esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Super-resolution reconstruction is a critical task in remote sensing image classification, and generative adversarial networks (GANs) have emerged as a dominant approach in this field. Traditional generative networks often produce low-quality images at resolutions like 256 \(\times\) 256, and current research on single-image super-resolution typically focuses on resolution enhancement factors of two to four (2\(\times\)-4\(\times\)), which do not meet practical application demands. Building upon the framework of StyleGAN, this study introduces a dual-style controlled super-resolution reconstruction network referred to as DSpix2 pix. It uses a fixed style vector (Style 1) from StyleGAN-v2, generated through its mapping network and applied to each layer in the generator. And an additional style vector (Style 2) is extracted from example images and injected into the decoder using AdIn, enhancing the balance of styles in the generated images. DSpix2 pix is capable of generating high-quality, smoother, noise-reduced, and more realistic super-resolution remote sensing images at 512 \(\times\) 512 and 1024 \(\times\) 1024 resolutions. In terms of visual metrics such as RMSE, PSNR, SSIM, and LPIPS, it outperforms traditional super-resolution networks like SRGAN and UNIT, with RMSE consistently exceeding 10. The network excels in 2\(\times\) and 4\(\times\) super-resolution tasks, demonstrating potential for remote sensing image interpretation, and shows promising results in 8x super-resolution tasks.  
c 2025 15, 1179
## 1 Introduction
This reduces the risk of potential issues during the implementation, ensuring a higher likelihood of mission success. So, the chosen MAPMTs are the Hamamatsu R7600-03-M16 (rugged version) and the APDs are an SMD version of the Hamamatsu S8664-55 (the same aboard the TSUBAME satellite, unfortunately lost in 2015 [20]). A precise calibration of each component of the system is essential to achieve the desired metrological characteristics of the polarimeter. To this end, this work addresses the issue of calibrating the response of the APDs under flight operational conditions [21]. Specifically, itis necessary to characterize the gain \(G_{APD}\) and energy resolution \(\delta E/E\) of these components as a function of temperature (from \(-20\)\({}^{\circ}\)C to \(+60\)\({}^{\circ}\)C--typical temperatures that include the range variation of the payload of Earth-orbiting CubeSats [22; 23]) and bias voltage \(V_{bias}\) (from 260 V to 410 V--typical bias voltage used on APDs; the lower limit corresponds to a low gain value, and the upper value is limited by the breakdown voltage in the device [24; 25]). Once the experimental dependence of the gain on temperature \(G_{APD}(T)\) is determined, given the temperature variability to which CubeSat payloads are subjected, a feedback system will be developed to maintain the APD gain stable despite \(T\) fluctuations by adjusting \(V_{bias}\). This work thus analyzes the challenge of calibrating APDs over such a broad parameter space, develops an appropriate measurement system, and preliminarily analyzes the data to establish possible control curves for stabilizing the gain of the APDs. This paper is organized as follows. In Section 2, a brief introduction to the APD is provided to the reader, focusing on the parameters (i.e., \(G_{APD}\) and \(\delta E/E\)) that will be characterized in this work, highlighting the expected dependencies of these to \(T\) and \(V_{bias}\). Then, in Section 3, the design of the measurement system is discussed in detail. In particular, first, the electronic circuits and measurement instrumentation for the characterization of \(G_{APD}\) and \(\delta E/E\) are presented, and then the system for the control of the temperature and humidity, which is based on a climate chamber, is introduced. Once the developed system is described, the measurement procedure and the system calibration and setup are discussed in Section 4. Due to the numerous components that elaborate the analog signal from the APD to the digitization stage, it becomes difficult, and it would not guarantee particularly accurate results, to attempt to calibrate the losses and gains introduced by each component in the line to assess the response of the entire system. To address this, it will be shown how a previously calibrated APD was used as a reference to calibrate the entire system. Finally, in Section 5, the measurements obtained are analyzed in detail, and conclusions are provided in Section 6.
## 3 Space Segment
### Platform and Launcher  
The EnMAP platform is a reuse of an existing design of OHB System AG space heritage. It comprises accurate orbit and attitude control and a high rate data processing chain and features three-axis stabilization and a mass memory system for data storage. The input image data stream of 866 Mbit/s is stored in five memory banks configured such that \"graceful degradation\" is tolerated, resulting in 512 Gbit of end-of-life memory capacity. Lossless data compression based on the Consultative Committee for Space Data Systems (CCSDS) Standard 122 is performed individually for each spectral band. This JPEG2000-based compression achieves an average compression ratio of 1.6 with a dedicated field programmable gate array (FPGA). The mass memory output is routed through a CCSDS coding unit directly to the 320-Mbit/s X-band downlink system. More information about the satellite platform can be found in [40].  
The satellite dimensions are compatible with the Indian Polar Satellite Launch Vehicle (PSLV). Launch is expected to take place from Sriharikota, India.  
Figure 7: On-board calibration units of the EnMAP instrument. For spectral and relative radiometric calibration, the Ulbricht integrating sphere (**left**) provides a homogeneous source of illumination for the spectrometer entrance slits via coupling optics and a mechanism that introduces a mirror into the telescope beam. Absolute radiometric calibration is performed by using the Sun to illuminate the entrance pupil of the telescope via a Spectralon full-aperture diffuser (**right**).
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 4 ESPEA for Solving the PVSP-MSS
### Local Search Based on Critical Paths  
In this paper, we propose local search technology to improve the solution quality by swapping the pickup vehicle operations on the critical path in the steel logistics park.  
The critical path directly affects the maximum completion time of scheduling; it is defined as the longest path of all paths from the start node to the end node and the operations in the critical path are defined as critical operations. Moving an operation on the critical path can optimize the current solution more efficiently [22]. In this paper, adjacent operations on the critical path in the same yard are called critical sets, and we design a critical path neighborhood structure based on the idea of critical operation movement. The detailed procedure of the local search technology based on critical paths is shown in Algorithm 4. In this local search technique, FindCriticalSetsOnCriticalPath() identifies all critical sets. LengthOfSet() determines the length of each set. As for LastTwoDiffWehides(), if the last two critical operations belong to different vehicles, then LastTwoDiffWehides() is true; otherwise, it is false. SwapLastTwoOps() represents swapping the last two critical operations. For FirstTwoDiffVehicles(), if the first two critical operations belong to different vehicles, then FirstTwoDiffVehicles() is true; otherwise, it is false. SwapFirstTwoOps() represents swapping the first two critical operations. An example of a local neighborhood based on the critical path is illustrated in Figure 9.  
Figure 8: An example of two-point crossover.  
Figure 9: An example of a local neighborhood based on the critical path.  
```
Input: Schedule S; Output: Improved schedule S'; \(critical\_sets\leftarrow\) FindCriticalSetsOnCriticalPath(\(S\)); ifLengthOfSet(\(set[0]\)) \(>1\)then ifLastTwoDiffVehicles(\(critical\_sets[0]\)) then  SwapLastTwoOps(\(critical\_sets[0]\))  endif endif ifLengthOfSet(\(set[-1]\)) \(>1\)then  ifFirstTwoDiffVehicles(\(critical\_sets[-1]\)) then  SwapFirstTwoOps(\(critical\_sets[-1]\))  endif endif forset incritical_setsdo ifLengthOfSet(\(set\)) == 2 then ifFirstTwoDiffVehicles(\(set\)) then  SwapFirstTwoOps(\(set\)) endif endif ifLengthOfSet(\(set\)) \(>=4\)then  ifFirstTwoOpsDiffVehicles(\(set\)) then  SwapFirstTwoOps(\(set\)) endif  endif endfor Return\(S^{\prime}\)
```  
**Algorithm 4** Local search based on critical paths
## 2 Recent Works
### Fairness Issues of UAV  
From 2018 to 2019, [PERSON] et al. [20] studied the problem of minimizing the maximum deployment delay and total deployment delay between UAVs, while considering fairness and coverage efficiency. However, this reference regarded UAVs as fixed and immobile aerial nodes without considering their movement. [PERSON] et al. [21] studied the problem of maximizing total energy and fairness in energy transmission when using movable UAVs to provide wireless power transfer services for ground devices. However, in this study, UAVs hover at a fixed location for a long time during charging. [PERSON] et al. [22] optimized the hovering time of UAVs and studied the fairness issue in the power supply network of UAVs based on it but only optimized the one-dimensional trajectory of UAV flight. [PERSON] et al. [23] studied the fairness issue of UAVs providing wireless communication services for ground users, where UAVs fly in a two-dimensional plane at a fixed height and introduced the concept of \(\alpha\)-fairness [24] to characterize fairness but did not consider the energy consumption of UAVs. [PERSON] et al. [25] considered the fairness of energy consumption between communication, hovering, and motion energy consumption of UAVs used for reconnaissance tasks and used a heuristic algorithm to solve this problem.  
From 2020 to 2021, [PERSON] et al. [26] proposed efficient and fair 3D UAV scheduling with energy replenishment. Under this model, UAVs can be charged while serving users. The author proposes a UAV control strategy based on a depth deterministic strategy gradient to ensure energy efficiency and fair coverage for each user in a large area, while simultaneously ensuring service durability. However, there is no research on energy efficiency fairness between different UAVs. [PERSON] et al. [27] formulated a fair energy-saving resource optimization problem, which maximizes the minimum energy efficiency of UAVsby optimizing the flight trajectory of multiple UAVs to achieve energy-saving fairness among UAVs.  
In 2022, [PERSON] et al. [28] introduced a new fairness index to ensure the fair distribution of service quality based on the coverage and service quality of UAVs. This study proposes an alternating algorithm based on proximal random gradient descent to optimize the position of unmanned aerial vehicles.
# Narrowband Internet of Things (NB-IoT):
## 4 Narrowband-IoT Possible Deployment Strategies
### Synchronous NB-IoT Deployment in All Small Cells  
This is the NB-IoT deployment strategy which is enabled in all the small cells by using the same physical resource blocks. All the small cells are synchronized in such a way that with the same PRBs, all the NB-IoT UEs are using the transmit power that is configured regardless of its maximum transmitting power capacity. This means that even though NB-IoT devices might support different power classes such as 14 dBm, 20 dBm, or 23 dBm, the NB-IoT devices will only be configured to use the minimum allowed transmit power in order to avoid causing the co-channel interference to other UEs using the same radio resources.In this strategy, power control may be the key feature to ensure the required performance. However, cell edge UEs may still suffer from the interference problem. This interference may highly be increased due to the low channel estimation quality of NB-IoT UEs associated by its reduced computational complexity.
## 3 Methodology
### Drifting Buoy Data Processing  
The information contained in the drifting buoy data is the specific time, latitude and longitude of each station. First, we calculate the distance between two continuous stations in the time series data of drifting buoys. Then the speed of the vector formed by the two points is calculated by using the time difference between the two points. For the calculation of the angle, any point is taken so that its projection with the remaining two points in the plane forms a right triangle, as shown in Figure 4. According to the distance formula, the distance between any two points can be calculated, and finally, the magnitude of the angle can be calculated according to the cosine theorem. The specific formula is as follows:  
\[C=\arccos(\cos(90-B_{\text{lat}})\times\cos(90-A_{\text{lat}})+\sin(90-B_{ \text{lat}})\times\sin(90-A_{\text{lat}})\times\cos(B_{\text{lon}}-A_{\text{lon }})) \tag{4}\]  
\[L=R\times C\times\pi/180^{\lx@math@degree} \tag{5}\]  
where \(R\) is the radius of the Earth, \(A_{\text{lat}}\) and \(A_{\text{lon}}\) represent latitude and longitude of point A, respectively, and \(B_{\text{lat}}\) and \(B_{\text{lon}}\) represent the latitude and longitude of point B, respectively. C represents the radial corresponding to the angle formed by points A and B. L is the arc length corresponding to the radian C, which is the distance between points A and B.
## 3 Results  
Table 1 presents the Global Warming Potential (GWP) of some of the most widespread refrigerants, both natural and synthetic, together with their main physical properties. The reported GWP values were collected from WMO 2018 [23]. The physical properties (molar mass (\(M\)), normal boiling point temperature (\(T_{\rm b}\)), critical temperature (\(T_{\rm c}\)), and critical pressure (\(P_{\rm c}\))) were collected from REFPROP 10.0 [24].  
The GWP of a compound compares its global warming impact to the impact due to the emission of a similar amount of a reference compound, usually taken as carbon dioxide (R744 in the ASHRAE designation) [25]. This value is estimated during a time horizon that is usually assumed to be equal to 100 years. In particular, the GWP results from the combination of the cumulative radiative forcing, both direct and indirect effects, and atmospheric lifetime of a compound (trace gas), together with the time horizon for its evaluation [6]. The radiative forcing is the change in net irradiance at the tropopause due to the change in atmospheric concentration of a trace gas resulting from a pulse release of that gas.  
Although the concept of GWP has several weaknesses, such as that it neglects the effect of reaction products resulting from breakdown and the assumption of a global mean concentration [6], this index is easy and the most commonly used environmental metric.  
The GWPs of blends are estimated as the mass-weighted averages of components' GWPs.  
The United Nations Environment Programme (UNEP) [26] proposed a classification scheme, distinguishing between very low (or ultra-low) (\(<\)-30), very low (\(<\)-100), low (\(<\)-300), moderate (\(<\)-1000), high (\(<\)-3000), very high (\(<\)-10,000) and ultra-high (\(>\)-10,000) GWP fluids.  
In recent years, many experimental vapor pressure and \(PvT\) data in the superheated vapor phase were measured by our research group with the isochoric apparatus for R1234 yf and R1234 ze(E) [27; 28; 29; 30], as summarized in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refrigerant** & **GWP** & **M (kg kmol\({}^{-1}\))** & **T\({}_{\rm b}\) (K)** & **T\({}_{\rm c}\) (K)** & **P\({}_{\rm c}\) (kPa)** \\ \hline R744 & 1 & 44.010 & 194.69 & 304.13 & 7377.3 \\ R32 & 705 & 52.024 & 221.50 & 351.26 & 5782.0 \\ R600a & \(<\)1 & 58.122 & 261.40 & 407.81 & 3629.0 \\ R134a & 1360 & 102.030 & 247.08 & 374.21 & 4059.3 \\ R1234 yf & \(<\)1 & 114.040 & 243.67 & 367.85 & 3382.2 \\ R1234 ze(E) & \(<\)1 & 114.040 & 254.18 & 382.51 & 3634.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global Warming Potential (GWP) and physical properties of the studied refrigerants.
## 2 The Applied Model
### Mathematical Models
#### 2.2.1 Control Equations  
This section utilizes the COMSOL finite element simulation platform to extract the acoustic coefficients of the model. The calculation utilizes the pressure acoustic-frequency domain module. The controlling equation is [24]:  
\[\
abla\cdot\left[-\frac{1}{\rho_{c}}(\
abla p_{t}-\mathbf{q}_{d})\right]-\frac{k_{ eq}^{2}p_{t}}{\rho_{c}}=Q_{m} \tag{1}\]  
\[p_{t}=p+p_{b} \tag{2}\]  
\[k_{eq}^{2}=(\frac{\omega}{c_{c}}) \tag{3}\]  
where  
\(\rho_{c}\)--material density, kg/m\({}^{3}\);  
\(p_{t}\)--total pressure, Pa;  
\(\mathbf{q}_{d}\)--dipole source, N/m\({}^{3}\);  
\(Q_{m}\)--monopole source, 1/s\({}^{2}\);  
\(p\)--pressure, Pa;  
\(p_{b}\)--background pressure, Pa;  
\(\omega\)--angular frequency;  
\(c\)--speed of sound in water, m/s.  
The solid-liquid interface is determined by the coupling equation [25] as follows:  
\[-n\cdot(-\frac{1}{\rho_{c}}(\
abla p_{t}-\mathbf{q}_{d}))=-\mathbf{n}\cdot\mathbf{u}_{tt} \tag{4}\]  
\[\mathbf{F}_{A}=p_{t}\mathbf{n} \tag{5}\]  
where \(\mathbf{n}\) represents the surface normal, \(\mathbf{u}_{tt}\) denotes the structural displacement, and \(\mathbf{F}_{A}\) represents the load that is acting on the structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Young’s Modulus (Pa)** & **Poisson’s Ratio** & **Loss Factor** & **Density (kg/m\({}^{3}\))** \\ \hline Substrate & 1.4 \(\times\) 10\({}^{8}\) & 0.49 & 0.48 & 1100 \\ Steel & 2.1 \(\times\) 10\({}^{11}\) & 0.3 & 0 & 7800 \\ Water & - & - & - & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
Figure 3: Cavity element model.
###### Abstract  
A concept that has been explored as a means to obtain decimeter-level positioning accuracy with global navigation satellite systems (GNSSs) is meta-signal processing (MSP), which treats several stand-alone GNSS signals as a single composite wideband signal. BeiDou Navigation Satellite System (BDS) III already offers with the BII+BIC signal a meta-signal, while the forthcoming Galileo (GAL) E1D could be combined with E1B or EIC if the E1D signal is broadcast with a frequency offset to the L1/E1 carrier frequency. This would boost the ranging performance of GAL open service (OS) in the upper L-band through MSP. However, the cross-correlation function (CCF) of meta-signals contain numerous high side-maxima which can, when wrongly identified as the main peak, lead to significant pseudo-range errors of multiple meters. The probability of such a false lock is known to increase with decreasing signal-to-noise ratios but can significantly increase even further due to imperfections in the analog hardware components (e.g., linear and non-linear effects of a high-power amplifier (HPA), an output multiplexer (OMUX), a transmitter filter, and a front-end receiver), as these can distort the CCF. One remaining question is whether meta-signals are a well-suited approach to reliably increase ranging performance in the presence of payload and receiver distortions. This study presents the first systematic assessment of the robustness of several potential meta-signal options enabled by a forthcoming GAL E1D signal for different levels of distortion. The results show significant performance gains but also indicate constraints regarding the choice of signals when considering MSP under the influence of distortions.  
GNSS; Galileo; meta-signal processing; dual-band tracking +
Footnote †: Copyright: © 2023 by the authors. Liconssee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
###### Abstract
The proposed multichannel filterbank was evaluated by two objective indices: perceptual evaluation of speech quality (PESQ) [20; 21] and short-time objective intelligibility (STOI) [22]. The results show that the gammachirp filterbank has good reconstruction performance, and the speech quality index of the processed signal is higher than that of the gammatone filterbank. From the subjective assessment experiments, it was found that the loudness compensation algorithm based on the gammachirp filterbank effectively improves the sentence intelligibility. The compensated signals also obtain higher speech evaluation indices for the hearing aid: hearing-aid speech perception index (HASPI) and hearing-aid speech quality index (HASQI) [23; 24]. The main contributions of this study are as follows:  
* We applied the gammachirp filterbank as a component in the loudness compensation system. The filter parameters were calculated based on the human auditory characteristics and the intensity of the input signal. The obtained multichannel gammachirp filterbanks model the frequency selectivity characteristics of the human ear;
* We introduced a new method of channel combination to reduce the computational complexity. The adjacent channels were merged by the characteristics of the special audiogram and the relationship between frequency ranges and speech intelligibility. After obtaining the personalized filterbank, each band carried out loudness compensation, based on the curve provided by the hearing-impaired patients;
* We conducted an objective evaluation and subjective assessment experiments. The results illustrate that the proposed method can considerably improve sentence intelligibility. The input speech can be reconstructed with fewer channels and satisfy different audiogram matching requirements.
## 2 Materials and Methods
### Earthquake Catalog  
Now it has become clear how to retrieve the data to be analyzed in order to predict a single earthquake, I want to explain how to obtain the complete earthquake dataset that has been compiled for this study and, in the next subsection, how to work with it.  
Figure 3: Illustration of a 3D matrix for enclosing a geographical area during several epochs t\({}_{1}\), t\({}_{2}\), t\({}_{3}\), t\({}_{4}\) included in the GIM files.  
Figure 2: Example GIM subgrid to analyze for possible TEC anomalies. A star indicates the location of the epicenter of the earthquake of magnitude \(M_{w}=7.8\) that actually occurred on 25 April 2015.  
The United States Geological Survey earthquake catalog [37] was used to retrieve the set of earthquakes of magnitude \(M_{w}\) 5.0 or higher from 1 January 2011 to 1 October 2024, that is, almost the last 14 years. Earthquakes of magnitudes around \(M_{w}\) 5.5 or lower (with those below \(M_{w}\) 5.0 simply excluded from this study) are understood to be little significant for the prediction. To overcome a part of the overwhelming dominance of these lower-magnitude events--which could hamper the estimation capabilities of the events with the largest magnitudes--as well as to alleviate the computational requirements where possible, I have simplified the list of earthquakes to deal with by randomly selecting only 10% of the events with magnitudes below or equal to \(M_{w}\) 5.5; 15% of the events with magnitudes from \(M_{w}\) 5.6 to \(M_{w}\) 6.0; 50% of the events with magnitudes from \(M_{w}\) 6.1 to \(M_{w}\) 6.5; and all the events with magnitudes higher than \(M_{w}\) 6.5. After this operative simplification, the list of earthquakes used for training the network displays a magnitude distribution, as shown in Table 1 and Figure 4.  
The earthquakes in the first group are essentially understood as having little significance for prediction purposes, so they are considered \"non-significant earthquakes\" for the scope of this study. Instead, this study aims to explore the largest magnitudes, with those in the groups of 7.0 and above as the main targets.