esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition  
Before capturing images, a number of coded targets, at least two, had to be placed near the object of interest to be utilized as ground control points for scaling the generated 3D data. In this study, four coded targets were placed around each specimen. Table 2 provides  
Figure 3: The study workflow.  
the number of images captured for each specimen. The same number of images was taken by both cameras for each specimen. Additionally, the images of each specimen were taken almost from the same positions, following the same track for both cameras. By doing so, the number of images, overlapping percentage, and camera movement were held the same for both cameras so that the accuracy and quality assessments would be entirely based on the camera specifications. The camera settings with which the images were captured are provided for both cameras in Table 3.  
The image quality values provided in Table 2 present the average quality value ranging from 0 to 1 of all captured images in the same data set. This value was computed using a built-in function in Agisoft Metashape software [42]. It estimates the overall quality of a given image based on its level of sharpness relative to the other images in the same data set. It was shown that the average image quality of the smartphone images (SP) was slightly higher than those of the digital camera (DC). This was due to the image format; the compact camera images were taken with RAW (NEF) format, which prevents any auto adjustment to the image in terms of its brightness and sharpness. However, with the JPG format, the images' level of brightness and sharpness were optimized automatically by a built-in feature in the smartphone camera (i.e., autofocus). Images with quality values less than 0.5 are recommended by Agisoft Metashape to be eliminated. In this study, all the images captured by both cameras had quality values greater than 0.7. Table 3 also shows that the area scanned with the smartphone camera was greater relative to the digital camera for all specimens. This is attributed mainly to the camera focal length. The shorter the focal length, the wider the angle of view, and the larger the captured area.
## 4 Results and Discussion
### Experimental Validation of Current Injection Models
#### 4.2.2 Heaviside Step Function
\right] \tag{37}\]  
Figure 14: Boundary illustration of the current injection model using the Heaviside step function for Case 2 (\(w\): electrode width). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental Configuration** & **Measured** & **Calculated** & \multicolumn{2}{c}{**Residuals**} & \multicolumn{1}{c}{**1 Res./Cal. **} \\ \hline Set \#1 & V1 & 0.384 & 0.949 & \(-0.565\) & 59.536 \\ \hline  & V2 & 0.964 & 1.058 & \(-0.094\) & 8.885 \\ \cline{2-6}  & V3 & 0.964 & 0.766 & 0.198 & 25.849 \\  
As a result, \(A_{n}\) and \(B_{n}\) can be obtained as follows:  
\[ \begin{split} A_{n}&=\frac{2\cdot\frac{\left[\frac{I}{ \sigma\cdot w}\right]}{n\pi\tanh\frac{(n\pi w)}{\pi\tanh\frac{(n\pi w)}{b}}} \int_{y_{1}-\frac{3}{2}}^{y_{1}+\frac{w}{2}}\cos(\frac{n\pi y}{b})dy+\frac{2 \left(-\frac{I}{\sigma\cdot w}\right)}{n\pi\sinh\frac{(n\pi y)}{b}}\int_{y_{2} -\frac{w}{2}}^{y_{2}+\frac{w}{2}}\cos(\frac{n\pi y}{b})dy\\ B_{n}&=-\frac{2\cdot\frac{I}{\sigma\cdot w}}{n\pi} \int_{y_{1}-\frac{w}{2}}^{y_{1}+\frac{w}{2}}\cos(\frac{n\pi y}{b})dy\end{split}  \tag{38}\]  
Furthermore, \(a_{0}\) and \(b_{0}\) are calculated as follows:  
\[ \begin{split} a_{0}&=\frac{1}{b}J_{0}^{b}-f(y)dy=- \frac{1}{b}J_{y_{1}-\frac{w}{2}}^{y_{1}+\frac{w}{2}}\left(\frac{I}{\sigma\cdot w }\right)dy=-\frac{1}{b}\cdot\left(\frac{I}{\sigma}\right)\\ b_{0}&=-\frac{a_{0}}{2}\end{split}  \tag{39}\]  
When all coefficients (\(A_{n}\), \(B_{n}\), \(a_{0}\), and \(b_{0}\)) are applied to Equation (33), function \(u(x,y)\) is the electric potential field over the rectangular domain. Table 6 shows a comparison between the measured and calculated voltages using the Heaviside step function. Throughout the fitted values, the electrical conductivity was 16.073 mS\(\cdot\)m\({}^{-1}\), and the RAE was 4.218%. Compared to the Dirac delta function, the RAE decreased from 4.372% to 4.218%. This reduction was evenly distributed across all data. This is clearly shown in the histogram and Q-Q plot of Figure 15. The asymmetry in the normal distribution was lower than that in the Dirac delta function. Although a heavy tail was also observed in the Q-Q plot of Case 2, the data were evenly distributed above and below the reference line of the plot in the \(-1\) to \(1\) interval of the standard normal quantiles. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental Configuration** & **Measured** & **Calculated** & \ \begin{tabular}{c} **Residual** \\ **(Mea\(\_-\)Cal.)** \\ \end{tabular}  & \begin{tabular}{c} **1 Res./Cal.** \\ **(\%)** \\ \end{tabular}  \\ \hline Set \#1 & V1 & 17.923 & 17.881 & 0.041 & 0.229 \\ \hline  & V2 & 8.716 & 8.816 & \(-0.101\) & 1.146 \\ \hline Set \#2 & V1 & 16.638 & 16.828 & \(-0.189\) & 1.123 \\ \hline  & V2 & 7.981 & 7.871 & 0.110 & 1.398 \\ \hline Set \#3 & V1 & 15.412 & 15.518 & \(-0.106\) & 0.683 \\ \hline  & V2 & 7.090 & 6.817 & 0.273 &
# Efficient Preamble Design Technique for Millimeter-Wave Cellular Systems with Beamforming
## 5 Conclusions  
In this paper, an efficient preamble design technique is proposed for OFDM-based mmWave cellular systems with beamforming, by combining two ZC sequences in the frequency domain. The parameter selection methods for the proposed preamble are also described for avoiding the performance degradation caused by the side-peaks (high correlation), STO, and ambiguities in the (GID, SID, BID). It was shown by simulation that the proposed techniques (Type-1 and Type-2) can obtain a significant complexity reduction in the CBID detection without a noticeable performance degradation compared to the previous technique. The number of complex multiplications required for the Type-2 preamble is reduced to 0.6% compared to the previous technique. The performance degradation caused by the side-peaks in the Type-2 preamble can be avoided by selecting appropriate root index pairs in the cell planning stage. With a similar computational complexity for the CBID detection, the proposed technique can provide an increase in the number of CIDs by a factor of 43, compared to the previous technique.  
**Acknowledgments: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1007) supervised by the IITP (Institute for Information & communications Technology Promotion), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057628).**  
**Author Contributions: [PERSON] and [PERSON] developed a preamble design technique for OFDM-based mmWave cellular systems with beamforming. [PERSON] supervised the project. Conflicts of Interest: The authors declare no conflicts of interest.**
## 4 System Implementation  
### Active and Reactive Power References  
Under unbalanced and distorted grid voltage conditions, not only the system total current will be distorted, but also the system power, DFIG torque and dc-link voltage will pulsate. To satisfy the demand of accessing network, the system total currents must be sinusoidal. Both the target of RSC and GSC are set to eliminate currents' unbalanced and harmonic components. Thus, the references of stator active and reactive power \(P_{\rm s}^{*}\), \(Q_{\rm s}^{*}\) are derived as  
\[P_{\rm s}^{*}=-1.5\Big{(}u_{\rm s\alpha}i_{\rm s\alpha}^{*}+u_{\rm s\beta}i_{ \rm s\beta}^{*}\Big{)};\ Q_{\rm s}^{*}=-1.5\Big{(}u_{\rm s\beta}i_{\rm s\alpha}^ {*}-u_{\rm s\alpha}i_{\rm s\beta}^{*}\Big{)} \tag{22}\]  
\[i_{\rm s\alpha}^{*}=-1.5(P_{\rm scom}u_{\rm s\alpha+}+Q_{\rm scom}u_{\rm s\beta +})/{\rm U}_{+}^{2};i_{\rm s\beta}^{*}=-1.5(P_{\rm scom}u_{\rm s\alpha+}-Q_{ \rm scom}u_{\rm s\beta+})/{\rm U}_{+}^{2} \tag{23}\]  
where \(U_{+}^{2}=u_{\rm s\beta+}^{2}+u_{\rm g\alpha+}^{2};P_{\rm ecom}\), \(Q_{\rm ecom}\) are the commands of stator average active and reactive power.  
Similarly, the references of GSC active and reactive power \(P_{\rm g}^{*}\), \(Q_{\rm g}^{*}\) are  
\[P_{\rm g}^{*}=-1.5(u_{\rm g\alpha+}i_{\rm g\alpha}^{*}+u_{\rm g\beta+}i_{\rm g \beta}^{*});Q_{\rm g}^{*}=-1.5(u_{\rm g\beta+}i_{\rm g\alpha}^{*}-u_{\rm g \alpha+}i_{\rm g\beta}^{*}) \tag{24}\]  
\[i_{\rm g\alpha}^{*}=-(u_{\rm s\beta+}Q_{\rm gcom}-u_{\rm s\alpha+}P_{\rm geom })/(1.5{\rm U}_{+}^{2})\ ;i_{\rm g\beta}^{*}=-(u_{\rm s\beta+}P_{\rm geom}-u_{\rm s\alpha+}Q_{\rm gcom })/(1.5{\rm U}_{+}^{2}) \tag{25}\]  
where, \(P_{\rm geom}\) and \(Q_{\rm ecom}\) represent the commands of the GSC average active and reactive power.  
Figure 5: Phase plane trajectory with different tracking reference. (**a**) Phase plane trajectory with different tracking references of ISMC; (**b**) Phase plane trajectory with different tracking references of MRSMC.
# Michelson Interferometric Methods for Full Optical Complex Convolution
## 4 Applications
This feature is particularly valuable for applications in artificial intelligence and signal processing, where convolution operations are prevalent and computationally intensive. The integration of these functionalities--multi-image recognition, secure hashing, and efficient convolution--represents a convergence of machine learning and optical data processing. This integration could lead to more efficient and secure data handling in various fields, from autonomous systems to financial technology. For future directions, we are exploring two promising directions for further development. First, we are investigating the potential of self-designed indium tin oxide (ITO)-based SLMs, which could significantly increase the system's operating speed and efficiency. Second, we are considering the transition of RCCM functionality to photonic integrated circuit (PIC) platforms. Recent advancements in PIC technology offer various options for implementing our system on a more compact and scalable platform. These developments could extend the RCCM's capabilities to other computation-intensive algorithms, such as those used in deep learning neural networks. We aim to push the boundaries of optical and photonic computing, addressing the growing demands of data processing in the digital age with innovative and secure solutions. This approach not only promises to enhance computational capabilities but also offers potential benefits in energy efficiency and processing speed for a wide range of applications. Figure 6: Concept of optical hashing algorithm and the scheme of the proposed full optical setup using RCCM for optical Fourier full convolution accelerator. An example of a 1-D compressed sequence read of the simulated camera reading that corresponds to MNIST input “5”, “0”, and “4”; The scheme of the proposed optical setup, where the source can be a single-mode fiber array  
collimated into free space or coupled in a photonic chip; diffusion can be achieved by an optical amplitude-only modulation by an SLM; convolution must be performed with complex amplitude-and-phase convolution in the Fourier domain; the optional final step of image classification with a heterogeneous convolutional 4F classifier can be alternatively replaced by a CMOS diode array (DA). All inserted figures along the axis show what pattern is presenting on the SLMs or the current status of the modulated signal carried by the beam.
# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 5 Experimental Evaluation
### Experiment Description  
**Dataset:** We evaluate our algorithms using publicly available datasets. The Adult dataset from the UC Irvine Machine Learning Repository ([[https://archive.ics.uci.edu/dataset/2/adult](https://archive.ics.uci.edu/dataset/2/adult)]([https://archive.ics.uci.edu/dataset/2/adult](https://archive.ics.uci.edu/dataset/2/adult)) (accessed on 10 September 2023)) and the Census dataset from the US full 1990 census ([[https://archive.ics.uci.edu/dataset/116/us+census+data+1990](https://archive.ics.uci.edu/dataset/116/us+census+data+1990)]([https://archive.ics.uci.edu/dataset/116/us+census+data+1990](https://archive.ics.uci.edu/dataset/116/us+census+data+1990)) (accessed on 10 September 2023)) are selected. These datasets are the de facto standards for the evaluation of anonymization. The Census dataset contains a one-percent sample of the Public Use Microdata Samples (PUMS) person records. The Adult dataset consists of 32,562 records with 14 attributes, of which three are numerical and the rest are categorical. The Census dataset consists of 2,458,285 records. All attributes are categorical. Six attributes from the Adult and Census datasets were used in our experiment. For the Adult dataset, we consider {age, workclass, marital} as attribute set \(QI_{1}\) and {occupation, relationship, native-country} as attribute set \(QI_{2}\). For the Census dataset, we consider {dAge, dAncstry1, dAncstry2} as attribute set \(QI_{1}\) and {iClass, dDepart, dHispanic } as attribute set \(QI_{2}\). The experiments simulate the condition of aggregated datasets from two different sources.  
**Experimental environment and algorithms**: The experiments are carried out on a machine featuring a 3.0 GHz Intel(R) Core(TM) i5 processor with 12 GB RAM. The operating system used is Ubuntu 22.04, and the implementation is developed and executed on an Intellij IDEA 2023. Java is used as the programming language, and the JDK version is 15. In addition, we implement the well-known Top-down [44], Mondrian [11], Incognito [10], and Flash [45] anonymity as our compared algorithms for privacy disclosure experiments. The experiments are repeated ten times, and the average result for each trial is calculated. The default parameters {_k,d,l_} are 20, 2, 3 and 200, 2, 3 on the Adult and Census datasets, respectively.  
**Experimental objectives**: We have three main objectives for the following experiments:  
**Privacy Disclosure Evaluation**--this evaluation tests the effects of a multi-source linkability attack using Algorithm 2. This verifies the effectiveness of the proposed method. **Data utility**--the experiments focus on information loss of the proposed anonymity algorithm under different conditions. **Efficiency**--experiments are conducted to evaluate the required overhead with different numbers of records.
## 3 Parametric Analysis
### Excitation Frequency  
The structural natural frequency can be obtained by \(\omega_{m}=\sqrt{g/l}\). \(\lambda\) is defined as \(\lambda=\omega/\omega_{m}\). \(\omega\) is the excitation frequency. The sinusoidal excitation frequency applied to the structure is defined as the excitation frequency. The influence of the excitation frequency on the control effect is analyzed as shown in Figure 7. It can be found that the control effect achieves peak value at an excitation frequency. The frequency is close to the structural natural frequency. When the excitation frequency is higher than the optimal frequency point, the control effect decreases as the excitation frequency increases.
## 3 Results
### The Impact of Site Structure on the Effectiveness of SIT Implementation
#### 3.5.3 Release Method  
In contrast, changing the method of releasing sterile males from a uniform aerial release to a ground-based weekly release from 100 fixed points in a 50 \(\times\) 50 m grid had a clear negative effect, resulting in significantly reduced SIT efficacy and less suppression of the wild fly population. With the exception of the scenario with higher release rates at site C, where the fly population was the lowest, this effect was consistent across all other cases (Figures 7 and 8). In general, to obtain an effect similar to using 50,000 pupae released evenly (aerial release), in the case of point release from the ground, it was necessary to use twice as many pupae, at least 100,000 per week.  
It should be emphasized, however, that in none of the locations, even in the best-case scenarios using 200,000 sterile pupae per week, did the local medfly population collapse and become exterminated, and in all cases, varying numbers of females, 499, 147, and 16 at sites A, B, and C, respectively, survived until the end of the year. Furthermore, despite the very low medfly population density at each site, a number of fertile females, i.e., 101, 100, and 111 from sites A, B, and C, respectively, left the treated area and disappeared into the external landscape.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Release Rate** & \multicolumn{2}{c}{**Release Method**} \\ \hline  & & **Aerial 1/Week** & **Aerial 2/Week** &
\begin{tabular}{c} **Ground** \\ **1/Week** \\ \end{tabular} \\ \hline  & **0 (NO IPM)** & & **21,999** & \\ Site A & 50 k & 4544 & 3707 & 9654 \\  & 100 k & 1245 & 1654 & 5057 \\  & 200 k & 499 & 1039 & 1935 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of females alive at the end of year.  
_Appl. Sci._**2025**, _15_, 6694  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Release Rate** & \multicolumn{3}{c}{**Release Method**} \\ \hline  & & **Aerial 1/Week** & **Aerial 2/Week** & \begin{tabular}{c} **Ground** \\ **1/Week** \\ \end{tabular} \\ \hline  & **0 (NO IPM)** & & **15.2** & \\ Site A & \begin{tabular}{c} 50 k \\ 100 k \\ 200 k \\ \end{tabular} & 5.3 & 4.7 & 9.8 \\ Site B & \begin{tabular}{c} 100 k \\ 200 k \\ \end{tabular} & 1.5 & 2.1 & 5.5 \\ Site C & \begin{tabular}{c} 200 k \\ \end{tabular} & 0.9 & 1.8 & 2.8 \\ \hline  & **0 (NO IPM)** & & **3.3** & \\ Site B & \begin{tabular}{c} 50 k \\ 100 k \\ \end{tabular} & 1.4 & 1.0 & 2.7 \\ Site B & \begin{tabular}{c} 100 k \\ 200 k \\ \end{tabular} & 0.6 & 0.2 & 0.9 \\ Site C & \begin{tabular}{c} 200 k \\ \end{tabular} & 0.5 & 0.3 & 1.6 \\ Site A & \
\begin{tabular}{c} **0 (NO IPM)** \\ 50 k \\ \end{tabular} &
\begin{tabular}{c} **7.3** \\ 1.8 \\ 0.4 \\ 0.1 \\ 0.  
Figure 7: The influence of the size of the sterile male cohort, the frequency of their release and the release method on the annual population profile of medfly in three hypothetical sites, A, B, and C with daily numbers of females present on the site, where 50 k, 100 k and 200 k denote 50,000, 100,000 and 200,000 pupae, respectively, delivered weekly.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 2 Related Works
### Dark Channel Prior  
Through extensive observations of outdoor clear images, [PERSON] et al. [13] found that at least one of three-color channels had extremely low intensity pixels in most image patches, excluding the sky region. Conversely, the minimum intensity of such pixels is almost equal to zero. Based on this observation, the dark channel of the haze-free image \(\mathbf{J}\) is given by:  
\[\mathbf{J}^{dark}(x,y)=min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]}\{\mathbf{J}^{c}(i,j )\}\}, \tag{3}\]where the superscript \(c\) is a color channel of \(\mathbf{J}\), \(\Omega(x,y)\) is a local patch centered at pixel \((x,y)\), and \((i,j)\) denotes the coordinates of pixels within \(\Omega(x,y)\). Using this equation, we first perform a channel-wise minimum operation to find the pixel minimum channel and then apply a minimum filter to get the dark channel. Assuming that \(A\) is always positive, we can divide both sides of Equation (1) by \(A\):  
\[\frac{\mathbf{I}(x,y)}{A}=\frac{\mathbf{J}(x,y)\mathbf{t}(x,y)}{A}+1-\mathbf{t}(x,y) \tag{4}\]  
To estimate the transmission map \(\mathbf{t}\), \(\mathbf{t}\) is assumed to be constant in local patches [13]: \(min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]}[\mathbf{t}(i,j)]\}=\mathbf{t}(x,y)\). By applying these two minimum operators to Equation (4), the estimated transmission map is obtained as follows:  
\[min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]}\left[\frac{\mathbf{\Gamma}(i,j)}{A^{c} }\right]\}=\mathbf{t}(x,y)min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]}\left[\frac{ \mathbf{\Gamma}(i,j)}{A^{c}}\right]\}+1-\mathbf{t}(x,y), \tag{5}\]  
\[\mathbf{t}(x,y)\approx 1-\omega\times min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]} \left[\frac{\mathbf{\Gamma}(i,j)}{A^{c}}\right]\}, \tag{6}\]  
since the assumption about the dark channel prior (\(\mathbf{J}^{dark}\approx 0\)) leads to \(min_{i,j\in\Omega(x,y)}\{min_{c\in[R,G,B]}[\mathbf{\Gamma}(i,j)/A^{c}]\}\approx 0\). The application-based positive constant \(0<\omega\leq 1\) is added to Equation (6) to optionally keep a small amount of haze for human depth perception [13]. Transmission map estimation requires that \(A\) is known in advance. The authors first select the top \(0.1\) percent brightest pixels in the dark channel to estimate \(A\). Amongst these pixels, the one with highest intensity in the input image \(\mathbf{I}\) is chosen as the atmospheric light \(A\).  
Since the authors assumed that \(\mathbf{t}\) is constant in local patches, however, artifact blocks arise in the estimated transmission map. Resolving this problem by using the computationally intensive soft matting [33] resulted in a very slow processing speed. Furthermore, the fact that the DCP may be found when observing a non-sky region can cause color distortion in the sky region of the dehazed image. DCP's drawbacks brought about a large room for improvement, and there have been several studies to extend the DCP in various directions [14; 15].
## 3 Results and Discussion
### Docking Study
For **3m**, **3n**, and **3o**, the presence of halogen atoms with their respective position on the benzene ring affected the binding pattern and orientation of the compound within the pocket (Figure 2e). Compounds **3f** (\(o\)-chloro phenyl) and **3o** (\(o\)-iodo phenyl) with IC\({}_{50}\) = 10 \(\pm\) 0.6 \(\upmu\)M showed two hydrogen bond interactions with His492 and Ala440 (Figure 2f), while 3m and 3n were stabilized by a single hydrogen bond with His492. These compounds also displayed hydrophobic interaction with Ala440, His593 and Met637. Compounds **3c** (IC\({}_{50}\) = 26 \(\pm\) 1\(\upmu\)M) and **3p** (IC\({}_{50}\) = 22 \(\pm\) 0.8 \(\upmu\)M), both with moderate biological activity, established two hydrogen bond interactions with His492 and Ala440. However, only one interaction with the metallocenter was observed (Figure 2g). The compounds with low biological activity, as compared to the standard and other pyrimidine derivatives such as **3i** (IC\({}_{50}\)= 66 \(\pm\) 2.4 \(\upmu\)M) and **5** (IC\({}_{50}\)= 42 \(\pm\) 2.3 \(\upmu\)M), established single hydrogen bond interaction with the active site residue Arg439 while compound mostly established by hydrophobic interaction with the active site residues (Figure 2h). However, no interaction with nickel was observed for **3i**. Indeed, the urease inhibition capacity of the synthesized pyrimidine derivatives is attributed to the mutual contribution of the distinct substitutions they bear. However, interaction with the metallocenter and hydrophilic interaction with Ala440, His492, His593, and Met637 are found to be crucial for the activity of these compounds.