esa-sceva/satcom-chunk-collection huggingface dataset:
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
###### Abstract  
Earth rotation parameters (ERP) are one of the key parameters in realization of the International Terrestrial Reference Frames (ITRF). At present, the International Laser Ranging Service (ILRS) generates the satellite laser ranging (SLR)-based ERP products only using SLR observations to Laser Geodynamics Satellite (LAGEOS) and Etalon satellites. Apart from these geodetic satellites, many low Earth orbit (LEO) satellites of Earth observation missions are also equipped with laser retroreflector arrays, and produce a large number of SLR observations, which are only used for orbit validation. In this study, we focus on the contribution of multiple LEO satellites to ERP estimation. The SLR and Global Positioning System (GPS) observations of the current seven LEO satellites (Swarm-A/B/C, Gravity Recovery and Climate Experiment (GRACE)-C/D, and Sentinel-3A/B) are used. Several schemes are designed to investigate the impact of LEO orbit improvement, the ERP quality of the single-LEO solutions, and the contribution of multiple LEO combinations. We find that ERP estimation using an ambiguity-fixed orbit can attain a better result than that using ambiguity-float orbit. The introduction of an ambiguity-fixed orbit contributes to an accuracy improvement of 0.5%, 1.1% and 15% for X pole, Y pole and station coordinates, respectively. In the multiple LEO satellite solutions, the quality of ERP and station coordinates can be improved gradually with the increase in the involved LEO satellites. The accuracy of X pole, Y pole and length-of-day (LOD) is improved by 57.5%, 57.6% and 43.8%, respectively, when the LEO number increases from three to seven. Moreover, the combination of multiple LEO satellites is able to weaken the orbit-related signal existing in the single-LEO solution. We also investigate the combination of LEO satellites and LAGEOS satellites in the ERP estimation. Compared to the LAGEOS solution, the combination leads to an accuracy improvement of 0.6445 ms, 0.6288 ms and 0.0276 ms for X pole, Y pole and LOD, respectively. In addition, we explore the feasibility of a one-step method, in which ERP and the orbit parameters are jointly determined, based on SLR and GPS observations, and present a detailed comparison between the one-step solution and two-step solution.  
onboard GPS; SLR; LEO; Earth rotation parameters; ambiguity fixing +
Footnote †: journal: Remote Sensing  
1
Footnote †: journal: Remote Sensing
## 4 Results and Discussion  
The correctness of Equation (12) is validated first, followed by a detailed analysis of the GEO residuals. Then, the performance of BDS-only and multi-GNSS PPP is assessed. Finally, a case study of the Mw 6.5 Jiuzhaigou earthquake is shown.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation
#### 2.3.3 OGC CityGML and IndoorGML
Although the CityGML LoD 4 is capable of describing indoor objects such as ceilings, windows, doors, furniture, etc., and indoor ADE can include indoor space features and indoor facility features for indoor facility management [85], the OGC IndoorGML standard aims to support location-based services for indoor navigation; however, CityGML has some missing elements for indoor applications, particularly indoor navigation. In this respect, indoorGML is a complementary standard to CityGML, KML, and IFC [86]. Based on GML 3.2.1, IndoorGML provides a cellular and structured space model, which supports semantic, geometric, topological, and multi-layered representation of indoor environments. While the basic concepts of IndoorGML are discussed in detail in [87, 88], [PERSON] et al. [11] presented a comparison between CityGML and IndoorGML; and, [PERSON] et al. [89] discussed the issues on the integration of IndoorGML and CityGML LoD 4 by two methods: automatic derivation of IndoorGML data from CityGML LoD 4 data set and external references from IndoorGML instance to an object in CityGML data. Future changes and improvements are being considered in the IndoorGML2.0 standard proposal [90].
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## 4 Numerical Simulations
### Discretization  
To implement the steepest descent method (or other gradient type schemes), one has to discretize the final time wave operator \(\mathbf{W}_{T}\) and its adjoint \(\mathbf{W}_{T}^{*}\). For that purpose we solve the forward and adjoint wave Equations (9) and (16) on a cubical grid with side length \(2L\) and nodes \(\mathbf{x}[\mathbf{i}]\coloneqq-(L,L,L)+\mathbf{i}\,2L/N_{x}\) for \(\mathbf{i}=(i_{1},i_{2},i_{3})\in\{0,\dots,N_{x}-1\}\) with the k-space method [52; 53], which we briefly recall in Appendix A. Note that the implementation of the k-space method yields a \(2L\)-periodic solution. The parameter \(L\) is chosen such that \(a+T<L\) which implies that inside \(B(0,R)\), for \(t\in[0,T]\), the solution of (16) coincides with its \(2L\)-periodic extension.  
We denote by \(\mathcal{F}\subseteq\mathbb{R}^{N_{x}\times N_{x}\times N_{x}}\) the set of all \(\mathbf{f}\in\mathbb{R}^{N_{x}\times N_{x}\times N_{x}}\) with \(\mathbf{f}[\mathbf{i}]=0\) for \(\mathbf{x}[\mathbf{i}]\
ot\in B(0,R)\). The discretized versions of \(\mathbf{W}_{T}\) and its adjoint \(\mathbf{W}_{T}^{*}\) are defined by \(\mathcal{W}\colon\mathcal{F}\to\mathbb{R}^{N_{x}\times N_{x}\times N_{x}}\colon \mathbf{f}\mapsto(\mathcal{K}\mathbf{f})(\,\cdot\,,N_{t})\) and \(\mathcal{W}^{\intercal}\colon\mathbb{R}^{N_{x}\times N_{x}\times N_{x}} \to\mathcal{F}\colon\mathbf{h}\mapsto\chi_{R}(\mathcal{K}\mathbf{h})(\,\cdot \,,N_{t})\), respectively, where \(\mathcal{K}\colon\mathbb{R}^{N\times N\times N}\to\mathbb{R}^{N_{x}\times N_{ x}\times N_{x}\times(N_{t}+1)}\) denotes the discretized wave propagation defined by the k-space method using the discrete time steps \(jT/N_{t}\) for \(0\leq j\leq N_{t}\), and \(\chi_{R}\) is the discretized indicator function of the ball \(B(0,R)\). The linear projection \(\mathbf{X}\) and its left inverse \(\mathbf{X}^{\sharp}\) are implemented using the Matlab build in functions for the Radon and inverse Radon transforms, respectively, with \(N_{\theta}\) equally spaced projection angles covering \([0,\pi]\). The resulting discretized transforms are denoted by \(\mathcal{X}\) and \(\mathcal{X}^{\sharp}\), respectively.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Signal Preprocessing  
Each ECG recording was segmented by a sliding window of 4096 samples. Figure 2 illustrates the segmentation of the ECG recording. There is an overlap of 150 samples between two adjacent windows. The design of the 150-sample overlap is for those incomplete QRS complexes located at the end of the 4096-sample segments, which would be misidentified as Not QRS peaks. They can be completely included in the beginning of the next 4096-sample segment because of the overlap of 150 samples. Two median filters were then applied to remove the low-frequency baseline drift [22]. Each ECG segment was first filtered by a median filter with a width of 200 ms (i.e., 72 samples at a sampling rate of 360 Hz) to remove the QRS and P waves; each segment was then filtered by a median filter with a width of 600 ms to remove the T waves. Hence, the baseline drift signal can be extracted by the output of the second median filter, and the baseline drift eliminated ECG recording can be obtained by subtracting the estimated baseline drift from the original ECG signal. Figure 3 shows the illustration of removing the low-frequency baseline drift, where the original ECG is the input signal, M1 and M2 are the outputs of the first and second median filters, respectively, and the filtered ECG is the baseline drift eliminated ECG. It is shown that most of the baseline drift can be removed. The high frequency components of the QRS wave were not pre-filtered out in the preprocessing stage.
# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
## 2 Materials and Methods
### Box Type and Quantity Recognition  
#### 2.2.1 The Dataset  
This research selected an object detection method based on deep learning. The success of deep learning in computer vision relies significantly on the availability of large datasets to avoid overfitting, especially when the neural network parameters are extensive. The more sufficient the dataset, the more effective features the model can extract, and the better the model fit. The dataset collected was captured in the Pyeong Hwa Automotive Factory. The sample images in the experiment were mainly from images and videos of boxes taken with mobile phones. All images are 1000 to 3000 pixels wide and 2000 to 5000 pixels high in a JPG file format. The dataset consists of boxes classified into four types: 04, 13, A, and Y.  
Figure 4: Example of binary classification: (**a**) box, and (**b**) no box.  
Figure 3: Selecting the RoI.  
Several frames were extracted from the videos. Since many of the frames were identical, eliminating some of them was necessary. The final dataset contained 1258 images; Figure 5 shows examples.  
The training and inference speed of the neural network will be reduced if the image resolution is too high. Since there are many types of boxes and backgrounds in one image, we cropped images to one stack, as seen in Figure 6. After cropping the image, more than 400 images are acquired for each class.  
Labeling datasets is one of the most important steps in object detection. To make a deep learning model learn properly, we need to neatly and correctly label each object in the images. After completing the image cropping, the four box classes were manually annotated using MakeSenseAI 1.10.0-alpha [16], as seen in Figure 7. During the annotation process, the rectangular tool was selected to annotate each stack of boxes. The marked  
Figure 5: Example images: (**a**) Class Y, (**b**) Class A, (**c**) Class 13, and (**d**) Class 04.  
Figure 6: Cropping an image for the final dataset.  
The numbers and distributions of objects in the training set are shown in Figure 8. Figure 8a shows the object names and corresponding amounts, indicating that the dataset encompasses an adequate number of instances for each box type. Figure 8b shows the distribution of tags. The x-axis is the ratio of the label center to the image width, and the y-axis is the ratio of the label center to the image height. As seen in the figure, the data are widely distributed and concentrated in the middle of the image. Figure 8c shows the sizes of the objects. The x-axis is the ratio of the label width to the image width, and the y-axis is the ratio of the label height to the image height.
## 2 Materials and Methods
's study [2], it was pointed out that using low-order Gaussian or Bessel beams to describe such high-intensity laser fields leads to inaccuracies. They pointed out that for tightly focused beams with a waist radius \(\omega_{0}\) (denoted as \(b_{0}\) in this paper) smaller than 4 \(\upmu\)m, the fifth-order correction terms satisfy both the accuracy and computational complexity requirements: For ultra-strong lasers, corrections beyond the fifth order are required to ensure accuracy in the description, and the fifth-order correction is sufficient for describing this situation, even for smaller \(\omega_{0}\) values. Therefore, further increasing the correction order may lead to redundant computational effort. Thus, our design conditions and the derivation of the formula use a fifth-order field description to more accurately describe tightly focused laser pulses while avoiding excessive computational overflow. Considering the rest mass of a single electron \(m_{e}=9.1096\times 10^{-31}\) kg and its charge \(-e=-1.6022\times 10^{-19}\) C, we determine and describe the electron's motion in a strong laser pulse using the following Lorentz equation and energy equation:  
\[\frac{d\mathbf{p}}{dt_{e}}=-e(\mathbf{E}+\mathbf{u}\times\mathbf{B}) \tag{13}\]  
\[\frac{d\Gamma}{dt_{e}}-ec(\mathbf{u}\cdot\mathbf{E}) \tag{14}\]  
where \(\Gamma=\gamma_{0}m_{e}c^{2}\) represents the electron's energy, defined by the formula \(\gamma_{0}^{-1}=\sqrt{1-\mathbf{u}^{2}}\), where \(\gamma_{0}\) is the Lorentz factor, representing the electron's normalized initial energy. The electron's momentum is \(\mathbf{p}=\Gamma\mathbf{v}/c^{2}\), where \(\mathbf{v}\) is the electron's velocity and \(\mathbf{u}=\mathbf{v}/c\). It is assumed that the direction of the electron's velocity is opposite to the direction of laser propagation. The time-varying electromagnetic field produced by a moving charged particle can be derived from the Lienard-Wiechert potential [24]. Therefore, the radiation power per unit solid angle can be calculated as follows:  
\[\frac{dP(t_{e})}{d\Omega}=\left[\frac{|\mathbf{n}\times[(\mathbf{n}-\mathbf{u})\times d_{t }\mathbf{u}]|^{2}}{(1-\mathbf{n}\cdot\mathbf{u})^{6}}\right]_{t} \tag{15}\]  
The radiation pulse power \(dP(t_{e})/d\Omega\) is normalized to \(e^{2}b_{0}^{2}/4\pi c\). The different time subscripts in Equation (15) indicate that the pulse received by the observer at time \(t\) is actually emitted by the electron at time \(t_{e}\). The data were obtained by solving the ordinary differential Equations (13) and (14) using the 4-5 Runge-Kutta-Fehlberg method (RKF45). A large-scale GPU parallel computation is performed on MATLAB R2023a, recording the numerical solutions for the electron's position, velocity, and acceleration at each step. The radiation at different spacetime states can be derived from Equation (15), and relevant plots of the electron's characteristics are obtained through data visualization.
## 4 System Simulation and Performance Analysis
### Performance Degradation Analysis  
The regulating performance of the SCRS is mainly affected by the dynamic characteristics of the electro-hydraulic actuator (including the reset spring and the dynamic characteristics of the solenoid valve) and the running state of the valve. If the dynamic characteristic of the electro-hydraulic actuator changes or there is valve leakage, the system regulation performance will degrade and even cause capacity regulation failure. Therefore, this section analyzes the degradation law of system regulation performance.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 2 Measurement Algorithms
### Space Vector Algorithm
The algorithm latency in this case is \(L=((N_{H}+\text{max}\{N_{M},N_{P},N_{F},N_{R}\})/2-1)T_{s}\), where \(N_{H}\), \(N_{M}\), \(N_{P}\), \(N_{F}\), \(N_{R}\) are the number of taps of the filters \(H\), \(M\), \(P\), \(F\) and \(R\), respectively.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 4 Temperature Correction of Data  
The examples discussed in the previous section clearly indicated the need for temperature correction of the obtained data. This section describes the proposed algorithm for such correction built in the course of routine processing of measurements from the Saint Petersburg Observatory. This algorithm includes four steps: (1) calculation of temperature correlations, (2) localization of temperature dependencies, (3) correction of variations, and (4) recalculation of baseline values. When determining the correlation between variometer readings and temperature, the G plots shown in Figures 2-5 were not applicable because they depended on the baseline values, while the latter depended on the variometer readings, which just need to be corrected. Therefore, a centered _G-mean_(_G_) plot was considered instead of G to construct the correlation. The correlation was determined using the least square method, which resulted in two coefficients \(a\) and \(b\), at which the graph \(a+bT(t)\), where \(T\) is temperature and \(t\) is time, lied closest to the graph _G_(_t_)-_mean_(_G_). The steps of the algorithm operation are illustrated below for each of the examples from the previous section.  
The vector measurements of the magnetic field were performed with a 1 s sampling rate, so the data of the scalar magnetometer, which measures the magnetic field every 3 s, were piecewise linearly interpolated and resampled with a period of 1 s. The algorithm for the temperature drift reduction was designed for analyzing 1-s geomagnetic data supplemented with constant offsets. In this section, all presented plots are based on these data, in contrast to the previous section, which illustrated 1-min baseline corrected measurements.
## 4 Performance Analysis
### Model Comparison
#### 4.4.1 Comparative Analysis  
Algorithm learning was conducted in the Geforce 1660 TI environment, as described above, and Figure 14 below is a graph comparing the LSTM and BI-LSTM learning results. In batch size 40, the execution time for each epoch was less than 10 s, and the same training was performed with 1000 epochs.  
As a result of learning, BI-LSTM obtained relatively superior results compared to LSTM. Both LSTM and BI-LSTM showed convergence around 200 epochs, but the MAE of the model learned through BI-LSTM obtained better results than LSTM. In the case of the LSTM model, it converges to 0.45 after 200 epochs, and in the case of BI-LSTM, it can be confirmed through the graph that it converges to 0.15 after 200 epochs. In terms of convergence speed, both algorithms obtained similar results, but BI-LSTM obtained an excellent result, about 2.5 times greater than the learning result of LSTM provided. Although there is no significant difference in learning speed between the two models, the figure shows that  
Figure 14: Comparison of the training model.  
Figure 13: Comparison between train data and test data of the model using the BI-LSTM algorithm.  
BI-LSTM is effective in predicting manufacturing process time. In addition, we proved that this paper can obtain meaningful results through comparative results.
## 4 Results and Discussion
### Unidirectional Specim with a Central Cut under Tension  
The unidirectional specimen with a cut across the width of one central ply loaded in tension was considered. In order to observe the delamination propagation deriving from the matrix crack, measurements on remote stresses (the ratio between the load and the cross-section area) were performed [49]. The geometry and the loading conditions are shown in Figure 3. The material properties of 977-2 HTS \([0^{o}_{7}]\) CFRP composite laminates are as follows: [PERSON]'s modulus \(E_{1}=144\) GPa, \(E_{2}=7.5\) GPa, shear modulus \(G_{12}=5.03\) GPa, [PERSON]'s ratio \(\upsilon_{12}=0.29,\upsilon_{23}=0.50\), tensile strength \(X_{T}=2290\) MPa, \(Y_{T}=47\) MPa, shear strength \(S=67\) MPa, and fracture energy \(G_{s}=1.55\) N/mm. In this problem, Mode II shear failure dominates in interlaminar failure, and delamination growth is promoted by the pre-given initial matrix crack before the overall fracture due to fiber tension breaking of other layers.  
A finite element mesh is shown in Figure 4, where half of the geometry (0.85 mm height) is chosen for the simulation because of the symmetry of the problem, and the plane strain state is considered with displacement control. The red line represents cohesive zone elements and is only assigned to the down interface in the figure (middle part in the whole area), for the interlaminar damage only occurred in this interface. The initial crack was formed by the killing element technique here. From the deformed shape showed in Figure 5, it can be found that the delamination by shear occurred in the interface between the two layers through nodal separation; of course, this is not intuitive, as in the Mode I failure. After the delamination had grown to some extent, the fiber damage initiated and evolu  
Figure 4: Finite element mesh.  
Figure 3: Configuration of a unidirectional specimen with a central cut.  
The remote stress-displacement curve predicted by our approach is shown in Figure 6. The remote stresses at delamination propagation were measured at the specimens with various cross-section areas by the German Aerospace Centre [49]. The mean value by experiments was 1753 MPa, while the simulated value by our approach was 1730.1 MPa. Both values match well, clearly verifying that our model can simulate the interactive behavior between the matrix cracks and the delaminations of the composite material.
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture
### Nanotechnology  
An architecture based on a lightweight and open software middleware layer aiming at achieving a balanced combination of RFIDs and Smart Objects is proposed by [PERSON] et al. in [75] directing towards a model for IoT which is generic and flexible in the hopes of optimizing the costs associated with RFID deployments as well as technical, social, and educational challenges. The model proposes the use of nanotechnology to address these issues by producing cheap, non-toxic, and disposable electronic devices. Interaction, awareness, and representation are salient features of smart objects making them complex entities. RFIDs are known for simplicity and scalability. A middleware for optimizing the use of both entities in the architecture hopes to resolve the aimed challenges of IoT. More research highlighting the use of smart and nanotechnology in IoT is presented by [PERSON] et al. in [76].  
In [3], [PERSON] et al. present the three-layered traditional IoT architecture as defined by China Mobile. Several shortcomings and challenges of the basic IoT models are highlighted in this research which include power and cost concerns, remote administration, sensor quality, and peripherals adaptability and flexibility. RFIDs are the most convenient form of sensors to gather information in terms of installation, ease of use, scalability, and power and cost efficiency; hence, they are the most popular choice for a traditional IoT structure. However, they are faced with many challenges. [PERSON] et al. in [77] highlight some of the major challenges faced by RFID technology. According to [77], RFIDs are prone electromagnetic interference. Collisions could occur as a result of multiple transmissions across the same wireless channel. Anti-collision protocols such as Query Tree, Binary Tree, and Frame Slotted ALOHA exhibit efficiency lesser than 50.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 1 Introduction
It enables reliability and escalates network performance, i.e., better throughput, reduced latency, and less bit error rate through parameter controlling from a layer to another [9]. To mark improvements in a tactical radio network, we propose a novel virtual sub-nets based cross-layer medium access control (VSCL-MAC) design that provides a self-forming and self-organizing intelligent approach to radio communications. It involves neighborhood discovery, which keeps nodes informed about their \(k\)-hop neighbors and helps nodes discover routes to these neighbors. Each SDR exchanges control frames to communicate intended transmission, establish routes for data transmission, and perform virtual confinement of radios, called sub-nets or subnetworks. This virtual division of the network enables simultaneous collision-free communication using a hybrid of TDMA/FDMA approaches. The distributed intelligent approach eliminates the neighbor discovery phase's periodical runs and enables efficient route discovery and simultaneous data transmission using FDMA for non-intermittent high network throughput in a time-distributed manner. The VSCL-MAC design supports unicasting and enables multihop communication within a uniform framework. It finds a route between source and destination using a common table, unlike other ad hoc routing protocols [10]. The design also provides dynamic slot allocation to simultaneous data transmission and assigns multiple slots to SDRs according to the data size, such as text or multimedia data. The adapted time slot allocation for radio transmission over multiple FDMA carriers ensures throughput maximization and QoS in tactical communication.