esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Review and Application in Mathematics Education  
In traditional primary mathematics classes, students are usually trained by teachers to learn how to quickly calculate results. After such learning, most students can only remember and understand the concepts. [PERSON] and [PERSON] also indicated that most students cannot learn high-order thinking skills and enhance their learning performance in traditional mathematics education [23]. To address this problem, past studies indicated that review and application are important learning activities to enhance students' learning performance in mathematics education [24; 25]. Through participating in review activities, students are able to become more proficient with related concepts and further internalize the concepts into individual knowledge. Furthermore, teachers should also provide opportunities for students to apply mathematical concepts to solve problems. By assigning application activities, students have to use and connect the relevant concepts they have learned to find solutions for completing the activities. [PERSON], [PERSON], [PERSON], and [PERSON] [26] presented that students' mathematical learning performance is positively correlated with their ability to apply the mathematical concepts they have learned.  
As mentioned above, this study applied a technology-enhanced board game to support teachers in developing review and application activities for students in traditional mathematics learning environments. By playing the board game, students are able to review and apply prime factorization concepts according to the rules of the board game and compete with their peers to become the winner. Students' learning performance can be thus enhanced by engaging in the game-based review and application activities. The following research questions were proposed to evaluate the effectiveness of the proposed approach.  
1. Is there any difference between the traditional exercise approach and the board game-supported exercise approach in terms of students' learning achievement in prime factorization learning?
2. Is there any difference between the traditional exercise approach and the board game-supported exercise approach in terms of students' learning attitude in prime factorization learning?
3. Is there any difference between the traditional exercise approach and the board game-supported exercise approach in terms of students' learning motivation in prime factorization learning?
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 3 Materials and Methods
### Decision Support Subsystem
#### 3.5.1 Interactive Monitoring Platform
* JWT (JSON Web Token): This stateless authentication mechanism ensures secure session control, allowing for only authorized users to access protected resources and functionalities. Figure 5: System architecture diagram. * RBAC (Role-Based Access Control): Role-based permission allocation restricts system operations according to user types (e.g., admin, operator, observer), preventing unauthorized actions and improving auditability. Figure 6 shows the homepage interface for the decision support subsystem. It can be seen from this figure that the pane is divided into five sections. **Part 1:** It is located in the top left corner of the user interface, contains a series of nested menus, and provides access to a variety of functions and settings. * System status: View the status of the WAN port, 5G module, and system information (system time, uptime, hardware version, etc.). * Advanced settings: Configure the connection type and negotiation mode of the WAN port. * Wireless settings: Set the basic parameters of the 5G wireless network (network mode, SSID name, enable SSID, channel width, etc.). * Router monitoring: View wireless signal quality and real-time data as well as historical charts acquired from all sensors located in the vessel (temperature and humidity, acceleration, magnetometer of the vessel, surveillance image, etc.). * 3D visualization: Three-dimensional display of vessels' attitude. **Part 2:** View the weather, CPU temperature, humidity, etc. **Part 3:** View network speed and online hosts quantities. **Part 4:** View upstream and downstream traffic. **Part 5:** View internet basic information (status enabled, IP address, MAC address, etc.)
## 3 Results and Discussion
### Single-Factor Experimental Results and Analysis
#### 3.3.2 Effect of Manganese Sand Dosage on the Iron and Manganese Ion Removal  
It can be seen from Figure 4 that, with the increase in manganese sand consumption, the system had more adsorption sites, and the removal rate of iron and manganese ions gradually increased. When the amount of manganese sand increased from 1.5 g/L to 3.5 g/L, the iron removal rate increased from 61.6% to 84.5%, and the manganese removal rate increased from 61.1% to 74.7%. When the dosage continued to increase from 3.5 to 4.5 g/L, the iron ion removal rate slowly increased from 84.5% to 85.2%, and the manganese ion removal rate slowly increased from 74.7% to 75.8%. This showed that increasing the dosage of manganese sand has little effect on adsorption in this range.  
With the increase in manganese sand dosage and adsorption sites, the removal rate of iron and manganese ions increased. However, an excessive amount of manganese sand also aggravated the collision between the particles, which was not conducive to the adsorption of ions, and the maximum amount of the adsorbed substances when the unit mass of the adsorbent reached equilibrium was reduced. Considering process costs and the regeneration cost of manganese sand, we considered the amount of manganese sand used in this test to be controlled at about 3.5 g/L. Therefore, the optimal dosage of manganese sand was determined to be 3.5 g/L.  
Figure 4: Effect of manganese sand dosage on the iron and manganese ion removal. R\({}_{1}\) is the iron ion removal rate; R\({}_{2}\) is the manganese ion removal rate.  
Figure 3: Effect of pH on the iron and manganese ions’ removal. R\({}_{1}\) is the iron ion removal rate; R\({}_{2}\) is the manganese ion removal rate.
## 4 Wireless Body Area Networks (WBANs)
### New Sensors Generations for BASNs  
In bio-monitoring, the 'physical world' which should be monitored is a living body. Thus, the sensor nodes should be placed in close proximity of the body of topic or implanted into the body, and they constitute a body-area network. The evolution of sensor nodes for bio-monitoring is driven by the \"disappearance\" requirement, and it leverages all technology options available, from the ever-shrinking standard microelectronic technology, to the emerging microfabrication processes allowing the integration of heterogeneous devices onto a small physical volume. As a result, we can clearly see a direction of rapid evolution as in [71]. In the next we will overview four new sensor generations for BASNs.
## 4 Electro-Thermal Modeling
### High-Fidelity Power Loss Model
#### 4.1.1. Conduction Power Loss  
In this method, conduction power loss is anticipated using datasheet characteristics by Equation (42).  
\[P_{cond_{loss}}=V_{ds}(t)I_{ds}(t) \tag{42}\]  
\(V_{ds}\) (V) and \(I_{ds}\) (A) represent forward saturation voltage and the drain current of MOSFET. \(V_{ds}\) and \(I_{ds}\) are functions of a junction temperature \(T_{j}\) (\({}^{\circ}\)C) and depend on one another.  
\[V_{ds}(t)=f\big{(}I_{ds}(t),\ T_{j}\big{)} \tag{43}\]  
The voltage could be calculated by interpolation using lookup tables. The lookup table reveals datasheet specified characteristics. Therefore, during each sampling step of a simulation, it can deliver information consistent with the datasheet specs of a power module. For example, Figure 12 shows the input and the output relation of a lookup table for the conduction loss [18,30,31,32,33,34].  
Figure 11. Inverter’s electro-thermal model block diagram.  
Figure 12. Lookup table to determine \(V_{ds}\) of MOSFET.  
Integrating instantaneous power loss over the switching cycle \(T_{s}=\frac{1}{f_{s}}\) would provide the average value of MOSFET conduction losses [30; 32], as shown in Equation (44).  
\[P_{CM}=\frac{1}{T_{s}}\int\limits_{0}^{T_{s}}P_{cond_{loss}}(t)dt \tag{44}\]  
In such a way, MOSFET conduction loss could be calculated by multiplying at every simulation time step a sample data of \(V_{ds}\) with \(I_{ds}\).  
Estimating conduction loss through a body diode is comparable to the above Equation (42) for MOSFET. The diode conduction loss could be found by Equation (45).  
\[P_{D\ cond_{loss}}=V_{f}(t)i_{f}(t) \tag{45}\]  
\(V_{f}\) (V) and \(I_{f}\) (A) represent a forward saturation voltage and the diode's current. \(V_{f}\) and \(I_{f}\) are functions of a junction temperature \(T_{j}\) and dependent on one another.  
\[V_{f}(t)=f\Big{(}I_{f}(t),\ T_{j}\Big{)} \tag{46}\]  
The average diode conduction losses across the switching period \(T_{s}\) (s) [30; 32], are expressed in Equation (47).  
\[P_{CD}=\frac{1}{T_{s}}\int\limits_{0}^{T_{s}}P_{D\ cond_{loss}}(t)dt \tag{47}\]
# Deep-Feature-Based Approach to Marine Debris Classification
## 4 Results  
### Deep CNN Architectures  
This section compares the final performance of marine debris classifiers when different deep CNN architectures are utilized to extract image features. For each architecture, three feature extraction-tuning schemes are employed and afterward compared. Table 5 showsthe values of quantitative metrics described in Section 3.4 computed on the test data that were not used for the network's training. The overall best performance was the fine-tuned Inception-ResNetV2 model architecture, which obtained the best results of all considered metrics, including 91.40% accuracy, 92.08% (macro) F1-score, and Kappa coefficient 0.89, while fine-tuned InceptionV3 network achieves the second-best result with 90.57% accuracy, 91.07% F1-score, and Kappa coefficient of 0.88. All deep feature extraction CNNs show better performance when their weights are in some way fine-tuned, either during the whole training process or only during the second phase of the training. The overall worst performance was the VGG19 network used as a fixed feature extractor with 77.15% accuracy, 78.15% F1-score, and 0.71 Kappa coefficient, while the lightweight MobileNetV2 architecture displayed the worst performance among the fine-tuned models. Compared to the simple neural network trained from scratch (with architecture as in Table 4), which achieves the accuracy of 46.96% and (macro) average precision 45.74%, recall 43.07%, and F1-score 43.68%, models that apply transfer learning show significantly better classification performance on test images of marine debris. A neural network trained from scratch suffers from severe overfitting; it shows good performance on the training data but has low generalization capability. Statistician [PERSON] demonstrated the importance of complementing numerical calculations with data visualizations for better perception of hidden data properties not captured by statistical analyses [71]. To illustrate high-dimensional features extracted with deep CNN architectures, we use the t-SNE [72] algorithm to project feature vectors into a visualizable two-dimensional space. Figure 17 shows two-dimensional t-SNE training feature projections for best-performing Inception-ResNetV2 and lightweight MobileNetV2 architecture when three different schemes of transfer learning are used. There is a notable difference in the class-wise separation of two-dimensional features when the model fine-tunes the pre-trained weights on the task of interest and when a deep feature extractor stays fixed. During the fine-tuning, a deep CNN extractor adjusts its weights so that new features incorporate details and peculiarities of marine images important for the characterization of each of the six considered classes. Thus, the fine-tuned features better discriminate between
## 1 Introduction
Moreover, the nanostructure of silver nanoparticles decorated on gold nanoparticles was reported as a sensitive medium for localized surface LSPR biosensing applications [42]. TSNPs are already being used as biosensors in research. For instance, [PERSON] et al. (2014) used a cytidine 5\({}^{\prime}\)-diphosphocholine-coated TSNP and AuTSNP (gold-coated TSNP) for detection of C-reactive protein, an inflammatory marker [38]. [PERSON] et al. (2015) successfully detected conformational changes in fibronectin, a protein related to tumor growth and cell survival, using AuTSNPs [43]. [PERSON] et al. (2023) monitored fibronectin behavior overtime within different complex cellular environments [44]. [PERSON] et al. (2018) built a nanoprobe by conjugating single-strand DNA to the TSNP surface, for specific detection of dengue virus [45]. [PERSON] et al. (2017) proposed a TSNP-AuNP nanocomposite that allows detection of glucose due to LSPR changes caused by TSNP etching [46]. [PERSON] et al. (2017) also applied TSNP etching color changes in the design of a colorimetric method for the detection of dopamine, which possesses a high affinity to Ag and formed a protective coating against chlorine [47]. A similar approach for the detection of mercury (Hg(II)) was reported, taking advantage of Ag/Hg amalgam formation to protect the TSNP from the effects of chlorine [48]. A slightly contrasting method reported by [PERSON] et al. (2017), measured the reduction in TSNP color intensity after the addition of Hg(II) alone, attributing such changes to both oxidation by Hg\({}^{+2}\) ions and amalgam formation [49]. The inconsistent findings regarding the antibacterial and biosensing applications of TSNPs can be attributed to the absence of convenient fabrication methods for producing well-defined anisotropic particles with distinct morphologies, as well as the variations in fabrication approaches employed across different studies. In this study, following up on our previous work, we aimed to prove that our TSNP preparation method can be used for successful incorporation into biopolymer blends (bacterial cellulose (BC) with polylactic acid (PLA), polycaprolactone (PCL), and polyhydroxybutyrate (PHB)), showing high potential for a broad spectrum of applications. Additionally, we investigated potential applications of these nanoplates in biosensing, specifically fibronectin (Fn) conformational behavior within dynamic environments and the detection of this protein with its antibody conjugate, Anti-Fn.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 3 Design and Optimization of Double-Telecentric Optical System
### Image-Space Telecentric Optical Path Design and Optimization  
By scaling the object-space telecentric system based on the image plane size, using the same operands for control, and setting up an optimization function, the optical system is optimized to achieve a telecentric optical system on the image side that meets the requirements. The system parameters are shown in Table 3. The system layout diagram is shown in Figure 4a, and the spot diagram of the image-space telecentric system is shown in Figure 4b.  
From Figure 4b, it can be observed that this is an image-space telecentric system with an Airy disk radius of 6.405 \(\upmu\)m. The optical system is divided into five fields of view, each representing the image height when the rays converge at the image plane after passing through the optical system. The upper-left field corresponds to an image height of 0 mm with an RMS spot radius of 1.266 \(\upmu\)m. The upper-right field corresponds to an image height of 5.257 mm with an RMS spot radius of 1.392 \(\upmu\)m. The middle field corresponds to an image height of 8.694 mm with an RMS spot radius of 1.567 \(\upmu\)m. The lower-left field corresponds to an image height of 12.035 mm with an RMS spot radius of 1.753 \(\upmu\)m. The lower-right field corresponds to an image height of 16.798 mm with an RMS spot radius of 1.961 \(\upmu\)m. All of these values are smaller than the Airy disk radius. In the spot diagrams of each field of view, the RMS spot radius is smaller than the Airy disk radius, indicating the optical system possesses high imaging capability.
## 4 Numerical Simulation Methods
### Development of Dissipated Energy Density  
In a closed system without heat exchange with the surroundings, based on the first law of thermodynamics [36], we can derive:  
\[W_{z}=W_{e}+W_{d} \tag{4}\]  
In the equation: \(W_{z}\) is the total input energy generated by external force work. \(W_{e}\) is the elastic strain energy that rocks can release. \(W_{d}\) is the dissipated energy.  
To express the dissipated energy in FLAC\({}^{\text{3D}}\), it requires editing Fish language to utilize pointer functions to capture the stress state of elements at different times, the strain increments within the time step, and the corresponding stress components of the elements at each time step. Subsequently, by using these data, the average stress components of the elements within the time step are calculated, and then the total energy of the elements is obtained. The specific calculation is shown in Equation (5).  
\[W_{z}=\sum_{i=1}^{n}\sigma_{i}\varepsilon_{i} \tag{5}\]  
In the equation: \(\sigma_{i}\) is the average stress component of the element. \(\varepsilon_{i}\) is the strain increment.  
The elastic strain energy that rocks can release is captured using pointer functions to obtain the stress \(\sigma_{t}\) of the element at time \(t\), calculated as shown in Equation (6).  
\[W_{e}=\frac{\sigma_{t}^{2}}{2E} \tag{6}\]  
Consequently, the formula for calculating the dissipated energy \(W_{d}\) is obtained as shown in Equation (7). The specific energy relationship is illustrated in Figure 8.  
\[W_{d}=\sum_{i=1}^{n}\sigma_{i}\varepsilon_{i}-\frac{\sigma_{t}^{2}}{2E} \tag{7}\]
## 4 Application Results
### The Case of the Tennessee Eastman Process
Incremental Pyraformer-DCCA continues to perform well, achieving an FDR of 97.47% and a FAR of 7.45%, indicating its effectiveness in detecting sticking faults with fewer spurious alerts. In the IDV (17) scenario, which involves unknown faults, Crossformer-DCCA and Autoformer-DCCA achieve nearly perfect detection rates (FDRs of 99.87% and 99.49%, respectively) but are accompanied by very high FARs above 44%, reflecting their tendency to flag too many anomalies. Fedformer-DCCA performs moderately, with an FDR of 94.68% and a FAR of 12.42%, while CCA delivers the lowest FDR of 85.73% but has a comparatively low FAR of 8.07%. Incremental Pyraformer-DCCA, again, stands out, with an FDR of 97.22% and a remarkably low FAR of 2.48%, making it highly robust in detecting unknown faults.
## 2 Materials and Methods
### Compression Test  
The real pressure were was collected using the AMI3037-2 pressure tester, with a sensor thickness of 1 mm, a measuring range of 0-34.0 kPa (measuring accuracy \(\pm\) 0.1 kPa), and the measurement duration of the single body part was 30 s. A virtual pressure measurement was performed directly using CLO 7.0 software. First, the corresponding preset virtual five materials in CLO 7.0 were selected, and their density, thickness, and other physical property parameters were adjusted [9]. Then, a virtual material strip was created on the corresponding body part, which was sewn and placed on the virtual body to test its pressure value.  
In this study, the pressure test was divided into two parts, and five materials were tested at each of the seven girths of the lower limb (Figure 2). The first part was the static pressure and tensile experiments on the material (carried out in the real and virtual environments, respectively). For this, the material was cut into 10 cm wide strips, with the lengths adjusted according to the girths of the corresponding body parts and were stretched for each girth: 5% (low strength), 10% (medium strength), 15% (medium-high strength), and 20% (high strength). Simultaneously, the pressure values of each part were collected, and six collection points were designated for each girth (Figure 2). The second part involved the dynamic and static pressure tests of the samples, which were also performed in both real and virtual environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Textile Materials (Face/Back Side)** & **Contents \%** & **Density, g/cm\({}^{2}\)** \\ M1 & 95 Polyester, 5 spandex & 210 \\ M2 & 96 Polyester, 4 spandex & 180 \\ M3 & 82 Polyester, 18 spandex & 180 \\ M4 & 85 Nylon, 15 spandex & 190 \\ M5 & 93 Polyester, 7 spandex & 200 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental material data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Device/Item** & **T2** & **T3** & **T7** & **T10** & **T11** \\ \hline G, cN/[cm-(°)] & 0.42 & 0.91 & 0.10 & 0.28 & 0.24 \\ HG, cN/cm & 0.85 & 1.97 & 0.33 & 0.13 & 0.05 \\ HG5, cN/cm & 0.91 & 1.71 & 0.30 & 0.30 & 0.21 \\ LT & 0.46 & 0.66 & 0.96 & 0.80 & 0.78 \\ EMT, 500 cN/cm & 27.83 & 52.84 & 195.06 & 125.44 & 70.71 \\ RC, \% & 54.01 & 56.96 & 50.70 & 47.58 & 50.93 \\ T0, mm & 1.15 & 0.87 & 0.71 & 0.62 & 0.81 \\ TM, mm & 0.83 & 0.65 & 0.43 & 0.42 & 0.51 \\ \hline \end{tabular}
\end{table}
Table 3: Indexes of materials tested by KES-FB1.. 4.  
The experiment was designed to be carried out in a closed laboratory, with a room temperature of 20 \(\pm\) 2 \({}^{\circ}\)C, a relative humidity of 55 \(\pm\) 5\({}^{\circ}\)\(\mathrm{\SIUnitSymbolMicro}\)rh, and a wind speed of \(\leq\)1 m/s to eliminate the influence of the external environment.  
The purpose of this study was to investigate the compressibility simulation effect of women's compression pants. Using 3D software to simulate the effect of the try-on and testing virtual pressure values, the optimization results were quantitatively analyzed, and real objective pressure test samples were made and analyzed.
## 4 Problem Formulation
### Objective Function and Problem Constraints  
The optimal controller gains are established based on the time domain simulation. Therefore, the controller design problem has been solved using PSO. The error in the DC voltage and measured active power of the CPL has been curtailed using a time-domain weighted objective function (J). The problem is optimally designed and formulated with the parameter constraints as follows:  
\[\text{Minimize }J=\int_{t=0}^{t=t_{sim}}\bigg{[}\Big{(}P_{m}-P_{ref}\Big{)}^{2}+ \Big{(}\text{V}_{dc}-\text{V}_{dcref}\Big{)}^{2}\bigg{]}.t\ dt \tag{21}\]  
Subject to  
\[\begin{array}{l}K^{min}\leq K\leq K^{max},\,m_{p}{}^{max}\leq m_{p}\leq m_{p} {}^{max},\,m_{PA}{}^{max}\leq m_{pAL}\leq m_{pA}{}^{max},\,n_{q}{}^{min}\leq n _{q}\leq n_{q}{}^{max},n_{q}{}_{AA}{}^{min}\leq n_{q}{}_{AL}\leq n_{q}{}_{AL} \max,\\ \text{and}\,k_{p}{}^{PL}\,\,\text{min}\leq k_{p}{}^{PL}\,\,\text{max},\,k_{p}{ }^{PL}\,\,\text{min}\leq k_{p}{}^{PL}\,\,\text{max},\,k_{p}{}^{PL}\,\,\text{ min}\leq k_{p}{}^{PL}\,\,\text{max}.\end{array} \tag{22}\]  
Bounded as \(K^{min}\leq K\leq K^{max}\), the controller parameters are K = [\(k_{pv}\), \(k_{iv}\), \(k_{pc}\), \(k_{ic}\), \(k_{pv}\), \(\_{AL}\), \(k_{iv}\), \(k_{pc}\), \(\_{AL}\), \(k_{ic}\), \(k_{ic}\), \(\_{AL}\)]\(T\). \(m_{p}\), \(n_{q}\), \(m_{p}\),\(\_{AL}\), and \(n_{q}\),\(\_{AL}\) are the power sharing gains. \(k_{p}{}^{PL}\) and \(k_{p}{}^{PL}\) are the PLL controller gains. To guarantee the minimum settling time, \(t\) is added.