esa-sceva/satcom-chunk-collection huggingface dataset:
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 2 Background
### Binaural-Based Ambisonics
Ambisonics also allows for a reduction of bandwidth and computational requirements needed for delivering immersive audio content in comparison to the traditional multichannel surround formats and object-based approaches [22]. Most of the binaural rendering systems supporting Ambisonics use computationally efficient spherically decomposed HRTF sets, i.e., pre-computed combinations of virtual loudspeakers and HRTFs [23]. Several HRTF manipulation techniques have been proposed for optimal binaural rendering of Ambisonic signals [20; 24; 25].
# A Game Model and Fault Recovery Algorithm for SDN Multi-Domain
## 4 SDN-MARA Algorithm
### Design of the SDN-MARA Algorithm
#### 4.3.2 Game Domain Construction Algorithm  
The selection of the target controller is based on the remaining capacity \(R_{i}\) of the controllers and the device's transition relationships \(h(i_{1},i_{2})\). The steps for selecting the target controller and constructing the game domain are illustrated in Figure 3 below.  
Firstly, initialize the set of target controllers by setting it to empty. Then, iterate through all the neighboring controllers of the faulty controller. Neighboring controllers are defined as those that meet \(h(i,j)\leq 2\), that is, the transition relationship between the controller \(i\) and the switch \(j\) does not exceed 2. During the iteration, check whether the current controller is already involved in another game domain. If the controller is already involved, it will not be considered. For controllers that are not involved in any other game activities, calculate the remaining capacity \(R_{i}\) of the relevant ones. The remaining capacity of the target controller needs to be greater than the minimum expected request rate \(pre_{min}\) of the out-of-control switches, which is the condition that needs to be satisfied, as shown in Equation (17):  
\[R_{i}>pre_{min}(v_{i}) \tag{17}\]  
Add all neighboring controllers that meet the conditions to the candidate set of target ones. Subsequently, add the out-of-control switches to this set. Finally, sort the controllers in the candidate set in descending order based on their remaining capacity. Assess the request demands of the out-of-control switches and, based on the assessment results, select one that meets the conditions from the candidate set as the final target controller set. The algorithm for constructing the game domain is presented in Algorithm 2.  
Figure 3: Flowchart for target controller selection and game domain construction.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 2 Materials and Methods  
Figure 1 shows the structure diagram of the proposed lightning mapping optimization using the PSO algorithm. The PSO algorithm was utilized to iteratively find the optimal best combination of lightning mapping hyperparameters. The algorithm was used to give a reliable estimation of lightning mapping with high accuracy of localization and to produce the best fitness values using the corresponding objective function. The general procedure of the proposed lightning mapping is divided into four phases, which are summarized as follows:  
Phase 1: This phase began by collecting a real lightning image. Signal editor toolbox (MATLAB 8.0 and Statistics Toolbox 8.1, The MathWorks, Inc., Natick, MA, USA) was used to draw the Cartesian coordinates of a real lightning map (azimuth and elevation) angles. The generated lightning map was used as the reference lightning signal for optimization.  
Phase 2: This phase introduced the proposed lightning event extraction approach. The main aim was to automatically extract the fast-electric field of the fast antenna (FA) with respect to the ITF data (VHF signals). The return strokes of the fast-electric field varied depending on the recorded ITF signals. Additionally, the shape of the return strokes in ITF signals could either be in a wide or narrow range. Identifying the starting and ending points of the VHF signal (lightning event) is considered a crucial step in the subsequent lightning event analysis instead of using a conventional human visual inspection. Adopting and applying more ITF signals in any sophisticated algorithm is a challenging task. In this regard, a novel lightning event extraction technique is proposed to ensure the effectiveness level of extracting the fast-electric field and VHF signal. The method uses an iterative windowed procedure, as detailed in Figure 2. We used the fast-electric field signal as a reference to search for the onset and offset of the lightning event.  
Phase 3 and Phase 4: These phases involved lightning data simulations and mapping optimization procedures.
## 4 Experiment Design
### Training Procedure  
#### 4.3.1 Data Processing  
The Potsdam dataset contains images the size of \(6000\times 6000\) pixels, too big to fit entirely into the GPU memory; thus, they were partitioned into multiple non-overlapping \(224\times 224\) tiles. This latter is the size of images in the ImageNet dataset [29] and was indeed selected to maximize the encoding capabilities of the RGB and DSM encoders that were pre-trained on such standard dataset. However, this setting is not binding, and the model is flexible on the size of the input images. As previously noted, the dataset is extremely unbalanced, and most of the patches extracted from the images do not contain any building pixel. By training the model on this dataset, the net will be biased towards the non-building class, and, in the evaluation phase, the performance metrics may stay high simply because the model is most of the time correctly predicting that the examined patch does not contain buildings. Thus, a data-balancing strategy is required to avoid the network to settle on a fairly high accuracy by simply ignoring the comparatively few building pixels altogether, which results into a useless trained network. Two different approaches can be used to tackle the problem. The first method implies using a weighted loss function during training (e.g., Weighted Binary Cross Entropy) that assigns a larger weight to samples containing buildings and, therefore, induces stronger changes in the net parameters when a building is being processed. The second method [33] suggests training the model only on positive examples, i.e., patches containing more than a pre-set number or percentage of building pixels in our case. This second approach was selected because it is expected not to affect the generalization capabilities of the network. The method was implemented by filtering the extracted patches so that only patches containing at least 5% of positive pixels (building pixels) survived. In the end, the number of effective training patches was 8800.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 3 Data
In any case, it is beyond the scope of this analysis to attempt to assess or improve on the registration. The ESA are planning to re-process all of their Sentinel-2 imagery with an improved ground reference image, but this has not yet been put in place for processing of Australian imagery [24].
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
## 4 Results and Discussion
### Elemental Composition Analysis (EDX)  
Energy dispersive X-ray (EDX) analysis was performed to confirm the quantitative composition of elements in the CdSe and ZnSe nanoparticles after irradiation with 120 kGy. The EDX patterns are shown in Figures 3 and 4 and the related data are listed in Table 1. The host material of CdSe nanoparticles exhibits four elemental peaks; three for cadmium located at -0.4, 3.1 and 3.3 keV, and one for Se located at 1.4 keV. For ZnSe nanoparticles, two elemental peaks related to Zn were found at -1.0 and 8.6 keV, and one for Se at -1.4 keV. The existence of a low intensity peak at -0.3 keV in both spectra is attributed to the carbon film used as a holder for the samples during EDX measurements. Data analysis from EDX patterns (Table 1) confirmed the stoichiometric composition of elements in the samples. One of the advantages of nanomaterials synthesized by the gamma radiolytic method is that the particles produced are pure, as indicated by their elemental composition, which has been established in previous works [25; 26; 27; 28; 29; 30; 31; 32].
## 5 Experiment
### Experimental Results
#### 5.3.2 Scalability
Meanwhile, the expiry rates of the hotspot and blind methods are notably very similar in most cases, and the effect of the number of robots is minimal in comparison to the effect on the other methods. It is observable in all setups (Figure 9d,e) that, regardless of the number of robots used, the expiry rate generally ranges between 20% and 70%. The performance is also not significantly reduced in certain setups but is significantly increased in others. Hence, blindly allocating robots or allocating them based on infection hotspots reduces the effect of inter-robot collisions and utilizes the available resources to achieve a more stable performance than that of the other methods. From the result distribution (Figure 9f), it is very clear that the centralized method's expiry rate result is highly inconsistent. In the 24-robot experiment, it has a significantly lower minimum-maximum-mean expiry rate value, while in the 592-robot experiment, it notably has the highest minimum-maximum-mean expiry rate value. Similarly, in regard to the ACO method, its results are more dispersed than those of the hotspot and blind methods when the number of robots is 24 and 144; its results are similar to theirs when the number of robots is 112 and 592, and its results are more compressed when the number of robots is 136. Furthermore, the ACO method results are sometimes positively skewed, while at other times, they are negatively skewed. This indicates that the ACO method is highly inconsistent and is not as scalable as the hotspot and blind methods when the ED_Foraging approach is adopted; the ACO method results in the best outcomes when the number of robots is intermediate (136) and the map size is medium (16 \(\times\) 16). Regarding the hotspot and blind methods, although the detailed results indicate that they have similar outcomes, when their distributions are compared (Figure 9f), the hotspot method results are more compressed than those of the blind method, particularly in the 24-, 136-, and 592-robot experiments. The hotspot method is slightly less compressed than the blind method in the 112-robot experiment, but it is positively skewed, indicating that most of its values are on the lower end of the scale.
### Merging MOOPs and Form Finding for a Performative Concrete Shell Structure  
The elegant complexity of structural and architectural shapes obtained through the form-finding method has become a broad prerogative for designers who try to merge structural stability with the compositive beauty of the architectural design. Thanks to additive manufacturing connected to 3D printing--which has become part of the construction industry--nowadays, free-form shape structures are allowed in the actual construction. Free-form shells, through proper design, allow us to obtain high-performance structures with maximum material savings, leading to projects characterized by wide spans thanks to their weight-to-height ratio and high rigidity. Furthermore, shell structures are spatially curved structures that can be defined as \"form resistant structures\" [20]. By introducing steel and concrete into 3D printing, the opportunity to define structures with complex geometries--with the possibility of exploring new shapes by exploiting free form shells--is increased.  
A structure obtained through form-finding can be differently designed according to the needs given by the specific project: a membrane can react purely to compression or purely to tensile stresses, depending on the forces applied in the design phase. The very low bending gives the advantages derived from the use of membrane within the structure. However, for concrete shells, in particular, bending can cause tension that accumulates in extreme fibers, leading to cracking. Furthermore, the material nonlinearity, in combination with the wrong geometry adopted for the structure, can cause failures giving the shell the characteristics of an \"imperfection-sensitive structure\" [21].  
Shells represent a great opportunity to produce innovative and sustainable architectures allowing the minimum weight of building elements. However, since shells are--by definition--sensitive to imperfections, it is clear how certain criteria regarding architectural shape must be respected. Therefore, a numerical model assisted by a two-phase optimization algorithm has been implemented: the result of shape optimization is directly connected with the solver adopted for the form-finding in order to have doubly performing results, reducing the calculation times, and providing high accuracy in the optimization process thanks to the FEA.  
This work aims at proposing a method to design--debating the challenges/advantages in designing--a thin concrete shell structure (50.0 m \(\times\) 10.0 m) and at investigating the efficiency of a thin continuous shell proposed as a large-span beam. The adopted methodology is based on: (i) 3D modeling in Rhinoceros 3D; (ii) finite element modeling in Gh-Karamba 3D; (iii) shape optimization in Gh-Octopus, based on hypervolume estimation algorithm for multi-objective optimization problems (MOOPs); (iv) a double-step optimization connecting Octopus results with Gh-Kangaroo, by which the last optimization, based on form-finding method, will be performed.
## 1 Introduction
These promising results suggested that the SIF alone provides a robust shortcut for estimating GPP, encouraging the wide use of OCO-2 SIF in carbon cycle studies at regional to global scales. However, due to their spatially and temporally sparse nature (Figure 1), the OCO-2 SIF data have to be aggregated to coarse spatial and temporal resolutions (e.g., 1\({}^{\circ}\), monthly) for global-scale monitoring of terrestrial photosynthesis. Most ecosystems are highly heterogeneous at spatial scales larger than 5-10 km [44], and the coarse spatial resolution of OCO-2 SIF remains a bottleneck for its wide applications and also hinders the comprehensive evaluation of SIF using ecosystem-level GPP and meteorological data [39]. Generating a new global SIF product with high spatiotemporal resolutions and a long data period based on OCO-2 SIF soundings would be valuable for measuring photosynthesis and providing important information on global carbon uptake at various spatial and temporal scales. Two recent studies produced 0.05\({}^{\circ}\) global spatially contiguous SIF datasets based on SIF from OCO-2 and surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS) using machine learning methods [45; 46]. Finer-resolution SIF products can have more flexible applications compared to the coarse-resolution SIF data directly aggregated from OCO-2 SIF soundings. Here we developed a new global 'OCO-2' SIF dataset (GOSIF) with fine spatial and temporal resolutions (i.e., 0.05\({}^{\circ}\), 8-day). We used not only discrete OCO-2 SIF soundings but also data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological data from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2). The selected meteorological variables could carry more environmental information (e.g., light use efficiency, LUE) than MODIS surface reflectance. During the model development, we explored how the predicted SIF was affected by the training sample size and land cover type. We then applied the model to estimate SIF for each 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid cell across the globe for each 8-day interval over the period 2000-2017. We compared our global SIF product with GPP from the MODIS GPP product and the FLUXNET 2015 Tier 1 dataset. We also examined the spatial and temporal variations and the trend in SIF across the globe using the resulting new 0.05\({}^{\circ}\) SIF data. Our new global, long-term SIF data product with 0.05\({}^{\circ}\) resolution, GOSIF, will benefit many applications in ecosystem functioning and carbon cycle studies. GOSIF will also help us better understand and use the fine-resolution (300 m) SIF data from the upcoming Fluorescence Explorer (FLEX) mission, the first mission specifically designed for mapping SIF [47], that is planned to be launched in 2022.
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods
### The Transfer Learning Framework for RUL Prediction
#### 2.3.1 The BLSTM Neural Networks  
To take advantage of the sequential nature of the sensor data in the turbine engine RUL prediction problem, recurrent neural networks (RNNs) are favored due to their ability to capture time-dependent relationships. However, conventional RNNs have the issue of vanishing and exploding gradient problems, which makes it extremely challenging to train such models. To address these issues, Long Short-Term Memory (LSTM), a gated recurrent neural network (RNN), was proposed in [35]. However, basic LSTM is only able to process the sequential data in forward directions. To capture both the past and future contexts, Bi-directional LSTM (BLSTM) [36] was proposed to process the sequence data in two directions including forward and backward directions with two separate hidden layers. Their outputs are then fed forward to the same output layer. This model has been shown to achieve good performance in machine health monitoring [25].  
In an LSTM/BLSTM network, an LSTM cell contains three gates, namely input gate \(\mathbf{i}_{t}\), forget gate \(\mathbf{f}_{t}\), and output gate \(\mathbf{o}_{t}\), which determine whether to use the input, whether to update the cell memory state, and whether to create an output, respectively. At each time step \(t\), the following equations define the BLSTM [25].  
\[\begin{split}\overrightarrow{\mathbf{i}}_{t}
# Elliptic-Curve Crypto Processor for RFID Applications
### Limitations in Existing Hardware Accelerators of RFID Applications  
Based on the discussions presented in Section 1.1, it can be observed that the accelerator architectures, published in [14, 18, 19, 20, 21, 22, 23, 24, 25, 26], are specifically designed to reduce hardware resources (area) and power parameters with the expense of higher clock cycles. Therefore, the increase in clock cycles ultimately reduces the performance of the entire architecture [14, 19, 20, 21, 22]. The reason for the utilization of extensive clock cycles is the use of different architectural styles, i.e., 8/16/32-bit (including datapath logic, memories, FFs), for the computation of PM operation. With low-area/power parameters, performance (latency) is also an essential parameter to be considered during the implementation of PM operation in ECC. Subsequently, the ambition of this work is to provide a low-latency (means high-performance) and low-area hardware accelerator architecture for RFID applications.
# A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
## 3 Experiments
### Dataset Realistic Constraints
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Formula** & **Description** \\ \hline Coverage & \(H(M(\mathbf{x}),M(\mathbf{c})),H=\left\{\begin{array}{ll}0,\,if\,y=\beta\\ 1,\,if\,y\
eq\beta\end{array}\right.\) & Verify if the counterfactual class (\(\hat{y}\)) has, indeed, a different class from the factual point class (\(y\)) using the same model (\(M\)) \\ Sparsity & \(\frac{1}{m}\sum\limits_{p=1}^{m}I(x_{p},c_{p}),I=\left\{\begin{array}{ll}1, \,if\,x_{p}=c_{p}\\ 0,\,if\,x_{p}\
eq c_{p}\end{array}\right.\) & Share of features in the counterfactual array (\(c_{p}\)) that are the same as the features in the factual array (\(x_{p}\)) for all \(m\) features. \\ Stability & \(\left\{\begin{array}{ll}1,if\,\,\sum\limits_{p=1}^{m}(c_{p,1}-c_{p,2})=0\\ 0,\,if\,\sum\limits_{p=1}^{m}(c_{p,1}-c_{p,2})\
eq 0\\ \end{array}\right.\) & Returns 1 if the same counterfactual is returned in two different runs (\(\mathbf{c_{1}}\) and \(\mathbf{c_{2}}\)) with same model and input data, returns 0 otherwise. \\ L2 & \(\sqrt{\sum\limits_{p=1}^{m}(x_{p}-c_{p})^{2}}\) & Euclidean (L2) distance between the factual point and the counterfactual point. \\ RUC & \(\left\{\begin{array}{ll}1,\,if\,c_{p}\in\mathbf{R}\\ 0,\,if\,c_{p}\
otin\mathbf{R}\end{array}\right.\) & Univariate Constraint (RUC), score is 1 if features are in the realistic space (\(\mathbf{R}\)), 0 if not. \\ RMC & \(\left\{\begin{array}{ll}1,\,if\,\left\{c_{1},c_{2},\_rand,c_{p}\right\}\in \mathbf{R}\\ 0,\,if\,\left\{c_{1},c_{2},\ldots,c_{m}\right\}\
otin\mathbf{R}\end{array}\right.\) & Multivariate Constraint (RMC), score is 1 if set of features are in realistic space, 0 if not. \\ MADD & \(\left\{\begin{array}{ll}\frac{1}{m_{\text{test}}}=\frac{1}{m_{\text{test}}} \left[\frac{x_{p}-c_{p}}{MADD}\right],\,if\,x_{p}\text{ is numerical}\\ \frac{1}{m_{\text{test}}}=\left[1(x_{p},c_{p})\right),\,if\,x_{p}\text{ is categorical}\end{array}\right.\) & MAD Distance (MADD), L1 distance between the factual point and the counterfactual point divided by the feature’s MAD, which is the Mean Absolute Deviation for the feature. \\ MD & \(\sqrt{(\mathbf{x}-\mathbf{u})\cdot\mathbf{C}^{-1}\cdot(\mathbf{x}-\mathbf{u})}\) & Mahalanobis Distance (MD) of the counterfactual point and the problem’s dataset. Where \(\mathbf{u}\) is the vector with the mean values of features and \(\mathbf{C}^{-1}\) is the inverse covariance matrix of the feature. \\ CT & \(t_{final}-t_{init}\) & Counterfactual generation Time (CT), time to generate a counterfactual in seconds. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics used in the study with their respective descriptions. (_radius_), following the equation \(area=\pi(radius)^{2}\). A table with all information about the constraints can be found in the Appendix A.
### Simplified Analytical Models  
#### 1.2.1 Sliding Block Model  
[PERSON] [8] proposed a model based on strategy of sliding block on a frictional sloping surface that predicted seismically induced ground deformations by integrating accelerations above the sliding block's yield acceleration to obtain its velocities. The angle of inclination and the factor of safety over sliding are associated to yield acceleration. When the driving force (seismic acceleration) equal to or greater than resisting force (yield acceleration), block will begin to slide. The total cumulative resulting deformation is then determined by integrating the sliding block velocity. [PERSON] et al. [18] used [PERSON]'s concept to introduce their model for predicting the permanent ground displacement expressed as  
\[D=N_{eq}T^{2}a_{p}f\left(\frac{a_{y}}{a_{p}}\right) \tag{1}\]  
where \(D\) is the lateral ground deformation, \(N_{eq}\) denote cycles number equivalent to uniform base motion, \(T\) denotes time interval (s), \(a_{y}\) denotes yield acceleration (\(g\)), \(a_{p}\) denotes peak acceleration (\(g\)), and \(f\) denotes dimensionless function that depends on base motion. [PERSON] et al. [19] also used [PERSON]'s concept, assuming an equivalent sinusoidal base acceleration record, to propose their model for predicting the permanent ground displacement expressed as:  
\[\log D=1.46\log I_{a}-6.642a_{y}+1.546 \tag{2}\]  
\(D\) denotes lateral ground deformation (cm), \(I_{a}\) presents arias intensity (m/s), and \(a_{y}\) presents yield acceleration (\(g\)).
## 3 VIDAF Architectural Framework  
An important novel outcome of this research is the proposed open architectural framework and infrastructure (VIDAF) that facilitates the development and exploration of reliable control systems for real-time applications, based primarily on standardized/general-purpose hardware/software architectural components rather than specialized/custom-build components. The infrastructure of this architectural framework enables the integration and communication of hardware/software components in real-time, making applications to work together as one integrated system.