esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Analyses  
All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS, version 23.0.0, IBM Corporation, Armonk, NY, USA). The Shapiro-Wilk test was performed to evaluate the normality of the data. Given the non-normal distributions, [PERSON]'s rank correlation analysis was employed to assess the relationship between the disengagement of attention and joint attention variables at T0. A Bonferroni correction was applied to adjust for multiple comparisons. Linear regression was used to examine whether disengagement latency at 12 months could predict joint attention behaviors at 24 months, as assessed through item b13 (entitled \"Initiation of Joint Attention\") of the ADOS-2 Toddler Module. Additionally, linear regression was used to test whether disengagement latency and JA behaviors at 12 months could predict socio-communicative abilities at 24 months (measured with the ADOS-2 Toddler Module) and developmental level at 24 months (assessed using the GMDS-ER). The developmental quotient, measured with the GMDS-ER, was included as a covariate in all analyses. The underlying assumptions were verified before applying the linear regression (linearity, independence, homoscedasticity and normality of residuals).  
At T1, outcome groups were categorized into ASD (n = 10) and NO-ASD (n = 25), excluding infants with neurodevelopmental disorders (NDD, n = 5). Despite the small number of infants diagnosed with ASD at 24 months (n = 10), we also applied binary logistic regression to determine whether disengagement of attention and JA behaviors at T0 could predict the diagnostic outcome at T1. The underlying assumptions were verified before applying the logistic regression (linearity, independence, multicollinearity and absence of outliers).
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 3 Results and Discussion  
### Empirical Thermoelectric Performance Model  
Figure 1 presents the empirical models for the total heat dissipated at the hot side of the thermoelectric module and the temperature difference (\(\Delta T\)) across the module, as a function of the heat pumped at the cold-side [12]. The legend shows the average operating temperature used when generating the experimental performance data, in \({}^{\circ}\)C. Figure 1 demonstrates that the \(\Delta T\) produced by the thermoelectric module is a linear function of the power transferred through the thermal element, with negative slope. The heat dissipated at the hot side rises linearly with the heat absorbed at the cold side of the module. Comparing the module's operation at 25 \({}^{\circ}\)C and 75 \({}^{\circ}\)C shows that a greater temperature difference is achievable at the higher operating temperature, however this requires greater heat removal from the cold side.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. Section 2 introduces the channel model and the formula definitions for the \(K\)-user MIMO interference channels. In Section 3, the proposed scheme for a three-user MIMO relay-aided broadcast channel is illustrated. Section 4 presents the precoded spatial-multiplexing MIMO system that assists in recovering the linear combination of intended and unintended symbols from the transmitter and relay. The generalization of the proposed scheme using the Alamouti space-time relay transmission technique is briefly discussed in Section 5. Section 6 describes the \(K\)-user relay-aided MIMO interference channel. Section 7 presents the numerical results and the paper is concluded in Section 8.
## 3 Detector Design  
The joint probability density functions (PDFs) of the data to be detected and the training samples under hypotheses \(\mathrm{H}_{1}\) and \(\mathrm{H}_{0}\) can be expressed as  
\[f_{1}(\mathbf{z},\mathbf{Z};\mathbf{\xi},\mathbf{R},\boldsymbol{\gamma})=\frac{ 1}{\pi^{N(L+1)}\prod\limits_{l=1}^{L}\gamma_{l}^{N}|\mathbf{R}|^{L+1}}\exp \left\{-\left((\mathbf{z}-\mathbf{H}\mathbf{\xi})^{\mathrm{H}}\mathbf{R}^{-1}( \mathbf{z}-\mathbf{H}\mathbf{\xi})+\sum\limits_{l=1}^{L}\frac{\mathbf{z}_{l}^{ \mathrm{H}}\mathbf{R}^{-1}\mathbf{z}_{l}}{\gamma_{l}}\right)\right\} \tag{3}\]  
and  
\[f_{0}(\mathbf{z},\mathbf{Z};\mathbf{R},\boldsymbol{\gamma})=\frac{1}{\pi^{N(L+ 1)}\prod\limits_{l=1}^{L}\gamma_{l}^{N}|\mathbf{R}|^{L+1}}\exp\left\{-\left( \mathbf{z}^{\mathrm{H}}\mathbf{R}^{-1}\mathbf{z}+\sum\limits_{l=1}^{L}\frac{ \mathbf{z}_{l}^{\mathrm{H}}\mathbf{R}^{-1}\mathbf{z}_{l}}{\gamma_{l}}\right)\right\} \tag{4}\]  
respectively, where \(\mathbf{Z}=[\mathbf{z}_{1},\mathbf{z}_{2},\cdots\mathbf{z}_{L}]\) denotes the matrix of training samples and \(\boldsymbol{\gamma}=[\gamma_{1},\gamma_{2},\cdots,\gamma_{L}]^{\mathrm{T}}\) represents the column vector of power mismatches between the training samples and the data to be detected. The following subsections will design detectors according to the GLRT criterion, Rao criterion, Wald criterion, Gradient criterion, and Durbin criterion.
## 3 Results
### Simulation Results from the Drop Test with 45\({}^{\circ}\) Inclined Flat Anvil  
The resultant accelerations were extracted from the node at the C.G. of the 6 YO THUMS head. The peak values of both linear and rotational accelerations for each impact location are shown in Figure 9. The peak linear accelerations at all impact locations were below 200 g, while all rotational accelerations were high and above 8000 rad/s\({}^{2}\). There was a high risk of mild traumatic brain injury. The inclined flat anvil caused a large rotational acceleration compared to the horizontal anvil. However, the peak linear acceleration was low compared to the DOT drop test.  
The maximum principal strain (MPS) of the brain is also another injury measure related to brain concussion and DAI [48]. Figure 10 shows the brain MPS at each impact  
Figure 8: The peak linear (PLA) and the rotational accelerations (RotA) obtained from the DOT drop tests at four impact locations.  
Figure 9: The peak linear (PLA) and the rotational accelerations (RotA) obtained from the inclined drop tests at four impact locations.  
location. The brain MPS, which exceeded 0.25, implied a high risk of brain DAI [38]. The red area in Figure 10 indicated the brain area with the MPS greater than 0.25. It was found that 55.78% of brain volume experienced a maximum principal strain greater than 0.25 (i.e., CSDM-0.25 = 55.75%) for the rear impact location. This implied a 50% risk of DAI [49]. The results expressed the risk of concussion and DAI in all four location drop tests. These corresponded to the high value of the rotational acceleration, which exceeded the limit of 8000 rad/s\({}^{2}\). At the rear impact location, the rotational acceleration was as high as 13,143 rad/s\({}^{2}\) and the volume of brain with the MPS greater than 0.25 was also very high. However, the peak linear acceleration at this location was only 137.2 g.  
From the simulation results, the DOT drop test provided critical loading conditions for the skull fracture, while the inclined drop tests provided critical loading conditions for brain injury. In the next section, the study of the effect of helmet parameters would consider the peak linear acceleration obtained from the DOT drop test and the rotational acceleration obtained from the inclined anvil drop test.
## 2 Proposed Method
### Texture-Less Surface Similarity Measure
#### 2.3.1 Global Representation  
Let \(\mathbf{p}=[q_{1},q_{2}\ldots q_{n},0]\) be the original vector composed of the gray-scale points within the image. The desired \(q_{n+1}\) must be obtained such that the inner product score between the augmented vector \(\mathbf{q}=[q_{1},q_{2}\ldots q_{n},q_{n+1}]\) and the normal vector \(\mathbf{n}=[0,0\ldots 0,1]\) of the ideal plane is bigger than \(\tau_{\text{score}}\). As shown in Figure 5, the initially ruffled gray-scale surface is extended to an approximate plane under the action of \(q_{n+1}\). For \(q_{n+1}\), bigger is not better. The specific value needs to be found such that the similarity of the two vectors just meets the threshold \(\tau_{\text{score}}\), as an excessive \(q_{n+1}\) will flatten all textured surfaces.  
The mean \(\mu_{p}\) and standard deviation \(\delta_{p}\) of \(\mathbf{p}\) are  
\[\mu_{p}=\frac{\sum\limits_{i=1}^{n}q_{i}}{n+1},\ \delta_{p}=\sqrt{\frac{\sum \limits_{i=1}^{n}\bigl{(}q_{i}-\mu_{p}\bigr{)}^{2}+\mu_{p}^{2}}{n+1}}. \tag{6}\]  
The mean \(\mu_{q}\) and standard deviation \(\delta_{q}\) of \(\mathbf{q}\) are  
\[\mu_{q}=\frac{n\overline{\mu_{q}}+q_{n+1}}{n+1},\ \delta_{q}=\sqrt{\frac{ \sum\limits_{i=1}^{n+1}\bigl{(}q_{i}-\mu_{q}\bigr{)}^{2}}{n+1}}. \tag{7}\]  
Let \(kq_{n+1}=\sum\limits_{i=1}^{n}q_{i}\); then,  
\[\mu_{q}=\lambda q_{n+1},\delta_{q}=\sqrt{\frac{\sum\limits_{i=1}^{n+1}\bigl{(} q_{i}-\lambda q_{n+1}\bigr{)}^{2}}{n+1}},\lambda=\frac{k+1}{n+1}. \tag{8}\]  
There is a problem when finding \(q_{n+1}\), whereby the gradient of the cosine function is subtle when the angle approaches zero; that is, the similarity function is insensitive to \(q_{n+1}\), as the augmented vector and the ideal normal vector change from cross to parallel. Therefore, the solution is obtained by calculating the perpendicularity (6) of \(\mathbf{q}\) and \(\overline{\mathbf{n}}=[n_{1},n_{2}\ldots n_{n},0]\), where \(|\overline{\mathbf{n}}|=1\). Here, \(\overline{\mathbf{n}}\) represents a specific vector in the \(\mathbf{R}_{\text{zz}}\) plane.  
Figure 5: Augmentation functions for the gray-scale surface. The green arrows represent the original vectors, the blue arrows represent the original components, the red arrows represent the augmented components, and the yellow arrow represents the augmented vector.  
\[T_{\text{global}}(\mathbf{x})=\langle\overline{\mathbf{n}},\mathbf{q}\rangle=\frac{ \sum_{i=1}^{n}\bigl{(}(q_{n}-\mu_{p})(q_{n}-\mu_{q})+(0-\mu_{p})(q_{n+1}-\mu_{q} )\bigr{)}}{\delta_{p}\delta_{q}(n+1)}. \tag{9}\]  
As shown in Figure 6, this global representation is suitable in the case of a vast change across the whole surface region. It is unsuitable for cases of local change, even if the change is dramatic, because the substation needs to be higher to affect the mean and variance.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Determination and Analysis of the Model Structure
According to the relationship between system frequency characteristics and the transfer function, the frequency response function \(G(j\omega)\) of single sub-channel ADC in the TIADC system identification model can be expressed as follows:  
\[G(j\omega)=G(s)|_{(s=j\omega)}=\frac{\omega_{n}^{2}}{\left(j\omega\right)^{2}+2 \xi\omega_{n}j\omega+\omega_{n}^{2}}\cdot\frac{(1+\Delta g)e^{\Delta t\cdot j \omega}}{j\omega/\omega_{c}+1}+\Delta o \tag{5}\]  
The time delay process \(e^{j\omega\Delta t}\) of the system is expanded in the form of the Taylor series as follows:  
\[e^{j\omega\Delta t}=1+j\omega\Delta t+\frac{\Delta t^{2}}{2}(j\omega)^{2}+\cdots \tag{6}\]  
In the Equation (6), each power of \(j\omega\) denotes that the output of the delay link contains the derivative of the input, because the sample-and-hold structure itself is a first-order structure, and its high-order change rate is very small, so the high-order terms can be omitted, and the approximate form is as follows:  
\[e^{j\omega\Delta t}\approx 1+j\omega\Delta t \tag{7}\]  
By combining Equations (5) and (7), the transfer function of the single sub-channel ADC equivalent model in the TIADC system can be expressed as follows:  
\[G(j\omega)=\frac{\omega_{n}^{2}}{\left(j\omega\right)^{2}+2\xi\omega_{n}j \omega+\omega_{n}^{2}}\cdot\frac{(1+\Delta g)(1+j\omega\Delta t)}{j\omega/ \omega_{c}+1}+\Delta o \tag{8}\]
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 4 Discussion
### Uncertainties of the Remote Sensing Data  
LAI, FAPAR, and CI satellite data were input to calculate the half-hourly FAPAR values across the FLUXNET sites in this paper. The quality of these inputs determined the accuracy of the GPP estimations. The MODIS LAI product (MCD15A2H), EBR FAPAR product, and CI product were used rather than measured data to drive the DDA model and the total APAR-based model. Such an application might have introduced uncertainties into the GPP estimations. In order to match the temporal scale of observations across the FLUXNET sites, a linear interpolation method was applied firstly for the EBR FAPAR product to obtain daily FAPAR and then to achieve half-hourly FAPAR records. Such an interpolation method might have introduced errors into the inputs of the GPP models. Additionally, LAI and CI data were used at temporal resolutions of eight days and a month, which might also have introduced errors into the GPP calculation at a half-hourly scale. In the future, high temporal and spatial resolution FAPAR, LAI and CI data should be used to constrain errors as well as for model construction and validation.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.3 Degree of Polarization  
Degree of polarization (DoP) is considered to be a very important parameter characterizing partially polarized EM waves. It can be derived from the Stokes vectors of any coherent radar modes, e.g., dual-pol, hybrid/compact and, of course, fully polarimetric SAR modes [32]:  
\[P=\frac{\sqrt{g_{1}^{2}+g_{12}^{2}+g_{3}^{2}}}{g_{10}^{2}}=\left(1-4\frac{ \left|\Gamma_{i}\right|}{\left(tr\Gamma_{i}\right)^{2}}\right)^{\frac{1}{2}} \tag{9}\]  
where \(g_{i}\) is Stokes vectors that can be used to describe both complete and partially polarized wave, and \(i\) stands for different polarization of transmission.  
\[g=\left[\begin{array}{c}g_{0}\\ g_{1}\\ g_{2}\\ g_{3}\end{array}\right]=\left[\begin{array}{c}\left\langle\left|E_{v}\right|^ {2}+\left|E_{h}\right|^{2}\right\rangle\\ \left|E_{v}\right|^{2}-\left|E_{h}\right|^{2}\right\rangle\\ 2\text{Re}\left\langle E_{h}E_{v}^{s}\right\rangle\\ 2\text{Im}\left\langle E_{h}E_{v}^{s}\right\rangle\end{array}\right] \tag{10}\]  
where \(E_{v}\) and \(E_{h}\) is vertically and horizontally received backscatter, respectively, and \(<\) \(>\) also stands for multilook by using an averaging window.  
DoP measures to what extent the scattered wave is deterministic and can be described by a polarimetric ellipse with fixed parameters. On the Poincare sphere, it represents the distance between the last three components normalized Stokes vectors and the origin [32]. It is 1 for complete polarized waves and 0 for fully unpolarized waves. For clean sea surfaces and weak-damping areas, the scattering mechanism can be described by the Bragg scattering; as a result, the DoP is large. For hydrocarbon slicks, random scattering mechanisms are dominating, and much lower DoP are observed.
# Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric
## 5 Scheme Analysis  
### Storage Efficiency Analysis  
In Hyperledger Fabric, the data stored is mainly the world state and the blockchain. The scaling mechanism proposed in the paper is that each node within a cluster only keeps a portion of the blocks belonging to this cluster but still needs to keep the entire world state database. In blockchain applications, the world state is similar to a traditional database where the current state information of specific entities is stored, so the size of the world state is usually predictable and grows slowly and linearly.  
Conversely, the main content saved by blockchain is the transaction information of entities in the world state, similar to database transaction log, and the storage cost grows rapidly. In different types of blockchain applications, the proportion of world state and blocks in the overall storage space will be different. In ring-overlap, only the block data is saved in pieces, and this proportion of the transactions to world state will affect the storage overhead. Let \(\ket{B}\) denote the size of the blockchain, \(\ket{S}\) was the world state data size, \(m\) be the number of clusters, \(n\) denote the number of nodes in a single cluster, and \(s\) be the number of copies of a block kept in a cluster. For the sake of discussion, only the case where \(\ket{B}/m\) is divisible by \(n\) is considered, and in the case where \(\ket{B}/m\) is much larger than \(n\), the computational difference can be ignored.  
In the new scheme, the number of blocks to be stored in each cluster is \((\ket{B})/m\), and the total amount of data that should be stored by each node is \(s/(m\times n)\times\ket{B}+\ket{S}\). Thus, the storage proportion of each node to the case without shading is:  
\[R_{v}=\frac{\frac{s}{m\times n}\times\ket{B}+\ket{S}}{\ket{B}+\ket{S}} \tag{1}\]  
Equation (1) shows that when the block data capacity is much larger than the world state data capacity, \(R_{v}\) tends to be \(s/mn\). If \(s=n\), it means that each node stores all the data of this cluster and the storage overhead is maximum; if \(s=1\), it means that the data of the whole network are equally distributed and stored on each node and the storage overhead is minimum.
## 4 Results of Numerical Simulations and Conclusions
### The Case of Having Two Scatterers  
First, in the case of having two scatterers, the results of imaging with the subspace migration algorithm using the output data by inputting the test data of No. 1, No. 400, and No. 500 into the developed model and ground truth data are shown in Figure 8. The parts highlighted in red in this figure are the locations of the scatterers. As a result of imaging with ground truth data and learning data in all three cases, it can be seen that the locations of the scatterers are the same.  
Figure 7: Visualization of real-part and imaginary-part of ground truth data and output data when there are three scatterers. (**a**) ground truth data (real-part); (**b**) output data (real-part); (**c**) ground truth data (imaginary-part); (**d**) output data (imaginary-part).  
In addition, in order to check whether the learning was successfully performed, we recreated the data. The imaging result of this recreated data using the subspace migration algorithm and the imaging result of the output data obtained by inputting this data into the learning model and the distributions of its singular values are shown in Figure 9. In this figure, the results of imaging the ground truth data and the output data show that there is little difference in the afterimages and the locations of the scatterers between two  
Figure 8: Imaging results for ground truth data and learning data with two scatterers. (**a**) true data No. 1; (**b**) learning data No. 1; (**c**) true data No. 400; (**d**) learning data No. 400; (**e**) true data No. 500; (**f**) learning data No. 500.  
images. In addition, when (a) and (c) are compared, although the distributions of singular values are somewhat different, there is no problem in finding the locations of the scatterers because the two singular values are relatively large.  
In addition, referring to Figure 10, as shown in (a), the difference between singular values is relatively small, so, as a result of imaging limited view data, it may not be possible to locate the scatterers as in (b). When the singular value is calculated with the full view data output by inputting this limited view data into the learning model, there are two points with relatively large singular values as shown in (c). The locations of the scatterers can be easily found as shown in the imaging result (d). To sum up, it can be seen that, if the number of scatterers is two, the developed artificial neural network model constructs the correct full view data from the limited view data well.  
Figure 9: Singular value distribution and imaging results for randomly generated scatter’s data. (a) SVD with true data; (b) imaging result with true data; (c) SVD with learning data; (d) imaging result with learning data.
## 4 Experiment Environment
### Validation of the Strategy  
In order to verify the effectiveness of the proposed frequency control algorithm under load change disturbances and DoS attacks, this subsection discusses the frequency deviation changes of power grids without the designed ADES controller. IEEE14 bus testing system is used for simulation. Assuming that the probability of data packet loss caused by DoS attacks is \(\eta_{ji}=0.05\), the attack period is 1-1.3 s, and the duration is 300 ms.  
If there is no ADES controller for auxiliary frequency control (the ADES controller is set not to participate in the frequency modulation process of power grids), the active power injected by BESS into power grids is \(\Delta P_{B_{i}}=0\). As shown in Figures 5 and 6, the frequency deviation change curve of the testing system and the current generator output curve can be obtained only by the active output adjustment of the generator participating in the frequency modulation process of power grids.  
As shown in Figure 5, the frequency of each bus presents an oscillating state under the influence of load change disturbances. In the control process, the power generation end participates in the frequency adjustment process by controlling the power output of the generator to weaken the influence of the load change on frequency deviation. However, the frequency deviation still exceeds its threshold 0.03 Hz at the end of the control process. Thus, ADES needs to be introduced to control the frequency deviation of power grids.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Description** & **Value** \\ \hline \(M_{i}\) & inertia constant & 0.05 \\ \(D_{i}\) & damping constant & 0.002 \\ \(T_{gi}\) & gas turbine constant & 0.2 \\ \(T_{di}\) & governor constant & 5 \\ \(R_{gi}\) & regulation constant & 0.5 \\ \(T_{ji}\) & synchronizing constant & 0.5 \\ \(T_{Bi}\) & Inertial time constant of BESS & 0.5 \\ \(b_{i}\) & frequency bias gain & 1 \\ \(K_{i}\) & tie-line bias control gain & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bus parameters.  
Figure 4: Multi-step random load change disturbances.