Data Used in This Study
#### 2.2.2 Meteorological Data  
The meteorological data, derived from 34 meteorological stations in Chongqing from 2001 to 2011, were provided by the climate center of Chongqing Meteorological Bureau. The data include monthly average temperature, monthly total precipitation, wind speed, sunshine hours, and relative humidity. A meteorological raster map with a spatial resolution of 1 km and a temporal resolution of 1 month was obtained by interpolating meteorological data from the ANUSPLIN [20], an interpolation software developed by the Australian National University using a smooth thin-plate spline method to enter.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
###### Abstract  
Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions to the global navigation satellite systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey the most popular approaches for robust regression (M-, S-, and MM-estimators) and how they can be adapted into a general methodology for robust GNSS positioning. We provide both theoretical insights and validation over experimental datasets, which serves in discussing the robust methods in detail.  
robust statistics; global navigation satellite systems (GNSS); multipath; single point positioning (SPP) +
Footnote †: journal: Sensors
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 3 System Model
### Personality Feature Extraction
#### 3.3.3 Text Analysis and Feature Extraction  
In the application of personality trait analysis, we found that many researchers used Linguistic Inquiry and Word Count (LIWC) [39], Medical Research Council (MRC), or N-grams as a way of extracting textual features. In this work, we used Jieba for preprocessing of text data such as word separation; this is the most commonly used word separation tool in Chinese NLP development and enables the computer to effectively recognize the character features in the dialogues. Considering that different personalities use different words, we did not use any deactivation vocabulary to remove the auxiliary words, but left each paragraph completely intact for the model to learn the semantic meaning expressed by the clients in each conversation. Finally, we set the maximum sentence length to 150 and included it in the vector space as a feature using word embedding; this eventually provided us with a 200-dimensional feature vector as the training target in our neural network. In this study, word embedding was performed by tencent-ailab-embedding-zh, using the Directional Skip Gram (DSG) algorithm [62] as a framework; the relative positions of words  
Figure 4: Sample video photos collected during the experiment.  
were additionally considered based on the word co-occurrence relationship, which can increase the semantic accuracy.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Training Details  
The proposed SMS-Net was trained using a single NVIDIA RTX 3090 GPU (NVIDIA Corporation, Santa Clara, CA, USA) with 24 GB memory. The model's hyperparameters were set with a batch size of 24 and 250 epochs using the Adam optimizer. The learning rate was \(2\times 10^{-3}\), decreasing by 0.1 at 170 and 200 epochs. Additionally, the input image sizes were adjusted for each dataset. The input images for the COCO and MPII datasets were resized to 256 \(\times\) 192 and 256 \(\times\) 256, respectively. Furthermore, data augmentation techniques, such as random rotation within a range of \([-30^{\circ},30^{\circ}]\), random scaling within [0.75, 1.25], and horizontal and vertical flips, were applied to both datasets.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Sensor Fusion
#### 3.6.1 Kalman Filters
In this regard, there has been significant research interest in Extended Kalman filters (EKF), Unscented Kalman filters (UKF) and Cubature Kalman filters (CKF) [105; 106; 107]. In [108], the EKF was used to estimate the lateral and longitudinal tire forces to improve the handling and safety of the vehicle. In [109], a dual extended Kalman filter (DEKF) was proposed which employs two parallel EKFs to estimate the position and state of the AV. In [110], a DEKF was applied to estimate the road friction parameters. However, EKFs are strongly influenced by the approximated noise and diverge when non-linearity of the model dominates [111]. An adaptive EKF (AEKF) was proposed by [PERSON] et al. [112], which adopts a traditional co-variance matching technique. Both matrices are updated continuously by making the filter residual co-variances consistent with the theoretical co-variances. A more accurate generalisation is to employ the Unscented Kalman Filter (UKF) [113]. UKF for vehicle dynamics is proposed in [114; 115]. A dual UKF (DUKF) has been applied to simultaneously estimate the sideslip angle and vehicle mass [116]. However, in a higher dimension filtering problem, the cubature Kalman filter (CKF) has shown better stability and accuracy [107; 117]. The improved performance is because of its ability to solve the multi-dimensional integral. Furthermore, in [118], the dual CKF (DCKF) is proposed, which performs better than the CKF under various friction coefficients when estimating the position and states of the AV. The UKF and CKF fall in the sampling based KFs and the performance of these algorithms depends upon the accuracy of instantiated sigma points [105]. Therefore, Particle filter (PF) and its variants, which are discussed in next section, are ideal for non-linear and non-Gaussian problems.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 2 Material, Thermal Analysis and Samples Preparation  
PC sheets with a thickness of 2.0 mm were bought from Polyfluo Asia Private Limited, Singapore. Some small pieces were cut out of the as-received PC sheet for thermal analysis.  
Thermogravimetric analysis (TGA) (TGA 2950, Surplus Solutions Inc., Woonsocket, RI, USA) was conducted from 25 \({}^{\circ}\)C to 850 \({}^{\circ}\)C at a ramp rate of 10 \({}^{\circ}\)C/min. According to Figure 2, there is virtually no weight loss before 400 \({}^{\circ}\)C, which confirms high thermal stability of this PC. The derivative weight (%/\({}^{\circ}\)C) is automatically worked out by the program of the TGA machine.  
Figure 1: Heating-responsive shape recovery after indentation at room temperature. (**a1,a2**) are 3D view and top view of the indent, respectively; (**b1,b2**) are 3D view and top view of the surface after heating, respectively. The scale bar in the subfigures of the right column is 15 \(\upmu\)m.  
Figure 2: Typical thermogravimetric analysis (TGA) result. Black line: weight; grey line: derivative weight.  
Differential scanning calorimetric (DSC) (DSC Q200, Coppell, TX, USA) tests were carried out between 25 \({}^{\circ}\)C and 380 \({}^{\circ}\)C at a ramp rate of 10 \({}^{\circ}\)C/min for two cycles. As revealed in Figure 3 (the result of the second cycle), the glass transition of this PC occurs between 130 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, and 142 \({}^{\circ}\)C may be defined as its glass transition temperature (\(T_{g}\)).  
Dog-bone shaped samples (refer to Figure 4 for dimensions) for uni-axial tensile test were cut out from the PC sheets.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 5 Discussions
### Relationship between Ground Movement and Groundwater Change
**  
the average of all the Persistent Scatterers (PSs) present within 300 m of the borehole station was used. This is to address the challenge of obtaining a PS point at the exact location of borehole. As the groundwater level of a borehole will affect its nearby areas, it is suitable to take the contribution of nearby PS points for a particular borehole station. Figure 10a-d shows time-series variation for four key boreholes and their related subsidence. Three out of the four locations selected (i.e., TQ2788a (Ashley Gardens, Westminster), TQ27_159 (Ram Brewery, High street, Wandsworth), and TQ28_153 (Kensington Gardens)) show decreasing trends for both groundwater and ground movement (subsidence). Additionally, from Figure 3 we can see that all three boreholes are surrounded by reddish-yellow patches, indicating subsidence in the corresponding area. On the contrary, a dark blue patch can be seen in Figure 3 surrounding borehole TQ27_282 (I Stratford Grove, Putney) suggesting overall uplift. Temporal groundwater variation and subsidence trends from Figure 10d suggest the same. This pattern of land movement for all the four borehole stations can be attributed to the change in groundwater, as no other major contributing factors for this behaviour can be found. Figure 1 shows the location of major faults in the study area. The faults might interfere with the groundwater flow, such that it can hinder the flow if fault material has low permeability, or it can act as flow conduits to promote the flow. The northern boundary fault has boreholes TQ38_241 (45-47 Gresham Street) and TQ38_263a (Victoria House, Southampton Row) on its southern and northern sides, respectively. The hydrographs from both the boreholes (Figure 11) shows comparable water levels and variation trends. The average change in groundwater for TQ38_241 (45-47 Gresham Street) is \(-\)1.1233 m and that for TQ38_263a (Victoria House, Southampton Row) is \(-\)1.279 m. Similarly, the subsidence pattern for both the boreholes shows similar trends, with an average value of \(-\)0.70 and \(-\)0.65 mm/year respectively. This suggests that this fault does not act as a barrier in this area. Figure 10: _Cont._  
While looking at boreholes on either side of Greenwich fault (Figure 1), the two boreholes selected for this analysis were TQ48_2 (Victoria docks) and TQ47_90 (Charlton Obh). The average change in groundwater for TQ48_2 is \(-\)1.386 m and that for TQ47_90 is \(-\)0.197 m. Similarly, the subsidence pattern for both the boreholes varies significantly with an average value of \(-\)0.634 and \(-\)1.44 mm/year, respectively. This suggests that the Greenwich fault acts as a flow barrier. In addition, Rotherhite Fault and Mitcham Fault at the centre of the basin are likely to act as flow barriers. However, there is no evidence for Wimbledon fault and Streatham fault to impede the groundwater flow [11].
## 2 Materials and Methods  
### Study Area  
For this study, 79 national parks across eight countries (South Africa, Zambia, Zimbabwe, Botswana, Namibia, Mozambique, Malawi, and Angola) in southern Africa were analyzed (Figure 1). Of the various protected area designations (e.g., national parks, game management areas, private game reserves), national parks were selected for their consistency in definition across countries. These parks were taken from the World Database on Protected Areas [40]. Given the spatial scale of the remote sensing techniques, only parks greater than 312,500 m\({}^{2}\) were studied because five or more pixels at the 250 m spatial resolution were needed to be able to establish trends. For this reason, the national parks of Swaziland Lesotho were not included in this study. Given the large variation in area across these 79 parks, using a natural breaks statistical technique, parks were broken into three main size classes: small, medium, and large (Figure 2). The small size class had a sample size of 41 and a mean area of 302 km\({}^{2}\). The medium size class had a sample size of 30 and a mean area of 4,771 km\({}^{2}\). The large size class had a sample size of 8 and a mean area of 23,284 km\({}^{2}\) (Figure 2). Buffer zones with sizes of 10 km, 20 km, and 30 km were created for the small, medium, and large parks, respectively. Buffers were created to be able to compare vegetation persistence inside and outside of the park. Buffer sizes chosen for each class are based on literature suggesting that buffers be approximately three times the size of the park [38]. Across this study region, climate varies greatly and was accounted for by comparing mean annual precipitation from the Global Historical Climate Network [41]. Mean annual precipitation of parks in this study varied from 52 to 1466 mm. Land cover variations were defined as savannas with varying amounts of grass/wood biomass. These differences were taken into account by using the Global Land Project Facility data on global land cover [42] (Table A1). Of these 79 national parks, 31 are considered to be transfrontier conservation areas, which can be defined as continuous conservation areas divided by geopolitical boundaries (namely country boundaries) that are cooperating together, but may still function under different management regimes [43].  
Figure 1: Southern Africa with national parks across the landscape and transfrontier parks highlighted.  
Figure 2: National parks used in the study by area, showing the breakdown of the three size area classes: small, medium, and large.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 4 Experimental Results  
The proposed schemes are implemented based on the reference software JM 19.0 ([[http://iphome.hhi.de/suehring/tml/](http://iphome.hhi.de/suehring/tml/)]([http://iphome.hhi.de/suehring/tml/](http://iphome.hhi.de/suehring/tml/))) for H.264/AVC. Six typical sequences with the resolution of \(352\times 288\) from Xiph.org video dataset are used in our experiments. These videos contain different motion and content, allowing for a wide range of payloads. The first 90 frames of each video are encoded with main profile, and the GOP structure is IPBPBPBPBPBPB, which means that there are six GOPs in total.  
To compare the performance of different 2D HS schemes fairly, the method proposed in [29] is modified to make it suitable for H.264/AVC video, and the objects used for data embedding in [37] is changed from motion vectors to DCT coefficients. Moreover, to reduce the impact of data embedding on video quality, only P frames and B frames are used for data embedding. In addition, the DCT coefficient pair is composed of two sequential coefficients in a zig-zag scanning order. There are 16 coefficients in a \(4\times 4\) block of H.264/AVC video, but only the 7 th to 16 th coefficients are selected, because modifying more low-frequency coefficients may cause larger video distortion.  
In the following sections, Ours\({}^{+}\) is used to denote our proposed scheme using the \((0,0)\) coefficient pairs, and Ours\({}^{-}\) denotes our proposed scheme without using the \((0,0)\) coefficient pairs. To present the comparison results more clearly, we also use gray cells to rank the results. There are three types of gray cells. The darker the cell, the higher the ranking of the result. In addition, the best results are displayed with underlined numbers.