esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 3 Data Preprocessing  
In the process of map information preprocessing, the coordinate data of NGSIM data set is first converted into vectorized vehicle trajectory and environment information. In this method, the dynamic vehicle trajectory and static object and lane line are represented as vector direction lines [23], and the trajectory and environment information of the vehicle to be predicted are encoded into vector matrix \(S\). To better represent the interrelationship between the lane and the predicted vehicle, we choose to use a 2D Cartesian coordinate system where the vehicle to be predicted is at the origin and the direction of travel is the X-axis. The vector from the two-dimensional coordinates of the central vehicle to each vehicle is used as the residual link vector to form a two-dimensional aerial view with the central vehicle as the reference. As shown in Figure 1.  
In the vehicle trajectory prediction problem addressed in this paper, the main task does not lie in designing networks to model social interactions. Therefore, we use the transformer network [16] architecture of MID based on the standard diffusion model as the encoder. The encoder network conceptualizes historical behaviors and social interactions as condition function, denoted by \(\mathbf{f}\), vehicle trajectories in the video, denoted by \((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{t},y_{t})\), can be conceptualized as a directed sequence signal over time. \(\mathbf{S}^{i}\in\mathbf{R}\) is interpreted as the acquired scene context encoding. This encoding includes the road map, the context vehicles, and the interaction between these scene elements, all centered on a specified baseline vehicle i. Throughout the training phase, the dataset is built around triples, including historical trajectory \((\mathbf{x},\mathbf{y})_{0:t}\), predicted trajectory \((\mathbf{x},\mathbf{y})_{t:T}\), and vehicle state information \(\mathbf{S}\).
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 2 Preliminaries
### LHZ FHS Set and Theoretical Bound  
Let integers \(L_{a}>0\), \(L_{c}>0\). Then, the LHZ \(L_{z}\) of \(\mathbf{S}\) is defined as follows.  
\[\begin{split} L_{z}=\min\{\max_{1\leq\tau\leq T^{\prime}}\{T^{ \prime}|H(\mathbf{x},\mathbf{x},\tau)\leq L_{a},\forall\mathbf{x}\in\mathbf{S} \},\\ \max_{0\leq\tau\leq T^{\prime}}\{T^{\prime}|H(\mathbf{x},\mathbf{y },\tau)\leq L_{c},\forall\mathbf{x},\mathbf{y}\in\mathbf{S},\mathbf{x}\
eq \mathbf{y}\}\}.\end{split} \tag{3}\]  
\(\mathbf{S}\) is called an LHZ FHS set if \(L_{z}>1\). In 2006, [PERSON] et al. [10] obtained the lower bound on the MPHC of the LHZ FHS set.  
**Lemma 1** (Peng-Fan bound).: _For any set \(\mathbf{S}\) of \(M\) FHSs with length \(N\) over \(\mathbb{F}\) of size \(q\), the low-hit zone \(Z\), and the MPHC \(H(\mathbf{S})\), one has_  
\[H(\mathbf{S})\geq\bigg{\lceil}\frac{(MZ+M-q)N}{(MZ+M-1)q}\bigg{\rceil}. \tag{4}\]  
**Definition 1**.: _Let \(\mathbf{S}\) be any LHZ FHS set. For the MPHC, \(\mathbf{S}\) is said to be optimal if its parameters let the equal sign in (4) be true._
## 4 Suggestion and Verification of Optimal Mixing Ratio for Waterproofing Performance  
### Suggestion for Optimal Superabsorbent Polymer Mixing Ratio  
The waterproofing performance of water-expandable polyurethane varies depending on the swelling force; that is, the degree of expansion when the coating material comes into contact with water. As previously identified, the minimum swelling rate for water-expandable polyurethane to exert its waterproofing performance was 150% for water-expandable polyurethane, which is commonly used in practice.  
Therefore, based on the swelling rate evaluation results according to the deterioration conditions in Table 7, the optimal mixing ratio of the superabsorbent polymer to ensure a swelling rate of 150% or higher is presented in Table 9. It is adjudged that the water-expandable polyurethane can stably secure waterproofing performance at a mixing ratio of 14% or higher for the hydrochloric acid treatment, the nitric acid treatment, and the sulfuric acid treatment, 13% or higher for the alkali treatment and the sodium chloride treatment, and 12% or higher for the freeze-thaw treatment and the repeated wet-dry treatment.
# Fabrication of a Simultaneous Highly Transparent and Highly Hydrophobic Fibrous Films
## 2 Materials and Methods
### Measurements and Characteristics
#### 2.3.5 Optical Transmittance Measurement  
The light transmittance transparency of the nanofiber scaffold samples was measured by UV-Visible transmission spectroscopy (Lambda 750 model). The electrospun fiber scaffold samples produced with various electrospinningning times of 10, 20, 30, and 40 s were examined. The light transmission area was 5 mm \(\times\) 25 mm, and the scanned sample area of the electrospun fibers on glass was 25 mm by 30 mm.
## 2 Research Methodology
### Pose Estimation System Construction
#### 2.3.3 Finish Alignment Matching  
After the coarse alignment matching through the SAC-IA algorithm, the iterative closest point (ICP) algorithm was applied to optimize the prediction and obtain the final 6-DoF pose [18]. The reference point cloud and target point cloud which have been transformed by the SAC-IA algorithm are considered as the input data for the ICP algorithm. Then, the nearest corresponding point, \(Q_{i}\), will be found inside the target point cloud, \(Q\), for each point, \(P_{i}\), of the reference point cloud, \(P\). The finishing point pairs are built up through the pair \(P_{i}\)-\(Q_{i}\). The error of the ICP finish alignment is defined by the acquired error of Euclidean distance between corresponding point pairs \(P_{i}\)-\(Q_{i}\) through the following formula:  
\[d_{k}=\frac{1}{N}\sum\limits_{1}^{N}\|Q_{i}-RP_{i}+T\| \tag{18}\]  
where \(d_{k}\) is the Euclidean distance, \(N\) is the number of iterations, and \(R\) and \(t\) are the rotation and the translation matrix of the finish alignment, respectively.  
Figure 5: Effect area diagram of estimated feature point for the dynamic workpiece using FPFH.
# _microorganisms_
## 2 Materials and Methods
### Primer Design for Real-Time qPCR  
Odorant-producing cyanobacteria were isolated from Paldang Lake, and pure cultures were obtained. The odorant synthase gene sequences were deposited with GenBank (accession numbers MT360266, MT515744, and ON365769-ON365771). The odorant gene sequences from the isolated odorous cyanobacteria (_Pseudanabaena_ sp. and _Anabaena_ sp.) and NCBI databases were used to establish the primer design for the real-time qPCR amplification of _mic_ and _geo_ using the SYBR Green system. Regions with high detectability were selected by aligning the isolated and referred sequences using BioEdit software 7.2 and were used to design the two primer sets (Table 1).  
Two 2-methylisoborneol (2-MIB) primers (3909F/4028R) were designed to identify _mic_, which encodes monoterpene cyclase, the enzyme that cyclizes 2-methyl GPP to 2-MIB [25; 26], and specifically, the region that encodes the highly conserved Mg\({}^{2+}\)-binding site. The target DNA fragment for the detection of geos  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Sequences, 5\({}^{\prime}\)\(\rightarrow\)3\({}^{\prime}\)** & **Product Size** & **Tm** \\
**(Gene)** & **Primer** & **Sequences, 5\({}^{\prime}\)\(\rightarrow\)3\({}^{\prime}\)** & **(bp)** & **(\({}^{\circ}\)C)** \\ \hline
2-MIB & 3909F & CAC CAG ATC TTT TCT TCG ATC & \\ (_mic_\({}^{1}\)) & 4028R & AAT CTG TAG CAC CAT GTT GAC & 140 & 59 \\ \hline Geosmin & 2014F & GCC CTT TGC GAC GAT TTC AA & \\ (_geo_\({}^{2}\)) & 2163R & GGA AGC ACT ATT ACG CAA TTC AAA & 150 & 59 \\ \hline \hline \end{tabular} \({}^{\dagger}\)_mic_ (_mibC_): _monoterpene cyclase gene; \({}^{2}\) geo_ (_gsy1_): _putative geosmin synthase gene._  
\end{table}
Table 1: Primers designed for quantification of _mic_ and _geo_.  
Figure 1: Water sampling locations in the North Han River Basin, South Korea.  
putative geosmin synthase sequence [27] that is part of the geosmin synthesis operon of _Anabaena urcanica_. The G-C content (%) of the selected regions and the potential for secondary structure formation were confirmed using OligoCalc (biotools.nubic.nothwastern.edu/ Oligocalc.html accessed on 12 September 2023).
# Review of Works Combining GNSS and InSAR in Europe
## 3 Temporal and Spatial Distribution of Scientific Production in Europe
### Field of Application
Other infrastructure applications were indirect, including the monitoring of subway construction in Bucharest, Romania [183], ground deformation detection focused on a new geothermal power plant at Reykjanes, Iceland [73], observation of ground deformation associated with the Kozloduy Nuclear Power Plants (NPPs), Bulgary [184], and experimental monitoring of localized deformation on the Roman aqueducs in Rome, Italy [27]. Glacial applications are represented by specific case studies in Iceland, which focused on the 3D surface motion of the glacier surface of the Gjalp volcano in Vatnajokull [28; 29]; France, with a time-series measure of the Argentiere glacier on the Mont Blanc Massif [185]; and northern Scotland (United Kingdom) through the investigation of the Glacial Isostatic Adjustment (GIA) [186]. In addition, some very particular studies of the combined use of GNSS and SAR data, categorized as Other (Figure 4), recorded the following: (i) sea level change detection along the coastline of Brest, France [187], along the North Sea and Baltic Sea Coast in Germany [188] and in the Northern Mediterranean Sea [189]; (ii) flood inundation modeling of Northamptonshire [190]; (iii) mine collapse monitoring in Rudna Mine, Poland [191]; (iv) surface and roughness and sediment texture characterization [192]; and (v) a new approach description of precise datum collection [193].
According to Eurostat, the EU produced 271.6 million tons of cereals in 2023 [22]. For Bulgaria, as part of the EU, the share of sunflower production from whole agriculture sector reaches 69.7% for 2023 or 1,764,594 tons in total [23]. The crop waste generated in this production in Bulgaria is estimated at about 3 million tons per year, which reveals the significant potential for utilizing sunflower crop waste through the production of bio-fertilizers. During the production of vegetable oil and biodiesel from sunflower seeds, the sunflower husks are a significant part of the residue, and they, like other plant waste,are most often burned or directly landfilled. Sunflower husks, however, contain various nutrients and trace elements that can be beneficial to plants and soil. Applying a two-stage approach to their utilization through initial combustion, in which they will first be utilized for energy production, followed by a second stage of utilization through the production of bio-fertilizers, will lead to significant benefits both in terms of increased energy efficiency and for ensuring ecological agriculture and bio-fertilization. Depending on the fertilization products used and the growing conditions of the sunflower, the ash obtained when burning waste sunflower husks can be rich in key elements, such as potassium, calcium, magnesium, and some trace elements, such as zinc, copper, and manganese [24; 25; 26; 27; 28]. Potassium carbonate, which is extracted from plant ash, has been used in agriculture for years as a fast-acting fertilizer [29; 30; 31; 32; 33; 34]. Sunflower husk ash can also be fully used as a biofertilizer with an NPK composition of 0-6-13, which makes it suitable for a wide range of agricultural applications [35]. All this, combined with the proven absence of hazardous substances and heavy metals in sunflower husk ash, defines the proposed approach as a modern ecological alternative [36]. Nonetheless, in the EU, as well as in Bulgaria itself, only the first stage of utilization of sunflower husks is mainly applied, namely their combustion for energy needs. The aim of this study is to design the full-scale technology necessary for self-sustaining double-stage circularity through the utilization of sunflower agriculture's waste in bio-fertilizers. The first stage of circularity covers the burning of sunflower husks for the production of thermal energy for industrial processes and heating, and the second one encompasses the bio-fertilization production from the obtained ash residue. The research experimentally investigates the full-scale technological approach regarding the second stage's implementation in the industry.
## 4 Results and Discussion  
### Numerical Model Verification  
In order to verify the accuracy of the numerical calculation model, the vertical final settlement of the vault of the right tunnel at different distances from the portal after the tunnel excavation in the numerical calculation and field monitoring was compared and analyzed, as shown in Figure 6. It can be seen from the figure that the curves of simulation results and monitoring results are similar in shape and size. The farther away from the portal, the greater the vertical deformation of the vault. The field monitoring value fluctuates on both sides of the simulation value. The maximum error between the simulation value and the monitoring value is 2.13 mm, the minimum error is 0.03 mm, and the average error is 1.03 mm. It can be seen that the numerical model has high accuracy.  
Figure 5: Buried pipeline: (**a**) Oblique drawing; (**b**) Side view.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Influencing Factor**} & **Condition 1** & **Condition 2** & **Condition 3** \\ \hline Relative position & Pipeline buried depth (m) & 1.5 & 2.0 & 2.5 \\ relationship between & Pipeline diameter (mm) & 400 & 600 & 1000 \\ underground pipeline & Pipeline wall thickness (mm) & 50 & 75 & 100 \\ and tunnel & Pipeline section shape & Square & Circle & - \\ Pipeline parameters & Pipeline material & Steel & Concrete & PVC \\ Tunnel construction & & & & Three-step \\ parameters & Tunnel excavation method & Full section method & core soil for three steps & excavation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influencing factors and parameter values of pipeline dynamic response.
# Investigation on Aerodynamic Noise Characteristics of Coaxial Rotor in Hover
## 4 Conclusions  
A CFD solver based on RANS equations and a high-efficiency trim method is established to simulate the unsteady loads of a two-bladed coaxial rotor in hover. Farassat 1A formulations are applied to predict the thickness and loading noise. The acoustic characteristics of the coaxial rotor are analyzed compared with the single rotor. The following conclusions can be drawn:  
(1) The noise of the coaxial rotor is rather different from a single rotor in the characteristics of the radiation and spectrum. This is mainly caused by two factors. One is the noise superposition effect of the upper and lower rotors, which are contra-rotating. The other is the complex unsteady aerodynamic interaction, especially the unsteady impulsive loads induced by the blade-meeting interaction, which leads to a new kind of impulsive loading noise.  
(2) The thickness noise of a single rotor distributes as concentric rings, which remains constant at different azimuths. Due to the noise superposition effect, the thickness noise SPL of a coaxial rotor shows cyclical fluctuations along the latitude (azimuth) angle. Moreover, at the blade-meeting azimuth angles, the magnitudes of thickness noise are enhanced.  
(3) For the single rotor, the highest SPL region of loading noise lies obliquely below the rotor disk, at an approximately -30\({}^{\circ}\) latitude angle. For the coaxial rotor, the highest SPL region is immediately below the rotor disk center. This is due to the fact that the noise of the coaxial rotor is mainly determined by the unsteady interaction loads, which causes the strongest noise below the rotor. The loading noise around the blade-meeting azimuths is also enhanced.  
(4) Regarding the frequency spectrum, for the single rotor, the frequency of loading noise is mainly lower than the 40 th harmonic of rotor rotation frequency, in this paper. For the coaxial rotor, the loading noise shows much stronger magnitude than the single rotor in the high-frequency band, due to the high-frequency unsteady interaction loads.  
Figure 16: Total noise SPL contour maps of different \(C_{\rm T}\) levels. (**a**) A0 case; (**b**) A1 case.  
**Author Contributions:** Conceptualization and project administration, H.Q.; validation and data curation, Y.Z. and L.J.; writing--original draft preparation, H.Q.; writing--review and editing, H.Q., P.W. and L.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 12102154) and the Foundation of Key Laboratory of Aerodynamic Noise Control of China Aerodynamics Research and Development Center (No. ANCL20200203).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available upon request by contact with the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Additionally, unstructured-mesh methods offer higher resolution, enabling more precise representation of the complex structures and anisotropic characteristics of subsurface media [36]. Currently, significant research achievements have been made in two-dimensional and three-dimensional anisotropic controlled-transmitter electromagnetic (CSEM) problems using unstructured-mesh finite element methods [37]. However, in the field of borehole-to-surface electromagnetic methods (BSEM), studies that consider resistivity anisotropy within a three-dimensional modeling framework remain relatively limited, particularly those employing edge-based finite element methods on unstructured meshes. While anisotropy has been recognized as a critical factor influencing electromagnetic responses in subsurface formations, most existing research still relies on simplified isotropic assumptions or two-dimensional approximations. The lack of comprehensive 3D numerical approaches that accurately incorporate anisotropic effects, especially in complex geological settings, restricts the applicability and precision of BSEM techniques in real-world reservoir monitoring and subsurface characterization. Therefore, there is a pressing need to develop robust and efficient 3D anisotropic modeling tools tailored to BSEM configurations. In recent years, several open-transmitter software packages have been developed for the forward and inverse modeling of electromagnetic methods, such as PETGEM [10], custEM [38], SimPEG [39], and emg3d [40]. Among these, PETGEM and custEM utilize tetrahedral meshes and support high-order basis functions. SimPEG employs the finite volume method and supports various mesh types, including staggered meshes, octree meshes, and cylindrical meshes. PETGEM can implement horizontal-vertical resistivity anisotropy, while custEM can achieve principal-axis and full-resistivity anisotropy. These open-transmitter codes provide support for the development of this method. This study aims to perform three-dimensional numerical simulations of reservoir dynamic monitoring models using the borehole-to-surface electromagnetic method based on an edge-based finite element approach with unstructured meshes. The objective is to evaluate the effectiveness of this method in monitoring reservoir dynamics and to demonstrate its advantages over traditional models that assume resistivity isotropy. To validate the accuracy of the proposed approach, comparisons are made with existing two-dimensional codes capable of handling anisotropic media. Furthermore, this study investigates the electromagnetic response characteristics of anisotropic reservoirs under borehole-to-surface monitoring scenarios.
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 2 System Model  
The representation of the NOMA structure is specified in Figure 1a. NOMA is considered to be one of the primary contenders for 5G radio. The characteristics of NOMA, such as low outages, high data rates, high bandwidth, and the ability to connect multiple devices, make it suitable for 5G. However, high PAPR significantly reduces the performance of NOMA wave forms [20].  
The NOMA signal can be expressed as:  
\[Y_{n}=\frac{1}{\sqrt{2}}\sum_{l=0}^{N-1}Y(l)\ \text{exp}\bigg{(}\frac{i6.28\ ln}{N} \bigg{)} \tag{1}\]  
where \(\ l=0,1\ldots,N-1\). The characteristics of filter used in NOMA is given as:  
\[h(t)=\delta(t-mT)\ \text{exp}(i2\pi f_{c}t) \tag{2}\]  
Figure 1: (a): Proposed F-NOMA System. (b): Proposed method.  
where \(f_{c}\) is the frequency of the carrier signal. The time domain NOMA with filter can be written as:  
\[Y(t)=\sum_{m=0}^{N-1}Y_{n}\delta(t-mT) \tag{3}\]  
From Equation (3), we can estimate the PAPR of the NOMA signal by:  
\[Y(t)_{PAPR}=\frac{Maximum_{t\in T}|Y(t)|^{2}}{\frac{1}{T}\int_{o}^{T}\!\left|Y( t)\right|^{2}\!dt} \tag{4}\]  
To lower the PAPR, the SLM algorithm is applied at the transmitter of the NOMA framework, shown in Figure 1b.  
The transmit symbol \(Y(t)\) can be expressed as:  
\[Y=\left[Y_{0},Y_{1},\;\ldots\;Y_{N-1}\right]^{T} \tag{5}\]  
The Equation (5) is mapped into S (sub-blocks). The phase rotation element (\(P^{s}\)) is used to obtain an ideal PAPR value, expressed as:  
\[P^{s}=\left[P^{s}{}_{0},P^{s}{}_{1},\;\ldots\;P^{s}{}_{N-1}\right]^{T} \tag{6}\]  
where \(P^{s}=\exp\left(j\theta^{s}_{p}\right)\) and \(\theta^{s}_{p}\) ranges among \(\left[0,360\right]\) for \(p=0,1,\;\ldots,N-1\), and \(s=1,2,\ldots,S\). The NOMA (\(\Upsilon\)) are scaled with \(P^{s}\) to obtain an ideal PAPR value given by:  
\[Z^{s}=\left[P^{s}*Y\right] \tag{7}\]  
\[Z^{s}=\left[Y^{s}{}_{0},Y^{s}{}_{1},\;\ldots\;Y^{s}{}_{N-1}\right]^{T}*\left[ P^{s}{}_{0},P^{s}{}_{1},\;\ldots\;P^{s}{}_{N-1}\right]^{T} \tag{8}\]  
In order to further estimate the peak value of NOMA symbols, an IFFT is applied on Equation (8), given as:  
\[z^{s}=\left[z^{s}{}_{0},z^{s}{}_{1},\;\ldots\;z^{s}{}_{N-1}\right]^{T} \tag{9}\]  
From Equation (9), the PAPR is estimated and the lowest PAPR signal is selected. The optimal PAPR is expressed as:  
\[PAPR_{optimum}=\frac{maximum_{t\in T}|z^{s}(t)|^{2}}{\frac{1}{T}\int_{o}^{T} \!\left|z^{s}(t)\right|^{2}\!dt} \tag{10}\]  
The performance of the proposed algorithm is weighed by estimating the Complementary Cumulative Distribution Function (CCDF), given as [21]:  
\[CCDF=Prob(PAPR\geq z^{s}{}_{th}) \tag{11}\]