esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Retarded Microwave Electromagnetic Motors
## 1 Introduction
If we take \(\vec{F}_{12}\) to be the operating force of body 1 on body 2 and \(\vec{F}_{21}\) to be the operating force of body 2 on body 1, (The bodies considered need not be point particles (although they might be) in the case where the bodies are extended, as the coordinates are of a representative point in the body, such as its center of mass), and if body 1 has a \(m_{1}\) mass, displacement \(\vec{x}_{1}\), velocity \(\vec{v}_{1}\), and acceleration \(\overrightarrow{ac}_{1}\), while body two has a \(m_{2}\) mass, displacement \(\vec{x}_{2}\), velocity \(\vec{v}_{2}\), and acceleration \(\overrightarrow{ac}_{2}\), then under [PERSON]'s second law, we have  
\[m_{1}\overrightarrow{ac}_{1}=m_{1}\frac{d\vec{v}_{1}}{dt}=m_{1}\frac{d^{2} \vec{x}_{1}}{dt^{2}}=\vec{F}_{21},\qquad m_{2}\overrightarrow{ac}_{2}=m_{2} \frac{d\vec{v}_{2}}{dt}=m_{2}\frac{d^{2}\vec{x}_{2}}{dt^{2}}=\vec{F}_{12} \tag{3}\]  
By adding up the above equations, we obtain the following equation for the system's total force \(\vec{F}_{T}\):  
\[\frac{d(m_{1}\vec{v}_{1}+m_{2}\vec{v}_{2})}{dt}=\vec{F}_{21}+\vec{F}_{12}\equiv \vec{F}_{T}. \tag{4}\]  
We define the center of mass location and velocity for a system of bodies located at \(\vec{x}_{1}\) and \(\vec{x}_{2}\) in terms of the total mass \(M=m_{1}+m_{2}\) as follows:  
\[\vec{x}_{cm}\equiv\frac{m_{1}\vec{x}_{1}+m_{2}\vec{x}_{2}}{M},\qquad\vec{v}_{ cm}\equiv\frac{d\vec{x}_{cm}}{dt}=\frac{m_{1}\vec{v}_{1}+m_{2}\vec{v}_{2}}{M}, \tag{5}\]  
We may write Equation (4) in the following form:  
\[M\frac{d\vec{v}_{cm}}{dt}=\frac{d\vec{p}_{T}}{dt}=\vec{F}_{T},\qquad\vec{p}_{ T}\equiv\vec{p}_{1}+\vec{p}_{2}=m_{1}\vec{v}_{1}+m_{2}\vec{v}_{2}, \tag{6}\]  
where \(\vec{p}_{T}\) is the system's total linear momentum and \(\vec{p}_{1},\vec{p}_{2}\) are the linear momenta of bodies 1 and 2, respectfully. Now, if [PERSON]'s third law is assumed, then it follows that \(\vec{F}_{21}=-\vec{F}_{12}\), and hence \(\vec{F}_{T}=0\). This entails that  
\[\frac{d\vec{v}_{cm}}{dt}=\frac{d\vec{p}_{T}}{dt}=0, \tag{7}\]  
Thus, if the center of mass is at rest at any time, then it will remain at rest for all time, and the total linear momentum is conserved. However, if retardation is assumed, then \(\vec{F}_{T}\
eq 0\), although for many (and most) practical systems, it can be assumed to be negligible in the non-relativistic limit. To see this, one can use the retarded potentials given in Equations (1) and (2) to calculate the fields and forces between two arbitrary systems. This calculation was performed in [11] and also in Appendix A of the current paper.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### The Parameters  
As illustrated in Figure 13, system parameters fall basically into two categories: Category I is associated with distance estimation and tracking: (a)AOA/TOA measurement noise; (b) transmission range; (c) number of sensor nodes; (d) compressing bound; (e) number of sub-cluster size; and (f) number of sampling points; Category II is associated with energy conservation: (1) the number  
Figure 12: The stochastic distribution of position estimation.  
of active sensor nodes; (2) the sampling area for the particle filter; (3) the distance estimation error; (4) distortion and compressing ratio; (5) data size; (6) the number of transmissions and receptions; and (7) the energy consumption. Figure 13 shows the impacts of system parameters on the tracking performance, such as distance estimation and energy conservation. Table 2 depicts the values of the simulation parameters for the target position estimate.
## 2 Geometric Modeling Approaches
### Modeling of Dielectric Constant of the Medium  
#### 2.3.1 Dielectric Property Modeling of the Snow Layer  
According to the liquid water content, snow can be classified into three categories [23]: dry snow, moist snow, and wet snow. Dry snow is primarily composed of a mixture of air and ice, whereas wet snow is more complex and consists mainly of water, ice, air, and dry snow. The corresponding dielectric constant is expressed in Equation (5).  
\[\sqrt{\varepsilon_{s}}=V_{a}+V_{i}\sqrt{\varepsilon_{i}}+V_{w}\sqrt{ \varepsilon_{w}} \tag{5}\]  
In Equation (5), \(V_{i}\), \(V_{w}\), and \(V_{a}\) denote the volume fractions of ice, water, and air, respectively. \(\varepsilon_{w}=80-j5\) and \(\varepsilon_{i}=3.15-j0.006\) represent the dielectric constants of water and ice, respectively. \(\varepsilon_{i}\) follows the formulation given in Equation (6).  
\[\begin{array}{l}\varepsilon_{i}=3.15+57.34(\frac{1}{f}+2.48\times 10^{-14} \sqrt{f})\\ \cdot exp(3.62\times 10^{-2}T)\end{array} \tag{6}\]
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 3 Online Rendering and Tracking
### Algorithmic Overview  
Let the world frame be denoted by \(\mathcal{F}_{W}\). We assume that a dense, high-fidelity, colored 3D point cloud of the environment, \(\mathcal{P}\subset\mathbb{R}^{3}\times\mathbb{R}^{3}\), has been pre-acquired using a state-of-the-art LiDAR odometry algorithm such as FastLIVO2. Each point \(\mathbf{P}_{i}\in\mathcal{P}\) is represented by its 3D coordinates \(\mathbf{X}_{i}=[X_{i},Y_{i},Z_{i}]^{T}\) in \(\mathcal{F}_{W}\) and its associated color information \(\mathbf{C}_{i}=[R_{i},G_{i},B_{i}]^{T}\).  
The tracking process is initialized with a known camera pose. For each subsequent frame, the algorithm performs the following steps:  
1. IMU-based Pose Prediction: Using the final pose from the previous frame, we integrate IMU measurements to predict the camera's pose for the current frame. This serves as a strong prior for the subsequent steps.
2. Synthetic View Rendering: Given the IMU-predicted camera pose, a synthetic RGBD image is rendered from the 3D point cloud \(\mathcal{P}\).
3. Keypoint Detection and Matching: Keypoints are detected in both the rendered synthetic image and the newly captured real camera image. These keypoints are then matched to establish correspondences.
4. Pose Estimation via PnP: The 3D-2D correspondences between the keypoints in the synthetic view (and thus, the 3D points in \(\mathcal{P}\)) and the keypoints in the real image are used to correct the IMU-based prediction and estimate the camera's final pose. This is formulated as a Perspective-n-Point (PnP) problem, where the objective is to minimize the reprojection error.
5. Iterative Refinement: The newly estimated pose serves as the corrected pose for the current frame, and the process is repeated, beginning with the next IMU prediction.
## 4 Simulation Works and Discussion
### Single Sink with Multi-Radios in a Round Robin Fashion
When the interface is receiving the transmitter is equal to one which is represented by an unbroken link in the diagram and zero otherwise which is represented by the broken link. In our third simulation scenario, we use a single sink with three radio interfaces; each radio interface is assigned to one of the three non-overlapping channels and six sending nodes to the sink, using the equations above in a round robin fashion. This assignment is semi-dynamic where two transmitting nodes are assign to the same channel and each radio interface at the sink switches among sending nodes on the same channel which gives a 2:1 ratio; two nodes transmit to one radio interface. Figure 12 shows the delay impact among the six sending nodes and the radio interfaces at the sink. MC-DCF with the multichannel multi-radio (MCMR) assignment performs significantly better than MC-DCF with multichannel single radio (MCSR). When comparing the outcome with the performance of MCSR in our previous work, we have seen an improvement of over 55% for delay. This outcome  
Figure 11: Round robin cycle. indicates that with multiple radio interfaces MC-DCF can reduce the high delay encountered with a single channel as the senders need not queue to wait for access to a single radio interface. Instead senders can be distributed among several interfaces. This also reduces the extensive work of a single interface switching between several sending nodes to receive their data packages. In Figure 13, we observe a similar trend to that of delay where MCMR obtaining higher packet delivery ratio of over 51% compare to MCSR that perform very poorly from our previous work. Therefore, having multiple radio interfaces at the sink node to receive data packets from the three non-overlapping channels have improve the performance packet delivery and reduce the traffic load experience by a single radio interface. Figure 14, shows the overall aggregate throughput for the total amount of data delivered to the sink. MCMR show an overall better performance of 49.6% in comparison to that of MCSR offered load. Figure 12: Delay impact comparison with one-many communications at sink node. Figure 13: Delivery impact comparison with one-many communications at sink node. The single sink with multiple channels and radio interfaces scenario demonstrated above for MC-DCF has shown an improvement in performance over a single sink with multiple channels and single radio interface. There has not been any significant improvement in the 802.11 DCF, as it is a contention based protocol design to operate on a single medium, where all nodes contend for the single medium.
###### Abstract  
RFID localization methods have been widely used in indoor positioning systems (IPS). Most localization techniques involve the use of multiple antennas and the placement of antennas and readers in order to ensure accurate positioning results. However, most localization techniques are complex and require high overhead costs in terms of needing multiple antennas and RFID readers. In this paper, we proposed a method to use a single antenna to perform all the reads and rely rather on the antenna beams to acquire multiple positioning data. A single array of antennas is configured to have multiple angles of operation and rely on different power levels as compared to regular antennas. By manipulating the beam pattern, direction and power, multiple sub-antennas can be conceived and the method utilizes antenna beams and relies mainly on one antenna to realize two-dimensional localization.  
indoor positioning system; RFID; LANDMARC; two-dimensional localization +
Footnote †: journal: Applied  
is more accurate and reliable. The LANDMARC approach is more flexible and dynamic and can achieve much more accurate and closer to real-time location sensing. Obviously, the placement of readers and reference tags is very important to the overall accuracy of the system. In order to implement LANDMARC, the RFID Electronic Product Code (EPC), which is a unique identifier per RFID tag, and the RSSI value are needed.  
The main contributions of this paper are listed in the following:  
1. A single xArray RFID reader is used to detect the location of the target tag by using reference tags and adaptive beam pattern changing. By analyzing the location accuracy of different beam patterns, the larger scale for indoor positioning systems can be achieved.
2. Optimizations between the effective range, number of reference tags, beam pattern and refresh rate are weighed and their tradeoffs are compared. The results show that the proposed method has a wide positioning range and better positioning accuracy.
3. The proposed system's superiority is highlighted, especially in its range-accuracy performance. It can be widely used in indoor positioning requirement scenarios, such as libraries, warehouses, and so on.
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 1 Introduction
However, heights of roadway embankments/retaining walls in Texas are typically up to 20 to 30 ft (6 to 9 m), and, therefore, evaluations of backfill materials for the entire embankment/wall height are required to identify the extent of low-quality backfills and determine a proper repair depth. Another challenge is that the evaluation methodology for roadway embankments/retaining walls should be inclusive of the entire components (i.e., pavement structure, backfill materials, drainage systems, movement of embankments/retaining walls, etc. ), not just the pavement structure and subgrade [38]. [PERSON] et al. [39] employed mobile Light Detection and Ranging (LiDAR) mapping for the precise monitoring of the performance of a mechanically stabilized earth (MSE) wall and showed the potential advantages of the LiDAR survey for the effective and reliable monitoring of MSE walls, as well as deriving global and local serviceability measures. Due to the aforementioned challenges, studies of the evaluations of in-service roadways built on retaining walls are exceedingly rare in the literature. The purpose of this case study is to (i) evaluate conditions of concrete pavement supported on an in-service MSE wall, (ii) identify potential mechanisms that might have led to the observed distresses, and (iii) determine repair strategies based on the collected data. To perform comprehensive investigations into the entire components, the authors have employed various evaluation methods. Pavement structures were evaluated through visual observations, FWD tests, and the coring of pavement structures. Additionally, the conditions of the MSE wall were assessed using DCP tests, LiDAR surveys, and soil borings. To overcome the limitation of shallow investigation depths for DCP tests, the authors also performed cone penetration tests (CPTs) and successfully characterized the conditions of backfill soils for the entire wall height. Subsequent sections of this paper provide detailed accounts of observed distresses in the field, followed by a systematic evaluation of pavement structures and MSE walls, along with insights into potential distress mechanisms.
## 3 Methodology
### Performance Metrics  
Various measures are employed to assess classifier performance. True Positives (TP) are the instances correctly labeled as positive, and True Negatives (TN) are the instances correctly labeled as negative. False Positives (FP) are instances mistakenly labeled as positive, and False Negatives (FN) are instances mistakenly labeled as negative. These metrics are computed using a confusion matrix, forming the foundation for evaluating performance. For a single class \(\mathrm{C_{i}}\), the terms \(\mathrm{TP_{i}}\), \(\mathrm{FN_{i}}\), \(\mathrm{TN_{i}}\), and \(\mathrm{FP_{i}}\) are utilized to evaluate class-specific metrics. The numerical values for \(\mathrm{C_{i}}\) are instrumental in calculating the following metrics:  
\[\begin{array}{c}Sensitivity=TP_{i}/(TP_{i}+FN_{i})\\ Specificity=TN_{i}/(TN_{i}+FP_{i})\\ Accuracy=(TP_{i}+TN_{i})/(TN_{i}+FP_{i}+TP_{i}+TN_{i})\\ Precision=TP_{i}/(TP_{i}+FP_{i})\\ F1 Score=2*Precision*Sensitivity/(Precision+Sensitivity)\end{array}\]  
Additionally, two widely used metrics for binary classification are the Receiver Operator Characteristic (ROC) curve and the Area under the ROC Curve (AUC). The ROC curve assesses the trade-off between True Positive Rate (TPR) and False Positive Rate (FPR), while the AUC represents the overall accuracy:  
\[TPR(Sensitivity)=TP/(TP+FN)\]  
\[FPR=FP/(FP+TN)\]  
AUC values surpassing 0.9 reflect classification accuracy nearing optimal levels, signifying a model with highly robust predictive capability.
## 2 Collective Detection Approach  
### Dependence on Assistance Data  
The concept of CD is intended to complement an existing Assisted-GNSS (A-GNSS) and positioning method. In fact, Collective Detection is an A-GNSS approach for direct positioning in which all information from satellites in view are combined in order to enable rapid acquisition, i.e., to reduce the time to first fix (TTFF) and increase the sensitivity of the receiver. The assistance data allows the GNSS receiver to reduce the search space by providing assistance information [13].  
It should be noted that for the application of the CD principle, apart from the RS position (for reducing the initial Mobile Station (MS) spatial uncertainty) and the ephemeris (for computation of the expected satellite position and velocity from azimuth and elevation angles), a new assistance parameter is required, which is the pseudorange measurements for all visible satellites as seen from the reference station. This is used in the code phase estimation of each candidate point in the position domain to estimate the user position.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 2 Multiple-Parallel Morphological Anti-Aliasing Algorithm
### Acquisition of the Blend Weight Coefficients  
After completing the classification of the horizontal and vertical aliasing patterns of the image, we need to calculate the blend weight coefficients for the pixels included in each aliasing pattern. Similarly, in our algorithm, the calculation of the blend weight coefficients is carried out in a single channel. Therefore, we need to calculate the blend weight coefficient for the pixels included in the aliasing patterns of the image in the R, G, and B channels. Next, we will take Figure 4, which only has an effective value in the R channel, as an example to demonstrate the process of the calculation of the blend weight coefficients.  
In the process of the calculation of the blend weight coefficients, we first connect the midpoint of each short edge and the long edge in each aliasing pattern to form a crossing edge. Since the crossing edge divides the pixels included in the aliasing pattern, we take the area divided by the crossing edge to each pixel in the aliasing pattern as the weight coefficient for color blending between the pixel and its adjacent pixels. In the image shown in Figure 4, the pixel edge [b2l, b1t, c1t, d2t] marked by the green line constitutes a horizontal \"L-shaped\" aliasing pattern. By connecting the midpoints of b2l and c1t, we can obtain the corresponding crossing edge marked by the black line. Since the area of the pixel b2 and the pixel c2 is divided by the crossing edge, and they are area1 = 1/3 and area2 = 1/24, respectively, the areas area1 and area2 will be used as the weight coefficients of the pixel b1 and the pixel c1 blended by the pixel above them, respectively, to participate in the calculation of the final anti-aliased color.  
To reduce the time and hardware resources consumed when calculating the areas divided by the crossing edge, we predefine an area texture, as shown in Figure 5. The area texture stores the area of each pixel in all aliasing patterns (the 16 aliasing patterns shown in Figure 6) of any length (less than 100) divided by its corresponding crossing edge.  
Figure 4: Process of the calculation of the blend weight coefficients.  
The definition of the area texture enables us to directly obtain the blend weight coefficients according to the type of the aliasing pattern and the position of the pixel in the aliasing pattern without complicated calculations, which greatly reduces the consumption of time and hardware resources.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Ultrawide Band (UWB) Technology  
The UWB is a short-range, low-power wireless technology that utilizes a sub-nanosecond radio pulse and transmits data in a wide bandwidth for strong multipath resistance and high precision [27]. It transmits short pulses, less than 1 ns over a large bandwidth, so higher accuracy between 20-30 cm can be achieved [2]. Although it can penetrate walls and resolve the multipath problem, its accuracy is significantly degraded when operating indoors [102]. In addition, the UWB hardware is expensive, so it is considered a high cost technology for wide scale applications [20]. Compared to Wi-Fi, the UWB availability is limited to only some smartphones [103].  
In [102], three commercial UWB-based localization systems using TDoA and AoA measurements, \"Ubinense\", \"BeSpoon\", and \"DecaWave\", were evaluated in a big industrial warehouse with diverse obstacles causing propagation loss and diffraction on the UWB radio signals. The Gaussian and Gamma distributions were also used for modeling the LoS and NLoS conditions, respectively, as well as a particle filter for updating the implementation of the non-normalized distribution function. It was concluded that, under NLoS conditions, the \"BeSpoon\" was slightly better than \"Decaware\" and significantly more reliable than \"Ubinense\", whereas under LoS conditions, the \"Decaware\" was more accurate, compared to \"DeSpoon\", since \"Decaware\" uses more advanced antennas. Regarding the measurements, the AoA measurements were more informative and accurate, with fewer outliers compared to TDoA measurements. Overall, the \"Decaware\" was found to be superior to both \"DeSpoon\" and \"Ubinense\".
# Topological Defect Evolutions Guided by Varying the Initial Azimuthal Orientation
## 4 Conclusions  
The topological defect evolutions of LCs are investigated via varying the initial azimuthal orientation of the square alignment lattice in a hybrid cell. As \(\alpha_{0}\) increases from \(0^{\circ}\) to \(90^{\circ}\), the twist deformations of the director \(n\) lead to significant director field variations surrounding \(+1\) and \(-1\) point defects. The Landau-de Gennes theory is adopted to analyze the director distributions and defect transformations. The simulations indicate that the directors rotate with increasing \(\alpha_{0}\), and there is no azimuth rotation for the \(+1\) point defect, while the \(-1\) point defects exist. Further theoretical analysis reveals that the \(-1\) point defect changes from a convergent type to a divergent type, while the type of \(+1\) point defect remains unchanged. Although twisted deformation is suppressed in the SmA phase, discontinuous LC layers are formed due to the inheritance of orientation order of the above textures. This results in various spiral TFCDs when \(\alpha_{0}\leq 45^{\circ}\). For larger \(\alpha_{0}\), the unit is divided into four individual lobes. TFCDs degenerate from infinite rotational symmetry to quadruple rotational symmetry, and the shape of the repeatable unit drastically changes with the variance of \(\alpha_{0}\). This work enriches the fundamental understanding on the self-organization of soft matter systems and may open the door for advanced applications of topological defects.  
[PERSON] and [PERSON]. ([PERSON]) contributed equally to the work. [PERSON], [PERSON] ([PERSON]), and [PERSON] designed the research; [PERSON] and [PERSON]. ([PERSON]) performed the research; [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON] ([PERSON]), and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2022 YFA1203703), the National Natural Science Foundation of China (NSFC) (62035008 and 13001281), and the University Research Project of Guangzhou Education Bureau (202235053).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no competing interests.  
Figure 4: TFCDs and split TFCDs in the S phase observed at 33.0 \({}^{\circ}\)C (top) and corresponding bright field images (bottom) when (**a**) \(\alpha_{0}=0^{\circ}\), (**b**) \(\alpha_{0}=30^{\circ}\), (**c**) \(\alpha_{0}=60^{\circ}\), and (**d**) \(\alpha_{0}=90^{\circ}\). The bottom right inset in (**a**) shows the layered configuration and director distribution of a single TFCD. The yellow lines mark the evolution of the TFCD units. The white arrows denote the directions of a pair of crossed polarizers. The scale bar indicates 30 \(\upmu\)m for all images.