esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
IoT: Internet of Things; SAR ADC: successive approximation register analog-to-digital converter; LSB: least significant bit; CMOS: Complementary Metal Oxide Semiconductor; ENOB: effective number of bits; SNDR: signal-to-noise and distortion ratio; SFDR: spurious-free dynamic range; FoM: figure of merit, DAC: digital-to-analog converter.
## 4 Experimental Results and Discussions
### QDFT Analysis  
A color image can be transformed into four real numbers \(A\), \(C\), \(D\), and \(E\) using a QDFT real transform. Figure 9 shows the \(24_{bit}\) color image and its red, green, and blue channels. The results of the \(24_{bit}\) color image that was operated by quaternion discrete Fourier transform are shown in Figure 10.  
After inverse QDFT transform using Equation (7), \(A_{\textit{{IQDFT}}}\) is negligible and can be approximately regarded as 0, this result also conforms to Equation (8). When the input is a pure quaternion, the result of \(\textit{{IQDFT}}\) can also be approximately regarded as a pure quaternion. When reconstructing the image, \(C_{\textit{{IQDFT}}}\), \(D_{\textit{{IQDFT}}}\), and \(E_{\textit{{IQDFT}}}\) are as red \(R^{\prime}\), green \(G^{\prime}\), and blue \(B^{\prime}\) channels, respectively. The experiment verifies that the difference between the reconstructed image and original image is approximately \(10^{-10}\). The difference  
Figure 10: The QDFT results of the real component **A**, the imaginary component **C**, the imaginary component **D**, and the imaginary component **E**.  
Figure 9: The color image Lena and its red channel, green channel, and blue channel.  
is very small, thus allowing the image to be almost completely restored. The reconstituting red channel \(R^{{}^{\prime}}\), green channel \(G^{{}^{\prime}}\), and blue channel \(B^{{}^{\prime}}\) are shown in Figure 11.
## 3 Results and Discussions
### Analysis of the Interaction between the Granary and Its Surrounding Soil  
Taking the medium dense sands around the granary as an example, the earth pressure theory of retaining walls is used to explain the interaction between the granary and soil under groundwater buoyancy from the displacement of the granary and the stress state of the sands around the granary. Figure 11 shows the collected images of the granary corresponding to eight time points during the experiment: Time 1, Time 2, and Time 3 are in the floating stage, Time 4, Time 5, and Time 6 are in the inclined stage, and Time 7 and Time 8 are in the unstable failure stage.  
In the floating stage shown in Figure 11a-c, the granary moves upward under the action of groundwater buoyancy, and the soils at the junction of the conical granary bottom and granary wall are squeezed to develop an upward uplift trend. At this stage, the junction of the conical granary bottom and granary wall bears passive water and soil pressure. With the increase of groundwater level, the granary displacement increases, as well as the soil deformation. The soils at the junction of the conical granary bottom and granary wall reach the limit equilibrium state, and the local soil around the granary develops shear failure. In the inclined stage, as shown in Figure 11d-f, due to the cohesive force of the conical bottom, the relative motion of soil particles decreases. The following under the conical granary bottom leads to the subsidence of the soil around the granary top. With the gradual increase of granary displacement, the original granary-soil relative stability system is broken. The soil at the junction of the conical granary bottom and granary wall is locally unstable and slips to the vacancy of the granary bottom, resulting in the shear failure of local soil around the granary.  
In the unstable failure stages shown in Figure 11g,h, with the increase of the inclination of the granary, the soil deviating from the inclination direction of the granary body passesits constraint and reaches the active limit state. At this point, the earth pressure acting on the inclined side of the granary is the minimum. The soils in the inclined direction of the granary are squeezed and the wall of the inclined side bears passive earth pressure. The displacement of the sands around the granary increase continuously to reach passive limit equilibrium state, and this produces continuous sliding. At this time, the soil around the granary expands from local deformation to overall deformation, the granary-soil stability system is completely broken, and the granary develops unstable failure. In summary, under the action of groundwater, the displacement of the granary is an important factor causing soil deformation and soil pressure change. The soil pressure acting on the granary cannot be separated from the displacement of the granary and the surrounding soil. For the stability of the granary-soil system, the granary and soil interact with, restrict, and influence each other.
## 3 Results
### Effect of the Process Gas Composition  
Figure 5a depicts the relative reduction fractions of the investigated pellet S1 over the reduction cycles with different H\({}_{2}\)/N\({}_{2}\) concentrations (cf. Table 2) at 700 \({}^{\circ}\)C. The corresponding oxidation fractions at different H\({}_{2}\)O/N\({}_{2}\) concentrations are illustrated in Figure 5b. The time needed to achieve a reduction of 80% of the maximum possible reduction (\(\gamma_{\rm Re}=0.2\)) decreases to less than 10%, from 411 min down to 38 min as the H\({}_{2}\) concentration in the reactive gas mixture increases from 10 to 100 vol.%, respectively. This result is in phase with the observations of [43]. The time needed for the oxidation reaction to reach \(\gamma_{\rm Ox}=0.8\) decreases by 86% (from \(t_{\rm Ox,r0_{1}}=220.8\) min down to \(t_{\rm Ox,r0_{4}}=31.3\) min) upon increasing the concentration of H\({}_{2}\)O in there active gas mixture from 10 to 80% (cf. Table 2). It can be also concluded that the reduction reactions are slower than the oxidation reactions. S1 requires 80.8 min to reach \(\gamma_{\rm Re}=0.2\) at a concentration of ro\({}_{3}\), while only 55.3 min are necessary to reach the equivalent oxidation fraction \(\gamma_{\rm Ox}=0.8\) at the same oxidizing gas concentration (ro\({}_{3}\)).  
Figure 4: Equilibrium partial pressure of oxygen in H\({}_{2}\)O/H\({}_{2}\) mixtures as a function of temperature (dashed lines) compared to the dissociation pressures of the different iron oxide reactions (solid lines) (calculated on the basis of [42]).
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 1 Introduction
#### Main Contributions  
The main contributions of this paper are enumerated as follows:  
* This paper presents an IoT network architecture for the collection, processing and distribution of environmental information.
* This architecture is based on LoRaWAN, with the addition of SDN and edge computing paradigms. This allows us to improve the scalability and the innovation capabilities of the proposed architecture.
* Based on the SDN network and the edge computing nodes, we propose an optimization of the MQTT protocol, protocol used to publish/subscribe data. For that purpose, MQTT proxies are deployed in edge nodes which multicast MQTT data to other edge nodes. Our solution does not require any modification to end nodes.
* Since multicast requires using UDP (User Datagragram Protocol) instead of TCP (Transport Control Protocol), which does not establish a connection, MQTT latency within the SDN is reduced.
* Although another multicast routing strategies could be employed, in our proposal the SDN controller creates source-based trees using the shortest paths, following an approach similar to PIM-SSM (Protocol Independent Multicast--Source Specific Mode). This further reduces the MQTT latency compared to other non-optimal multicast routing based on, e.g., shared trees. An scalability analysis shows that the computation time for multicast paths is reasonable for large networks (up to a few thousand of edge nodes or, equivalently, tens of thousands of LoRaWAN networks).
* We have created a control protocol, _MQTTZMULTICAST_, which maps MQTT topics to multicast IP addresses. MQTTZMULTICAST also includes the required signaling to join/leave a multicast group (i.e., an MQTT topic), thus removing the need for IGMP or similar protocols. This further reduces the traffic within the SDN network.
* A prototype has been developed as Proof of Concept. Our prototype includes real LoRaWAN equipment connected to an SDN network based on _Mininet_ and the RYU SDN controller. Different LoRaWAN nodes send data through the SDN network over MQTT, received by an MQTT subscriber connected to an edge node, and finally visualized using Grafana.
* Our testbed environment is released as an open-source project [3], allowing for reproducible research.  
This article is organized into the following sections. The current section introduces the objectives of the work and its context. Section 2 conducts a review of the State of the Art. Section 3 gives an overview of LoRaWAN. Section 4 describes the design of the IoT network architecture, whereas the MQTT enhancements are explained in Section 5. Section 6 shows our network prototype, carried out as a proof of concept. Finally, Section 7 presents the performance evaluation results, concluding the article in Section 8.
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Timeout with Quorum based Join  
The implementation of a timeout value can be useful in several situations, for example to understand when a particular computing process should be arrested before exhausting the system resources or because its execution is taking so long that is preventing the execution of other components. In cloud computing, it can be used to model either user behavior, as detailed in Section 3.1.1, or specific cloud features. In the latter case, two notable examples are spot instances on Amazon Web Services, and maximum execution times in Function as a Service (FaaS) Serverless deployments.  
Spot instances are very cheap virtual machines that are provided with a bidding process. The user places a bid, defining the maximum price she is willing to pay for running the VM. The provider, based on its current workload and the offered price, decide whether to assign the VM or not. If the user receives a VM, this might be later shut down by the provider, in the case of changes in the workload that would increase the price of the VMs to an amount larger than the user bid. This event can be modeled as a timeout that might occur after a random amount of time.  
Serverless computing, is currently a very popular cloud software deployment paradigm: users write their applications as a set of functions that exploits other cloud services (such as authentication and storage), and that is written in a specific programming language. The cloud provider decides where such functions will be run, based on the users' requests. Due to small overhead, functions can be started and parallelized in a relatively quick way, reducing the users costs and increasing the providers resource utilization. However, to keep a balance in the overall architecture, all functions executions are characterized by a maximum running time. Should they take longer, they will be interrupted, and all the partial computation will be lost. This type of behavior can be easily modeled with a timeout event of constant duration.
## 1 Introduction
### Finite Element Method as a Failure Pressure Prediction and Data Generation Tool of Corroded Pipes  
In this approach, the issue of having a limited amount of real-life data to generate training data for the ANN model can be overcome by using a Finite Element Method (FEM). FEM has been widely applied in the industry and among researchers for predicting the failure pressure or residual strength of a corroded pipeline. Using FEM, [PERSON] et al. in 2005 assessed the failure of an API 5L X65 gas pipe at multiple corrosion defect regions with a maximum percentage error of less than 10.00% [5]. Similarly, [PERSON] et al. [8] utilised FEM for burst test analysis of a corroded API 5L X52 grade steel pipe with a maximum percentage error of less than 5.00%. The results of this study also showed that FEM is a reliable tool to analyse the effects of defect geometries on the failure pressure of the pipe.  
In a study conducted by [PERSON] et al. [24], an ANN was developed for the failure pressure prediction of a pipe with interacting corrosion defects using FEM to generate training data for ANN. A feed forward neural network was utilised with a backpropagation algorithm. Their ANN was developed with four neurons in the input layer, five neurons in the hidden layer, and one neuron in the output layer. The input parameters are the normalised defect length, depth, longitudinal spacing, and circumferential spacing. The number of neurons in the hidden layer was calculated using Equation (1), where \(N_{h}\) is the number of hidden neurons, \(N_{i}\) is the number of inputs while \(N_{o}\) is the number of outputs. The developed ANN was validated against burst tests, and it was revealed that ANN can predict the failure pressure of a corroded pipe to a high level of accuracy, with a maximum percentage difference of 2.00%.  
Figure 1: A feedforward neural network with two hidden layers.  
\[N_{h}=2\sqrt{N_{i}+N_{o}} \tag{1}\]
# A Study of Wind Shear Influences on the Aerodynamic Performances of a UAV Airfoil
## 4 Conclusions  
Shear wind has great influences on the aerodynamic performances of a UAV, and the effect of shear wind on the velocity distributions, lifts and lift-drag ratios are investigated. In this paper, we evaluate that:  
1. Three wind shear models are established to present a comparative analysis of the altitude impact on velocity. Shear wind velocity gradient has a significant influence on both velocity profile and velocity gradient which affects pressure and fluctuating velocity distribution around the airfoil. A positive gradient reduces velocities near the airfoil leading edge, but a negative gradient increases them. On the other hand, a positive gradient and logarithmic distribution will reduce the lift curve slope, the maximum lift coefficient and the stall angle of attack. Conversely, a negative gradient will increase the slope of the lift curve, maximum lift coefficient, and stall angle of attack.  
Figure 14: Lift-drag ratio of airfoil with different velocity distribution of shear wind.  
Figure 13: The lift-drag ratio of an airfoil in Linear distributed shear wind: (**a**) Positive gradients; (**b**) Positive gradient versus negative gradient.  
2. A positive gradient of shear wind increases the maximum lift-drag ratio and its corresponding angle of attack. Therefore, a UAV can obtain a larger lift-drag ratio when suffering a positive gradient shear wind. On the contrary, high lift and high drag can be achieved for the negative gradient case which can greatly reduce the take-off and landing distance of the UAV. The vertical take-off and landing capability is expected to be realized through the comprehensive optimization design of the UAV.  
The findings in the current research can be used for the UAV design to achieve long-range flight and they provide a foundation for further experimental and computational studies on bird flight with low energy consumption.  
Conceptualization, Y.Y. and D.L.; methodology, D.L.; software, Y.Y.; validation, L.L.; formal analysis, Y.Y.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y. and L.L.; funding acquisition, Y.Y and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 3 Design of Metallic Cross-Type Slot Transmitarray
### Design of Proposed Transmitarray Antenna  
For the basic TA experiment, when the beam-steering angle for converting the source wave into a plane wave was set to 0\({}^{\circ}\), the phase distribution result for a 10 \(\times\) 10 unit cell could be obtained, as shown in Figure 9a. By retaining only the unit cells where the wave radiated from the designed patch antenna reaches the TA, the beam could be focused, thereby improving the aperture efficiency. Additionally, to further enhance the aperture efficiency by reducing the array area, six cells at the corners were removed, forming a circular array with a total of 52 cells, as shown in Figure 9b,c. Additionally, the distance from the substrate--where the patch antenna and SP were designed--to the TA was 43 mm. The previously mentioned F/D ratio was calculated as 0.25, yielding a focal length of 40 mm. However, since the optimal size (D) may not be precisely 160 mm, the height was adjusted within an acceptable range based on the empirical electromagnetic simulation results. It is important to note that as the TA and sidewall are metallic structures, they should not come into contact; thus, a small gap was required.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Hlayer & 8.83 mm \\ Hcell & 27.39 mm \\ Lcell & 20 mm \\ Lslot & 15.5\(\sim\)17.8 mm \\ W & 2 mm \\ Copper plate thickness & 0.6 mm \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of cross-type metallic unit cell.  
Figure 8: Transmittance response of cross-type metallic unit cell.  
The entire structure was supported by a PVC-type support, which held the four-layer metal plate in place. The designed TA was verified by comparing the simulation results with actual measurements. Since the maximum gain of the source antenna was observed at 9 GHz, the TA achieved a gain of 18.3 dB, resulting in an aperture efficiency of 29.3%, as shown in Figure 10. This was calculated using Equation (1). In this study, the effective area was considered circular, and thus \(A_{eff}=\pi r^{2}\) was used for the calculation, with r set to 80 mm.  
\[\eta_{a}(\%)=\frac{G{\lambda_{o}}^{2}}{4\pi A_{eff}} \tag{1}\]
## 5 Conclusions
Regarding the use of GPM for evaluating the performance of the three MeteoSwiss products, the smallest dispersion is observed between GPM and CombiPrecip (scatter \(\sim\)1.8 dB), followed by GPM and RADAR (scatter \(\sim\)2.2 dB), whereas a larger disagreement was found between GPM and GAUGE (scatter \(\sim\)2.4 dB). GPM confirms that, for precipitation mapping in the Alpine region, the best results are obtained by combining ground-based radar and rain-gauge measurements using a geostatistical approach. One file with four additional images is available online at www.mdpi.com/2072-4292/9/11/1147/s1. Figure S1 shows the Effective Height of Visibility of the weather radar network. Figure S2 shows estimate of annual precipitation amounts derived using MeteoSwiss weather radar observations synchronous with the GPM satellite overpasses. Figure S3 shows the corresponding GPM estimates. Figure S4 shows the corresponding estimates based on the MeteoSwiss product called CombiPrecip, which is a geostatistical merging of synchronous rain gauge measurements and ground-based radar observations. **Acknowledgments:** The authors would like to thank the Swiss Confederation. The GPM data were provided by the NASA DAAC at the Goddard Space Flight Center. The authors would like to thank the anonymous reviewers and the academic editor for helpful and stimulating comments. **Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the comparisons among the four different precipitation products; [PERSON] conceived and programmed all the routines to extract and process satellite, ground-based radar, gauge, and CombiPrecip data; he conceived and developed the routines for georeferencing the GPM latitude/longitude cells on the Swiss conformal Cartesian grid used by MeteoSwiss; [PERSON] and [PERSON] contributed analysis tools and materials; [PERSON] analyzed the data and prepared the figures; he also prepared figures in Supplementary File 1, together with [PERSON]. [PERSON] wrote the paper after feedback, suggestions, comments, and changes by [PERSON], [PERSON], [PERSON], and [PERSON]; the paper was corrected and checked for English usage by [PERSON] and [PERSON]. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Problem Formulation
### Equations of Motion  
The equations of motion over the rotating spherical Earth for the vehicle's gliding phase are established as follows [7]:  
\[\left\{\begin{array}{l}\dot{v}=-\frac{\rho v^{2}S_{m}C_{D}}{2m}+g_{r}^{ \prime}\sin\theta+g_{\omega v}(\cos\sigma\cos\theta\cos\phi+\sin\theta\sin\phi) \\ +\omega_{e}^{2}r(\cos^{2}\phi\sin\theta-\cos\phi\sin\phi\cos\sigma\cos\theta) \\ \dot{\theta}=\frac{\rho v^{2}S_{m}C_{L}\cos v}{2 mv}-\frac{\rho v^{2}S_{m}C_{Z} \cos v}{2 mv}+\frac{v\cos\theta}{r}+\frac{g_{r}^{\prime}\cos\theta}{v}\\ +\frac{\delta vv(\cos\theta\sin\phi-\cos\sigma\sin\theta\cos\phi)}{v}-2\omega _{e}\sin\sigma\cos\phi\\ +\frac{\omega_{e}^{2}r(\sin\phi\cos\sigma\sin\theta+\cos^{2}\phi\cos\theta)}{ v}\\ \dot{\sigma}=-\frac{\rho v^{2}S_{m}C_{L}\sin v}{2 mv\cos\theta}-\frac{\rho v^{2}S_{m}C_{Z}\cos v}{2 mv\cos\theta}+ \frac{v\tan\phi\cos\theta\sin\sigma}{r}\\ -\frac{\delta vv\cos\phi}{v\cos\theta}-2\omega_{e}(\sin\phi-\cos\sigma\tan \theta\cos\phi)+\frac{\omega_{e}^{2}r(\sin\sigma\sin\phi\cos\phi)}{v\cos\theta} \\ \dot{\phi}=\frac{\gamma\cos\theta\cos\sigma}{r}\\ \dot{\lambda}=\frac{\gamma\cos\theta\sin\sigma}{r\cos\phi}\\ \dot{r}=v\sin\theta\end{array}\right. \tag{1}\]  
where \(v\) is the velocity relative to the Earth, \(\theta\) is the flight path angle, \(v\) is the bank angle, \(\sigma\) is the heading angle at which a vehicle's velocity is projected onto a local horizontal plane concerning the local north, clockwise is the positive direction, \(\lambda\) is the longitude, \(\phi\) is the latitude, and \(r\) is the radius from Earth's center to the vehicle center of mass. \(m\) is the mass of the vehicle, and \(S_{m}\) is the reference area. \(\omega_{e}\) is the angular velocity of the Earth's rotation, \(g_{r}\prime\) and \(g_{\omega v}\) are the component of the Earth's gravitational acceleration in the direction of the geocentric radius vector and the angular velocity of the Earth's rotation, respectively. \(C_{L}\), \(C_{D}\), and \(C_{Z}\) are the lift coefficient, drag coefficient, and lateral force coefficient, respectively. \(\rho\) is the atmospheric density.
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
###### Abstract  
In this paper, we propose a multiple-input multiple-output receiver with deep learning. To obtain the high performance of the maximum likelihood receiver while maintaining the low computational complexity of the linear receiver, we apply a signal-to-noise ratio-based receiver-type decision method. Specifically, the criterion and threshold, which are based on the ratio between the symbol error probabilities of both receivers, to decide the receiver type are presented. For this, we analyze the signal-to-noise ratio gain of the zero-forcing receiver and exploit it as an input feature of the DNN. The simulation results demonstrate that the proposed receiver achieves a symbol error rate performance nearly identical to that of the maximum likelihood receiver with low computational complexity.  
multiple-input multiple-output; receiver; low-complexity; deep learning Article