esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 A Compressed Sensing Based Ultra-Wideband Cognitive Radio
### Filter-based Compressed Sensing  
Random filter based CS system for discrete time signals was proposed in [132]. This idea can be extended to continuous time signals. We use \(*\) to denote the convolution process in a linear time-invariant (LTI) system. Assume that there is an analog signal \(x(t),t\in\left[0,T_{x}\right]\) which is \(K\)-sparse over some basis \(\Psi\):  
\[x\left(t\right)=\sum_{n=0}^{N-1}\Psi_{n}\left(t\right)\theta_{n}=\Psi\left(t \right)\theta \tag{5}\]  
where  
\[\Psi\left(t\right)=\left[\Psi_{0}\left(t\right),\Psi_{1}\left(t\right),..., \Psi_{N-1}\left(t\right)\right] \tag{6}\]  
\[\theta=\left[\theta_{0},\theta_{1},...,\theta_{N-1}\right]^{T} \tag{7}\]  
Note that there are only \(K\) non-zeros in \(\theta\). \(x(t)\) is then fed into a length-\(L\) FIR filter \(h(t)\):  
\[h\left(t\right)=\sum_{i=0}^{L-1}h_{i}\delta\left(t-iT_{h}\right) \tag{8}\]  
where \(T_{h}\) is the time delay between each filter tap.  
The output \(y(t)=h(t)*x(t)\) is then uniformly sampled with sampling period \(T_{s}\). \(T_{s}\) follows the relation \(T_{s}/T_{h}=q\), where \(q\) is a positive integer. \(M\) samples are collected so that \(M\cdot T_{s}=\left\lfloor L\cdot T_{h}+T_{x}\right\rfloor\), where \(\left(L\cdot T_{h}+T_{x}\right)\) is the duration of \(y(t)\).  
Now we have the down-sampled output signal \(y(mT_{s}),m=1,2,...,M-1\):  
\[\begin{array}{l}y\left(mT_{s}\right)=h\left(mT_{s}\right)*x(mT_{s})\\ =\int_{0}^{T_{y}}h\left(mT_{s}-\tau\right)x\left(\tau\right)d\tau\\ =\int_{0}^{T_{y}}\left[\sum_{i=0}^{L-1}h_{i}\delta\left(mT_{s}-iT_{h}-\tau \right)\right]\!x\left(\tau\right)d\tau\\ =\sum\limits_{i=0}^{L-1}h_{i}x\left(mT_{s}-iT_{h}\right)\\ =\Phi x\end{array} \tag{9}\]  
where \(\Phi\) is a \(quasi-Toeplitz\) matrix and  
\[x=\left[x\left(0\right),x\left(T_{h}\right),...,x\left(\left(M-1\right)qT_{h} \right)\right]^{T}=\Psi\theta \tag{10}\]\[\Psi=\left[\Psi\left(0\right),\Psi\left(T_{h}\right),...,\Psi\left(\left(M-1\right) qT_{h}\right)\right]^{T} \tag{11}\]  
A \(quasi-Toeplitz\) matrix has such property: each row of \(\Phi\) has \(L\) non-zero entries and each row is a copy of the row above, shifted right by \(q\) places.  
Let \(y_{m}=y(mT_{s})\), we have  
\[y=\left[y_{0,}y_{1},...,y_{M-1}\right]^{T} \tag{12}\]  
Combining Equations 5, 6, 7, 9, 10, 11 and 12, we have:  
\[y=\Phi\Psi\theta=\Theta\theta \tag{13}\]  
Now the problem becomes recovering \(N\times 1\) vector \(\theta\) from the \(M\times 1\) measurement vector \(y\), which is exactly the same as the problem posed in Equation 4. The number of measurements for successful recovery depends on the sparsity \(K\), duration of the analog signal \(T_{x}\), filter length \(L\) and the incoherence between \(\Phi\) and \(\Psi\). Numerical results in Section 6.3 show that when \(x(t)\) is sparse and \(h(t)\) is a PN sequence, \(\theta\) can be reconstructed successfully with a reduced sampling rate, requiring only \(M<<N\) measurements. Note that measurement \(y\) is a projection from \(x\) via an FIR filter. We use this feature to design our proposed system.
## 2 Materials and Methods
### Experimental Design Scheme  
Response Surface Methodology (RSM) was used to construct a five-factor and five-level orthogonal experimental design covering five independent variables with five different levels for each variable. Compared with previous research schemes [18; 22], this scheme included more dimensions of response indicators in the evaluation of the dry rice quality, so as to comprehensively consider the influencing factors of the dry rice quality. In this study, the Central Composite Design (CCD) was selected as the basic framework of RSM experimental design. A total of 59 experimental units were designed, including 32 factor design points, 10 axial points and 17 central points, in order to deeply analyze the interaction effects between the studied variables.  
The effect of temperature on the output results in this study can be found in detail in previous studies by scholars. The temperature selection interval is determined by GB/T21015-2007 \"Technical Specification for Paddy Drying\" [32], which states that the permissible heating temperature of the paddy in drying is \(\leq\)40 \({}^{\circ}\)C. And, when the paddy drying temperature exceeds 45 \({}^{\circ}\)C, the internal starch arrangement of rice is chaotic, free fatty acid content increases, and the degree of aging of rice increases, resulting in a decline in taste, so a drying temperature not exceeding 45 \({}^{\circ}\)C is appropriate [33]. Therefore, the drying temperature should not exceed 45 \({}^{\circ}\)C. The relative humidity selection interval was obtained from the relative humidity inside the machine collected in the previous period on multiple circulating paddy dryers. The collected data showed that the ambient relative humidity interval was about 40-65% during the drying operation of paddy grains undergoing drying in the dryer. The initial moisture interval selected in this study covers the moisture of the drying object in the actual dryer. The purpose of studying the effect of initial moisture on the output term is to demonstrate that the drying profiles of grains with different moisture gradients under the same drying conditions are not the same. The wind-speed selection interval was obtained from the in-machine wind speeds collected in the previous period on multiple circulating paddy dryers [27]. Therefore, five key process parameters and their corresponding variation ranges were selected: drying temperature (T, 35 \({}^{\circ}\)C to 55 \({}^{\circ}\)C), air relative humidity (RH, 30% to 50%), initial moisture content (MC, 20% to 28%), wind speed (V, 0.36 m/s to 0.84 m/s) and tempering ratio (TR, 1 to 4). The specific level settings for each variable are detailed in Table 1. The response parameters of interest in this study covered the key characteristics of rice during drying, such as net drying time and cumulative effective accumulated temperature, and multiple nutritional quality indicators of dried rice, including protein content, fat content, amylose content and amylopectin content. The protocol and results of the response surface experiment are shown in Table 1.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 3 Manifolds-Based Low-Rank Dictionary Pair Learning
### Problem Formulation  
Assume that we have \(N\) training image sets (gallery videos), \(\{X_{i}^{k}\}_{i=1}^{N}\), which belong to \(K\) classes, where \(X_{i}^{k}=\{x_{i,1}^{k},x_{i,2}^{k},\cdots,x_{i,m_{i}}^{k}\}\in\mathcal{R}^{p \times m_{i}}\) denotes the \(ith\) gallery image set which belongs to \(k\)th class. Here, \(x_{i,j}^{k}\in\mathcal{R}^{p\times 1}\) denotes the \(j\)th image coming from \(i\)th gallery video, \(m_{i}\) is the number of sample images in \(X_{i}^{k}\), and \(p\) denotes the dimensions of the image sample. We also assume that in the \(kth\) class, there are \(n_{k}\) image sets. The important notations in this paper are summarized in Table 1.  
To benefit from the advantages of static modeling method, we first use the following covariance matrix (point on a Symmetric Positive Definite (SPD) manifold) and linear subspace (point on a Grassmann Manifold (GM)) to model image sets, and we developed two submethods: MbLRDPL-SPD and MbLRDPL-GM.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 3 TTP-GPR for Coal Seam Thickness Prediction
### Structural Element Transition Probability
The change from one state pair to other state pair is called state pair transition. Figure 5 shows the state diagram of the structural element. In Figure 5, there are eight state pairs between the known data and the predicted data, and the yellow marking shows an example of a state pair. Assuming that there are n states and there are n possible state at each point, so the total number of state pairs is \(n^{2}\) and the number of possible state pair transitions are \(n^{4}\). If using the state pairs directly solves the transition probabilities, then the computational complexity is huge. Fortunately,  
Figure 4: Diagram of overlapping with structural elements movement. Figure 5: Diagram of structural elements state. in our application, the state of a prediction point is fixed, i.e., the second state of the state pair is determined. Thus, the transition probability can be obtained by the first state of the state pair. The total state transition number is \(n^{2}\). The state transition probability can be calculated, as follows [19]:  
\[ \begin{array}{l}P(St)=P\big{\{}st_{i,j-1}=s_{i}|st_{i,j}=s_{j}\big{\}}=P_{s_{i }s_{j}}(m)\\ \left\{\begin{array}{l}0\leq P_{s_{i}s_{j}}(m)\leq 1,s_{i},s_{j}\in S\\ \sum_{i,j\in S}P_{s_{i}s_{j}}(m)=1,s_{i},s_{j}\in S\end{array}\right.\end{array}  \tag{13}\]  
where, \(P_{s_{i}s_{j}}(m)\) is approximated by the state transition frequency [20] and m represents the number of steps of the structure element movement. In this paper, one-step transition probability is used, and thus, m = 1. For an individual structural element, state transition probability between pairs of points can describe its spatial characteristics and the effect of equidistance, for which the data input form is shown in Figure 6. For the convenience of data processing in the STTP-GPR algorithm and other compared algorithms, the 3 \(\times\) 3 structural element is then transformed into vector that expresses the thickness around the prediction points, and it adds the spatial information descripted by transition probability. As we decompose the structural element to the original coal seam thickness and spatial information, and combine the coal seam data and spatial information to predict unknown points, this decomposition method does not affect the prediction results.
###### Abstract
It was found that the addition of head rotation reduced the ambiguity between front and back and that DNNs outperformed GMMs, with DNNs proving better at discerning between the front and back hemispheres. Work presented by [PERSON] et al. [4] investigated the problem of DoA analysis of the component parts of a BRIR. They used the continuous-wavelet transform to create a frequency domain representation of the signal, which is used to compute the ILD and ITD across frequency bands. The DoA is then computed by iterating over a database of reference HRIRs and finding the reference HRIR with the closest matching ILD and ITD values to the component of the BRIR being analysed; the DoA is then assumed to be the same as the reference HRIR. They reported mean angular errors between 28.7\({}^{\circ}\) and 54.4\({}^{\circ}\) for the component parts of the measured BRIRs. This paper presents a novel approach for the spatial analysis of two-channel BRIRs, using a binaural model fronted NN to estimate the azimuthal direction of arrival for the direct sound and reflected components (direct sound is used to refer to the signal emitted by a loudspeaker arriving at the receiver, and the reflected component refers to a reflected copy of the emitted signal arriving at the receiver after incidence with a reflective surface) of the BRIRs. It develops and extends the approach adopted in [15] in terms of the processing used by the binaural model to extract the interaural cues, the use of a cascade-correlation neural network as opposed to the multi-layer perceptron to map the binaural cues to the direction of arrival classes, the nature of the sound components being analysed--short pulses relating to the direct sound and reflected components of a BRIR as opposed to continuous speech signals--and the method by which measurement orientations are implemented and analysed by the NN. In this paper, multiple measurement orientations are presented simultaneously to the NN, whereas in [15], multiple orientations are presented as rotations produced by a motorised head with the signals being analysed separately by the NN, which allowed for active sound source localisation in an environment. The following sections are organised as follows; in Section 2, the implementation of the binaural model and NN, the data model used and the methodology used to generate a test dataset are discussed; Section 3 presents the test results; Section 4 discusses the findings; and Section 5 concludes the paper.
###### Abstract  
The operative temperature of a photovoltaic cell influences the electric conversion yield. This can be enhanced by cooling the panel. Among the studied solutions, phase change materials (PCM) exploit latent heat and absorb a large amount of energy at a nearly constant temperature. PCMs suffer from a low thermal conductivity. Under these premises the paper presents a hybrid graphene/fins/PCM cooling system to maximize efficiency gains and thermal recovery. An indoor laboratory characterization, under a solar simulator, compares the proposed model with a reference one (an identical, simple PV module) under fixed environmental conditions. Outdoor tests investigate the performances of the two systems under natural conditions. Indoor results show that the front temperature of the proposed PCM integrated module is averagely 6 \({}^{\circ}\)C less, with a peak of 8 \({}^{\circ}\)C, than the reference case. This means an increase in the electric yield of about 3%. Outdoor investigations prove that, when the PCM is solid and during the melting phase, the proposed system is averagely 1.12 \({}^{\circ}\)C and 4.87 \({}^{\circ}\)C colder than the reference case, respectively. The thermal efficiency is 30% and 65%, respectively. Once the melting process is completed, the performance becomes worse, and the hybrid panel is almost 3 \({}^{\circ}\)C warmer than the simple panel.  
Keywords:phase change material; thermal storage; photovoltaic; nanoparticles; graphene; solar simulator; solar radiation +
Footnote †: journal: Applied
# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
## 3 Proposed method
### Base Layer Synthesis
If the local variance difference image (\(Var_{diff}\)) is negative, it indicates an area with a relatively large amount of detail in the near-infrared image, and if it is positive, it indicates an area with a relatively large amount of detailed information in the visible-light image. For this purpose, when the variance difference image is negative, it is normalized to a value between 0 and 0.5, and when it is positive, it is normalized to a value between 0.5 and 1. \[MAP_{var}=\left\{ \begin{array}{ll}0.5\biggl{(}\frac{Var_{diff}}{\max\left(Var_ {diff}\right)}+1\biggr{)}\,&for\ Var_{diff}\geq 0\\ -0.5\biggl{(}\frac{Var_{diff}}{\min\left(Var_{diff}\right)}-1\biggr{)}\,&for\ Var_{diff}<0\end{array} \right. \tag{16}\]  
where \(MAP_{var}\) is a distributed map in which \(Var_{diff}\) has a value between 0 and 1, and the proposed regularization expression is applied. A final detailed layer composite map is created by applying a blur function to the variance map, as follows:  
\[MAP_{detail}=G[MAP_{var}] \tag{17}\]\[G[I(i,j)]=I(i,j)\sum_{y=-hw}^{hw}\sum_{x=-hw}^{hw}\exp\left(-\left(\frac{(i+x)^{2}}{ 2\sigma_{x}{}^{2}}+\frac{(i+y)^{2}}{2\sigma_{y}{}^{2}}\right)\right) \tag{18}\]  
where the \(G\) function represents a two-dimensional Gaussian filter and \(\sigma_{x}\), \(\sigma_{y}\) are the standard deviations in the x and y directions, respectively. \(MAP_{detail}\) is a detailed layer synthesis map that is blurred by applying a Gaussian filter to \(MAP_{var}\) for natural synthesis. The larger the kernel size of Gaussian function, the more natural detail layer fusion is possible, but a tradeoff occurs due to a decrease in detail contrast and an increase in the amount of computation. Thus, in this study, the kernel size (7, 7) was applied for this Gaussian filter. Using the generated detailed layer synthesis map (\(MAP_{detail}\)), the synthesis is performed as shown in the following equation:  
\[L_{detail}=MAP_{detail}L_{D_{VIS}}+(1-MAP_{detail})L_{D_{IR}} \tag{19}\]  
where \(L_{detail}\) is the luminance detailed layer synthesis result, and the detailed layers of the day and night images generated above are synthesized using the alpha blending method using the detailed layer synthesis map as a weight. \(L_{D_{VIS}}\) and \(L_{D_{IR}}\) are detail layer images of visible and near-infrared images, respectively. Figure 10 shows the sublayers of the visible and near-infrared images and the results of the synthesis of the sublayers. The details of the cup and box parts are well-preserved. Figure 10: Result of detail layer synthesis: (**a**) visible-light image detail layer, (**b**) near-infrared image detail layer, (**c**) detail layer synthesis result. The final synthesized luminance channel \(L_{fused}\) is generated as follows by synthesizing the independently synthesized base layer and detailed layer using the above process. \[L_{fused}=L_{base}+L_{detail} \tag{20}\]
# Phonon Scattering and Thermal Conductivity of Actinide Oxides with Defects
## 2 Methods
### Sample Preparation for Actinide Oxides with Fission Gases  
As shown in Figure 3, two fission gas bubbles are generated in a simulation box to keep the symmetry of RNEMD. In all structures simulated, the number of unit cells in \(y\) and \(z\) directions is fixed to be 5 while the number of unit cells in \(x\) is fixed to be 100. During RNEMD, however, the middle portion is selected as the hot bath and two ends are selected as the cold baths, reducing the characteristic sample length to be half of the total simulation box size. Therefore, the characteristic sample length considered in the present paper corresponds to 50 unit cells (\(N_{x}\) = 50) of the UO\({}_{2}\) cubic structure. The total numbers of atoms (Kr and/or Xe) in fission gases are fixed to be 30 while varying their compositions. Five different gas units are simulated in this research (Figure 3b-f); 30 Kr, 20 Kr/10 Xe, 15 Kr/15 Xe, 10 Kr/20 Xe, and 30 Xe are selected to see the effects of the fission gas composition on thermal transport in UO\({}_{2}\). A custom MATLAB program is used to prepare simulation structures. Once a structure with \(5\times 5\times 100\) unit cells of UO\({}_{2}\) is created, spaces that correspond to \(3\times 3\times 3\) unit cells in the pristine UO\({}_{2}\) structure are emptied and fission gases are inserted in the empty spaces. A periodic boundary condition is used in the direction of the thermal conductivity calculation to mimic an infinitely large structure. However, it should be noted that phonons with wavelength larger than the size of the simulation box cannot be simulated in MD simulations. Since only one sample length (\(N_{x}\) = 50) is used in the current study, the effects of sample length and phonon boundary scattering are not examined. The authors will provide a more rigorous study by simulating fission gas bubbles in samples with various sample lengths to eliminate the effect of phonon boundary scattering in the near future.  
Figure 2: Unit cell of PuO\({}_{2}\) or UO\({}_{2}\) with various defects.
# Moving Deep Learning to the Edge
## 4 Deep Learning Inference on Edge Devices  
Deep neural networks are very compute-intensive, while edge computing platforms are limited in computational capacity compared to cloud centers. Within the edge devices, there are different computing platforms with different computing capacities from edge servers to end user devices.  
Edge servers or nodes offer a considerable centralized or distributed computing power and memory, but serve many requests, and therefore, the available resources are shared by many clients.  
Furthermore, they were designed mostly for batch processing and not for stream processing. Therefore, several solutions have been proposed to enhance the execution of deep learning on edge nodes.  
On the other side, end devices have the lowest computing and memory resources, but are usually used by a single user only. Therefore, as we move from the end devices to the edge nodes and servers, the computational capacity and memory increase, but the computational requests also increase. The migration of deep learning inference to the edge introduces a new service on edge devices. Deep neural networks are more computational and memory demanding than other machine learning algorithms. Therefore, straightforward integration of deep learning models on the edge would create inefficient solutions. Several approaches have been followed to find solutions to reduce the computing time, memory storage, and energy consumption of DNN.  
One research direction is at the algorithmic level looking for new network models that, besides accuracy, also consider computing, memory, and energy aspects. The execution of these networks can be further improved with methods to reduce the number of parameters and computations of the model. Another direction is to design dedicated architectures for deep learning with high performance and energy efficiencies. A fourth dimension is relative to the design of edge computing architectures.  
The design of deep learning solutions considering these four design dimensions will generate the most efficient solutions to run deep learning on the edge. The tradeoffs created by these four design dimensions vary with the type of edge device. Edge servers must be more generic since they offer services to a large set of clients. Therefore, in this case, deep learning models and architectures must be more generic. Furthermore, DNN may be more elaborate with higher accuracy since the computing platforms have a higher capacity. As we move to the end devices, the services are more specific, and the model must be optimized for specific applications. However, since the computing platforms have a lower capacity, the network models tradeoff accuracy for computation, memory, and energy.  
In the following sections, each of these design dimensions is reviewed.
###### Abstract  
(1) Background: The femoroacetabular impingement (FAI) type cam leads to a conflict between the acetabular rim and a bony thickening of the femoral neck junction. While maximal excursions in flexion, addiction and internal rotation provoke pain, the aim of this study was to analyze if a cam morphology shows an impact on gait pattern. (2) Methods: Fifty-five patients with end-stage hip osteoarthritis performed gait analysis before hip replacement as well as three, six and 12 months postoperatively. Thirty-three (60%) of them presented an FAI type cam. An ANOVA was used to compare the hip angles in sagittal, frontal and transversal planes between patients with a FAI type cam (group \"+cam\") and without (group \"\(-\)cam\"). (3) Results: Before surgery the patients of the +cam-group showed a tendency towards a reduced flexion and internal rotation at the heel strike (\(p>0.05\)). Over time, the differences were adjusted by total hip arthroplasty. (4) Conclusions: We did not find any differences in the gait analysis of patients with a FAI type cam compared to patients without.  
Keywords:hip deformity; femoroacetabular impingement (FAI); gait analysis; range of motion; heel strike; toe off +
Footnote †: journal:  
rim, the type cam affects the femoral neck with anterior-lateral bone thickening [7]. In clinical examination, the conflict between the cam morphology and the acetabular rim could be provoked with a maximal excursion in flexion, adduction and internal rotation (FADIR or anterior impingement test) [8]. This test leads to pain and shows the restrictions in the maximum excursions caused by the bony conflict in the presence of an FAI [9,10]. Some studies showed that the presence of a cam deformity influences the gait pattern and joint moments [11,12]. In spite of joint-preserving surgeries, differences in healthy individuals were notable or with a questionable clinical relevance [13, 14, 15]. Due to studies showing differences in the gait pattern of patients with a FAI type cam, the aim of the current study was to analyze if the functionality of the affected joint was comparable after THA, or if there were notable remaining changes. Another goal was to compare the gait pattern of patients with hip OA depend on the presence of an FAI type cam.