esa-sceva/satcom-chunk-collection huggingface dataset:
# New Evolutionary-Based Techniques for Image Registration
## 5 The Proposed Evolutionary Algorithms for Image Registration
### Hybridization between ES Search and APSO  
The main aim of the hybrid approach introduced next is to find a trade-off between the accuracy and the efficiency of the obtained algorithms. The resulted techniques combine the ability of auto-adaptive ES search to correctly identify the direction of fitness increase to find out a global optimum solution with the strong quick convergence ability of APSO.  
The proposed hybrid technique is a two-stage mechanism that first uses an ES search mechanism to detect the direction of fitness increase and a certain population, Pop, then applies one of the proposed variants of APSO using a subset of good individuals \(X_{0}\subseteq Pop\) to quickly reach an optimal solution. The idea behind this hybridization relies on the following remarks. Despite the fact that ES mechanisms are in general very suitable to rapidly identify promising areas of the space search, they are less good for finely tuning the solutions. A more efficient method might be to incorporate a more systematic search of the vicinity of good solutions by adding a search mechanism with a quick convergence ability.  
The ES component of the hybrid technique is characterized by: \(\sigma_{ini}\), the initial values of \(\sigma-\)parameter; \(\epsilon_{\sigma}\), the minimum value of each step size; NMax, the maximum number of generations; and the threshold parameter \(\tau\) controlling the desired sub-optimal fitness value, \(\tau<1\). The parameters of APSO are: MaxGeneration, the maximum number of firefly populations, \(\beta_{0},\gamma\) the parameters of the updating rule, and n the APSO population size.  
The computation scheme of the proposed method is described as follows
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.6 Key Sensitivity Analysis  
A strong encryption system should be highly sensitive to even the smallest alteration to the secret keys [13]. Assume the control settings and beginning values that are used to encrypt plain photos \(\left(x_{0}^{1}\right)\) and (\(r^{1}\),\(r^{2}\),...,\(r^{4}\)) in order to test the key sensitivity. Use the new key to decode the image after the encryption process by adding \(10^{-16}\) to any beginning condition or control parameter. As a result, Figure 11 shows the key sensitivity test demonstrates how sensitive the proposed encryption system is to the security key. That indicates the least amount of secret key modification during the decoding procedure. The outcome will be an image that is entirely unencrypted.
## 3 Results  
MEM was detected and confirmed by UPLC in all 54 samples dissolved in demineralized water with ascorbic acid. A one-way ANOVA was used to compare the materials within each time point, as shown in Table 1.  
The drug release on day 1 was statistically nonsignificant in both sterile and non-sterile groups of M, MP, and MC, yielding 5.57, 6.80, and 5.82 mcg/mL in the sterile group and 5.33, 5.77, and 5.75 mcg/mL in the non-sterile group, respectively, as shown in Figure 1A-F. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean \(\pm\) SD**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{5-6}  & & & & & **Lower Boundary** & **Upper Boundary** \\ \hline \hline \multirow{4}{*}{M} & Day 1 & 3 & \(5.57\pm 0.14\) & \(5.21\) & 5.92 \\ \cline{2-6}  & Day 3 & 3 & \(55.50\pm 1.55\) & 51.66 & 59.34 \\ \cline{2-6}  & Day 7 & 3 & \(163.81\pm 1.06\) & 161.19 & 166.44 \\ \hline \multirow{4}{*}{MP} & Day 1 & 3 & \(6.80\pm 1.16\) & 3.92 & 9.68 \\ \cline{2-6}  & Day 3 & 3 & \(73.59\pm 1.75\) & 69.23 & 77.94 \\ \cline{2-6}  & Day 7 & 3 & \(205.53\pm 1.95\) & 200.69 & 210.38 \\ \hline \multirow{4}{*}{MC} & Day 1 & 3 & \(5.82\pm 0.56\) & 4.43 & 7.21 \\ \cline{2-6}  & Day 3 & 3 & \(64.81\pm 0.60\) & 63.32 & 66.31 \\ \cline{2-6}  & Day 7 & 3 & \(218.86\pm 1.67\) & 214.71 & 223.02 \\ \hline \multirow{4}{*}{M} & \multicolumn{4}{c}{Non-sterile} \\ \cline{2-6}  & Day 1 & 3 & \(5.33\pm 0.18\) & 4.88 & 5.77 \\ \cline{2-6}  & Day 3 & 3 & \(51.10\pm 1.46\) & 47.46 & 54.74 \\ \cline{2-6}  & Day 7 & 3 & \(165.07\pm 1.77\) & 160.66 & 169.47 \\ \hline \multirow{4}{*}{MP} & Day 1 & 3 & \(5.77\pm 0.20\) & 5.28 & 6.26 \\ \cline{2-6}  & Day 3 & 3 & \(62.07\pm 0.29\) & 61.36 & 62.79 \\ \cline{2-6}  & Day 7 & 3 & \(191.55\pm 0.93\) & 189.25 & 193.84 \\ \hline \multirow{4}{*}{MC} & Day 1 & 3 & \(5.75\pm 0.43\) & 4.68 & 6.83 \\ \cline{2-6}  & Day 3 & 3 & \(62.17\pm 1.52\) & 58.39 & 65.96 \\ \cline{1-1} \cline{2-6}  & Day 7 & 3 & \(217.23\pm 1.92\) & 212.46 & 222 \\ \hline \hline \end{tabular}
* Statistically significant at \(p\leq 0.05\).  
\end{table} 
Table 1: Comparison within each sterile and non-sterile materials (M, MP, MC) over 1, 3, and 7 days. The highest MEM concentration in all the groups was observed in sterile MC at day 7 resulting in 218.86 mcg/mL, while the lowest was in non-sterile M at day 1 by 5.33 mcg/mL, as shown in Table 1 and Figures 1D and 3C. A two-way ANOVA repeated measurement was used to confirm the presence of the interaction between time and each material concentration, as shown in Table 2, in which no statistically significant difference was noted between the materials during day 1 (\(p=0.19\)). Statistically significant differences were perceived by days 3 and 7 (\(p=0.00\)), as shown in Figures 2 and 3. The overall difference between the sterile and non-sterile groups was statistically nonsignificant using a \(t\)-test, except MP at day 3 and 7 (\(p=0.006\), 0.00), in addition to M in day 3 (\(p=0.023\)), as shown in Table 3.
## 3 Proposed Algorithm  
The proposed algorithm ELQSSM--ML is discussed in this section. Initially, the Multi-Rate Mobile Adhoc Network is developed where each node is deployed with an Omni-directional aerial and posse's similar radio specifications. Let the MANET consists of \(N\) arbitrarily circulated relay nodes \(r_{n},n\in\{1,\ldots,N\}\), situated in the vicinity of the origin and target nodes. Every node distribuues a similar transmission channel and transmit the packet at a consistent rate. The data transfer is at a higher rate when compared to the direct transfer from a given origin to a destination. The nodes are normally disseminated, with the origin containing the data being forwarded to the target. Moreover, \(N\) relay nodes will help to send the accurately decoded data to the target at the minimum transfer cost, and so the total throughput is increased.  
All of the nodes help to identify the gain in channel among self and target nodes through the SN Ratio of the packets that arrive, which are measured in terms of distance, the loss in energy of a particular route, and the fade factor for correct decoding of data. The channel between the two nodes is considered to be independent. All the nodes are half-duplex with a condition to exhibit a high level of energy during transfer. The IEEE802.1 standard is adopted in the physical layer that assists multi-rate ability among the transfers.  
Each node is defined by an equal primary power, and the current power of every node is produced in a random way prior to the transfer task for representing the real-world  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.No** & **Method** & **Advantage** & **Disadvantage** \\ \hline \multirow{2}{*}{1} & EECRM & Low cost & Do not address Lifespan issue \\  & & Less energy wastage & The residual energy is not used \\ \hline \multirow{2}{*}{2} & EECRP-PSO & Optimized energy usage & The time taken for message delivery is long \\  & The Source node is always backed up with energy & The algorithm has huge implementation cost \\ \hline \multirow{2}{*}{3} & QSSM-ML & The source and sink had equal energy distribution & Implementation cost \\  & The average waiting time for a node to get energy is less & The problem of local minimal optimization \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing methods.  
quality of the node's energy range in the network. In the collaborative transfer mode, the origin forwards its data to the target through the elected optimum relay node in a dual-hop fashion. Moreover, the highest rate collaborator is applied at the target for decoding the arriving data at the PHY layer if many independent paths collaborate.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Model Performance Evaluation  
In this study, the performance of each model is evaluated based on four types of performance indices. The evaluation includes both the absolute and relative aspects of the errors, such as the root mean square error (_RMSE_), mean absolute error (_MAE_), correlation coefficient (_R_), Nash-Sutcliffe efficiency (_NSE_) and mean absolute percentage error (MAPE).
## 2 Materials and Methods  
Figure 1 shows a schematic diagram of the electro-optic detection system based on the free space detection method. The THz pulses are emitted by the MMW Accelerator at Ariel University. The frequency of these pulses is 0.11 THz, 10 \(\upmu\)s width, and 5 kW power [16]. A CW Fiber laser from keysight Technologies model N7748A is used in this experiment as pump laser. The laser was set at 1535 nm wavelength, 40 mW power. A manual fiber polarization controller from Thorlabs (Item FPC031) was used to control the polarization of the laser. An Erbium-Doped Fiber Amplifier (EDFA) was used to amplify the laser at 200 mW. A 100 GHz fiber filter (from E-TEK Dynamics), which is appropriate to the 1535 nm wavelength, was used to filter the noise around 1535 nm after amplifying the laser. A focus collimator was used to focus the laser beam into the nonlinear crystal in the free space. Two Off-Axis parabolic gold mirrors (from Thorlabs, item MPD399-M03), were used to focus the THz pulse into the nonlinear crystal. A hole was made in one of the mirrors to allow the laser to pass through it.  
A GaAs crystal with orientation <110> and 1 mm thickness was used as a nonlinear crystal. Two Broadband mirrors (from Thorlabs, item BB1-E04) were used to direct the laser beam into the photodetector. Two polarizers applicable to the 1535 nm wavelength were used to produce a cross-polarization process. The photodetector that used was a Thorlabs, item DET10C. An RF Amplifier (HD communications corp, item HD29225) was used to increase the electric signal of the photodetector at 30 dB. In order to analyze the data an oscilloscope was used (Keysight, item DSOX3104A), with a bandwidth of 1 GHz and a max sample rate of 5 Gigasamples (GSa)/s.  
The use of fiber-based systems provides a good method for increasing the optical output signal and improving the signal-to-noise ratio. The use of the EDFA can increase the optical output signal up to 30 dB. The schematic diagram for the electro-optic detection system based on the fiber coupling detection method is shown in Figure 2. The difference between the free space detection system and the fiber coupling detection system is found in the part between the coupling of the laser beam into the fiber and the photodetector.  
Figure 1: Schismatic diagram for free space electro-optic detection system.  
A focus collimator was installing in the second polarizer couplingand focused the laser into the fiber. An EDFA was used to amplify the output signal after the coupling system, the output signal was amplified at 30 db. Then, the output signal was put through a 200 GHz filter to remove the noise around the increased pulse. A fiber adapter was used to connect the fiber to the photodetector. Then, the RF amplifier increases the electric signal by 30 dB. The data were analyzed by the oscilloscope.
## 3 Results
### F1-Confidence Curves  
The F1 score is a commonly used metric for evaluating object detection models [27]. It considers both the precision and recall of the model to compute a score between 0 and 1, with 1 being perfect precision and recall. The F1 score is calculated as the harmonic mean of precision and recall  
\[F1=2*\frac{precision*recall}{precision+recall}\]  
where:  
* Precision is the percentage of predicted bounding boxes that accurately match ground truth objects. It measures how many of the detections are actually correct.
* Recall is the percentage of actual objects that are correctly detected. It measures how many of the ground truth objects are found.  
The F1 score is a useful metric because both precision and recall are important in object detection--the model is expected to detect as many real objects as possible without too many incorrectly detected boxes. The F1 score balances these two metrics into a single value. A model with a high F1 score performs well in both avoiding false positives and not missing detections.  
So, in summary, the F1 score provides a measure of the accuracy of an object detection model by combining precision and recall. It provides a balanced evaluation of the detector's performance. Maximizing the F1 score leads to models that are reliable in detecting objects correctly without many mistakes.  
The F1-Confidence curve is a metric used to evaluate the performance of object detection models [28]. It shows the relationship between the confidence threshold and the F1 score. A high F1 score at a high confidence indicates a good detector. The area under the F1-Confidence curve summarizes the overall detection performance.  
Figure 7a,b show the F1-Confidence curves for the initial YOLOv8 and our modified YOLOv8, respectively. From them, our modified YOLOv8 has produced better outcomes than the original YOLOv8 in the following areas:  
1. Higher peak F1 score. The maximum F1 score indicates the model's best performance. The initial YOLOv8's maximum F1 score is 0.86, whereas ours is 0.95, indicating an improvement of 0.09.
2. Wider curve and flatter peak. A wider curve and flatter peak indicates the F1 score is stable across different thresholds, revealing a more robust model. A narrow peak means high sensitivity to thresholds. Our model's F1-Confidence curve has a wider curve and flatter peak than the initial YOLOv8.
3. Larger area under the curve. The area under the F1-Confidence curve summarizes performance across all thresholds. A larger area indicates a better model.  
Figure 7: The F1–Confidence curves for the original YOLOv8 (**a**) and our modified YOLOv8 (**b**).
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 2 Selection Criteria  
It was decided to search the literature from January 2006 (when the publications on machine learning in emotion recognition started emerging) to April 2019. The used databases were ACM [9], IEEE [10], BASE [11], and Springer [12]. These databases were chosen because of their relevance in Computer Vision.  
The searching strategy consisted of Open Access articles, journals, conference objects, and manuscripts. There are several keywords and variants that cover the FER field; therefore, it was decided to limit the searched keywords to the most popular two within this topic: \"Emotion Recognition\" or \"Facial Expression Recognition\". The full text of the works was considered during the searching process. The results were the following:  
* ACM: 239 papers,
* IEEE: 118 papers,
* BASE: 78 papers,
* Springer: 369 papers.  
All the papers were assessed, resulting in a total of 783 non-duplicate papers. The selection criteria for this systematic review only include works that explore FER using Computer Vision. First, all the titles and abstracts were quickly appraised in order to exclude studies that were not relevant to the scope of this systematic review or did not meet the selection criteria, resulting in 185 papers. Second, the full text of the remaining papers was carefully evaluated in order to filter out the remaining studies that did not meet the selection criteria, resulting in 112 papers. The following categories of papers were excluded:  
1. Theoretical studies,Studies that are not related with Computer Vision,
* Surveys and Thesis,
* Dataset publications,
* Older iterations of the same studies.  
Figure 2 sums up the search and exclusion steps of this systematic review. It is important to mention that some included papers did not present results, they were not conclusive or their procedures and/or results deviate from the mainstream scope; therefore, they were not included in Section 7 [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. However, those papers still contributed to the remaining sections.  
Figure 3 shows how many papers were found for each year in the searched time frame, displaying a clear increase of interest in emotion recognition. It only appears to decrease in 2019 because, as mentioned above, the searched time frame goes from January 2006 to April 2019.
## 2 Materials and Methods
### Mechanical and AE Test  
AE analysis is employed to monitor micro-crack initiation and propagation within the CGCMs during mechanical testing. It captures transient elastic waves generated by the release of energy from internal microstructural changes, providing real-time data on damage onset and progression. This approach enables the assessment of the material's mechanical behavior, identification of critical stress points, and determination of failure modes. Consequently, AE analysis offers valuable insights into the material's integrity and durability, facilitating a comprehensive evaluation of performance under various additive conditions. In this study, a PCI-2 AE system (manufactured by Physical Acoustic Corporation, Princeton Junction, Princeton, NJ, USA) was used to capture AE signals during mechanical testing. Two probes, attached to the CGCM specimen surface, collected acoustic emission signals simultaneously during loading. The sampling frequency was set to 140 kHz, with a signal amplification of 45 dB.  
Compression tests were then carried out using a YNS 2000 Servo hydraulic testing system (Changchun Machinery Factory, Changchun, China) at a strain rate of 0.05 mm/min. The layout of the test equipment and CGA-based specimen is shown in Figure 3.
## 5 Results and Discussion
### Comparison of the BE-LMTree Model with Benchmark  
Because this is the first time that the BE-LMTree model is investigated for landslide modeling, the validity of the proposed model therefore was evaluated and compared with the benchmark. We select support vector machine (SVMC) as a benchmark because SVMC has proven efficient and outperforms other conventional methods [38; 91]. For constructing the SVMC model, the radial basic function (RBF) kernel [41; 92; 93] was selected and the grid-search method [94; 95; 96] was used to derive the best the regularization (C = 9) and kernel width (\(\gamma\) = 0.245). In addition, the performance of the LMTree model was also included to present the merit of the proposed BE-LMTree model that is an integration of the Bagging ensemble and the LMTree.  
The result is shown in Figures 7, 8, and 10. Using the training dataset, the CLA of the SVMC model (90.08%) and the LMTree model (92.03%) is slightly lower than CLA (93.81%) of the BE-LMTree model. Regarding LLR, the SVMC model (7.93) and the LMTree model (13.13) have lower values when compared to that of the BE-LMTree model (17.31). The other detailed metrics of the two models are shown in Figure 7. Overall, the BE-LMTree model performs better than the SVMC model and the LMTree model in the training dataset.  
Using the validation dataset, the prediction performance of the SVMC model and the LMTree model is evaluated (Figure 8). It could be seen that the proposed BE-LMTree model (CLA = 87.98, LLR = 5.89) has a higher prediction performance when compared to those of the SVMC model (CLA = 86.45%, LLR = 5.09) and the LMTree model (CLA = 82.85%, LLR = 4.05). The global prediction capabilities of the three landslide models are assessed using the ROC curve and AUC (Figure 10). It could be been that the proposed BE-LMTree model (AUC = 0.834) is slightly higher than those of the SVMC model (AUC = 0.825) and the LMTree model (AUC = 0.813). Other detailed prediction performances of the three models are presented in Figure 8. Based on the aforementioned analysis, it could be concluded that the proposed BE-LMTree model is capable of producing the best landslide susceptibility result for this study area.  
Figure 10: Receiver Operating Characteristic (ROC) curve and Area Under the curve (AUC) of the BE-LMTree model, the LMTree model, and the SVMC model using the validation dataset. SE: Standard Error; CI: Confidence Interval.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 2 Mini-Crack Detection System Based on Laser Excited Thermography
### Detection System  
The schematic of the mini-crack detection system based on laser excited thermography is shown in Figure 2, and the system is composed of a CW laser scanning device, an image acquisition device, and a control device. The surface of the conveyor belt is heated by a CW laser beam, and an infrared camera records the corresponding thermal response simultaneously. Then, the thermal response is transmitted to the image processing unit.  
The CW laser scanning device consists of a CW laser beam generator and a collimator. When the conveyor belt runs continuously, the space-fixed CW laser beam actively scans the conveyor belt surface and generates thermal waves. The CW laser scanning device receives the control signal and emits a CW laser beam to the conveyor belt. Then the collimator is used as a beam generator to transformed CW point laser beam into line laser. The infrared radiation intensity from the conveyor belt depends on materials in different layers and belt speed when the moving belt heated by the same laser beam. In this paper, a CW laser with the power of 15 W is used as a heat source. The conveyor belt which consists of three-layer composite rubber materials operates at maximum running speed of 1 m/s.  
The image acquisition device consists of a focusing lens, a beam splitting membrane and an infrared camera. A focusing lens is used to adjust the focal length of the infrared camera. Beam splitting membranes are used to separate the visible and the infrared beam. The spectrum working range of the beam splitting membrane is 6.5-7.5 um [42]. The beam splitting membranes can prevent the light beyond the limited wavelength from entering the thermal image, which can avoid the influence of infrared of infrared and visible light in the environment on infrared thermal imaging to a certain extent. Finally, the infrared camera captures the thermal wave on the conveyor belt.  
The control unit is used to control the operation of the conveyor belt and the start-up of the CW laser scanning device. To avoid surface overheating, the CW laser scanning device is usually started after the conveyor belt operates normally and shut down before the conveyor stops running.  
Figure 2: Schematic diagram of longitudinal tear detection system based on laser excited thermography.