esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Prediction Results  
To verify the validity of the model, the proposed model was validated in MATLAB R2018a. In this paper, the net load data of the upper bus of a city's PV substation in the first 15 days of May 2017 were used. The sampling interval of the net load data is 5 min, and the installed capacity of the distributed PV power station is approximately 50 MW. The data from 1-11 May were selected as the training set to model the prediction model, and the parameters of the model were adjusted by cross-validation. The data from 12-15 May were selected as the samples of the prediction test, where 12 May was sunny, and 13-15 May were cloudy.
## 3 Experiments
### Verification of the Evaluation Method of the Angular Misaligments of a Small Cylinder
By using the setup, the arc-profile of the workpiece was first obtained at a \(Y\)-position of \(y_{1}\). After that, the workpiece was made to travel along the \(Y\)-direction together with the holder unit by using the linear stage. After that, another arc-profile was obtained at a \(Y\)-position of \(y_{2}\) (\(=y_{1}+\Delta y\)). Figure 15a shows an example of the arc-profile obtained during the experiments. For each of the obtained arc-profiles, a circle was fitted based on the least-squares method to obtain the peak coordinate as shown in Figure 15b. After that, by using the obtained data, \(\theta_{\mathrm{Z}}\) was evaluated based on Equation (18). In the experiments, \(\Delta y\) was set to 4 mm. Five repetitive trials were made by following the above procedure without the demounting and remounting operations of the holder unit at each trial, and the standard deviation of the measurement of \(\theta_{\mathrm{Z}}\) was evaluated to be 0.006\({}^{\circ}\), as shown in Figure 15c. Following the verification of the measurement repeatability, an attempt was made to detect the angular displacement \(\theta_{Z}\) of the pin gauge. By using the rotary stage, the workpiece was rotated about the \(Z\)-axis in steps of approximately 0.023\({}^{\circ}\), corresponding to the resolution of the rotary stage. The angular displacement of the workpiece was evaluated by both the proposed technique and the commercial autocollimator at each step. Figure 16 shows the results. As can be seen in the figure, the maximum deviation of the detected \(\theta_{Z}\) from the reading of the autocollimator was less than 0.01\({}^{\circ}\). These experimental results demonstrate the feasibility of the proposed technique for measurement of \(\theta_{\mathrm{Z}}\). By using the obtained \(\theta_{X}\) and \(\theta_{Z}\), the angular misalignment of the workpiece can thus be compensated. Figure 16: The angular displacement of the workpiece about the \(Z\)-axis measured by the proposed method. Figure 14: Setup for the verification of the technique for measurement of \(\theta_{\mathrm{Z}}\). Figure 15: Evaluation of the \(\theta_{Z}\) and its repeatability: (**a**) measured profile (**b**) arc-profile cut from the profile in (**a**) and its deviation from a fitting circle; (**c**) repeatability of \(\theta_{Z}\) measurement.
## 5 Experimental Results
### Noise Attacks
#### 5.2.1 Salt and Pepper Noise  
Salt and pepper noise is added to an image by the option of both choosing noise (with 255-pixel worth) and irregular dullness (with 0-pixel esteem) all around the image. This model is otherwise called information drop noise because it is measured by the number of salt and pepper crystals on the image. The sample salt and pepper noise attacks on images and the watermark extracted from it with PSNR and NCC values are provided in Figure 8 with a density of 0.5.
## 2 Methods
### Covariance Pooling Module
#### 2.3.2 Acceleration Covariance Normalization  
The square root of a covariance matrix is itself positive definite, and such a matrix admits a unique root that can be computed via EIG or SVD. However, EIG and SVD computations are generally slower on GPU than on CPU [32; 41; 42]. To solve this problem, MPN-COV utilizes Newton-Schultz iteration to accelerate the calculation of covariance normalization [41; 42].  
In Equation (5), when \(\alpha=\frac{1}{2}\), \(Z=F^{\frac{1}{2}}=Y=Udiag(\lambda_{i}^{\frac{1}{2}})U^{T}\), let \(Y_{0}=\hat{F}\), \(Z_{0}=I\) for \(n=1,\ldots,d\), after [34], the coupled iteration can be written as:  
\[\begin{cases}Y_{n}=\frac{1}{2}Y_{n-1}(3I-Z_{n-1}Y_{n-1})\\ Z_{n}=\frac{1}{2}(3I-Z_{n-1}Y_{n-1})Z_{n-1}\end{cases} \tag{6}\]  
Equation (6) involves only matrix multiplication, making the process highly compatible with GPU-based parallel processing. Simultaneously, compared to the precise matrix square roots computed using the EIG method, our approach achieves higher precision with fewer iterations. We set the iteration count to 5 in our method. To promote stable convergence of the Newton-Schultz iteration, the covariance matrix is handled as:  
\[\hat{F}=\frac{1}{tr(F)}F \tag{7}\]  
where \(tr(\cdot)\) represents the matrix trace. The transformation applied to the covariance matrix in Equation (7) may scale down the data, potentially degrading network performance. Thus, a post-compensation step aligned with the pre-processing is necessary after the Newton-Schulz iteration:  
\[Z=\sqrt{tr(F)}Y_{n} \tag{8}\]
###### Abstract  
The instability of the winding-cushion structure is one of the primary causes of transformer failures. Insulation cushion compression and offset are the predominant forms leading to structural instability. Therefore, this paper, using the SFSZ7-31500/110 transformer as an example, first derives the theoretical formula for mechanical stress calculation. It clarifies the key influencing parameters of the winding-cushion block structure on the axial bending stress of the winding. Subsequently, an electromagnetic force finite element calculation model is established to obtain the axial force distribution in the winding and the distribution of unbalanced displacement during short-circuit processes. Based on the force and offset distribution, a specific cushion block compression and offset test platform is constructed. By setting different cushion block variables, the effects of cushion block unbalanced height and cushion block offset on the winding's bending elastic modulus are determined. Finally, a simulation model for stress calculation of the winding-cushion block structure is established, revealing the influence pattern of cushion block compression and offset instability on the axial strength of the winding. The results of this study indicate that the greater the uneven cushion block height, the lower the axial strength of the winding. Under the same cushion block offset angle, winding structures with non-uniform cushion block offsets exhibit the worst axial stability. When the offset angles are 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\), the maximum axial bending stress of the winding increases by 1.73\(\%\), 3.46\(\%\), and 7.82\(\%\), respectively. Increasing the offset angle exacerbates the decrease in the axial strength of the winding up to a certain extent. The findings in this study have significant implications for enhancing a transformer's short-circuit resistance.  
power transformers; short-circuit impacts; winding strength; cushion blocks; axial bending stress +
Footnote †: journal: Applied Sciences
# Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation
## 3 The Proposed Method
### Objective Function of SSRR-AGLP
 =tr(FF^{T}-2W^{T}XF^{T}+W^{T}XX^{T}W)+\beta tr(F(D-R)F^{T}) \tag{27}\] \[+tr((F-Y)U(F-Y)^{T})+tr(\Lambda F)\]  
Setting the derivative with respect to \(F\) to zero, we obtain  
\[\frac{\partial\eta(F,\Lambda)}{\partial F}=2F-2W^{T}X+2\beta F(D-R)+2(F-Y)U+ \Lambda=0 \tag{28}\]  
According to the KKT condition \(\Lambda_{ij}W_{ij}=0\)[51], we obtain  
\[(2F-2W^{T}X+2\beta F(D-R)+2(F-Y)U)_{ij}\delta_{ij}=0 \tag{29}\]According to Equation (29), the updating rule of \(F\) is  
\[F_{ij}=:F\frac{\left[W^{T}X+\beta FR+YU\right]_{ij}}{\left[F+\beta FD+FU\right]_{ ij}} \tag{30}\]  
.2.3. Fix Weight Matrix \(S\) and Prediction Label Matrix \(F\) to Solve Transformation Matrix \(W\)  
Removing the items that are not related to the matrix \(W\) in Equation (18), the optimization problem of the variable \(W\) can be obtained as follows:  
\[ \begin{array}{l}\min_{W}\varepsilon(W)=||F-W^{T}X||_{2}^{2}+\gamma||W||_{2} ^{2}\\ s.t.W\geq 0\end{array}  \tag{31}\]  
Through a series of algebra formulations, the Equation (31) is simplified to  
\[ \begin{array}{l}\min_{W}\varepsilon(W)=tr(FF^{T}-2W^{T}XF^{T}+W^{T}XX^{T}W)+ \gamma tr(W^{T}W)\\ s.t.W\geq 0\end{array}  \tag{32}\]  
To solve the above problem, we need to introduce the Lagrange multiplier matrix \(\theta\). The Lagrange function of Equation (32) is:  
\[\eta(W)=tr(FF^{T}-2W^{T}XF^{T}+W^{T}XX^{T}W)+\gamma tr(W^{T}W)+tr(\theta W) \tag{33}\]  
The partial derivation of Equation (33) with respect to \(W\) is  
\[\frac{\partial\eta(W,\theta)}{\partial W}=-2 XF^{T}+2 XX^{T}W+2\gamma W+\theta \tag{34}\]  
Setting the derivative is equal to zero, we obtain  
\[\frac{\partial\eta(W,\theta)}{\partial W}=-2 XF^{T}+2 XX^{T}W+2\gamma W+\theta=0 \tag{35}\]  
According to the KKT condition \(\theta_{ij}W_{ij}=0\)[51], we obtain  
\[(-2 XF^{T}+2 XX^{T}W+2\gamma W)_{ij}\theta_{ij}=0 \tag{36}\]  
According to Equation (36), the updating rule of \(W\) is:  
\[W_{ij}=:W_{ij}\frac{\left[XF^{T}\right]_{ij}}{\left[XX^{T}W+\gamma W\right]} \tag{37}\]
## 4 Experiments and Results  
### ECG Datasets for Individual Identification  
The PTB electrocardiogram (ECG) database was used to evaluate the performance of the proposed methodology. The PTB-ECG database comprises 549 high-resolution ECG recordings obtained from 290 patients. This dataset includes both healthy individuals  
Figure 5: Ensemble neural network for individual identification using ECG signal.  
and patients presenting various cardiac pathologies. The recordings were acquired from 15 leads, including 12 standard leads and the Frank XYZ leads. Participants' ages ranged from 17 to 87 years, comprising 209 males and 81 females. Data for each subject were acquired in the form of 1-5 record files. The ECG signals were sampled at 1000 samples/s with a 16-bit resolution. Each record included medical summaries containing age, sex, and diagnostic information. PTB-ECG encompasses nine diagnostic categories, including various cardiac conditions, as shown in Figure 6[48; 49].
## 1 Introduction
(2019) monitored tree stand age based on SIF signals, incorporating HyPlant and LiDAR data together over a managed loblolly pine forest. Whereas, [PERSON] et al. (2019) aimed to evaluate the capability of hyperspectral RS approaches (including SIF retrieved from HyPlant) for detecting physiological and physical effects caused by chemical agents over a commercial grass farm. The first published maps of HyPlant data by [PERSON] et al. (2019) were limited to observations of croplands and showed a large spatial variability of SIF signals between different crop types. Our study consequently aims to report on the first SIF maps retrieved close to the telluric oxygen absorption bands around 760 and 687 nm over a peatland ecosystem and its surrounding. Together with the functional trait proxy SIF, classical VIs sensitive to biochemical and structural traits were analyzed for the peatland ecosystem. SIF and VIs were grouped and analyzed for different vegetation types (i.e., forest, grassland, peatland), thereby allowing us for the first time to quantitatively analyze the variability of VIs, SIF, and related traits in these ecosystems. Further, this study also provides novel insight on how SIF and VIs are related to species diversity and functional diversity in a fen peatland.
## 4 Conclusions  
In this study, a series of matrix-type random coding MSs is designed to explore the RCS reduction characteristic. The coding method of MS with different ratio of \"0\" and \"1\" units is proposed, and then, the RCS reduction mechanism of different arrangements is discussed theoretically. To analyze the energy scattering characteristic of random coding sequences, the designed MSs were simulated to get the RCS reduction curves and the scattering patterns under normal incidence. For MS samples of coding \(a\) and \(b\), the reflectance was less than \(-10\) dB on average under \(x\)- and \(y\)-polarized wave incidence in the frequency range of 5.8-15.5 GHz, the average RCS reduction is basically larger than 10 dB under normal incidence in the frequency range of 5.5-15 GHz. The matrix-type coding MS presents a broadband RCS reduction characteristic. At oblique incidence, an effective RCS reduction characteristic can also be achieved. Compared with previous works [21; 22; 23; 24; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42], our design of the matrix-type random coding MSs has better broadband RCS reduction and wide-angle incidence tolerance. The designed MS is expected to have potential applications in the field of stealth.  
[PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data and contributed reagents/materials/analysis tools; [PERSON] wrote the paper; and [PERSON] and [PERSON] revised the paper.  
This work was supported by the National Natural Science Foundation of China (U1435209, and 61605147), and the Natural Science Foundation of Hubei China (Grant No. 2017 CFB588).  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Sample Pretreatment  
Ten commercial EVOO samples produced from five countries including Spain (S1-S4), Italy (I1-I3), Greece (G1), Portugal (P1), and Australia (A1) were purchased from local grocery stores in China. Each sample was 300-500 mL, stored in their respective original glass containers at ambient temperature, and kept sealed until analysis. Because Spain and Italy are the main producers of olive oil, the corresponding sample sets consist of four and three different manufactures, respectively. The other three EVOO origins were from the other countries to compare the possible differences between geographic location. Three commercial vegetable oils including corn, soybean, and sunflower oil were selected as possible adulterants and were purchased from local groceries.  
To simulate the adulteration of EVOOs, a series of binary blended oils were prepared by adding either corn oil, soybean oil or sunflower oil into EVOOs at percentages ranging from 10% to 50% at a 10% interval (\(v/v\)). The samples were then vortexed for 1 min until forming a homogenous suspension. The pure and the adulterated EVOOs were directly transferred to a microtiter plate without further pretreatment. The sample volume was 200 \(\mu\)L for each sample. All samples were prepared in triplicates.
## 3 Results and Discussion
### Structural Stability
The decrease in mass density with Cr content is due to the smallest density of Cr and the increase in lattice constant. The densities of Co and Fe are larger and the lattice constants decrease, causing the mass density to increase with the content of Co and Fe. Ni has the highest density, but the lattice constants increase with Ni content, and therefore the density of the alloy increases first (\(x\leq 1.57\)) and then decreases (\(x>1.57\)). That is, the relative effect of Ni content on the mass and volume of the alloy is different with different content. When \(x>1.57\) of Ni, the mass increase of the alloy is less than the volume increase, and the density appears to decrease.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
## 4 Discussion
### Reactions to Receiving an ASR  
One of the most striking findings was students' feelings after receiving an ASR: The three most common responses were feeling bad, feeling apathetic or indifferent, and experiencing academic pressure. Likewise, when asked about concerns they had when they  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Change** & **Example** & **n** & **\%** \\ \hline Make ASRs more evident & “make it an alert” & 7 & 4.2 \\  & “Perhaps make them more common? I honestly can’t remember & \\ Have them sent out earlier & any other time I have gotten one, aside from once from my & 7 & 4.2 \\ or more frequently & professor. More feedback for how a student may be doing in a class & 5 & 3.0 \\ Visual implementation in & “Don’t make them stay there forever if you recover the grade” & 5 & 3.0 \\ MyASU (student portal) & “To always have a follow up with the student (if it was negative)” & 5 & 3.0 \\ Meet with people/ & “I would make them more accessible to students” & 5 & 3.0 \\ student follow-up & “I would make them more accessible to students” & 5 & 3.0 \\ Change the implementation & “have it set up a mandatory meeting with advisor” & 3 & 1.8 \\ of ASRs & “I would exploit more information on what they are used for” & 3 & 1.8 \\  & “Change the name of it. Comes off as negative just from receiving & 2 & 1.2 \\ Get rid of ASRs & “Don’t send them out if your doing well because it causes stress & 2 & 1.2 \\ Make ASR experience more & & - & - \\ constructive & & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: _Cont._received their ASR, 40% or more of the sample reported concerns over: maintaining their GPA, failing the class, their permanent academic record, and their instructor's perception of them as a student. Some of these concerns may be barriers to re-engagement in the course. However, these concerns, such as students' worries about an instructor's judgment, can be addressed during meetings with advisors [10].  
Simultaneously, students agreed that they felt supported and noticed by the instructor after receiving an ASR, and that they felt motivated and able to stay and improve in the course, in line with prior work on student responses to academic early alerts [10; 11; 43]. Further, a search of student knowledge and stances expressed via posts by student users on social media sites (e.g., Reddit) revealed that even a negative ASR can be perceived positively as an instructor's attempt to help [44]. Together, these findings paint a nuanced picture of student responses to ASRs: Students experience negative emotions and hold understandable concerns about what an ASR means for them but are motivated to persist in the course and have self-efficacy to do so.
## 4 Vortex Shedding Characteristics by Numerical Simulations
### Numerical Analysis of VIV Performance  
Figures 17 and 18 depict the static flow around vortex distribution at the peak response point of the vertical VIV at +3\({}^{\circ}\) and +5\({}^{\circ}\) wind angles of attack for the cross-section during the construction and completion stages. The construction stage section demonstrates good aerodynamic performance, with the incoming flow producing a vortex only behind the lower chord. The remaining section does not produce a vortex of sufficient strength and scope. Hence, the wake cannot produce a sufficiently sizeable vortex-induced force, leading to the absence of the VIV phenomenon in the construction stage section. However, the presence of the railing, maintenance vehicle track, and sidewalk corbel causes non-negligible vortices near these accessory structures, which continue to shed to the wake. As a result, the intensity and range of wake vortices are significantly higher than those of the construction stage section. Among the vortex shedding of various frequencies and sizes, one vortex with a shedding frequency similar to the natural frequency of the truss girder drives the VIV at +3\({}^{\circ}\) and +5\({}^{\circ}\) wind angles of attack in the completion stage section.  
In order to find these vortices that cause VIV in the truss girder, 29 measurement points were positioned at various locations close to the girder to record wind speed time-history curves. The placement of the measurement points is depicted in Figure 19. Specifically, measurement points P1-P13 were situated around the upper deck plate, while P14 and P15 were positioned behind the windward and leeward diagonal bracing members, respectively. Measurement points P16-P29 encircled the lower deck, with P23 and P29 behind the maintenance track. Table 7 presents the characteristic frequencies of wind speed time-history curves recorded at measurement points and their corresponding Strouhal numbers.  
Figure 17: Vorticity contour of construction stage at a certain moment. (**a**) \(\alpha=+3^{\circ}\). (**b**) \(\alpha=+5^{\circ}\).  
Figure 18: Vorticity contour of completion stage at a certain moment. (**a**) \(\alpha=+3^{\circ}\). (**b**) \(\alpha=+5^{\circ}\).  
The Strouhal number of measurement points P1-P6 and P13 is 0.51, consistent with the Strouhal number of 0.47 observed at the peak response point of vertical VIV during wind tunnel tests. Similarly, the Strouhal numbers of measurement point P22 are 0.31 and 0.64, corresponding to the Strouhal numbers of 0.36 and 0.64 for minor and major torsional VIV in the tests, respectively. The above phenomenon indicates that the vortex shedding in the wake area of the upper deck or above the upper deck may cause the vertical VIV of the truss girder, while the vortex shedding in the wake area of the lower deck may cause the torsional VIV. Therefore, optimizing the sections in these areas should be prioritized when considering aerodynamic measures to suppress VIV in truss girder bridges.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Atmospheric Correction Band to Band
#### 4.2.1 V03 Method  
The V03 methodology uses the regression function with user-defined restrictions for estimating the surface albedo, \(\alpha\), (Equation (11)):  
\[\alpha=0.043+0.082\cdotp_{1}+0.064\cdotp_{2}+0.173\cdotp_{3}+0.114\cdotp_{4}+0. 237\cdotp_{5}+0.252\cdotp_{6}+0.034\cdotp_{7} \tag{11}\]  
being  
\(\rho_{\lambda}\): monochromatic reflectance of the spectral band \(\lambda\) specified for Landsat 8.  
Fulfilling the restrictions regarding the regression coefficients given in Equation (12):  
\[\sum\
olimits_{(i=0)}^{7}c_{i}=1\ and\ c_{i}\geq 0,\ for\ i=1,\ldots,7, \tag{12}\]  
This restricted methodology takes into account the physics of surface albedo and provides more realistic values.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 3 Proposed Model
### The DNN Multilayered LSTM Model  
This two-stage model includes data preprocessing and the hybrid DNN multilayered LSTM model. The preprocessing stage involves filling the missing values with the mean values, eliminating any outliers in the dataset, and data normalization for effective training. The data processing stage receives the IHEPC data, which are cleaned and passed onto the DNN layer, as in the first module. They are then transferred to the multilayered LSTM model, which is made up of two layers. The multilayer LSTM is the second module of the model. The purpose of the LSTM layers is information analysis and the prediction of the time series. The LSTM layers are then connected to the dense layer, which changes the output dimensionality of the LSTM layers.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 3 Tests and Evaluation
### Evaluations Based on the Simulation Test
It can be seen that the RPF algorithm took the most time, which was about 18.4 times, 8.9 times, 8.2 times, 2.3 times, 3.8 times, and 1.1 times more than that of Bancroft, LS, EKF, UKF, CKF, and PF, respectively. This conclusion is consistent with the results from the simulation experiment. Figure 11: CDF of 3D positioning error in the field experiment.