esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we proposed the universal and efficient DL-based scheme to predict the optimal beamforming vector in the downlink scenario or the transmit power allocation in the uplink scenario with very low computational complexity. We trained the constructed DNN to minimize the MSE-based loss function. The numerical results showed that the proposed scheme could precisely approximate the solutions (i.e., labels) with low computational complexity. An important future work for UAV wireless communications is to maximize the achievable rate using the proposed scheme.  
Conceptualization and methodology, [PERSON]-R.K. and [PERSON]-H.P.; validation and formal analysis, [PERSON]-R.K. and [PERSON]-[PERSON]; investigation and resources, [PERSON]-W.L. and [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]-R.K. and [PERSON]-H.P.; writing--review and editing, supervision, and project administration, [PERSON]-[PERSON] and [PERSON]-W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the ICT R&D program of MSIT/IITP. [2019-0-00499, Development of Identification and Frequency Management Technology of Small Drones at Low Altitude].  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Adam & Adaptive Moment Estimation \\ BS & Base Station \\ CSI & Channel State Information \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ ELU & Exponential Linear Unit \\ IoT & Internet of Things \\ LoRa & Long Range \\ LP & Linear Programming \\ LPWAN & Low-Power Wide-Area Network \\ \end{tabular}  
Figure 9: Minimum transmit power versus the SINR threshold for the uplink with \(v=300\) km/h.
## 3 Design and Computation of Orthogonal Simulation Experiment
### Mathematical Model in FLAC\({}^{\text{3D}}\)  
In our simulations, the pre-defined mathematical models in the software of FLAC\({}^{\text{3D}}\) were employed without modification. These models are briefly described as follows (mainly from \"FLAC\({}^{\text{3D}}\) user's guide\") and can be regarded as the foundation of our tests.  
There are some conventions that should be noted first: in the Lagrangian formulation adopted in the FLAC\({}^{\text{3D}}\), a point in the medium is characterized by the vector components \(\mathbf{x}_{i}\), \(\mathbf{u}_{i}\), \(\mathbf{v}_{i}\) and \(d\mathbf{v}_{j}\)/\(dt\) (\(i\) = 1, 3) of position, displacement, velocity and acceleration, respectively; the symbol \(\mathbf{a}_{i}\) denotes component \(i\) of the vector \(\mathbf{a}\) in a Cartesian system of reference axes; \(\mathbf{a}_{ij}\) is component (\(i,j\)) of tensor \(\mathbf{a}\); and \(\mathbf{a}_{,i}\) is the partial derivative of \(\mathbf{a}\). A bold letter designates a vector or a tensor, similarly hereinafter.  
(1) Stress  
The stress state at a point of the medium is characterized by the symmetric stress tensor \(\sigma_{ij}\). The traction vector \(\mathbf{t}\) on a face with unit normal \(\mathbf{n}\) is given by [PERSON]'s formulae:  
\[\mathbf{t}_{i}=\sigma_{ij}\mathbf{n}_{i} \tag{1}\]  
(2) Rate of Strain and Rate of Rotation  
If the particles of the medium move with velocity \(\mathbf{v}\), the medium experiences an infinitesimal strain determined by the translations \(\mathbf{v}_{i}dt\) in an infinitesimal time \(dt\) and the corresponding components of the strain-rate tensor are:  
\[\xi_{ij}=\frac{1}{2}\Big{(}\mathbf{v}_{i,\ j}+\mathbf{v}_{j,i}\Big{)} \tag{2}\]  
The components of the rotation-rate tensor are:  
\[\mathbf{\omega}_{ij}=\frac{1}{2}\Big{(}\mathbf{v}_{i,\ j}-\mathbf{v}_{j,i}\Big{)} \tag{3}\](3) Equations of Motion and Equilibrium  
Application of the continuum form of the momentum principle yields [PERSON]'s equations of motion:  
\[\sigma_{ij,j}+\rho\mathbf{b}_{i}=\rho\frac{d\mathbf{v}_{i}}{dt} \tag{4}\]  
where \(\rho\) is the mass per unit volume of the medium and \(\mathbf{b}\) is the body force per unit mass.  
In the case of static equilibrium of the medium, the acceleration \(d\mathbf{v}/dt\) is zero. Then, the above equation reduces to the partial differential equations of equilibrium:  
\[\sigma_{ij,j}+\rho\mathbf{b}_{i}=0 \tag{5}\]  
(4) Constitutive Model  
In excavation studies, the Mohr-Coulomb yield criterion is usually employed [45; 46; 47]:  
\[f^{\prime}=\sigma_{max}-\sigma_{min}\frac{1+\sin\varphi}{1-\sin\varphi}+2c\sqrt {\frac{1+\sin\varphi}{1-\sin\varphi}} \tag{6}\]  
where \(f^{\prime}\) is the shear yield function; \(\sigma_{max}\) and \(\sigma_{min}\) are the maximum and minimum principal stresses, respectively; \(\varphi\) is the internal friction angle; and \(c\) is the cohesion.
## 4 Results and Discussion
### Monthly Energy Gain  
Although the annual analysis shows the overall behaviour of the mounting systems, a monthly analysis was necessary to determine the most appropriate mounting system for each period of time. Therefore, for this section the monthly energy gain (\(MEG\)) was analysed. Equation (26) was used for this purpose. The monthly energy gain for the \(PV\) module mounting systems under study is shown in Figure 12:  
Figure 11: Annual energy gain.  
Figure 10: Representation of the \(R_{b}\) parameter throughout the year.  
The following observations can be deduced from Figure 12:  
1. In the months of October, November, December, January, and February, the fixed tilt angle system performed better than the horizontal single-axis tracker, both in the theoretical study and in the study under real operating conditions. In the other months, the horizontal single-axis tracker performed better. Therefore, during the months of greatest incident solar irradiance, the horizontal single-axis tracker performed better Figure 10, which shows the variation of \(R_{b}\), explains this trend.
2. In the theoretical study, the highest value of \(MEG\) was in June and the worst was in December. These results were true in both the scenarios under study. In Scenario 1, these results were approximately \(43\%\) and \(-29\%\), respectively, and in Scenario 2 they were approximately \(47\%\) and \(-22\%\), respectively.
3. In the study under real operating conditions, the best month was July and the worst month was December. This was true in both the scenarios under study. In Scenario 1, these results were approximately \(30\%\) and \(-24\%\), respectively, and in Scenario 2, approximately \(30\%\) and \(-21\%\), respectively.
## 3 Results
### Model Test Using Second Dataset  
In the results of training and testing using the first dataset, it may be considered that both accuracy and binary cross-entropy cost are acceptable. However, it is crucial for the suggested model to investigate the feasibility of newly added gait data since the method can be operated for monitoring the conditions of the walking environment where lots of newly added data are generated. In this consideration, the second dataset is inputted into the suggested model. The second dataset was collected at the same experimental site as the first dataset, but not with the same participants. Thus, the second dataset can be used to check whether the trained model is feasible for newly added data. Figure 7 presents the test results using the second dataset. In Figure 7, both test results using the first and second datasets are presented for comparison to each other. The result of the test accuracy of the second dataset is very similar to that of the first dataset, as shown in Figure 7a. Figure 7b presents that there is a small gap between the binary cross-entropy cost of the first dataset (for a test) and that of the second dataset. This comparison between two datasets indicates that the suggested model can be effectively operated using newly added data.  
In this study, two gait classes are used: _Normal Gait and Abnormal Gait_. To analyze the performance of the model in more detail, the gait classification results are analyzed. Figure 6 shows the classified activities and performance metrics of both the first and second datasets. The test results of the first dataset as shown in Figure 8a present 96.8% accuracy, 99.2% precision, 96.4% recall, and 0.978 F1-score. The test results of the second dataset also show very similar results to that of the first dataset (see Figure 8b). These two test results with high values of performance metrics such as accuracy, precision, recall, and F-1 score indicate that the suggested model can distinguish between normal and abnormal gaits. This result may lead us to allow the gait classification method to monitor the walking environment and identify environment barriers hindering pedestrians' walkability.
## 3 MS Monitoring System for the Spillway Tunnel
### Principles of MS Monitoring  
The weak structural planes (micro-fractures) contained in a discontinuous rock mass will go through a process consisting of initiation, propagation, and penetration when the stress and temperature change, and this process will release strain energy in the form of elastic waves. Such elastic waves released along with the development of micro-fractures are called microseisms, and the monitoring method of inverting seismological parameters based on elastic waves to analyze the stability of the rock mass is called MS monitoring. A valid MS event can be detected only after at least four sensors have collected valid elastic wave signals for parsing the temporal and spatial parameters (time t and spatial coordinates X, Y, and Z) from the exact starting point, as shown in Figure 2. In addition, the collected elastic waves can also be used to invert the important source information such as the magnitude, energy released, apparent stress, and apparent volume. Therefore, the macroscopic deformation failure trend of the surrounding rock can be predicted based on the analysis of the temporal, spatial, and intensity as well as other source parameters, thus ensuring safe construction.  
Figure 2: Schematic of MS monitoring.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 3 Proposed Low-Complexity Iterative Decoding for LDPC-Based Product Codes  
In this section, the proposed iterative decoding scheme is presented. Meanwhile, the performance of the LDPC-based product code with iterative decoding is verified through software simulation.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### Datasets for Tropical Meteorology and Climatology
The Jet Propulsion Laboratory (JPL) has developed a tropical cyclone information system that provides near-real-time data and visualization for multi-parameter satellite and airborne observations [43]. In addition to the TRMM lightning data mentioned above, a large collection of NASA field campaign datasets is available at NASA GHRC [25]. More information about these projects and datasets can be found on their websites. A single website, to be described next, has been developed to allow searching and accessing data from NASA DAACs.
## 4 Results and Discussion  
### State Detection Based on FNN  
In Figure 8, each cycle corresponds to a current signal. At each state, 100 (300 in total) current signals are randomly selected to train and test the FNN model, and then 20 (60 in total) current signals are randomly selected from each stage to verify the accuracy of the FNN. The current signals extracted under each state of the HSV were decomposed, and the original signal was decomposed into eight frequency bands. The feature vectors under each state were obtained according to Equations (1) and (2).  
The number of neurons in the input layer of the FNN was equal to the number of eigenvalues in the training sample. Since there were eight eigenvalues for each feature vector, there were an equivalent number of neurons in the input layer. The number of input layer nodes, output layer nodes, and membership functions are 8, 1, and 18, respectively. The corresponding \(m\) and \(n\) in Figure 1c are 18 and 8, respectively. The training times, expected error, and learning rate \(\eta\) of the neural network were 6000, 0.1, and 0.001, respectively. Figure 10 shows the confusion matrix generated through enumeration of the output results of test sample data. From the figure, the number of correct classifications is displayed on the diagonal of the matrix. The FNN prediction accuracy of the state of the HSV was \((19+18+19)/(19+1+2+18+1+19)=93.3\%\). Therefore, the FNN could meet the requirements of degradation state detection.
## 2 Materials and Methods
### Long Short-Term Memory Neural Networks (LSTM)  
During the late 1990s, Hochreiter & Schmidhuber [19] provided the solution for exploding and vanishing gradients in the form of an upgraded variant of the RNN, namely, Long Short-term Memory (LSTM) which is capable of dealing with long-term dependencies. To overcome the problems associated with long-term dependencies, the LSTM cell (Figure 5) contains a so-called \"Cell State\" which enables almost unobstructed information flow through time.  
Further, the LSTM cell has three building blocks, called \"gates\", which regulate the data flow through the cell. The \"Forget Gate\" (Equation (5)) regulates how to modify the \"Cell State\" according to previous (\(t\)-1) and current (\(t\)) timestep.  
\[f^{(t)}=\sigma\Big{(}W_{fhh}h^{(t-1)}+W_{fwh}x^{(t)}+b_{fh}\Big{)} \tag{5}\]  
\[i^{(t)}=\sigma\Big{(}W_{ihh}h^{(t-1)}+W_{ixh}x^{(t)}+b_{ih}\Big{)} \tag{6}\]  
\[\dot{c}^{(t)}=\tanh\Big{(}W_{chh}h^{(t-1)}+W_{cxb}x^{(t)}+b_{ch}\Big{)} \tag{7}\]  
Figure 5: The structure of a Long Short-term Memory cell (according to: [29]).  
\[c^{(t)}=f^{(t)}\odot c^{(t-1)}+i^{(t)}\odot\dot{c}^{(t)} \tag{8}\]  
\[o^{(t)}=\sigma\Big{(}W_{oth}h^{(t-1)}+W_{osh}x^{(t)}+b_{oh}\Big{)} \tag{9}\]  
\[h^{(t)}=o^{(t)}\odot\tanh\Big{(}c^{(t)}\Big{)} \tag{10}\]  
where: \(\odot\) denotes the Hadamard product (elements-wise vector multiplication), \(\sigma\) denotes sigmoidal function, \(W\) are the network weights and \(b\) are the biases, \(x(t)\) is the input at current timestep, while \(h(t\)-1) is the \"Hidden State\" (same as for an RNN cell) of the previous timestep.  
Next, the _\"Input Gate\"_ (Equations (6) and (7)) regulates how much of the current information (timestep \(t\)) will be added to the \"Cell State\". The \"Cell State\" gets modified by the \"Forget\" and \"Input Gates\" following the Equation (8). The \"Output Gate\" (Equation (9)) regulates the outflow of the data from the cell, which is affected by the current input data and the \"Cell State\". The final output of a LSTM cell is calculated following the Equation (10) and is also the new \"Hidden State\" which is passed to the next timestep.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 2 Proposed Antenna and Parametric Sweep
### Effects of Different Antenna Iteration Orders  
Figure 2 shows the procedure for constructing a cardioid fractal slot antenna with different iteration orders (and similarly to a majority of fractals with the same IF). The generator shape for this fractal antenna is a cardioid described by Equation (1). The starting point for this antenna is the zeroth iteration fractal, i.e., the cardioid antenna shown on Figure 2a. Additionally, this figure shows the generation of the fractal structure in which each iteration has the same iteration factor IF = 0.68 (\(IF=a_{2}/a_{1}=a_{3}/a_{2}=a_{4}/a_{3}=a_{5}/a_{4}\)).  
When examining the reflection coefficient of the antenna with zeroth iteration order and the antenna with the first iteration order seen in Figure 2b, it is observable that the cardioid in Figure 2b has a wider operating range. If the condition for the antenna operating range is taken to be \(S_{11}<-10\) dB, in the case of the zeroth iteration, this condition is fulfilled in two frequency bands: 1.8-3.5 GHz and 5.9-30 GHz, with the antennas having second and third iteration orders where the condition is met in a narrower band 1.8-2.57 GHz. The best characteristic in terms of broadband operation was obtained with a cardioid with the first iteration order (Figure 2c), whereas the \(S_{11}\) characteristic deteriorates by further increasing the iteration order, as is the case of a cardioid with the second and third iteration order (Figure 2d).  
By analyzing the results of simulations as well as fractal geometry that can be found in the literature, it was decided that the proposed antenna should be designed using fractal geometry, which changes the iteration factor in each subsequent iteration. In this manner, greater flexibility in design is achieved.
## 3 Results and Discussion
### Desorption  
Regeneration of saturated adsorbents is of vital significance for actual application. In view of the fact that NaOH can result in deprotonation of the fixed amine groups resin and NaCl can disturb electrostatic attraction between the net positively charged resin surface and PTSA anion, the mixed solution of 8% NaOH and 5% NaCl was chosen as the eluent, and the desorption efficiencies of five resins are presented in Figure 8. Notably, the final static desorption rate of each resin was above 80%, hinting that the PTSA adsorption on the resins was reversible. Meanwhile, it clearly suggested that electrostatic interaction played a leading role in the PTSA adsorption on resins. Moreover, a decreasing trend in the functional group density of resins was favorable for the desorption performance of the resin (Figure 8).  
Figure 7: X-ray photoelectron spectroscopy (XPS) spectra of (**a**) S2p of SD1 resin, (**b**) S2p of SD5 resin, (**c**) N1s of SD1 resin and (**d**) N1s of SD5 resin before and after adsorption.
## 1 Introduction
### Related Works and Contributions
In this paper, we consider the distributed rate adjustment for heterogeneous CSMA/CA networks. Compared with the aforementioned literature, the unique contributions of this work are summarized as follows:  
* A mathematical characterization of heterogeneous traffics is provided, which enables each node to use CCA information to accurately determine the aggregate transmission rate of all other nodes. Compared with previous methods [8; 22; 23] that can only be applied for characterization and estimation in homogeneous networks, the proposed algorithm does not assume homogeneity of the network nor prior knowledge on nodes coexisting in the networks. It can thus be used for estimating both homogeneous and heterogeneous traffics;
* Based on the above mathematical framework, a distributed algorithm is proposed to predict the packet success rate and tune the transmission rate to meet the throughput demand. The algorithm is accurate with only an average error of 0.43% for homogeneous networks and 0.524% for heterogeneous networks. * The algorithm is fully distributed and does not require any central coordination. Moreover, a change detection mechanism is also developed to allow each node to react promptly to on-going traffic changes and re-adjust its transmission rate to meet the throughput demand. The rest of the paper is organized as follows. Section 2 reviews the CSMA/CA algorithm and discusses the model used throughout the paper. Section 3 derives the distributed algorithm, followed by the detectability analysis and the detection mechanism in Section 4. Extensive experimental results are presented in Section 5. Section 6 concludes the paper.
## 5 Conclusions  
In this study, we propose a high-accuracy reconstruction method for airborne STIL. Three types of points are selected from different airlines or scanning bands, and iterated as weighted variables using the PSO algorithm. The optimization results show that the elevation accuracy is always maintained in a small range despite the complex coupling relationship between different parameters. This means that high-precision reconstruction of 3D point clouds can be achieved without the need to measure placement errors of the LiDAR system during mapping missions. Among several optimization parameters, the best optimization setting increases the elevation accuracy to within 0.3 m.  
Conceptualization, R.F.; methodology, X.W.; software, X.W.; validation, X.W., Z.C. and C.D.; formal analysis, X.W. and R.F.; data curation, Z.D.; writing--original draft preparation, X.W.; writing--review and editing, Z.C.; visualization, X.W.; project administration, D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62192774 and No. 62305085).  
Not applicable.  
Figure 12: A comparison of misalignment with and without PSO parameters; (**a**) before PSO; (**b**) after PSO.  
Figure 13: Coincidence of point clouds under different air routes; (**a**) the test zone; (**b**) before PSO; (**c**) after PSO.  
Data Availability Statement: According to the requirements of the project regulatory department, streak images and point-cloud data cannot be provided.
## 3 Simulation Implementation  
### Simulating the Photon States at the Transmitter, Noise, and Receiver End  
In the UV scattering optical communication system under consideration, OOK modulation is employed. The simulation conditions at the transmitter end are as follows: a communication rate of 300 kbps is set, and the LED array operates with a forward voltage of 1 V. As the LEDs switch on and off, the data signal is transmitted into the atmospheric channel.  
We simulated the optoelectronic characteristics of the system at different stages, as shown in the figure above. Figure 6a depicts the square wave output waveform of the signal transmission, with the LED emitting light at a high level. Figure 6b illustrates the simulated environmental noise interference. Figure 6c presents the discrete photocurrent received by the PMT. It can be observed that the occurrence frequency and amplitude of noise exhibit certain randomness. When introduced into the data signal, this noise, further amplified by the PMT and operational amplifier circuitry, can interfere with the communication quality of the system.  
Figure 6: (a) Amplitude variation of the signal at the transmitting end. (b) The amplitude of introduced noise. (c) Signal amplitude at the receiving end.
## 3 Methods
### Transmitting Messages
In this way, we are able to use the most efficient transmission path to improve transmission efficiency and reduce message drop rate and delay.