esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Balance  
There were no main effects of group on sway indices, nor group x time interactions except a single group x time interaction (F (1.38) = 4.18, \(p\) = 0.0480) on the COP amplitude in the A/P plane, which was caused by the detrimental effect of time (increase in COP amplitude) on postural steadiness in the CON group on a foam surface only (\(p\) = 0.0450).  
Several COP parameters exhibited the main effects of the support surface. The transfer from hard to foam surface increased COP variability in the M/L (F (1.38) = 34.49, \(p\) < 0.0001) and A/P (F (1.38) = 11.58, \(p\) = 0.0016) planes, and increased COP mean speed in the M/L (F (1.38) = 24.53, \(p\) < 0.0001) and A/P (F (1.38) = 24.73, \(p\) < 0.0001) planes as compared to standing on a firm surface. The same change in support surface decreased COP entropy (F (1.38) = 8.84, \(p\) = 0.0051), COP frequency (F (1.38) = 6.64, \(p\) = 0.0140), and COP fractality (F (1.38) = 4.06, \(p\) < 0.0512) in the A/P plane. The detailed results are presented in Table 2.  
There were also main effects of time that elapsed from pre- to post-training balance assessment on the COP parameters. All significant changes occurred in the AP plane and on firm surface only and indicated a decrease in the COP measures between the first and second measurements (Table 2). The main effects of time were observed on the following sway measures: frequency (F (1.38) = 5.06, \(p\) = 0.0301), entropy (F (1.38) = 6.61, \(p\) = 0.0140), and fractality (F (1.38) = 6.11, \(p\) = 0.0180). These decreases were caused exclusively by the respective changes in the control group, and the corresponding \(p\)-values for the Tukey post hoc test were \(p\) = 0.0005 for COP frequency, \(p\) = 0.0410 for COP entropy, and \(p\) = 0.0074 for COP fractality.  
Figure 2: Force error values before (Pre) and after (Post) the intervention for two groups (EXP and CON) and both legs (right and left). (**A**) Absolute error; * \(p\) < 0.001, significantly different from Pre; * \(p\) < 0.005, significantly different from CON. (**B**) Variable error. (**C**) Constant error; * \(p\) < 0.05, significantly different from Pre; * \(p\) < 0.01, significantly different from CON.
Our institution previously conducted a motion analysis of patients who underwent ACL reconstruction surgery using our unique system, which synchronises surface electromyography with a motion capture system and force plates [41]. However, there are limited reports on motion analysis using a combination of motion capture systems, force plates, and surface electromyography in patients who have undergone TKA [42]. To further improve postoperative function after TKA, a modern implant with a shape similar to a biological knee has been developed. Although kinematic analyses of modern implants using 2D-3D registration have been performed [7; 8], the kinetics and muscle activity remain unclear. Therefore, we aimed to compare the kinematics, kinetics, and muscle activity of patients who underwent TKA during sit-to-stand (STS) movement using a modern implant that mimics the normal knee function and a conventional implant, by analysing data from a motion capture system synchronised with force plates and surface electromyography. We hypothesised that patients who underwent TKA using modern implants would show kinematics, kinetics, and muscle activity similar to those of healthy individuals.
## 5 Discussion
### Field Experiment: Prediction of Saline Tracer Displacement  
Acquired and \"predicted\" datasets were inverted with the open source ResIPy software package [62] and the \"pyres\" Python wrapper package, which allows for creating a flexible user interface for modelling and inversion of ERT datasets using the NumPy, SciPy, and Matplotlib Python packages [62; 63]. In particular, the time-lapse inversion strategy used was difference inversion, after LaBrecque and Yang [69], because small changes of conductivity are better resolved, as in this experimental case, and high-quality images with fewer artefacts are produced.  
In detail, the resistivity obtained by the inversion of background data (10 July) serves as the a priori model in the difference inversion. There are several advantages to this method. First, convergence is fast since the inverse routine needs only to find small perturbations about a good initial guess. Second, systematic errors, such as those due to errors in field configuration and discretization errors in the forward modelling algorithm, tend to be cancelled [69].  
Then, CH-ERT imaging of tracer transport is best presented using images of resistivity change (%) relative to the pre-tracer condition according to the following:  
\[d(\%)=\left[\frac{\rho_{t}-\rho_{0}}{\rho_{0}}\right]\times 100 \tag{9}\]  
where 0% indicates no change between datasets, whereas negative values indicate an electrical resistivity decrease.  
Images of electrical resistivity changes between \(-30\) and \(-42\) m (Figure 8) allow us to delineate salt plume evolution: they show evidence for a prevalent transport pathway under 30 m depth. Moreover, statistical predicted datasets seem to predict the tracer phenomenon well in the real aquifer studied here.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 1 Introduction
Current common operational schemes mainly employ horizontal  
Figure 1: Comparison of constant-flow hydraulic fracturing and variable-flow hydraulic fracturing: (**a**) constant-flow hydraulic fracturing; (**b**) variable-flow hydraulic fracturing. wells. A typical horizontal well is composed of three parts: a vertical or slightly inclined section drilled downward, known as the vertical section; after reaching the reservoir, the well inclination angle gradually changes to 90\({}^{\circ}\), parallel to the reservoir, known as the build-up section; and a horizontal section that extends for a certain length, known as the horizontal section. Since conventional vertical wells only have a vertical section, there is a lack of research on the build-up and horizontal sections in horizontal wells. Fluctuating pressure also exists in long-distance oil pipelines, and the MacCormack algorithm is commonly used for simulating transient flow within pipelines [28; 29]. Moreover, research on pipeline leakage monitoring based on pressure fluctuations within the pipeline has matured [30; 31], providing a reference for the study of pressure wave propagation in the stable section of horizontal wells. In the second chapter of this paper, a transient flow model of fracturing fluids within horizontal well pipelines will be established. The third chapter will introduce two methods used to solve the model and verify the accuracy of these methods. The fourth chapter will analyze the variation characteristics of bottom hole pressure in horizontal wells under different fluid injection schemes and well depths. The fifth chapter will present some conclusions drawn from this study. This research is helpful for engineers to choose appropriate fluid injection schemes.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 3 Proposed Method
### Fast Mode Selection  
In other methods, affine prediction is evaluated for each block, which takes a significant amount of time. In Reference [10], there are two affine prediction modes, which take even more time. We propose heuristics to avoid computing all possible modes and save on encoding time.  
We decide if affine models should be used over translation first by looking at the variance of the optical flow in a given block. The variance is computed as in Equation (13).  
\[\bar{x} =\sum_{i=0}^{N}\sum_{j=0}^{N}\frac{flow_{x}(i,j)}{N^{2}} \tag{9}\] \[\sigma_{x}^{2} =\sum_{i=0}^{N}\sum_{j=0}^{N}\frac{(flow_{x}(i,j)-\bar{x})^{2}}{N ^{2}}\] (10) \[\bar{y} =\sum_{i=0}^{N}\sum_{j=0}^{N}\frac{flow_{y}(i,j)}{N^{2}}\] (11) \[\sigma_{y}^{2} =\sum_{i=0}^{N}\sum_{j=0}^{N}\frac{(flow_{y}(i,j)-\bar{y})^{2}}{N ^{2}}\] (12) \[\sigma_{xy} =\sqrt{\sigma_{x}^{2}+\sigma_{y}^{2}} \tag{13}\]  
In these equations, \(flow_{x}(i,j)\) and \(flow_{y}(i,j)\) represent the optical flow at the position \((i,j)\). When the resulting variance \(\sigma_{xy}\) is very small, translation for the whole block is likely to be very accurate, as every pixel has the same displacement. The opposite case, where the variance is very high, mostly represents large discontinuities in the motion vector we should use to predict the current block. It is very likely that splitting the block into smaller subblocks is preferable.  
We decide on two threshold values for these cases, resulting in the following:  
1. translation if \(\sigma_{xy}<0.01\)
2. affine if \(0.01<\sigma_{xy}<4\)
3. split block if \(\sigma_{xy}>4\)  
To determine the best threshold values, we ran tests on a few sequences. For the lower bound, 0.01 was determined experimentally to avoid skipping the numerous cases where the best parameter is 1 and the variance would be around 0.05. For the higher bound, we checked the variance of the sequences and values over 1 correlated heavily with object boundaries, but setting the threshold to 1 made the skipping too eager, so we increased it to 4 to allow for some margin of error.  
Then, to see which 3-parameter model would fit best, the absolute values of \(s\) and \(r\) are compared, and the model corresponding with the highest value is selected. In case neither is bigger than a small threshold, set to a tenth of the minimal non-zero value for the affine parameter, affine motion estimation is skipped for the current block. While in most cases the variance heuristics catch those blocks, some outliers can affect the variance greatly.  
To predict values for other pictures in the picture reference list, the displacement is scaled proportionally to the temporal distance between the frames. This approximation is typically accurate enough when the movement stays similar. For example, if the first reference picture is at a distance of 1 and the second at a distance of 2, the displacement values are doubled.
## 4 Evaluation of Seismic Performance
### Displacement Ductility  
Structures should exhibit not only good load capacity but also proper deformation capacity to avoid brittle failure under an earthquake. Displacement ductility is a measurement of the ability of the shear wall to deform in the elasto-plastic stage without significant degradation of strength. The appropriate definition of the shear wall's ductility has been discussed in the literature [23; 24; 25; 26]. In this study, ductility is defined as the ratio between the top displacement corresponding to 0.85 \(F_{p}\) in the post-peak stage and the yield displacement of the specimen. The yielding point of the test wall was determined according to the equal energy method [27], as shown in Figure 12. Accordingly, the ductility is calculated by the equation below.  
\[\mu=\frac{D_{u}}{D_{y}} \tag{1}\]  
where \(D_{u}\) is the displacement corresponding to the post-peak load equal to 0.85 \(F_{p}\), \(D_{y}\) is the yield displacement of the specimen. Each specimen's \(D_{u}\), \(D_{y}\), and \(\mu\) are summarized in Table 3.  
As seen in Table 3, all the specimens exhibited relatively high ductility for flexural failure featured by toe crushing and flexural yielding of vertical steel. Each specimen had its own post-yielding  
Figure 12: Determination of the yielding point.  
deformation characteristics due to different combinations of construction technology and axial compression. CMW-1 had a comparable ductility value to PMW-1 with the same compression ratio of 0.10 while CMW-2 had a higher ductility value (5.0) than that of PMW-2 with a compression ratio of 0.15. With the increase of the axial load, the ductility of traditional RMSW and precast RMSW had decreases of 7% and 12%, respectively. The results proved that the axial load has a detrimental effect on the displacement ductility of shear walls, which is more obvious for precast RMSW. In addition, Table 3 also shows that, on average, the post-peak lateral load of both traditional and precast RMSW decreased to 0.85 \(F_{p}\) at approximately 1.1% drift.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 3 Survey of Existing 5G Satellite Integration (SATis5) and Associated Challenges  
A survey on 5G-Non-Traditional Network (5G-NTN) focusing on SATis5 architectures and associated challenges was conducted. Based on the survey results [6, 7, 8, 9, 10, 11, 12], this section provides an overview of existing SATis5 architectures, the current proposed SATis5 roadmaps, and associated technical challenges. Section 3.1 presents an overview of existing SATis5 architecture options and provides references related to system interfaces and standards. Section 3.2 describes an overview of SATis5 roadmaps. Section 3.3 provides a summary of technical challenges associated with existing SATis5 architecture options.
## 2 Design
### Sensing, Control, Communication, and Localization
#### 2.4.4 Localization  
In this study, our aim was to use local sensors to guide the robots to dock with each other without a global/external positioning system. In short ranges (with distance between two robot modules less than 2 m), SMMRob can locate itself using the robots' internal sensors and cameras with respect to other robots (relative positioning will be presented in Section 3.3.1). Initially, these robots will be deployed in short ranges to find other modules using cameras and they can search for others randomly. Meanwhile, to provide a wide-ranging application in future, an ultra-wideband (UWB) positioning system is integrated. Each modular robot is equipped with a UWB terminal chip or called tag, and four positioning anchors distributed in four different positions. UWB can provide sub-meter-level positioning accuracy. The UWB system will be used for further research in the future and is not used in this paper.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 3 The AVQ for Image Sequences (AVQ\({}_{\text{IS}}\)) Algorithm
```
1.\(SD\leftarrow\text{Use an }IDH\) for the initialization
2.for\(i\gets 1\)to\(N\)do
3.\(\textit{frame}_{i}\gets i\)-th frame of \(IS\)
4.\(\textit{frame}_{i}\)\(\leftarrow\)fAVQ\({}_{\text{Compression}}\)(\(\textit{frame}_{i}\), \(SD\))
5.endfor
6.\(IS^{C}\leftarrow\){\(\textit{frame}_{1}\)\({}^{C}\), \(\ldots\), \(\textit{frame}_{i}\)\({}^{C}\), \(\ldots\), \(\textit{frame}_{N}\)\({}^{C}\)}
```  
**Algorithm 3** Pseudo-code of the _compression stage_ (AVQ\({}_{\text{IS}}\) algorithm). In Algorithm 4, we outline the pseudo-code of the decompression stage. It should be noted that the decompression stage is substantially symmetrical with respect to the compression one. Figure 8: The shared dictionary used by each one of the _fAVQ_ instances.
# Distributed Graph Diameter Approximation
## 7 Experiments
### Scalability  
The goal of the scalability experiments is twofold. One the one hand, we want to assess the overhead incurred by a distributed algorithm over a sequential one, in case the graph is small enough so that the latter can be run. On the other hand, we want to compare the scalability with respect to the graph size of ClusterDiameter versus DeltaStepping. To this purpose, we used as benchmarks the original USA network and the two inflated versions with \(S=5,10\). We note that both the original USA network and the inflated version with \(S=5\) fit in the memory of a single machine. We did not experiment with the sk-2005 and twitter-2010 graphs, since it was unclear how to downscale them  
Figure 5: Time employed to build clusterings of USA with different radius guesses (**left**), size of the auxiliary graph for each guess (**center**), and diameter approximation obtained from each clustering (**right**). All axes are in logarithmic scale, except for the \(y\) axis of the rightmost plot.  
to fit the memory of a single machine, while preserving the topological properties. As a fast sequential baseline, we consider a single run of [PERSON]'s algorithms from an arbitrary node, which gives a 2-approximation to the diameter.  
The results of the experiments are reported in Figure 6. On the original USA network, as well as on its fivefold scaled up version, the sequential approach is considerably faster than both ClusterDiameter and DeltaStepping, suggesting that when the graph can fit into main memory it is hard to beat a simple algorithm with low overhead. However, when the graph no longer fits into main memory, as is the case of USA-x10, the simple sequential approach is no longer viable. Considering DeltaStepping, it is clearly slower than ClusterDiameter, even on small inputs, failing to meet the five hours timeout on USA-x10, On the other hand, ClusterDiameter exhibits a good scalability in the size of the graph, suggesting that it can handle much larger instances, even in the case of very sparse graphs with very large unweighted diameters.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Control Plane Security  
The control plane functions as the central intelligence of a Kubernetes cluster, managing scheduling, resource distribution, and the intended state of all workloads. Securing the control plane is essential for safeguarding the Kubernetes ecosystem's integrity, availability, and confidentiality. Due to their elevated rights and access to necessary configuration and state data, these components are prime targets for attackers. This section delineates critical security protocols for fortifying the principal control plane elements: the Kubernetes API server, etcd, and the scheduler. Each topic delineates certain configuration modifications, certificate administration, and access controls that mitigate attack surfaces and avert unauthorized interactions with the fundamental orchestration logic.
## 4 Practical Application of the E-Commerce Platform
### Forecasting Store Sales  
The third application is a time series forecast that makes predictions based on historical sale transactions with time stamps. It involves building models to make trend and seasonal observations that help merchants make better store planning, e.g., inventory planning, where the decision maker can determine the optimal quantity and timing for his/her inventory. This application was carried out on an actual data set from three e-stores. The data covers daily sales recorded for approximately four years, from January 2018 to October 2022, with more than 74,000 online sales records that contain information such as order date and sales amount. In the application, Prophet [31] is used, which is an open-source framework developed by Facebook's data science team and can be used for forecasting time series sales data.  
In forecasting, patterns of the data are important. Based on Figure 7, the data shows a seasonal monthly pattern per year in the three online stores. For example, e-store 3 (see Figure 7c) has a changing trend that appeared in August 2021. Note that Figure 7 shows the sales forecast for the three e-stores with plotted the actual values of our time series (black dots) and the forecasted values (blue line). In addition, the model can forecast sales based on the patterns in the actual data, although there are outliers in the data. See, for example, e-store1 in Figure 7a.  
Figure 6: A visualization showing the distribution of customers for an e-store. Each dot represents a city. Each city is associated with a number of cursomers’ orders.  
Figure 7: Prediction results using FB-prophet. Black dots represent the actual data points, while the blue line represents the FB-prophet predictions.
###### Abstract  
This work introduces a high-performance multi-input multi-output (MIMO) antenna design to operate at the 28 GHz band. The proposed four-port MIMO antenna, in which each port comprises a 1 \(\times\) 8 series-fed array, achieves peak gains of 13 dBi along with bandwidths of 1 GHz. Enhanced antenna performance is achieved through the optimal spacing of antenna elements and a decoupling methodology comprising a well-designed metamaterial unit cell, leading to reduced interference between antenna arrays. The design shows significantly suppressed mutual coupling to be less than \(-40\) dB, a diversity gain that is very close to 10 dB, an envelope correlation coefficient of 0.00012, and a channel capacity loss of 0.147 bit/s/Hz, at 28 GHz. The experimental assessments confirmed these developments, endorsing the suggested design as a robust contender for 5G mmWave communications.  
a 2025 6 1
###### Abstract  
This paper proposes a novel THz band 2-D wide-angle scanning phased-array antenna (PAA) based on a decoupling surface. The simulated S\({}_{11}\) bandwidth under periodic boundary conditions is 106-119 GHz, with stable gain within the bandwidth. The designed decoupling surface effectively reduces the coupling between elements, and the simulated active VSWR performance and ground surface current distribution under periodic boundary conditions confirm this. An \(8\times 8\) (64-element) planar PAA is modeled and simulated in CST2022 to verify the beam-scanning performance of the PAA. According to the simulation results, a 2-D wide-angle scanning of \(\pm 48^{\circ}\) is achieved in the 106-114 GHz range, while in the 115-119 GHz range, a wide-angle scanning of \(\pm 48^{\circ}\) is achieved on the E-plane, and the beam-scanning range on the H-plane reaches \(\pm 40^{\circ}\). Moreover, the normal peak gain is stably maintained at 21.9-22.8 dBi, with a normalized radiation efficiency as high as 95%, and the scanning radiation efficiency is higher than 81%. Due to its stable gain and 2-D wide-angle scanning performance, the proposed PAA has a broad application prospect in terahertz wireless communication equipment.  
THz band; decoupling surface; two-dimensional wide-angle scanning; phased-array antenna +
Footnote †: journal: Applied  
Article  
Designing a Novel THz Band 2-D Wide-Angle Scanning Phased-Array Phased-Array Antenna Based on a Decoupling Surface. _Appl. Sci._**2024**, 14, 8618. [[https://doi.org/10.3300/appl4196618](https://doi.org/10.3300/appl4196618)]([https://doi.org/10.3300/appl4196618](https://doi.org/10.3300/appl4196618))
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
## 2 Experimental Details
### Preparation of Graphene Films by DIW  
In this experiment, four different kinds of dispersion solvents were used, i.e., deionized water and anhydrous ethanol in four different ratios (0 ethanol, 10% ethanol, 20% ethanol, and 30% ethanol, all in volume fraction). The graphene suspension (0.3 g), dispersion solvent (10 mL), and sodium dodecyl sulfate (0.015 g) were put into a beaker and sonicated for 20 min to obtain the conductive graphene ink. Then, the conductive graphene ink was transferred into a syringe equipped with a gas pump. The PET substrate was fixed on the sliding operation table with a heating plate, and the distance between the needle and the substrate was controlled to be less than 5 mm. Next, the graphene film was directly written on the substrate under continuous heating (100 \({}^{\circ}\)C) using four types of conductive inks, denoted as **G-0**, **G-10**, **G-20**, and **G-30** (with 0-30 vol.% ethanol in the solvent). The robotic arm's moving speed and extrusion flow rate were optimized to obtain graphene films with certain widths. The deposition process was repeated several times to obtain a graphene film with a certain thickness. After the full evaporation of the solvent, the as-printed graphene film was hot-pressed at 300 MPa for 5 min. The grid-patterned GFTE was fabricated by DIW following the same procedure. As shown in Figure 1, three types of grid patterns were fabricated with different transparent aperture sizes, i.e., 2 mm \(\times\) 2 mm, 3 mm \(\times\) 3 mm, and 4 mm \(\times\) 4 mm (denoted as **G-2 \(\times\) 2**, **G-3 \(\times\) 3**, and **G-4 \(\times\) 4**, respectively).