esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Multi-Controlled (Rotation) Gates  
Another common intermediate gate is the multi-controlled (rotation) gate. This is a one-qubit gate with \(k\) control bits. Rather than just applying a gate when all control bits are zero, the applied operation to the target qubit can be different for each of the \(2^{k}\) possible classical values of the control qubits.  
This is written as \(F_{m}^{k}(U(2))\), which is a fully or multi-controlled \(U(2)\) gate with \(k\) control qubits, with the target qubit at position \(m\). The circuit representation of this gate is shown in Figure 2. To indicate that an operation is applied for either state of the control bits, a square control box is used.  
These multi-controlled gates correspond to a (block) diagonal unitary matrix, which is why they show up frequently in decomposition schemes. This is shown in Equation (4).  
\[F_{m}^{k}(U(2))=diag_{j}(U(2)_{j})=\begin{bmatrix}U(2)_{0}&&\\ &\ddots&\\ &&U(2)_{2^{k}}\end{bmatrix} \tag{4}\]  
A multi-controlled rotation gate around axis \(a\) corresponds to the matrix shown in Equation (5). This can be any axis, but in the paper, the multi-controlled \(R_{y}\) and \(R_{z}\) axes are mainly used.  
\[F_{m}^{k}(R_{a})=diag_{j}\big{(}R_{a}(\theta_{j})\big{)}=\begin{bmatrix}R_{a} (\theta_{0})&&\\ &\ddots&\\ &&R_{a}(\theta_{2^{k}})\end{bmatrix} \tag{5}\]
## 7 Numerical Results  
### Conventional Design Results  
As previously mentioned, the SFPB1 and TFPB1 configurations are derived via the heuristic ELF design procedure (Figure 6) without implementing any optimization. They result in more conservative solutions and they are used for comparison with the proposed single- and dual- optimized schemes. Due to the geometry of the tank, the process resulted in 60 identical isolators, symmetrically placed in both schemes, as shown in Figure 3. It is noted that rectangular isolator grids are usually used in buildings, while, based on engineering experience and problem characteristics, non-identical isolators can be applied. For instance, [PERSON] and [PERSON] [63], based on axial load variation, heuristically selected SFPB with different properties to more efficiently isolate a six-story reinforced concrete building, with an L-shaped plan under near-fault excitations.  
The ELF process results in an SFPB1 scheme with 60 SFPB isolators with identical properties, i.e., \(\mu\) = 0.03 and \(R\) = 3.2 m. The fundamental period of the system is 3.23 s, the effective damping is 24.4% and the displacement capacity is 0.29 m. Analogously, the uniform parameters of the 60 isolators in TFPB1 scheme are: friction coefficient values for the outer sliding surfaces, \(\mu_{1}\) = \(\mu_{4}\) = 0.09 and the inner sliding surfaces, \(\mu_{2}\) = \(\mu_{3}\) = 0.03, radius of curvature values for the outer surfaces, \(R_{1}\) = \(R_{4}\) = 3.2 m, and the inner surfaces, \(R_{2}\) = \(R_{3}\) = 1.07 m. The fundamental period of the system is 2.87 s, the effective damping is 22.84% and the displacement capacity is 0.784 m, much higher compared to SFPB1, which enables them to withstand higher PGA levels, as will be shown in the sequence.
# Stereo Digital Image Correlation in MATLAB
## 3 Implementation
### ADIC3D Function  
The main function ADIC3D, outlined in Table 5, sets up the DIC problem and calls the appropriate subroutines. The output variables are preassigned in lines 9-12 to save input data and efficiently store computed data. ADIC3D allows subsets to be analyzed in a flexible manner by assigning Xos, SubSize, SubShape, and SFOrder on a per subset and per image basis. However, despite being capable of this, ADIC3D assigns the same SubSize, SubShape, and SFOrder to each subset as is conventional for DIC. Subsets containing invalid pixels, identified by Mask, are eliminated.  
The subroutine StereoMatch is called to perform stereo matching. StereoMatch has input variables n (the total number of images in each image series), ResultData, FileNames1, FileNames2, and StopCritVal. It returns the subset positions in the FIS2 stored as ProcData2(d).Xos and stereo matching correlation results P, C, Iter, and StopVal stored in Stereo.  
Temporal matching is performed on each image series separately in lines 15 and 17 using the ImCorr subroutine. ImCorr's inputs are n, ProcData1 or ProcData2, FileNames1 or FileNames2, RefStrat and StopCritVal. It returns P, C, Iter, and StopVal for each subset throughout the image series stored in ProcData1 or ProcData2 for the first and second image series respectively.  
Finally, subroutine CSTrans determines coordinates and displacements in the world CS based on inputs n, ResultData, WorldCTs, ImgCTs, and RefStrat. CSTrans returns Xow, Uw, and CamParams stored in DispTrans. Note that within the subroutines ResultData is shortened to RD and ProcData is shortened to PD where appropriate.
## 4 Conclusions
Based on a general assessment of the Ra-226, Th-232, and K-40 activity concentrations relative to the UNSCEAR global medians, the Artuklu, Derik, Kiziltepe, Mazdag, Midyat, and Omerli districts are at a higher natural radioactivity risk, while Dargecit, Nussaybin, Savur, and Yesilli have lower risk levels. When comparing anthropogenic Cs-137 concentrations with the Turkish average, higher values were found not only in naturally high-radioactivity districts, such as Artuklu, Mazdag, Midyat, and Omerli, but also in Dargecit, Savur, and Yesilli, where natural radioactivity levels are lower. Nussaybin was found to be the least risky district in terms of both natural radionuclide and Cs-137 concentrations. The comparison of radiological parameters with UNSCEAR global medians indicated that all districts in Mardin had safe Ra\({}_{\rm eq}\) and H\({}_{\rm ex}\) values. Similarly, all districts except Omerli had ELCR values below the UNSCEAR global median. The D\({}_{\rm R}\) parameter exceeded the UNSCEAR global median only in Omerli and Nussaybin, while the remaining eight districts were within safe limits. For the AEDE parameter, seven districts had values below the UNSCEAR global median, except for Omerli, Midyat, and Mazdag. The AGDE parameter was the most frequently exceeded parameter, with six districts (Artuklu, Derik, Kiziltepe, Mazdag, Midyat, and Omerli) surpassing the UNSCEAR global median, while Dargecit, Nussaybin, Savur, and Yesilli had lower values. None of these four districts exceeded the UNSCEAR global medians for Ra\({}_{\rm eq}\), D\({}_{\rm R}\), H\({}_{\rm ex}\), AEDE, AGDE, or ELCR parameters. Considering both natural radionuclide activity concentrations and radiological parameters, Nussaybin district was found to have the lowest surface soil radiation risk, as it remained below the UNSCEAR global medians and had a Cs-137 concentration lower than the Turkish average. When selecting new residential areas, the radiological parameters examined in this study should be taken into account alongside local geological conditions. Further detailed radiological measurements should be conducted with a higher number of soil samples to ensure more accurate assessments of the selected regions. Conceptualization, E.C., N.S. and S.N. ; methodology, N.S. and S.N. ; visualization, S.N. ; formal analysis, N.S. and S.N. ; investigation, E.C., N.S. and S.N. ; writing--original draft preparation, N.S. and S.N. ; writing--review and editing, E.C., N.S. and S.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors thank the undergraduate students [PERSON], [PERSON], [PERSON], and [PERSON] for their help during the data processing stage of this study. The authors declare no conflicts of interest.
# EmbeddedPigDet--Fast and Accurate Pig Detection for Embedded Board Implementations
## 2 Background
It is also essential to meet the execution speed requirement, in order to process the successive video frames without delay. However, many previous methods did not report the execution times. Among the previous methods of reporting the execution times, only the deep learning-based method [44] can satisfy both requirements of individual pig detection. However, none of the previous methods reported the pig detection using embedded boards. In a large-scale pig farm, a \"cost effective\" solution is definitely required. As explained, a low-cost solution (rather than a typical PC-based solution) is additionally required due to the severe ammonia gas in a closed pig room. However, since low-cost embedded boards have more limited computing power than typical PCs, the fast and accurate detection of individual pigs for \"on-device\" pig monitoring applications is very challenging. To the best of our knowledge, this paper is the first report on how to improve the accuracy of individual pig detection with an embedded board, while satisfying the execution speed requirement, by using the complementary techniques of light-weight image processing and deep learning. That is, we first remove less-important \(3\times 3\) convolutional filters of a deep learning-based object detector, in order to obtain a light-weight version of it. Then, we generate a three-channel composite image as an input image for the light-weight object detector, in order to improve its accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Target** & **Data Size** & \begin{tabular}{c} **No. of Pigs** \\ **in a Pen** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Technique** \\ \end{tabular} & \
\begin{tabular}{c} **Individual** \\ **Detection of Pigs** \\ \end{tabular} &
\begin{tabular}{c} **Execution Time** \\ **(ms)** \\ \end{tabular} & **Reference** \\ \hline \multirow{8}{*}{PC} & Not Specified & Not Specified & Image Processing & No & Not Specified & [10] \\  & \(720\times 540\) & 12 & Image Processing & Yes & 220 (PC) & [11] \\  & \(768\times 576\) & Not Specified & Image Processing & No & 1000 (PC) & [12] \\  & \(768\times 576\) & Not Specified & Image Processing & No & 500 (PC) & [13] \\  & \(150\times 113\) & Not Specified & Image Processing & No & 250 (PC) & [14] \\  & \(640\times 480\) & 9 & Learning & No & Not Specified & [15] \\  & \(720\times 576\) & 9 & Image Processing & Yes & Not Specified & [16] \\  & \(1280\times 720\) & 7–13 & Image Processing & Yes & Not Specified & [17] \\  & Not Specified & 3 & Image Processing & Yes & Not Specified & [18] \\  & \(352\times 288\) & Not Specified & Learning & No & 236 (PC) & [19] \\  & \(640\times 480\) & 22–23 & Image Processing & Yes & Not Specified & [20] \\  & \(640\times 480\) & 22 & Image Processing & Yes & Not Specified & [21] \\  & Not Specified & \(17\)–20 & Image Processing & Yes & Not Specified & [22] \\  & \(256\times 256\) & Not Specified & Image Processing & No & Not Specified & [23] \\ \hline \end{tabular}
\end{table} 
Table 1: Some of the recent pig detection results (published during 2010–2019).
## 3 Results Analysis
### Experimental Results for Time Measurements
#### 3.1.3 IO/Network-Bound Load Test Results  
Further, the impact of interrupt handling caused by IO-bound tasks, such as network load, etc., is illustrated in Figures 8 and 9. Time values from _TSC_-based time operations show almost native overhead on all platforms, except Virtual Box. Although VMware ESXi has median values (Figure 8c) of data acquired with _TSC_ timer higher than on Host OS, Xen, and QEMU, it has significantly lower spread than on QEMU, Host OS and Xen. This also can be seen in Figure 9, where the tails of distributions from VMware ESXi datasets indicate lower time cost values than in other cases. Nevertheless, the virtualized _TSC_ timer on QEMU shows an unexpected advantage: its median overhead value is even lower than the median value obtained on the non-virtualized hardware on which QEMU is placed. This can be seen in the CCDF plot (Figure 9 yellow line), where 90% of outcomes are concentrated below 17 ns and past this value the distribution line falls steeply and then follows the behavior of other _TSC_ distributions. For Xen and Virtual Box, tails are much longer. At the same time, the probability of having time cost values greater than median value is greater for all cases; however, median value occurs with a probability around 0.99. The impact of IO/network load can be characterized by a long tail in the middle area of the CCDF plot, which means higher variance of measurements. This effect appears mostly on Virtual Box and Xen.
## 4 Conclusions  
We have developed an effective geometry-based cycle slip detection and repair method with TDCP. The detection method is based on ILAM, and takes advantage of the greater number of satellites with GPS + BDS to improve the detection ability. The integer validation is utilized in repair process, including success probability and decimal test, which can detect the outliers and improve the reliability.  
The experimental results demonstrate that ILAM can detect the cycle slips exactly. The detectable cycle slips number increases when there are more number of satellites with GPS + BDS, and the mean value increases by 4-fold, from two to 10, compared to GPS. In repair, the accuracy of float solutions with GPS + BDS improves by 25% compared with GPS, and the mean RMSs of float solutions are 0.018 and 0.024 cycles, respectively. With the use of the decimal test for validation, non-integer outliers can be detected, and compared to GPS, the maximum decimal bias reduce by 1/3, from 0.3 to 0.2 with GPS + BDS. All above results imply that the proposed method can take full advantage of multi-constellation. In future work, the applications such as RTK and PPP with high sampling rate are expected to use this proposed method to fix cycle slips without limitations on the number of frequencies.  
**Acknowledgments:** The authors appreciate FJCORS for providing GNSS data. This work was supported by the National Key Research and Development Program of China (No. 2016 YFB0800405, No. 2016 YFB0501803 and No. 2016 YFB050220), National Nature Science Foundation of China (41304014 and 41674017). Final thanks to all colleagues who put forward valuable opinions for this article.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments and wrote the paper, performed the experiments; [PERSON] and [PERSON] performed the field test and provided the LiDAR, IMU, GPS raw data and tree reference; [PERSON], [PERSON], [PERSON] and [PERSON], reviewed the paper and gave constructive advice.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Laser Scribing Mechanism and Theoretical Model  
### Mechanism and Process Parameters of Laser Scribing  
Laser scribing is a laser application based on laser ablation, which irradiates a high-energy laser beam on a maskant of parts to remove the maskant on the metal surface without damaging the substrate. The main mechanism of laser scribing is the ablation effect, which means the thermal effect generated by the high-energy laser to eliminate the combination of the maskant and the substrate by evaporation, plume, ionization and explosion [32]. The mechanism of laser scribing is shown in Figure 9.  
The main components of chemical milling maskant are organic polymers and is similar to plastic, resin or rubber, which have very high laser absorptivity at a 10.6 um wavelength. The thermal conductivity of organic polymer materials is much less conductive to heat than metals, and the heat conduction loss in processing is also very low. Among them, the thermal conductivity of resin glass is \(1.9\times 10^{-1}\) W/(m\(\cdot\)K), the thermal conductivity of rubber is \(1.69\times 10^{-1}\) W/(m\(\cdot\)K), and the thermal conductivity of titanium \(\lambda=15.24\) W/(m\(\cdot\)K) [33]. Due to the poor thermal conductivity of organic polymers, there is an obvious temperature difference between laser heating areas and non-heating areas. On the premise of no energy accumulation, it can ensure that the processing slit width and heat affected area is small. In most cases, the basic optical properties of polymers are not only determined by polymer macromolecules but also have a decisive relationship with added materials such as dyes and softeners.  
The laser scribing quality under argon protection/no argon protection is compared for AC850 protective maskant. The results show that there was little difference in the quality of laser scribing under argon protection and without argon protection [17]. According to the laser ablation mechanism of organic polymers, the phase transition of laser scribing from a solid state to a gas excited state is carried out by chemical decomposition [30].  
Figure 8: The maskant incomplete cut. (**a**) Incomplete cut scribing line; (**b**) The scribing lines are not continuous and smooth.  
Figure 9: Mechanism of laser scribing.  
Additionally, the complex macromolecules break down into smaller molecules, volatile elements and vapors. Therefore, the laser scribing mechanism of the AC850 maskant is mainly vaporization or sublimation after material decomposition.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 1 Introduction
The dynamics equation of the overall method is relatively simple, but because of the incessant change of reference points, the solution is complicated. Since the reference point of the separation method is fixed, the calculation of the force and moment of the cargo in the helicopter is clearer, and it is convenient to study the flight dynamics characteristics of helicopters in the process of cargo airdrop. For the research of the helicopter internal cargo airdrop, this article uses the above separation method, but with some novelty; it mainly refers to the research method of the cargo airdrop of the fixed wing conveyer [36], and involves the difference between the helicopter cargo airdrop and the fixed wing conveyer cargo airdrop. The dynamics modeling of the fixed wing conveyer cargo airdrop is mainly in focus on the force acting on the aircraft before the airdrop leaves the aircraft. However, there is less consideration on the sharp change in the force acting on the fuselage when the cargo is dropped at the end of the cabin. In order to study the effect of the cargo on the helicopter trim and stability from the beginning of the movement away from the cabin to the complete departure from the cabin room, this article has completed the dynamic modeling analysis of the process of the helicopter cargo airdrop. The main research content of this article is to present a methodology of CH-47 series tandem rotor helicopter flight dynamics model under heavy cargo airdrop conditions in order to analyze the trims and stabilities. The article is organized as follows. First, a cargo airdrop model is proposed. Following this, a tandem rotor helicopter flight dynamics model under heavy cargo airdrop condition is established. The trim algorithm and simulation method are developed. Verification of the flight dynamics model has been conducted. The trim characteristics analysis of the helicopter/cargo system is conducted. Stability features of the tandem rotor helicopter in the process of airdrop are also analyzed thoroughly.
## 3 Results and Discussion
### Experiment Results
Figure 6a,b are structural images of SD-OCT obtained using 100% data and structural image of CS-ISAM obtained using 25% data, respectively. The blue and red curves in Figure 6c show the lateral distribution of the structures within the yellow dashed boxes at the depth of 500 \(\upmu\)m from the focal plane in Figure 6a,b, respectively. The result of Figure 6c indicates that the diffusion of the structure was reduced by approximately 2.2 times at a depth of approximately 5.2 Rayleigh ranges above the focus plane, which verifies the effectiveness of CS-ISAM. Consequently, both simulation and experiment results show that the improvement effect of ISAM and CS-ISAM on lateral resolution was almost the same, which proves the effectiveness of the method used in this paper. We also tried to reconstruct the image using much less than 25% data. However, because of the low signal to noise ratio (SNR) of the image and the limitations of the CS reconstruction algorithm used in this paper, the results were not perfect. However, we believe that 25% data compression rate is enough to prove the practical significance of this method. In the following work, we will explore the impact of different CS reconstruction algorithms on this method to improve the speed and performance of reconstruction.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 3 Improved PSO Algorithm  
Building upon the standard PSO algorithm, this section introduces four enhancement strategies: composite chaotic mapping for population initialization, adaptive inertia weights, an elite particle learning mechanism, and an ordinary particle mutation strategy. Each of them enhances specific aspects of the standard algorithm, including population initialization, inertia weights, particle velocity and position updates, and cross-mutation. We provide a detailed description of the algorithmic formulas, parameters, and the significance of each improved strategy. Furthermore, a flowchart illustrating the improved adaptive PSO algorithm, referred to as APSO, along with the steps for its implementation, is also presented.
## 3 Proposed Methodology
### Proposed GATransformer
#### 3.2.2 Channel Pruning Approach  
The proposed progressive channel pruning technique efficiently prunes superfluous channels while retaining model performance using unique ID assignment and attention mechanisms. Each layer's channel weights in the overall model are flattened into a tensor T to identify each weight as a channel weight node with an ID. This ID-based approach simplifies attention score calculation and channel identification during pruning. The GAT module processes the flattened tensor T to compute the dependencies among channel weight nodes, resulting in the attention scores. These scores capture the intra-layer relationships between channels. Subsequently, the multi-head attention (MHA) mechanism evaluates cross-layer correlations using the GAT output. The MHA output is refined through a feed-forward network (FFN) to extract deeper features. Finally, the production of the MHA is linearly transformed, producing the pruned model. This strategy leverages attention mechanisms at  
Figure 6: Architecture of LMA with channel pruning to obtain a pruned model.  
intra-layer and inter-layer scales, combining the GAT module for local dependency analysis and the Transformer module for global correlation.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 2 Materials
### Multi-Wavelength LiDAR
The multi-wavelength LiDAR was rasterized into 0.5 m grid data that included DSMs, DTMs, and intensities from different wavelengths. Because the average point density of the test area was about 70.7 points/m\({}^{2}\), the height of DSM was the highest point in the cell, and the intensity map selected the intensity of the highest point in the cell. The ground point for DTM was automatically generated by a non-ground point filtering method in TerraScan Software, and the ground points were then placed into the grid by the kriging interpolation method.
## 2 Patients and Methods
### Whole Exome Sequencing and Mutation Analysis  
Genomic DNA was extracted, with consent, from whole blood following standard procedures. The DNA samples of the patients were subject to whole exome sequencing following processing using a SureSelect V6+UTR-post target capture kit. Genomics analysis toolkit (GATK) germline mutation workflow version 3.8.1 (Cambridge, IL, USA) [13] was utilized to identify variants. The sequencing reads were aligned to the human genome reference (hg19) using BWA-MEM version 0.7.17 [13; 14] to generate BAM files. These BAM files were processed by GATK HaplotypeCaller to identify SNVs and small indels resulting in individual GVCF files. These GVCF files were consolidated into a single joint genotyped VCF file format, listing all genotypes in separate columns. Ensembl variant effect predictor tool (version 95) [15] was used to predict pathogenic effects of each variant.