esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
#### Comparison Between CMM and NIIM
Finally, to evaluate the effectiveness of NIIM, an error study was computed to measure the contour profile from deviations obtained from CMM and NIIM. This assessment, as described in [49], involves the average relative error (\(ARE\)), the root mean square error (\(RMSE\)), and the correlation coefficient \(R^{2}\) represented by Equations (10)-(12), respectively. The resulting metrics are summarized in Table 14.  
\[ARE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{11}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{ y})^{2}} \tag{12}\]  
where \(n\) is the number of the form deviation observations, \(y_{i}\) are the actual form deviation values, and \(\hat{y}_{i}\) are the predicted form deviation values. Several observations can be made in the results between CMM and NIIM: CMM demonstrates a lower ARE (0.0257) than NIIM (0.0301), indicating slightly better performance regarding the average error. Similarly, CMM exhibits a lower RMSE (0.0781) than NIIM (0.0812), suggesting slightly better overall accuracy in measurements. CMM shows a higher \(R^{2}\) (0.9318) than NIIM (0.9265), implying a stronger linear relationship between the predicted and observed values for CMM measurements. CMM achieves a slightly higher accuracy of 97.43% compared to the accuracy of NIIM at 96.99%.
## Appendix B  
The mean (A1) is calculated as follows:  
\[\text{mean}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\] (A1)  
where \(N\) is the number of observations and \(x_{i}\) represents each observation. The standard deviation (A2) calculates the extent to which the numbers in the dataset are spread out from the mean.  
\[\text{stddev}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\text{mean})^{2}}\] (A2)  
where mean is calculated. Based on Z score (A3), the normalization is expressed as follows:  
\[x_{\text{normalized}}=\frac{x-\text{mean}}{\text{stddev}}\] (A3)  
where \(x\) is an individual data point and mean and stddev are the previously calculated values.  
_Model Evaluation Metrics_  
Accuracy is the most commonly used metric in model evaluation and refers to the number of correctly labeled data points divided by the total number of examples. In otherwords, it is the rate of all predictions for both classes [41]. Accuracy for binary classification is expressed as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
In the case of imbalanced data, accuracy is not a suitable metric when used in this form. If 1% of examples belong to the minority class, we can achieve an exaggerated accuracy of 99% by predicting the majority class [41].  
The precision or positive predictive value for the binary case can be interpreted as being true in n% (it refers to the percentage of correct positive predictions) of cases, as follows:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{11}\]  
and the reverse is called recall or the true-positive rate, corresponding to the number of correctly predicted positive samples or the number of correctly diagnosed patients with the disease [41].  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{12}\]  
The F1 Score is the weighted harmonic mean of precision and recall [41].  
\[\text{F1 Score}=\frac{2\times Recall\times Precision}{Recall+Precision} \tag{13}\]  
Ba accuracy is obtained from the rows of the confusion matrix and is the average recall for each distinct category in the dataset [41]. \(Recall_{0}=\frac{TP}{TP+FN}\), and \(Recall_{1}=\frac{TP}{TP+FN}\)  
\[\text{Ba Accuracy}=\frac{(Recall_{0}+Recall_{1})}{2}. \tag{14}\]
## 3 Results and Discussion
In our case, we performed PLE measurements to understand the correlation between PLE with PL and UV-vis absorption spectra. The measurement was done by fixing the detection wavelength of 410 \(\pm\) 10 nm with varying excitation wavelengths ranging 225-400 nm. As shown in Figure 5a (black curve), the PLE spectrum displayed the two distinct peaks at 233 nm and 310 nm, which were consistent with the two absorption edges of the absorption spectrum (blue curve) [63]. The PLQY of 3 \(\upmu\)M concentration of R6G solution in methanol has been found to be \(\sim\)100% under visible excitation and can be used as the reference material to estimate the PLQY of other emissive materials [64]. The PLQY of MQDs was estimated using the following equation [65], which is presented in Figure 5b. \[\Phi_{MQDs}=\Phi_{Ref}\ \frac{I_{MQDs}^{PL}/\ A_{MQDs}}{I_{Ref}^{PL}/\ A_{ Ref}} \tag{1}\]  
Here, \(\Phi_{MQDs}\) is the QY of the MQDs to be measured, \(\Phi_{Ref}\) is the QY of the reference, \(I_{MQDs}^{PL}\) represents the PL intensity of the MQDs, \(I_{Ref}^{PL}\) represents the PL intensity of the reference, \(A_{MQDs}\) is the absorption of the MQDs, and \(A_{Ref}\) is the absorption of the reference. The maximum PLQY was estimated to be \(\sim\)6.9% with 310 nm excitation, which is consistent with the PLE and PL results presented in Figure 5a. Furthermore, excitation wavelength (within UV, ranging from 230 nm to 390 nm) dependent PL spectra were obtained, which are broad in shape owing to the inhomogeneous size distribution of MQDs in aqueous solution [61; 66], as shown in Figure 5c. Red shifts in the PL peak positions along with a decrease in intensity were observed for the excitation wavelengths from 310 nm to 390 nm, which may be due to the various sizes of the MQDs dispersed in the aqueous medium. Under the higher wavelength (lower energy) excitation, the small MQDs with a large band gap will be less excited, resulting in a lesser intensity due to the loss of emission at short wavelengths along with red-shifted peak positions and vice-versa. Remarkably,  
Figure 4: HR–TEM images (**a**) low, (**b**) high magnification, inset FFT image and (**c**) size distribution curve of the synthesized Ti\({}_{2}\)N MQDs. we observed a high intensity of the emission under excitation at \(\sim\) 230 nm and 310 nm, which may be due to most of the MQDs being excited under this illumination. This is quite consistent with our absorption, PLE, and PLQY results shown in Figure 5a,b.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
## 5 Simulation Results
### Extended Kalman Filter with Interval Analysis  
Figure 6 shows a localization similar to Figure 5. Indeed, in this case, the localization points of the two Kalman filters are close to each other. However, in Figure 6, the localization jumps of the interval analysis has an impact on the Kalman filter in the initial transition phase. Nevertheless, in the second scenario Figure 9, instead of diverging, the interval extended Kalman filter converge towards the real position of the drone after the transition phase.
## 3 Case Study  
To evaluate the proposed GOPML approach, we applied it to the TEP dataset and SKAB dataset. The experiments conducted on these datasets aimed to assess the efficacy of the method in small sample scenarios and under fine-grained working conditions. The results demonstrate that the method exhibited a high accuracy in small sample fault diagnosis. Moreover, it showed a commendable performance in the transferability of fault diagnosis across various scenes.
## 3 Results and Discussion
### Fermentation Process and Product Characterization
#### 3.3.1 First Fermentation Stage  
During alcoholic fermentation, samples were taken from the reactor and analyzed for their sugar and alcohol contents. The results, presented in Figure 7, illustrate the changes observed throughout the process. As depicted, the sugars in the tomato juice were gradually converted into alcohol (ethanol), leading to a decrease in the sugar content and an increase in the alcohol concentration. The initial sugar content was 12.21 \(\pm\) 1.05 \({}^{\circ}\)Brix which decreased to 3.73 \(\pm\) 0.91 \({}^{\circ}\)Brix after 72 h. The final alcohol content in the wine produced from tomato juice was 5.58 \(\pm\) 0.21%. This result was similar to previous findings in which tomato wine showed a maximum ethanol production rate in medium of 5.8% after 3 days [22]. However, the alcohol level in the reactor appeared to have reached its final value as early as the beginning of the second day (after 25-28 h). This suggests that the alcoholic fermentation process could have been terminated earlier than the day three, reducing both the processing time and fermentation costs.  
The final product of alcoholic fermentation served as an intermediate stage in the production of acetic acid. However, measurements were performed to characterize it, ensuring comprehensive monitoring of the fermentation process (Table 4). An analysis of the intermediate product's properties indicated that it retained a significant portion of its lycopene content (10.02 \(\pm\) 0.64 mg/100 g) while also achieving the required ethanol concentration for acetic acid production. According to this specification, the alcohol content in the wine used for acetic acid production should range between 5.5 and 7%. This ethanol percentage was achieved by adjusting the initial sugar concentration to 12.21 \(\pm\) 1.05 \({}^{\circ}\)Brix. As shown in Table 4, the final sugar content and TDSs exhibited minimal variation, confirming the accuracy of the refractive index measurement of the tomato wine.
## 1 Introduction
### Test Results  
Figure 3 represents the tensile load-displacement curve of the brazed-joint specimen with butt-joint angle \(\alpha=0^{\circ}\). Three sets of repeated tests were performed on the specimens, and the load-displacement curves showed good agreement. The results indicated that as the tensile load increased, plastic deformation occurred in the base material. When the load  
Figure 1: Structural dimensions of butt brazing joints.  
Figure 2: Specim loading diagram.  
reached its maximum value, the specimen instantaneously fractured in the weld seam of the brazed joint.  
Figure 4 shows the load-life curves of the butt-brazed joint specimens with \(\alpha\) = 0\({}^{\circ}\) under cyclic displacements X = [0.4 mm, 0.5 mm, 0.6 mm]. The maximum fatigue loads of the specimens were 3842 N, 4340 N, and 4745 N, and their fatigue lives were 158,955, 55,219, and 45,886 cycles, respectively.  
Figure 5 shows the three stages of fatigue softening of the butt-brazed joints under cyclic displacements of X = [0.4 mm, 0.5 mm, 0.6 mm]. In Stage I, the fatigue load decreased slightly, and the brazed weld of the butt-brazed joint showed initial softening characteristics. In Stage II, the fatigue load remained stable for a long time. In Stage III, the fatigue load decreased rapidly, and the brazed weld of the butt-brazed joint exhibited severe softening characteristics; fatigue cracks initiated and propagated at the brazed weld, leading to the fracture of the specimen.  
Figure 4: Load-life curves of brazed joints under different cyclic displacements.  
Figure 3: Tensile load-displacement curve of the specimen.  
Figure 5: \(X=[0.4\) mm, \(0.5\) mm, \(0.6\) mm] Three stages of fatigue softening of butt-brazed joints.  
Figure 6 shows the fatigue life curves of butt-brazed joints under different butt angles \(\alpha=[0^{\circ},\,15^{\circ},\,30^{\circ},\,45^{\circ}]\). The fatigue lives of the specimens at the time of fatigue fracture were 55,219, 145,510, 198,628, and 251,282 cycles, respectively. Under the same cyclic displacement, the fatigue life of the butt-brazed joint increased with the butt angle.  
During the testing process, it was observed that the fracture occurred at the weld seam, and the interface failure of the brazing layer exhibited instantaneous and rapid propagation characteristics. Figure 7 illustrates the fatigue crack propagation fracture morphology of brazed joints at different angles. Microscopic analysis of the brazed joint fracture revealed that due to plastic fracture during fatigue loading, multiple micro-voids appeared at the fracture surface, accompanied by a large number of fatigue striations, which were evenly distributed. As can be seen in the figure, with the increase in butt angle, the area of the brazed joint scarf face increases, resulting in enhanced connection strength, while the width of secondary cracks and the number of fatigue striations gradually decrease.
## 6 Cost and Benefit Analysis
### Equipment Cost  
Equipment cost is a primary factor in evaluating any new technology. Compared to traditional single-motor drive systems, this system employs multiple low-speed, high-torque external rotor permanent magnet motors. Although the number of motors is higher, the cost of each individual motor is relatively low. Therefore, the overall equipment cost offers a certain advantage in large-scale applications. Additionally, the modular design of the multi-motor distributed drive system facilitates easier installation and maintenance. While the initial installation cost is higher, the maintenance costs are lower. The modular design not only simplifies the replacement and repair of individual motors but also reduces downtime, thereby lowering maintenance costs and minimizing production disruptions.
## 4 Numerical Results and Discussion
### Out-of-Plane Simulation
#### 4.1.2 Damage Kinetics
Figure 37: The simulated failure process of the specimen under out-of-plane loading: (**a**) 155 \(\upmu\)s; (**b**) 170 \(\upmu\)s; (**c**) 200 \(\upmu\)s; (**d**) 410 \(\upmu\)s.  
Figure 38: The fracture pattern of \(8\times 8\times 4\) specimens under 1.0 MPa loading. Figure 39: The contours of \(8\times 8\times 8\) specimen at 155 \(\upmu\)s (unit: cm-\(\upmu\)s-g): (**a**) effective stress of sub-laminates; (**b**) effective strain of sub-laminates; (**c**) damage degree of cohesive elements. As shown for the contours of the cylindrical specimen in Figure 40, the effective stress, effective strain, and damage degree were higher at the center than at the two end faces in contact with the bars. Therefore, the cylindrical specimen tended to fail at the center. By comparing Figures 29 and 30, it can be seen that the effective strain of the cylindrical specimen was higher at the center, while the effective strain of the cubic specimen was higher at the two end faces in contact with the bars. This difference between cylindrical and cubic specimens is similar to the findings in Reference [37].
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 3 Results
### Diebold-Mariano (DM) Test
This stage allowed for validating the model's ability to accurately replicate real conditions outside the training set, without loss of precision or signs of overfitting. Finally, the forecast segment, highlighted in red, projects a moderate continuation of maritime traffic levels. The model's consistency with previous patterns suggests that similar operational conditions are expected in the future, reinforcing the model's usefulness for short-term planning. Figure 14 shows the time series of the number of vessels, segmented into training (yellow), testing (blue), and forecasting (red) phases. An initial increasing trend was observed, followed by stabilization and then a sustained projection, suggesting consistency in the dynamics of maritime traffic. As a complement to the analysis conducted, a comparative representation is presented below between the empirical observations and the forecasts generated by the various evaluated models (Figure 15). This integrated visualization enabled a direct examination of discrepancies in the predictive performance of each model and facilitated the visual identification of the one that exhibited the greatest degree of fit to the historical series of vessel traffic through the Panama Canal.
## 1 Introduction
### Challenges  
The hourly variation of electricity demand shows that demand-side nonlinear characteristics add to the challenge of balancing the grid. In addition, integration of solar and wind power along with other energy mix scenarios increases the uncertainty of generation. Load profiles are influenced by seasonal and cyclic pattern of the atmosphere as well as by human behavior and other external factors [6; 7]. While researchers have included these variables in their models when developing forecasting models, the predicted value may not exactly match the true value.  
Depending on the impact of such influencing parameters, researchers are continuously seeking to improve on existing forecasting models in order to minimize the incidence of under- and over-forecasting [8]. In this context, the major challenges for researchers is to minimize the prediction error by developing sophisticated models and testing them on various datasets, such as industrial loads, residential loads, aggregated loads, etc. [9; 10]. This challenge has motivated researchers to develop robust forecasting models that can improve accuracy when predicting electricity demand and reduce the financial costs for utility companies. The three main areas that need to be tackled are forecasting accuracy, sensitivity to parameters, and complexity of training [1].
## 2 Review of the Standard PSO and Its Variants
### 2.2 Neighborhood topology adjustments  
The PSO particles with different neighborhood topologies are connected together through different structures. In these structures, the particles have different rules for communicating and sharing information, which can affect the diffusion speed of the information in the population and drive the whole population's evolution. Therefore, neighborhood topology adjustments can also effectively improve the performance of PSO. Many neighborhood topologies have been proposed such as the star [3], ring [30], wheel [31], and Von Neumann topologies [32]. These foregoing neighborhood topologies belong to static topologies and the pattern of information acquisition is fixed, which contradicts the distinct requirements at the different evolutionary stages. Therefore, many dynamic topologies have been developed in which the neighborhood can be refreshed and regrouped depending on the particles' parameters in the evolution process. In [33], a dynamic neighborhood that is introduced contains only a few particles during the initial stages of the optimization. With the increase in generations, the number of particles in the neighborhood gradually increases until all particles are included. This enhances the exploration abilities of the algorithm in the earlier stage and makes the algorithm focus more on exploitation at the end of the PSO run. Furthermore, in [26], the nonparametric PSO (NP-PSO) constructs two different topologies, making it perform global and local search over the search space with a fast convergence speed. In the dynamic neighborhood learning PSO (DNLPSO) [34], the exemplar particle is selected from a neighborhood dynamically updated at certain intervals, which is conducive to preserving the diversity of the population to discourage premature convergence.  
The multipopulation mechanism has received extensive attention because of its advantages in maintaining population diversity. In the DMS-PSO [11] and the dynamic multiswarm global PSO (DMS-GPSO) [35], the entire population is divided into many small swarms and various regrouping schemes and information interaction methods have been designed to perform dynamic updating of these swarms. In addition, in the multiswarm ensemble PSO (MPEPSO) [36], all particles are divided into four subswarms including three indicator subswarms and one reward subswarm. AMPSO [29] contains three subswarms, namely, an exploration swarm, an artificial exploitation swarm, and an artificial convergence swarm. In the above two algorithms, the subswarms update their velocities through different strategies, with the intention of balancing exploration and exploitation.
## 4 Proposed Architecture  
IoT is an emerging technology that enhances every area with its real-time monitoring application from any remote location. Moreover, the advanced and robust wireless communication technologies meet the requirement of IoT. In fire safety, the significance of establishing a real-time evacuation path is possible with a personal area network (LAN) and wide area network (WAN) for overcoming the network connectivity in terms of cost, energy consumption, and license band. With this advantage, in this study, a novel and hybrid IoT-enabled architecture based on 2.4 GHz Zigbee and LoRa communication protocol is created and is shown in Figure 5.  
Figure 4: Zigbee Architecture.  
Figure 3: Zigbee Comparison with Bluetooth and Wi-Fi.  
The hybrid architecture is the amalgamation of five different components namely end device, evacuation path display controller, safety operation controller, vision node, and gateway. The end device and vision node are the primary devices that are deployed in the building for sensing parameters including smoke, fire, temperature, and gas through the attached sensors and camera module. The end device is embedded with a 2.4 GHz Zigbee and LoRa RF module. The 2.4 GHz Zigbee enables \"N\" number end devices to transmit the sensory data to the evacuation path display controller and safety operation controller. Here, the safety operation controller analyzes the data, and in the case of a fire, it immediately triggers the sprinkler system at the place where the fire was initiated. The evacuation path display controller also analyzes the sensory data received from the end device and provides the visualization of the safe evacuation path for the people in the building.  
Moreover, to enhance the detection of fire accidents, a vision node is also positioned at a place near the end device. The vision node is loaded with an analytic model, which can distinguish the fire accurately, and it communicates the visuals to the local server and cloud server through internet connectivity. As discussed earlier, the end device is embedded with a LoRa module, the LoRa module enables transmission of the sensor data to the gateway via LoRa communication. This gateway logs the sensory data into the cloud server via Wi-Fi. The cloud server records the sensory data and visual data to assist the fire authorities with continuous vision of the building fire system.
## 4 Fault Identification Method Based on IPSO-WNN
### WNN Hidden Layer Output Optimization  
The standard WNN structure diagram is shown in Figure 9.  
In Figure 9, \(X\) and \(Y\) denote input samples and predicted outputs; \(\omega_{ij}\) are the weights connecting output layer node i and implicit layer node \(j\); \(\omega_{ik}\) is the weight connecting node \(j\) in the implicit layer and node \(k\) in the output layer; and \(\psi(x)\) is the dbN function.  
There are \(n\) input nodes in WNN. The input sequence \(X=(x_{1},x_{2},\ldots,x_{n})\) is processed by the dbN function and the output value is obtained in the hidden layer.  
The calculated values of the standard WNN implied layer are susceptible to the influence of the input layer data, with large fluctuations between values and a single calculation method, which cannot meet the requirements of this paper for fault identification accuracy. Therefore, using quantified connection weights between input and implied layers, this paper seeks to reduce the influence of single input data. The calculation is shown in Equation (13):  
\[u_{j}=\psi\left[\frac{\sum_{l=1}^{n}\omega_{ij}x_{i}-b_{j}}{a_{j}}\right],j=1, 2,3,\ldots,l \tag{13}\]  
where \(u\) is the output value of the implicit layer nodes, \(n\) is the number of nodes in the input layer, and \(l\) is the number of implied layers, and \(a,b\) denote the scaling factor and translation factor of the dbN function, respectively.  
After the implicit layer processing, the output value obtained is given in Equation (14).  
\[y_{k}=\sum_{j=1}^{l}\omega_{jk}u_{j},k=1,2,3,\ldots,m \tag{14}\]  
where \(y_{k}\) is the actual output of the network, and \(m\) is the number of output layer nodes.  
The error function of WNN is usually expressed as the mean squared difference between the actual output value and the desired output value, as shown in Equation (15).  
\[E_{D}=\sum_{k=1}^{m}(y_{k}-y_{k})^{2} \tag{15}\]  
where \(E_{D}\) is the training error, and \(y_{k}\) is the desired output.