esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Human-Environment Coordinated Development Zoning Management of HF and EQ
#### 4.3.2 Dynamic Evolution Zoning of HF-EQ  
Next, this study calculated the CCD change rate from 2000 to 2020, with the identified dynamic evolution zones presented in Figure 9. Overall, D1 is the dominant dynamic category among the five lake-ring urban agglomerations. In contrast, D2 decreases progressively from west to east, with proportions of 45.79% in DTU, 29.37% in PYU, 19.03% in CHU, and 10.90% in HZU, but shows a rebound in the easternmost THU (23.53%). This distribution pattern indicates that excessively high and low CCD values can undermine human-environment coordinated development. For each category, (1) D1 displayed a sheet-like or belt-like pattern, widely distributed in each urban agglomeration's water-front, low-hill, and plain water network areas. This category often highly overlapped with low-CCD areas. (2) D2 in DTU and PYU was mainly located in urban core areas and ecological forest lands, while in CHU, HZU, and THU, it was primarily found in urban-rural transitional zones, mixed agricultural and construction areas, and ecological edge regions. This category highly overlapped with CCD buffer zones and high-value areas. The above patterns indicate that mixed and transformed land use patterns are key influencing factors for fluctuating human-environment system coordination. As the urbanization process advances, the interplay between HF and EQ becomes increasingly complex, emphasizing the urgent need to strengthen the regulation of deteriorating high-CCD areas.
## 3 Results
### Molecular Analysis of Putative Cotton Plants  
The presence of the Cry1 Ac gene integrated into the genome of newly transformed cotton plants were verified by PCR amplification for the CLCuV-Ac and PK2-Ac transgene, respectively (Figure 3). To evaluate Cry1 Ac endotoxin level driven by two different promoters, the relative accumulation of Cry1 Ac endotoxin protein expression driven by CaMV35S (PK2-Ac) and the bidirectional begomoviral promoter (CLCuV-Ac), respectively, were monitored at 15-day intervals for 8 weeks. Consistent expression of the endotoxin Cry1 Ac gene driven by the CLCuV-BuC1 promoter (CLCuV-Ac) was observed for the two transgenic cotton plants obtained here. The Cry1 Ac protein was detected by ELISA in transformed cotton plants expressing the Cry1 Ac gene driven by the CaMV35S promoter, however, relative endotoxin accumulation declined over time, compared to plants expressing the transgene driven by the begomoviral bidirectional C1 promoter (Figure 4). The maximum Cry1 Ac protein was detected in leaves, followed by bolls, squares, and then the anthers. All the recorded differences were highly significant as revealed by a one-way ANOVA followed by [PERSON]'s HSD analysis. The statistical analysis revealed that there were significant differences (\(p<0.05\)) for all cotton plants (Figure 4). The lowest coefficient of variance was observed for PK2-Ac 90-day-old plants, while it was higher for CLCuV-Ac plants (Figure 4). No Cry1 Ac endotoxin was detected in the roots of transgenic cotton plants (Figure 5).  
Figure 3: Detection of the Cry1 Ac gene fragment in transformed cotton plants by PCR amplification. Lanes 2–9 showed the amplicons obtained for the Cry1 Ac gene (565 bp) in putatively transformed cotton plants. Lane 1 contains the 1 Kob p DNA marker. Lanes 2–5 contains amplicons from the CLCuV-Ac transformed cotton plants. Lanes 6–9 contains amplicons from PK2-Ac transformed cotton plants. Lane 10 is the negative control, and Lane 11 contains the amplicons from the plasmid CLCuV-Ac used as the positive control.
# A Survey on Change Detection and Time Series Analysis with Applications
###### Abstract  
With the advent of the digital computer, time series analysis has gained wide attention and is being applied to many fields of science. This paper reviews many traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses with their advantages and weaknesses. First, Fourier and least-squares-based spectral analysis methods and spectral leakage attenuation methods are reviewed. Second, several time-frequency decomposition methods are described in detail. Third, several change or breakpoints detection methods are briefly reviewed. Finally, some of the applications of the methods in various fields, such as geodesy, geophysics, remote sensing, astronomy, hydrology, finance, and medicine, are listed in a table. The main focus of this paper is reviewing the most recent methods for analyzing non-stationary time series that may not be sampled at equally spaced time intervals without the need for any interpolation prior to the analysis. Understanding the methods presented herein is worthwhile to further develop and apply them for unraveling our universe.  
applied sciences; change detection; Fourier transform; least-squares; non-stationary; spectral analysis; time series; trend analysis; unequally spaced; wavelet analysis 202111641[[https://doi.org/10.3390/app1136141](https://doi.org/10.3390/app1136141)]([https://doi.org/10.3390/app1136141](https://doi.org/10.3390/app1136141))
## 4 Discussion and Examples
\begin{array}{cccc}0.9&0. 7&-0.8&1.2\\ 0.8&0.9&1.1&-0.6\\ -1.1&0.7&-0.6&0.7\\ -0.8&0.3&0.9&0.9\end{array} \right].\]  
For the above model, hypothesis \(\mathcal{H}_{1}\) holds for \(\mathcal{F}_{j}=1\), \(j=1,2,3,4\). Moreover, conditions (9) and (10) of Theorem 1 hold, since  
\[7.6=\mathcal{L}_{1}=\min_{1\leq h\leq n}(2a_{h})>\max_{1\leq h\leq n}\sum_{j= 1}^{n}\left(\mathcal{F}_{j}|w_{hj}|+\mathcal{F}_{h}|w_{jh}|\right)=\mathcal{L }_{2}=7.2>0,\]  
and  
\[0<\mathcal{L}=\mathcal{L}_{1}-\mathcal{L}_{2}=0.4<1.\]  
Figure 5: The global practical Mittag-Leffler stability behavior of the states of the model discussed in Example 2. In addition, condition (18) of Theorem 2 is satisfied for  
\[0<\hat{\mathcal{L}}=\min_{1\leq h\leq n}\left[a_{h}-\mathcal{F}_{h}\sum_{j=1}^{n} \left|w_{jn}\right|\right]=0.3<1.\]  
However, for specific \(\Theta_{h}\), \(h=1,2,3,4\), the model may not be stable or practically stable, even in cases when both conditions (11) and (19) are satisfied. A simulation is shown in Figure 6 for \(\Theta_{h}=0.8\), \(h=1,2,3,4\) such that conditions (11) and (19) are satisfied. Hence, external bias contributes to the stability behavior of a state. However, if we consider impulsive controllers in the corresponding response system (5) defined as  
\[V_{hl}(\mathcal{U}_{h}(\eta_{l}-1))=\left(1-\frac{1}{3h}\right)\mathcal{U}_{h }(\eta_{l}-1), \tag{29}\]  
at the the impulsive instances \(\eta_{l}\) that satisfy (4), then all conditions of Theorems 1 and 2 hold for \(\rho_{hl}=\frac{1}{3h}\), \(h=1,2,3,4\), \(l\in\mathbb{N}_{1}\). Therefore, the impulsive control model (5) is globally practically Mittag-Leffler stable with the trajectories shown in Figure 7.  
Figure 6: The unstable behavior of the states of the impulse-free model discussed in Example 3. Figure 7: The global practical Mittag-Leffler stable behavior of the states of the impulsive control model discussed in Example 3. **Remark 19**. : _The constructed examples demonstrate the efficacy of the established criteria. They also show that, depending on the parameters of the designed model, it is necessary to apply different criteria, which motivates the search for diverse criteria. In addition, Example 3 demonstrates that suitable impulsive controllers can be used to stabilize the unstable behavior of an impulse-free model._
# Escaping Printable Encoded Streams to Embed Out-of-Band Data
## 4 Printable Encodings and Case Studies of Payload Data Embedding
### Error Detection and Correction Information Embedding  
The stream of printable encoded data may be stuffed with sequences belonging to \(\mathcal{U}\) that encode a Cyclic Redundancy Check (CRC) [17] of a portion of data that has to be controlled for errors.  
It is possible to encode a CRC of length \(l\leq\lfloor\log_{2}(t^{v}-2^{n})\rfloor\) bits using a subset \(\mathcal{C}\) of \(2^{l}\) sequences in \(\mathcal{U}\) associating every CRC binary string of length \(l\) to one sequence in \(\mathcal{C}\) (Figure 0(a)). In this case, the proposed framework is instantiated with \(\mathcal{Z}=\mathcal{C}\).  
The maximum values of \(l\) for the encodings in Table 1 are 11 for Base41, 14 for Base45, and 27 for Base85. Longer CRC codes may be stuffed by simply concatenating more unused sequences (Figure 0(b)) and also in this case \(\mathcal{Z}=\mathcal{C}\) or, considering a single unused sequence \(\mathsf{S}_{c}\), \(\mathcal{Z}=\{\mathsf{S}_{c}\}\), as a preamble for a fixed number of legal sequences belonging to \(\mathcal{E}\) each carrying \(n\) bits of the CRC (Figure 0(c)) (see [18] for a comprehensive list of CRC polynomials).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoding** & **Encoded Bit** & \begin{tabular}{c} **Number of** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Alphabet** \\ **Smbols** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Cardiinality** \\ \end{tabular} &
\begin{tabular}{c} **Unused** \\ **Sequences** \\ \end{tabular} \\  & \(n\) & \(v\) & \(t\) & \(t^{v}-2^{n}\) \\ \hline Base16 [3] & 8 & 2 & 16 & 0 \\ Base32 [3] & 40 & 8 & 32 & 0 \\ Base64 [3] & 24 & 4 & 64 & 0 \\ Base41 [1] & 16 & 3 & 41 & 3385 \\ Base45 [5] & 16 & 3 & 45 & 25,589 \\ Base85 [9] & 32 & 5 & 85 & 142,085,829 \\ Base91 [11] & 13 & 2 & 91 & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some printable encodings with their main parameters and number of unused sequences.  
**Example 1**.: _Considering the Base41 encoding [1], an implementation of Figure 1a is to employ \(2048\) of the \(3385\) unused sequences available to stuff CRCs of length \(l=\log_{2}2048=11\) bits computed on the previous bit string for error detection._  
**Example 2**.: _Using the same Base41 encoding [1], an implementation of Figure 1b is to employ \(2048\) of the \(3385\) unused sequences available and concatenate three of them to stuff CRCs of length \(3\,l=3\log_{2}2.048=33\) bits computed on the previous bit string for error detection._  
**Example 3**.: _A possible implementation of Figure 1c with Base45 [5] is to employ one of the \(25{,}589\) unused sequences available (see Table 1) to specify that the following two sequences belonging to \(\mathcal{E}\) (each one encoding \(16\) bits) will encode a \(2\times 16=32\) bits CRC._
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology
### Foundation-Damage Classification Approach
#### 3.4.2 Deep Learning of Partially Untrained Databanks  
The foundation of the caisson system sustains various types of damage after extreme events such as storms or tsunamis. For training a CNN model, vibration data can be collected directly from these damaged caissons and labeled according to the severity of the damage. However, a concern is the availability of such data. With variations in shape, location, and severity of foundation damage, each collected sample differs and may not correctly represent feasible damage scenarios. Therefore, it is necessary to train the CNN model by utilizing limited data as well as to recognize unseen damage cases by classifying them into the trained databank of the CNN model. To assess the capability of the 1-D CNN model, certain damage levels were excluded to create partially untrained databanks.  
As shown in Figure 6, noise levels ranging from 1% to 5% were added to the 12 ensembles to create the training and validation datasets. Three untrained cases were designed to examine the effect of missing data on the performance of the 1-D CNN. In Case 1, the training and validation set excluded three damage levels: D2, D4, and D6 (from eight damage levels D0-D7), while in Case 2 and Case 3, the training and validation set excluded four damage levels (Case 2: D1, D3, D5, and D7; Case 3: D1, D2, D5, and D6). For the evaluation dataset, the remaining three ensembles were injected with noise levels from 1% to 5% with intervals of 1%.  
Figure 5: Configuration of the noise-contaminated databank.  
Figure 6: Configuration for a partially untrained databank.
## 3 \(n\)-Type Dopants
The host lattices are different; however, the key feature of the highly electronegative F in Si and Ge is that it can passivate the dangling bonds formed by vacancies. In essence, as the F atoms saturate the dangling bonds (formed by the vacancies) they concurrently solve the low activation and high diffusion of donor atom problems in Ge [97]. This is because the donor atom diffusion is vacancy mediated, whereas they are also necessary for the formation of the large donor atom-vacancy clusters that deactivate the donor profile. In the DFT study by [PERSON] et al. [97], it was calculated that F captures vacancies to form F\({}_{\text{n}}V_{\text{m}}\) clusters, in effect depriving these vacancies from the donor atoms. Although this early study employed relatively crude mass action analysis arguments, it inspired experimental work to investigate the interaction of F with defects in Ge. Interestingly, [PERSON] et al. [104] used X-ray absorption near edge structure (XANES) spectroscopy to study the local structure of ion-implanted F-doped Ge and determined that F\({}_{\text{n}}V_{\text{m}}\), and in particular F\({}_{\text{6}}V_{\text{2}}\) clusters, form in agreement with the DFT results [97]. Considering the diffusion of donor atoms in the presence of F, [PERSON] et al. [2] determined that F retards As diffusion in Ge (refer to Figure 2). [PERSON] et al. [100] determined that F implantation passivates vacancies at around 500 \({}^{\circ}\)C, and this is linked to the enhancement in Ge-MOSFET performance. It should be stressed that the search for efficient defect engineering strategies in the case of \(n\)-type doped Ge is a good example of the compatibility and complementary information that can be achieved when utilizing experiment and DFT calculations.
# Microwave Photonic Frequency Conversion Based on a Wavelength Swept Laser
## 2 Operation Principle
### Frequency Conversion Principle
The conversion coefficient \(\kappa\) is the ratio of the output frequency to the input frequency. When the sign of the WSL chirp rate and \(\beta_{2}\) were the same, \(\kappa\) was less than 1 and the signal was down-converted. While when the sign of the WSL chirp rate and \(\beta_{2}\) were the opposite, \(\kappa\) was larger than 1 and the signal was up-converted. The mathematical expression of the frequency conversion spectrum is expressed in (9), but the impact of system parameters, including \(L\), \(r\), \(\tau\) and \(\omega_{r}\) on the conversion performance, need to be optimized.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Encryption Process
#### 2.4.2 Stage-Merging Bit Scrambling  
In this part, through the redesign of the card-selection and card-exchange processes of the Knuth shuffle algorithm, the two-stage scrambling process can be merged to accomplish the double scrambling of pixel position and pixel value, with a better performance of encryption compared to single- and adjacent-pixel scrambling. The steps are as follows:  
1. Rearrange the image \(I\) in columns and rows, and convert all pixel values to binary to obtain a one-dimensional sequence _I_'.
2. Select the three pixels \(f\), \(s\), and \(t\) that will be bit scrambled each time as follows: \[K_{1}^{\prime}(i)=\text{mod}(\text{floor}(K_{1}(i)\times 10^{14}),\,MN-i-1)+1\] (5) \[f=I^{\prime}(MN-i)\] (6) \[s=I^{\prime}(K_{1}^{\prime}(i))\] (7) \[t=I^{\prime}(MN-i+1)\] (8) where floor represents rounding down, \(i=1,2,\ldots,MN-2\).
3. After each selection of pixels \(f\), \(s\), and \(t\), the binary bits will be recombined. The 8 bits of pixel \(f\) are recorded as \(f_{1}f_{2}f_{3}f_{4}f_{5}f_{6}f_{7}f_{8}\), \(s\) and \(t\) are similar. In addition, the method of recombination is shown in Figure 5, which is divided into four types. Choose the corresponding method for recombination. The calculation of \(K_{2}^{\prime}(i)\) is as follows: \[K_{2}^{\prime}(i)=\text{mod}(\text{floor}(K_{2}(i)\times 10^{10}),4)+1\] (9) where \(i=1,2,\ldots,MN-2\).
4. Perform a right cyclic shift on the recombined bit sequence _bitStr_1 to obtain a bit sequence _bitStr_2: \[bitStr_{2}=\text{cirshift}(bitStr_{1},K_{2}^{\prime\prime}(i))\] (10) where cirshift represents the right circular shift, which moves the sequence to the right, and the shifted low bits are repositioned to the high bits. \(K_{2}^{\prime\prime}(i)\) represents the number of shift bits from 1 to 7, expressed as: \[K_{2}^{\prime\prime}(i)=\text{mod}(\text{floor}(K_{2}(i)\times 10^{14}),7)+1\] (11) where \(i=1,2,\ldots,MN-2\).
5. Divide the bit sequence _bitStr_2 into three blocks every 8 bits, and reassign \(f\), \(s\), and \(t\) respectively. Thus far, the three pixels selected in this round have completed the bit-scrambling operation.
6. Repeat steps (2) to (5) until the entire sequence is scrambled, and then convert to a decimal sequence \(P\).  
Figure 6 shows the whole process of stage-merging bit scrambling for image sequences.
# Sparse Indoor Camera Positioning with Fiducial Markers
## 3 Proposed Method
### Automatic Estimation of Metagroup Edges  
Obtaining metagroup edges requires determining the set marker \(C(g^{k},g^{t})\) that has not moved between two groups. This can carried out manually by annotating them at the time of recording the images; however, it is a process subject to human error. We propose a method to automatically obtain these markers, knowing that a connection exists between the two groups. Our solution relies on the following idea. Starting with an empty set \(C(g^{k},g^{t})\), we add the pair of markers whose relative position in both groups is most similar and compute the rigid transform \(\gamma^{k,t}\) that moves their corners from one group to another. Then, we keep adding markers to the group as long as the transform error is below a threshold. Let us formally describe it below.  
Our algorithm starts by computing the relative pose between all marker pairs within the two groups:  
\[\Phi(g^{k})=\{\varphi_{m_{i},m_{q}}^{g^{k}}\},\Phi(g^{t})=\{\varphi_{m_{i},m_{ q}}^{g^{t}}\} \tag{32}\]  
As a starting point for \(C(g^{k},g^{t})\), we use the pair of markers \((m_{i},m_{q})\) with the most similar relative position in the two groups, i.e.,  
\[C(g^{k},g^{t})=\{\underset{(m_{i},m_{q})}{\arg\min}\|\varphi_{m_{i},m_{q}}^{g^{ k}}-\varphi_{m_{i},m_{q}}^{g^{t}}\|^{2}\}. \tag{33}\]  
The corners of the markers expressed in the reference system of the groups, i.e.,  
\[m_{i,l}^{g^{k}}=\varphi_{m_{i}}^{g^{k}}\cdot c_{l},m_{q,l}^{g^{k}}=\varphi_{m _{q}}^{g^{k}}\cdot c_{l}, \tag{34}\]  
are used to obtain transform between the groups, \(\gamma^{k,t}\), using the Horn transform. Then, we can calculate the error of the transform by analyzing how far these corners are when we move them from one group to another:  
\[E\Big{(}\gamma^{k,t}\Big{)}=\sum_{m_{i}\in C(g^{k},g^{t})}\ \sum_{l=1}^{4}\|m_{i,l}^{g^{k}}- \Big{(}\gamma^{k,t}\cdot m_{i,l}^{g^{t}}\Big{)}\|^{2}. \tag{35}\]  
The more fixed markers found between the groups in \(C(g^{k},g^{t})\), the more accurate the transform \(\gamma^{k,t}\) will be, so we want to add all the common fixed markers. We proceed iteratively by adding the next unselected marker that produces the smallest increment in the error \(E\Big{(}\gamma^{k,t}\Big{)}\), and we stop when the error added by a particular marker is above a given threshold \(\tau_{e}\).  
The above method has a problem, though: the pair initially selected by Equation (33) may not be correct. It may occur that some of the moved markers were placed in positions that were very similar to each other. If that happens, they could be selected in Equation (33), and the process would end with a few markers selected in \(C(g^{k},g^{t})\). To overcome that problem, we repeat the process several times by selecting the starting set \(C(g^{k},g^{t})\) as not only the best pair of markers but also the second and the third, etc. Thus, multiple \(C_{c}(g^{k},g^{t})\) sets are obtained. Amongst them, we select the one with more markers, and in the case of a tie, we choose the one with the lowest error (Equation (35)).
## 3 Results and Discussion
### Characterization Analysis and Mechanism
#### 3.3.4 FT-IR Analysis  
The samples, both before and after MC treatment for 180 min, were prepared for FT-IR analysis. The spectra in Figure 6 offer valuable insights into the structural and functional properties of the milled samples. In non-milled samples, the peaks at 1499 and 1321 cm\({}^{-1}\) represented the chain vibrations of the aromatic ring, while the peaks at 962, 763, and 695 cm\({}^{-1}\) were attributed to the stretching vibration of the C-Br bond. Additionally, the peak at 1210 cm\({}^{-1}\) denoted the asymmetrical stretching vibration of the C-O-C bond in BDE 209 [43]. Interestingly, all the abovementioned peaks disappeared in the 180 min ball-milled sample, suggesting that BDE 209 was degraded completely, and the bromine was removed from the organic structure. These observations align with the findings from the degradation and debromination experiments.
# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 3 System Description
#### Keystroke Dynamics Analyzer Agents
In the validation process, 61% accuracy was reached, which is lower than the accuracy achieved in state-of-art aspect-based sentiment analysis using supervised machine learning (68.0% to 77.2%) [9], and is on the low side of the accuracies of techniques for discriminating between different affective or valence states using keystroke dynamics (57% to 95.6%) [20]. This could be caused by the dataset of keystroke dynamics data, as it is not a very big dataset and was made using self-reporting by young people. In the case of the keystroke dynamics stress analyzer agent, we used the same ANN architecture as the previous agent, as it was found experimentally to be the best for model accuracy. Again, the keystroke data in the dataset was used for the training, but this time using the stress labels to train the model. Finally, in the validation process, 64% accuracy was reached this time; that is better than the case of the keystroke dynamics sentiment analyzer agent, and is higher than the accuracies found by using different machine learning methods for stress strength detection as shown in [6], but is lower than the accuracy of 75% reported by [14] when detecting stress via a combination of keystroke dynamics and linguistic analysis. There were machine learning methods employed for detecting stress using keystroke dynamics with user personalized models (detection of stress with models trained to identify stress in a concrete user), which reached nearly 90% accuracy [20], and 96.76% to 99.5% accuracy detecting stress states based on the time of the day [21]. However, since these methods are based on user personalized models, they are not comparable to our approach, which is based in user-independent models (we use the same models to predict stress in all the users instead of personalized models).