esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Processing Methods
### Spectrum Reconstruction
#### 3.1.2. Radiance Transfer Model  
In order to convert the radiance spectra into absorbance spectra reflecting the composition and concentration information of the target gas, a radiance transfer model needs to be established [31]. In practical application scenarios, the SMWIR-IFTS mainly uses a low elevation or approximate horizontal angle for detection, and a simplified three-layer radiance transfer model can be used, as shown in Figure 4.  
Figure 2: Flowchart of the overall processing.  
The background radiation from the third layer passes through the target gas in the second layer and the atmosphere in the first layer before reaching the SMWIR-IFTS detector. The total radiance intensity \(R_{in}\) transmitted to the detector can be expressed as follows:  
\[R_{in}=(1-\tau_{atm})B_{atm}+\tau_{atm}\Big{[}(1-\tau_{gas})B_{gas}+\tau_{gas}R _{bg}\Big{]} \tag{1}\]  
where \(\tau_{atm}\) is the atmospheric transmittance, \(B_{atm}\) is the blackbody radiance intensity at the atmospheric temperature, \(\tau_{gas}\) is the transmittance of the target gas, \(B_{gas}\) is the blackbody radiance intensity at the target gas temperature, and \(R_{bg}\) is the background radiance intensity. Within a certain detection distance, the atmospheric transmittance is approximated as 1, and the absorbance of the target gas Agas can be simplified as follows:  
\[A_{gas}=\log_{10}\Big{(}\frac{1}{\tau_{gas}}\Big{)}=\log_{10}\Big{(}\frac{R_{bg }-B_{gas}}{R_{in}-B_{gas}}\Big{)} \tag{2}\]  
According to the Beer-Lambert law, the absorbance spectrum of the target gas is closely related to its concentration. However, during the detection of the target gas by SMWIR-IFTS, the acquired spectra are prone to be influenced by external environmental  
Figure 4: Schematic diagram of three-layer radiance transfer model.  
Figure 3: (**a**) Interferogram data cube; (**b**) interference image sequence; (**c**) baseline of interferometric intensity sequence; (**d**) apodization of interferometric intensity sequence; (**e**) phase correction of interferometric intensity sequence; (**f**) reconstructed radiance spectrum of each temperature sampling point.  
factors, leading to poor spectral consistency. Therefore, the research focus of this paper is on how to achieve high-precision gas concentration inversion from the low-consistency absorbance spectra detected by SMWIR-IFTS.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### Characteristics of 5G Networks and Devices
#### 2.1.1 5G Devices  
Future 5G devices will enable various types of critical services with different requirements of data rate, latency, reliability, and security, including enhanced mobile broadband (eMBB), massive machine type of communication (mMTC), and ultra-reliable and low-latency communication (URLLC) scenarios. Therefore, different users with diverse QoS requirements should be associated with different types of access points.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 4 Temperature Correction of Data
### Data for December 2021  
The impact of temperature on the behavior of the _G-mean_(G) graph is most visible in December 2021 (Figure 12). However, in terms of temperature correction, this case turned out to be fairly straightforward.  
The _G-mean_(_G) graph correlated well with the temperature graph throughout the month, so in the spt.txt file, the reference interval is set to a whole month:  
\"2021-12-01 00:00:00; 2022-01-01 00:00:00\".  
As usual, this interval was indicated in Figure 12 by a red segment, and the green segments marked possible correction interval limits. Obviously, for December 2021, the automatic mode for the localization interval selection was well suited, as it ensured that the \(a+bT\) graph crossed zero at its ends. Thus, the approximate beginning of the interval could be the vicinity of the right-hand boundary of the green segment located in Figure 12 at the beginning of November after the bay of the \(a+bT\) graph. The approximate end of the interval could be the vicinity of the left-hand boundary of the green segment going from December 2021 to January 2022. As a result of the algorithm execution, the corresponding line in the spc.txt file will be overwritten as  
\"a2021-12; 2021-11-05 14:20:00; 2021-12-29 09:21:00; \(-\)2.1600; 0.1346\".  
The automatically chosen localization interval in Figure 12 was shown in blue and ran from 5 November to 29 December 2021, covering almost two months. Notably, it intersected with the localization interval for November 2021 (see previous sub-section) from 5-8 November, so at the intersection, the correction procedure accounted for both intervals. For variometer readings falling within this intersection, the \(VC(t)\) correction term is  
\[VC(t)=\sum_{i}(a_{i}+b_{i}T_{t}), \tag{11}\]  
where \(i\) is the ordinal numbers of the overlapping localization intervals.  
Figure 13 shows plots \(G\) for December 2021 derived from the temperature adjusted variations only (in blue), and the recalculated baseline values (in orange).  
Figure 12: Correlation and localization in December 2021. Notations are the same as in Figure 6.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Comparative Experiment
In addition, the model had an average MC error of 0.047, Brooks-Gelman-Rubin (BGR) of 0.98, and model fitting performance (Deviance Information Criterion-DIC) of 322. The comparative analysis showed that the proposed RNN model outperformed both MLP and BLR in terms of training and validation accuracies (Table 8). The BLR model performed better than MLP on the training dataset; however, its accuracy on the validation dataset was less than the MLP model. The MLP model uses only local contexts and therefore does not capture the spatial and temporal correlations in the dataset. The hidden units of the RNN model contain historical information from previous states, hence increasing the information about the data structure, which may be the main reason for its high validation accuracy over the other two methods. Building BLR models is more difficult than NN models, because they require expert domain knowledge and effective feature engineering processes. The NN model automatically captures the underlying structure of the dataset and extracts different levels of features abstractions. However, building deep learning models with various modules (e.g., fully connected networks, LSTM, convolutional layers) can be a challenging task. In addition, the BLR models are less prone to over-fitting of the training dataset than NN models, because they involve simpler relationships between the outcome and the explanatory variables. Complex networks with more hidden units and many modules often tend to over-fit more, because they detect almost any possible interaction so that the model becomes too specific to the training dataset. Therefore, optimization of network structures is very critical to avoid over-fitting and build practical prediction models. In computing the importance of an explanatory factor, the BLR models could easily calculate the factor importance and the confidence intervals of the predicted probabilities. In contrast, NN methods, which are not built primarily for statistical use, cannot easily calculate the factor importance or generate confidence intervals of the predicted probabilities unless extensive computations were done. In addition, based on the estimated weight of each accident related factor, the factors could be ranked by scaling the weights into the range of 1-100 and then giving them a rank from 1 to 9 as shown in Table 9. The three methods (MLP, BLR, and RNN) did not agree on the factors' ranking. For example, the RNN model ranked accident time as the most influential factor in injury severity, whereas accident time was ranked 2 and 7 by the BLR and MLP models, respectively. Both the RNN and BLR models agreed on several factors' ranking i.e., accident location (6), accident reported cause (5), and collision type (8). The correlation (\(R^{2}\)) between the RNN and BLR ranks was 0.72. In contrast, RNN and MLP did not agree on the ranking of the factors and their ranking correlation was the lowest (0.38).
# Static and Moving Target Imaging Using Harmonic Radar
## 2 Harmonic Radar
### Unambiguous Range and Range Resolution  
In a wideband harmonic radar system, the transmit frequency \(f_{t}\) is stepped over a bandwidth \(B\) centered around a frequency \(f_{0}\) in steps of \(\Delta f\). Consequently, the nth harmonic received at the radar, \(f_{r}\), has a bandwidth of \(nB\) centered around \(nf_{0}\), due to the linear multiplication of the fundamental frequency band. For a harmonic radar operating at the nth harmonic, the maximum unambiguous range to the target, \(R_{\max}\), and the down range resolution,\(\Delta R\), are given by, respectively [52]  
\[R_{\max}=\frac{c}{2n\Delta f} \tag{4}\]  
and  
\[\Delta R=\frac{c}{2 nB} \tag{5}\]  
where \(c\) is the speed of light.
###### Abstract  
This paper describes a compact multi-port sub-6 GHz multiple-input multiple-output (MIMO) antenna system tailored for 5G NR mobile terminals operating in the n77 (3.3-4.2 GHz), n78 (3.3-3.8 GHz), and n79 (4.4-5.0 GHz) frequency bands. The proposed design leverages a shared coupling approach that exploits the smartphone metal frame as the radiating element, facilitating efficient integration within the spatial constraints of modern mobile devices. A two-stage method is used to mitigate the mutual coupling and correlation issues typically encountered when designing compact MIMO configurations. Initially, a four-port structure is used to evaluate broadband impedance and spatial feasibility. Based on the observed limitations in terms of isolation and the envelope correlation coefficient (ECC), the final configuration was reconfigured as an optimized two-port layout with a refined coupling geometry and effective current path control. The fabricated two-port prototype exhibited a measured voltage standing wave ratio below 3:1 across the n78 band on both ports, with the isolation levels attaining \(-\)12.4 dB and ECCs below 0.12. The radiation efficiency exceeded \(-\)6 dB across the operational band, and the radiation patterns were stable at 3.3, 3.5, and 3.8 GHz, confirming that the system was appropriate for MIMO deployment. The antenna supports asymmetric per-port efficiency targets ranging from \(-\)4.5 to \(-\)10 dB. These are the realistic layout constraints of commercial smartphones. In summary, this study shows that a metal frame integrated two-port MIMO antenna enables wideband sub-6 GHz operation by meeting the key impedance and system-level performance requirements. Our method can be used to develop a scalable platform assisting future multi-band antenna integration in mass-market 5G smartphones.  
multi-port antenna; coupled antenna; 5G NR; sub-6 GHz; MIMO +
Footnote †: journal: applied sciences  
Article  
Design and Performance Evaluation of a Sub-6 GHz Multi-Port Coupled Antenna for 5G NR Mobile Applications
# _Applied Sciences_
## 4 Discussion and Conclusions
### Privacy-Utility Trade-off Considerations  
While parameter \(N\) influences the confidentiality level of the obfuscation method that uses non-bijective functions, the degree of privacy provided by the VAE-based approach is dependent on the level of noise added during encoding, and the number of channels obtained at the bottleneck. When several channels are employed, the information is shared between them, resulting in fewer details being preserved in one channel and in more robust security. Furthermore, the valuable information is not evenly dispersed across the different channels, and one may select a particular representation to fulfill a specific requirement. Hence, the clinical user may select between different options for the trade-off between accuracy and privacy (e.g., categorical choice: very high accuracy, high accuracy, balanced, high privacy, very high privacy). For example, a very high privacy requirement may be chosen if easily recognizable patient features are present in the images (MRI data [35]).  
Regarding the classification accuracy, although its value is still above 80% when the obfuscation approaches are combined, which is acceptable in the context of a privacy-accuracy trade-off, there is still room for improvement. The purpose of the classification experiments is not to achieve state-of-the-art performance on obfuscated images but to compare the results when the same architecture and different obfuscation techniques are employed. Because the classification task can be successfully performed even when using small values of N, we sought to explore the existence of structural dissimilarities between the LCA and the RCA, which could allow for a superior reconstruction for one of the classes. Figure 18 depicts two different samples (one LCA and one RCA angiographic acquisition) and their dichromatic obfuscated counterparts. Although assigning the proper category solely by visually inspecting the binary images is difficult, it is clear that some characteristics are preserved even when \(N=2\), which allows for a relatively accurate DL-based classification. The scores given by the expert readers for LCA and RCA reconstructions are recorded separately, to see if such differentiating details allow for a more qualitative recovery for specific samples. In terms of vessel scores, there is no substantial difference between the two categories, according to the visual inspection. However, it appears that the background can be better reconstructed for RCA views.  
To demonstrate that there is no statistically significant difference between the two groups, we compute the p-value. The scores are first standardized into the t-score. The p-value is calculated by considering a two-tailed hypothesis. A comparison between the results obtained for the three obfuscation approaches, where the vessels and the background are separately assessed, is presented in Table 7. As the significance level is set to 0.05 and all computed p-values exceed this threshold, we can confirm that the difference between reconstructed RCA and LCA frames is not statistically significant.
## 2 System Model of RSST-SR-DCSK  
The primary limitation of DCSK systems lies in dedicating half of the bit duration to transmitting reference samples for non-carried information, leading to lower data rates and diminished energy efficiency. Consequently, it is crucial to develop new chaos-based communication systems that offer higher data rates and improved energy efficiency. To tackle this challenge, this paper introduces an enhanced RSST-SR-DCSK system designed to boost energy efficiency and transmission rates. This section details several vital elements of the system, encompassing the signal frame structure, and the modulation and demodulation processes of the RSST-SR-DCSK system.
## 6 Conclusions  
The proposed semantic based stream management system is characterised by: (i) continuous situation awareness and capability to handle theoretically infinite data streams due to the lazy evaluation mechanism; (ii) aggregating heterogeneous sensors based on the ontologies translated as strict rules; (iii) handling noise and contradictory information inherently in the context of many sensors, due to the plausible reasoning mechanism. The system represents a step towards building real-time stream processors for knowledge-rich applications.  
With streams being approximate, omniscient rationality is not assumed when performing reasoning tasks on streams. Consequently, we argue that plausible reasoning for real time decision making is adequate. One particularity of our system consists of applying an efficient non-monotonic rule based system [7] when reasoning on gradually occurring stream data. The inference is based on several algorithms, which is in line with the proof layers defined in the Semantic Web stack. Moreover, all the Haskell language is available to extend or adapt the existing code. The efficiency of data driven computation in functional reactive programming is supported by the lazy evaluation mechanism, which allows to use values before they can be known.  
In order to apply our PSRH system to a different domain, three tasks are necessary:  
1. to translate the domain specific ontologies into strict rules, which is automatically performed by the mapping module;
2. to design plausible rules and priorities by a domain expert;
3. to import the most adequate sensor ontology for the current problem (for instance SWEET or W3S SSN Ontology).  
During the translation part, some knowledge from the ontology may be lost. The axioms stating a subclass of a complex class which is a disjunction cannot be translated into PL, as in \(CheapMilk\sqsubseteq LowFatMilk\sqcup NoBrandMilk\). Also, subclasses of a complex class expression which is existential quantified cannot be translated, such as \(Sensor\sqsubseteq\exists hasPart.Battery\). Our solution allows this limitation of expressivity in order to perform reasoning in real time within the efficient plausible logic framework.  
The current implementation was tested only on the proof of concept scenario described in Section 5. More extensive experiments are needed to test the large scale efficiency and scalability of the proposed system. At the moment, we are able to support our solution based on the results reported in [7] and based on the reduced complexity of description logic programs [8].
# Decoupled MPC Power Balancing Strategy for Coupled Inductor Flying Capacitor DC-DC Converter
## 3 Decoupled MPC Power Balancing Strategy for Coupled Inductor FCDC
### Decoupled Mathematical Model of Coupled Inductor FCDC
\]  
Thus, the expressions for the voltage and current in (3) under the s domain can be rewritten as:  
\[\mathbf{U}(s)= \begin{bmatrix}r_{\text{a}}+s(L_{\text{a}}+M)&0\\ 0&r_{\text{b}}+s(L_{\text{b}}-M)\end{bmatrix} \mathbf{I}(s) \tag{6}\]  
Substituting (6) into (2) and rewriting the result with inverse Laplace transform, the expressions of inductor voltage and current in the time domain are obtained as follows:  
\[ \begin{bmatrix}u_{\text{a}}\\ u_{\text{b}}\end{bmatrix} = \begin{bmatrix}L_{\text{a}}+M&0\\ 0&L_{\text{b}}-M\end{bmatrix}\begin{bmatrix}\frac{d_{\text{a}}}{dt}&\frac{d_{ \text{b}}}{dt}\end{bmatrix} ^{T}+ \begin{bmatrix}r_{\text{a}}i_{\text{a}}\\ r_{\text{b}}i_{\text{b}}\end{bmatrix}  \tag{7}\]  
As shown in (7), there is no coupling relationship for the basic voltage equation of the coupled inductor. Thus, substituting (7) into (2), we can have:  
\[\frac{d}{dt} \begin{bmatrix}i_{\text{a}}\\ i_{\text{b}}\\ u_{\text{fa}}\\ u_{\text{fb}}\end{bmatrix} = \begin{bmatrix}\frac{-r_{\text{a}}}{(L_{\text{a}}+M) }&0&\frac{d_{\text{a}}-d_{\text{d}}}{(L_{\text{a}}+M)}&0\\ 0&\frac{-r_{\text{b}}}{(L_{\text{b}}-M)}&0&\frac{d_{\text{b}}-d_{\text{b}}}{(L _{\text{b}}-M)}\\ \frac{d_{\text{a}}-d_{\text{d}}}{C_{\text{fa}}}&0&0&0\\ 0&\frac{d_{\text{b}}-d_{\text{b}}}{C_{\text{fb}}}&0&0\end{bmatrix} \times \begin{bmatrix}i_{\text{a}}\\ i_{\text{b}}\\ u_{\text{fa}}\\ u_{\text{fb}}\end{bmatrix} + \begin{bmatrix}\frac{d_{\text{a}}}{(L_{\text{a}}+M) }&\frac{-1}{(L_{\text{a}}+M)}\\ \frac{d_{\text{b}}}{(L_{\text{b}}-M)}&-1\\ 0&0\\ 0&0\end{bmatrix}\begin{bmatrix}u_{\text{i}}\\ u_{\text{o}}\end{bmatrix}  \tag{8}\]  
It can be seen from (8) that there is no coupling between the two phase circuits under the coupled inductor, that is, the mathematical model of coupled inductor FCDC has been decoupled.
## 1 Introduction
[18] employed nanoindentation techniques to measure the hardness, elastic modulus, and fracture toughness of fragmented coal, observing that although the hardness and elastic modulus remained relatively constant with an increasing peak load, the fracture toughness exhibited a pronounced increasing trend. [PERSON] et al. [19] investigated eight types of coal using nanoindentation tests, an XRD analysis, and FESEM-EDS imaging and demonstrated that the nanoscale mechanical properties of coal are strongly correlated with the coal rank, mineral composition, and microstructure. [PERSON] et al. [20] conducted nanoindentation experiments to analyze the variations in the elastic modulus and hardness of coal samples before and after freezing and further investigated the correlation between these two properties. The results indicated that the porosity of the coal samples increased to varying extents after freezing and that the elastic modulus and hardness exhibited a positive correlation. [PERSON] et al. [21] conducted depth indentation tests on three different types of coal and found that the mechanical properties of the coal matrix change with the coal grade, whereas the inclusion properties do not change as significantly. [PERSON] et al. [22] combined modern nanoindentation technology with transmitted-light microscopy and proposed the use of a classic exponential weight function to describe the relationship between the effective elastic contact modulus of the sample and the indentation depth and verified the effectiveness of this method based on different maximum indentation depths. In summary, although numerous studies have utilized nanoindentation and related experimental techniques to investigate the micromechanical properties of rocks such as shale, sandstone, and coal, research specifically targeting the surface damage and mechanical behavior of coal under the hygrothermal conditions typical of deep mining environments remains relatively scarce. Therefore, conducting an in-depth investigation into the evolution of microscopic damage and the mechanical response of coal under hygrothermal conditions holds substantial theoretical and engineering value for elucidating the deterioration mechanisms of coal's mechanical properties in complex deep mining environments.
## 2 Model of Dynamic Imaging
### Image Sharpness Evaluation  
SSIM and MSE are common image sharpness assessment tools that can compare the difference between two images of the same size. After the degraded image of the frequency domain filtering is obtained, SSIM and MSE are used to analyze the regularity of image quality degradation caused by satellite vibration.  
The formula for SSIM is expressed as:  
\[\text{SSIM}(x_{i},y_{i})=[l(x_{i},y_{i})]^{\tau_{1}}[c(x_{i},y_{i})]^{\tau_{2}}[s (x_{i},y_{i})]^{\tau_{3}} \tag{20}\]  
where \([l(x_{i},y_{i})]^{\tau_{1}}\) is brightness distortion; \([c(x_{i},y_{i})]^{\tau_{2}}\) is contrast distortion; \([s(x_{i},y_{i})]^{\tau_{3}}\) is structural distortion. \(\tau_{1}\), \(\tau_{2}\), and \(\tau_{3}\) are the corresponding weight coefficients, respectively. The value range of SSIM is [0, 1]. The larger the value of SSIM is, the smaller the image distortion is.  
The formula for MSE is expressed as:  
\[\text{MSE}=\frac{1}{MN}\sum_{x=1}^{M}\sum_{y=1}^{N}\left[o(x_{i},y_{i})-\xi[g( x_{i},y_{i})]\right]^{2} \tag{21}\]  
where \(o(x_{i},y_{i})\) and \(\xi[g(x_{i},y_{i})]\) are the pixel values of the reference image, and the evaluated image at M and N are also the image size. The higher the value of MSE is, the greater the degree of the distortion is.