esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective. switchgear; PD; mechanical vibration; anomaly detection; audio signal processing; SVM; pattern recognition +
Footnote †: journal: Applied Sciences
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### GNSS Ntrip Caster (CAS) and GNSS Receiver Control Service (GCS)  
The _testbed_ includes a service that encapsulates a GNSS NTRIP caster from the Bundesamt fur Kartographie und Geodasie (BKG) ([50]). This caster streams the data from the _testbed_ GNSS base receiver(s) to users that need to navigate accurately using RTK (who need GNSS measurements from reference receivers). In addition to the _testbed_ receivers, this service allows also to relay already existing  
Figure 3: High-level Command and Control service (CAC) service description, showing the interaction of the service with the front-end.  
data from receivers that belong to other networks. The _testbed_ NTRIP caster offers the _sourcetable_ with a list of _mountpoints_ in which the users of the _testbed_ will be able to connect to.  
Besides the caster service, the _testbed_ has the GNSS Receiver Control service, that provide a means to monitor the status of the GNSS receivers of the _testbed_. Its API allows the front-end to provide a visual display in real time of the GNSS receiver status as well as timing information in different time-scales (i.e., local, GPS, GNSS, and UTC).
# Area and Performance Estimates of Finite State Machines in Reconfigurable Systems
## 2 Related Works
### Estimates of FSMs
#### 3.2.3 Other Estimates for FSMs

Table 11: Comparison of the sequential state encoding method using presented estimates with known FSM state encoding methods in terms of performance (the best solutions are in bold). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FSM**} & _seq\_code_ & _Quartus_ & _JEDI_ & _s\_method_ & _Fmax/FS_ & _Fmax/FQ_ & _Fmax/FJ_ \\  & _FS_ & _FQ_ & _FJ_ & _Fmax_ & & \\ \hline bbara & 430 & 451 & **547** & 529 & **1.230** & 1.173 & 0.967 \\ bbsse & 345 & **397** & 341 & 357 & 1.035 & 0.899 & 1.047 \\ beecount & 633 & 636 & **637** & 633 & 1.000 & 0.995 & 0.994 \\ cse & 291 & 302 & 319 & **323** & 1.1



According to Table 11, using the proposed estimates in the _s_method_ method on average improves the FSM performance by 7.3% over the _seq_code_ method, 3.5% over the _Quartus_ method, and 4.0% over the _JEDI_ method. The greatest performance superiority of the _s_method_ method is observed over the _seq_code_ method in example bbara (23.0% better), over the _Quartus_ method in example pma (27.6% better), and over the _JEDI_ method in example dk16 (19.5% better). The _s_method_ method achieves the largest number (16) of best area solutions, of which 15 solutions are unique. ## 5 Conclusions

In this paper, four FSM area estimates are proposed to estimate the quality of FSM state encoding: _e_FPGA_ for implementing FSMs in FPGAs; _e_CPLD_ for implementing FSMs in CPLDs; _classic_ for implementing FSMs on elements of small and medium degree of integration; and _terms_ for considering the largest number of FSM parameters. Three estimates of FSM performance are proposed: _seq_dec_, when sequential decomposition of Boolean functions is used; _par_dec_, when parallel decomposition of Boolean functions is used; and _avg_dec_, when an unknown method of Boolean function decomposition is used. In addition, two estimates that do not directly aim at computing area or delay are proposed: _diff_w_ to ensure that the complexity of the FSM transition functions is approximately equal and _max_w_ to minimize the maximum complexity of the FSM transition functions. It is shown how new estimates of the area and performance of FSMs can be generated from the _diff_w_ and _max_w_ estimates. The expressions and algorithms for the computation of each estimate are given. A comparison of the efficiency of the estimates between each other on the benchmark of FSMs when using the proposed estimates in the sequential state coding method [8] is performed. The use of the considered estimates reduces the area of FSMs on average from
# Implementation of the 3-30-300 Green City Concept: Warsaw Case Study
## 2 Materials and Methods
### Case Study
#### 2.2.2 Data  
The data used in the study were obtained from the geoportal.gov.pl portal [30]. It is an online platform providing access to the state geodetic and cartographic data resource made available by the Central Office of Geodesy and Cartography [31]. It allows users to browse maps and search for specific locations. It also provides the possibility to download spatial data free of charge. The Geoportal is a tool that is widely used by offices, businesses, researchers, students, and ordinary citizens. Owing to the integration of multiple data sources and analytical functions, the portal supports decision-making and planning processes, contributing to sustainable development and effective spatial management [32].  
To analyze the visibility of trees from individual squares (simulating dwellings), data called \"3D Tree Models\" were used [33]. These are data containing 3D Tree Models that  
Figure 3: Parks and forests over 1 hectare based on BDOT10k.  
are representations of trees over 4 m in height. The data are provided free of charge in City GML format. It is worth noting that the standard in which they are represented is LoD1 [34]. The 3D models of the trees were generated automatically on the basis of current data in the state geodetic and cartographic resource. Each model provided has the same attributes (object identifier, data source, validity of source material, tree height, tree ground level height).  
For the remaining analyses, BDOT10k data were used [35]. This is a dataset containing the spatial locations of topographic objects together with their basic descriptive characteristics. Examples of data contained in this collection include communication networks, forests, buildings, etc. The content and detail of this database correspond to a topographic map at a scale of 1:10,000. The use of OpenStreetMap (OSM) data has also been investigated [36; 37]. The aim of this project is to provide a free, editable map of the world. Data collection and updating are based on a collaborative community of users who create and update map data. Each user can add information about roads, buildings, and other geographical features, so that the map is constantly updated and relatively accurate. A detailed analysis showed, however, that these data are not as accurate as the BDOT10k data; thus, they were eventually abandoned [38].
## 1 Introduction
### Generalized Gradient Vector Flow and Divergence Map
 \tag{7}\]  
Figure 2: Edge extraction for a test image with arbitrary contours. positive values, defining regions from which the vector field comes out. Moreover, the sides of areas that delimit parts of image inside which the field is null (see enlarged image in the red box of Figure 3), collapse each other, especially in conjunction with long and deep concavities. Therefore, these curves, with positive divergence values, demarcate edge sections with high curvatures (Figure 4) and a great geometrical significance which concur in forming the skeleton of our figure. Upon variation of the vector field \(\bar{v}\) in time, there is a consequent variation of the related divergence map. As can be seen in Figure 5, where a circular initial contour is superimposed on the divergence map, the parts of the deformable curve that are positioned in areas from which the vector field diverges in the direction of boundaries can be pushed towards them. On the contrary, those traits that are inside the regions where the vector field is null remain trapped into their interior. Consequently, the divergence analysis of the external force field \(\bar{v}\) can be very useful in feature extraction, since it allows to delimit regions from which the field flow originated. Furthermore, we could note that the divergence map put in evidence the presence of curves pointing to the most significant geometric parts of boundaries with high curvature values. In this way, the geometrical shape of the extracted objects with their most significant characteristics will result well defined. After these considerations, in this paper, we suggest to use the map contour of divergence to select automatically an initial curve for the deformation process. To this end, given an image \(I(x,\,y)\), at first it has been evaluated \(\bar{v}\) and its divergence map in the gray color space \(C=[0,255]\), as follows:  
\[I_{D}:D\subset R^{2}\rightarrow\left[0,255\right],\ I_{D}=div\left(\frac{\bar{v }}{|\bar{v}|}\right)\]  
then we have generated a contour map of the field divergence \(I_{D}\), in order to visualize its level sets, _i.e._, the set of points in the image domain \(D\) where divergence is constant (Figure 6a). Subsequently, a level curve of high intensity, corresponding to high divergence values has been automatically selected as initial contour (Figure 6b). Figure 4: Test image and its divergence map. In this way, the chosen curve certainly encloses areas from which the field diverges, thereby pushing the deformable contour towards object edges, where sinks or zones of maximum field convergence are localized. As can be seen in Figure 6b, edges have been detected correctly.
###### Abstract  
The Changchun Observatory of the National Astronomical Observatories, Chinese Academy of Sciences, and the Shanghai Astronomical Observatory are used to generate very short arc (VSA) angle observations of objects in low Earth orbit (LEO) and geostationary orbit (GEO) with their ground-based electrical-optical telescope arrays (EA), the Changchun EA and SAO FocusGEO, respectively. These observations are used in this paper. The range-searching (RS) algorithm for initial orbit determination (IOD) is improved through the multiple combinations of observations and the dynamic range-searching step length. Two different computation modes (the normal mode and the refining mode) of the IOD computation process are proposed. The geometrical method for the association is used. The IOD and association methods are extended to the real optical observations for both LEO and GEO objects. The results show that the IOD success rate of arcs from the LEO objects is about 91%, the error of the semimajor axis (SMA) of the initial orbital elements is less than 50 km, and the correlation accuracy rate is about 89%. The IOD success rate of arcs from the GEO objects is higher than 88%, and the correlation accuracy rate is greater than 87%. The recent COSMOS 1408 antisatellite test (ASAT) generated a large amount of debris. The algorithm of this paper and the observations of Changchun EA are used to initially identify new debris, possibly from the ASAT through initial orbit determination and track association. Finally, 64 suspected new pieces of debris can be found. The results show the effectiveness of the IOD and the correlation algorithm, as well as the potential application of the optical-electrical array in studying space events.  
Keywords:space debris; optical telescope; initial orbit determination; track association; orbit determination; antisatellite +
Footnote †: journal: Appl. Sci. 2023, 13, 13224. [[https://doi.org/10.3390/appl32413224](https://doi.org/10.3390/appl32413224)]([https://doi.org/10.3390/appl32413224](https://doi.org/10.3390/appl32413224))
###### Abstract  
This study aimed to offer an alternative way for delivering the benefits of the mandarin fruit juice to consumers via spray drying microencapsulation. Two mandarin cultivars, [PERSON] (A) and [PERSON] (RS), were studied. Three types of juice sample were prepared, i.e., the whole fruit juice (A3 & RS3), the flavedo-removed fruit juice (A2 & RS2), and the peel-removed fruit juice (A1 & RS1) samples. Gum Acacia and maltodextrin (ratio of 1:1, \(w/w\)) were chosen as wall matrices for aiding the drying of the juice samples while using a microfluidic-jet spray dryer. The properties of the fruit powder (colour, water activity, bulk/trapped density, solubility, hygroscopicity, morphology) and the retention of major phytochemicals (i.e., phenolic and volatile compounds) were examined. The results showed that the powders produced from the whole fruit juices (A3 and RS3) gave higher yellow colour with a regular wrinkled surface than other powders (A1 & RS1, and A2 & RS2). The water activity of mandarin powders was in a range of 0.14 to 0.25, and the solubility was around 74% with no significant difference among all of the powders. The whole fruit powders had a significantly higher concentration of phenolic compounds (A3, 1023 ug/100 mg vs. A2, 809 ug/100 mg vs. A1, 653 ug/100 mg) and aroma compounds (A3, 775,558 ug/L vs. A2, 125,617 ug/L vs. A1, 12,590 ug/L). This study contributed to the delivery of phenolic and flavour compounds of the mandarin fruits, at the same time minimising waste generation during processing. It also gave insight into the production of spray-dried powders from the whole mandarin fruits.  
mandarin; microencapsulation; spray drying; phenolic compound; aroma compound; fruit powder; PCA 10.399/foods10010123
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 4 Proposed Approach
### Constraint Framework  
Inspired by GANet, we use its target prediction model to divide the target area of the vehicle, which is used as the target area constraint [27] of our model.  
The future distribution of trajectories learned due to the denoising model can be expressed as \((P(Y;f))\), all we choose to express the same form \((M(S;C))\) as a quadratic distribution representing the constraint function. The combined joint distribution function of the model is expressed as \((L=S_{1}P(Y;f)\cdot S_{2}M(S;C))\), where \(S_{1}\) and \(S_{2}\) are used as control coefficients.  
\[\mathcal{L}_{1}(M((\mathbf{x},\mathbf{y})))=\frac{\sum[(M(\mathbf{Y};\mathbf{f}, \sigma)-G_{\text{GA}})\odot\mathbf{N}_{\text{GA}}]}{\sum[\mathbf{N}_{\text{GA }}]} \tag{11}\]  
where \(G_{GA}\in R^{Nt*Nh}\) is the target area tensor,and \(N_{GA}\) represents the binary mask tensor's values indicating the positions in \(N_{GA}\) utilized to apply constraints to the trajectory.  
Secondly, in a real driving environment, possible future path conflicts between multiple vehicles will lead to disastrous consequences. Based on this, we design a new repulsive collision module to reduce the danger of approaching or colliding between vehicles that interact with each other. The constraint functionality of this module is formally defined as follows.  
\[\mathbf{R}=\text{max}\Big{(}\Big{(}1-\tfrac{1}{r}\Delta(M(\mathbf{Y})),0\Big{)} \tag{12}\]  
\[\mathcal{L}_{2}(M((\mathbf{x},\mathbf{y})))=\tfrac{\
abla\mathbf{R}}{\sum(R\geq 0)} \tag{13}\]  
In our framework, the repulsive force value at each time step is denoted by R. The pairwise distance function between all pairs of noise-reduced vehicles across each time step is denoted as \((\Delta(M(Y;f,\sigma)))\). Furthermore, the repeller radius is designated as r.  
The repulsion effect is quantified by a scalar R, representing the strength of the repulsion at each time step. The identity tensor is systematically extended to all \(N_{t}\) time steps, providing a consistent and structured basis for computation in the entire inference time step. Finally, the repeller radius r defines the region within which the repulsion forces become influential, shaping the effective area of repulsion.
# A Study of Forest Phenology Prediction Based on GRU Models
## 4 Discussion
The results showed that the values of MSE, RMSE, and MAE for the GRU model range from 0 to 1, indicating high prediction accuracy. The performance of the GRU model in phenology prediction is good. The GRU model predicted 60 days of data and was consistent with the trends of forest growth. It was observed that the overall trend of the GRU model showed a relatively flatchange The reason for this is that the GRU model introduces addition when updating from t to t\(-\)1, which prevents gradient dispersion and mitigates the problem of gradient vanishing. Therefore, it can predict the changes in phenological growth. The GRU model has fewer parameters, faster computation, and reduced overfitting risk, which allows it to anticipate changes ahead of time.
## 4 Discussion
[34], the absolute REMPI cross section of toluene at 266 nm (approx. 45% of the cross section at 266.8 nm) and 248 nm (approx. 50% of the cross section at 266 nm) could be calculated. With these two-photon REMPI cross sections of toluene (\(\sigma_{\text{tol,266 nm}}\) = 5.4 \(\times\) 10\({}^{-35}\) cm\({}^{4}\); \(\sigma_{\text{tol,248 nm}}\) = 2.7 \(\times\) 10\({}^{-35}\) cm\({}^{4}\)) and the \(\sigma_{\text{rel,tol}}\) determined in this work, an estimation of absolute two-photon ionization cross section of all investigated compounds could be carried out (Table 2). Absolute REMPI cross sections of single-core aromatics at 248 nm were lower than those obtained at 266 nm, which is in good agreement with the REMPI spectra presented in References [34; 35; 44]. For naphthalene, acenaphtene, and fluorene, a similar behavior would be expected. However, absolute REMPI cross sections were higher at 248 nm, which cannot be explained at this point and further studies are necessary. On the other hand, absolute REMPI cross sections for phenanthrene and pyrene were higher at 248 nm, as expected from the REMPI spectra. Phenanthrene showed the strongest REMPI response at \(\sim\)250 nm, which was attributed to the S\({}_{0}\)\(\rightarrow\) S\({}_{4}\) transition [45], resulting in the highest absolute REMPI cross section of all investigated compounds. In Table 2, ionization energies (_IE_) are also shown. (1 + 1) REMPI is only possible if the energy of two photons is higher than the _IE_. For all investigated analytes, the two-photon absorption at 266 nm and 248 nm (4.66 eV and 5 eV, respectively) exceeded the _IE_. Thus, absolute REMPI cross sections at 266 nm increased with decreasing IEs, although this trend is not applicable to explain ionization yields in REMPI. For effective REMPI processes, the lifetime of the excited state should be sufficiently long compared to the laser pulse duration, which was true for all of the compounds in this study. The results of this study suggested that the principle of relPICS can be used for a system-independent quantification of PAHs in gaseous matrices. For single-core aromatics, the observed deviations of \(\sigma_{\text{rel,tol}}\) between both systems were attributed to differences in UV laser generation. Preliminary results obtained with an additional REMPI-TOFMS at 266 nm (UV generation by the fourth harmonic generation of 1064 nm of Nd:YAG) for \(p\)-xylene and 1,2,4-trimethylbenzene confirmed the cross section values of these compounds, as determined by PITOF 2. **Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data, and wrote the paper with the input of all authors. **Funding:** This research was funded by the German Research Foundation (DFG) grant number ZI 764/12-1. **Acknowledgments:** We would like to thank [PERSON] (University of Rostock, Chair of Analytical Chemistry) and [PERSON] (University of Rostock, Chair of Soil Science) for the theoretical and experimental support. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Control Allocation
#### 2.4.2 The Advantage of Control Data Chain Connectivity  
The directional gain of airborne radar will affect the power of the echo signal in different enemy or foe situations, so it was necessary to modify the traditional control data link model to receive the power of the data link. For this radar, the antenna gain as a function of the scanning angle \(\varphi\) and the direction angle \(\theta\) was as shown in Equation (10):  
\[G(\theta,\varphi)=F^{2}(\theta)G_{0}\cos\varphi \tag{10}\]  
where \(F^{2}(\theta)\) is the antenna gain function of each directional angle and \(G_{0}\) is the gain in the normal direction of the antenna array.  
In the control data link, the received power of the load agent is shown in Equation (11):  
\[P_{sm}=\frac{G(\theta,\varphi)G_{mt}G_{pt}P_{p}}{L}{(\frac{\lambda}{4\pi R})} ^{2} \tag{11}\]  
where \(G_{mt}\) is the receiving gain of the load agent data link antenna; \(G_{pt}\) and \(P_{p}\) are the transmission gain and transmission power of the air relay control agent data link antenna, respectively; \(L\) is the loss factor.  
In the back-transmission data link, the received power of the air relay control agent is shown by Equation (12):  
\[P_{sp}=\frac{G(\theta,\varphi)G_{mt}G_{pr}P_{m}}{L}{(\frac{\lambda}{4\pi R})} ^{2} \tag{12}\]  
where \(G_{mt}\) is the receiving gain of the data link antenna of the air relay control agent; \(G_{pr}\) and \(P_{m}\) are the transmission gain and transmission power of the load agent data link antenna, respectively; \(L\) is the loss factor.  
It can be seen from Equation (10) that the angle and distance will affect the communication ability of the air relay control agent when communicating with the load agent. Therefore, the target detection probability of the Swerling 0 model [34] was used to represent the connectivity advantage of the data link.  
The connectivity advantage of the control data link \(Q_{2}\) was as shown in Equation (13):  
\[Q_{2}=\frac{erfc(\frac{V}{\sqrt{2}})}{2}-\frac{eV^{2}}{2\sqrt{2\pi}}[C_{3}(V^ {2}-1)+C_{4}V(3-V^{2})-C_{6}V(V^{4}-10V^{2}+15)],V=P_{sm}/N \tag{13}\]  
where \(G_{3}\), \(G_{4}\), and \(G_{6}\) are the Gram-Charlier series coefficients, which usually vary with the type of target fluctuation.  
The connectivity advantage of the backhaul data link \(Q_{3}\) was as shown in Equation (14):  
\[Q_{3}=\frac{erfc(\frac{V}{\sqrt{2}})}{2}-\frac{eV^{2}}{2\sqrt{2\pi}}[C_{3}(V ^{2}-1)+C_{4}V(3-V^{2})-C_{6}V(V^{4}-10V^{2}+15)],V=P_{sp}/N \tag{14}\]