esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 2 Observatory Measurements
### Ionospheric Parameters  
ASJI has two different type of ionospheric sounders operative during the Austral summer: a Vertical Incidence Sounder (VIS) and an Oblique Incidence Sounder (OIS) whose transmitter (Tx) was located in the ASJI and its receiver (Rx) in Spain. Figure 1[12] shows a simple operation scheme of the vertical incidence and of the oblique incidence.  
During the 2004 expedition, the VIS was installed in the ASJI. It records a vertical incidence ionogram every 10 min providing information about the particularities of the ionosphere and helping to model the climatology and the effects of the temporal geomagnetic disturbances. More details on the VIS soundings are given in [13] and details on the instrument, the Advanced Ionospheric Sounder (AIS), developed by the Instituto Nazionale de Geofisica e Vulcanologia (INGV) of Rome, Italy, can be obtained in [14].  
The OIS, developed by La Salle, is used to analyze and characterize the 12,760 km ionospheric channel between Antarctica and Spain [6] during the austral summer, when the ASJI is operative. The key parameters obtained are: (i) link availability; (ii) power delay profile of the channel and (iii) frequency dispersion. These parameters are used to model the HF radio link, with the OIS becoming another useful sensor of the ionosphere. The transmitter antenna is located in the ASJI (see picture in Figure 2a, with the detail of the radials in Figure 2b). More details on the HF data transmission will be described below, in Section 3.  
Figure 1: The particular layers of the ionosphere (D, E, F1 and F2) have different characteristics (electron densities and heights) depending on the effects of solar ionizing radiation on the neutral atmosphere [12].  
Figure 2: (**a**) The OIS installed in the ASJI, with the 32 radials improving the propagation angle and the transmitter in a second plane; (**b**) Detail of the 32 radials in the monopole antenna.
## 4 Simulation and Analysis
### Parameter-Setting Analysis
It can be seen that the upper-layer model of parcel-receiving station location and the lower-layer model of public air-route planning influence each other, and the bi-layer optimization model proposed in this paper is valuable. Considering the cost of the selected parcel-receiving station count, the cost of the average picking distance, the cost of picking fairness, and the cost of public air-route operation, the proportion of upper-layer model weight and lower-layer model weight are selected as 0.5 and 0.5. With this weight combination, the four costs of the bi-layer optimization model solved by GA-MMAS are the most balanced, where the cost of the upper-layer model is reduced by 5.12% on average, and the cost of the lower-layer model is reduced by 4.48%.
## 2 Materials and Methods  
### Materials  
The following materials were used for this study: Polyisobutylene Oppanol N100 (N100) (density 0.92 g/cm\({}^{3}\), molecular weight Mw -1,100,000), supplied by BASF (Poznan, Poland); butyl rubber IIR1675 (density 0.92 g/cm\({}^{3}\), Mooney viscosity 46-56 ML (1 + 8) at 125 \({}^{\circ}\)C, degree of unsaturation 1.4-1.8% mol), supplied by Konimpex Sp. z o.o. (Konin, Poland); polybutene plasticizer PIB32, with a density of 0.92 g/cm\({}^{3}\) and a number average molecular weight Mn of 1300, supplied by DMR sp. z o.o. (Warsaw, Poland); surface-modified mineral filler Omyacarb 2t (CaCO\({}_{3}\)) with a density of 2.70 g/cm\({}^{3}\), average particle size of 2 \(\upmu\)m, and oil absorption of 11 g/100 g of filler, supplied by Omya sp. z o.o. (Oldrzychowice Klodzkie, Poland); silanes: (3-Mercaptopropyl)trimethoxysilane (MPTES), (3-Aminopropyl)triethoxysilane (APTES), Vinyltrimethoxysilane (VTMOS), and (3-Glycidoxypropyl)trimethoxysilane (GLYMO), supplied by Brentnag sp. z o.o. (KedzierzynKozle, Poland); and Norperox BIPB-40 (BIPB), Di(tert-butylperoxyisopropyl)benzene, supplied by Nordmann-Rassmann sp. z o.o. (Warsaw, Poland).
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
###### Abstract  
In this paper, we propose a compact and highly isolated four-port ultra-wideband MIMO antenna. The antenna elements achieve broadband characteristics by etching a metamaterial structure on the radiating patch and stepping the coplanar waveguide feed. The test results show that the unit antenna can operate from 1.8 GHz to 16.38 GHz with an absolute bandwidth of 14.58 GHz and a relative bandwidth of 160.4% with good radiation properties and gain. After that, a compact four-cell ultra-wideband MIMO antenna is designed by using polarization diversity technology with an overall size of 51.2 mm \(\times\) 51.2 mm \(\times\) 1.524 mm. The MIMO antenna can operate from 1 GHz to 17 GHz with an absolute bandwidth of 16 GHz and a relative bandwidth of 177.78%. To reduce the coupling between cells, four angled slits are etched on the common ground to improve the isolation of the MIMO antennas to 27.8 dB. The performance parameters of the proposed MIMO antennas are further validated through simulation analysis and measurements. Moreover, the diversity properties of MIMO antennas are analyzed in detail, demonstrating the applicability of the proposed antennas in UWB communication systems, which can also be used for satellite mobile communications and satellite fixed communication services.  
UWB; MIMO antenna; high isolation; compact; polarization diversity Article
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion  
### Deep Learning Performance  
The training is based on supervised learning, where the LSTM is made aware of all 24 classification variations. The number of epochs and hidden units is varied to evaluate their impacts on the performance results in terms of learning time (s) and prediction accuracy (%). The results are averaged over three simulation runs with percentages rounded to the nearest 0.01% and time rounded to the nearest second.  
Table 10 shows the effects of increasing the number of hidden units on the performances in each simulation run with the number of epochs fixed at 1000. The results illustrate an example of all three types of capacity fitting [32]:  
1. _Underfitting_: where the solution is not sufficiently complex to understand the data, causing a bias underfitting issue. This can be seen with 5 and 10 hidden units;
2. _Overfitting_: where the solution learns the training data but fails to generalize the training set for new unseen testing data. This can be slightly observed with 40 hidden units;
3. _Appropriate fit_: where the solution can generalize as well as learn the trend to predict new data accurately. This is observed with 20 hidden units.  
Table 11 further shows the effects of increasing the number of epochs on the performances in each simulation run with the number of hidden units fixed at 20. Similarly, all three types of fits are visible. Based on the results obtained from varying these two parameters, the combination of 20 hidden units and 1000 epochs is chosen for the evaluation of handover and throughput performances in the next section.
# Future Wireless Networking Experiments Escaping Simulations
## 2 Theoretical Comparison of the Testbeds
### Resources Available for Wireless Experiments  
POWDER base stations are composed of software-defined radio devices from National Instruments (NI) connected to Commscope and Keysight antennas. In addition, some sites are equipped with massive MIMO technology from Skylark Wireless.  
Each device has one or more dedicated 10G links to aggregation switches at the Fort Douglas aggregation point. Each Fixed Endpoint (FE) installation in POWDER contains an ensemble of Software-Defined Radio (SDR) equipment from National Instruments (NI) with complementary small form-factor compute nodes. There is no wired backhaul, though the platform does provide seamless access via cellular/Wi-Fi to resources in an FE installation. Endpoints are mounted at human height level on the sides of buildings.  
The COSMOS testbed developed and integrated second generation radios in sandbox 2 to support wide-band Full Duplex (FD) wireless [17]. FD radio leverages techniques of frequency domain equalisation. In addition, the C-RAN architecture has been integrated with an optical x-haul network. Due to its high capacity, the optical x-haul network can support the investigation of high data rate and low latency applications. Finally, COSMOS also enables vehicles to wirelessly share in-vehicle sensor data with other vehicles and the edge cloud server.  
The CityLab testbed starts from a multi-technology approach. Hence, The gateways can utilise different communication technologies such as Wi-Fi (at both 2.4 GHz and 5 GHz), Bluetooth, Zigbee (at 2.4 GHz and 868 MHz), or sub-GHz radios (at 868 MHz and 433 MHz).  
Figure 2: Overview of Wi-Lib1.t and Wi-Lib2.t.  
In W-iLab.t, the embedded PCs run a Linux distribution (it is possible to choose the desired version). Some nodes are equipped with two Atheros-based Wi-Fi interfaces. The set of possible experiments includes using the sensors or enabling one or two wireless interfaces for a broad range of experiments.
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 2 Related Work
### Surface Reconstruction with Gaussians  
The Gaussians in 3 DGS are distributed and do not align with object surfaces, making mesh generation challenging. SuGaR [25] introduced regularization terms that minimize both volume density differences and normal differences to better align Gaussians with object surfaces. SuGaR also employed Poisson reconstruction to obtain a triangle mesh. However, SuGaR involves computationally intensive regularization processes and often generates inaccurate meshes for planar surfaces due to difficulties in precisely aligning volumetric Gaussians to flat geometries. To address these issues, several studies have proposed regularization terms to flatten 3D Gaussians into 2D Gaussians [27, 28]. Building on these approaches, 2D Gaussian Splatting (2 DGS) [10] further develops this concept by reconstructing object surfaces with fully 2D Gaussians defined in the local tangent space. Furthermore, an exact homogeneous transformation for 2D Gaussians, along with regularization terms, facilitates efficient and accurate surface reconstruction.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 5 Discussion
### LRP Implementation and Interpretability  
The implementation of LRP-\(\gamma\) (\(\gamma\) = 2.5) for model interpretation initially showed limited explainability when analyzing normal signal conditions. The relevance score distributions across neurons, while showing varying patterns, did not provide immediately clear insights into the model's decision-making process. However, this limitation led to a crucial discovery when examining anomalous conditions.  
The most significant finding emerges from the analysis of artificially introduced anomalies. Consistent increases in LRP scores during anomalous periods were observed across all features, with particularly pronounced effects when anomalies were introduced to specific parameters. This relationship between anomalies and elevated LRP scores suggests a novel approach to anomaly detection in GNSS signals. The results show clear patterns of increased relevance scores corresponding to specific anomalies, with growth rates ranging from 7.34% to 32.48% depending on the feature and anomaly duration.  
This discovery has important implications for GNSS applications. Traditional anomaly detection methods often rely on direct signal analysis or black-box machine learning approaches. The LRP-based method offers a new perspective by providing interpretable insights into how the model identifies anomalies. This could be particularly valuable in safety-critical applications where understanding the reasoning behind anomaly detection is crucial.
## 3 Results and Discussion
### Characterization of Bi@Fe/CN
The high-resolution Fe 2p XPS spectrum (Figure 4e) shows two spin-orbit centers centered at the binding energies of 711 eV and 725 eV, corresponding to Fe 2p\({}_{3/2}\) and Fe 2p\({}_{1/2}\), respectively. The Fe 2p\({}_{3/2}\) peak can be decomposed into two peaks at 710.3 eV and 712.2 eV, corresponding to Fe\({}^{2+}\) and Fe\({}^{3+}\), respectively, indicating the coexistence of Fe\({}^{2+}\) and Fe\({}^{3+}\) in the catalyst [35]. Additionally, as seen in the high-resolution Bi 4f XPS spectrum (Figure 4f), the peaks at 158.8 eV and 164.1 eV are associated with the Bi 4f\({}_{7/2}\) and Bi 4f\({}_{5/2}\) orbitals of Bi\({}^{3+}\), respectively [36]. Figure 4: XPS spectra of Bi@Fe/CN, (**a**) survey XPS spectra, (**b**) C 1s spectra, (**c**) N 1s spectra, (**d**) O 1s spectra, (**e**) Fe 2p spectra, and (**f**) Bi 4f spectra.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## Materials and Methods
### Experimental Protocol  
A series of experiments was carried out to evaluate the performance of MD-EMBR for TrOC degradation. At the start of the experiment, a mixture of the selected TrOCs (each at 20 \(\upmu\)g/L) in Milli-Q water was added to the bioreactor. Lacase was added to the bioreactor for achieving an initial enzymatic activity of 95-100 \(\upmu\)M\({}_{\text{(DMP)}}\)/min. The media from the glass enzymatic bioreactor and water from the permeate tank were recirculated in their respective flow channels separated by the membrane. A chiller (SC100-A10, Thermo Scientific, Waltham, MA, USA) was used to regulate the temperature of the permeate tank at 10 \(\pm\) 0.1 \({}^{\circ}\)C. The permeate tank was also placed on a precision balance (Mettler Toledo Inc., Columbus, OH, USA) to monitor permeate flux. The recirculation flow rate of both feed  
Figure 1: Schematic representation of the membrane distillation—enzymatic membrane bioreactor (MD-EMBR).  
and the distillate was controlled at 1 L/min (corresponding to the cross flow velocity of 9 cm/s) using two rotameters.  
Duplicate samples from the enzymatic bioreactor (100 mL each) and permeate tank (500 mL each) were taken after operating the MD-EMBR for 12 h. After evaluating the lacase-catalyzed degradation of TrOCs in MD-EMBR, the possible improvement in TrOC degradation was assessed with the addition of three redox-mediators (HBT, VA and SA) at two different concentrations (0.25 and 0.5 mM) via separate runs. Again duplicate samples from the enzymatic bioreactor and permeate tank were collected for the quantification of TrOCs.  
Samples collected from the enzymatic bioreactor were diluted to 500 mL with Milli-Q water and were filtered through 0.45 \(\upmu\)m glass fiber filter paper (Filtech, Wollongong, NSW, Australia). The pH of samples was adjusted to 2-2.5 using 4 M H\({}_{2}\)SO\({}_{4}\) before solid phase extraction (SPE) and GC/MS analysis. For toxicity analysis, undiluted samples from the enzymatic bioreactor and permeate tank were collected in 2 mL amber vials at the end of each experiment, and stored at 4 \({}^{\circ}\)C until analysis.
## 5 Conclusions  
This paper presented some fundamentals and the progress of microwave liquid crystals, concerning its performance metric with a focus on its response times, as well as for phase shifters in different topologies, starting from well-known LC-tuned waveguide phase shifters and planar inverted microstrip delay lines toward innovative loaded line and nanowire membrane-filled LC concepts with the newest results, featuring different microwave performances and response times, ranging from tens of seconds  
Figure 38: Single-beam ESA with a fully analog architecture for point-to-point communications (**above**) and multi-beam ESA with hybrid analog/digital architecture, using subarrays, for point-to-multipoint communications (**below**) [255].  
down to few tens of milliseconds. For manufacturing low-cost large-scale ESAs with very low power consumption for single- and multiple-beam steering, low-profile planar loaded-line phase shifter stacks with 4 \(\upmu\)m thin integrated MLC varactors with switch-off response times of about 30 ms for 400\({}^{\circ}\) differential phase shift at room temperature are currently assembled into multi-stack, flat-panel, beam-steering phased arrays, where the MLC phase shifter stacks with a FoM \(>\) 120\({}^{\circ}\)/dB over 2.5 GHz bandwidth are fabricated in a standard LCD process. Beam-steering within \(\pm\)60\({}^{\circ}\) is in the range of 10 ms in practice, which is less than the response time above, since not all phase shifters of the array require a full 360\({}^{\circ}\) phase shift at the same time during the beam-steering process. In a few cases, it might need more than 10 ms to reduce the side lobe level for fitting the mask requirements. Further developments are going into three directions:  
1. Next-generation microwave LCs are aiming for higher low-temperature stability in the range of -30 \({}^{\circ}\)C, higher anisotropy in relative permittivity up to 1.2, and a larger ratio of rotational viscosity over the elastic constant \(\frac{\gamma_{\mathrm{rot}}}{K_{\mathrm{11}}}<0.02\) for even faster switch-off response times of less than 25 ms for an LC layer height of 4 \(\upmu\)m.
2. Progress in the processing and manufacturing of the phase shifter stack (1) with a lower LC layer height \(h_{LC}<\) 2.5 \(\upmu\)m, which reduces the response time significantly, down to below 10 ms, without affecting the other performance parameters, (2) using thinner glass with low dielectric constant and low dielectric losses, and (3) with compact loaded lines for 360\({}^{\circ}\) phase shift.
3. Progress in the assembly technology of the whole electronically steerable antenna, including the electronics for the antenna control unit, the feeding network, and the radiator stack.  
With the modular concept and the hybrid analog/digital architecture, these smart antennas are flexible in size to meet the specific requirements for operating in future satellite ground and user terminals, but also in 5G mm-wave systems.
In reference [13], the Background Subtraction idea is introduced into airborne SAR moving target detection, which can effectively extract the motion trajectory of moving targets. This method is applied to the spotlight mode, which can generate an image sequence to obtain the background. And thanks to the accurate position information of the platform, all images can be formed in unified image coordinates, i.e., no registration is needed. However, when the spaceborne SAR platform repeatedly observes the same scene, due to the influence of orbit offsets at different times, the geometric positioning error of the image is large, so background extraction and change detection cannot be directly performed. In addition, there are relatively few studies and discussions on target-level change detection in high-resolution spaceborne SAR images. Reference [14] proposed a moving target monitoring method with high framerate spaceborne Synthetic Aperture Radar SAR images. Reference [15] explored methods for urban change detection using multi-temporal spaceborne SAR data. [PERSON] et al. developed an object-based change detection algorithm that can generate change maps at different scales [16]. Therefore, compared with airborne SAR images, introducing Background Subtraction into spaceborne SAR time-series images has several differences, which makes it a challenging task. Based on the aforementioned analysis, this paper proposes an improved Logarithm Background Subtraction method, entitled SAR-SIFT-Logarithm Background Subtraction, for change detection using spaceborne SAR time-series data.The proposed method employs SAR-SIFT image registration [17; 18; 19] to obtain accurately coregistered image sequence, which is followed by the Logarithm Background Subtraction algorithm for image change detection. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images are coregistered by a SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the part that remained unchanged throughout the time period is modeled with a median filter to obtain the static background. The change information is then obtained via subtraction of background and CFAR detection and clustering. The method is experimentally validated using Sentinel-1 and PAZ-1 time-series datasets along with detailed truth data. The structure of this paper is as follows: Section 2 introduces the dataset used in the paper and outlines the experimental design; Section 3 presents the proposed SAR-SIFT-Logarithm Background Subtraction. In Section 4, the detailed experimental results are presented. Section 5 contains the discussion of the experimental results. Section 6 contains the conclusion.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 4 Results  
### Experimental Evaluation of Trained DNN Models  
Figure 9 depicts trends observed in the calculated average values of R\({}^{2}\) and MAPE, obtained during \(5\times 5\)-fold cross-validation of the morning and afternoon prediction DNN models, in accordance with changes in hidden-layer conditions. As can be seen, the average value of R\({}^{2}\) for validation data is equal to 0.99 with increase in the number of hidden-layer nodes, irrespective of the number of hidden layers. MAPE values for validation data decrease with increase in the number of hidden-layer nodes, irrespective of the number of hidden layers. The highest MAPE value for the validation data was 6.08% when the number of hidden layers was three and the number of corresponding nodes was 30, whereas the lowest MAPE value of 4.78% was observed for four hidden layers and 50 hidden-layer nodes. Table 6 describes the mean and standard deviation of MAPE of DNN models for validation data, in accordance with the number of rounds of 5-fold cross validation. The lowest mean and standard deviation of MAPE were analyzed in (4, 50), (5, 50), and (4, 40) of hidden layer configuration conditions. Table 7 lists the results of t-significance test for DNN models. In the table, '\(p<0.05\)' indicates that the difference of MAPE of two models was statistically significant. In contrast, 'NS' means the predictive performance of two models were both equal to each other. The results of the t-significance test for MAPE of DNN models shows that DNN models with (4, 40), (5, 50), and (4, 40) of hidden layer configuration follow with no statistically significant differences in the predictive performance. Figure 9: Results of \(5\times 5\)-fold cross validation for predicting ore production during morning. In this study, the DNN model corresponding to the MAPE value for validation data was considered to realize optimum performance in predicting morning ore production. The optimum model comprised four hidden layers and 50 hidden-layer nodes. Similar to Figure 9, Figure 10 depicts the trends for R\({}^{2}\) and MAPE values corresponding to the DNN model for prediction of afternoon ore production. The average value of R\({}^{2}\) was equal to 0.99, regardless of the number of hidden layers and hidden-layer nodes. The average value of MAPE values, in general, decrease with increase in hidden-layer nodes. The highest MAPE value of 7.00% corresponds to three hidden layers and 30 hidden-layer nodes, whereas its lowest value of 5.22% corresponds to five hidden layers and 50 hidden-layer nodes.