esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
###### Abstract  
Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of \(640\times 480\). The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.  
deep learning: edge device; object detection; precision agriculture; TPU accelerator +
Footnote †: journal:  
002 2022
## 3 Image Processing Procedure
### Image Registration and Subtraction  
In a CT image of PM parts, there may be a very small difference in grey levels between defects and the part, even after image enhancement. Some parts are hollow; this means that a different background (surroundings of the part) with a different grey level would appear in the image that could shade out the shape of the part itself and defects. This makes it very difficult to segment the defects from the part and its background. Potentially, some complex part geometries could also make the situation worse. Image subtraction can potentially resolve these issues. The acquired CT image with possible defects, referred to as the target image below, is subtracted from a reference image, e.g., a corresponding defect-free CT image with a similar grey level distribution except for the defected regions, to obtain a difference image with the part image removed and defects standing out. Prior to the subtraction operation, it is indispensable to register the target image with the reference image because the two images may be acquired in different CT scanning conditions, i.e., translation, scale, and rotation may occur and generate significant false artefacts in the processed image. Since CT images are grey scale images with different grey levels for different constituents, the target image can be registered to the reference image automatically based on its grey level distributions [24; 25]. First, a transform type is specified and an initial transformation matrix is determined internally, both of which specify the image transformation applied to the target image with bilinear interpolation. Secondly, the transformed target image is compared with the reference image by computing a metric value (i.e., the measures of similarity). Finally, a stop condition (normally a certain metric value) is checked by the optimiser. When the stop condition has been met (or a specified maximum number of iterations has been reached) the process will stop; otherwise it will continue after optimising the transformation matrix.
# Automatic Defect Detection for Web Offset Printing Based on Machine Vision
## 2 Methodology
### Image Registration Based on Shape Context with Concave-Convex Feature
#### 2.2.2 Shape Context Based on Concave-Convex Feature  
Inspired by shape context, a simple yet effective image registration method based on the shape's concave-convex feature is presented. As shown in Figure 9, a rectangular coordinate system with the shape center is set up, and then a descriptor is given for each point, finally the shape descriptor is obtained by counting the distribution of all point descriptors.  
Figure 8: Illustration of shape context.  
Figure 7: One dimensional pyramid.  
For a point \(p_{i}\) as shown in Figure 8, its descriptor is defined as \(\lambda_{i}=(d_{i},q_{i},\theta_{i})\), where \(d_{i}\) is the distance from \(p_{i}\) to o, \(q_{i}\) is the quadrant number of \(p_{i}\), and \(\theta_{i}\) represents the shape concave-convex feature of \(p_{i}\) relative to \(p_{i-1}\) and \(p_{i+1}\). It is worth noting that our proposed shape concave-convex feature only relies on its two adjacent points, not all points used in Figure 7. For distance \(d_{i}\), it is quantized to a trisection according to the maximum distance \(d_{\max}\). The value of \(q_{i}\) ranges from 1 to 4. The range of \(\theta_{i}\) is between 0 and \(\pi\), and is uniformly quantized to six segments. Considering all combinations of \(d_{i}\), \(q_{i}\) and \(\theta_{i}\) values, there are 72 (\(3\times 4\times 6\)) different combinations. Thus, we build a histogram with 72 bins by counting all points on the shape, the histogram is used as the shape descriptor.  
Let \(f(p_{i})=f(d_{i},q_{i},\theta_{i})\in[1,72]\), then the histogram is constructed as follows.  
\[h(k)=\left\{\#p_{i}\left|f(p_{i})=k,i=1,2,\cdots,N\right.\right\}\quad k=1,2, \cdots,72 \tag{4}\]  
where \(N\) is the number of points on the shape.  
If there are two shapes, their corresponding histograms are \(h_{p}\) and \(h_{q}\), a similarity measure between two shapes is defined as follows:  
\[S=\frac{1}{2}\sum_{k=1}^{72}\frac{[h_{p}(k)-h_{q}(k)]^{2}}{h_{p}(k)+h_{q}(k)} \tag{5}\]  
In the process of print production, the distortion of a printed image can be approximately regarded as rigid distortion. Therefore, the transformation parameter can be evaluated by Equation (6).  
\[\left(\begin{array}{c}\hat{x}\\ \hat{y}\end{array}\right)=\left(\begin{array}{cc}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{array}\right)\left(\begin{array}{c}x\\ y\end{array}\right)+\left(\begin{array}{c}t_{x}\\ t_{y}\end{array}\right) \tag{6}\]  
where \(\theta\) is the angle of the rotation, \(t_{x}\) and \(t_{y}\) are the translation along the X-axis and Y-axis, respectively.
###### Abstract  
Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.  
Antenna group; massive MIMO; reference signal (RS) +
Footnote †: journal: Sensors
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## 3 Proposed Regularized Least Squares Ensemble Model
### The Regularization Strategy in the Least Squares System
\begin{array}{l}\hat{w}_{e}=\text{Vdiag}(\frac{d_{i}}{d_{i}+1})\mathbf{U}^{ \text{T}}\mathbf{Y}_{\text{train}}\\ \hat{w}_{e}=[\hat{w}_{e1},\hat{w}_{e2},\hat{w}_{e3}],j=1,2,3\end{array}  \tag{18}\]

\(\hat{w}_{1}\) is the initial weight factors for \(\lambda_{l}\), and \(\hat{w}_{2}\) is the initial weight factors for \(\lambda_{r}\). 6:Calculate the weight factors with Equations (15) and (16) for \(\hat{w}_{1}\) and \(\hat{w}_{2}\), separately, and construct the \(\hat{f}_{e}(\lambda_{l})\) and \(\hat{f}_{e}(\lambda_{r})\) with Equation (1). 7:Calculate the \(RMSE\) of \(\hat{f}_{e}(\lambda_{l})\) and \(\hat{f}_{e}(\lambda_{r})\) with Equation (20), and the current optimal \(\lambda_{c}\) values are calculated as:

\[ \begin{array}{l}\text{if }RMSE(\hat{f}_{e}(\lambda_{\min}))<RMSE(\hat{f}_{e}( \lambda_{\max})),\\ \lambda_{c}=\lambda_{\min},\lambda_{r}=\lambda_{\text{mid}}\\ \text{else}\\ \lambda_{c}=\lambda_{\max},\lambda_{l}=\lambda_{\text{mid}}\end{array}  \tag{19}\]

Back to 2
8:The optimal \(\lambda^{*}\) is equal to the \(\lambda_{c}\) after iteration, and it can be used to construct the RLS-EM. Output:\(\lambda^{*}\). ```  
**Algorithm 2** Search for the optimal regularization parameter \(\lambda^{*}\)
## 2 Theoretical Background of the Best-Estimate Methodology
Without entering in the mathematical and theoretical details described in [4; 5], the core equations solved in BeSYC are:  
\[\alpha^{be}=\alpha-\left(C_{\alpha\alpha}\cdot S_{r\alpha}^{T}-C_{\alpha r} \right)\cdot D_{rr}^{-1}\cdot\left(r_{comp}-r\right) \tag{7}\]  
\[r^{be}=r-\left(C_{\alpha r}\cdot S_{r\alpha}^{T}-C_{rr}\right)\cdot D_{rr}^{-1 }\cdot\left(r_{comp}-r\right) \tag{8}\]  
\[C_{\alpha\alpha}^{be}=C_{\alpha\alpha}-\left(C_{\alpha\alpha}\cdot S_{r\alpha} ^{T}-C_{\alpha r}\right)\cdot D_{rr}^{-1}\cdot\left(C_{\alpha\alpha}\cdot S_{ r\alpha}^{T}-C_{\alpha r}\right)^{T} \tag{9}\]  
\[C_{rr}^{be}=C_{rr}-\left(C_{\alpha r}^{T}\cdot S_{r\alpha}^{T}-C_{rr}\right) \cdot D_{rr}^{-1}\cdot\left(C_{\alpha r}^{T}\cdot S_{r\alpha}^{T}-C_{rr}\right) ^{T} \tag{10}\]  
\[C_{\alpha r}^{be}=C_{\alpha r}-\left(C_{\alpha\alpha}\cdot S_{r\alpha}^{T}-C_{ \alpha r}\right)\cdot D_{rr}^{-1}\cdot\left(C_{\alpha r}^{T}\cdot S_{r\alpha}^ {T}-C_{rr}\right)^{T} \tag{11}\]  
\[D_{rr}=S_{r\alpha}\cdot C_{\alpha\alpha}\cdot S_{r\alpha}^{T}-S_{r\alpha} \cdot C_{\alpha r}-C_{\alpha r}^{T}\cdot S_{r\alpha}^{T}+C_{rr} \tag{12}\]  
\[\chi^{2}=\left(r_{comp}-r_{exp}\right)\cdot D_{rr}\cdot\left(r_{comp}-r_{exp}\right) \tag{13}\]  
where:* \(\alpha\) and \(r\) are the column vectors containing the nominal values of the parameters and the responses, respectively;
* \(r_{comp}\) is a column vector containing the values of the responses achieved from the code run adopting the parameters' nominal values (\(\alpha\)). This vector is arranged as the \(r\) vector;
* \(C_{\alpha\alpha}\) and \(C_{rr}\) are the variance-covariance matrix of the parameters and the responses, respectively;
* \(C_{\alpha r}\) is the matrix containing the correlations between experimental responses and parameters;
* \(S_{\alpha\alpha}\) is the sensitivity matrix;
* All the terms with the be superscript are the best-estimate/calibrated equivalent of the elements described above, while the superscript \(T\) denotes transposed elements;
* \(\chi^{2}\) refers to the \(\chi^{2}\) distribution, and it is used as indicator of the agreement between computed and experimental responses. Even if diagonal \(C_{\alpha\alpha}\) and \(C_{rr}\) matrices are provided, the mathematical framework provides block diagonal matrices as output because the BEPU methodology is capable to evaluate the correlations between the parameters and the responses at any time node. For this reason, the BeSYC software capitalizes these results, also calculating the Pearson, the Spearman, and the Kendall's Tau correlation coefficients for both parameters and responses, which are not part of the general mathematical framework described in [4; 5].
# Sulfur-Doped Carbon Nanohorn Bifunctional Electrocatalyst for Water Splitting
## 4 Conclusions  
In summary, we used a facile doping strategy to introduce sulfur atoms into the graphitic network of CNHs, and we tested S-CNHs against OER and HER. The S-CNHs proved to be a highly efficient bifunctional electrocatalyst towards water splitting due to the high S doping levels. Doping with sulfur effectively modified the electronic structure of CNHs, which along with high conductivity helped to achieve low overpotential values for both OER and HER. Specifically, S-CNHs showed low overpotential for oxygen and hydrogen evolution reaction, at 1.63 and \(-\)0.2 V vs. RHE, respectively, and excellent durability and stability. Additionally, S-CNHs exhibited significantly lower Tafel slope and lower current resistance compared to oxidized and pristine CNHs for both electrocatalytic reactions. The easy preparation method, high activity and excellent durability of S-CNHs make them a promising alternative for replacing noble-metal-based electrocatalysts for water splitting.  
Conceptualization, A.K. and N.T.; validation, A.K. and N.T.; investigation, A.K. and N.T.; TEM imaging, R.A.; writing--original draft preparation, A.K. and N.T.; writing--review and editing, A.K., R.A. and N.T.; visualization, A.K., R.A. and N.T.; project administration, A.K. and N.T.; funding acquisition, A.K. and R.A. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning\" in the context of the project \"Reinforcement of Postdoctoral Researchers-2 nd Cycle\" (MIS 5033021), implemented by the State Scholarships Foundation (IKY). R.A. gratefully acknowledges the support from the Spanish Ministry of Economy and Competitiveness (MINECO) and the MICINN through project grants MAT2016-79776-P (AEI/FEDER, UE) and PID2019-104739 GB-I00/AEI/0.10339/501100011033 as well as from the European Union H2020 program \"ESTEEM3\" (823717) and Flag-ERA GATES (JTC-PCI2018-093137).  
HRTEM and XPS were conducted at the Laboratorio de Microscopias Avanzadas (LMA), Universidad de Zaragoza, Spain. We thank [PERSON] (LMA) for the XPS acquisition.  
The authors declare no conflict of interest.
## 3 Data and Methods
### Correction for Sea Surface Reflection of Sky Radiation  
The reflection of downward radiation (including atmospheric emission and extraterrestrial radiation) is a significant contribution to the radiometer measured brightness temperature. The extraterrestrial radiation is about 2.725 K caused by the cosmic microwave background radiation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\theta_{i}\) & 16\({}^{\circ}\) & 26\({}^{\circ}\) & 36\({}^{\circ}\) & 45\({}^{\circ}\) & 60\({}^{\circ}\) \\ \hline V-pol & 12 & 23 & 28 & 11 & 9 \\ \hline H-pol & 12 & 23 & 28 & 11 & 9 \\ \hline \end{tabular}
\end{table}
Table 1: Number of measurements at each incidence angle (\(\theta_{i}\) ) and polarization. V-pol and H-pol are the vertical and horizontal polarizations, respectively.  
Figure 3: Ambient blackbody calibration target.  
related to the Big Bang nucleosynthesis and continuum radiation [36]. Although the impact of galactic radiation (hydrogen emission and continuum radiation) could be strong when these radiation sources are limited to the specular reflection into the main antenna beam, these contaminated observations could easily be discarded because reflectivity of the sea surface is large (in the order of 0.7) and the galactic signal scattered from the sea surface is near specular reflection [10; 17; 37]. Thus, the steady and continuous downward radiation could be due to the cosmic background radiation plus the emission from the atmosphere. The sea surface reflected sky radiation could be estimated by the L-band radiative transfer model developed in our previous work [38]. In this model (Figure 4), the two-scale model was used to approximate the sea surface reflected radiation towards the radiometer at different observation angles, and the downwelling atmospheric emission and radiation attenuation was obtained by using the millimeter propagation model developed by [PERSON] [39; 40]. In addition, the profiles of the atmosphere properties were obtained from the National Center for Environmental Prediction (NCEP) FNL data, in which the whole atmosphere was divided into 26 layers, and for every layer, the gas extinction and emission were calculated with several input data including the air temperature, pressure, and relative humidity.
## 3 Method
### Numerical Calculation of Spherical Coefficients  
Let us introduce several different grids for numerical calculations for the method proposed.  
The numerical integration (step P5) of the square-integrable function defined on the two-dimensional sphere is performed using quadrature formulae. This function is evaluated at some nodes in the sphere. For example, this set of points could be Fliege-Maier's set of points on the sphere for low-error integration of spherical functions [50]. The mentioned grid can be used for integration by directly summating the function evaluated at grid points and weighted with the respective weights. Another option could be to use \(t\)-designs [49], which constitute uniform arrangements on the sphere for which spherical polynomials up to degree \(t\) can be integrated precisely by summating their values at the points defined by the t-design. One more way could be to use a grid defined by the icosphere [51].  
The Gauss' quadrature formulae with the mentioned designs provide fast and accurate calculations of spherical harmonic coefficients. In [50], it was shown that for pre-computed nodes and weights, the integration error drops to \(10^{-12}\) for many functions.  
Another approach could be performing the spherical harmonic transform (SHT) in the least-squares sense. Using values \(f\big{(}\theta_{j},\varphi_{j}\big{)}\) (see Formula (4)) at nodes \(\big{(}\theta_{j},\varphi_{j}\big{)}\) on the grid, the spherical harmonic coefficients \(f_{lm}\) could be computed using the Moore-Penrose pseudo-inverse of a matrix [53]. The following least-squares problem is solved with respect to variables \(f_{lm}\) as follows:  
\[f(\theta_{j},\varphi_{j})=\sum_{l=0}^{L_{\text{max}}}\sum_{m=-l}^{l}f_{lm}Y_{lm }(\theta_{j},\varphi_{j}),\qquad j=0,\;\ldots,\;p.\]  
In step P7, it is necessary to reconstruct a function at the sphere by its spherical harmonic coefficients. This reconstruction requires calculating the function at the preferably uniform grid. We used a tessellation [51] built from an icosahedron. To obtain a tessellation, the following steps are performed: (i) divide each triangular face of the icosahedron into four smaller triangles by connecting the midpoints of the three sides; (ii) normalize the new triangle vertices on the unit sphere; and (iii) repeat these steps until the desired solution is reached.  
After applying five such iterations to the original icosahedron, we obtained an icosphere containing 10,242 vertices. Each vertex of this triangular tessellation has six adjacent triangles, except for the original twelve icosahedral vertices with five adjacent triangles.
# Measurement of Instantaneous Mass Flow Rate of Polypropylene Gasification Products in Airflow
## 5 Results and Discussion  
### Yield of Gasification Products with Hydrogen-Assisted Ignition  
Figure 12 shows the time histories of gas mass flow rates at GG inlet, \(G_{\text{in}}(t)\), and outlet, \(G_{\text{out}}(t)\), as well as the mass flow rates of gasification products, \(G_{\text{out}}(t)-G_{\text{in}}(t)\), calculated based on experimental data for test fires #1 to #3 with hydrogen-assisted ignition. For calculating \(G_{\text{in}}(t)\), \(G_{\text{out}}(t)\), and \(G_{\text{out}}(t)-G_{\text{in}}(t)\), the procedure described in Section 3 was used. In test fire #1 without combustion, calculations show the appearance of an additional mass flow rate at GG outlet due to hydrogen supply. This additional mass flow rate does not exceed 3 g/s and can be readily considered in calculations using realistic properties of hydrogen and air for air-to-fuel ratio \(\alpha\approx 1.5\). In test fires #2 and #3 with hydrogen-assisted ignition, the duration of a high-temperature ignition pulse is 0.5 s. Therefore, the total yield of sample gasification products can include a noticeable contribution of the ignition stage. The adopted approach to processing the experimental data allows separating the yield of gasification products during ignition (with air temperature \(T_{\text{0 in}}=\) 1600-1800 K) and during further combustion at a low temperature of incoming air. This separation is clearly seen in Figure 12.  
Table 1 shows the operation parameters of gasification process in test fires with hydrogen-assisted ignition. Upon ignition, the yield of gasification products reaches 107-121 g/s due to the high air temperature at sample inlet. Later, when the temperature of the incoming air decreases to an ambient temperature, the mass flow rate of gasification products decreases to 72-82 g/s. The measurements of PP sample mass before and after test fires confirm a significant decrease in sample mass (by 283-338 g) due to PP gasification. The time-averaged ratio of the total mass of air to the total yield of PP gasification products varies from 2.46 to 2.87.
# Advances in High-Precision NO2 Measurement by Quantum Cascade Laser Absorption Spectroscopy
## 2 Materials and Methods
### Field Inter-Comparison Setup and Instrumentation
#### 2.2.1 Commercial NO\({}_{2}\) Instruments  
For the field campaign, we used the following commercial instruments: a CLD, a CAPS and another QCLAS. All instruments, apart from the CLD, and calibration units were located in the annex room of the monitoring station. The CLD device (T200 NO/NO\({}_{2}\)/NO\({}_{\rm X}\) analyser, Teledyne Technologies Inc., Thousand Oaks, CA, USA) is the standard model  
Figure 6: Influence of water vapor on the retrieved NO\({}_{2}\) amount fraction. The negative bias (\(\Delta\)NO\({}_{2}\), corresponding to the difference between wet and dry conditions) is plotted as a function of the absolute humidity of the sample at different pressures. The confidence intervals (\(\pm\) 95%) of the linear regressions are indicated by shaded areas. The error bars indicate the 1 \(\sigma\) standard deviation of the measured \(\Delta\)NO\({}_{2}\) and the uncertainties of the water measurements (k = 1).  
used by NABEL, and it is permanently installed in the main room of the station. It uses a molybdenum converter to reduce ambient NO2 to NO. The instrument has a LOD of 200 pptv for NO2. The CAPS device (T500U NO2 Analyser, Teledyne Technologies Inc., Thousand Oaks, CA, USA) is based on NO2 light absorption at 450 nm in a resonant optical cavity and has a LOD of about 40 pptv. Both the CLD and the CAPS use a drier in their inlet to mitigate water interferences. The second QCLAS (NO2 Analyzer, MIRO Analytical AG, Dubendorf, Switzerland; hereafter, QCL-MIRO) is a newly available commercial instrument, which is based on the same technique as our custom-made QCLAS (hereafter, QCL-Empa). Furthermore, it also deploys the same laser-driving scheme, DAQ processes and spectral evaluation software [46], but it uses an NO2 absorption line around 1630 cm\({}^{-1}\). The entire spectrometer is built into a 19\({}^{\circ}\)/4U rack and uses an AMAC-76 multipass cell (OPL = 76 m; Aerodyne Research Inc, Billerica, MA, USA). The LOD of this instrument is specified to 15 pptv. The instrument normal operation includes an automated re-zeroing cycle with 10 min period.