esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Machine and Deep Learning Algorithms
This architecture is particularly useful to provide additional context from the future and the past to the network [30] and to make effective predictions and decisions in a natural language task. **Gated recurrent unit (GRU)** is a type of RNN architecture introduced by [PERSON] et al. in 2014. This model is similar to the LSTM architecture, which is designed to solve the vanishing gradient problem associated with traditional RNN. GRU simplifies the LSTM  
Figure 8: The architecture of Word and BERT embedding with DL algorithms. Figure 7: The architecture of TF-IDF and ML algorithms. architecture by reducing the number of parameters, making it easier to compute and often faster to train. GRUs, like LSTMs, are designed to remember forgotten information over long or short period; this is beneficial for modeling sequence data where context is important. The key difference between GRUs and LSTMs lies in their structure: LSTMs utilize three gates while GRUs operate with just two gates, the update and reset gates [31]. **Convolutional neural network** (CNN) is a class of deep learning algorithms specially designed for image recognition and computer vision. It learns to recognize patterns in images by mimicking the human visual system [32]. However, CNNs have been shown to be successful in the domain of NLP. They provide significant improvements over NLP tasks such as sentence classification and sentiment analysis. A convolutional neural network is based on two principal layers: convolutional layers and a pooling layer. Convolutional layers apply a set of filters (or kernels) to the input data, with each filter detecting specific features, such as edges, textures, or patterns. The pooling layer reduces the spatial dimensions of the feature map. This layer helps retain the most significant features. In this study, a CNN is combined with a BiLSTM network. This hybrid approach enables the CNN to detect local sentiment-related patterns (positive or negative) within text sequences, while the BiLSTM considers the broader context to interpret the significance of these patterns within the overall text. This combination enhances classification accuracy by leveraging both localized sentiment detection and contextual understanding.
## 1 Introduction
To improve the effectiveness of the controller design, several optimization algorithms have been used to adjust the parameters, such as particle swarm optimization (PSO) [20], invasive weed optimization (IWO) [21], gray wolf optimization (GWO) [22], biogeography-based optimization (BBO) [23], and genetic algorithms (GAs). In [13; 24], the PSO algorithm was used for the adjustment of the fractional order PID controller parameters. The results showed the parameters given by the PSO led to a precise and strong anti-disturbance control. Another work used a modified firefly algorithm (MFA)-PSO-based fractional order PID controller [25] for an effective torque and speed regulation strategy for a BLDC motor. Other optimization algorithms, such as the IWO [26] and GWO [27], were also applied to fractional order controllers to improve their performance. In this research, the fractional order fuzzy controller is implemented in a synchronized gantry-type X-Y platform. Several optimization algorithms, namely PSO, IWO, BBO, and GWO, are integrated with the FOFPID for precision trajectory tracking control, and their relative performances are compared. The integral of the time-weighted absolute error (ITAE) is an index for the optimization of the controller parameters. Two PMLSMs set up in parallel on the Y-axis rails move a beam simultaneously, while another PMLSM moves the head set up on the beam along the X-axis. A MATLAB system ID toolbox is used to find the parameters of the PMLSM transfer functions. The simulation and experimental results for the different reference contours are demonstrated to reveal the effectiveness of the proposed controller, with improved tracking performance. This paper is organized as follows: Section 2 presents the system dynamics and model identification. Section 3 describes the fractional-order fuzzy PID controller. The PSOoptimization algorithm used in this work is introduced in Section 4. The simulation and experimental results are demonstrated in Section 5, and Section 6 presents the conclusions drawn from the results.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 2 Materials and Methods
### Treatment Water Analysis and Processing
#### 2.6.3 Isoflavones Trapping and Recovery  
Previous studies already showed the ability of synthetic resins to fix IFs [41]. Therefore, after cleaning of the pilot treatment water by PREcart PP 2 cartridge filtration, specific adsorption resins were tested for IF trapping and for water recycling.  
As a pre-selection step, a jar test was performed, wherein 100 mL of water was exposed to 20 mg of resin under stirring for 2 h, before IFs were assayed. Ten different materials were tested to allow a preliminary selection: Lewati\({}^{\text{\textregistered}}\) VPOC-1064 (Lanxess, Cologne, Germany), AD3008 ([PERSON], Paris, France), PAD900 ([PERSON], Paris, France), PAD950 ([PERSON], Paris, France), SEPLITE LXA838 (Sun Resin, Shaanxi, China), SEPLITE LXA94 (Sun Resin, Shaanxi, China), Relite EXA 90 (Resindion, Binasco, Italy) and Optiore\({}^{\text{TM}}\) SD-2, Amberlite\({}^{\text{TM}}\) XAD7 HP, Amberlite\({}^{\text{TM}}\) FPX66, the last three supplied by Dupont\({}^{\text{\textregistered}}\) (Paris, France).  
The two most promising resins were selected for open circuit column tests to determine the maximum volume of water that can be treated by the resin before performance loss (that is, the achievement of saturation), and to define the IFs' recovery conditions. For this final selection, 212 mL of the pre-selected resin was pre-wetted, then inserted into a 3 cm diameter column and gradually saturated with 4.2 L of water, representing 20 times the bed volume of the resin. Five samples were collected, each after passing 850 mL of filtrate through the column. The desorption function was checked using either demineralized water or ethanol, or mixtures of both in different volume proportions: 70:30, 50:50 and 30:70.
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 3 Results and Discussion
### Synthesis
#### 3.1.9 (E)-3-(pyridin-4-yl)-1-(4-((2-hydroxy-benzyl)amino)phenyl)prop-2-ene-1-one (A9)  
Red solid, very soluble in ethanol and methanol, slightly soluble in acetone, poorly soluble in water; bp: 229-230 \({}^{\circ}\)C; EIMS (\(m/z\)): 329.1303 [M \(-\) H]\({}^{-}\), (MW = 330.14); UV (\(\lambda_{\rm max}\) nm, EtOH): 378, 397; \({}^{1}\)H-NMR (500 MHz, DMSO-\(d_{6}\)) 8 ppm: 4.31 (d, 2H, \(J\) = 6 Hz, CH\({}_{2}\)), 6.67 (d, 2H, \(J\) = 7.5 Hz, H3', H5'), 6.74 (t, 1H, \(J\) = 7.5 Hz, \(J\) = 8 Hz, NH), 6.84 (d, 1H, \(J\) = 8 Hz, H3\"), 7.07 (t, 1H, \(J\) = 7 Hz, \(J\) = 7.5 Hz, H5\"), 7.15-7.19 (m, 2H, H4\", H6\"), 7.54 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\alpha}\)), 7.77 (d, \(J\) = 6 Hz, 2H, H2, H6), 7.96 (d, 2H, \(J\) = 9 Hz, H2', H6'), 8.07 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\beta}\)), 8.63 (d, 2H, \(J\) = 5.5 Hz, H3, H5), 9.62 (s, 1H, OH); \({}^{13}\)C-NMR (125 MHz, DMSO-\(d_{6}\)) 8 ppm: 56 (CH\({}_{2}\)), 114.9 (C3', C5'), 118.8 (C3\"), 122.3 (C5\"), 124.6 (C4\"), 124.9 (C1\", C2, C6), 126.8 (C1'), 127.9 (C\(\alpha\)), 128.3 (C4'), 131.2 (C2', C6'), 138.4 (C1), 142.3 (C1, C\({}_{\beta}\)), 150.2 (C3, C5), 153.4 (C4'), 155 (C2\"), 185.5 (C=O).
###### Abstract
The low cost is, above all, what makes the procedure interesting as a method for screening particularly in countries with limited resources where high technology techniques are not available. Breast self-examination (BSE) [14] is the most common method of early-stage breast cancer detection. The sensitivity of BSE is related to significant changes in mechanical properties of tissue in the course of cancer development. BSE, or regularly examining one's own breasts, could be used to find early signs of breast cancer, when it is more likely to be treated successfully. It is a simple non-invasive procedure that can be performed by all women, allowing them to become comfortable with their own bodies [15]. Self-palpation should be performed once a month, several days after the beginning of menstruation when the breasts are softer. A woman palps her breast with the pads of her finger to detect either superficial or deeper lumps. The breast is assumed to be divided into four quadrants, each of which is checked separately. Several common patterns are designed to ensure complete coverage of the breast also including \"the axillary tail\" (the piece of breast tissue which extends under the armpit) [14]. A number of wearable systems in support of BSE are already patented and commercialized. Among these, thermal technology, which estimates thermal maps of the breast using infrared sensors, is the most common. Other examples of intelligent bras show additional parameters such as the differences between oxygenated and non-oxygenated hemoglobin, and the evaluation of the levels of oxygenation, which have a different distribution in healthy and diseased tissues [16; 17; 18; 19]. Diseased tissue is in fact characterized by a higher metabolism and therefore develops areas with higher temperature. In this study, we present a non-invasive wearable device named Palprest to simulate breast self-examination using pressure sensing textiles. As far as we know, the use of the tactile of technology on a wearable device to provide a tool for home screening has not been proposed, as all current devices are based on different techniques (heat and oxygen levels). Palprest is not conceived to be a diagnostic tool but as a support that, in combination with other screening methods, can increase the odds of early detection for better prognosis. In the following sections, we outline the results of a survey conducted to better understand the reasons why women do not practice BSE. The device concept is introduced, and then the finite element (FE) analysis of the mechanics underlying the working principle is presented. The physical implementation of the device and its characterization are discussed. Finally, the proposed solution is validated using a breast phantom with an embedded inclusion.
## 3 Results and Discussion
### Extension to the MROM Modelling Investigation for Samples Printed in TBO  
To make sure that the MROM is effective to predict the samples printed in the TBO, an additional investigation was conducted by printing samples with different percentages of 50%, 70%, and 90% and performing modelling processes with both ROM and MROM. Figure 25 and Table 9 show the details of this complementary investigation. The MROM generated the error percentages to fall below 10% for all the samples printed in the TBO. The results for the new samples were also very close to the experimental results, showing the high accuracy of the MROM, regardless of the infill percentage. It clearly shows that the MROM is capable of very accurately predicting the mechanical behaviour of the additional samples.
## Materials and Methods
### Reducing Antioxidant Power Based on FRAP Assay  
The FRAP assay describes ferric reducing antioxidant power, the ability of an antioxidant to reduce Fe\({}^{3+}\)-TPTZ complex to Fe\({}^{2+}\)-TPTZ [27; 32]. Various concentrations of each test compound (e.g., rice sprout extracts or standard antioxidant gallic acid was mixed with 1 mL DMSO. The FRAP reagent comprised of 300 mM acetate buffer (pH 3.6), 10 mM 2,4,6-tris(2-pyridyl)-s-triazine (TPTZ) solution and 20 mM FeCl\({}_{3}\)-6H\({}_{2}\)O in a 10:1:1 ratio. The reaction mixture was pipetted into each well of a 96-well plate and incubated for 30 min in the dark at room temperature. The absorbance of the colored product (ferrous tripyridyltriazine complex) was measured at 593 nm with the microplate spectrophotometer (EnSight\({}^{\text{TM}}\)) against a blank with deionized water. The experiments were repeated in five replicates. The FRAP value was calculated from the standard curve of FeSO\({}_{4}\) (y = 0.5065 - 0.3073, R\({}^{2}\) = 0.9782) and expressed in mmol Fe\({}^{2+}\) per gram of extract (mmole Fe\({}^{2+}\)/g extract). Gallic acid was used as a positive control (y = 0.3821x + 0.0653, R\({}^{2}\) = 0.998).
## 3 Results
### Visual Inspection Performed by UAV and IR Camera
According to previous experience gained from experiments at UNIZA, at first, it is demanding to define the initial environment in order to consider performing a visual inspection with UAV and a long-wave infrared (IR) camera. Secondly, it is necessary to define an appropriate flight path for monitoring desired locations and also determine the time intervals of UAV's movement with respect to the defined trajectory and gradually end the monitoring process itself. An UAV with the IR camera is represented as a point in three-dimensional space. Before the inspection begins, it is necessary to determine the starting (ending) point of take-off of the unmanned aerial vehicle, its altitude for each reference point within the previous defined trajectory, also the total estimated time of the entire visual inspection. All data scanned by the camera have to be examined as soon as possible. At the end of the operation, the result is comprehensive information on the basis of which the pilot decides whether it is needed to perform a more detailed inspection in case certain irregularities were detected during the visual inspection process [35]. Figure 5 shows a diagram that defines all the steps of the process of implementing a visual inspection using a UAV with a long-wave IR camera attached. The regular camera was able to capture defects that occurred only on the outer surfaces of the aircraft airframe, as shown in Figure 6.  
Unlike the conventional camera, whose image consists of pixels, the infrared camera detects and measures the infrared energy of objects without the direct contact [36]. The camera converts that infrared data into an electronic image that shows the apparent surface temperature of the object being measure. With the combination of these two cameras, the inspector has a multi-spectral image of damage. In this case, the Mavic 2 Enterprise series is proposed as an example of a drone that is capable of performing IR visual inspection. This specific portable drone is available in three different variants--Advanced, Dual, and Zoom. The main difference is the quality of their cameras. The Advanced and Dual versions carry two sensors--visual and thermal, while the Zoom variant carries only one visual sensor  
Figure 5: Flowchart of visual inspection using UAV and IR camera. Source: Authors. Figure 6: In-flight (vision) screen of the operator performing the PA34-220T SENECA V airframe inspection, Source: [30]. with zoom capabilities. The weight of these models is about 904 g without accessories and their maximum take-off weight is 1100 g, which is quite light. The Advanced version presents the ideal option for IR visual inspection due to its superior resolution of a visual 48-megapixel sensor, thermal resolution of HD 640 \(\times\) 512 px at 30 Hz with a spectral band of 8-14 um, and pixel pitch of 12 um. The thermal camera provides16\(\times\) digital zoom functionality. The Operating temperature is from \(-\)10 to 40 \({}^{\circ}\)C. The operator is able to switch between visual, thermal, or split-view [37].
# A Review on Internet of Things for Defense and Public Safety
## 2 Compelling COTS IoT Applications
Another contribution of USTRANSCOM is fleet management, which uses RFID trackers to monitor palletized shipments among major transit hubs. * Mining: embedded sensors on equipment and vehicles offer a more precise picture of ground operations and enable real-time monitoring of equipment. This new technology, together with autonomous mining systems, is transforming daily operations. An IoT deployment reduces expenditure on infrastructure and machines by reducing outages and maintenance, energy consumption and environmental impact, while significantly improving productivity and mine safety by reducing injuries and fatalities. A renowned example is the autonomous drilling system of Rio Tinto (Perth, Australia), which includes tunneling machines, trains, autonomous haulage systems, and driverless trucks. This mission-control site manages operations of 15 mines, 31 pits, 4 port terminals and a 1600 km rail network [48]. The above-mentioned examples illustrate some of the ways in which the private sector is leveraging IoT and enabling new business models. Today, European research projects like RERUM [49], RELYonIt [50], FIESTA-IoT [51], BIGIoT [52], or bloTope [53] intend to tackle the most relevant shortcomings of commercial IoT. Among the different projects, EU-funded METIS-II [54] must be highlighted: the researchers created a 5G radio-access network design and provided the technical enablers needed for an efficient integration. Other concerns, like environmental issues or green deployment schemes for IoT [55], still remain open.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 7 Forecasting Results  
In this work, the proposed forecasting model was applied to the Hu-Si wind farm on Penghu Island, Taiwan. The Hu-Si wind farm has six wind turbines, and the capacity of each turbine is 0.9 MW.  
Figure 11 shows the three-hour-ahead deterministic forecasting results by XGBoost with data preprocessing and tuned parameters. Table 2 lists four important parameters in the XGBoost model. Accordingly, the blue line in Figure 11 denotes forecasting values, while the orange line stands for actual values.  
Figure 11: Testing sample of three-hour-ahead by the proposed model.  
Figure 10: Proposed three-hour-ahead probabilistic forecasting model.  
It can be seen clearly that the proposed XGBoost approach can track the actual data well. The error created from the proposed XGBoost is small; the RMSE on the training set and forecasting set are 0.177 MW and 0.352 MW, respectively.  
To demonstrate the superiority of the proposed method, the forecasting results by the proposed method are compared with those by other forecasting models:  
* Method 1a,b: Proposed model with different NWPs scenarios: (a) RWRF and WEPS, (b) WRFD and WEPS.
* Method 2: XGBoost with standard dimension reduction.
* Method 3: ANN model with standard dimension reduction.  
Method 1a,b was selected in order to demonstrate the importance for considering various NWP models. This study focuses on the importance of various NWP methods on wind power forecasting. In the proposed method, the inputs include WRFD, RWRF, and WEPS, while the inputs of Method 1a only include RWRF and WEPS, and the inputs of Method 1b include WRFD and WEPS. The purpose for distinguishing different NWP inputs is to demonstrate and compare the forecasting results by considering different NWP models; Method 2 was selected to highlight the importance of the dimension reduction technique; Method 3 was selected to reveal the importance of the training AI method.  
Figures 12-14 present the results of probabilistic forecasting for the Hu-Si wind farm in the prediction intervals from 50 to 90% considering different NWP models as Method 1. Figures 15 and 16 display the comparison of wind power forecasts by the proposed model with Method 2 and Method 3, respectively. To examine the accuracy of PIs (PI validity) and demonstrate the efficiency of the proposed method (method performance), Tables 3-7 summarize the values of PICP, PINAW, and CWC, respectively.  
Figure 14: Three-h-ahead probabilistic forecasting results by Method 1b (WRFD, WEPS).  
Figure 13: Three-h-ahead probabilistic forecasting results by Method 1a (RWRF, WEPS).  
Figure 15: Comparison of wind power forecasting results by the proposed method and Method 2.  
Figure 16: Comparison of wind power forecasting results by the proposed method and Method 3.
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 4 Development of a Pantograph Slide Wear Model
### Mathematical Model of the Friction Coefficient  
As shown in Equation (6), the friction coefficient directly affects the pantograph-catenary current-carrying frictional energy dissipation. To accurately calculate the pantograph slide wear under varying friction coefficients, the influence of the friction coefficient on the wear performance of the pantograph slide is considered. Based on the research findings on pantograph-catenary frictional forces [26], a mathematical model for the pantograph-catenary frictional force, incorporating the current density, sliding speed, and contact pressure, is introduced under the same experimental conditions, as shown in Equation (8):  
\[F_{f}=\left(0.072-3\times 10^{-8}J\right)v+\left(0.101-3\times 10^{-8}J\right)F \tag{8}\]  
In the equation, \(F_{f}\) denotes the pantograph-catenary frictional force; \(J\) is the current density; \(v\) represents the sliding speed; and \(F\) is the contact pressure.  
The relationship between the current density \(J\) and the current intensity \(I\) in the experiment is given by  
\[J=3.34\times 10^{3}I \tag{9}\]  
According to [PERSON]'s law, the mathematical expression for the frictional force is given by  
\[F_{f}=\mu F \tag{10}\]  
Therefore, by combining Equations (8) to (10), the mathematical model for the pantograph slide friction coefficient is derived as  
\[\mu=\frac{\left(0.072-1.002\times 10^{-4}I\right)v}{F}-1.002\times 10^{-4}I+0.101 \tag{11}\]  
The model's applicability is strictly constrained to the validated operational range: current intensity 100 A \(<I<\) 250 A, normal force 80 N \(<F<\) 110 N, and operation speed 60 km/h \(<v<\) 120 km/h. Experimental data indicate that the model prediction accuracy may decline or even fail when the variables exceed this range, primarily due to the nonlinear characteristics of the thermo-mechanical coupling at the contact interface and material frictional behavior, which evolve with parameter variations. It is worth emphasizing that statistical analysis shows that this defined range covers more than 90% of the train's actual normal operating conditions, including typical scenarios such as startup acceleration, uniform cruising, and conventional braking. Thus, the model provides effective support for the analysis of pantograph-catenary sliding friction characteristics.