esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
Various studies have used machine learning (ML) and DL methodologies to classify emotions in speech. Recognizing emotions from speech signals represents a significant yet complex aspect of human-computer interactions [8; 9]. Furthermore, numerous approaches have been used in the field of SER to identify emotions from speech signals, including well-known methods for speech analysis and classification. For example, feature extraction involves transforming speech waveforms into parametric representations that may include the use of statistical features or spectrograms. Recognizing emotions can also encompass retrieving diverse characteristics from speech signals, such as paralinguistic qualities like pitch, intensity, and the MFCC. Several scholars have proposed integrating prosodic and spectral data for a more complete SER analysis. Although substantial research has been conducted on this topic, it remains difficult for machines to discern emotions.
## 3 Double-Beam System Traversed by a Moving Force
### Parametric Studies
#### 3.3.2 Contact Stiffness Ratio  
Figure 15 shows maximum dimensionless displacements (\(\overline{w}_{\rm 1,max}\)) and (\(\overline{w}_{\rm 2,max}\)) versus the contact stiffness ratio (\(s_{\rm e}\)) for different boundary conditions when \(\alpha=0.5\), \(s_{\rm m}=1\) and \(s_{\rm k}=1\). Several characteristics can be found from the figure:  
1. \(\overline{w}_{\rm 1,max}\) generally decreases with \(s_{\rm e}\) and the opposite trend is true for \(\overline{w}_{\rm 2,max}\);  
Figure 14: Maximum displacement ratios \(\overline{w}_{\rm 1,max}\) and \(\overline{w}_{\rm 2,max}\) versus speed ratio \(\alpha\) for different boundary conditions when \(s_{\rm e}=100\), \(s_{\rm m}=1\) and \(s_{\rm k}=1\).  
Figure 13: Comparison between the results from the Modal Superposition (MS) method in this study and the results from the Equation Decoupled (ED) method by Hilal [19] for \(\alpha=0.5\), (**a,b**) \(s_{\rm e}=10\), (**c,d**) \(s_{\rm e}=100\).  
2. The varying rates of \(\overline{w}_{i,\max}\ (i=1,\ 2)\) are very large when \(s_{\rm e}\) is below a turning point and become much smaller when \(s_{\rm e}\) is beyond the turning point;
3. With the increase of \(s_{\rm e}\), \(\overline{w}_{1,\max}\) and \(\overline{w}_{2,\max}\) tend to be the same value which is half of that for a single beam with the same boundary conditions and at the same values of \(\alpha\), \(s_{\rm m}\) and \(s_{\rm k}\);
4. The difference between \(\overline{w}_{1,\max}\) and \(\overline{w}_{2,\max}\) is generally smaller for looser boundary conditions.
# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
###### Abstract  
Nonlinear devices and channel interference can significantly impact the received signal in visible light communication (VLC). While recent research has explored receiver recovery using deep learning, existing approaches often involve replacing traditional channel estimation and equalization modules with neural network models. However, these models introduce additional data processing steps after fast Fourier transform (_FFT_), leading to increased complexity. To address these challenges, this study introduces a novel direct time-domain waveform equalization approach using a bidirectional gated recurrent unit (BiGRU) neural network for indoor VLC employing direct current (DC)-biased orthogonal frequency division multiplexing (DCO-OFDM). Unlike previous methods, our proposed scheme utilizes time-domain waveform data from photodiode outputs for direct balancing, harnessing the potent nonlinear processing capabilities of the BigGRU model. We first analyze the nonlinear processing capacity of the BigGRU model and subsequently compare the performance of different receiving methods on a constructed indoor visible-light communication platform. Experimental results demonstrate that the BiGRU-based approach exhibits low complexity and exceptional nonlinear channel learning capabilities. Notably, the proposed method outperforms other strategies in terms of bit error rate without the need for pilot signals. These findings validate the potential of the BiGRU-based DCO-OFDM receiving scheme as a promising solution for future VLC systems.  
BiGRU; DCO-OFDM; deep learning; visible light communication; nonlinear equalization +
Footnote †: journal: _Photonics_
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 7 Conclusions  
This paper presents an analysis of the IM performance of the HDDM-AGC method in terms of \(C/N_{0}\) and position solution based on high-end geodetic grade and low-cost commercial receivers. For each receiver type, different instances are used, either connected to a clean antenna signal for reference, to a signal with interference, or a signal processed by an HDDM-AGC. To provide a comparison with the state of the art, the built-in mitigation features of the high-end receiver are also enabled in the tests. The HDDM-AGC is implemented on FPGA. The most significant bit of its digital I&Q output is sent to a 1-bit DAC for up-conversion to the L1 band. This way it is used as input for the commercial receivers.  
Several interference types are considered in the tests. The results show that the HDDM has competitive mitigation capabilities, but a fair comparison is limited by the physical setup of the study. The most significant limitation of this study is the 1-bit DAC, which resulted in an unfair comparison of the HDDM performance. Therefore, it is suggested to repeat this study with an improved DAC for external comparisons to limit unnecessary losses. Nevertheless, the HDDM showed competitive mitigation capabilities, despite this limitation. Furthermore, the results also showed degradation in PVT accuracy using IM (although availability improved), an aspect that requires more investigations.  
The following are available online at [[https://www.mdpi.com/article/10.3390/s22020679/s1](https://www.mdpi.com/article/10.3390/s22020679/s1)]([https://www.mdpi.com/article/10.3390/s22020679/s1](https://www.mdpi.com/article/10.3390/s22020679/s1)).  
Conceptualization, J.R.v.d.M. and F.G.; methodology, J.R.v.d.M. and F.G.; software, F.G.; validation, F.G., J.R.v.d.M., S.U. and D.C.F.; formal analysis, F.G., J.R.v.d.M. and D.C.F.; investigation, F.G., J.R.v.d.M. S.U. and D.C.F.; resources, F.G. and A.R.; data curation, J.R.v.d.M. and D.C.F. writing--original draft preparation, F.G. and J.R.v.d.M.; writing--review and editing, F.G., J.R.v.d.M., S.U. A.R. and D.C.F.; visualization, J.R.v.d.M.; supervision, F.G.; project administration, A.R.; funding acquisition, W.F. and A.R. All authors have read and agreed to the published version of the manuscript.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The full results of all SVIDs analyzed in this study are available as Supplementary Material to the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 4 Discussion
### Comparing the SAR Transmit Antenna Pattern Using Transponder Recordings  
To further investigate the different sensitivity levels of both SAR instruments, transponder recordings were investigated. The DLR transponders are able to detect and record the transmitted signals of the SAR instrument during an overpass. These recordings are usually used to verify the antenna pointing and the transmit patterns in azimuth direction [24].  
The maximum level of the transmit azimuth pattern measured by each transponder is estimated. For each transponder these gain values are averaged over at least 10 overpasses for a given constellation, i.e., same look angle and same internal transponder settings. These averaged gain values are shown as data points in Figure 13 for S-1A (blue points) and S-1B (red points), whereby the free space losses were already considered. For each setting a low standard deviation (below 0.2 dB) is found.  
Figure 12: Measured radar brightness (VH polarization) as a function of elevation angle for S-1A (blue dots) and S-1B (red dots) for calm ocean regions. In addition, the annotated noise levels are depicted by lines for both SAR instruments. The detected radar brightness is a measure for the noise equivalent beta zero (NEBZ) if the SAR receiving power is dominated by the instrument noise, i.e., the SNR is in the order of 0 dB.  
In order to compare the measurements with the theoretical expected gain shape, the SAR transmit elevation pattern derived from the Sentinel-1 antenna model is plotted as a function of look angle in Figure 13 (lines). The measured gain values match well the shape of the theoretical transmit elevation pattern. For all three transponders and all three geometric configurations used, a higher transmit pattern gain is found for S-1B compared to S-1A of about 0.7 dB on average.  
It is concluded that the higher sensitivity of S-1B compared to S-1A arises on the one hand from a higher transmit power of about 0.7 dB. However, to explain the difference of about 1.4 dB between the NEBZ levels of both SAR instruments (see Figure 12) the remaining gain of 0.7 dB has to be related to a higher receive pattern gain of S-1B. The found reciprocity of transmit and receive pattern gain may indicate that the higher sensitivity of the S-1B SAR instrument arises mainly from the antenna radiation elements themselves.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 4 Datasets
### Geoid Model  
The regional NKG2015 quasi-geoid model [76] was used to retrieve \(DT_{SA}\) from SA-derived SSH data, as explained in Section 2. It is referred to as the Geodetic Reference System 1980 (GRS80) ellipsoid and covers the area from 53\({}^{\circ}\) to 73\({}^{\circ}\)N and from 0\({}^{\circ}\) to 34\({}^{\circ}\)E, with a grid spacing of 0.01\({}^{\circ}\)\(\times\) 0.02\({}^{\circ}\). NKG2015 has a good agreement with GNSS/levelling control points, with a standard deviation (STD) of 3.0 cm [76]. Various studies have estimated the NKG2015 geoid error that could reach up to 15 cm over certain marine areas. Such deficient geoid modelling areas were associated with the poor coverage of marine gravity data. Contrastingly, the global geoid models that are customarily used by the satellite altimetry community are less accurate and with a poorer spatial resolution (typically 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), e.g., EGM2008). It should be noted that the geoid model deficiencies do not affect the trend estimation studies; however, a precise geoid is needed for the examination of oceanographic processes (e.g., [50; 51]). Although, the geoid model may also be constrained by some problematic areas, which were investigated by [33; 51]. These areas are avoided in this study.
## 9 Future Research Directions and Challenges
### Fingerprinting and Deep Learning Applied to CSI  
Compared to range-based methods, fingerprinting has more potential to deliver higher localisation precision. On the one hand, range-based methods are limited by the accuracy of the model they apply to extract ranges or angles. On the other hand, fingerprinting may utilise the signal information to its full extent. Mainly, CSI data contain latent knowledge that can be captured by the complex AI approaches linked to fingerprinting. Hence, appropriate deep-learning models may be the key to exploiting the CSI large matrix structure effectively to hallucinate necessary abstract features for localisation. For example, deep neural networks may be able to decode these high-dimensional matrices to localise the obstacles around the receiver from the NLOS path.  
Furthermore, if multi-array antennae are deployed at the receiver and transmitter, CSIs contain even richer information, since different versions of the same signal would be available as separate fingerprints. Therefore, effectively profiting from CSI information may prevent the need for several BSs for localisation. It is shown in [118] that just one BS might suffice. Lastly, in this method, the main disadvantage of the traditional RF-based method, i.e., NLOS situations, is circumvented, under which these approaches become erroneous and unreliable.  
There are challenges in fingerprinting that are worth much more attention. The availability of data and the collection of enough data is not trivial. The collected dataset is not entirely reliable, as the environment constantly evolves. Transfer learning as one possible solution is suggested to exploit synthetic data or already available datasets. In this area, there still seems to be much room to investigate the integration of deep learning with CSI fingerprints.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 2 Principles and Classification of Reflector Antennas
### Types and Characteristics of Reflector Antennas  
Reflector antennas can be classified based on their characteristics. The most fundamental classification is based on the geometric shape of the reflector. According to this criterion, reflector antennas can be categorized into five types [21]: parabolic reflector antennas, parabolic cylindrical antennas, elliptical reflector antennas, spherical reflector antennas, and dual-reflector antennas. The characteristics and applications of each type are discussed below.
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 3 Method
### Cascade Spherical Sweeping  
We propose a cascade spherical sweeping approach, enabling high accuracy, while reducing resource use. The initial stage using the low-resolution spherical cost volume sweeps more hypothetical spheres for a coarse estimate. Based on this estimation, in the high-resolution space, we perform spherical sweeping with fewer but more accurate hypothetical spheres, leading to better results.  
As shown in Figure 5, the proposed cascade spherical sweeping consists of three stages. Let \(L_{t}\) represent the number of hypothetical spheres in stage \(t\). The shape of the corresponding initial spherical cost volume is \(H_{t}\times W_{t}\times C\times L_{t}\), with the sweeping interval \(\lambda_{t}\). \(h_{t}\) is a parameter defined by Equation (12), which is used to determine the sweeping range. In stage 1, we construct the spherical cost volume over the entire hypothetical spherical space at a low resolution, where \(\lambda_{1}=\frac{N}{L_{1}}\). In the subsequent stages, each stage utilizes the outcome of the previous one as the basic sphere \(n_{t}^{pred}\) to initiate sweeping, with the range determined by applying offsets based on \(h_{t}\), which is determined as  
\[S_{t,min}=\begin{cases}0&\text{if }n_{t}^{pred}-h_{t}\frac{L_{t}}{2}<0\\ N-h_{t}L_{t}&\text{if }n_{t}^{pred}+h_{t}\frac{L_{t}}{2}\geq N\,,\\ n_{t}^{pred}-h_{t}\frac{L_{t}}{2}&\text{otherwise}\end{cases} \tag{10}\]  
\[S_{t,max}=\begin{cases}h_{t}L_{t}-1&\text{if }n_{t}^{pred}-h_{t}\frac{L_{t}}{2}<0 \\ N-1&\text{if }n_{t}^{pred}+h_{t}\frac{L_{t}}{2}\geq N\,,\\ n_{t}^{pred}+h_{t}\frac{L_{t}}{2}&\text{otherwise}\end{cases} \tag{11}\]  
where \(S_{t,min}\) and \(S_{t,max}\) denote the lower and upper limits of the sweeping range in stage \(t\), respectively. \(n_{t}^{pred}\) is obtained using Equation (1) but using the upsampled \(SC_{t}^{*}\).
## 3 Research Case Study
### Experimental Tests
#### 3.1.1 Data Gathering  
Data gathering with the fixed camera in terms of height and location on the ground was carried out twice. The setup and subject of the case study are illustrated in Figure 4.  
When fixing the camera to the ground, the location of its stand was marked for the second data gathering session. Additionally, the drainage hole was a potential place for defect expansion and the extraction of data for the durability index due to these changes was carried out. After a comparison of the images, significant changes were found to exist between the first data gathering session and the second, which was encouraging for the research aim.  
The RC elements might be damaged on the surface due to environmental reasons, poor construction quality, bad curing, and other internal or chemical damage reasons. The surface damage is as follows:  
* Cracking;
* Chazing;
* Blistering;
* Delamination;
* Dusting;
* Curling;
* Efflorescence;
* Scaling and spalling.  
It is necessary to mention these surface damages are considered a threat to concrete durability.
## 2 Data Containers for an Optical Encryption  
### QR Code as a Data Container  
To the best of our knowledge, the only full-fledged data container, commonly used in an optical encryption, is QR code. QR codes were developed as 2D bar codes for machine vision systems (as pointed in [3]). The design of QR codes was determined by the requirements of their application: they have to have high resistance to damage (up to a partial loss) and should be easy to read in various conditions.  
The robustness of QR codes against damage is provided by the use of the Reed-Solomon error correction codes (RS). In the same way as BCH, RS can maintain the integrity of the data within and correct errors that occur. This ability is realized by adding extra information to the block of the useful data. The number of correctable errors is determined by the parameters of a code (the length of the input data block and the volume of extra information).  
An important difference between RS and BCH is their format: RS codes operate in the byte format, while BCH--in the bit one. The byte format is a major disadvantage of QR codes, when they are used in an optical encryption. For binary QR code, a byte format of RS means that it needs 8 modules (QR code elements) to represent minimal fragment of encoded data visually. If even one module within is damaged, the whole 8-modules fragment is incorrect [46].  
QR code has only four available error correction levels of approximately 7%, 15%, 25% or 30%; however, due to the byte format, these percentages represent not errors in modules but in whole blocks of eight of them, resulting in much lower error correction capabilities than expected [48].  
Other shortcomings of QR codes include an abundance of service elements, associated with positioning and orientation. Such elements are used for the correction of QR code placement before its decoding and are unnecessary for an optical encryption because it rarely leads to notable image shift or rotation. These elements reduce the area available for data recording and do not allow the decoding of QR code in case of their damage, although the error correction limit may not yet be reached.  
Secondly, common QR codes have a small range of available-to-user parameters. Maximum size is 177 \(\times\) 177 modules with strictly 1:1 aspect ratio. The error correction levels are limited to four fixed values. The maximum number of interconnected codes, which contain one file, is 16; therefore, their maximum data volume is only about 50 kB.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 2D Electrochemical Process Operational Conditions
#### 3.1.5 Effect of Initial CBZ Concentration on CBZ Removal  
The influence of the initial CBZ concentration, ranging from 2.5 to 10 mg/L, on its removal was studied. Figure 6 shows that the degradation efficiency of CBZ decreased as the initial CBZ concentration increased. This may be attributed to the presence of refractory functional groups, such as the benzene ring, which may inhibit the catalytic reaction [45]. Although the degradation was much faster at lower concentrations, the initial concentration of 10 mg/L, used previously, was considered adequate for the following experiments, because this was the least favorable situation, and at the same time allowed the evaluation of the influence of the process variables within a wider range.  
Figure 4: Effect of pH on the removal of CBZ from aqueous solutions (operational conditions: j = 6.67 mA/cm\({}^{2}\); distance = 3.5 cm; NaCl = 0.1 M; CBZi = 10 mg/L; reaction time = 10 min; cathode = STS; anode = MMO).  
Figure 5: Speciation of CBZ as a function of the pH of the aqueous solution (adapted from [48]).  
Based on the results of these studies, we concluded that the optimum conditions for the removal of CBZ using the 2D electrochemical process were: current density = 6.67 mA/cm\({}^{2}\), electrolyte (NaCl) concentration = 0.1 M, pH = 7, interelectrode distance = 3.5 cm, reaction time = 10 min, and initial CBZ concentration = 10 mg/L, using MMO as the anode and STS as the cathode. Under these conditions, the removal rate of CBZ was up to 95%.
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Copper Oxide (CuO)
Similarly, with methane and carbon monoxide, adsorption on the CuO surface leads to electron donation, affecting the concentration of charge carriers and producing a detectable change in resistance [66] (Figure 7). Operating temperature is another important factor in CuO-based gas sensors, as temperature affects the kinetics of gas adsorption and desorption. CuO sensors often exhibit optimal performance in the temperature range of 100-300 \({}^{\circ}\)C, depending on the specific gas and nanostructure used. Lower temperatures can hinder gas adsorption, reducing sensitivity, while higher temperatures might lead to desorption of the gas molecules before they can significantly alter the material's electrical properties. Conversely, excessively high operating temperatures can lead to the desorption of gas molecules from the CuO surface before they can significantly alter the material's electrical properties [67]. At elevated temperatures, the adsorbed gas molecules gain sufficient kinetic energy to overcome the adsorption energy, leading to rapid desorption. This rapid desorption reduces the interaction time between the gas molecules and the CuO surface, diminishing the signal change and potentially leading to false negatives [67]. This effect is particularly pronounced for gases with low adsorption energies, which tend to desorb more readily at higher temperatures. Therefore, precise temperature control is often required to optimize CuO's response to different gases, especially in industrial applications where rapid fluctuations in gas concentration occur [68]. CuO has also been investigated in composite form with other metal oxides, such as ZnO and SnO\({}_{2}\), to enhance gas sensing performance through synergistic effects. For example, CuO-ZnO composite sensors have shown high sensitivity and selectivity for hydrogen and carbon monoxide, as the heterojunctions formed at the interface between CuO and ZnO improve charge separation and facilitate electron-hole pair interactions with adsorbed gas molecules. Specifically, in one study, the response of the composite sensor to 100 ppm of H\({}_{2}\) was significantly higher than its response to 400 ppm of CO\({}_{2}\), 25 ppm of NH\({}_{3}\), 10 ppm of acetone, and 25 ppm of ethanol at an operating temperature of 350 \({}^{\circ}\)C [69]. CuO-SnO\({}_{2}\) composites similarly leverage the high surface area and reactivity of SnO\({}_{2}\) while utilizing CuO's p-type properties to enhance the response to reducing gases, making these composites suitable for use in environmental monitoring and industrial safety devices [69]. The combination of CuO's inherent p-type behavior, narrow bandgap, and versatile response to reducing gases makes it a highly effective material for gas sensing applications. Advances in nanostructuring, doping, and composite formation continue to expand the range and efficacy of CuO-based sensors, making them valuable tools in industrial safety, environmental monitoring, and other critical applications where sensitivity and selectivity are paramount.