esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Preliminary Detection of Suspected Human Targets
After the same preprocessing on multiple sets of images from multiple exposures, a complete reflectance panorama is formed in _Scenario 2_ as shown in Figure 14. Obviously, it is visually difficult to observe the ground suspected target in this complex scenario. Especially for the target T9-T12, it is almost impossible to find them, due to their color characteristics being similar to the background. Then, the proposed method based on multispectral information only is applied to preliminarily detect suspected human targets automatically as shown in Figure 15. It is clear that all the 12 suspected human targets were still successfully detected in such a complex environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Algorithms** & **Accuracy** & **F\({}_{1}\) Score** \\ \hline DT & 99.76\% & 98.08\% \\ BP & 99.58\% & 99.62\% \\ RF & 99.37\% & 99.41\% \\ SVM & 99.65\% & 99.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition performance comparison of the state-of-art machine learning algorithms. Figure 14: The complete reflectance panorama in _Scenario 2_. However, we need to note that only suspected target T\({}_{12}\) is a real human target. In other words, just based on the multispectral information alone, we cannot distinguish clothes and true human targets just relying on multispectral images. Therefore, this complex _Scenario 2_ is taken as a typical example to test the effectiveness of the human subject property re-identification method based on vital sign information proposed in this paper.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 5 The Calibration of Extrinsic Parameters
### Calibration of the Rotation Matrix  
Considering the general model we built, we need to calibrate the actual direction vectors of the camera's horizontal and pitch rotation axes and then calculate the rotation matrix between the left and right camera coordinates.
## 4 Proposed CH Selection Algorithm Based Bacterial Foraging Paradigm
### The Proposed CH Selection Algorithm  
In the proposed CH selection algorithm, a new approach that is based on bacterial foraging belongs to methods, called as swarm optimization, computational intelligence, or metaheuristic, such as swarm optimization, genetic algorithm, ant, and bee colony algorithms is presented. In all proposed algorithms, \(BC(Bacterial\ Cell)\) and \(BBC(Best\ Bacterial\ Cell)\) represent the sensor node and CH, respectively.  
In the proposed algorithm, some of the advanced nodes in the heterogeneous WSN have been assigned to the choice of CH, which can provide the best energy balance of the bacterial cells. In our algorithm, each bacterial cell (\(BC\)) was associated with a CH. The best BC can be assigned as CH. With this inspiration, it is possible to identify the best cell in the WSN in the determination of the best cell in terms of energy, degree, and balance load, in order to find bacteria in food and to assign the most appropriate advanced nodes as CH.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 8 Experimental Results
### A Case Study
This number of code lines is drastically reduced (measured in thousands of code lines) when the optimizations are applied to the applications in Figure 13(b). The results of the number of code lines are constant under different design variants hence the generic range (_T1-T8_) is used. The number of code lines differs with the application and the optimization type used. _Opt4_ yields the fewest number of code lines by reducing the non-optimized number of code lines by a factor of 164,425. This is followed by _opt2_ which shortens the code length by the factor of 142,084 and finally optimizations _opt1_ and _opt3_ which reduce the number of code lines by factors of 10,252 and 9542 respectively. Generally, the number of code lines is directly proportional to the number of FSM states shown in Figure 13.  
One of the benefits of using our design approach is to reduce developer time and improve designer productivity. Our compiler framework allows benchmarking of the execution time that elapses from the design description using SDF-AP model to the FPGA bitstream creation. This time combines the SDF-AP analysis, VHDL generation and the RTL synthesis using the ISE. The time lapse is measured in hours (h) and the obtained results for each SDR application are shown in Figure 15. For each application, the time lapse refers to the total time period taken to generate VHDL and RTL using all the four optimizations techniques. The amount of time taken by the compiler framework to perform SDF-AP analysis and to generate VHDL code is shown in Figure 14(a). In addition, the total time taken by the ISE synthesize RTL is shown in Figure 14(b). The biggest designs namely the OFDM-TX and OFDM-RX of IEEE 802.22 take respective 117 and 53 h to generate while the smallest designs namely the OFDM-TX and OFDM-RX of IEEE 802.11a take respective 2.59 and 2.58 h to generate. To compute the buffer size of the SDF-AP channels, arrays are required to keep the model access patterns. For the applications with long access patterns, the on-heap memory of Java Virtual Machine (JVM) does not  
Figure 13: The total number of FSM states for each SDR application. handle caching of gigabytes of data. A workaround to this problem is using the off-heap memory which enables storing data outside the heap in the OS memory part. Because there is no JVM involved, the off-heap introduces the overhead of serializing and deserializing the long arrays to corresponding objects. There is an additional cost of dealing with native memory which does not exist in on-heap memory leads to the delayed analysis of SDF-AP model when the application access patterns are long as show in Figure 15a.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 3 Experiment and Discussion
### Ablation Study  
We investigated how the designed FFT_Filter block in FFT_YOLOX influenced the whole performance of the detection model. Table 3 (The best results are illustrated with bold format) exhibits the performances in detail. The 0.5\(-\)0.95 AP metric of original YOLOX-S was taken as a baseline, demonstrating that the accuracy of 0.5\(-\)0.95 AP is 0.480 without FFT_Filter block. Then, we removed the FFT_Filter to replace the 3 \(\times\) 3 block with the dark2-5 blocks of the YOLOX_S model. To illustrate, we designed four different models (exp2, exp3, exp4, exp5), which adds CSP_FFT block in dark2-5, dark3-5, dark4-5, and dark5 modules, respectively. As shown in Table 3, the proposed FFT_YOLOX (exp2) model, where CSP_FFT blocks were introduced in all dark2-dark5 modules of the original YOLOX-S model, had the highest accuracy of 0.5\(-\)0.95 AP with fewest model parameters and lowest FLOPs. Specifically, the accuracy of 0.5\(-\)0.95 AP of FFT_YOLOX (exp2) model, where only one CSP_FFT block was introduced as the dark5 module of the original YOLOX-S model, is also at a high level. This demonstrates that our proposed FFT_Filter enhanced the ability of global feature extraction even within a deep layer of a convolution network. Furthermore, for qualitative comparison, the detection results of different models are shown in Figure 5. Furthermore, to make the illustration of detection results be clearer, two important areas on the detected images are enlarged to highlight the comparison performances of different  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **AP\({}_{0.5}\)–0.95** & **AP\({}_{0.5}\)** & **AP\({}_{0.75}\)** & **AP\({}_{small}\)** & **AP\({}_{medium}\)** & **AP\({}_{large}\)** & **Params** & **FLOPs** \\ \hline YOLOX-S & 0.480 & 0.829 & 0.506 & 0.207 & 0.418 & 0.531 & 8.94 M & 26.64 G \\ FFT\_YOLOX & **0.483** & **0.832** & **0.510** & **0.242** & **0.421** & **0.533** & **8.49 M** & **25.18 G** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparisons on URPC dataset with YOLOX-S and FFT_YOLOX.  
Figure 4: The qualitative comparison results of the original YOLOX-S and the proposed FFT_YOLOX model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Holothurian** & **Echinus** & **Scallop** & **Starfish** & **Params** & **FLOPs** \\ \hline YOLOX-S & 0.375 & 0.480 & **0.476** & 0.498 & 8.94 M & 26.64 G \\ FFT\_YOLOX & **0.377** & **0.485** & 0.472 & **0.501** & **8.49 M** & **25.18 G** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Each class AP (0.5\(-\)0.95) comparisons on URPC dataset with YOLOX-S and FFT_YOLOX.
## 3 Results  
As shown in Figure 1, 4096 articles were identified. After duplicates were removed and preliminary screening was conducted, 75 articles underwent full-text review. Fifty-two studies (Table 1) were eligible for inclusion. From these studies, we identified the following 26 factors that influence Ni-Ti rotary instrument torque and force: type of sample [17], canal curvature [17; 18], cross-sectional design [11; 15; 19; 20; 21], taper [21], blade [15], pitch length [11; 12; 13; 14; 15], helix angle [13; 15; 20], rake angle [11; 12; 12; 15], cutting efficiency [11; 12], instrument size [9; 11; 12; 22; 23; 24], glide-path preparation [25; 26], canal size [27; 28; 29; 30; 31], contact area [32; 33], preparation technique [32; 33; 34; 35; 36; 37; 38], preparation time [21; 39], insertion depth [28; 29; 30; 31; 32; 33; 34; 36; 37; 38], insertion rate [41; 46], displacement [40], motor [39], kinematics [3; 4; 20; 38; 47; 48; 49; 50; 51; 52; 53; 54; 52; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 7 \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Author/Year** & **Type of Study** & **Type of Sample** & **Instrument** & **Preparation Technique** & **Result** \\ \hline [PERSON] et al., 1999 [23] & In vitro & Mandibular incisors & ProFile & Step-back \& crown-down & Students \& Endodontist (T) \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & & \\  & & & & & & & \\  & & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Summary of reviewed studies analyzing torque and force generated by nickel-titanium rotary instruments during root canal preparation.
## 1 Introduction
In addition to being used for comparative static analysis, multiplicative SDA can also be used for spatial difference analysis. The development of the multiplicative SDA method has enabled decomposition analysis at the aggregate and sectoral levels. The main decomposition method used in multiplicative SDA is the multiplicative D&L method [16], which is similar to the generalized Fisher's index method in the IDA [22]. Initially, D&L could only be used to obtain decomposition results at the aggregate level. [PERSON] and [PERSON] [23] then introduced attribution analysis, estimating the contribution of each department at the sectoral level. [PERSON] and [PERSON] [7] introduce four models for calculating a country's aggregate intensity using the I-O framework, which includes taking the I-O model, decomposition method selection, imports assumption, and GDP calculation method into account, and they further improve the multiplicative SDA framework. The authors of [24] refine the multiplicative SDA framework and define it as a spatial SDA. [PERSON] et al. [25] use the three-layer SDA method to analyze the main driving factors of energy intensity changes in China. However, few studies use spatial SDA to analyze differences in carbon intensity, energy intensity, or other environmental indicators between regions. In summary, the literature mainly focuses on the driving factors behind intertemporal changes in energy intensity in various countries and lacks comparative studies on international differences in energy intensity as well as the causes of these differences. Considering the current state of research in this area, this study's contributions are as follows. (1) We first analyze the spatial differences in energy intensity among the G20 countries at the aggregate and sectoral levels. (2) By employing the multiplicative SDA method, we decompose the driving factors of energy intensity differencesamong the G20 countries into sectoral intensity, input structure, and final demand structure effects. Further, we evaluate the energy reduction potential for selected high-energy-intensity countries. (3) Considering the differences in energy intensity among sectors, we use multiplicative SDA to decompose the embodied energy intensity (EEI) drivers of key sectors and further identify the correlations between key sectors that drive the differences in energy intensity among the G20 countries. The remaining paper is organized as follows. Section 2 explains the theoretical model and data used in this study. Section 3 shows the spatial differences in energy intensity among the G20 countries at the aggregate and sectoral levels. Section 4 analyzes the driving factors of energy intensity differences among the G20 countries. Section 5 offers conclusions and policy implications.
## 4 Application in Circular Model Selection with Significant Challenges  
An efficient and environmentally friendly packaging circular model that focuses on recycling resources is implemented to minimize waste and reduce the harmful environmental impact while supporting long-term economic stability So, in order to determine which packaging circular model has a high-impact, we decided to identify by using the objectives, challenges and circular models from the previous literature. To assess the circular model three professors represented as the experts (Es) from SC background were selected, E1, E2, and E3, as well as three circular models.  
Five objectives represented as criteria are considered which are: Economic Objectives (O1), Environmental Objectives (O2), Social Responsibility Objectives (O3), Sustainable Objectives (O4), and Time-based Objectives (O5). Additionally, fifteen challenges are represented as the sub-criteria evaluate the circular model including: Transportation Cost (C1), Production Cost (C2), Inventory Cost (C3), Emissions (C4), Resource Utilization (C5), Waste Reduction (C6), Job Creation (C7), Community Impact (C8), Safety and Health (C9), Energy Efficiency (C10), Life-cycle Impact (C11), Renewable Resource Usage (C12), Flow Time (C13), Makespan (C14), and Lead Time (C15) and three circular models which are Biodegradable packaging (CM1), Compostable packaging (CM2), and Recyclable packaging (CM3). These 5 criteria, 15 suitable sub-criteria, and 3 alternatives are taken from literature based on expert opinions. In this work, the idea of attributes is condensed to the notation \(\mu_{\text{P}}\)\(\in\) [0, 1] for fuzzified occurrences. Z-numbers match the values of characteristics. As stated by Figure 5 depicts the hierarchical structure of the objectives, and challenges to research circular model in order to identify the highly sustainable circular model. A thorough questionnaire on the linked factors for influencing circular model selection was created for the relation matrix in order to rate the criteria. Using the linguistic evaluation factors shown in Table 5, experts assessed the criteria and sub-criteria. For the initial decision matrix based on Table 6 linguistic assessment factor, experts evaluated for the circular models.  
Figure 5: Hierarchy framework for packaging circular model.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Plasma Irradiation
#### 2.7.4 Other Molecules  
Fluoride  
[PERSON] et al. [126] treated MoS\({}_{2}\) multi-layers under fluoride plasma (SF\({}_{6}\), CHF\({}_{3}\), and CF\({}_{4}\)) for selected-area \(p\)-doping to form \(p\)-\(n\) MoS\({}_{2}\) junctions and diodes.  
Chlorine  
[PERSON] et al. [116] irradiated exfoliated MoS\({}_{2}\) few-layers (5-15 layers) under low-energy Cl (200 eV and \(1\times 10^{13}\)-\(10^{15}\) ions/cm\({}^{2}\)). The natural \(n\)-type MoS\({}_{2}\) layers were doped to \(p\)-type and the electric conductivity was reduced after irradiation.  
Phosphorus  
[PERSON] et al. [132] reported a compatible, controllable, and area selective phosphorus plasma immersion ion implantation process for \(p\)-type doping of mechanically exfoliated MoS\({}_{2}\) layers using PH\({}_{3}\)-He plasma. Homogeneous \(p\)-\(n\) MoS\({}_{2}\) junction diodes were fabricated.  
Gallium  
Mono-layer MoS\({}_{2}\) heterojunctions, such as intrinsic GaN/\(p\)-type MoS\({}_{2}\) heterojunction, were also irradiated by Ga plasma under UHV conditions [98]. The values of VBM were reduced to 0.2 eV for Ga-irradiated MoS\({}_{2}\) layers.  
Combination  
[PERSON] et al. [133] exposed mechanically exfoliated MoS\({}_{2}\) few-layers to an O\({}_{2}\)-Ar plasma (O\({}_{2}\):Ar = 1:3) for 2-28 s. The frequency of the plasma was 13.52 MHz. An oxide phase was generated under the plasma exposure, changing the electrical conductivity and carrier mobility of the 2D materials.  
[PERSON] et al. [98] irradiated MoS\({}_{2}\) under N\({}_{2}^{*}\) plasma for 1-3 min and then under Ga flux. The VBM was changed.  
[PERSON] et al. [134] treated MoS\({}_{2}\) vertical layers in UV-ozone. The ozone treatment increased super wettability and enhanced hydrogen evolution reaction of the material by changing edge chemistry and surface defects.  
[PERSON] et al. [19] treated MoS\({}_{2}\) layers in UV-ozone and then fabricated the layers into organic photovoltaic cells. The open-circuit voltage, fill factor, and power conversion efficiency increased significantly compared with un-irradiated MoS\({}_{2}\)-based solar cells.
## 7 Conclusions  
This paper takes the heat exchanger outlet temperature control system as the control object. Aiming at the problems of large delay, nonlinearity, and multiple interferences, a feedforward cascade compound control system based on IPFC is proposed. Through the analysis of the simulation results, the following conclusions are drawn.  
1. In this paper, the predictive functional control algorithm and PID control algorithm are combined, and the incremental PID control algorithm is used to improve the optimization objective function of the predictive functional control algorithm. By predicting the future output value of the controlled system and optimizing the control parameters of the controlled object, the analytical solution to the control quantity is obtained. The IPFC control algorithm has the structural characteristics of proportion, integration, and differentiation and has the advantages of strong robustness of the predictive functional control algorithm and good anti-interference of the PID control algorithm;
2. Compared with the feedforward cascade composite control scheme with PID as the main controller, before adding disturbance, it can be seen from the simulation comparison diagram that the control scheme proposed in this paper has a smaller overshot and adjustment time, which also confirms the effectiveness of the improved control algorithm proposed in this paper;
3. In the case of model matching and model mismatch, simulation is made for the three interference channels acting separately and simultaneously. Through the analysis of simulation comparison curves, it can be concluded that the feedforward cascade compound control system based on IPFC proposed in this paper has strong disturbance suppression ability and can correct or minimize the interference before it enters the process. The disturbance does not affect the controlled object.  
Conceptualization, B.X.; methodology, B.X. and W.M.; software, W.M.; validation, B.X. and W.M.; formal analysis, W.M.; investigation, B.X.; data curation, W.M.; writing--original draft preparation, W.M.; writing--review and editing, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Optimization Method for Improving Efficiency of Thermal Field Reconstruction in Concrete Dam
## 3 Optimization Method for Thermal Field Reconstruction
### Monitoring Point Number Optimization  
To reduce the calculation load of thermal field reconstruction and improve the response speed, it is crucial to investigate the impact of the number of temperature-monitoring points on the reconstruction results of the thermal field. A total of 58 monitoring points, spaced at 0.5 m intervals, were selected from the 7#-44 concrete block of the WDD dam. Subsequent datasets were generated by setting monitoring point intervals to 1.0 m, 1.5 m, 2.0 m, and 2.5 m, respectively, as illustrated in Figure 11.  
By comparing the interpolation results of these datasets with varying intervals (Figure 12), the effect of the number (or density) of temperature measurement points on thermal field reconstruction was analyzed. The analysis revealed that, as the number of temperature-monitoring points decreased, the reconstructed thermal fields remained nearly identical. Minor differences were observed only in the center of the dam block, with the maximum deviation being less than 1 \({}^{\circ}\)C. The temperature reconstructions near the upstream surface, downstream surface, and transverse joint surface were virtually identical. Even with a reduced number of monitoring points, the accuracy of the reconstructed thermal field continued to meet the temperature control requirements (e.g., within 1 \({}^{\circ}\)C). In summary, the Kriging interpolation algorithm, combined with the \"Z\" pattern of fiber embedding, allows for a reduction in the number of temperature-monitoring points while still ensuring the accuracy of the reconstructed thermal field.  
Figure 11: Monitoring data at different intervals.