esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Difference between Climate Normals and Climatologies
### Cloud Top Pressure  
Figures 8-10 show the similar results to Figures 4, 6 and 7, but for the cloud top pressure. Here, we note that CN_WMO could not be computed over the desert regions in the tropics, as well as over the parts of Greenland and Antarctica. There are also some differences between CN_WMO and Clim30 over other regions peripheral to these data gaps. These gaps in climate normals of cloud top pressure can be explained by the following factors. First and foremost, it is physically possible that, in the desert regions and few other tropical regions, clouds may not develop every day and clear sky conditions could prevail for longer times within a month. This is especially the case during the drier summer and winter months. This means that the D5 and/or D11 conditions are not satisfied in practice, since the cloud top pressures are naturally retrieved only when clouds are present. It is, therefore, clear that the WMO guidelines are not suitable in this case.  
The situation over the Antarctic and Greenland is even more interesting. Here, the data gaps in climate normals are not just due to the prevailing clear sky conditions, but also due to the fact that the cloud detection algorithm in CLARA-A3 may miss clouds over the bright and cold polar regions, especially in cases of extremely cold surface temperatures [21; 48], and then no other cloud properties are retrieved downstream. This shows that the cloud detection sensitivity of climate data records could also play an important role while computing the climate normals. The spatial features in the data gaps in Figures 9 and 10 support these explanations. The impact of orbital drift is also seen in Figure 10 in the northern hemispheric dry belt (10-30N) when the number of longitude grid points fulfilling the D5 and D11 conditions increases gradually in the first decade when NOAA-11 (1991-1995) and NOAA-14 (1995-2000) satellites were active.  
Figure 8: Same as in Figure 1, but for the cloud top pressure (in hPa).  
Another noticeable feature in Figure 8 is that the difference between CN_WMO and Clim30 is exactly identical to the difference between CN_WMO and CN_MB. This means that the climate normals based only on the monthly mean data are exactly identical to the 30-year climatology. The data gaps in CN_WMO are driven mainly by the fulfilment of the D5 and D11 conditions that are applied to the daily means and are not relevant to CN_MB. This further means that no added benefit is obtained by computing the climate normals based only on monthly means compared to the respective 30-year climatology.
## 2 Problem Description and Model  
Based on the previous literature, the SAOSP can be described as a combination optimization problem that aims to arrange the observation sequence of candidate tasks and determine their observation time in the selected VTWs to gain the maximum observation profit. In this section, a detailed description is given and a mathematical model of SAOSP is built with complex constraints and an optimization objective.
## 5 Results and Discussion  
### Simulation  
It is recommended to use the ANSYS flow field model (CFD) to properly organize the secondary airflow circulation in regenerative air. The pickup head inlet and suction port are important devices for the effective implementation of secondary airflow recirculation in the system. The exact assessment of the velocity and trajectory of the airflow moving through the pickup head inlet and suction port is shown in Figure 6. As we know that the sweepers move at a certain speed during the road cleaning process, the secondary particle airflow through the pickup head inlet ducts moves towards the suction duct for circulation in the system. The ambient airflow should not exert a negative pressure around the suction port during the addition of recirculated secondary airflow to the system. If the suction port airflow rate is higher than the secondary airflow passing through the inlet ducts, it results in less resistance when the surrounding airflow is joined. In addition, the wind resistance must always be taken into account, which affects the complete circulation of the secondary airflow recirculated in the system as a result of the movement of the sweeper at different speeds during operation.  
Figure 7 shows the velocity and trajectory of the particles. Road cleaners are not always highly efficient, because the particles on the road surface have different structures and sizes. Studies have shown that to improve the assimilation efficiency of particles located on the road surface, the airflow rate in the assimilation port must be higher than the  
Figure 6: The velocity and trajectory of the secondary airflow through the inlet and suction port of the pickup head: (**a**) velocity contour; (**b**) pressure contour.  
velocity of the moving particles. This was used to accurately predict the particle trajectories and particle absorption efficiency of the discrete particle model (DPM). Depending on the type and structure of the particles, the effect on the absorption efficiency of the particles makes it possible to obtain precise data. When we increased the suction port air flow rate to 35 m/s, the suction sand particle efficiency was greater than 0.91 \(\eta\), whereas in clay particles, this efficiency was 0.88 \(\eta\).
## 2 Background  
We provide here a detailed description and useful definitions and terminology for the ecosystem of a multi-UAV system.  
**Uncerewed Aerial Vehicle (UAV)**  
An uncrewed aerial vehicle or Remotely Piloted Aircraft (RPA), also referred to as a drone, is an aircraft that does not require a human pilot onboard. It flies autonomously or remotely through an onboard computer, remote control, or a combination of the two. It is a type of Uncrewed Aerial System (UAS), also known as a Remotely Piloted Aircraft System (RPAS) [8], which is defined as an aircraft system that operates without direct human intervention in the aircraft [46]. Most of the time, the interaction with the aircraft is made possible through a ground control station, a remote controller, a data transmission system, and dedicated hardware and software support (see Figure 2).  
**Autonomous UAV**  
Autonomous UAVs are a specific case of aerial vehicles that can fly to a given location without the assistance of a remote human. This type of vehicle is able to perceive, interact, and act with respect to its environment in an autonomous way for decision making on a mission [7].
# The Effect of Biogas Origin on the Electricity Production by Solid Oxide Fuel Cells
## 1 Introduction  
Solid Oxide Fuel Cells (SOFCs) are electrochemical devices which are able to convert the chemical energy of the feed stream to electricity embodied in combined heat and power application [1]. These devices became popular during recent decades due to the great variety of fuels that they can be fed with (such as ethanol, methanol, etc.), a fact that can be mainly attributed to the high temperature where SOFCs usually operate [2].  
It is also known that biomass can be converted into biogas (a mixture consisting mainly of CH\({}_{4}\) and CO\({}_{2}\)) through several bioprocesses, where biogas composition strongly depends on the biomass raw materials, as Table 1 presents (for details, see Section 2).  
By using these procedures, biogas can be converted into syngas, a complex gas consisting mainly of CH\({}_{4}\) (non-combustible), H\({}_{2}\), CO, CO\({}_{2}\) and a small amount of steam, thus being partially suitable for utilization in an SOFC to produce electricity [3, 4]. Obviously, the analogy between H\({}_{2}\) and CO\({}_{2}\), which are the main components for fuel cell operation while the other components remain intact through the device by following the main powerplant's path, notably affects the SOFC's performance because the higher the hydrogen content, the more electricity is produced [5]. This analogy is attributed to the origin of biogas, i.e., by the biomass from which the biogas has been produced, while the operating conditions selected for the conversion of organic material to biogas are also important for the electricity produced [6].  
In this context, several layouts of stand-alone power plants have been proposed, where a fuel cell is inevitably included [7; 8; 9]. In the present project, a thermodynamics-based model for the optimization of the operation of SOFC-based power plants is presented under specific assumptions (for details, see Section 2). The calculations of the system's performance and efficiency are based on mass, energy and exergy balances, where the processes involved have been mathematically described. In order to examine the importance of biomass quality on the electricity produced, the model presented here may be supplied by several feed streams with different CH\({}_{4}\)/CO\({}_{2}\) ratios, thus representing different biomass resources and production processes [10]. This concept constitutes the innovation of this work, since it is able to simulate experimentally unattainable cases, just by applying a great variety of different values for each parameter.  
To specify the above, the present research work embodies an extensive parametric analysis on such FC based systems through a simulation model, which is able to use as the main supply fuel not only pure CH\({}_{4}\)[11; 12] but a variety of different qualities of biogas referring to real life scenarios as they are produced by different raw materials through the anaerobic digestion process.
## 3 Results and Discussion
### Mechanical Deformations Due to Gas Pressures  
First, a piston displacement curve was measured. For that purpose, a fixed reference is taken, which for this case, was the white paper shown in Figure 7. This reference was considered as the zero position of the piston and this is the first frame of the processing. From this frame to the next one, the piston displacement was calculated by the PIV methodology [25] through a correlation function. In this way, the displacement curve was developed frame by frame until the piston returned to the initial reference point.  
To verify that the measured piston position was correct, a comparison was performed between the measured trace at valve overlap TDC and the calculated trace from GT-Power. We used a correction factor of 11% to the measured displacement curve because the grid paper and piston were in different planes, inducing a perspective error in the transformation from pixels to millimeters. In Figure 8, a good fit can be seen between the piston displacement trace calculated from GT-Power (without deformation) and the measured one at valve overlap TCD (which was used during the study as the reference without deformation.)  
The same correction factor was used in the piston displacement at firing TDC. Figure 9 shows that the deformation occurred at 1.1 bar of intake pressure. The maximum in-cylinder pressure was 31 bar and the deformation found at TDC was less than 0.03 mm. It can be seen that for this engine condition,  
Figure 8: Measured piston displacement versus the calculated one.  
Figure 7: Reference used in the piston position measurements.  
the mechanical deformation was negligible, and the in-cylinder volume was not significantly affected. On the other hand, Figure 10 shows the deformation at 2.7 bar. For this case, the maximum in-cylinder pressure was 80 bar and the maximum deformation at TDC was 0.5 mm. It is possible to note that as the cylinder pressure increases, the deformation becomes significant. It is important to take into account that a deformation of 0.5 mm is enough to induce an error in the instantaneous volume trace of around 8% and consequently in the heat release rate and thermodynamic characterization. Considering that deformation increases linearly with pressure, as proven by [PERSON] et al. [17], it is possible to develop a linear function where deformation is directly dependent on cylinder pressure as presented earlier in Equation (3).
## 1 Introduction
Compared to ANN and the method that manually adjusts the three parameters one by one to obtain an accurate prediction model, the intelligence algorithm LHS-SVR can automatically calculate the parameter combinations one by one in the search space to find the optimal value, which improves the computational efficiency to a certain extent. Based on the selection, crossover and mutation operators, the GA-SVR can self-adaptively and dynamically adjust the processes of selection, crossover and mutation and rapidly generate optimal results, which greatly improves the computational efficiency compared to LHS-SVR and ANN. After comparisons, the modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < LHS-SVR < GA-SVR. Besides, LHS-SVR and GA-SVR only need representative training samples from the research and then automatically adjust the three parameters \(C\), \(\gamma\) and \(\zeta\) to obtain the accurate SVR. The modeling efficiencies of LHS-SVR and GA-SVR are higher than the empirical/semiempirical model and the analytical model. In finite element software, if the software needs to invoke stress-strain data, which are not beforehand inputted into the software, it mainly calculates unknown stress-strain data by mathematical interpolation means. However, hot flow behaviors of materials at different conditions (such as different strain rates and temperatures) are highly non-linear and complicated. The mathematical interpolation method cannot correctly predict the hot flow behaviors of materials and will obtain inaccurate simulation results. The stresses outside experimental conditions were predicted by the well-trained LHS-SVR, which enhances the simulation precision of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles.
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
## 3 Methods
### Local Feature Learning Groups  
In the speech emotion analysis task, the order of speech segments is very important for analyzing the emotion of speech. There is a strong correlation between successive audio segments and therefore a great similarity in the sentiment present in them. The framework proposed in this paper also captures the correlations between consecutive audio segments by processing them and using them in the sequence prediction process to identify and analyze their expressed sentiments. As shown in Figure 3, we employ a local feature learning group that contains four identical local feature learning blocks. To sustain spatial and temporal trails, for instance, spatio-temporal information in sequential speech pieces, the conformer structure is used in this framework. Convolution operations are applied to transform input-to-state and state-to-state transitions within the conformer. Conformer catches spatio-temporal trails while convolution is in operation to better explore the correlation between speech segments. If \(x_{i}\) is used to denote the i-th input of the conformer layer, the output \(y_{i}\) is:  
\[\tilde{x_{i}}=x_{i}+\frac{1}{2}FFN(x_{i}) \tag{1}\]  
\[x_{i}^{\prime}=\tilde{x_{i}}+MHSA(\tilde{x_{i}}) \tag{2}\]  
\[x_{i}^{\prime\prime}=x_{i}^{\prime}+CONV(x_{i}^{\prime}) \tag{3}\]  
\[y_{i}=Layernorm(x_{i}^{\prime\prime}+\frac{1}{2}FFN(x_{i}^{\prime\prime}) \tag{4}\]  
Here, FFN, MHSA and Conv represent the feed forward network, multi-headed self-attentive network and convolution operation, respectively.  
Figure 2: Example of extracting MFCC features from audio signals.  
For the calculation of MHSA, we may set \(Q\in R^{L\times D}\), \(K\in R^{S\times D}\), \(V\in R^{S\times D}\), \(K=V\), \(d_{K}=D\), The self-attention formula is:  
\[SA(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{K}}})V \tag{5}\]  
MHSA firstly performs linear dimension-raising operations on \(Q\), \(K\) and \(V\) (that is, full connection operation, parameters are not shared), output, \(\tilde{Q}\in R^{L\times\tilde{D}\times H}\), \(\tilde{K}\in R^{S\times D\times H}\), \(\tilde{V}\in R^{S\times D\times H}\), \(\tilde{K}=\tilde{V}\), \(d_{\tilde{K}}=\tilde{D}\times H\), then press \"head\" to do \(H\) times and the final result is spliced.  
\[Head_{i}=SA(\tilde{Q_{i}},\tilde{K_{i}},\tilde{V_{i}}) \tag{6}\]  
\[O=Concat(Head_{1},Head_{2},...,Head_{H}) \tag{7}\]  
Among them \(\tilde{Q_{i}}=R^{L\times\tilde{D}}\), \(\tilde{K_{i}}=R^{S\times D}\), \(\tilde{V_{i}}=R^{S\times\tilde{D}}\), \(Concat_{i=1}^{H}\tilde{Q_{i}}=\tilde{Q}\), \(Concat_{i=1}^{H}\tilde{K_{i}}=\tilde{k}\), \(Concat_{i=1}^{H}\tilde{v_{i}}=\tilde{v}\).
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Assessment Strategies
#### 3.3.3. Most Common Used Measurement Devices  
About the devices used in the considered studies, most spot measurement studies were conducted using spectrum analyzer, isotropic antennas and different narrowband devices, while most personal measurement studies were conducted using narrow band portable devices and personal distributed exposimeters. Commonly used exposimeters include various versions of EME Spy (SATIMO, Courtaboeuf, France), ESM 140 (Maschek Electronik, Bad Worishofen, Germany), Narda exposimeters (Nardalert S3, RadMan, and RadMan XT) [Narda Safety Test Solutions, New York, NY, USA], ExpoM-RF (Fields at Work GmbH, Zurich, Switzerland), and personal distributed exposimeters (PDEs) (Ghent University/iMinds, Ghent, Belgium) [36]. The Narda exposimeters are broadband exposimeters, whereas the others are narrowband. All these devices have very different characteristics, in terms of number of frequency bands that can be discriminate (e.g., 20 bands in EME Spy 200, 16 bands for ExpoM-RF, 8 bands for the ESM 140), the total bandwith, the sampling interval (e.g., equal to 0.5-10 s for ESM 140 and Nardalert S3, 4-255 s for the EME Spy 200, and 3-6000 s for ExpoM-RF), sensitivity in terms of lower detection threshold, size and weight. For a detailed description of all these devices characteristics, see [24].
## 3 Results and Discussion
### Confirmatory Test  
In the last, a confirmatory test is conducted with the optimal input polishing parameters (\(T_{g}~{}=\) 0.019 Nm, \(\omega\) = 4500 rpm, \(F_{z}\) =16 N, and \(V_{f}\) = 5 mm/s) of the proposed system achieved previously by using Taguchi method and ANOVA. A comparison between the tested and the expected results from the linear regression model for the polishing efficiency and the torque is presented in in Table 8. A good agreement is found between the experimental and the predicted results that confirm that the experimentation based on the Taguchi method is properly conducted to explore the system response for the different levels of the input parameters (control factors). Figure 9 shows the watch bezel before and after the polishing using optimized polishing parameters on the selected polishing area.
###### Abstract  
The complex and dynamic space environment is both exciting and challenging in this NewSpace era. In particular, Low-Earth Orbits are being realigned and reinvented for various purposes using suitable technological advancement. This paper is focused on the major parameters that can be analyzed to attain orbit control and autonomy of small-satellite constellations for real-time applications. By applying industry experience to graduate research, this work addresses the related concerns in a realistic manner. Currently, global small-satellite constellation solutions are too expensive and inaccessible for many nations to help in their data reception requirements. This issue was addressed, and some of the main aspects relating to low-cost and high-benefit technical synthesis, in addition to utilization for deep-space missions, were also discussed in detail. In conclusion, this paper demonstrated a strategic approach to optimize the coverage and performance, and reduce the cost of small-satellite constellations, compared with present day constellations, allowing the data to be relayed faster and with precision. This will benefit the industry in the development of low-cost constellations, and effectively assist in disaster management and deep-space communication relays. Autonomous orbit selection and navigation can be established for better path alignment for satellites to efficiently propagate and deliver the required data.  
coverage; optimization; constellation; small satellites; deep space Article  
[PERSON] A Study of Autonomous Small Satellite Constellations for Disaster Management and Deep Space Strategy. _Remote Sens._**2022**, _14_, 6148. [[https://doi.org/10.3390/rs14236148](https://doi.org/10.3390/rs14236148)]([https://doi.org/10.3390/rs14236148](https://doi.org/10.3390/rs14236148))
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 3 MCNN Framework for Geophysical Phenomenon Classification
### Multichannel Feature Extraction
Subsequently, the parameters \(\theta_{j}^{t}\) of the unfrozen layers in Equation (19) and the ultimate classifier layer undergo iterative optimization using the gradient descent method, taking into account the [PERSON] optimization algorithm, as described in [65]:  
\[\theta_{j}^{t}=\theta_{j}^{t-1}-\frac{an\hat{m}_{t}}{\sqrt{\hat{v}_{t}}+\varepsilon}. \tag{19}\]  
Here, the terms \(\hat{m}_{t}\) and \(\hat{v}_{t}\) represent the bias-corrected first and second moments, respectively, and are iteratively updated according to the following expressions (20):  
\[\hat{m}_{t}=\frac{\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}}{1-\beta_{1}}. \tag{20}\]  
Both \(\beta_{1}\) and \(\beta_{2}\) represent the exponential decay rates associated with moment estimates. The gradient \(g_{t}\) can be calculated by using the stochastic objective function \(f(\theta)\):  
\[g_{t}=\
abla_{\theta}f_{t}(\theta_{j}^{t-1}). \tag{21}\]  
Appropriate parameters for the classification of ocean SAR imagettes have been determined through a series of experiments, resulting in the optimal settings of \(\varepsilon=10^{-8}\), \(\alpha=10^{-3}\), \(\beta_{1}=0.9\), and \(\beta_{2}=0.999\). The choice of employing the Adam optimization algorithm is grounded in its efficacy in handling nonstationary objectives and effectively addressing challenges associated with highly noisy and sparse gradients commonly encountered in the context of ocean SAR imagery. Following the extraction of multichannel features using the Inception-ResNet architecture, feature concentration is performed to derive classification outcomes. In this investigation, the utilization of the softmax function is employed, leveraging its capacity to acquire discriminative yet generative compact vector representations for image classification, as discussed in prior work [66]. Let there be \(C\) classes in the assessment dataset. Denoting the original image input as \(K\) and the MCNN parameter as \(\theta\), the softmax function is applied to ascertain the probability of \(y\) belonging to the k-th class:  
\[P(y_{k}=1|K,\theta)=\frac{e^{K^{T}\theta_{k}}}{\sum_{j=1}^{C}e^{K^{T}\theta_{j }}}. \tag{22}\]  
Here, \(P(y_{k}=1|K,\theta)\) represents the probability of the image belonging to the k-th class, \(e\) denotes the exponential function, and \(\theta_{k}\) signifies the parameters corresponding to the k-th class. This formulation allows for the derivation of a probability distribution over the multiple classes for effective image classification. For inputs, denoted as \(K\), not falling within the confines of any predefined classes (\(P(y_{k}=1|K,\theta)<0.5\), \(k=1,2,\ldots,C\)), a categorization process is implemented to assign such inputs to a distinct and specific category.