esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiband and Lossless Compression of Hyperspectral Images
## 3 Lossless Multiband Compression for Hyperspectral Images (LMBHI)
### 3-D MultiBand Linear Predictor (3D-MBLP)  
The Multiband Linear Predictor (3D-MBLP) uses a prediction context by considering two parameters:  
* \(B\): number of the previous bands, that are considered for the prediction;
* \(N\): number of the samples for the current and each previous band, which will be used for the creation of the prediction context.  
First of all, we define a bi-dimensional enumeration \(E\), graphically represented in Figure 2. The main aim of such an enumeration, is to permit the relative indexing of the pixels with respect to the pixel which is currently under analysis (which has 0 as index in Figure 2).  
In order to define the prediction context of 3D-MBLP, we use the following notations:  
* \(I_{i,j}\): indicates the \(i\)-th pixel of the \(j\)-th band, according to the enumeration \(E\);
* \(I_{0,j}\): denotes the pixel that has the same spatial coordinates of \(X\), of the \(j\)-th band, according to the enumeration \(E\).  
In the following, we suppose that the current band is the \(k\)-th band. In particular, by using our notations, it is possible to observe that \(X\) can be also addressed as \(I_{0,k}\) In detail, the 3D-MBLP predictor is based on the least squares optimization technique and the prediction is computed by means of the Equation (4).  
\[\hat{X}=\sum_{i=1}^{B}\alpha_{i}\cdot I_{0,\;k-i} \tag{4}\]  
The coefficients: \(\alpha_{0}=[\alpha_{1},\;\ldots,\;\alpha_{B}]^{T}\) are chosen to minimize the energy of the prediction error described by the Equation (5).  
\[P=\sum_{i=1}^{N}\left(I_{i,\;k}-\hat{I}_{i,\;k}\right)^{2} \tag{5}\]  
\(P\) can be rewritten in matrix notation by means of the following equation:  
\[P=(C\alpha-X)^{T}\cdot(C\alpha-X)\]  
where  
\[C=\left[\begin{array}{cccc}I_{1,\;k-1}&\cdots&I_{1,\;k-B}\\ \vdots&\ddots&\vdots\\ I_{N,\;k-1}&\ldots&I_{N,\;k-B}\end{array}\right]\]  
and \(X=\left[I_{1,k},\ldots,I_{N,k}\right]^{T}\).  
Figure 2: The enumeration \(E\) we used for the relative indexing with respect to the current pixel, identified with 0 as index.  
Subsequently, by taking the derivate of \(P\) and by setting it to zero, we obtain the optimal coefficients by means of the Equation (6).  
\[\left(C^{T}C\right)\alpha_{0}=\left(C^{T}X\right) \tag{6}\]  
Once the coefficients \(\alpha_{0}\), which solve the linear system Equation (6), are computed, the prediction, \(\hat{X}\), of the current pixel \(X\), can be calculated.
# Seismic Behavior and Modeling of Ductile Composite Steel-Trussed Concrete Beam to Column Joints
## 1 Introduction
A different load pattern was used by [13; 14] during testing on post-tensioned glulam beam-column joints. In addition to the seismic forces and axial stress applied to the column, additional masses were placed on the beam to simulate the shear stress due to gravitational loads present in the seismic action combination. In order to easily equip with efficient and cost-effective seismic damage-proof connections of RC moment-resisting framed structures endowed with semi-prefabricated steel-trussed concrete beams and standard RC pillars cast-in-place, a recent study has developed specific energy dissipating systems for hybrid beam-to-column connections using both experimental, numerical, and analytical methods [15; 16; 17; 18]. Generally, the use of friction dampers prevents damage to the structural elements, improves the expected performance, and limits damage to the panel zone [19], while the use of devices based on shape memory alloys (SMA) is more effective at recovering strains and reducing residual deflections [20]. In the present study, the seismic performance of a composite steel-trussed concrete beam ductile joint, patented as the MTR\(\otimes\)system by Metal-Ri S.r.l. [[https://www.metalri.it](https://www.metalri.it)]([https://www.metalri.it](https://www.metalri.it)) (accessed on 8 October 2023), is investigated by means of experimental tests performed on two experimental specimens extracted from a prototype building realized with the same beam and with different columns, made of reinforced concrete (RC) or concrete-filled tube (CFT). The experimental models were subjected to quasi-static cyclic tests with different experimental setups and test apparatus. Nonlinear numerical lumped plasticity models and fiber models have been implemented in order to reproduce the experimental outcomes. The objective of the investigation is to verify the bending strength and ductile capacity of the beam-to-column connections when subject to gravitational and seismic loads in accordance with the requirements provided in the Italian seismic code.
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 5 Simulation
### Performance Metrics  
As defined in this subsection, the performance is evaluated using the root mean square errors (RMSEs) and the bias norms (BNorms). The RMSE and BNorm of the target position estimation are  
\[\text{RMSE}_{k}^{pos} =\sqrt{\frac{1}{M_{0}}\sum_{i=1}^{M_{0}}\left\|\hat{x}_{k|k}^{i}(1 :2)-x_{k}^{i}(1:2)\right\|^{2}}, \tag{64}\] \[\text{BNorm}_{k}^{pos} =\|\frac{1}{M_{0}}\sum_{i=1}^{M_{0}}\left(\hat{x}_{k|k}^{i}(1:2)-x _{k}^{i}(1:2)\right)\| \tag{65}\]  
\begin{table}
\begin{tabular}{l} \hline \hline \(1\). Initialization \\ \(\hat{x}_{0}=\hat{x}_{0}\) \\ \(P_{0}=E\big{[}(x_{0}-x_{0})(x_{0}-x_{0})^{T}\big{]}\) \\ \hline \(2\). Predict \\ \(\hat{x}_{k|k-1}=F_{k-1}\hat{x}_{k-1}\) \\ \(P_{k|k-1}=F_{k-1}P_{k-1|k-1}F_{k-1}^{T}+DQ_{k}D^{T}\) \\ \hline \(3\). Filter Gain \\ \(K_{1}=\sum_{j=1}^{n}\lambda_{j}e^{-\frac{x_{j}^{2}}{2}}P_{k|k-1}\big{(}H_{k}^{ 1}\big{)}^{T}\) \\ \(K_{2}=H_{k}P_{k|k-1}H_{k}^{-1}+\sum_{i=1}^{n}\rho_{i}e^{-\frac{x_{i}^{2}-1}{2 }}[P_{k|k-1}(1,1)+P_{k|k-1}(2,2)]+R_{k}\) \\ \(K_{k}=K_{1}K_{2}^{-1}\) \\ \hline \(4\). Update \\ \(\hat{x}_{k}=\hat{x}_{k|k-1}+K_{k}(x_{k}-H_{k}\hat{x}_{k|k-1})\) \\ \(P_{k|k}=P_{k|k-1}-K_{k}K_{1}^{T}\) \\ \hline \(5\). When the nonlinear constraint is \(x_{k}^{T}Mx_{k}+m^{T}x_{k}+x_{k}^{T}m+m_{0}=0\) \\ \(\text{Use the iterative method to find }\lambda\) \\ \(\text{Then }\hat{x}_{k}=G^{-1}V(I+\lambda_{k}\mathbb{Z}^{T}\mathbb{Z})^{-1}e( \lambda_{k})\) \\ \hline \(6\). \(k=k+1\), go to \(2\). \\ \hline \end{tabular}
\end{table}
Table 3: PL-MMSE with Nonlinear Constraints Algorithm.  
where \(\xi^{i}_{k|k}(1:2)\) is the estimated target position and \(x^{i}_{k}(1:2)\) is the true target position at time \(kT\) at the \(i\)th run, respectively.  
The RMSE and BNorm of the target velocity estimation are  
\[\text{RMSE}_{k}^{pd} =\sqrt{\frac{1}{M_{0}}\sum_{i=1}^{M_{0}}\|\hat{\pi}^{i}_{k|k}(3:4)- x^{i}_{k}(3:4)\|}^{2}, \tag{66}\] \[\text{BNorm}_{k}^{pd} =\|\frac{1}{M_{0}}\sum_{i=1}^{M_{0}}\left(\hat{\pi}^{i}_{k|k}(3:4) -x^{i}_{k}(3:4)\right)\| \tag{67}\]  
where \(\hat{\pi}^{i}_{k|k}(3:4)\) is the estimated target velocity and \(x^{i}_{k}(3:4)\) is the true target velocity at time \(kT\) at the \(i\)th run.  
Similarly, the time-averaged RMSE, BNorm of the target position and velocity estimation are  
\[\text{RMSE}_{avg}^{pos} =\sqrt{\frac{1}{M_{0}B}\sum_{i=1}^{M_{0}}\sum_{k=L_{0}}^{N}\| \hat{\pi}^{i}_{k|k}(1:2)-x^{i}_{k}(1:2)\|}^{2}, \tag{68}\] \[\text{BNorm}_{avg}^{pos} =\frac{1}{B}\sum_{k=L_{0}}^{N}\|\frac{1}{M_{0}}\sum_{i=1}^{M_{0}} \left(\hat{\pi}^{i}_{k|k}(1:2)-x^{i}_{k}(1:2)\right)\|,\] (69) \[\text{RMSE}_{avg}^{vol} =\sqrt{\frac{1}{M_{0}B}\sum_{i=1}^{M_{0}}\sum_{k=L_{0}}^{N}\| \hat{\pi}^{i}_{k|k}(3:4)-x^{i}_{k}(3:4)\|}^{2},\] (70) \[\text{BNorm}_{avg}^{vol} =\frac{1}{B}\sum_{k=L_{0}}^{N}\|\frac{1}{M_{0}}\sum_{i=1}^{M_{0}} \left(\hat{\pi}^{i}_{k|k}(3:4)-x^{i}_{k}(3:4)\right)\|. \tag{71}\]  
Here, we set \(B=N-L_{0}+1\) with \(L_{0}=50\) where \(L_{0}\) is an offset parameter to reduce the time-averaged metrics affected by the initial tracking errors in the simulations.
## 3 Theoretical Approach to Linear Attenuation Factor of Composite Materials  
The attenuated radiation intensity can be calculated using the linear attenuation coefficient of the composite and the thickness of the shielding material.  
\[I=I_{0}-I_{t} \tag{1}\]  
\[I_{t}=I_{0}e^{-\mu compt} \tag{2}\]  
where \(I_{0}\) = the intensity incident on the shield; \(I_{t}\) = the amount of radiation passing through the shielding material of thickness t; \(\mu_{compt}\) = effective attenuation factor of the composite; \(t\) = thickness of the shielding material.  
By substituting Equation (2) into Equation (1), Equation (3) can be obtained.  
\[I=I_{0}\big{(}1-e^{-\mu compt}\big{)} \tag{3}\]  
Using Equation (3), it is possible to theoretically calculate the linear attenuation coefficient of the composite material ignoring the attenuation due to refraction and scattering at the interface of the material constituting the composite material. The radiation dose shielded by the layered composite made of two materials is equal to the sum of the radiation dose shielded by material A and the radiation dose shielded by material B. Since radiation passing through the composite passes through material A and then through material B, the initial dose through material B is equal to the dose through material A. Therefore, by Equation (3), the amount of radiation shielded by the composite made of two materials can be calculated as follows.  
\[\begin{array}{ll}I=(I_{0}&-I_{0}e^{-\mu_{1}I_{1}})+\big{(}I_{0}e^{-\mu_{1}I_ {1}}-I_{0}e^{-\mu_{1}I_{1}}e^{-\mu_{2}I_{2}}\big{)}\\ &=I_{0}\Big{[}1-e^{-\mu_{1}I_{1}}+e^{-\mu_{1}I_{1}}-e^{-(\mu_{1}I_{1}+\mu_{2}I_ {2})}\Big{]}\\ &=I_{0}\Big{[}1-e^{-(\mu_{1}I_{1}+\mu_{2}I_{2})}\,\Big{]}\end{array} \tag{4}\]  
where \(\mu_{1}\) = effective attenuation factor of A; \(\mu_{2}\) = effective attenuation factor of B; \(t_{1}\), \(t_{2}\) = thickness of A and B, respectively.  
By Equations (3) and (4), the effective attenuation coefficient of the composite can be calculated as follows.  
\[\begin{array}{ll}\mu_{compt}=\mu_{1}t_{1}+\mu_{2}t_{2}\\ \mu_{compt}=\frac{\mu_{1}t_{1}+\mu_{2}t_{2}}{t}\,[\mathrm{cm}^{-1}]\end{array} \tag{5}\]  
where \(\mu_{compt}\) = Calculated value of effective attenuation factor of composite; \(t\) = Thickness of A + Thickness of B (\(t_{1}+t_{2}\)).  
If the effective attenuation coefficient of the materials constituting the composite material and the ratio of each material are known through Equation (5), the theoretical effective attenuation coefficient of the composite material can be calculated.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 5 Enhancing Encoding Compression Efficiency
### Preprocessing Methods Based on Spectral Analysis  
In recent years, to further enhance the efficiency of encoding compression, researchers have conducted preprocessing of test data based on spectral analysis. In test compression, spectral analysis is used to extract key test vector information components that determine fault coverage from the test vectors. Researchers have found that high-quality test vector sets exhibit characteristics related to the spectrum. By performing preprocessing based on spectral analysis, it is possible to generate test sets with high fault coverage while effectively reducing the amount of test data, achieving test compression. In practical applications, [PERSON] [100] proposed the use of Walsh-Hadamard transform for digital circuit analysis, which can detect fixed faults in circuits. [PERSON] applied this transformation to the Built-In Self-Test (BIST) and ATPG for timing circuits [31]. [PERSON] applied spectral analysis to test vector preprocessing at the RTL (Register Transfer Level) [32]. Subsequently, [PERSON] completed test compression for fixed faults [101] and transition faults [102] in microprocessor testing. In 2010, [PERSON] extended the applicability of this method to very large-scale digital circuits, demonstrating that the Walsh-Hadamard transform can effectively analyze the principal components and residual components in the test set, and the inverse transform can recover the principal components into the original test vectors [100] (Figure 6).  
The decompressor based on Walsh-Hadamard transform consists of counters and the XOR network, where the bit length of the counters is determined by the order of the Hadamard matrix. Logic gates are used to selectively control the outputs of the counters; then, the Walsh function, or the principal component, is generated through the XOR network. The compressed data stored on the ATE are output through the decoder as the residual component. The principal component and the residual component are restored to the original test vectors through XOR gates and then input into the scan circuit.
## 2 Method for a Strain Field Measurement on the Bearing Plate
\[\Delta\omega=C_{\varepsilon}\Delta\varepsilon+C_{T}\Delta T=C_{\varepsilon}( \varepsilon+\alpha\Delta T)+C_{T}\Delta T \tag{1}\]  
In this equation, \(C_{\varepsilon}\), is the strain coefficient and \(C_{T}\) is the coefficient of the sensor signal change due to the refractive index of the sensing optical fiber. The proposed method mentioned in the previous section measuring strains at two different locations use the strain difference to determine the tendon force also works for excluding the temperature effect. If two sensor signals on the bearing plate are obtained (one (\(\Delta\omega_{a}\)) near the anchor head and the other (\(\Delta\omega_{b}\)) at a little distance from it) and their difference is proportional to the applied load, \(\Delta F\) on the anchor, the anchor force change can be described as follows. \[ \begin{split}\Delta F=C(\Delta\omega_{a}-\Delta\omega_{b})=& C[C_{\varepsilon}(\varepsilon_{a}+\alpha\Delta T)+C_{T}\Delta T-C_{ \varepsilon}(\varepsilon_{b}+\alpha\Delta T)-C_{T}\Delta T])\\ =& CC_{\varepsilon}(\varepsilon_{a}-\varepsilon_{b })=C^{*}(\varepsilon_{a}-\varepsilon_{b})\end{split}  \tag{2}\]  
In the above equation, \(\varepsilon_{a}\) and \(\varepsilon_{b}\) are the mechanical strains at each location A and B. \(\alpha\) is the coefficient of thermal expansion and \(\Delta T\) is the amount of temperature change. \(C^{*}\) is the linear correlation coefficient between the strain difference and the anchor force change. In this equation, we observe that the thermal effects are eliminated, and only the strain change due to anchor force is considered. This works when the temperature of the whole bearing plate is the same, and there is no local constraint suppressing the thermal strain on the measuring field. To apply this equation to our study, we measure the distributed strain at two circumferential locations on the surface of the bearing plate where we can neglect the temperature variation, and we also find two strains, which are the averaged strains  
Figure 3: Angular strain distribution of the bearing plate under 10 ton anchor force, (**a**) nodes of calculated strains and (**b**) strains due to nodes. calculated from the measured distributed strains. We measure the thermal strain on top of the bearing plate and confirmed that the thermal strain is the same on the measuring field in Section 5.
# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 3 Energy Characteristic Analysis of Microseismic Waveform Signals
The processing results are shown in Figure 14.  
By analyzing the results, it can be deduced that the other signals generated in the acquisition process are almost straight lines in the graph after EEMD processing, and the curve of the rock fracture event signal is smoother after noise reduction, which indicates that the effect of the wavelet threshold method is significant. The  
Figure 12: Contrast diagram of rock fracture signal denoising. To compare and analyze the difference between rock blasting and rock fracture signals, eight-layer decomposition is selected as the same decomposition level of the two types of signals. The energy of each decomposition level of the two signals after decomposition is shown in Figure 16a,b. Figure 14: Contrast diagram of rock blasting signal noise reduction. Figure 13: EEMD decomposition diagram and spectrum of rock fracture signal. From Figure 16a, it can be observed that the signal energy of the rock fracture signal collected by the sensor is mainly concentrated in the IMF 1 frequency band, which accounts for 89.96% of the total energy. From Figure 16b, it can be observed that the signal energy of the blasting signal collected by the sensor is mainly concentrated in the IMF1 and IMF2 bands, which account for 31.01% and 61.43% of the total energy, respectively. For 50 groups of the two types of signals extracted using the microseismic monitoring equipment, the wavelet threshold method is used for noise reduction, and EEMD is subsequently used for eight-layer decomposition of the denoised signal. The results of the rock fracture signal decomposition are shown in \[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]0 The results of the rock blasting signal decomposition are shown in \[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]1  
\[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]2 (**a**) Energy distribution diagram of each frequency band of rock fracture signal; (**b**) energy distribution diagram of rock blasting signal in each frequency band. \[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]3 EEMD decomposition diagram and spectrum of rock blasting signal. The following conclusions are drawn by comparing Figures 17 and 18. 1. Most of the energy of rock fracture signal is concentrated in the IMF1 frequency band, and the distribution energy in the IMF1 frequency band accounts for more than the total energy of the other frequency bands. 2. In the rock blasting signals, most of the energy is concentrated in the IMF 2, IMF 3, and IMF 4 bands; however, a few signal events were mainly observed in the IMF 1 energy band. 3. The signal category can be preliminarily judged by verifying whether the main energy is concentrated in the IMF 1 frequency band. \[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]4 Histogram of energy percentage of each frequency band in total energy of rupture signal. \[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]5 Histogram of energy percentage of each frequency band in total energy of blasting signal.
# Impact of Lowering Speed Limit on Urban Transportation Network
###### Abstract  
The purpose of this study is to analyze the impact of lowering speed limit on an urban transportation network. A microscopic traffic simulation model, Vissim was utilized to measure the impact. Also, various traffic inputs were tested with different signal coordination scenarios to investigate the impact in different traffic conditions. It was found that during early morning hours with very light traffic, the impact of lowering speed limit was significant. During congested time periods, including level of service E and F, the travel speed reduction from lowering speed limit was not significant. As suggested in other studies, the results demonstrated that lowering the speed limit does not have a significant impact on average travel speed in congested traffic networks. Also, different signal coordination was tested. As expected, signal coordination based on the lowered speed limit performed better than the case with signal coordination based on the previous higher speed limit. The results of this study are expected to provide insights when considering lowering speed limit for existing traffic networks.  
traffic simulation; simulation evaluation; speed limit lowering; policy evaluation; signal coordination evaluation +
Footnote †: journal:  
approach has a limitation in calculating differences in emissions, since it does have the capability of reflecting disaggregated acceleration and deceleration data. It has been reported that vehicle emissions increase in unstable traffic flow conditions including stop-and-go traffic conditions and high acceleration/deceleration traffic conditions. If an average speed is applied in the emission calculation, it is believed to have a greater chance of underestimating vehicle emission, compared to using disaggregate data. Therefore, it is necessary to use microscopic approach utilizing trajectory data of induvial vehicles to realistically calculate the impact on vehicle emission from lowering speed limit. In this study, MOVES OP (operation) mode, a microscopic emission analysis technique, was used to analyze the environmental impact on vehicle emission from lowering speed limit.  
The objective of this paper is to investigate the impact on traffic operation and environments from lowering speed limit. Average travel speed and travel time were compared for traffic operational impact analysis, while vehicle emissions were estimated for traffic environmental impact analysis. Various traffic conditions and signal coordination were tested for more comprehensive analysis.
## 2 Measurement Principles and Methods
### Sub-Pixel Edge Detection and Fitting
#### 2.1.3 Hyperbolic Tangent Fitting (HTF) Method  
[PERSON] et al. proposed the hyperbolic tangent fitting method as an edge model. This approach aligns the actual edge gray-level distribution with the model to enhance sub-pixel edge detection accuracy. The hyperbolic tangent function, an S-type function, is generally expressed as follows:  
\[f(x)=A\cdot\tanh(B(x-C))+D \tag{12}\]  
where \(A\) is the curve's magnitude, \(B\) is the slope, \(C\) is the curve's center position (i.e., the edge position), and \(D\) is the offset.  
In sub-pixel edge detection, a set of grayscale values \(I(x)\) from edge pixels is analyzed, where \(x\) indicates the pixel position. The objective is to determine the optimal values of \(A\), \(B\), \(C\), and \(D\) that best fit these data. Initially, a rough edge location is identified using initial edge detection, and the surrounding gray values are extracted. Subsequently, a fitting window encompassing a series of pixel points near the edge is chosen in the vicinity of this initial edge location. The method employs least squares to determine the optimal parameters \(A\), \(B\), \(C\), and \(D\), thereby minimizing the discrepancy between the fitted function \(f(x)\) and the actual gray value \(I(x)\). This is accomplished by addressing the following optimization problem:  
\[\min_{A,B,C,D}\sum_{x}\left[I(x)-(A\cdot\tanh(B(x-C))+D)\right]^{2} \tag{13}\]The optimization problem can be solved using a nonlinear least-squares algorithm, and in this paper the Levenberg-Marquardt algorithm is used to solve it.