esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 UAV Power Sources
### Methanol Fuel Cells  
The typical method for producing hydrogen involves splitting water and natural gas, which results in a high production cost. The methods for storing hydrogen by compression and liquefaction, however, are considerably more expensive. To increase the amount of hydrogen for the compression process, the storing pressure rises from 200 bar to 700 bar. It is required to use more durable tanks, whose weight is meant to be high, under such high pressure. When hydrogen is controlled by a cryogenic system at 20.4 K, it is in liquid condition and ready for the liquefaction process. As a result, liquid fuel-feeding Direct Methanol Fuel Cells (DMFCs), which provide higher specific energy and possibly longer endurance, have been utilized by UAVs. Additionally, because of its liquid phase, methanol is considerably simpler to transport, store, and handle. As a result, elaborate and expensive auxiliary facilities are avoided, which in turn lowers the cost. Because of the resources from biomass, methanol is also more accessible than hydrogen, which further reduces the cost. The use of DMFCs in UAVs is being hampered by two problems, though. One is the poorer energy efficiency compared to hydrogen fuel cells, which is caused by the slow kinetics of the methanol oxidation process (MOR), and the other is the issue of CO species poisoning the catalyst during MOR, which leads to performance degradation over time [51].  
Proton exchange membrane fuel cells (PEMFCs), which have an anode DL, an anode CL, a PEM, a cathode CL, and a cathode DL, can simply be used as a model for how DMFCs should be constructed. Liquid methanol is given to the DL and diffuses to the CL, where it is oxidized in the presence of water to produce carbon dioxide, protons, and electrons:  
\[CH_{3}OH+H_{2}O\to CO_{2}+6H^{+}+6e^{-} \tag{6}\]  
\[E_{a}^{0}=0.02\mathrm{V} \tag{7}\]  
Following that, the produced protons are delivered by PEM to the cathode, where they participate in the ORR process:  
\[3/2O_{2}+6H^{+}+6e^{-}\to 3H_{2}O \tag{8}\]  
\[E_{c}^{0}=1.23\mathrm{V} \tag{9}\]  
When the previous two equations on the anode and cathode are combined, the overall reaction can be represented as follows:  
\[CH_{3}OH+3/2O_{2}\to CO_{2}+2H_{2}O \tag{10}\]  
\[E^{0}=1.21\mathrm{V} \tag{11}\]  
DMFCs have a theoretical voltage that is lower than that of hydrogen fuel cells. Similarly, activation, Ohmic, and concentration losses lead a DMFC's real voltage to be significantly lower than its theoretical value. Furthermore, the practical operating voltage of DMFC is substantially lower than that of hydrogen fuel cells due to the methanol crossoverphenomenon. The oxygen on the cathode reacting with the methanol delivered from the anode to the cathode resulting in low voltage and mixed potential.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 4 Results
### Experimental Environment and Contrasting Models  
The hardware used in this experiment includes an i7-12700K CPU, NVIDIA GTX 3090 Ti 24 GB graphics card, and 128 GB RAM. The software environment comprises Windows Subsystem for Linux (WSL), PyTorch 1.11.0, and Python 3.8.  
To evaluate the efficiency of the algorithms proposed in this paper, traditional target detection methods such as CEM [14], SID [18], MF [54] and sparse representation method as DSE [32], as well as deep learning models like BLTSC [41] and OS-VAE [45], were selected for comparison. All comparison algorithms were configured with the optimal parameters specified in the respective papers.
## 2 Materials and Methods
### Positioning an Object on the Ground  
With a focal length f = 0.1 m (Figure 4), the FOV will be \(\gamma\) = 10\({}^{-2}\) rad. The instantaneous size of the ground object is about 5.75 km for an orbit altitude of 575 km. The resulting area size corresponds to a GOSAT TANSO-FTS observation footprint of 10.5 km [47]. The Carbon Dioxide Monitoring Mission (MicroCarb) has a Nadir pixel size of \(4.5\times 9\) km\({}^{2}\) and The Geostationary Carbon Cycle Observatory (GeoCarb) has a Nadir pixel size of \(10\times 10\) km\({}^{2}\)[20].  
The positioning accuracy provided by the satellite's orientation system is 0.1\({}^{\circ}\), which corresponds to 1 km on the Earth's surface (the orientation error is 17%). The average motion of the satellite and its subsatellite point are:  
\[n=\sqrt{\frac{\mu_{E}}{(R_{E}+h)^{3}}}=0.0625\ \mathrm{deg/s}, \tag{1}\]  
where \(\mu_{E}\) is Earth's standard gravitational parameter, \(R_{E}\) is the Earth's radius and orbital altitude \(h\) = 575 km. Then, the velocity of the subsatellite point will be \(V_{E}\) = \(R_{E}\)\(\times\) n = 6.95 km/s. We assume that the FTIR spectrometer interferogram measuring rate is 10 Hz and the measurement time t is 0.1 s. The satellite orientation error will be s\({}_{E}\) = V\({}_{E}\)\(\times\) t = 695 m (12%). The resulting error is adequately correlated with the size of the object on the ground and allows scanning of the Earth's surface based on the relative motion of the satellite in orbit.  
For the described FTIR spectrometer, the SNR in the recorded IR spectrum was estimated for a registration time of 0.1 s and in an orbit of 575 km. The calculation took into account the parameters of the spectrometer, such as the NEP of the detector, entrance aperture and transmission of the optical system, which is equal to 0.3. The estimated SNR for spectral band 2.0-2.4 \(\upmu\)m is about 450.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Unit** & **Value** \\ \hline Spectral range: & & \\ O\({}_{2}\) & \(\upmu\)m & 0.75–0.80 \\ CO\({}_{2}\) and CH\({}_{4}\) & \(\upmu\)m & 2.0–2.4 \\ Spectral resolution & cm\({}^{-1}\) & 2 \\ FOV & rad & \(10^{-2}\) \\ Entrance aperture & mm & 100 \\ Power consumption & W & up to 100 \\ Dimensions & mm & \(268\times 208\times 216\) \\ Weight & kg & no more than 10 \\ \hline \end{tabular}
\end{table}
Table 2: The compact low-resolution FTIR spectrometer characteristics.
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 2 Related work
### Meteorological Data Storage  
With huge amounts of data being generated periodically in meteorological fields, the conventional IBM, Oracle and EMC architectures can no longer cope; hence, cloud computing becomes the best way to store data. [PERSON] in Emory University presented Hadoop-GIS [15], which is a scalable and high performance spatial data warehousing system for running large-scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through a spatial query engine called RESQUE that utilizes both global and customizable local spatial indexing to achieve efficient query processing. [PERSON] and [PERSON] [16] achieved a linear quad-tree retrieval structure in the HBase. It uses MapReduce to insert data and create an index and also uses MapReduce to retrieve the index structure. [PERSON] from the University of Connecticut also introduced a parallel approach of MapReduce [17] for improving the query performance of geospatial ontology for disaster response. The approach focuses on parallelizing the spatial join computations of GeoSPARQL queries. All these existing systems work well in batch computations but the time for MapReduce is too long to satisfy the demands of real-time systems. Although efforts are being made to improve the real-time capability of meteorologically distributed systems, the most efficient and effective method is yet to be developed.
## 5 Experimental Evaluation  
The proposed greedy algorithm (GH-MWDDS) was implemented in ANSI C++ using GCC 7.4.0 for compiling the software. Its performance was compared with three recent local search approaches available from the related literature [23]: Local Search (LS), Fixed Depth (FD), and Variable Depth (VD). In order to conduct a fair comparison to the three local search algorithms (LS, FD and VD) we used the original source code provided by the authors of [23]. GH-MWDDS and the three local search algorithms were experimentally evaluated on a laptop equipped with a 64-bit 2.5-GHz Intel(r) Core(tm) i5-7200U processor and 8 GB of RAM. We applied GH-MWDDS to a set of 640 problem instances that are described below. Moreover, in order to test the usefulness of our ILP model, we applied the ILP solver ILOG CPLEX 20.01 in single-threaded mode to all problem instances. The time limit for each application of CPLEX was set to 2 CPU hours. Moreover, the experiments with CPLEX were performed on a cluster of machines with two Intel(r) Xeon(r) Silver 4210 CPUs with 10 cores of 2.20 GHz and 92 Gbytes of RAM.
## 4 Flight Load Prediction Case Study
### Sensitivity Analysis of Flight Parameters  
Based on the proposed mean impact value method and trained GA-enhanced ELM neural network, the MIVs of the 13 flight parameters are calculated and shown in Table 4.  
Table 4 shows that the vertical load factor and angle of attack are strongly correlated with the wing bending moment, while the pitch angle and rate are almost uncorrelated with the wing bending moment. By removing the pitch rate and pitch angle from the input flight parameters, the mean relative error, mean squared error, and maximum relative error based on the testing dataset are 0.90858%, 1.9973, and 4.8335%, respectively, which demonstrates that the prediction accuracy is almost not affected by removing these two flight parameters.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 1 Introduction  
Remote Sensing (RS, see Abbreviations for the list of acronyms) systems provide valuable information for mapping and monitoring different oceanographic parameters. RS is a practical tool to monitor oceans due to the remoteness and broad coverage of these environments. For example, satellites acquire multi-temporal Near Real Time (NRT) datasets over large areas, which makes them suitable for analyzing the changes in oceanographic variables [1]. Moreover, several types of microwave RS systems, such as Synthetic Aperture Radar (SAR) and scatterometers, can work during both daytime and nighttime and almostin any weather conditions, which can be very helpful in the continuous monitoring of oceans [1; 2; 3; 4].  
Considering the importance of ocean environments and the advantages of RS technology for ocean studies, various research works have been conducted so far to investigate the potential of RS systems to derive different oceanographic parameters. However, currently, there is not a study that comprehensively discusses various applications of RS in the oceans. Therefore, this study discusses and reviews the most important applications of RS systems for oceanographic studies. The first part of this review paper was about six applications of RS in the oceans (i.e., Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD)). Part II of this study discusses nine other applications (i.e., Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCH), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery) through nine subsections.  
In each subsection, the introduction of the application is first provided. Then, it is discussed how various RS systems are being employed to study that particular application. Finally, the advantages and limitations of each system are discussed. It should be noted that the main focus of this review paper is on the spaceborne active RS systems for oceanographic applications. However, some non-spaceborne RS systems, such as Sound Navigation and Ranging (SONAR) and High Frequency (HF) radars are also discussed due to their important applications in ocean environments.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 3 Methodology
### Communication Model
The Orthogonal Frequency Division Multiplexing (OFDM) modulation is applied last, before transmission. To simulate the effect of ambient noise on the message, we introduced additive white Gaussian noise (AWGN) into the channel. As specified in the 802.11p standard [24], the standard parameters for OFDM modulation were used (see Table 2). In contrast to the encoding process, the decoding is initiated by the receiving device in reverse as it was on the transmitting side. In the initial phase, the receiving device assesses the message reception power. If this is below the receiving sensitivity of the modulation threshold, then the module assumes that the message has not been received. Conversely, if the reception power is above the sensitivity threshold, the device attempts to decode the message. Decoding is a crucial aspect of this layer, as it enables the recipient to obtain the information they require from the sender.
## 2 Materials and Methods
### SWCNT Film Optoelectronic Properties Characterization and Control  
Optical spectroscopy with unpolarized light was performed on SWCNT films deposited on a glass side, in the UV-visible (VIS)-NIR range, to characterize the optical properties of several SWCNT films. In Figure 1a, the optical transmittance (\(T\)) as a function of the incident light wavelength, \(\lambda\), is shown. The optical transmittance is dominated by the valley centered at about 283 nm due to the light absorption phenomenon of surface plasmon resonance, which is typical of carbon-based thin films and nanostructures [26]. In the spectrum, it is also possible to identify the \(S_{33}\) (300-400 nm), the \(M_{11}\) (400-550 nm), the \(S_{22}\) (550-900 nm), and the \(S_{11}\) (800-1600 nm) electronic transitions between van Hove singularities due to the 90% of semiconducting (Si) and 10% of metallic (Mi) multi-chiral SWCNT mixture [7]. In Figure 1b, the measured \(T\) value of SWCNT films at fixed incident wavelength \(\lambda\) = 550 nm vs their sheet resistance (\(R_{S}\)) is reported. Thinner films have higher transmittance and are characterized by higher \(R_{S}\). The variation of the transmittance of CNT films with their sheet resistance (\(R_{S}\)) follows the relation  
\[T(\lambda)=\left(1+\frac{Z_{0}\sigma_{ac}(\lambda)}{2R_{S}\sigma_{dc}}\right) ^{-2} \tag{1}\]  
where \(Z_{0}\) = 377 \(\Omega\) is the free space impedance, \(\lambda\) is the photon wavelength, and \(\sigma_{ac}\) and \(\sigma_{dc}\) are the optical and electrical conductivity, respectively. The red curve in Figure 1a, has been obtained by fitting the Equation (1) where the only free parameter is the ratio \(\sigma_{ac}/\sigma_{dc}\). The fit returned a value for such an optical and electrical conductance ratio equal to 1.2 \(\pm\) 0.1. The relationship in Equation (1) evidences that \(T\) and \(R_{S}\) are in strong competition in PD devices: for high \(R_{S}\), the charge collection is limited, while low \(R_{S}\) means that a minor number of photons can reach the NSH junction. For this reason, the film realization process is significant because by only controlling the SWCNT dispersion volume used in the filtration process, the optical and electrical properties can be modulated, depending on the film thickness. In the following, SWCNT films with a thickness ranging between 10 and 60 nm were used and a comparison of their performances in the NSH-based PD reported [27].
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 2 Fully Autonomous Recovery System
### Development Challenges and System Advantages  
#### 2.1.1 Difficulties and Challenges of System Development  
The system is required to overcome the complex conditions of the surface of the sea, to achieve high-precision moving-target positioning and attitude determination (perception challenge). Sea wind and waves have an impact on the attitude of the USV at all times, and it is difficult to maintain it consistently. This brings about an uncertainty in the landing process of the UAV and makes it difficult for the recovery system to retrieve the UAV without damage.  
The system needs to have active/passive control capabilities for energy compliance conversion during recovery process, such as converting the kinetic energy when a UAV hits an arresting cable into the potential energy of the arresting cable (control challenges). It also needs to overcome the uncertainty of the position of the UAV and have a compliance-grasping ability during the grasping and placement process.  
A collaboration of different technologies is necessary to coordinate amidst dynamic differences and environmental uncertainties (coordination challenges). In this case, a heterogeneous coordination between the arresting cable system and the fixed-wing UAV proved difficult.
## 4 Results and Discussion  
The performance of machine learning (ML) models developed in this study are evaluated mainly using mean absolute percentage error (MAPE) and mean absolute error (MAE) in predictions. MAPE is defined as:  
\[\text{MAPE}=\frac{1}{n}\cdot\sum_{i=1}^{n}\biggl{(}\left|\frac{\tilde{y}_{i}-y _{i}}{y_{i}}\right|\biggr{)} \tag{6}\]  
where \(y\) is the actual (experimental) value of AAR, \(\tilde{y}\) is the output value (AAR predicted by a particular model), and \"\(i\)\" goes from 1 up to the number of predictions (\(n\)). Note that MAE is a similar error measure that calculates the error in terms of the absolute difference between the predicted and experimental values of AAR (i.e., MAE does not normalize the difference between experimental and predicted values). A multivariate linear regression (MLR) ML model is also developed using the same dataset, the same input features, and supervised learning technique. It is used as the baseline model for comparison of performances of the nonlinear ML models developed in this study. All the ML models and deep learning ANN models in this study are developed in the Python programming platform using the implementations of the standard classes available in Scikit-Learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)), accessed on 1 June 2023) and TensorFlow and Keras ([[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/)), accessed on 1 June 2023) libraries of modules.
## 3 The DOB Controller Design and Performance Analysis  
Before designing the DOB controller, we first need to present the inner acceleration controller. In this paper, \(C_{a}\) is designed as follows:  
\[C_{a}=\frac{\frac{s^{2}}{\tilde{\omega}_{n}^{2}}+\frac{2\tilde{\omega}}{\tilde {\omega}_{n}}s+1}{s}\cdot\frac{K_{a}}{T_{1}s+1} \tag{6}\]  
The quadratic differential element is to compensate the paltform's mechanical resonance, the integrator is used to partly compensate the quadratic differential of the acceleration object, and the inertial element is a low-pass filter to eliminate the high-frequency noise, in which \(T_{1}\) should be very small. Thus, \(C_{a}\tilde{G}_{a}\) is presented as Equation (7):  
\[C_{a}\tilde{G}_{a}=(\frac{\frac{\tilde{\omega}_{n}^{2}}{\tilde{\omega}_{n}^{2 }}+\frac{2\tilde{\omega}}{\tilde{\omega}_{n}}s+1}{s}\cdot(\frac{K_{a}}{\frac{ s^{2}}{\tilde{\omega}_{n}^{2}}+\frac{2\tilde{\omega}}{\tilde{\omega}_{n}^{2}}s+1} \frac{1}{T_{e}s+1})=\frac{KK_{a}s}{(T_{1}s+1)(T_{e}s+1)} \tag{7}\]  
Figure 4: The structure of the TLC with DOB. The velocity is acquired by the acceleration integral and CCD signal would help to correct the velocity. \(C_{p}\) is the velocity controller.  
According to the analysis in Section 2.2, the ideal DOB controller is descripted as follows:  
\[C_{f}=\left(C_{a}\widetilde{G}_{a}\right)^{-1}=\frac{(T_{1}s+1)(T_{e}s+1)}{KK_{a }s} \tag{8}\]  
In Equation (8), since the order of numerator is higher than the one of denominator, it cannot be accomplished in physics. Therefore, we need to transform the \(C_{f}\) to be realizable:  
\[C_{f}=\frac{(T_{1}s+1)(T_{e}s+1)}{KK_{a}s}\approx\frac{T_{1}s+1}{KK_{a}s}\ (T_{e}<<1) \tag{9}\]  
In Equation (9), as \(T_{e}\) is much smaller than 1, the simplification is reasonable in low and medium frequency, resulting in sacrificing some DSA in high-frequency domain, which can be acceptable.  
As the DSA improvement brought by DOB is owing to the numerator of \(E_{TLC-DOB}\), we use \(E_{DOB}\) to represent the numerator. Now we focus on the value of the numerator presented as follows:  
\[\begin{array}{ll}E_{DOB}=1-C_{a}\widetilde{G}_{a}C_{f}&=1-\left(\frac{KK_{a }s}{(T_{1}s+1)(T_{e}s+1)}\right)\cdot\left(\frac{T_{1}s+1}{KK_{a}s}\right)\\ &\qquad=1-\frac{1}{T_{e}s}+1=\frac{T_{e}s}{T_{e}s+1}\end{array} \tag{10}\]  
Obviously, \(E_{DOB}\) is a high-pass filter. The red line in Figure 5 is the simulation of \(E_{DOB}\). The improvement of DSA is apparent in low and medium frequency, and as the frequency goes down, the effect is stronger, while in high-frequency there is no improvement which meets our design. However, in fact, the actual \(E_{DOB}\) will not exactly fit the red line. Since in low frequency the acceleration signal is weak which is susceptible by noise, the observed disturbance by accelerometers is not very accurate which leads to insufficient improvement of DSA in low frequency. Considering that, the blue line is the real \(E_{DOB}\), which will rise as the frequency goes down. That is the reason we continue to add a composited velocity loop to enhance the DSA in low frequency.  
Figure 5: The simulation of \(E_{DOB}\).
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 3 Massive MIMO Performance Gains
### Array Gain  
One of the main important advantages of massive MIMO technology is to increase the power efficiency compared to MIMO system. A system with very large antenna arrays at base station in which each antenna has a small physical size, such system requires a small amount of transmit power. Therefore, the power radiated by terminals could be made inversely related to the square-root of the number of base station antennas with no reduction in performance if the base station has imperfect channel estimation, while in the case of perfect estimation the radiated power could be made inversely related to the number of antennas. The reduction of terminals' transmit power will drain their batteries slower on the multi-access channel while reducing the emitted RF power would help in cutting the power consumption of the base station on the broadcast channel [24]. Another benefit of a large number of antennas is the ability to increase the tolerance to noise as well as the interference power, hence improving the signal to noise plus interference ratio (SINR) which means that interference can be reduced in massive MIMO systems by exploiting the spatial dimension to increase the separation between users.  
In general, an array gain is defined as the ratio between the received power in massive MIMO (\(P_{re}^{Massive\;MIMO}\)) and the received power in MIMO system (\(P_{re}^{MIMO}\)) [28], which is given by  
\[G_{array}=\frac{P_{re}^{Massive\;MIMO}}{P_{re}^{MIMO}}, \tag{30}\]  
with multi-cell massive MIMO system under imperfect conditions, the radiated power required by terminals is \(p_{u}\propto 1/\sqrt{M}\), where \(p_{u}\) is the transmitted power by terminals and \(M\) is the number of BS's antennas, while under perfect conditions, the radiated power required by terminals is \(p_{u}\propto 1/M\)[29]. With massive MIMO, a system with an unlimited number of antennas at the base station, the transmitter must have knowledge of the channel state information to exploit the array gain. In this paper, two cases of CSI are considered in order to derive an expression for array gain in multi-cell massive MIMO system, then a formula will be derived for the received signal power at terminals in the downlink channel.