esa-sceva/satcom-chunk-collection huggingface dataset:
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 4 Validation Using the International Parallel Test Benchmarks
### Experimental Campaign
#### 4.1.2 Material and Testing Procedure  
Toyoura sand is a benchmark geomaterial used in the International Parallel Test [12]. It consists of quartz, limestone, and mica, among other materials. The test described here is performed on dry samples of uniformly graded material, with round-shaped particles larger than 75 \(\upmu\)m in diameter. The physical properties of the Toyoura sand are shown in Table 1, and its grain size distribution is presented in Figure 11.  
The Toyoura sand specimens are prepared according to the dry tamping method. The material is left to dry in an oven for 24 h to remove any trace of initial moisture. Then, five layers of geomaterial, dosed to achieve the desired void ratio, are gradually poured into the mould with constant drop height. Each soil layer is levelled and compacted to the desired height with a tamper.  
The bender elements are installed in a container filled with Toyoura sand. It consists of a cylindrical acrylic mould, a base plane, and a top plate (see Figure 12). The mould is 200 mm in height and has an internal diameter of 100 mm. The transmitter and receiver bender elements are embedded in the centres of the base and top platens. The top platen allows for the application of vertical pressure through a pressure nozzle, at the top of the specimen, under K0 conditions.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
### Energy Consumption
#### 5.5.2 All-Camera Strategy  
This strategy relies solely on cameras without PIR sensors. With this approach, we must deploy more cameras and fix their positions and directions to ensure the same seamless coverage as the above strategy, as there are no PIR sensors aiding in positioning. And all the cameras must be continuously capturing the field, with image processing running constantly. We measure that it takes 8 min and 2 s to capture and process 36 images, consuming 0.001 kWh of energy (equivalent to 179.253 Wh per day) for each non-rotating camera and its connected Raspberry Pi. So, one clear problem with this strategy is that there is a 13.4 s interval between two captures, which could pose significant issues. Animals intruding the field during these intervals may not be detected.  
In addition to maintaining a distance of 40 m between the cameras and the sensing boundary, the angle \(\beta\) also needs to be adjusted to match the field of view of the cameras, as illustrated in Figure 13.  
Under this deployment, the number of cameras required, \(C_{2}\), is  
\[C_{2}=\frac{1000}{31.734\times 2}\times 4\approx 64 \tag{5}\]  
where 31.734 is the distance between points \(C\) and \(D\), as marked in Figure 13.  
Hence, the total energy consumption (Wh) for this strategy is  
\[P_{2}=(C_{2}\times 179.253)=11,472.192 \tag{6}\]
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O\({}_{2}\) absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the COSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published COSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially.  
SIF; data-driven method; retrieval window; Doppler effect; sensitive +
Footnote †: journal: Remote Sensing
## 3 Energy-Saving Optimization Model
### Energy-Saving Optimization Algorithm Flow  
Based on a clear evaluation method of energy-saving costs proposed in Section 2, an energy-saving optimization model for residential appliance management is formulated. We set energy-saving costs as a constraint, as shown in Equation (9), where \(\xi\) is the risk coefficient in the energy-saving optimization strategy. When the probability of misoperation is smaller, the risk of energy saving is less. As for the objective function, we hope to maximize energy efficiency, or in other words, minimize the electricity consumption, as shown in Equation (10), where \(E_{i}[0,t]\) is the electricity consumption and \(p_{i}(t)\) is the power of appliance \(i\) throughout \([0,t]\). This represents the instantaneous energy-saving model when \(t_{a}=0\) in the time dimension, and represents the accumulative energy-saving model in the temperature dimension when \(t_{a}>0\).  
\[SC_{i}[t_{0},t_{0}+t_{a}]\leq\xi, \tag{9}\]  
\[\text{min}E_{i}[t_{0},t_{0}+t_{a}]=\int\limits_{t_{0}}^{t_{0}+t_{a}}p_{i}(t)dt. \tag{10}\]  
In particular, appliances of the same kind and of the same impact on electric effect can be combined for optimization, such as several air conditioners in the same area, when the total electricity consumption is set as the optimization target. Therefore, marking a set of the appliances which have the same impact on the electric effect in the region \(r\) as \(l_{r}^{n}\), and marking the electricity consumption within the period \([0,t]\) as \(\text{min}E_{l_{r}^{n}}[0,t]\), the target function is:  
\[\text{min}E_{l_{r}^{n}}[t_{0},t_{0}+t_{a}]=\int\limits_{t_{0}}^{t_{0}+t_{a}} \biggl{(}\sum\limits_{i\in l_{r}^{n}}p_{i}(t)\biggr{)}dt. \tag{11}\]  
Based on the model formulated above, the energy-saving optimization algorithm flow is designed in the time and temperature dimensions respectively, which are introduced in detail in the next subsections.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 1 Introduction
Once trained, IDFlow can easily generate multiple degraded LR images given the HR version by exploring the learnt distribution, maintaining the stochastic nature of image degradation. Thus, we introduce IDFlow to generate large-scale realistic training data from an arbitrary HR image dataset for SR model training. We conduct extensive experiments on existing real-world datasets. The results demonstrate that not only can IDFlow generate realistic LR images close to the real-world images, but it can also further improve the real-world performance of the existing SR models. Compared with the state-of-the-art SR methods, the SR models trained with IDFlow can produce cleaner SR results with richer textures from real-world noisy images.
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
###### Abstract  
To study the deformation and failure mechanisms of soft rock mining roadways, the 1506 return airway of Anyang Coal Mine is taken as the engineering background. Based on limit analysis theory, a failure model based on a rigid slider system is constructed to assess the failure of the soft rock surrounding a roadway. The formulas for calculating the self-weight power of the slider in the velocity discontinuity line of the rock surrounding the roadway, the work power of the surrounding rock pressure, and the energy dissipation rate of the velocity discontinuity line are derived, and the upper limit objective function of the velocity discontinuity line height is obtained. The failure characteristics and fracture evolution process of the surrounding rock under different mining stresses are analyzed by means of physical similarity simulations. The simulation results show that shear failure occurs first on the roadway side due to stress concentration. The fissures expand along the bottom angle of the roadway to the blind support area and the low-intensity support area. The cracks weaken the support strength of the angled anchor cable and bolt in the roadway shoulder. Under the action of roof pressure, the status of the rock mass inside and outside the shear slip zone changes from static to dynamic. This causes deformation and failure of the roadway roof, side, and floor.  
limit analysis theory; soft rock roadways; failure mechanism; similar simulation +
Footnote †: journal: Applied sciences
## Abbreviations  
The following abbreviations are used in this manuscript:  
IDS Intrusion Detection System R2L Remote-to-Local U2R User-to-Root SFS Sequence Forward Selection DT Decision Tree RNN Recurrent Neural Network LSTM Long Short-Term Memory GRU Gated Recurrent Unit DoS Denial of Service PCA Principle Component Analysis DBMdeep Boltzmann machine DBNdeep belief network LDA Linear Discriminant Analysis CF criterion function KNN K Nearest Neighbour SVM Support Vector Machine ROC Receive Operating Characteristic TP True Positives FN False Negatives FP False Positives TN True Negatives TPR True Positive Rate
## 4 Probabilistic Fatigue Reliability Assessment
### Fatigue Limit State  
Under the repeated stresses, the fatigue performance and service life prediction of suspenders can be evaluated by the fatigue reliability analysis with a well-defined fatigue limit-state function. The limit-state function adopted in fatigue reliability analysis was derived based on the \(S\)-\(N\) approach and Miner's rule given below:  
\[g(\textbf{X})=D_{\Delta}-D_{y}=D_{\Delta}-(N/A)\cdot(S_{re}^{*})^{m}, \tag{16}\]  
where \(D_{\Delta}\) = the critical fatigue damage, which is assumed as lognormal with a mean value of 1.0 and a coefficient of variance (COV) of 0.3 [33]; \(D_{y}\) = the fatigue damage accumulation index at \(y\)th year; and the effective number of cycles, \(N\), is computed from Equation (15), which is treated as a random variable with a COV of 0.2, and \(A\) is also assumed as a random variable. All random variables used for fatigue reliability analysis on suspenders are listed in Table 5.
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
###### Abstract  
In this paper, we propose an effective directional Bayer color filter array (CFA) demosaicking algorithm based on residual interpolation (RI). The proposed directional interpolation algorithm aims to reduce computational complexity and get more accurate interpolated pixel values in the complex edge areas. We use the horizontal and vertical weights to combine and smooth color difference estimations. Compared with four directional weights in minimized Laplacian residual interpolation, the proposed algorithm not only guarantees the quality of color images but also reduces the computational complexity. Generally, the directional estimations may be inaccurately calculated because of the false edge information in irregular edges. We alleviate it by using a new method to calculate the directional color difference estimations. Experimental results show that the proposed algorithm provides outstanding performance compared with some previous algorithms, especially in the complex edge areas. In addition, it has lower computational complexity and better visual effect.  
image demosaicking; color filter array (CFA); Bayer pattern; directional interpolation; residual interpolation (RI) +
Footnote †: journal: Applied Physics Letters
## 4 Transcriptional Regulation during Salinity and Drought Stresses
### Basic Helix-Loop-Helix (bHLH)  
The bHLH family of transcription factors is the second largest family in plants after the MYB family. In _Arabidopsis_, 162 members of bHLH have been identified, and 167 have been identified in rice plants [73]. As the name suggests, the bHLH family of transcription factors contains a highly conserved basic/helix-loop-helix unique structural domain consisting of two parts: basic amino acid and the helix-loop-helix region (HLH). Findings have determined that the bHLH protein predominantly attaches to the core DNA sequence motif called E-box (5-CANNTG 3), where N could be any nucleotide; the most common form of E-box is the G-box (CACGTG), which is also a binding sequence for the MYC family [74; 75].  
A growing body of evidence proves that the bHLH TFs are strictly involved in plants' response to a variety of abiotic stresses such as drought and salinity [74; 76]. [PERSON] et al. [77] conducted a transcriptome-wide expression analysis of bHLH genes from tea (_C. sinensis_) and revealed that a total of 39 _CsbHLH_ were upregulated under the exposure of the plant to drought stress. Moreover, transcriptomic studies in _Arabidopsis_ roots under salt exposure have shown that at least 15 bHLH genes are upregulated in response to the stress, which implicates their functional role in salt stress responses in plants. There is other evidence suggesting the crucial involvement of the bHLH transcription factor family in plants' response to abiotic stresses.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## Appendix A  
The LEO data used in the article is GRACE-A data in 2007 when no real time precise orbit and clock products are available. Until now, there is no public archive available for the storage of real time orbit and clock products. Thanks to the GFZ real time working group, they have stored these real time products since April 26 th, 2018, which make it possible for us to demonstrate the orbit difference of LEO POD by using precise GPS products and real time products. We updated our software recently to conduct GRACE Follow On (GRACE-FO) POD since GRACE mission was ended in October, 2017. GRACE-FO mission is the successor of the GRACE mission, the orbit of GRACE-FO satellite (GRACE-C and GRACE-D) is nearly the same as that of GRACE with the orbital inclination of 89 degree. Moreover, GRACE-FO observatory builds on the design of the original GRACE, but incorporates a number of improvements based on lessons learned [44].  
In this part, we firstly evaluate one week GFZ real time products (2019, DOY 216-222) with respect to CODE final products. Then GRACE-C POD with empirical force model is conducted to demonstrate how much difference is expected due to the use of real time products instead of precise CODE products.  
Figure 14 shows the RMS values of GPS orbit difference and STD of GPS clock difference between CODE products and GFZ real time products. The averaged RMS values of all GPS satellites for along-track, cross-track, and radial directions are 3.5 cm, 2.7 cm, and 2.4 cm, respectively. The STD values of GPS clock difference are generally smaller than 0.2 ns for all GPS satellites. The averaged STD value of all satellites is 0.14 ns, which is about 4.0 cm in distance. With these products, GRACE-C satellite POD is conducted. Since GRACE-FO is built on the design of GRACE, all the force models used for GRACE can be adopted for GRACE-FO as shown in Table 1. The PSO of GRACE-C provided by JPL are used as references for our orbit evaluation ([[https://podaac-tools.jpl.nasa.gov/drive/files/allData/gracefo/L1B/JPL/RL04/ASCII](https://podaac-tools.jpl.nasa.gov/drive/files/allData/gracefo/L1B/JPL/RL04/ASCII)]([https://podaac-tools.jpl.nasa.gov/drive/files/allData/gracefo/L1B/JPL/RL04/ASCII](https://podaac-tools.jpl.nasa.gov/drive/files/allData/gracefo/L1B/JPL/RL04/ASCII))). The averaged RMS of GRACE-C orbits with respect to the PSO is shown in Figure 15.  
From Figure 15, we can see that the RMS values of orbit by using GFZ real time products are slightly larger than those of CODE final products. The differences are about 8 mm, 3 mm, and 2 mm for along-track, cross-track, and radial directions, respectively. By using real time products, the orbit precision for 3D will degrade about 1 cm.
# Improvements of the xAAL Home Automation System
## 4 The Basic Schema
### Changes in xAAL Schemas
#### 4.2.3 On the Use of CBOR Tags  
In CBOR, a data item can be enclosed by a tag to give it additional semantics. Such a tag gives an indication of how data should be interpreted. For instance, tag 0 says that a text string has to be interpreted as a date, tag 1 is for dates since POSIX Epoch (1970-01-01), tag 32 says that a text string is a URI, tag 37 says that a binary string is a UUID, etc. A small set of tags is part of the standard (RFC 7049); additional tags are defined by a dedicated IANA registry [29].  
There are pros and cons on using CBOR tags. Tags values are carried with data, this consumes space. On the other hand, the receiver gets in-line semantic indication to interpret data without havingto check any side specification documents. This is particularly useful when CBOR is used for general (de)serialization in a programming environment: many CBOR libraries widely use tags to provide automatic casting of data into dedicated specific programming object types. Conversely, tags are somehow redundant with information provided within xAAL schemas.  
Well, on the basis of these considerations, it has been settled that tags were not used for low-layers of the xAAL communication protocol (that is to say the Security Layer and the Application Layer as defined below). Indeed, these layers are of a small and rigid shape, without any room for choice or interpretation of data. These data items are relating to the xAAL communication protocol itself.  
However, data items relating to attributes, methods and notifications of xAAL devices are of more complex shapes. Therefore, xAAL schemas may use CBOR tags in their data type definitions; for instance, UUID, date, URI. Note that, if tags are specified in an xAAL schema, then they are mandatory into the _body_ part of the Application Layer for corresponding actions. Tags management may lead to optional behaviors by decoders, but they are not optional in data items written in messages once they have been specified by a CDDL rule (as in xAAL schemas).
## 2 The Mathematical Model
### The Mathematical Model of the RBF Neural Network  
The RBF neural network is an artificial neural network that closely resembles a multi-layer feedforward network. It features a three-layer architecture with a single hidden layer. Known for its exceptional local approximation capabilities, this network offers a straightforward and practical training approach. Its versatility has made it a popular choice for applications in system modeling, control, and various other fields. Refer to Figure 2 for a topology diagram of the network.  
As shown in Figure 2, \(I=[I_{1},I_{2},I_{3}]^{T}\) is the input vector; \(J=[J_{1},J_{2},J_{3},J_{4},J_{5},J_{6}]^{T}\) is the radial basis vector; and \(O=[O_{1},O_{2},O_{3}]^{T}\) is the weight vector.  
The diagram illustrates that the neuron nodes representing the input signal constitute the input layer of the network's first layer. The number of neurons in this layer is determined by the dimensions of the input data. The second layer is the hidden layer, which transforms the input layer's data into a higher-dimensional space, facilitating the classification of different data types. The number of neurons in the hidden layer can be adjusted flexibly, and multiple neurons can be structured to address the system's complexity. This design allows for both an effective approximation performance and real-time processing. The third layer is the output layer, which generates outputs based on weighted contributions from the hidden layer and typically consists of a single neuron output structure.  
Moreover, the components labeled \(\beta_{01}\), \(\beta_{02}\), and \(\beta_{03}\) in the network, along with the ESO nonlinear function factor in the active disturbance rejection controller, can be restructured simultaneously. In an attempt to improve the influence of the hidden layer's excitation function, the Gaussian function is generally selected.  
Figure 2: Topology of the RBF neural network.
## 4 Numerical Examples
### Square Al\({}_{2}\)O\({}_{3}\) Plate  
Consider a fully simply supported square plate with length \(a\), thickness \(h\), as shown in Figure 5. The plate is assumed to be made of aluminum (Al) and aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), and the corresponding material properties are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Young’s Modulus (GPa)** & **Poisson’s Ratio** & **Density (kg/m\({}^{3}\))** \\ \hline Al & 70 & 0.3 & 2707 \\ Ti & 110.25 & 0.288 & - \\ Al\({}_{2}\)O\({}_{3}\) & 380 & 0.3 & 3800 \\ ZrO\({}_{2}\) & 278.41 & 0.288 & - \\ SiC & 427 & 0.17 & 3210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of ceramics and metals.  
Figure 4: Central deflections of a homogeneous square plate with various aspect ratios. (**a**) Simply supported; (**b**) clamped.  
Assuming the length \(a=1\), the thickness \(h=0.25\), and the gradient index \(g=10\), and the top surface of the plate is subjected to a transverse bi-sinusoidal load \(\varrho_{0}\text{sin}(\pi x/a)\sin(\pi y/a)\) with \(\varrho_{0}=4\times 10^{9}\), numerical tests indicate that using fourth-order NURBS basis functions with a \(10\times 10\) mesh in the SDF-based IGA provide converged response results for any SDF truncation numbers.  
Defining nondimensional deflection and stress responses as \(\overline{w}=\frac{10h^{3}E_{x}}{a^{4}\varrho_{0}}w\big{(}\frac{a}{2},\frac{a} {2},0\big{)}\), \(\overline{\sigma}_{xx}=\frac{h}{a\varrho_{0}}\sigma_{xx}\Big{(}\frac{a}{2}, \frac{a}{2},\frac{a}{2}\Big{)}\), \(\overline{\tau}_{xz}=\frac{h}{a\varrho_{0}}\tau_{xz}\Big{(}0,\frac{a}{2}, \frac{a}{2}\Big{)}\), Figure 6 shows the convergence of the results with respect to the SDF truncation numbers. The deflection \(\overline{w}\) converges as \(M\geq 3\) and \(N\geq 3\); however, for the normal stress \(\overline{\sigma}_{xx}\) and the shear stress \(\overline{\tau}_{xz}\), converged results can only be obtained by setting \(M\geq 5\) and \(N\geq 6\).  
The converged nondimensional 3D responses are shown in Figure 7. It is noted that the shear stress \(\overline{\tau}_{xz}\) on the top and bottom surfaces of the plate is zero, satisfying the traction-free boundary conditions [50].  
As shown in Table 2, the nondimensional deflection and stress results for various gradient indexes and aspect ratios obtained using the SDF-based IGA (\(M=5\), \(N=6\)) are compared with available references in the literature. Quasi-3D [15] represents results based on CUF; TSDT-IGA [42] represents IGA solutions based on Reddy's TSDT; FSDT [15] are analytical solutions based on the FSDT. It is found that all four methods yield generally acceptable deflection results. However, the FSDT is unable to provide accurate stress results for both thick and thin FGM plates. The TSDT-IGA method shows improved but limited accuracy stress results, especially for thick FGM plates. In all cases, the SDF-based IGA results conform very well with the quasi-3D results, indicating the accuracy and reliability of the present method.