esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 3 Experimental Results and Discussion
### Evaluation Criterion  
In this paper, the evaluation criteria consist of two parts, blind pixel detection evaluation criterion and flickering pixel suppression evaluation criterion.  
We adopt the number of detected blind pixels, the false detection rate of detected blind pixels, and the average running time per frame to evaluate the effect of blind pixel detection. The number of detected blind pixels (\(N_{bp}\)) denotes the number of constant blind pixels and flickering pixels detected by a certain method. The false detection rate of detected blind pixels is given by Equation (12).  
\[R_{fbp}=\frac{N_{fbp}}{N_{bp}}\cdot 100\%, \tag{12}\]  
where \(N_{fbp}\) is the number of normal pixels that are falsely detected as blind pixels by a method. In this paper, we obtain calibration data, and \(N_{fbp}\) can be measured by the method given in national standard [24]. Finally, the average running time per frame (\(T_{pf}\)) can present the operating efficiency of a method. The proposed method and other methods are implemented under MATLAB R2018a with an Intel Core 2.80 GHz processor and 8 GB of physical memory.  
The number of suppressed flickering pixels, the miss suppression rate of flickering pixels, and the false suppression rate of flickering pixels form the evaluation criterion for flickering pixel suppression. The number of suppressed flickering pixels (\(N_{sf}\)) means the total number of frames suppressed for all flickering pixels. The miss suppression rate of flickering pixel (\(R_{msf}\)) is defined in Equation (13).  
\[R_{msf}=\frac{\sum_{n=1}^{N}N_{msf}(n)}{\sum_{n=1}^{N}N_{rf}(n)}\cdot 100\%, \tag{13}\]  
where \(N\) is the flickering pixel amount, and \(N_{msf}(n)\) refers to the frame amount that should be suppressed but not be suppressed of the \(n\)\(th\) flickering pixels. \(N_{rf}(n)\) is the frame amount of blinking of \(n\)\(th\) flickering pixel. Similarly, false suppression rate of flickering pixel (\(R_{fsf}\)) is given by Equation (14).  
\[R_{sf}=\frac{\sum_{n=1}^{N}N_{sf}(n)}{\sum_{n=1}^{N}N_{rf}(n)}\cdot 100\%, \tag{14}\]  
where \(N_{sf}(n)\) refers to the frame amount that false suppressed of the \(n\)\(th\) flickering pixel. As is mentioned above, the evaluation of flickering pixel suppression uses simulation data.  
Figure 6: Experimental data acquisition details (**a**) gathering method of the sky background, (**b**) semi-physical simulation method of deep space background, (**c**) the detail of how the IRPT moves in deep space background of semi-physical simulation.  
Therefore, the \(N_{rf}(n)\), \(N_{fsf}(n)\), and \(N_{msf}(n)\) in Equations (13) and (14) can be recorded during simulation.
## 3 Test Results and Analysis
### Peak Stress and Strain at Peak Stress  
The peak stress \(q_{\text{max}}\) and strain at peak stress \(\varepsilon_{q\text{max}}\) diagrams of PCS samples can be obtained from the relevant data in Figure 2, as shown in Figures 3 and 4.  
Figure 3: Peak deviatoric stress of PCS (fiber-cement-treated subgrade soil with polypropylene (PP) fiber).  
Figure 2: Deviatoric stress-axial strain curve with various fiber mass contents; (**a**) 0%o fiber mass content; (**b**) 2%o fiber mass content; (**c**) 4%o fiber mass content; (**d**) 6%o fiber mass content; (**e**) 10%o fiber mass content.  
It can be seen from Figures 3 and 4 that the peak stress and strain at peak stress of PCS samples gradually increase with the increase in confining pressure given a constant fiber mass content. In addition, under the same confining pressure, the peak stress and strain at peak stress of PCS samples first increase and then decrease with the increase in fiber mass content. When the fiber mass content is 6%o, the peak stress and strain at peak stress reach the maximum. Compared with the 0%o PCS sample, with the change in confining pressure, the peak stress increases from 24% to 29% and the strain at peak stress increases from 85% to 120%.  
The above results show that the modification effect of fiber increases with the increase in confining pressure; among them, the ductility is significantly improved.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 3 Proposed Scheme  
In this section, we elaborate on our RDH scheme for encrypted color halftone image. The block diagram of our method is illustrated in Figure 1.  
Figure 1: Block diagram of the proposed scheme.  
The overall procedure of proposed RDH-EH algorithm can be briefly described as follows. First, the image owner encrypts the original halftone image \(\mathbf{O}\) using encryption key \(K_{e}\) and then sends the produced cover image \(\mathbf{I}\) to the service provider. The provider first converts \(\mathbf{I}\) into channel images \(\mathbf{R}\), \(\mathbf{G}\), and \(\mathbf{B}\). For cover image \(\mathbf{R}\), he copies it into two identical cover image \(\mathbf{R}^{\prime}\) and \(\mathbf{R}^{\prime\prime}\). After the first WPC embedding procedure is complete, two stego image \(\mathbf{F}_{R^{\prime}}\) and \(\mathbf{F}_{R^{\prime\prime}}\) are generated. The stego image \(\mathbf{F}_{R^{\prime}}\) is the cover image of the second WPC embedding process and is copied into two identical images \(\mathbf{F}_{R^{\prime}}^{\prime}\) and \(\mathbf{F}_{R^{\prime\prime}}^{\prime\prime}\). There will be stego images \(\mathbf{S}_{R^{\prime}}\) and \(\mathbf{S}_{R^{\prime\prime}}\) after the second WPC. Next, the service provider uses \(\mathbf{S}_{R^{\prime}}\) in the third embedding procedure and also copies it into two images \(\mathbf{S}_{R^{\prime}}^{\prime}\) and \(\mathbf{S}_{R^{\prime\prime}}^{\prime\prime}\). After that, two stego images \(\mathbf{T}_{R^{\prime}}\) and \(\mathbf{T}_{R^{\prime\prime}}\) are produced. Then, the provider combines \(\mathbf{T}_{R^{\prime}}\), \(\mathbf{F}_{R^{\prime\prime}}\), \(\mathbf{S}_{R^{\prime\prime}}\), and \(\mathbf{T}_{R^{\prime\prime}}\) into one image \(\mathbf{C}_{R}\); the specific way of combining them is elaborated in Section 3.2. For cover images \(\mathbf{G}\) and \(\mathbf{B}\), be executing the same embedding process as described above, the stego images \(\mathbf{C}_{G}\) and \(\mathbf{C}_{B}\) are generated. Finally, the service provider merges stego images \(\mathbf{C}_{R}\), \(\mathbf{C}_{G}\), and \(\mathbf{C}_{B}\) into one stego image \(\mathbf{C}\).  
On the receiver's side, depending on the limits of authority, he can do different operations to the image. If the receiver has only the data-hiding key \(K_{d}\), he can extract the watermark \(\mathbf{b}\). If he has both \(K_{e}\) and \(K_{d}\), the receiver can extract the watermark \(\mathbf{b}\) and recover the original image \(\mathbf{O}\). Because we combine four images into one stego image during the embedding procedure, if the receiver has only the \(K_{e}\), he does not have access to the content of the original image after decryption.
## 3 Results and Discussion
### Analysis of Contour Reconstruction Reliability for S02
In this case, the FBS-based analysis correctly ignored this protuberance, showing more robustness against \"ghostly\" features than the CMM. In any case, a robust contour-detection procedure must be aware of the possible presence of these abnormalities and apply adequate treatments to avoid their negative influence on contour reconstruction. Finally, the relevance of the annular cropping strategy can be observed in Figure 13.  
Processing the whole 502 image with a \(T15/S_{f}15\) combination provided several unlikely contour candidates. These segments could be located inside the top layer, such as A or B details, or in the background, such as detail C (Figure 13). Although this combination did only introduce few unlikely contour candidates without annular cropping, they had a huge influence on MQI results (Table 5). Figure 11: Polar graphs of \(R_{d}\) calculated for S02 including reference CMM data (red dots) and three different test conditions (blue dots). Figure 12: Samples of S02 original scans showing a seam in the 180\({}^{\circ}\) orientation (**left**) and a “ghostly” feature in the 293\({}^{\circ}\) orientation (**right**). At the same time, the large segment in detail A reduced the apparent \(R_{d}\) of its corresponding sectors. A similar, discontinuous phenomenon is present in detail B. On the other hand, the small segment in detail B artificially enlarged \(R_{d}\). When annular cropping was applied (Figure 13, right), the effect of such localised errors was minimised, although not completely removed. For example, the detail D shows a multi-contour issue that lies inside the remaining area after the annular cropping step, and its effect on \(R_{d}\) can be clearly seen. Contrary to the usual situation when recognising contours on digital images, the specific characteristics of AM layer contour characterisation allow for an a priori coarse approximation of the test target location in the image. Accordingly, this approximation could be used to generate a reduced area of interest, cropping the rest of the image, and avoiding external features to introduce noise during contour characterisation. The FBS-based contour characterisation of AM layers should take advantage of this particularity, as it was the case of the analysis presented here.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
###### Abstract  
In this paper, a phase-scheduled-command filtered-x least mean square (FXLMS) algorithm with a phase error between the disturbance and command signal is analyzed in detail. The influence of the phase error on the control effort, convergence time constant and performance of convergence is explained for both stationary and nonstationary disturbance signal cases. An estimation ratio, in both stationary and nonstationary disturbance cases, of the pseudo-error MSE convergence performance is also developed and discussed. Simulation results were collected to verify the analysis. The results show that, for both stationary and nonstationary disturbance, the phase error may heavily increase the distance of the optimum vector from the initial value, leading to a large control effort, a large convergence time constant and poor convergence performance. The estimation ratio also has a satisfactory performance for estimating the influence of the phase error.  
active noise control; phase-scheduled-command FXLMS; active sound profiling; phase error +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of Soil pH on Odor Activity Value (OAV) of Key Compounds in Tea Leaves
[47] found that geraniol was one of the key compounds in the aroma formation of black tea, and its content determined the intensity of the aroma of black tea. [PERSON] et al. [48] analyzed the degrees of contribution of the main aroma compounds to the intensity of the aroma of different black teas and found that geraniol was the compound that made the greatest contribution in black tea. It can be seen that the formation of the aroma characteristics of tea leaves is closely related to the geraniol content in the tea leaves, and the key to the decrease in the aroma quality of the Tieguanyin tea leaves is soil acidification, as the acidification caused a decrease in the geraniol content in the tea leaves. The odor characteristics of the compounds in tea can be mainly divided into six types, namely woody, floral, burnt, green, fruity and fatty [24]. Based on the determination of the compound contents and with reference to the contribution of different compounds to different odor characteristics (Table S1), this study further divided the odor characteristics of the key compounds into six categories, namely woody, floral, burnt, green, fruity and fatty. An analysis of the effect of soil pH on odor characteristics showed (Figure 2B) that with an increase in the soil pH (3.29-5.32), the OAV values of the woody, floral and burnt odor characteristics of the tea leaves showed a significant increase, while the OAV values of the green and fruity characteristics decreased and then increased, and the OAV value of the fatty characteristic showed a change in volatility. A further analysis revealed that the floral OAV value the floral characteristic was the largest among the different odor characteristics of tea leaves, and the floral odor presented a significant trend of increasing with an increase in the soil pH. Secondly, this study also found that the floral odor characteristics of the Tieguanyin tea mainly came from the contribution of geraniol compounds (Tables S3-S6). It has been reported that geraniol is a monoterenoid, and in plant biosynthesis, lauricene is the raw material for the synthesis of geraniol, whereas when the soil pH decreases, the levels of expression of monoterense synthase genes and lauricene synthase genes in tea tree leaves show a decrease which, in turn, decreases the geraniol content [41]. Furthermore, a floral fragrance is the main aroma characteristic of Tieguanyin tea, and the stronger the aroma, the better the aroma quality of the tea and vice versa [49; 50; 51]. It can be seen that soil acidification caused a decrease in the aroma quality of Tieguanyin tea, mainly due to the decrease in the geraniol content in the tea leaves which, in turn, led to a decrease in the strength of the floral odor characteristics.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 5 Results
### Optimal 2D Sparse Array
#### 5.1.2 Imaging  
To validate the performance of the presented configuration, a model based on a 1951 USAF MIL-STD-150A resolution test chart was used as the OUT. This type of geometry consists of several groups of bars (in this specific case modeled as metal plates) separated by a certain distance and is widely used to analyze and validate imaging systems. The CAD model used in the following simulation is shown in Figure 7a, and Table 1 specifies its geometry.  
To perform the experiment, the OUT was placed at a standoff distance of 3 m. As can be seen in Figure 7b, the reconstruction replicates all the details of the original image. This shows that the system has a very high resolution, since it is able to reconstruct all the elements of the two groups of bars, which indicates that it is able to detect targets with an accuracy of less than 1 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Resolution (rr) Analysis**} \\ \hline \multicolumn{3}{c}{**Distance between Bars**} \\ \hline \hline  & \multicolumn{2}{c}{**Group Number**} \\ \hline
**Element** & \(-\)**2** & \(-\)**1** \\ \hline
1 & 2.5 cm & 0.85 cm \\ \hline
2 & 1.75 cm & 0.75 cm \\ \hline
3 & 1.5 cm & 0.5 cm \\ \hline
4 & 1 cm & 0.25 cm \\ \hline
5 & & 0.2 cm \\ \hline
6 & & 0.1 cm \\ \hline \multicolumn{3}{c}{Vertical spacing between elements} & 1 cm \\ \hline
2 ar width (bw) & 1 cm \\ \hline
2 ar length (bl) & \(3 bw+2 rr\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Custom 1951 USAF MIL-STD-150A model geometry definition.  
Figure 6: Non-optimized array configuration and its corresponding PSF. (**a**) Array configuration. (**b**) PSF.  
Runtime Simulation Performance  
For imaging purposes, an XZ plane measuring 36.5 cm by 31 cm was defined, with a fine 2.5 mm discretization, resulting in a total of 18.375 pixels in the image plane. As detailed in Section 2.2, each \(\text{tx}_{\text{m}}\)-\(\text{rx}_{\text{n}}\) pair generates an individual image, leading to a substantial total of 3.136 different images to be combined within this architecture.  
Remarkably, HW-1 processes these images in a mere 0.03 s, but with the utilization of HW-2 this processing time is dramatically reduced to an impressive 0.008 s. Such exceptional performance gains can be attributed to harnessing GPU resources and leveraging MATLAB's robust vector computing capabilities. Our unwavering commitment to efficiency and rapid processing drives us to continually improve our software. We actively explore alternative approaches to optimize performance, consistently pushing the boundaries of what can be achieved in the field.
## 2 Mathematical Modeling  
This section develops a mathematical model to investigate the PSA process. The mathematical model includes mass, momentum, energy balance equations, adsorption isotherms, and kinetics. Meanwhile, the mathematical model is based on the following assumptions:  
1. The ideal gas law is applicable.
2. An axially dispersed plug flow model is used to describe the flow pattern.
3. Radial variations in temperature, pressure, concentration, and velocity are negligible.
4. The inter-phase mass transfer coefficient is expressed using the linear driving force (LDF) model.
5. The aging of adsorbents is not considered.
# Foremost Walks and Paths in Interval Temporal Graphs +
## 6 Linear Combination of Optimization Criteria  
[PERSON] et al. (2010) present a polynomial time algorithm that optimizes any linear combination of the eight optimization criteria \(foremost\), \(reverse\_foremost\), \(fastest\), \(shortest\), \(min\_hop\), \(cheapest\), \(most\_likely\), and \(min\_wait\) in a contact-sequence temporal graph. In this section, we show that, when time is discrete, we can use the algorithm of [PERSON] et al. (2010) to solve the \(mhf\), \(mwf\) and \(mcf\) problems in polynomial time for contact-sequence graphs. This algorithm may also be used to find \(mhf\), \(mwf\), and \(mcf\) paths in interval temporal graphs by first transforming the interval-temporal graph into an equivalent contact sequence graph as described in Section 1. Since this transformation may increase the number of edges by an exponential amount, this approach does npt result in a polynomial time algorithm for interval-temporal graphs. However, it does result in a pseudopolynomial time algorithm as shown in Theorem 17.  
To solve \(mhf\), for example, for contact-sequence graphs, we perform the following:  
1. Set the coefficient for every criterion other than \(min\_hop\) and \(foremost\) to 0.
2. The coefficient for the foremost criterion may be set to any integer that is greater than or equal to the number of vertices, \(n\), in the contact-sequence graph.
3. Set the coefficient for the min-hop criterion to 1.  
With these settings, [PERSON] et al. (2010) will find walks that minimize \(h(s,v)=c_{f}*t_{a}(v)+hops(v)\), where \(c_{f}\) is the coefficient for the foremost criterion, \(t_{a}(v)\) is the time at which the walk from \(s\) arrives at \(v\), and \(hops(v)\) is the number of hops in the walk. Since a min-hop walk is necessarily a min-hop path, it has no more than \(n-1\) hops. If we examine the digits of \(mhf(s,v)\), which is a non-negative integer, using the radix \(c_{f}\), the least significant digit is the number of hops and the remaining digits give \(t_{a}\). Hence, \(mhf(s,v)\) is minimized by min-hop foremost paths to \(v\) and not by any other path or walk.  
The strategy for \(mwf\) and \(mcf\) is similar. In the case of \(mwf\), the function to optimize is \(mwf(s,v)=c_{f}*t_{a}(v)+wait(s,v)\) and for \(wcf\), the optimization function is \(wcf(s,v)=c_{f}*t_{a}(v)+cost(s,v)\), where \(wait(s,v)\) is the total wait time on the walk from \(s\) to \(v\) and \(cost(s,v)\) is its cost. For \(mwf(s,v)\), \(c_{f}\) must be larger than the maximum total wait time on an optimal foremost walk from \(s\) (a simple bound is the maximum arrival time of a foremost path to reachable vertices), and for \(mcf(s,v)\), \(c_{f}\) must be larger than the maximum cost of an optimal foremost path (a simple bound to use is the sum of the maximum cost of each edge). For simplicity, we use \(c_{f}=2^{32}\) in our experiments, as this is large enough for our data sets.  
It is easy to see how the above modeling strategy may be used to find, say, min-cost foremost paths with the fewest number of hops.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
###### Abstract  
Land surface albedo plays an extremely important role in the surface energy budget, by determining the proportion of incoming solar radiation, which is available to drive photosynthesis and surface heating, and that which is reflected directly back to space. In northern high latitude regions, the albedo of snow-covered vegetation and open, leafless forest canopies in winter, is quite high, while the albedo of boreal evergreen conifers can either be quite low (even with extensive snow lying under the canopy) or rather bright depending on the structure and density of the canopy. Here, we present the further development and evaluation of a 30 m Landsat albedo product, including an operational blue-sky albedo product, for application in the circumpolar domain. The surface reflectances from the Landsat satellite constellation are coupled with surface anisotropy information (Bidirectional Reflectance Distribution Function, BRDF) from the MODerate-resolution Imaging Spectroradiometer (MODIS). The product is extensively validated across diverse land cover and conditions and performs well with root mean squared error of 0.0395 and negligible bias when compared to coincident tower-based albedo measurements. The development of this Landsat albedo products allows for better capture of ephemeral, heterogeneous and dynamic surface conditions at the landscape scale across the circumpolar domain.  
albedo; Landsat; Arctic; boreal; circumpolar Article
## 4 Methods
### CRF Layer  
When performing named entity recognition tasks in the field of power marketing, the DFENN model, although capable of extracting global and local features of text sequences, does not handle the dependencies between adjacent tags. To solve this problem, Conditional Random Fields (CRF) are introduced, and CRF can obtain the globally optimal tag sequence by considering the relationship between neighboring tags. For the input sequence \(X=(X_{1},X_{2},\ldots,X_{n})\), extracting features can obtain the output matrix \(P=(P_{1},P_{2},\ldots,P_{n})\), and for the prediction sequence \(Y=(Y_{1},Y_{2},\ldots,Y_{n})\), its score function is calculated as shown in Equation (17).  
\[S(X,Y)=\sum\
olimits_{i=0}^{n}A_{y_{i},y_{i+1}}+\sum\
olimits_{i=1}^{n}P_{i, y_{i}} \tag{17}\]  
In the equation, \(A_{y_{i},y_{i+1}}\) represents the fraction of \(y_{i}\) transferred to \(y_{i+1}\), and \(P_{i,y_{i}}\) represents the fraction of the character predicted to be the first label. Firstly, the softmax layer is used to count the probability of all possible labels identified, and finally the label sequence with the highest probability is output.  
When building the corpus in the field of power marketing, due to the large difference in the frequency of some entity types in the power marketing data, it leads to the problem of unbalanced sample distribution in the corpus. For example, the number of entity types \"equ\" far exceeds the number of \"name\" entity types, which leads to an imbalance in the distribution of the loss function during the training process, and eventually the model will prefer the labels with a large number of samples, and for the samples. The recognition performance of labels with fewer samples is poor. In this paper, we use the focal loss function to alleviate the problem caused by the unbalanced distribution of samples, and the predicted entities are passed through the focal loss function to obtain the loss values, and the algorithm is shown in Equation (18).  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\text{log}(p_{t}) \tag{18}\]  
In the equation, \(p_{t}\) represents the probability that the sample is correctly identified, and the larger its value represents the easier the sample is identified. When the sample is correctly identified, \(p_{t}\to 1\), whereas when the sample is incorrectly identified, \(1-p_{t}\to 1\). In this paper, by adjusting for different values of \(\alpha\) and \(\gamma\) in the focal loss function, \(\alpha\) set to 0.25 and \(\gamma\) set to 2 can achieve the best results. The model reduces the loss for accurately identified samples during the training process and increases the weight of the hard-to-identify samples in the loss function, thus, the model's recognition performance improves.