esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparison of Methods  
The comparison of methods primarily focused on the accuracy metric, as most other researchers did not employ additional metrics like the AUC-ROC or AUC-PRC. The comparison of methods based on the KDEF, CK+, and FER2013 is depicted in Tables 8-10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Angry & 0.62 & 0.62 & 0.62 \\ Disgust & 0.81 & 0.71 & 0.76 \\ Fear & 0.58 & 0.51 & 0.54 \\ Happy & 0.87 & 0.88 & 0.87 \\ Neutral & 0.63 & 0.66 & 0.64 \\ Sad & 0.57 & 0.61 & 0.59 \\ Surprise & 0.81 & 0.80 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of fine-tuned VGG16 model with histogram equalization on different classes in the FER2013 dataset.  
Figure 4: Confusion matrix for fine-tuned VGG16 model with histogram equalization on CK+ dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Type** & **Accuracy** \\ \hline Pathamidam [61] & Hybrid CNN & 89.58\% \\ [PERSON] et al. [62] & ACD & 99.12\% \\ [PERSON] et al. [65] & Efficient-SwishNet & 88.3\% \\ [PERSON] et al. [68] & MobileNet + SVM & 86.4\% \\ [PERSON] et al. [73] & Feed-Forward Network & 88.87\% \\ [PERSON] et al. [72] & Pre-trained VGG19 & 93\% \\ Proposed Model & Histogram + Pre-trained VGG19 & **95.92**\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of methods on KDEF dataset.  
Our model demonstrated an exceptional performance on the KDEF dataset compared to state-of-the-art works. The closest contender was the model in the study by [PERSON] et al., achieving an accuracy of nearly 93% using a transfer learning model based on VGG19. On the CK+ dataset, our model also showcases supremacy. Although the work of [PERSON] et al. achieved the same accuracy, their model was notably complex, with each convolutional block comprising approximately 18 layers.  
However, the discussion primarily centers around the FER2013 dataset, where our model exhibited a relatively good performance. We utilized the filtered FER2013 dataset from the work of [PERSON] et al., where their single five-layer model achieved an accuracy of approximately 70.09%. Despite attempting to implement the same model on the filtered FER2013 dataset, we achieved only a 68.27% accuracy. Therefore, we implemented our own pipeline, achieving an accuracy of almost 69.65%. Moreover, despite employing a simpler model based on pre-trained VGG16, our model compares favorably with other works that utilize more complex architectures. Notably, our model takes less time because of the lower number of parameters that it is using.
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 3 Simulation Results
### Simulation Results of Planar Two-Link Manipulator  
The two-link manipulator experiment was mainly used to verify the feasibility of the reinforcement-learning algorithm in the field of manipulator-path tracking. The specific parameter settings for the experiment are detailed in Section 2.3.1. The tracking-curve results of this experiment are shown in Figure 5.  
The blue line represents the real operating end path, while the red line represents the desired-goal path. The tracking findings in Figure 5 show that the deep-reinforcement-learning-based strategy completely succeeds in tracking the target path. The experimental findings in the simulation environment are displayed in Figure 6.  
Figure 4: The redundant-manipulator-simulation system.  
Figure 5: Path-tracking curve of two-link manipulator based on DQN.  
The experimental results show that it is completely feasible to use the deep-reinforcement-learning algorithm to achieve path tracking with a simple two-link manipulator.
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
## 3 Cascaded Enhanced HHTWT  
### Operating Principle of CE-HHTWT  
In order to amplify the power of HHTWT, a novel structure is proposed on the basis of HHTWT, named cascaded enhanced HHTWT. It is featured by introduction of another electron beam that forms dual-beam THz band CE-HHTWT.  
CE-HHTWT is demonstrated in Figure 12. Connecting an amplification section to the output port of the HHTWT forms dual-beam THz band CE-HHTWT. The THz signal enters the amplification section from the radiation section. Beam-wave interaction occurs between a new electron beam and input signal of amplification section. Compared with the HHTWT, the efficiency and the output power of dual-beam CE-HHTWT is improved.
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 2 Structure and Principle  
The schematic diagram of the dual-view integral imaging 3D display system by using MHOE is shown in Figure 1, which consists of an MHOE, projector I and II. The MHOE is used as a projection screen, which records the wavefronts of a micro-lens array from two different directions by using two-step fabrication methods. Projection beams I are projected by projector I, which contain elemental image array (EIA) I, and projection beams I are projected on the MHOE with a pair of angles \(\theta_{2}\) and \(\theta_{3}\), which satisfies the Bragg condition. Then, 3D images I are reconstructed in the left viewing zone. Projection beams II with EIA II are projected by projector II, and projection beams II are incident onto the MHOE with a pair of angles \(\theta_{5}\) and \(\theta_{6}\). Then the Bragg condition is satisfied and 3D images II are reconstructed in the right viewing zone. Finally, two different 3D images are reconstructed in the dual viewing zones, respectively.
## 3 Clustering Methods to Compact the Visual Information
### Cluster with a Self-Organizing Map Neural Network  
As a second alternative, Self-Organizing Maps (SOM) have been chosen to carry out the clustering evaluation in this work. This algorithm was introduced by [PERSON] (1986), and it is an effective option to carry out a mapping distribution when the data present a high dimensionality [PERSON] (1986). This algorithm has been commonly used for clustering or reducing the dimensionality of data. Therefore, in this work, the input data are the set of global appearance descriptors calculated with one of the methods described in Section 2. The size of the neural network map (\(W_{SOM}\times H_{SOM}=n_{c}\)) is chosen. After the training step, the data will be grouped into \(n_{c}\) different clusters.  
Self-organizing maps automatically learn to classify input vectors according to their similarity and topology in the input space. They differ from competitive layers in that neighbouring neurons in the SOM learn to recognize neighbouring sections of the input space. Thus, self-organizing maps learn both the distribution (as the competitive layers do) and topology of the input vectors with which they are trained. The neurons can be arranged in a grid, hexagonal, or random topology. The self-organizing map network identifies a wining neuron \(i^{*}\) using the same procedure as employed by the competitive layer, but instead of updating only the winning neuron, all neurons within a certain neighbourhood \(N_{i^{*}}(d)\) of the winning neuron are updated.
## 2 Prediction Algorithm
### OS-ELM with Forgetting Mechanism (FOS-ELM)  
In practice, training data are time sensitive. In other words, data are only valid within a certain time period. In the process of the FOS-ELM, the data obtained earlier than a certain time can no longer be used under the forgetting mechanism, since the outdated data may make the prediction less accurate [20]. As solar radiation and temperature vary seasonally, the FOS-ELM is more appropriate for the PV power prediction model. The algorithm of FOS-ELM is presented as follows.  
Step 1: Initialization, which is the same as Step 1 in the OS-ELM.  
Step 2: Online learning with the forgetting mechanism.  
When the \(\left(k+1\right)^{th}\) chunk of new data \(\left\{\left(x_{i},y_{i}\right)\right\}\) is ready,  
1. Calculate the partial hidden layer output matrix \(\mathbf{H}_{k+1}\), which corresponds to the latest data.
2. Estimate the new \(\mathbf{P}_{k+1}\) and \(\mathbf{\beta}^{\left(k+1\right)}\) based on (9) and (10).
3. Set \(k=k+1\), and then go back to Step 2.  
It is obvious that the difference between FOS-ELM and OS-ELM is the addition of the forgetting mechanism, which can not only get rid of the outdated data to avoid their interference in the training networks, but also can reveal the timeliness of the data.
###### Abstract  
The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.  
Global Navigation Satellite System (GNSS); Global Positioning System (GPS); low-power electronics; smart devices; smartphone; wearables; measurements; Android +
Footnote †: journal: Electronics Electronics
## 2 System and Channel Models
### FSO Link
\(\sigma_{\ln L}^{2}(\cdot)\) is the large-scale log-irradiance variance and the unified expression can be given by:  
\[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]1 \[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]2  
where \(u\in\{l_{0},L_{0}\}\), \(\eta(l_{0})=\frac{8.56}{1+0.18d^{2}+0.20\sigma_{1}^{2}Q_{l}^{1/6}}\), \(\eta(L_{0})=\frac{Q_{0l}\beta l(l_{0})}{Q_{0}+\eta(l_{0})}\), and \(Q_{0}=\frac{64\pi^{2}l}{\beta L_{0}^{2}}\). The PDF of pointing error impairments \(I_{p}\) is given as [30]:  
\[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]3  
where \(A_{0}=[\text{erf}(y)]^{2}\) is the fraction of collected power, \(\text{erf}(\cdot)\) is the error function and \(v=(\sqrt{\pi a})/(\sqrt{2}w_{z})\). \(w_{z}\) is the beam waist, and \(a\) is the receiver's aperture radius. \(m=w_{zeq}/2\sigma_{s}\) is the ratio between the beam radius and the pointing error displacement standard deviation at the receiver. \(w_{zeq}^{2}=w_{z}^{2}\frac{\sqrt{\pi}\text{erf}(v)}{2\exp(-v^{2})}\). Note that \(m\rightarrow\infty\) is considered as no pointing errors. and  
\[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]4  
where \(J_{1}=\big{[}1-b,1,1+m^{2}\big{]}\) and \(J_{2}=\big{[}a,m^{2},0\big{]}\). \(\Gamma(\cdot)\) is the Gamma function, and \(G_{p,q}^{m,n}[\cdot]\) is the Meijer's G function [31]. The instantaneous SNR for the FSO link is \(\gamma_{FSO}=\frac{(\eta_{R}I)^{t}}{N_{02}}\). Applying the transformation, the PDF and CDF of \(\gamma_{FSO}\) can be obtained as [27]:  
\[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]5  
and  
\[y_{D}=I^{\frac{t}{2}}G\left[1+(\eta_{e})^{\frac{t}{2}}(h_{RF}x+n_{SR})\right]+ n_{RD} \tag{5}\]6  
where \(\varphi_{m}=\frac{am^{2}}{(b-1)(1+m^{2})}\). \(\tilde{\gamma}_{FSO}=\frac{(\eta_{L}E[I])^{t}}{N_{02}}\) is the average SNR. \(E[\cdot]\) is the expectation operator.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 5 Natural Resource Extraction
### Above-Ground Mining Operations  
Above-ground mining operations consist of open-pits (Section 5.2.1), waste piles, and tailings ponds and dams (Section 5.2.2). Accessible case studies of open-pit monitoring using SAR and InSAR within Australia are limited for commercial reasons, but for effective use of the information, mine site operators crucially require reliable, easy to interpret, and timely (within 24-48 h of acquisition) observations to effectively act upon potential risks [51].
## 1 Introduction
### A Summary of SAR Research and Applications for Rice Mapping
Theoretical models, based on radiative transfer equations, are particularly useful for understanding the complex backscattering mechanisms; however, they require a significant amount of input parameters that are tuned to the surrounding conditions at the test site. Supervised classifiers rely on a substantial set of good-quality training data to ensure a good classification, and there is a risk of over-fitting the classification. Image ratio approaches that divide the intensity values from pairs of images are an efficient way to extract information on rice crop dynamics and are particularly useful when the SAR time series has few images or sparse images through the rice season. Temporal feature extraction from sparse SAR time series meets one of our aims, but the method needs to be adapted to use more comprehensive multi-temporal information for rice area mapping. A further consideration in the choice of algorithm in this study is that very few studies have tested the robustness of rice detection algorithms across a range of conditions and a range of countries. Varietal choice, crop establishment methods and crop management practices can have a significant effect on the structure of both plant and canopy, the duration and growth rate of the plant and water content. This contextual information on the characteristics of different rice cropping systems must be considered in the development of a robust rice detection algorithm for application over diverse rice environments. For this reason, we propose a rule-based classification approach to rice area mapping that is based on a small number of rules and parameters that can be quickly fine-tuned from site to site and season to season. These rules and parameters are derived from the above-noted relationships between backscatter and the development of the rice crop, and they build on the temporal feature approach [25] by exploiting regularly acquired, multi-temporal SAR information. Conceptually, the classification approach is based on rules that are agronomically meaningful and, thus, easily understood and easily fine-tuned based on the local knowledge of the rice-growing environment and the key rice-growing stages.
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## 3 Deriving Solar Flux Values Using Observations of a Mobile Radar Working at the X-Band
### The Swiss Confederation Dual-Polarization X-Band Weather Radar
For the Payerne field experiment, ITSG\({}_{\rm{rp}}\) was \(-\)56.2 (\(-\)56.7) dBm, for the horizontal (vertical) channel, while the corresponding levels at the output of the A/D, ITSG\({}_{\rm{dBadu}}\) were 66.4 and 64.9 dBadu, respectively. Figure 1 shows examples of S\({}_{\rm{rp}}\) acquired using the horizontal channel every 30 min during two consecutive winter days. The observations show similar and representative time trends: since attenuation by the Earth's atmosphere is not corrected, a maximum is expected around mid-day in correspondence to the largest angle of elevation value. However, it is worth recalling that the signal variability also depends on the variation of the temperature inside the antenna-mounted receiver box. Our procedure to read the daily solar maximum is very simple: we just keep the daily 2 nd MAX between 10 and 14 UTC and round it to the nearest 0.05 dB. Hence, our daily estimate of S\({}_{\rm{rp}}\) for both 21 and 22 February is \(-\)101.9 dBm. 1. As far as the source of the reference signal is concerned, it is important to note the following three limitations of the ITSG solution compared to the more advanced solution implemented in the dual-polarization MeteoSwiss weather radar network that has recently been installed in the framework of the Rad4 Alp project [22]. The ITSG signal cannot be used for continuous monitoring of the receiver. It can only be injected on demand, as long as the radar is offline. 2. It is plausible that the sensitivity of the ITSG to temperature is not less than the Rx sensitivity itself (see, for instance, the considerations on atmospheric attenuation at the end of Section 3.2.3 and the variability observed in Figure 1). The monochromatic signal of ITSG does not homogeneously fill the whole matched-filter band width. The last two limitations are addressed in the Swiss Confederation weather radar network (Rad4 Alp) by using a white signal, generated by a noise source (NS): as described in [23], the selected NS shows high stability and reproducibility, and its sensitivity to temperature is considerably smaller than the overall Rx amplifying chain. Furthermore, the NS white signal completely fills the whole matched-filter bandwidth. As for the first limitation, the present solution in the X-band radar does not allow a continuous monitoring of the Rx chain: the ITSG\({}_{\text{rp}}\) and ITSG\({}_{\text{dBadu}}\) terms in Equation (3) are both assumed to be constant. Instead, with the (C-band) Rad4 Alp calibration concept, slightly different values in dBadu at the output of the A/D converter could correspond to the same injected reference value.
## 3 Results and Discussion  
### Result and Visualization of Cartesian Mesh Generation  
Using the parallel algorithm, a simplified Hubble Space Telescope model was meshed as shown in Figure 8a. The STL file of the model contained \(1.9\times 10^{4}\) triangular facets. The three-dimensional Cartesian mesh contained \(9.6\times 10^{9}\) mesh cells as shown in Figure 8c. The total running time of mesh generation was 23.61 s. Slicing display was used to show the quality of mesh in different view. Figure 8b is the slicing photo in a view; the boundaries between different materials are displayed clearly. As can be seen, the mesh generated using proposed algorithm had a high degree for matching the original model, and could satisfy the computational requirement of three-dimensional numerical simulation.  
Figure 7: Flowchart of Cartesian mesh generation algorithm based on the graphics processing unit (GPU) parallel ray casting method.  
The proposed parallel algorithm was also applicable when the computational domain contained a large number of geometries. As shown in Figure 9, \(2.43\times 10^{11}\) mesh cells were generated using the proposed parallel algorithm. Figure 9a is an aircraft model containing 134,473 facets. The total running time of mesh generation was \(5.44\times 10^{2}\) s. Displaying trillions of 3D mesh in an image is difficult to implement. To show the result of mesh generation, the engine and the wheel of the aircraft were extracted as shown in Figure 9b,c.  
Figure 8: Cartesian mesh generation result of a simplified Hubble Space Telescope model, (**a**) Hubble Space Telescope model; (**b**) projection result from one perspective; (**c**) result of mesh generation.
# Fate of Soil Carbon Transported by Erosional Processes
## 4 Soil Erosion and the Global Carbon Budget
### Water Erosion  
The GCB is strongly affected by accelerated erosion by water and reportedly emits \(\sim\)1.1 Pg C/yr. [2]. However, the C emitted by erosion is not accounted for in the GCB. In India, [PERSON] et al. [28] reported that erosion transported about 114.4 Tg of C/yr. of which 34.6 Tg C is emitted into the atmosphere. Using 13C isotopic signature in abandoned Mediterranean agricultural land, [PERSON] et al. [9] observed that previous estimates have not considered that erosion transported SOC can be released to the atmosphere as a range of GHGs (CO\({}_{2}\), CH\({}_{4}\), N\({}_{2}\)O). The data by [PERSON] and colleagues from U.K. rivers showed that soil erosion is a net source of GHGs with median emission factors of 5.5, 4.4, and 0.3 Mg CO\({}_{2}\) eq/yr. for one Mg of fluvial C, gross C erosion, and gross soil erosion respectively. [PERSON] and colleagues concluded that gross soil erosion would only be a sink of both C and GHGs only if all the following criteria are met: (i) gross soil erosion rates are \(<\)91 Mg/km\({}^{2}\cdot\)yr., (ii) the eroded C is completely replaced by new SOM, and (iii) if less than half of the gross erosion makes it into the ocean network. Similar conclusions were arrived at by [PERSON] et al. [5]. In this context, [PERSON] and [PERSON] [29] also highlighted the importance of sheet erosion and its impact on the lateral transport of SOC and the attendant impact on GCB. Based on a study involving data from 240 runoff plots over entire rainy seasons from different regions of the world, [PERSON] and [PERSON] reported that the media in particulate organic carbon loss (POCI) was 9.9 g C/m\({}^{2}\cdot\)yr. with the highest value of 10.8 g C/m\({}^{2}\cdot\)yr. for semi-arid soils followed by 6.4 g C/m\({}^{2}\cdot\)yr. for tropical soils and 1.7 g C/m\({}^{2}\cdot\)yr. for temperate soils. Assuming the mean POCL of 27.2 g C/m\({}^{2}\cdot\)yr., the total amount of SOC displaced by sheet erosion from its source was estimated at 1.32 \(\pm\) 0.20 Gt C/yr. or about 13.2% of the 10 Gt of C emitted from the fossil fuel combustion. [PERSON] and [PERSON] also observed that erosion-induced CO\({}_{2}\) emissions may be limited in clayey soils but severe in sandy soils, and that sheet erosion is an important and efficient mechanism of detachment and transport of surficial material (e.g., silt, clay, SOC).  
It is also recognized that soil erosion can be a sink of C in coastal ecosystems and steep terrains. For example, a study in New Zealand documented that transport of sediments and POC from coastal steep lands is a sink. [PERSON] [30] observed that the North Island of New Zealand exports. 1.9 Tg of POC/yr. to the sea and sequesters 1.25 \(\pm\) 0.3 to 0.6 Tg POC-yr. through regenerating soils. In contrast, the South Island of New Zealand exports 2.9 \(\pm\) 0.7-1.5 Tg POC/yr. and sequesters about the same amount. With 80% efficiency of burial at seas, New Zealand has a net sink of 3.1 \(\pm\) 2-2.5 Tg C/yr.