esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental and Numerical Methods
### Numerical Calculation Model and Effective Verification
#### 2.3.4 Verification of Calculation Results  
Numerical calculation is performed according to the attack angle and initial projectile velocity measured in the tests. The calculation results are compared with the experimental data. With the measured projectile positions in water at different times, the corresponding velocities can be calculated and fitted. The velocity comparison is shown in Figure 5, and the maximum error is within 15%. It can be considered that the velocity attenuation trend of the numerical calculation is in good agreement with the experimental results.  
Figure 6 gives a comparison between the measured load curve in the projectile water entry test and numerically calculated loads. The shock wave peak overpressure measured in Test 1 is 19.1 MPa, and the initial shock wave pressure in the simulation is 15.6 MPa, with an error of 18%, whereas the error between simulation and experimental results in Test 3 is 22%. The reason for this difference is: the grids in the water domain are slightly larger, so the shock wave attenuation speed is faster than the actual situati  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & \(\rho_{0}\) **(kg/m\({}^{3}\))** & \(v_{\mathrm{d}}\) & \(C\) **(m/s)** & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(\gamma_{0}\) & \(a\) & \(C_{4}\) & \(C_{5}\) & \(E_{0}\) **(J/m\({}^{3}\))** \\ \hline water & 1000 & 0.89 & 1448 & 1.98 & 0 & 0 & 0.11 & 3 & & & 0 \\ air & 1.22 & & & & & & & 0.4 & 0.4 & \(2.53\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material parameters of fluid.  
Figure 5: Comparison of tested and simulated velocities. (**a**) Test 1 velocity curve; (**b**) test 3 velocity curve.  
grid size will increase the computing time at a geometric rate. Therefore, it is considered that the numerical calculation gives reasonable results on the premise of ensuring accuracy, and the error is within the acceptable range.  
Figure 7 and Table 5 show a comparison of the remaining projectile after the test and numerical simulation. The side erosion unique to the water entry of the projectile at an attack angle and the asymmetric shape of the nose are successfully simulated, and the error between the calculated and experimental residual mass is very small, demonstrating that the failure modes of the projectile obtained by numerical calculation agrees well with the experimental data.  
To sum up, the proposed numerical calculation methods and numerical models can well simulate the water entry of the projectile, and check with the experimental data, striking a good balance between accuracy and efficiency.
## 1 Introduction  
To suppress nonlinear issues and enlarge transceiver sensitivity, the design of long-distance optical transmission networks (LDOTNs) is considered a fruitful solution [1]. The enhanced digital applications and online services' demands have led researchers to focus on deploying new strategies for upgrading current LDOTNs. Nowadays, LDOTNs are widely used in marine environments and earthquake warnings because of their high order of accuracy and huge transmission capacity capabilities [2]. Thus, upcoming generations of optical systems must necessarily be elastic and more efficiently utilize the available optical spectrum; this in turn improves spectral efficiency and the overall network capacity. Furthermore, the complexity and cost of optical networks should be kept as low as possible for practical implementation [3]. Approaches such as constellation shaping (CS) [4] and soft decoding are promising procedures for fulfilling the existing applications of LDOTNs and suppressing the issues generated by nonlinearities in optical fiber. The CS is further classified into two major types, the first of which is known as probabilistic CS [5] and the second which is called geometric CS [6]. The constellations of quadrature amplitude modulation (QAM) [7] are varied by applying these types of CS. Moreover, the transceiver sensitivity is enhanced with the help of these methods, allowing long distances and high data rates [8]. Hence, the proposed model becomes flexible compared to current LDOTNs. Additive white Gaussian noise (AWGN) is a better approximated technique for coherent LDOTN, which works based on Gaussian constellation [9]. The symbiotic information data [10] are enhanced by applying Maxwell-Boltzmann probability distribution. CS is examined to a standard 16 QAM and 64 QAM constellation over AWGN multiple signals. The AWGN approximation is valid for multichannel LDOTN, although there are some conditions for which AWGN is not applicable. In cases such as that of the single span system [11], low-symbol-rate transmission [12] and low-dispersion fiber [13], the AWGN is not accurate and produces nonlinearities. A high order of nonlinearities is generateddue to these conditions in the form of phase and polarization impairments (PPis) [14]. As a result, the statistical approach of multichannel LDOTNs is changed. In addition, these impacts become increasingly negative with the launching of CS. Thus, Gaussian constellations add nonlinearities to LDOTNS, which explains why such constellations are limited for multichannel LDOTNs. The nonlinear phase shift noises (NLPns) are mainly created by PPis, which are similar to the phase noise of laser parameters. NLPns consist of long autocorrelation NLPns [15] and short autocorrelation NLPns [16]. To suppress long autocorrelation NLPns, the carrier phase recovery (CPE) procedure is applied, while short autocorrelation NLPns, disturbing the received outcomes, cannot be organized by CPE. Thus, the goals of LDOTNs are highly degraded by short autocorrelation NLPns.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 4 Methodology
### Experimental Setup  
#### 4.2.1 Gateway and End Node Setup Hardware  
The discussion in this section is on the process of selecting hardware and configuring devices. The configuration of the device primarily consists of two parts:  
* Gateway (with Raspberry Pi)
* End Node (with Arduino)  
The RAK-Wireless Raspberry Pi HAT was utilized as the gateway in this experiment. The RAK-Wireless 2245 gateway is a Pi HAT which connects with Raspberry Pi over GPIO PINS. Raspberry Pi is derived from Linux Debian 32-bit Operating Software (OS). It is the best option as ARMv7 is the minimum system requirement to run the LoRaWAN gateway system. Any board computer with an ARMv7 processor can connect to the gateway over GPIO. The transceiver module consists of two LoRa chips (SX1301 and SX125x), which are coupled to form a gateway module. It has the potential to access up to 8 channels simultaneously, which in turn allows it to reach a greater number of end nodes. The  
Figure 5: Relation between elevation and slant range distance at different levels by path loss calculation obtained from Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elevation (deg)** & **Slant Range (km)** & **FSPL (dB)** \\ \hline
20\({}^{\circ}\) & 1293.8 & 142.4 \\
30\({}^{\circ}\) & 992.9 & 140.1 \\
40\({}^{\circ}\) & 812.1 & 138.4 \\
50\({}^{\circ}\) & 698.9 & 137.1 \\
60\({}^{\circ}\  
gateway block diagram and module are shown in Figure 6[26]. ADR is configured using Chirpstack network stack architecture [27].  
Figure 6b demonstrates the RAK2245 LoRaWAN gateway stacked with the Raspberry Pi over GPIO pins, making the spacecraft prototype. The gateway does not need an external power supply as it receives power from the Raspberry Pi.  
Table 4 shows typical transmit parameters of LoRaWAN gateway, highlighting the frequency range modulation schemes and threshold limits along with certain power levels. Meanwhile, Table 5 shows receive sensitivity levels at different spreading factors of the LoRaWAN gateway operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Condition** & **Min** & **Max** \\ \hline Frequency & & 433 MHz & 435 MHz \\ Modulation & FSK/LoRaTM & & \\ Tx Power vs. Temperature & Power Level & \(-3\) kHz & +3 kHz \\ Tx Power vs. Temperature & Setting:20 & \(-5\) dBm & +5 dBm \\ TX Power Variation & & \(-1.5\) dBm & +1.5 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Frequency and modulation hardware parameters of the LoRaWAN gateway used in an experiment.  
Figure 6: (**a**) Block diagram [26] and (**b**) module of LoRaWAN gateway together with the Raspberry Pi used in this study.
## 5 Weather Forecasting Models
### A Global Measurements Integration Model
For example, military missions involving Remotely Piloted Aircrafts (RPAs) have experienced substantial losses over the past three decades, amounting to over $100 million in taxpayer expenses. Given that RPAs are unmanned, they rely heavily on accurate environmental awareness to operate safely and effectively. A significant portion of these losses can be attributed to limitations in conventional weather forecasting technologies, which fail to provide the required resolution and timeliness for dynamic mission environments. Therefore, the integration of near-real-time weather forecasting capabilities into military operations could serve as a critical support tool for RPA operators, significantly reducing operational losses and enhancing mission safety. Moreover, by delivering high-resolution, accurate weather data remotely, this technology may reduce or even eliminate the need for deploying dedicated weather sensors at or near mission locations [34].
## 3 Experiments  
A scanning electron microscope (SEM, JEOL JSM-7800F) was used to examine the surface morphology of the nanocone array and the silver nanosphere array fabricated on the nanocone array. EDS analysis was also performed using the same SEM system. UV-vis absorption spectrum measurements were performed using a HITACHI U3900 spectrophotometer at room temperature in the range of 400-800 nm. The SERS active substrates were placed perpendicular to the light beam to obtain the absorption spectra.  
For the SERS studies, the developed surface enhanced Raman scattering sensors were tested using analytes including R6G and DEHP. The developed nanostructured SERS substrates were first immersed in different concentrations of R6G solution and DEHP methanol solution for 12 h, then rinsed with deionized water and dried in air. The SERS spectrometer used was a microscope Raman spectrometer (Tokyo Instruments. Nanofinder 30) with 632.8 nm HeNe laser excitation, and the power of the laser source was set to 0.1 mW. The focused laser spot on the surface of the sample has a diameter of approximately 2 \(\upmu\)m and a penetration depth of about 4 \(\upmu\)m. The integration time per measurement is 10 s.
# Influence of Aging Conditions on the Dynamic Stiffness of EPDM and EVA Rail Pads
###### Abstract  
The railway sector plays a crucial role in sustainable transportation by reducing greenhouse gas emissions while supporting an increasing volume of freight and passenger transport. Rail pads, essential components in railway infrastructure, mitigate vibrations and distribute loads; however, their long-term performance is influenced by environmental and mechanical degradation, affecting track durability and maintenance costs. Despite their significance, the degradation mechanisms impacting the dynamic stiffness of EPDM (Ethylene Propylene Diene Monomer) and EVA (Ethylene Vinyl Acetate) rail pads remain insufficiently characterized. This study examines the effects of mechanical and chemical aging on the stiffness of these materials through 864 dynamic stiffness tests, analyzing three types of rail pads under mechanical cycling (up to 2,000,000 cycles), UV (ultraviolet light) exposure (100-500 h), and hydrocarbon exposure (100-500 h). Mechanical aging increases stiffness across all pads, with Pad C (EVA) exhibiting the most pronounced increase (27%). The effects of UV exposure vary by material, leading to a stiffness reduction of up to 11.5% in Pad B (EPDM), whereas Pad C (EVA) experiences a 9.5% increase under prolonged exposure. Hydrocarbon exposure also presents material-dependent behavior, with Pad A (EPDM) experiencing an 11.5% stiffness reduction at low exposure but partial recovery at higher exposure, while Pad C (EVA) shows a 5% increase in stiffness under prolonged exposure. These findings offer valuable insights into the aging mechanisms of rail pads and underscore the importance of considering degradation effects in track maintenance strategies.  
dynamic stiffness; rail pads; polymer degradation; railway maintenance; mechanical properties +
Footnote †: journal: Applied sciences
## 5 Conclusions
\begin{tabular}{l l} \(A\) & Heat exchange area, m\({}^{2}\) \\ \(A_{c}\) & Duct cross-sectional area of the airflow channel, m\({}^{2}\) \\ \(C_{p}\) & Specific heat at constant pressure, kJ kg\({}^{-1}\) K\({}^{-1}\) \\ \(D_{c}\) & Dehumidification capacity, kg h\({}^{-1}\) \\ \(D\) & Mass diffusion coefficient of water vapor in the air, m\({}^{-2}\) s\({}^{-1}\) \\ \(DWD\) & Desiccant wheel dehumidification \\ f & Mass per unit length of one air channel, kg m\({}^{-1}\) \\ h & Enthalpy, kJ kg\({}^{-1}\) \\ \(HD\) & Hybrid dehumidification \\ \(HPD\) & Heat pump drying \\ K & Heat transfer coefficient of the heat exchanger, W m\({}^{-2}\) K\({}^{-1}\) \\ \(K_{y}\) & Mass transfer coefficient, kg m\({}^{-2}\) s\({}^{-1}\) \\ \(m_{rj}\) & The mass flow rate of the compressor, kg s\({}^{-1}\) \\ \(Nu\) & Nusselt number \\ \(p\) & Pressure, Pa \\ \(Q\) & Heat transfer capacity, kW \\ \(RH\) & Relative humidity \\ \(s\) & The boundary length of a channel, m \\ \(Sh\) & Sherwood number \\ \(SMER\) & Specific moisture extraction rate, kg kW\({}^{-1}\) h\({}^{-1}\) \\ \(T\) & Temperature, K \\ \(t\) & Temperature, \({}^{\circ}\)C \\ \(V_{th}\) & Theoretical exhaust volume of the compressor, m\({}^{3}\) s\({}^{-1}\) \\ \(vi\) & Specific volume, m\({}^{3}\) kg\({}^{-1}\) \\ \(W\) & Energy consumption, kW \\ \(w\) & Desiccant water content, kg kg\({}^{-1}\) \\ \(Y\) & Air moisture content, kg kg\({}^{-1}\) \\ \(Y_{w}\) & The moisture content of the air in equilibrium with the desiccant, kg kg\({}^{-1}\) \\
**Greek symbols** \\ \(\Delta t\) & Logarithmic mean temperature difference, \({}^{\circ}\)C \\ \(\alpha\) & Heat transfer coefficient, W m\({}^{-1}\) K\({}^{-1}\) \\ \(\lambda\) & Thermal conductivity, W m\({}^{-2}\) K\({}^{-1}\) \\ \(\tau\) & Time, s \\ \(\rho\) & Density, kg m\({}^{-3}\) \\ \(\eta_{v}\) & Volumetric efficiency of the compressor \\
**Subscript** \\ \(a\) & Air \\ \(c\) & Condenser \\ \(d\) & Silica gel \\ \(e\) & Evaporator \\ \(et\) & Electric heating \\ \(fan\) & Fan \\ \(i\) & Inlet \\ \(l\) & Saturated water \\ \(m\) & Matrix materials \\ \(o\) & Outlet \\ \(r\) & Refrigerant \\ \(tot\) & Total energy consumption of hybrid dehumidification system \\ \(v\) & Water vapor \\ \end{tabular}
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 4 Method Verification
### Verifying the Effectiveness of SSO
In this example, there are 11 population sizes (300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500), 5 crossover rates (0.4, 0.5, 0.6, 0.7, 0.8), and 4 mutation rates (0.05, 0.06, 0.07, 0.08). Therefore, a total of 220 (=11 \(\times\) 5 \(\times\) 4) GA experiments are conducted, and the experimental results are saved along with the space interference level (\(f_{SC}\)) and the number of evolved generations (\(g\)). The results are shown in Figure 9. The results of sensitivity analysis present that the interference level (\(f_{SC}\)) is the minimum of 17.79, and the maximum is 24.35. The minimum _fsc_ was searched for eight parameter combinations, [320-0.5-0.05], [340-0.7-0.07], [400-0.4-0.05], [400-0.6-0.07], [400-0.8-0.06], [420-0.7-0.08], [460-0.5-0.06], and [500-0.4-0.07], respectively. It was confirmed that the obtained results with the initial set of GA parameters (i.e., _PS_ = 400, _CR_ = 0.4, _MR_ = 0.05) were the optimal solution.
## 3 Results
### Silica Nanoflakes Characterization
Figure 5 shows the FTIR spectra of both types of silica flakes functionalized with folic acid and loaded with MTX. The band at 1398 cm\({}^{-1}\) observed in both spectra indicates the existence of hydrogen bonds that could be formed between the drug and the silica flakes (Figure 5). It could also be related to the C-H bond stretching vibrations [38; 39]. The band centered at 1450 cm\({}^{-1}\) in both samples corresponds to the symmetric stretching mode of the COO- group, indicating the successful interaction of MTX and silica flakes [40]. It is also noteworthy that after packing the drug, in both samples, the peak corresponded to the N-H bonds disappears. Therefore, it can be assumed that the drug interacts with silica flakes not only by hydrogen bonding, but it also attaches to amino groups, thus forming an amide bond, as confirmed by the peak at 1540 cm\({}^{-1}\), corresponding to the C-N bond [41]. Figure 4: Raman spectra of silica flakes functionalized with folic acid: mSiO\({}_{2}\) after LDH Mg/Al_FA (**A**) and mSiO\({}_{2}\) after GO_FA (**B**). Figure 5: FTIR spectra of silica flakes functionalized with folic acid loaded with methotrexate (MTX): mSiO\({}_{2}\) after GO_FA (**A**,**B**) and mSiO\({}_{2}\) after LDH Mg/Al_FA (**C**,**D**). The thermogravimetric analysis (Figure S5) was carried out for silica flakes functionalized with folic acid and loaded with drug to determine the weight percentage of organic substances in the obtained structures. The obtained results allowed calculating that in the sample after functionalization with folic acid, 20 wt % of mSiO\({}_{2}\) after LDH Mg/Al belongs to organic molecules and 23 wt % of mSiO\({}_{2}\) after GO belongs to organic molecules. While for the samples after drug loading, the weight percentage of organic components was 50 wt % and 49 wt % for mSiO\({}_{2}\) after LDH Mg/Al and mSiO\({}_{2}\) after GO, respectively.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 7 Conclusions  
This review outlines recent advancements in the development of graphene-based materials with enhanced surface area, emphasizing their potential in energy storage and other emerging applications. The main insights from the literature are summarized below:  
* Surface area and capacitance: Extensive research demonstrates that enhancing the surface area of carbon-based materials decreases resistance in supercapacitors by offering a greater number of electrochemically active sites, which, in turn, leads to improved capacitance. Hybrid nanomaterials have significantly enhanced the performance of electrochemical materials in energy storage and conversion. However, more research is needed to understand the synthesis, structure, and properties of these materials.
* Synergistic interactions: Potential synergistic effects may result from these interactions, which alter the structure and geometry of materials produced atop graphene and carbon nanotubes. To comprehend charge transfer mechanisms, it is also essential to investigate the electrical and chemical structures at the interface of graphene, carbon nanotubes, and supporting materials.
* Physical property modulation: An increased surface-to-volume ratio in nanomaterials significantly enhances their optical, electrical, thermal, and mechanical properties. It improves light absorption (including surface plasmon resonance), boosts conductivity, and increases active sites for reactions and adsorption. It also affects surface energy and wettability, influencing behaviors like adhesion and liquid spreading. These effects are highly dependent on the material's composition, structure, and environment.  
In conclusion, this review highlights the pivotal influence of surface area on optimizing the properties and functionality of graphene-based materials, particularly for energy storage and related technologies. It advocates for future studies to delve deeper into the relationships between surface architecture, material interfaces, and device performance, paving the way for the strategic development of advanced energy systems.  
[PERSON] and [PERSON] conceived the conceptual design; [PERSON] and [PERSON] interpreted and wrote the original draft; and [PERSON] and [PERSON] provided the comments for textual improvement. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korean National Research Foundation (NRF-2021R1F1A-1048388).  
Not applicable.  
The authors declare no conflicts of interest.
## 4 Conclusions  
The paper presents the performance obtained by an uplink massive MIMO system with relay as intermediary between source and destination, using LDPC channel coding and OFDM modulation when active users are separated using complex spreading codes from four new sets of codes, MUSA, MUSA0, EMUSA, and EMUSA0. For the MUSA set, 32 codes with low correlation properties have been selected as the intercorrelation should be as low as possible. For the MUSA0 set, 25 codes, in ascending order of their intercorrelation value, have been chosen from the MUSA set previously defined, and the rest of the 7 codes have been replaced with ones with zero cross-correlation. The extended MUSA (EMUSA) set of codes was created by extending the MUSA and MUSA0 from length 8 to length 32, obtaining thus EMUSA and EMUSA0 sets. The results were obtained using the QPSK modulation scheme, when the transmission was made over a channel affected by Rayleigh fading using a MIMO configuration of (\(1\times 26\times 30\)) and (\(1\times 30\times 30\)), with 26 or 30 antennas at the relay.  
The performance was highlighted by comparing them with classical Walsh and PN spreading codes, and the new sets of codes proved to outperform the classical ones. This paper also analyzed the situation in which the number of receive antennas at the relay was different from the number of receive antennas at the destination, and some discussion was made with respect to the compromise that has to be reached between the processing complexity, total rate, and occupied bandwidth and the required level of performances in terms of BER obtained at certain SNRs.  
Another novel idea, that was described and tested within this paper, was the one in which complex spreading codes cPN were obtained starting from PN spreading codes. The results obtained are promising, leading to very low BER values, showing that in this case, the number of antennas can be reduced and, thus, the overall system cost.  
As future work, we will focus our attention on implementing a higher number of parallel relays between source and destination and comparing their performance when using DF or amplify-and-forward (AF) protocols and trying to find a compromise between performance, hardware, and a low cost of implementation.  
Conceptualization, M.-G.B., C.F., and S.H.; methodology, M.-G.B. and C.F.; software, M.-G.B., validation, M.-G.B., C.F., and S.H.; formal analysis, M.-G.B. and C.F.; investigation, M.-G.B. and C.F.; resources, M.-G.B., C.F., and S.H.; writing--original draft preparation, M.-G.B., C.F., and S.H.; writing--review and editing, M.-G.B., C.F., and S.H.; visualization, M.-G.B. and C.F.; supervision, M.-G.B. and S.H.; project administration, M.-G.B. and S.H.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by University Politehnica of Bucharest PubArt funding program.  
The authors declare no conflict of interest.
# An Efficient Explainability of Deep Models on Medical Images
## 3 Materials and Methods
### Dataset
#### 3.2.1 COVID-19 Radiography Dataset  
It was developed by a team of researchers from Qatar University, Doha, Qatar and Dhaka University, Bangladesh, in collaboration with physicians. It includes chest X-ray images of patients with COVID-19, as well as normal images, viral pneumonia images and lung opacity images. The database includes a total of 3616 positive cases of COVID-19, 10,192 normal images, 6012 cases of pulmonary opacity and 1345 cases of viral pneumonia (show Figure 2).