esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ITFWO for Various OPF Problems
### Basic OPF Solutions
#### 4.1.3. Type 3: Including the Voltage Deviation (V.D.) and Fuel Cost  
In this type of OPF problems has been considered \(Fcost\) and V.D. in order to increase service and security indexes at buses as follows:  
\[\begin{array}{l}J_{5}=\sum\limits_{i=1}^{NG}\alpha_{i}+b_{i}P_{Gi}+c_{i}P_{Gi} ^{2}+\Phi_{v}\sum\limits_{i=1}^{NL}\left|VL_{i}-1.0\right|+\lambda_{P}(\,P_{G1 }-P_{G1}^{\lim}\,)^{2}\\ +\lambda_{S}\sum\limits_{i=1}^{NTL}\left(\,S_{i}-S_{i}^{\lim}\right)^{2}+ \lambda_{Q}\sum\limits_{i=1}^{NG}\left(\,Q_{Gi}-Q_{Gi}^{\lim}\right)^{2}+ \lambda_{V}\sum\limits_{i=1}^{NL}\left(\,VL_{i}-VL_{i}^{\lim}\right)^{2}\end{array} \tag{42}\]  
where, the amount of \(\phi_{v}\) has been choosen 100 [80].  
The best setting for control parameters has been achieved by ITFWO has been given in Table 1. Moreover, the simulation solutions of the methods have been given in Table 4, ITFWO has significantly decreased this multiobjective OPF. In addition, the optimization processes of the problem with the studied methods have been in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Fuel Cost (S/h)** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** & \(J_{5}\) \\ \hline SSO [102] & 803.73 & 0.365 & 9.841 & 0.1044 & 814.1700 \\ SpDEA [99] & 803.0290 & - & 9.0949 & 0.2799 & 831.0190 \\ MFO [80] & 803.7911 & 0.36355 & 9.8685 & 0.10563 & 814.3541 \\ MPSO [80] & 803.9787 & 0.3636 & 9.9242 & 0.1202 & 815.9987 \\ DA-APSO [103] & 802.63 & - & - & 0.1164 & 814.2700 \\ MNSGA-II [104] & 805.0076 & - & - & 0.0989 & 814.8976 \\ MOMICA [104] & 804.9611 & 0.3552 & 9.8212 & 0.0952 & 814.4811 \\ PSO-SSO [102] & 803.9899 & 0.367 & 9.961 & 0.0940 & 813.3899 \\ TFWO [1] & 803.416 & 0.365 & 9.795 & 0.101 & 813.5160 \\ EMSA [101] & 803.4286 & 0.3643 & 9.7894 & 0.1073 & 814.1586 \\ PSO [102] & 804.477 & 0.368 & 10.129 & 0.126 & 817.0770 \\ BB-MOPSO [104] & 804.9639 & - & - & 0.1021 & 815.1739 \\ TFWO & 804.1210 & 0.3640 & 10.0753 & 0.0979 & 813.9110 \\ ITFWO & 803.8167 & 0.3639 & 9.8804 & 0.0941 & 813.2267 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The optimization results for Type 3.  
Figure 5: Optimization process for Type 3.
## 5 Conclusions  
In this paper, we use the time-gated AQ-SPAD as a receiver to achieve better communication performance by optimizing the gate-ON time. The photon counting model based on external time-gated AQ-SPAD is established according to counting characteristics and ISI distortion. The BER model of the time-gated AQ-SPAD receiver is constructed. In addition, we use the minimum BER as an optimized target, and determine the optimal gate-ON time according to the signal photon flux and background photon flux. Numerical experiments show that the gated mode enhances the background of the AQ-SPAD receiver. Moreover, the gated mode has better BER performance than the free-running mode, especially when the signal light flux or the background light flux is higher, and the BER performance of the time-gated mode is apparently better than the free-running mode.  
Conceptualization, Y.M. and Y.Z.; methodology, Y.M.; validation, Y.M. and X.D.; formal analysis, Y.M.; writing--original draft preparation,Y.M.; writing--review and editing, Y.Z., C.W., Z.Y. and X.D. All authors have read and agreed to the published version of the manuscript.  
The research of this paper was funded in part by the National Key Research and Development Project under Grant No. 2018 YFB1801903, in part by the National Natural Science Foundation of China (NSFC) under Grant 61901524 and in part by the China Postdoctoral Science Foundation under Grant No. 2019M663477.  
Not applicable.  
Figure 7: Performance comparison of time-gated SPAD and free-running SPAD. (**a**) \(N_{g}=16\). (**b**) \(N_{g}=64\).
# A Low-Cost IoT-Based Bidirectional Torque Measurement System with Strain Gauge Technology
## 3 Materials and Methods
Figure 3 highlights the practical integration of the sensor and the calibration process. Figure 3: Experimental setup with torque wrench: 1—clamped end; 2—torque sensor; 3—half shaft; 4—torque wrench. Figure 2: The schematic diagram of the hardware configuration. Each component is labeled for clarity. The process was repeated five times per torque value to ensure repeatability in both directions (clockwise and counterclockwise). The average recorded data for each increment was plotted, and a fifth-order polynomial regression was performed to establish the nonlinear relationship between the digital output and the applied torque. The resulting polynomial coefficients were programmed into the ESP8266 firmware, enabling real-time conversion of raw sensor readings into torque values. Regression analysis was used to validate the polynomial model's ability to capture the sensor's bidirectional response, with error metrics such as mean absolute error (MAE) and standard deviation (SD) calculated for each calibration point. This approach aligns with best practices for torque sensor calibration in nonlinear systems, as detailed in recent literature [3, 35, 36, 37, 38]. The polynomial coefficients and uncertainty analysis follow guidelines for high-precision metrology outlined in the guidelines in SMITH and the work of [PERSON] et al. [7, 22, 39, 40, 41]. The Wheatstone bridge configuration provides inherent thermal compensation, as mentioned in the system description. The HX711 module demonstrates relatively stable performance with temperature variations within its specified operating range [8]. However, long-term stability requires periodic recalibration, the frequency of which depends on operating conditions and measurement requirements. Currently, filtering techniques are not implemented in the presented results. However, advanced filtering methods such as Kalman filtering [42] can significantly improve measurement stability and precision. Future implementations may incorporate artificial intelligence-based algorithms to reduce or eliminate the need for frequent recalibration.
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
## 3 Governing Equations Derivation  
In this section, we describe the theoretical formulation here adopted to study the thermal buckling and vibration of a sandwich beam with an FGM core of thickness \(h_{c}\) and SMA hybrid layers of thickness \(h_{a}\), as depicted in Figure 1.  
Figure 1: Sandwich beam with an FGM core and SMA reinforcement layers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Properties** & **Materials** & \(\mathbf{P_{0}}\) & \(\mathbf{P_{-1}}\) & \(\mathbf{P_{1}}\) & \(\mathbf{P_{2}}\) & \(\mathbf{P_{3}}\) \\ \hline Young’s elasticity & Ti-6 Al-4V & \(122.56\times 10^{9}\) & 0 & \(-4.586\times 10^{-4}\) & 0 & 0 \\ modulus & Zirconia & \(244.27\times 10^{9}\) & 0 & \(-1.371\times 10^{-3}\) & \(1.214\times 10^{-6}\) & \(-3.681\times 10^{-10}\) \\ \hline Coefficient of Thermal Expansion & Ti-6 Al-4V & \(7.5788\times 10^{-6}\) & 0 & \(6.638\times 10^{-4}\) & \(-3.147\times 10^{-6}\) & 0 \\  & Zirconia & \(12.766\times 10^{-6}\) & 0 & \(-1.491\times 10^{-3}\) & \(1.006\times 10^{-5}\) & \(-6.778\times 10^{-11}\) \\ \hline density & Ti-6 Al-4V & \(4429\) & 0 & 0 & 0 & 0 \\  & Zirconia & \(5700\) & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: TD constants of metal and ceramic phases.
## 4 Experiment and Discussion
### Results for the Accuracy of the Measurement Distance and Inventory Location Recognition
The regression analysis for the recognition rate was conducted by increasing the number of stock items in each zone from one to 15 and increasing the tag recognition interval from 0.2 to 7 sec, thus the recognition rate was obtained as in Equation (5) with R-square = 94.1%, P-value = 0.00. R-square is a statistical measure for the proportion of the variance for a dependent variable that is explained by an independent variable, or variables, in a regression model [41], and the P-value, or probability value, is the probability of obtaining test results as extreme as the results actually observed for the statistical hypothesis testing, assuming that the null hypothesis is correct [42]. \[\text{The recognition rate}=125\ -\ 6.02\times N\ -\ 18.6\times\text{tag recognition interval} \tag{5}\]  
The regression formula further supports the experiment results, as it explicitly shows the association of the tag recognition interval with the recognition rate. According to Equation (5), we could calculate that, for the recognition rate to be 100%, the tag recognition interval of the RFID reader should be approximately 0.05, 0.37, and 0.70 sec when the number of loaded stock items is four, three, and two, respectively. In addition, as the number of stock items increases, the tag recognition interval needs to be decreased to retain the recognition rate. Furthermore, even if the tag recognition interval is small, the recognition rate could be lowered when the number of stock items is large.
## 4 Methodology
### Data Encryption: Pre-Processing
#### 4.2.4 Compilation and Encryption  
Once the model is quantized, it is converted into a Concrete-ML-based execution process and compiled. This step is considered part of the pre-processing phase as it prepares the model for efficient execution on encrypted data [44]. Finally, inference is performed directly on encrypted data, ensuring privacy without compromising the model's predictive capabilities.
# Document Image Shadow Removal Based on Illumination Correction Method
## 2 Related Works
### Document Shadow Removal Dataset
#### 2.1.2 The Synthetic Datasets  
The FSDSRD dataset [18] is the first synthetic dataset available for the shadow removal of document images. It comprises a quad of shadow images, non-shadow images, shadow masks, and foreground masks. FSDSRD creates documents by synthesizing text, graphics, and textures and then uses the graphics renderer to add synthetic shadows to create various shading conditions. Additionally, various colored textures were added to help train models learn document background colors and maintain color consistency between inputs and outputs. FSDSRD offers a wide variety of content, textures, and shadow types, addressing the issue of limited diversity in real document image datasets. However, FSDSRD is a fully synthetic dataset, lacking background diversity. Moreover, the synthesized shadows are black, which significantly differ from real shadows, making the models trained on FSDSRD unable to simulate the characteristics of real-world datasets.  
The SDCSRD dataset [19] contains 17,624 images divided into training, validation, and test sets in a 12:3:1 ratio. Compared to FSDSRD, it contains more images with a colorful background and two different font styles: handwritten and artistic. This diversity can help improve the model's robustness in handling different font styles and complex background conditions and promote the overall enhancement of the model's text recognition ability in complex visual environments.
## 3 The 3D Motion-Planning Algorithm
### The 3D Intraoperative Motion Planner  
The computational cost of a 3D motion planner is substantially higher than that of a planar motion planner. To design a 3D intraoperative motion planner, frequency is among the key parameters that should be considered. We propose a modified RRT*-based motion planner, owing to its high trajectory planning efficiency. The superiority of the modified motion planner will be proved by comparing it with other motion planners. A static motion-planning algorithm is used to generate multiple candidates, and it selects the best trajectory as one of the inputs to the intraoperative motion planner. The outline of the static motion-planning algorithm is shown in Algorithm 1. Figure 3: (**a**) The insertion trajectories displayed in the front view; (**b**) the rotation angles of the green segment and the blue segment, respectively; (**c**) the proximity of the obstacle to the blue segment and the green segment, respectively. The three inputs into the static motion-planning algorithm are the position and tangential orientation of the insertion point \(q_{ins}\), the target point \(q_{tar}\), and set of obstacles \(Q_{obs}\). The output of the algorithm is the best trajectory evaluated among several options, by using the cost function expressed in Equation (9). The static motion planner is revised from a goal-biased RRT* algorithm [34]. First, a tree \(t_{i}\) is initialized and the environmental map \(E\) is constructed. Then, a point \(q_{samp}\) is sampled using the _RandomSample_ function to emphasize the purpose of target searching. This sampling point is either the target point or a random point. Later, the nearest point \(q_{near}\) in the tree \(t_{i}\) is searched for and the feasibility of the segment starting from the nearest point to the sampling point is checked. The _ConstrainsFree_ function checks for this feasibility in terms of the kinematics constraints (bounded curvature, insertion angle, and so forth) and environmental constraints (obstacle avoidance). If the constraint conditions are not satisfied, the algorithm repeats the procedures mentioned in lines 3-11. Otherwise, it calculates the kinematic input \(u\).
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 4 Simulation and Analysis
### Simulation of Path Optimization  
In this section, the proposed APSD-RRT algorithm is compared with the RRT and RRT* algorithms. The quality of the path is an important parameter in this experiment, which is measured by the initial path length (IPL) and the final path length (FPL). The corresponding time cost is also recorded, which is indicated by the initial time (IT) and the final time (FT). The statistical data based on the experiment results are presented in Table 3.  
The RRT algorithm aims to find a feasible solution, but it does not consider the path optimization. Once it finds the solution, the algorithm stops running. As shown in the first column of Figure 7, the paths generated by the RRT algorithm are tortuous, and do not meet the high-efficiency and energy-saving requirements of practical applications. The vectors obtained by the APS-RRT planner are relatively biased towards the target states; the quality of the path shows a slight improvement compared to the RRT algorithm, but there are many sharp and redundant nodes in the path. Compared with the RRT algorithm, the initial path lengths in the four environments are shortened by 9.4%, 6.3%, 8%, and 3.3%, respectively. Compared with the RRT* algorithm, the quality of the initial path generated in Map1 is slightly improved; in the other three environments, the initial path lengths are lengthened by 2.2%, 4.8% and 8%, respectively, but the time spent finding the initial path in the four environments is reduced by 67.9%, 67.7%, 90.2%, and 78.2%, respectively. The RRT* algorithm achieved the final path with the best quality in this experiment, but the RRT* algorithm spends the most computing resources on the node optimization. It is clear that many nodes have no impact on the final result, especially in the environments with few obstacles, such as Map3, where there are many redundant nodes in the search tree. In comparison, the APSD-RRT algorithm is equipped with the post-processing methods to enhance the solution obtained by the APS-RRT planner. Benefiting from the planner's speed when reaching a feasible solution, the APSD-RRT algorithm can generate a high-quality path within an acceptable time. The final path generated by the APSD-RRT algorithm is straight and smooth with few corners. It should be noted that the APSD-RRT algorithm shows an increase in the time cost of 26.4% on average and has the same memory usage than the APS-RRT planner, but it exhibits enhanced performance in terms of the quality of the path, shortening the path length by about 13% on average. In the four different environments, the APSD-RRT algorithm shortens the final path length by about 19% on average compared with the RRT algorithm. In comparison with the RRT* algorithm, the APSD-RRT algorithm results in a higher-quality path in Map4; the APSD-RRT algorithm lengthens the path length by about 2.9% on average in the other three environments, but it has significant advantages in terms of the time cost and memory usage. Although the APSD-RRT algorithm exhibits a larger standard deviation in the performance
# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 5 Discussion  
This study considers three sentiment classification problems, and data are trained by manual tagging. In the literature, [67] performed sentiment analysis on various product groups using RandomizedSearchCV sklearn libraries and obtained the best classification performance result with SVM, similar to the result of this study. In another study on sentiment analysis of various product reviews in Turkish, [68] compared the results of manual and automatic labeling with various deep learning approaches and obtained the best performance result with manual labeling. In [69], with a dimension-based approach, [PERSON] performed sentiment analysis with LTSM and CRF models using a Turkish sentiment dictionary and obtained an f-1 score of 86.75%. In another study [70], a new model was proposed for Turkish reviews of electronic products with manual labeling, and an accuracy of 84.23% was obtained using word2 vec and RF. As seen in sentiment analysis studies conducted with Turkish data in the literature, Turkish is a language that requires human intervention due to its grammar structure and rich usage. In this study, a human performs manual labeling, and high performance is achieved by learning with various machine learning methods.  
The study can be enriched regarding customer satisfaction or product reviews of cosmetic brands with relatively low market share and negative words or can be developed for use in brand comparison research. Due to the large data size, this study detects the general sentiment of consumers. Dimension-based sentiment analysis can be performed by using smaller data sizes or by reducing the existing dataset, and sentiment-attributed features can be revealed with algorithms such as cluster analysis, cause analysis, and latent Dirichlet allocation.
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 2 Materials and Methods
### Data Analysis  
#### 2.6.1 Exploratory Analysis  
An initial exploratory analysis was conducted on the data obtained after image processing. The data on the variables were summarized using descriptive statistics (mean, standard deviation, median, and minimum and maximum value), based on the image source (mobile phone or scanner) and nitrogen concentration group (group 1 or group 2), as established from the threshold given by the median nitrogen concentration from the laboratory analysis. For mobile phone images, 12 photos belonged to group 1 and 60 to group 2, totaling 72 images. For scanner images, 72 belonged to group 1 and 78 to group 2, totaling 150 images.  
In addition to descriptive statistics, a \(t\)-test was used to compare the groups within each image source, considering the F-test for homoscedasticity and a significance level of 5% (\(p<0.05\)). Correlation matrices were also obtained for both groups using the Corrplot package in R (version 0.92) [30], for both the mobile phone and printer data, to visually assess the associations between variables and identify potential variables for exclusion in the development of discriminant models. In addition, correlation coefficients were also used to evaluate the relationship between the nitrogen concentration and the variables obtained from the DIP for each database.
## 1 Introduction
### Related Work
This model outperformed state-of-the-art missing data imputation models. Some studies, including the study carried out in [19], demonstrated the effectiveness and efficiency of deep learning (DL) methods compared to the ARIMA model and back propagation neural network model. A convolutional neural network (CNN)-based DL model was proposed in [14] for imputing missing values in weather data of an individual weather station on a temporal basis. The performance of the model was evaluated using various stations nearest to the stations without missing values. This study used five optimizers (Rmsprop, Adam, Nadam, Stochastic Gradient Descend, (SGD), and Adagrad) and found that the SGD optimizer provides the most accurate results in predicting missing values. Past studies reveal that the application of spatial kriging and DL methodology in missing value imputation of weather data has promising results. However, studies that impute missing values in spatiotemporal weather data using hybrid models are extremely rare, especially when there is a lower number of weather gauging stations. The existing research [5; 17; 20], which applies hybrid models, used more than 50 weather stations when interpolating spatiotemporal missing data. Most importantly, so far, no study has applied a spatiotemporal hybrid model to impute missing values among rainfall data at the Ratnapura gauging station. Therefore, to reduce this gap in the literature and to utilize the potential of spatial kriging, machine learning, and deep learning techniques in dealing with missing values, this study proposes a hybrid model by integrating them. Our target is to verify its appropriateness when the periods with missing values of rainfall gauging stations are different and discontinuous. Our focus is also given to the scenario where data are available only for a few rainfall stations. The rest of this paper is organized as follows. The next section describes the adopted missing values imputation methods and the development of the new approach. The results and discussion section, followed by the conclusion are lined up last.
## 4 Parametric Study FEM Buried Pipe under Haul Road
### Analysis of Results
#### 4.3.1 Surface Pressure  
It is important to note that the pressure applied on the surface dissipates with respect to depth, so the pressure that the pipe will assume will depends on the height of the top backfill of the trench. As shown in Figure 22, the pressure of 1250 kPa, applied on the surface, decreases as the measurement is deeper; hence, pipes installed at 1 m depth will receive much greater load than those installed at 3 m depth. Therefore, it will be important to evaluate the pipe and verify if the deflection and stresses applied on it are within acceptable limits.  
For example, for a 48\({}^{\prime\prime}\) pipe with standard wall thickness (0.375\({}^{\prime\prime}\)) and with an allowable stress of 138 MPa installed at 1 m depth, the pressure produced by a CAT 797F truck with a weight of 600 tons generates maximum stresses under the [PERSON] pipe failure criteria of 79 Mpa at the crown of pipe. Considering a design safety factor of 2, the design limit stress is 69 Mpa, and hence the installation condition at a depth of 1 m is not recommended, because it exceeds the design limits at the pipe crown (See Figure 23).  
Figure 21: Example of one of the FEM cases run for the parametric model: soil and pipe stress results for a pipe installation with 48\({}^{\prime\prime}\) + 0.8 m width, lateral backfill type EM1, bedding height 1/12 of 48\({}^{\prime\prime}\) buried 2 m high under a tire pressure load of a CAT-797F truck (Stress in kPa).  
Figure 22: Curve of vertical forces applied by a CAT-797F truck and the weight of the upper backfill, with respect to depth (Stress in kPa).