esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiellipsoidal Mapping Algorithm
## 3 Ellipsoidal Surfaces Optimization  
In 2017, we presented the Hyperellipsoidal Neuron (HN) [10], where the neuron represents an hyperellipsoidal decision surface. The HN is capable of deforming the decision surface into geometric entities, such as a pair of planes, and spheres and pseudocylinders. In this paper, we use the same propagation of the HN for representing the ellipsoidal surfaces.  
We use the parametrization functions \(\psi_{1}(\cdot)\) and \(\psi_{2}(\cdot)\) defined in (10) and (11) respectively.  
\[\psi_{1}(x)=\left[x_{1},\cdots,x_{n},1,-\frac{1}{2}x_{1}^{2},\cdots,-\frac{1}{ 2}x_{n}^{2}\right]^{T} \tag{10}\]  
Finally, the fitness function for the DE algorithm in (12) is designed, where every cluster \(S_{i}\), calculated by k-means++, is used for adapting the semiaxes \(\{r_{x},r_{y},r_{z}\}\) of an ellipsoid with center \(c_{i}\). The first term penalizes the distance from every point in the cluster to the ellipsoid surface (outside or inside) by applying the root mean square error (RMSE), and the second term penalizes the density of each cluster \(S_{i}\) by computing the ratio of the volume of the entity to the number of points contained in \(S_{i}\), i.e., \(S_{i}\).  
\[(r_{x},r_{y},r_{z})=\underset{(r_{x}r_{y}r_{z})}{\arg\min}\quad\alpha\sqrt{ \frac{1}{S_{i}}\sum_{X\in S_{i}}\left[\psi_{1}(X)^{T}\psi_{2}(E)\right]^{2}}+ \frac{4(1-\alpha)}{3S_{i}}\tau r_{x}r_{y}r_{z} \tag{12}\]  
The free parameter \(\alpha\) controls how much the ellipsoid can grow; \(\alpha\) and the parameter \(k\) control the granularity of the ellipsoidal map. The granularity refers to the number and size of the ellipsoids that represent an object.
## 3 Results
### Correlations Between Remote Sensing-Based Indicators and Apodemus Agarrius Population Density  
Figure 5 shows that the maximum cross-correlation coefficient indicates that PsnNet, at a lag of 3 months, appears to play a role in rodent population density. The results (Figure 6) indicated that the population density of _A. Agarrius_ was positively correlated with NDVI, GPP and PsnNet, with a lag of 2 months, 2-3 months and 2-3 months, respectively. The population density of _A. Agarrius_ was negatively correlated with LST, with a 6-month lag. The highest correlation coefficient was for the relationship between the capture rate of _A. Agarrius_ and PsnNet, with a 3-month lag (\(r=0.46\), \(p<0.01\)). The PsnNet was high on an annual basis during March to September, which was the breeding season for _A. Agarrius_, although the value of PsnNet in June was low due to the spring harvest. The capture rate of _A. Agarrius_ was relatively high in the breeding season and relatively low over the winter period, from December to February. Therefore, the results indicated that the seasonal pattern of PsnNet peaked and coincided with the capture rate of _A. Agarrius_.  
Figure 4: Remote sensing-based indicators of crop-land in Hu County. NDVI, normalized difference vegetation index; FPAR, fraction of photosynthetically active radiation absorbed by vegetation; LAI, leaf area index; GPP, gross primary productivity; PsnNet, net photosynthesis; LST, land surface temperature.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Lag (month)** & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \(r\) & 0.19 & 0.31 ** & 0.41 ** & 0.42 ** & 0.39 ** & 0.31 ** & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-correlations between the number of hemorrhagic fever with renal syndrome (HFRS) cases and the rodent population density.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 2 Related Work
### Reinforcement Learning
Building on these foundations, several recent studies have explored the innovative applications of reinforcement learning to emerging vehicular challenges. [PERSON] et al. [75] applied DRL to non-lane-based toll-plaza trajectories. [PERSON] et al. [76] and [PERSON] et al. [77] explored RL-driven route optimization and energy-aware scheduling in IoV and logistics networks, respectively; [PERSON] et al. [78] introduced a hybrid-state, risk-aware DRL decision model that improves maneuver safety in highway scenarios; [PERSON] et al. [79] leveraged vision transformer-based DRL for end-to-end visual self-driving control; and [PERSON] et al. [80] proposed a hybrid DRL-kinematic autopilot framework that boosts decision reliability on motorways. Although these works reflect the growing versatility of RL in autonomous driving, none of them jointly consider the role of wireless bandwidth maps in the decision-making process, leaving a critical research gap that our study addresses. Our research identifies and addresses this gap by introducing a novel reinforcement learning framework for autonomous vehicle path planning that is explicitly aware of bandwidth heterogeneity. This approach is motivated by several factors, including the critical challenge of planning routes with adequate signal availability for connected autonomous vehicles. Recent research has highlighted users' concerns when navigation systems suggest routes with poor signal coverage [81], underscoring the importance of this issue. Our work is distinctive in that it considers not only the traditional objectives of route planning, such as minimizing drive time and avoiding traffic, but also prioritizes high-bandwidth roads to meet the vehicle's data transfer requirements. In doing so, our work fills a critical gap in the existing research, demonstrating the untapped potential of RL to improve the efficiency and efficacy of autonomous vehicles in urban environments with heterogeneous bandwidth conditions.
# Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
## Materials and Methods
### The Area Considered  
In this study, WV-2 imagery concerning the area around Vesuvius (Campania, Italy) was considered (Figure 1). The dataset was acquired on 22 April 2015 and supplied by the provider as Ortho Ready Standard product (ORS2A), which means it had already been rectified using the average terrain elevation of the scene (low accuracy) [33]. The scene is georeferenced in the UTM/WGS84 33 N zone T coordinates system and extends from 40\({}^{\circ}\)46\({}^{\prime}\)22.06\({}^{\prime\prime}\) north to 40\({}^{\circ}\)51\({}^{\prime}\)46.53\({}^{\prime\prime}\) north in latitude and 14\({}^{\circ}\)20\({}^{\prime}\)2.74\({}^{\prime\prime}\) east to 14\({}^{\circ}\)24\({}^{\prime}\)40.70\({}^{\prime\prime}\) east in longitude.  
Figure 1: WorldView-2 (WV-2) image and its location in Campania. The coordinates reported on the map are referred to as UTM/WGS84 (33 N—zone T) and expressed in meters.  
This area is particularly interesting due to both its anthropological and natural features. In fact, most of the area is within the Vesuvius National Park, founded in 5 June 1995, which houses 610 vegetable species and 229 vertebrates and invertebrates [34]. The area also has one of the highest urban densities in Europe and includes the archeological remains of Pompeii, the ancient town that was destroyed by the eruption of Vesuvius in 79 AD. Thus, a high-performance VHRSI such as WV-2 would clearly support research activities in many fields, if corrected and georeferenced.
## 1 Introduction  
Reliable determination of seismic wave attenuation (which inverse is the Quality factor Q) in a geological strata is of importance from an industrial point of view. The Q, as one of the seismic attributes, can provide information about the mechanical properties of rocks, as well as indicate subtle structural changes [1]. The ratio of attenuation values for different wave types (P/S), or its anomalous values, allow inferences regarding reservoir parameters and pore space saturation with hydrocarbons [2; 3; 4]. It should also be noted that the attenuation can be considered as a filter that modifies the frequency spectrum of seismic waves, thus providing important information about the recorded wavefield [5]. The narrowing of the spectral band of the seismic data due to energy absorption decreases its vertical resolution. Authors like [6; 7; 8; 9] propose convolutional models that have potential to increase the vertical resolution of the seismic data, whereas other approaches use complex machine learning techniques for spectral enhancement [10; 11]. Considerable difficulties are posed by the imaging of thin layers, usually occurring in a series of increased shale content, where signal interference occurs. An increase in the dominant frequency of the seismic signal in such situation makes it possible to at least partially isolate such overlays.  
In this study, to increase the higher frequency content of the seismic signal, we use Inverse Q-Filtering (IQF), which allows us to achieve a higher vertical resolution of the section. The IQF procedure involves subjecting the seismic traces to a minimal-phase deterministic filter, compensating for the effect of the exponential loss of higher frequencies and increasing the distortion of the signal as the seismic wave propagates through the geological strata.
## 2 Key Technology Discussion
### Back-Off Efficiency Improvement  
The inherent design of the Doherty amplifier allows for improved efficiency at power back-off (PBO) levels due to its load modulation characteristics. As the input power decreases, the peaking amplifier is progressively disengaged, allowing the carrier amplifier to operate more efficiently under lower power conditions. This dynamic adjustment of load impedance enables the DPA to maintain higher efficiency compared to conventional amplifiers, which often suffer from significant efficiency drops at back-off levels. Recent  
Figure 1: Simulated IM3 and fundamental signal vs. input power: (**a,b**) at lower frequency; and (**c,d**) at higher frequency.  
Figure 2: Simulated PDRs vs. input power with different bias currents.  
designs have focused on optimizing the asymmetry between the carrier and peaking amplifiers to enhance efficiency even further. By fine-tuning the bias points and operating conditions of each amplifier, it is possible to achieve a broader range of efficient operation while minimizing distortion. In addition, three-way Doherty configuration is a typical back-off efficiency improvement technique.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 2 Materials and Methods
### Standardized Precipitation Enopotranspiration Index  
SPEI was proposed by [PERSON] et al. [19]. The calculation of SPEI was based on monthly precipitation and temperature and was characterized by multiple timescales. SPEI's central concept is the construction of a meteorological water cycle to describe the accumulated water deficit or surplus, which can then be used to characterize drought [49]. High to low SPEI means relatively wet to relatively dry [50]. In this study, SPEI was calculated separately on 1-12 month timescales to assess the different durations of the water deficit. The specific calculation method for SPEI is given below.  
First, [PERSON]'s method was employed to estimate the monthly potential evapotranspiration (\(PET_{j}\), mm):  
\[PET_{j}=16K\left(\frac{10T_{j}}{I}\right)^{m} \tag{1}\]  
where \(T_{j}\) is the monthly temperature of \(j\); \({}^{\circ}\)C; \(I\) is the annual heat index, which is the sum of 12 monthly index values; \(m\) is the coefficient, depending on \(I\); \(m=0.492+1.79\times 10^{-2}I-7.71\times 10^{-5}I^{2}+6.75\times 10^{-7}I^{3}\); and \(K\) is the correction factor for the latitude and month functions.  
The accumulated moisture deficit sequence \(X\), with different timescales, was constructed and its probability distributions were calculated. A month's cumulative moisture deficit is the sum of the previous \(k-1\) months and the current month's moisture deficit, and \(k\) is the timescale, \(k=1\), \(\cdots\), \(12\).  
\[X_{i}^{k}=\sum D_{i} \tag{2}\]  
\[D_{i}=P_{i}-PET_{i} \tag{3}\]  
where \(P_{i}\) is the monthly precipitation (mm), \(D_{i}\) is the monthly moisture deficit (mm), and \(i=1\), \(\cdots\), \(n\), \(n\) is the number of samples in a time series. The calculation of the probability distribution function of the cumulative water deficit series is performed by introducing a three-parameter logarithm-logistics probability distribution function.  
\[f(x)=\frac{\beta}{\alpha}\left(\frac{x-\gamma}{\alpha}\right)^{\beta-1}\left[ 1+\left(\frac{x-\gamma}{\alpha}\right)^{\beta}\right]^{-2} \tag{4}\]  
\(\alpha\), \(\beta\), and \(\gamma\) are the scale, shape, and position parameters, respectively, for the \(D\) values in the range (\(\gamma<D<\infty\)). The probability distribution function of \(D\) is given according to the log-logistic distribution.  
\[F(x)=\left(1+\left(\frac{\alpha}{x-\gamma}\right)^{\beta}\right)^{-1} \tag{5}\]  
Finally, SPEI was obtained as the standardized value of \(F(x)\).  
\[SPEI=W-\frac{c_{0}+c_{1}W+c_{2}W^{2}}{1+d_{1}W+d_{2}W^{2}+d_{3}W^{3}} \tag{6}\]  
\[W=\sqrt{-2\ln(P)} \tag{7}\]  
For \(P\leq 0.5\), \(P\) is the probability of exceeding the defined \(D\) value, \(P=1-F(x)\). If \(P>0.5\), \(P\) is replaced by \(1-P\) and the resultant SPEI is the opposite; \(c_{0}\), \(c_{1}\), \(c_{2}\), \(d_{1}\), \(d_{2}\) and \(d_{3}\) are constants.
## 3 Results and Discussion
### Optimization of Connector Geometry and Determination of Ultimate Bearing Capacity  
3.1 Connector Geometry Optimization: Sensitivity of Connector Performance to Bolt Hole-Edge Distance  
Figure 9a shows the load-displacement curves computed via FEA for models with various hole-edge distances. It can be seen that curves are very similar if the hole-edge distance is between \(1.0d_{0}\) and \(1.5d_{0}\). For the very small hole-edge distance of \(0.7d_{0}\), the significantly lighter honeycomb sandwich structure and pad section host very high stresses. The stress in the bolt hole section ranges between 522 and 590 MPa, which exceeds the common yield strength of aluminum alloys and produces damage in the connection plate.  
The ultimate load is about 36 kN for all connectors with hole-edge distances greater or equal to \(1.0d_{0}\) but drops down to 21.64 kN (i.e., 40% reduction in shear load bearing capacity) for the hole-edge distance of \(0.7d_{0}\).  
Based on these results, we modified the geometry of the high-strength bolt connector setting the hole-edge distance equal to \(1.5d_{0}\). Load-displacement curves of optimized models including different values of the bolt diameter are compared with those relative to the original design. Figure 9b shows that, regardless of bolt diameter, a hole-edge distance of \(1.5d_{0}\) allows to reach the same performance as for the original design. In summary, the minimum dimensions of the connection plate and pad can be optimized to be 1.5 times the bolt hole diameter to ensure that the connector has sufficient bearing capacity yet reducing connector material consumption.  
Figure 9: Load-displacement curves determined by ANSYS for (**a**) models with different hole-edge distances; (**b**) models with different bolt diameters.
## 3 Results  
### EACEE Inhibited NO Production and iNOS Protein Expression in LPS-Stimulated RAW 264.7 Macrophages  
To investigate the cell viability of EACEE in RAW 264.7 macrophages, we conducted the MTT assay. EACEE had significant toxicity over 62.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (Figure 1A). Therefore, we performed further studies using the nontoxic concentrations of EACEE (12.5, 25, and 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). iNOS, an enzyme that produces NO, is activated during the inflammatory response and oxidative stress [13]. To observe the inhibitory effect of EACEE on NO production in LPS-stimulated macrophages, cells were pretreated with EACEE for 1 \(\mathrm{h}\) and stimulated with LPS for 48 \(\mathrm{h}\). EACEE exerted dose-dependent inhibitory effects on NO release in LPS-stimulated cells (Figure 1B). In particular, EACEE (50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) suppressed excess NO production by 40.5% in LPS-stimulated macrophages, which is superior to the effect of N6-(1-Iminoethyl) lysine (NIL, positive control). Stimulation with LPS significantly increased iNOS protein expression compared with that in vehicle-treated cells (1.00 \(\pm\) 0.03 vs. 0.02 \(\pm\) 0.048). Compared with those in LPS-stimulated macrophages, macrophages treated with 12.5, 25, and 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL EACEE showed significantly reduced iNOS protein levels, by 17.72%, 18.27%, and 23.70%, respectively (Figure 1C).
## 5 Modeling of the Proposed Protocols  
This section presents the modeling of the proposed DDSec and DDACap security protocols. The UEs should be able to access and share restricted services even in the case where certain roaming agreements do not apply. At this security level, the UE can use some of the primary, secondary and federated security context such as preshared session keys and federated IDs, however it is still capable of generating its own cryptographic primitives and security vectors, as explained in Section 4. It leverages on security parameters and guarantees used in network access security [50] and service level security [39] mechanisms. These protocols apply cryptographic primitives such as those specified in 5G standard [1] to achieve the security requirements of 5G-enabled D2D communications. The cryptographic primitives used include symmetric and asymmetric encryption, one-way hash function, digital signature and message authentication code.
## 3 Application of 5G+ Big Data in Fully-Mechanized Mining Face with a Super-Large Mining Height
This enables data collection and analysis of the shearer, support, three machines, pump station, mobile substation, comprehensive protector, feed equipment, combined switch, automatic emulsion proportioning equipment, as well as the remote control of these devices. Real-time monitoring and control of the working status of each subsystem are realized. Based on the centralized control system, the console driver can monitor the working status of each subsystem in real time. The system is characterized by fault display and recording functions, which can help maintenance personnel quickly find problems and greatly improve maintenance efficiency. Furthermore, the integration of \"three posts into one\" is realized on the basis of the centralized control system [43]. The centralized control center can remotely monitor and control the main production equipment of the fully-mechanized mining face in real time. By applying information technology, it is a mine safety monitoring system integrating \"worker, machine, environment, and management\" to improve mine safety. It can reduce the number of workers in high-risk areas and help achieve the ultimate goal of \"safety through absence of personnel\" in underground coal mines, thereby providing high safety benefits. To address the issue of roof control in the 12,404 fully-mechanized mining face with a super-large mining height, the \"playing football\" type roof control mode of the fully-mechanized mining face with a super-large mining height is proposed under the background of 5G+ big data.
# Optical Emission Spectroscopy as a Diagnostic Tool for Characterization of Atmospheric Plasma Jets
###### Abstract  
A suitable technique for localized surface treatment of solid materials is an atmospheric pressure plasma jet (APPI). The properties of the APPJ plasma often depend on small details like the concentration of gaseous impurities what influences the surface kinetics. The simplest and often most useful configuration of the APPJ is presented, characterized by optical emission spectroscopy (OES), and results are discussed in view of various papers. Furthermore, results of additional recent papers on the characterization of the APPJ by OES are presented as well. Because the APPJ is operating at atmospheric pressure, even the water vapor traces may significantly alter the type and concentration of reactive species. The APPJ sustained in noble gases represents a source of vacuum ultraviolet (VUV) radiation that is absorbed in the surface of the treated material, thus causing bond scission. The addition of minute amounts of reactive gases causes significant suppression of VUV radiation and the formation of reactive radicals. These radicals such as OH, O, N, NO, O\({}_{3}\), and alike interact chemically with the surface causing its functionalization. Huge gradients of these radicals have been reported, so the surface finish is limited to the area reached by the radicals. Particularly OH radicals significantly prevail in the OES spectra, even when using very pure noble gas. They may cause suppression of other spectral features. OH radicals are especially pronounced in Ar plasmas. Their density decreases exponentially with a distance from the APPJ orifice.  
atmospheric pressure plasma jet (APPI); optical emission spectroscopy (OES); plasma-surface interactions; local surface modification; polymers; functionalization +
Footnote †: journal: Physics Letters B
## 5 Results and Discussion
### Characteristics of the Time Domain.  
Figure 13a,b show the impulse response signals collected at distances of 0.4 m and 13 m away from the impact location, respectively. The maximum amplitude of the impulse response signals decreases as the propagation distance increases, which indicates that the energy of the impulse response signal decreases due to energy attenuation. Moreover, the closer the vibration source is to the sensor, the earlier the impulse response signal begins to oscillate.  
Figure 14a,b show the CCC obtained by the CCF method and the modified GCC method, respectively. The time delay estimation only depends on the shift value of the maximum peak in the cross-correlation coefficient with respect to the central point, and can be calculated as the product of the sampling period and the shift of the cross-correlation function maximum peak [34]. Two maximum peaks can be observed, and the time delay estimations are \(-0.00020\) seconds and \(-0.00034\) seconds, respectively. The distance estimations between the leak and sensor 1 are 1.83 m and 1.99 m, respectively. Since the relative location error in this study is equal to the quotient of the leakage location error and the distance between the leak and sensor 1, the real distance between the simulated leak orifice and sensor 1 is 2.16 m, so the relative location errors are 15% and 8%, respectively. The maximum peak amplitudes of the two CCCs are 0.168 and 0.236, respectively. Hence, the modified pre-filter can improve the accuracy of the time delay estimation and increase the degree of the correlation between the two measured signals.  
Figure 14: Cross-correlation coefficients (CCCs) obtained by two methods: (**a**) CCF location method, and (**b**) modified GCC location method.  
Figure 13: Impulse response signals acquired at different distances from sensor 1: (**a**) 0.4m and (**b**) 13m.