esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization of SM-OGFC by Response Surface Methodology (RSM)  
### Central Composite Design  
In regards to the optimization of SM-OGFC, several factors, such as the asphalt-aggregate ratio, silane coupling agent content, compaction times and mixing temperature, could remarkably influence the compressive strength SM-OGFC. However, if just a single variable of the aforementioned factors is analyzed, cross-impact of the variables would usually be neglected. On the other hand, a complete experimental design to explore the relationships of the exploratory variables is often time-consuming. In this context, response surface methodology (RSM) has been suggested to find the most valuable point, or called optimization to simplify the experimental design and simultaneously, maximize the production, or minimize the cost, side reactions, etc. RSM has been applied in many fields, such as chemical reactions, industry manufacture and many other product optimization. In regards to the RSM, central composite design (CCD) is often used to support the optimization of a two-level factorial or fraction factorial design, which is usually coded as \(-1\) and \(1\) so as to simplify the experimental expression and calculation [22; 23; 24]. [PERSON] et al. applied a central composite design (CCD) with response surface methodology (RSM) to optimize the mix design parameters of cold bitumen emulsion mixture, namely bitumen emulsion content (BEC), pre-wetting water content (PWC) and curing temperature (CT) [25]. [PERSON] et al. designed an experimental scheme to optimize three preparation parameters of styrene-butadiene-styrene (SBS)-modified asphalt mixture reinforced with eco-friendly basalt fiber based on response surface methodology (RSM) [26].  
In this study, asphalt-aggregate ratio and silane coupling agent content are considered as key factors in the preparation of SM-OGFC samples; compaction times and mixing temperature are also considered as vital factor in the preparation study. Experiments are designed with six central points. Table 4 shows the range of asphalt-aggregate ratio, silane coupling agent content, compaction times and mixing temperature respectively. The Marshall stability, \(-15\)\({}^{\circ}\)C splitting strength, and spring-thawing stability are considered as the response of the software. In this study, CCD approach suggests 30 experiments; six of them are replicated as the central point. Table 5 reveals the proffered experiments and results.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 1 Introduction
Based on the requirements and design, manipulators have multiple joints known as \"Degrees-of-Freedom\" or DoFs. The use of industrial manipulators can be dated back to 1954 when General Motors began using Unimate, a hydraulic arm, to lift heavy loads [10]. A mobile manipulator combines an industrial or robotic arm with a mobile platform. In the literature, mobile manipulators can be dated back to 1992, when the mobile robot lab at LAAS developed \"Hilare 2 bis\" [11], shown in Figure 1. A mobile manipulation system benefits from both the manipulator's dexterity and the mobile platform's mobility. Because of these advantages, mobile manipulators can be used across industries for various challenging tasks to reduce the risks to human workers, increase efficiency, and maximize financial benefits. Figure 1: Upgraded Hilare 2 bis from LAAS with 6-wheel robot and a manipulator [11]. Only 28% of track inspection and repairs involve robotic and autonomous system (RAS) techniques, even though railway track faults cause 17% of train delays and 33.1% of passenger train cancellations [5]. A mobile manipulator is a particular type of robot with a mobile base and a robotic arm. With the help of multiple sensors and computational algorithms, mobile manipulators can perform various tasks, such as cleaning a nuclear reactor [12] or inspecting a railway tunnel [13]. With the recent advancement in sensor and computation technologies, the mobile manipulator has revealed many promising use cases in maintenance technologies across various industries [14; 15; 16; 17]. These promising results substantiated an increment in productivity, efficiency, and safety for the human operators [18; 19]. This research aims to discuss the prospects of adopting mobile manipulators for railway track inspection and repair tasks, as observed in other industries. This paper will begin by reviewing the current state of the art maintenance machines for railway track inspection and repair tasks. Afterward, robotic technologies for railway track inspection and repair tasks will be discussed in Section 3. Section 4 reviews the definition, classification, and usage of the mobile manipulators found across other industries during the last 20 years for inspection and repair. Next, a mobile manipulator made of a commercially available unmanned ground vehicle (UGV) and an industrial manipulator for railway track inspection and repair tasks will be introduced in Section 5. The benefits of adopting mobile manipulators for track inspection and repair will be presented in Section 6. Then, Section 7 will discuss the challenges and requirements of the use of a mobile manipulator for track maintenance. Finally, Section 8 will present our conclusions.
# High-Precision Manufacture and Alignment of Image Slicer Based on Thin Glass Bonding
## 6 Measurement  
### Test Method  
Parallel light is used for the image slicer error detection. Parallel light illuminates first a beam splitter prism and then the image slicer. The light from the image slicer is then reflected by the prism and finally focused on a CCD detector to measure the distance between the focus of each two image slices. According to the design, the distance between the focus of each two image slices is 2 mm. The measurement optical path is shown in Figure 14. The actual structure of the optical system is shown in Figure 15. A 4D interferometer laser is used as a parallel light source. The pixel size of the CCD is 2.4 \(\mu\)m, which means the CCD meets the test requirements. The CCD is placed on a closed-loop control, high-precision, five-dimensional adjustment frame with XYZ movement accuracy of 0.002 mm. The distance beyond the measurement range of a CCD single frame is measured using the five-dimensional adjustment frame.  
Figure 13: Practical fabrication process: (**a**) the stack fixture consists of two high-precision orthogonal prisms; (**b**) thin glass of different length; (**c**) using the stack fixture aligns different length thin glass on the right side; (**d**) the auxiliary blocks are bonded on the top and bottom of stack block; (**e**) the sphere is produced on the front side of glass slice; (**f**) using the stack fixture aligns different length thin glass on the left side.  
Surface error and roughness will be measured using a 4D interferometer and white light interferometer.
## 3 Experiments
### Datasets and Preprocessing
#### 3.1.3 WHU-RS19  
To evaluate the network's performance on different scene reconstruction results, this paper also selected WHU-RS19 as the test set for the comparative experiments. The WHU-RS19 dataset consists of 950 aerial images. The size of each image in the dataset is 600 \(\times\) 600 pixels. The WHU-RS19 dataset contains 19 categories of land use types, and each category consists of 50 images.  
Figure 4: Spatial distribution of Sentinel-2 and GaoFen-2.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### Software
#### 3.4.4 Stimulation and Sending the Values to the Database  
The fifth and last phase complements the previous phase, in that the comparison of the obtained values is made with the previously defined value. In this specific case, to conduct the tests, whenever the value obtained and handled by the EMG sensor was higher than the set value, a stimulation pulse was sent, as shown in the code of Figure 8.  
This stimulus is produced by the PWM technique, which sends an impulse with an amplitude value and duration obtained earlier, when the parameter checks on the _Google Sheet_ were conducted.  
Next, the counter that was started with the system and that had a value equal to zero was incremented by one. This value represents the number of apneas detected and treated, for each use of the device, i.e., ideally for each night of use.  
The number of apneas and the interval of hours in which they may be more prevalent is extremely relevant for the medical doctor who is following the therapy. Through these data, the professional can see if the device is working as desired and can apply a more personalized therapy to each patient during the duration of treatment. The parametrization of the electrostimulation should be left to the medical doctor, who is the only one able to modify the parameters (see Figure 9).  
Figure 8: Code 7—stimulation pulse.  
Figure 7: Code 6—simulation of data acquired by electromyography sensor.  
For each apneic event, the system increments the counter once, saves that value, and registers the time of the event. It then sends these data to the _Google Sheet_, as can be seen in Figure 10.  
In order to send these data to the _Google Sheet_, it is necessary to use a script, which allows the access the page and write the value in an available cell, saving it automatically.
## 3 FDIA Detection Model Based on GAN-GRU and Transformer
### FDIA Detection Based on GAN-GRU Data Balancing and the Transformer Classifier  
In this paper, a false data detection method is proposed, as depicted in Figure 5. To address the challenge of a low detection rate resulting from imbalanced data, GAN-GRU and the Transformer techniques are employed. The approach functions as a generator and discriminator by integrating a GRU neural network within the GAN framework. Throughout the training process, both the GAN network and the GRU network construct recurrent neural networks to generate a limited number of data samples, thereby achieving a balanced representation of diverse sample types in the training dataset. The balanced dataset is divided into training and test subsets, and the Transformer technique is utilized to detect and classify anomalous data. Ultimately, a normalized confusion matrix is obtained through the comparison of predicted and actual values.  
Figure 5: Flowchart of FDIA detection based on GAN-GRU data balancing and the Transformer classifier.  
By synergistically employing data balancing and classification models, we attain an enhanced capacity to discern and differentiate various forms of FDIAs. The data-balancing processing model effectively equalizes the dataset through the generation of synthetic attack data, thus providing ample training samples. Leveraging the Transformer classification-based detection model allows us to gain insights into the interrelations among distinct attack types and normal data, enabling precise classification determinations. This elevates the efficacy and resilience of the FDIA identification model, contributing to the amelioration of security challenges in power systems. This approach not only curtails the rate of false alarms but also enhances the system's adeptness at handling disparate data distributions, thus furnishing a robust underpinning for the secure and dependable operation of power systems.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 2 Attitude and Orbit Determination Model  
In this section, the basic mathematical model, namely, the state model and the observation model, for the autonomous attitude and orbit determination problem of a two-spacecraft system using angle-only measurements is first presented. In addition, the observability matrix is introduced to analyze the observability of the system. Finally, the widely used fifth-order cubature Kalman filter is reviewed for later estimation.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 4 Implementation Details  
In this section, we provide the implementation details of the algorithm. First, the considerations of wavelet decomposition, column gradient equalization, and guided filter are elaborated. Then the entire procedure is used as a quick reference.
## 3 System Architecture  
The proposed system consists of two main modules, namely the Fuzzy Inference Module and the Reinforcement Learning Optimization Module. These modules work together to create an adaptive user interface (UI) tailored to individual learning behaviors. In particular, the Fuzzy Inference Module is responsible for classifying students into different performance categories based on the time they spend on exercises, using triangular membership functions. As such, it assigns fuzzy weights that determine the level of UI adjustments needed. However, in order to overcome the limitation of the static fuzzy logic system to adjust to real-time variations in student behavior, the Reinforcement Learning Optimization Module is utilized to continuously refine the membership function thresholds.  
Thus, this ensures that fuzzy weights remain accurate and responsive to individual learning patterns. Together, these two modules allow the UI to dynamically adapt, providing a more effective learning experience that modifies hints, guidance, and difficulty levels in a way that best supports the learner. Figure 1 illustrates the system's architecture.
## 2 Materials and Methods
### Impact of Grinding Conditions  
The influence of the grinding conditions was studied using two different laboratory mills. A laboratory roller mill (Micromill, UMR IATE prototype; Montpellier, France [19]) is used to generate compressive/shear stresses during the grinding process. A laboratory vibratory impact ball mill (MM400, Retsch(r), Haan, Germany) in single ball configuration is used to generate mainly impact stresses during grinding.  
To investigate different grinding conditions, yellow split peas were ground by modulating the setting parameters on both mills (Table 1), using a fractional factorial experimental design with 4 factors and 2 levels per factor (2\({}^{4-1}\)). For each mill, a total of 8 different experimental grinding conditions were investigated (Tables 2 and 3). The ball mill was also used with a load of 10 g of yellow split peas per grinding jar, a single steel ball (diameter = 25 mm), and a grinding frequency of 20 Hz, for different grinding times (between 30 and 300 sec). It should be noted that in all trials, the grinding conditions were deliberately set after preliminary tests to minimize heating constraints. Only run 7 of the ball mill experimental design and kinetic runs longer than 4 min generated uncontrolled heating within the grinding jars, which could have affected the state of the constituents of the pea powders produced. Such effects were not evaluated in this study.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mill Setting Parameters** & \
\begin{tabular}{c} **Natural Lower Values** \\ **(coded = \(-\)1)** \\ \end{tabular} &
\begin{tabular}{c} **Natural Higher Values** \\ **(coded = +1)** \\ \end{tabular} \\ \hline Roller mill & & & \\ Cylinder type: plain (P) or splined (S) & S-S-S-S & S-P-P-P \\ Cylinder gap reduction rate (mm) & 0.9–0.6–0.4–0.05 & 0.9–0.2–0.05–0.05 \\ Rapid cylinder rotation speed (rpm) & 400 & 800 \\ Speed differential & 1.33 & 2.5 \\ \hline Ball Mill & & & \\ Grinding time (min) & 1 & 4 \\ Ball diameter (mm) & 20 & 25 \\ Grinding frequency (Hz) & 20 & 30 \\ Product mass per jar (g) & 5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental domain explored to study the influence of the roller mill and of the ball mill setting parameters on the characteristics of the yellow pea powders produced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Essays** & **Factor 1 (F1)** & **Factor 2 (F2)** & **Factor 3 (F3)** & **Factor 4 (F4)** \\ \hline e1 & S-P-P-P & 0.9–0.6–0.4–0.05 & 400 & 1.33 \\ e2 & S-S-S-S & 0.9–0.2–0.05–0.05 & 400 & 1.33 \\ e3 & S-S-S-S & 0.9–0.6–0.4–0.05 & 800 & 1.33 \\ e4 & S-P-P-P & 0.9–0.2–0.05–0.05 & 800 & 1.33 \\ e5 & S-S-S-S & 0.9–0.6–0.4–0.05 & 400 & 2.5 \\ e6 & S-P-P-P & 0.9–0.2–0.05–0.05 & 400 & 2.5 \\ e7 & S-P-P-P & 0.9–0.6–0.4–0.05 & 800 & 2.5 \\ e8 & S-S-S-S & 0.9–0.2–0.05–0.05 & 800 & 2.5 \\ \hline \hline \end{tabular} F1 = cylinder type; F2 = cylinder gap reduction rate (mm); F3 = rapid cylinder rotation speed (rpm); F4 = speed differential.  
\end{table}
Table 2: Roller mill experimental design.  
All grinding conditions were performed at least in triplicate.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.2.3 Interference Parameters Estimation  
The interference parameter estimation algorithm mainly analyzes the spectrum of the received signal to distinguish the interference frequency points from the non-interference frequency points. At present, some typical methods have been proposed to locate the frequency components of interference. These methods mainly include continuous mean elimination (CME) algorithm, forward continuous mean elimination (FCME) algorithm, FCME algorithm based on double threshold and FCME algorithm based on adjacent cluster merging (acc-fcme) [26]. With these methods, the frequency band, frequency bandwidth and Interference to noise ratio (Jnr) of interference can also be further estimated.
## 3 On-Chip Circuit Implementation
### Implant Modulator and Clock Recovery  
The data modulator at the implant uses backscattering based on load shift. As shown in Figure 4c, a switch M\({}_{\text{N5}}\) is used to shunt the resistor R\({}_{3}\) with the secondary \(LC\) tank. In the non-stimulation time, the equivalent loading resistance across the secondary coil is around 12.5 k\(\Omega\). During backscattering, the input data controls whether R\({}_{3}\) should be shunt or not. Effectiveness of backscattering depends on the amount of load shift, but if R\({}_{3}\) is small, too much power would be dissipated, and the voltage on the secondary LC tank may be reduced too much that affects the clock recovery circuit. After careful consideration, R\({}_{3}\) is designed to be 500 \(\Omega\). The threshold voltage of M\({}_{\text{N5}}\) is 0.5 V, and it can be completely turned on and off by V\({}_{\text{DC}}\) and 0 V, respectively.  
The clock recovery circuit is shown in Figure 4d. It recovers the clock from the secondary resonant \(LC\) tank to drive the digital controller. The high skew and low skew inverters I\({}_{\text{h1}}\) and I\({}_{\text{I2}}\) together with M\({}_{\text{P4}}\) and M\({}_{\text{N6}}\) form a Schmitt trigger comparator for noise rejection. When V\({}_{\text{AC1}}\) goes high, M\({}_{\text{N6}}\) is turned off first, and after the designed dead-time M\({}_{\text{P4}}\) is turned on. Similarly, when V\({}_{\text{AC1}}\) goes low, M\({}_{\text{P4}}\) is turned off first and after the dead time, M\({}_{\text{N6}}\) is turned on. The transistors of I\({}_{\text{h1}}\) and I\({}_{\text{I2}}\) are very small to reduce the short-circuit current. The isolation inverter I\({}_{\text{b3}}\) and the tiny feedback inverter I\({}_{\text{I4}}\) are used to hold the voltage level during the dead-time durations.  
Figure 4: (**a**) Complementary metal-oxide-semiconductor (CMOS) rectifier; (**b**) Voltage protection circuit; (**c**) Implant modulator; (**d**) Clock recovery circuit; (**e**) Simulated rectifier output vs. loading.
## 2 Materials and Methods
### Programming and Calibration of the Device
#### 2.4.2 Second Round of Calibration  
Since the errors after the first calibration were greater than 10%, a second calibration was performed. Although with the adjustment the errors could be lowered, there was no guarantee that under other conditions this adjustment could be contradictory. With the adjustment after the first calibration, the second calibration was performed. In the subsequent data, there was still a significant difference between the values monitored by the two instruments. Figure 5 shows the second calibration line. This still resulted in an average error of more than 10%, as shown in Table 4. Despite this, progress was favourable (when adjusting) and the graphs began to look similar in low and medium CO2 concentration, as shown in Figure 6. In addition, several conclusions could be drawn from this calibration:  
* The occupant of the living space must be far away from both instruments (prototype and equipment). This avoids falsification of data by possible direct exhalation on them.
* Data collection must not be started until 15 min after a change in the atmosphere. These changes are caused by the opening of door and window. After 15 min, the atmosphere becomes stable again.  
Figure 4: Measurements of the first calibration and adjustment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**First Calibration**} & **Datasheet** & **After Adjustment** \\ \cline{2-3}  & **(_y_ = 3.1249\(x\) - 1251.6)** & **(_y_ = 3.3237\(x\) - 1281.2)** \\ \hline MAE & 114.74 ppm & 43.03 ppm \\ ME & 111.85 ppm & 0.05 ppm \\ RMSE & 127.92 ppm & 55.41 ppm \\ MAPE & 11.10\% & 04.49\% \\ FIT & 72.32\% & 89.46\% \\ R\({}^{2}\) & & 98.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical parameters related to the errors of the first calibration.  
Figure 5: Linear regression of the second calibration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Second Calibration**} & \multicolumn{2}{c}{**Before Adjustment**} & **After Adjustment** \\ \cline{2-3}  & \(y\) = 3.3237\(x\) – 1281.2** & **2.9497\(x\) – 1120.8** \\ \hline MAE & 180.34 ppm & 41.65 ppm \\ ME & \(-\)180.34 ppm & 1.17 ppm \\ RMSE & 225.37 ppm & 55.17 ppm \\ MAPE & 10.44\% & 2.97\% \\ FIT & 82.70\% & 95.56\% \\ R\({}^{2}\) & 99.68\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical parameters related to the errors of the second calibration.  
Figure 6: Measurements of the second calibration and adjustment.
## 3 Materials and Methods
### Plant and Image Selection
* When there were images of the same location with the same timestamp, we kept a single copy, breaking ties with the following: (1) largest area surrounding the plant contained; (2) least cloudy area; (3) latest download timestamp; and (4) random selection. 3. Weather filters. Ambient temperature and relative humidity were obtained for each plant as detailed in Appendix A.3. Images were excluded from FGD models when ambient weather conditions were unfavorable for plume visibility. At high temperatures and/or low relative humidity, the water vapor in the flue stack does not readily condense, plume visibility is reduced, and our models have no signal to detect. The warmer the temperature, the more humid it needs to be for water vapor plumes to be visible, eventually becoming very faint at high temperatures regardless of the humidity. At colder temperatures, however, even very dry conditions will still result in a visible plume. Therefore, we used empirically derived cutoff rules for plume visibility: * Exclude images in which the ambient temperature is \(\geq\)14 \({}^{\circ}\)C and relative humidity is \(\leq\)26%. * Exclude images in which the ambient temperature is \(\geq\)24 \({}^{\circ}\)C and relative humidity is \(\leq\)36%. * Exclude images in which the ambient temperature is \(\geq\)32 \({}^{\circ}\)C. After applying the above plant, image, and weather filters, our training dataset consisted of 74 plants for NDT and 99 for FGD during the years 2015-2022. Table 1 lists the number of training images for each satellite before and after filtering.