esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Survey
### Sc-Mopso-Dg
#### 3.3.3 Addition and Deletion of Rendezvous Point  
An issue with moving a solution from one class to another is the need to add more information to the solution when the destination class is higher or to delete older information from the solution when the destination class is lower. To handle this, an algorithm for addition and deletion was developed. The algorithm receives the destination class as the class input, an exemplar that represents a leader selected from the destination class; and OldSolution, which represents the subject solution that is being moved from the source class to the destination class.  
As shown, the Algorithm 3 starts operating by initiating newSolution, which represents the solution after being moved to the destination class. First, newSolution takes all the information of the oldSolution. Next, assuming that all solutions have the same number of mobile sinks, the differences between the number of rendezvous points in the oldSolution and in the corresponding newSolution is calculated. There are two possibilities: (1) the difference is negative, which means that rendezvous points are to be added; (2) the difference is positive, which means that rendezvous points are to be deleted. In the first case, we randomly select one rendezvous point from the exemplar and add it to the solution until equality. In the second case, we select one rendezvous point from the subject solution and delete it until the difference is 0, which means that the solution is already transformed to the new class.  
```
1:Input
2:1-class: class for moving to it.
3:2-exemplar: individual solution of new class.
4:3-oldSolution: the particle needed to moved.
5:Output
6:1-newSolution
7:Start Algorithm
8:1:newSolution = oldSolution
9:for each ms of newSolution do
10: difference = number of RV(ms) of exemplar \(-\) number of RV(ms)
11: if difference > 0 then
12: for i = 1 until difference do
13: select RVs randomly from exemplar
14: add them to ms newSolution
15: end for
16: else if difference < 0 then
17: for i = 1 until difference do
18: delete RV randomly
19: end for
20: end if
21: end for
22:end Algorithm
```  
**Algorithm 3** Pseudocode of adding and removing rendezvous points
### Feature Selection
Thus, embedded methods endeavor to find the best feature subset during model building by selecting each feature individually. Furthermore, they derive significant advantages in terms of model interaction, accuracy, fewer variables, and computational cost than previous approaches. **Information Gain** (IG) [36] is one of the most widely used approaches in preparing features from a filter-based approach. That is, IG provides a classification ranking of all attributes (features) related to the target (class). Then a threshold is assigned to select several features according to the order obtained. Accordingly, a feature that strongly correlates with the target is considered a relevant feature and irrelevant (or redundant) otherwise. However, a weakness of the IG criterion is a bias favoring features with more values, especially when they are not more informative. Thus, IG between the feature in \(X\) and the variable (target) \(y\) is given here in Equation (1):  
\[\text{IG}=H(Y)-H(Y|X)=H(X)-H(X|Y) \tag{1}\]  
where \(H(x)\) is the entropy of \(x\) given \(y\). The entropy of \(y\) is defined by Equation (2):  
\[H(y)=-\sum_{y\in Y}p(y)\log_{2}(p(y)) \tag{2}\]  
where \(p(y)\) is the marginal probability of \(y\) on all values of \(Y\). Note, \(Y\) is a finite set. Moreover, the conditional entropy of \(Y\) given the random variable \(X\) is shown in Equation (3):  
\[H(Y|X)=-\sum_{x\in X}p(x)\ \sum_{y\in Y}(p(Y|X)\log_{2}(p(Y|X)) \tag{3}\]  
where \(p(y|\ x)\) is the conditional probability of \(y\) given \(x\). IG is a symmetrical measure such as IG (\(x,y\)) = IG (\(y,x\)), as shown in Equation (1). The information gained about \(Y\) after observing \(X\) is equal to the information gained about \(X\) after observing \(Y\). **Gain Ratio** (GR) [37] is the non-symmetrical measure introduced to compensate for the bias of the IG attribute evaluation. The GR formula is given in Equation (4):  
\[\text{GR}=\text{IG}/(H(X)) \tag{4}\]  
Accordingly, to ensure a high-performing predictive model, we have applied two feature selection methods, i.e., IG and GR, over the collected dataset. The experiment has been conducted three times to extract various sets of features. The outcomes are reported in Table 1. Indeed, feature selection methods are essential for improving model performance by consuming fewer computing resources, accelerating the training process, and overcoming the overfitting/underfitting issues.
## 5 Laboratory Test Verification
The theoretical \(\Delta_{b}\) differs from the experimental \(\Delta_{b}\) by about 12% on average. It is obvious that with the change of \(\alpha_{mv}\), the identified steel beam frequency changes significantly. As \(\alpha_{mv}\) increases, the degree of the identified bridges frequency deviation increases. If the bridge frequency deviation rate \(\mid\Delta_{b}\mid\) < 10% is used as the condition for successful identification, the steel beam frequency is successfully identified only when the light vehicle and double-piece beam are used. This corresponds to the conditions for identifying success in Section 4.3, that is \(\alpha_{mv}\) < 0.1. In this case, the changing pattern of \(\Delta_{b}\) is in good agreement with the theory, that is, \(\mid\Delta_{b}\mid\) decreases with the increase of \(\alpha_{v}\). After the test vehicle speed is converted into a dimensionless parameter by using the expression in Table A1, the value of \(\alpha_{v}\) is only 0.027-0.094 due to the limitation of the experimental conditions. However, in the parameter analysis in Section 4, the value of \(\alpha_{v}\) ranges from 0.03 to 0.75. As a result, in the test results, the effect of \(\alpha_{v}\) on \(\mid\Delta_{b}\mid\) cannot be well reflected. In this study, the magnitudes of these frequency deviation rates are also greater than those frequency deviation rates calculated by Equation (11). Although, experimental results are limited because of the chosen scaling of the experimental models, the bridge frequency deviation obtained by the bridge dynamic response should be taken seriously. In particular, the bridge frequency deviation caused by passing vehicles must be excluded. The overall test results are in good agreement with the theoretical calculations and mostly conform to the pattern described in the previous section. However, there are inevitable deviations between the test results and theoretical calculations. For the abnormal phenomena in the above test results, possible reasons for preliminary analysis are as follows: 1. One degree-of-freedom oscillator is used as a vehicle model in the theoretical derivation, which is not the case for an actual vehicle. 2. The vehicle damping and beam damping are ignored in the theoretical calculation. 3. Improper installation of vehicle components and unsmoothed wheels will affect the interaction between the vehicle and bridge to a certain extent, which, in turn, interferes with the signal. 4. The vehicle will experience a short period of acceleration until reaching the desired speed. Although a previous study [51] shows that little difference exists between the vehicle passing the bridge at a constant speed or constant acceleration. Due to the large vehicle-to-bridge mass ratio in this test, the influence of the non-uniform speed may be amplified, resulting in errors. Figure 14: Layout of experiment: (**a**) laboratory model; (**b**) vehicle; and (**c**) steel beam.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 4 Implementation Details and Results
### Performance Evaluation  
If the body area has been segmented and the bright control is applied, there will be less light in the human body. A reduction in the projected light will lead to the camera obtaining a darker area in the picture, which would also affect the next segmentation due to the difference in the incoming image. We sequentially adjusted the dimming brightness level in 50 increments to measure this effect.
# Challenges and Possibilities of ICT-Mediated Assessment in Virtual Teaching and Learning Processes
## 3 Results
### Systematic Review of ICT-Mediated Assessment
#### 3.1.3 What Are the Assessment Possibilities Offered by the Current ICT Platforms? We establish two categories to answer the question about the ICT-mediated assessment possibilities (Table 2). First, we analyzed the digital tools and strategies to incorporate ICT-mediated evaluation. Then, we analyze the pedagogical approaches considered in the studies. From the review, we can identify several technological tools that enhance the assessment process, for instance, the use of e-portfolios and mobile devices. Education programs use E-portfolios for teacher training. However, these have the potential to be used in several fields of knowledge [20; 21; 93]. Students build E-portfolios to demonstrate skills acquired in the training process. One of its advantages is the opportunity for constant monitoring of student progress, allowing the personalization of the learning process [20; 21]. While E-portfolios are assessment instruments, they also allow students to develop documentation and reporting skills [93]. However, its implementation requires teachers and students to use several digital tools, highlighting the importance of digital literacy [20; 21]. On the other hand, mobile learning is a trend in educational research that seeks to exploit the ubiquity of mobile devices, such as cell phones and tablets, to encourage and enhance learning. The use of mobile devices for learning processes allows the development of evaluation activities of various types. These may include the traditional multiple-choice questionnaires [94], but also the construction of multimedia material (photographs, audios, and videos) to evidence the appropriation of knowledge and development of skills [94]. Teachers can promote a collaborative environment using mobile devices [94] but also can use these devices to develop outdoor assessment activities [89; 95], highlighting the possibility to evaluate at any time and any place [95]. A standard research question is  
Figure 7: Number of documents categorized by field of knowledge. the willingness and acceptability of students to use their mobile phones as assessment tools; the studies report both positive and negative perceptions [52; 89; 94; 96; 97]. Additionally, there are other strategies for the incorporation of ICT-mediated assessment in learning processes, such as self-assessment, peer-assessment, gamification, augmented reality, learning analytics, adaptive assessment, and automated assessment. Table 3 summarizes these key strategies and challenges. In addition to the tools mentioned above, the systematic review revealed several platforms and software developed or used to improve assessment activities. Table 4 summarizes some of the software and platforms identified in the review.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
###### Abstract
Therefore, this research integrates multi-scale convolutional neural network (MCNN) and transformer models. The proposed MCNN-transformer model is applied in MPWSS and evaluated in comparison with six machine learning and deep learning methods. This method can thereby reduce the acceleration of wear at the mud pump shaft seal caused by sensor failure and improve the reliability of the MPWSS. This paper introduces a hybrid model for predicting the shaft seal water pressure of MPWSS in CSD. This model assists operators in accurately adjusting the seal water pump speed and seal water flow rate when the shaft seal water pressure sensor of MPWSS fails. The primary contributions and innovations of this research can be succinctly summarized as follows:  
1. This article comprehensively analyzes the principles and failure mechanisms of the MPWSS, and summarizes its challenges. 2. This article combines MCNN and transformer methods for the first time to solve the challenge of shaft seal water pressure sensor failure in MPWSS of CSD. Applying MCNN to process the original feature signals and input them into the transformer model can effectively eliminate the differences between heterogeneous data and the original data, facilitating the transformer model to capture data features better and improve the accuracy of sealing water pressure. 3. To demonstrate the superiority of the proposed MCNN-transformer model, its performance is compared with six other benchmark models. 4. In terms of feature selection, considering the mechanism of underwater pumps and based on prior knowledge and expert experience, the MPWSS key index selection method is proposed, and this method with the is combined stacked model of Random Forest (RF) and XGBoost, which can better excavate the relationship between shaft end seal water pressure and other sensing parameters. The paper is organized as follows. In Section 2, the principle and structure of the mud pump are introduced, and the composition of the MPWSS and its working mechanism are briefly outlined. In Section 3, based on the actual monitoring data, a complete data preprocessing and feature selection model is proposed. In Section 4, the MCNN-transformer model and six other benchmark models are proposed and compared. In Section 5, the results of seven proposed models are discussed. Finally, Section 6 provides conclusions and prospects for future research.
## 10 Conclusions  
This paper has discussed PCI, a non-linear system identification technique to improve the performance of the integrated RISS/GNSS system. Two versions of RISS were used, one based on the single-axis gyroscope, along with an odometer, proposed by the author, integrated with GNSS, and the other incorporating two accelerometers to calculate pitch and roll. The complementary strengths of GNSS and RISS can be synergized, and optimal performance would be achieved during GNSS outages. First, loosely coupled and then tightly coupled integration schemes were considered. Enhancements for both integration techniques were suggested, successfully implemented, and tested for real road trajectory data using KF. As demonstrated by the results, PCI can handle the gyroscope's non-linear azimuth errors and stochastic sensor errors for the first algorithm. The performance of the 2D positioning RMSE KF-PCI solution was improved by about 77% as compared to the KF solution for the multiple GNSS outages of 2 min. For the second algorithm, PCI is utilized for modeling the residual pseudorange correlated error. In the case of the availability of three satellites, the performance improvement for the proposed KF-PCI solution was approximately 39% compared to the KF solution for the one-minute partial outage. The results demonstrated the worth and effectiveness of the proposed system identification techniques for enhancing the integrated navigation system at various phases.  
[PERSON], [PERSON], and [PERSON] proposed the idea, conceived and designed the algorithm, performed the experiments, and wrote the manuscript; [PERSON] participated in the experimental work, analyzed results, and reviewed the simulation results; [PERSON] and [PERSON] supervised the activities, reviewed the manuscript, and provided important suggestions to improve the algorithm. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).  
**Institutional Review Board Statement**: Not Applicable.  
**Informed Consent Statement**: Not Applicable.  
**Data Availability Statement**: Not Applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**The Number of Visible Satellites**} & \multirow{2}{*}{**Outage Dur. (s)**} & \multicolumn{2}{c}{**RMS Error in Position (Meter)**} \\ \cline{3-4}  & & **KF** & **KF-PCI** \\ \hline
3 & 60 & 7.5 & 4.6 \\ \hline
2 & 60 & 10.4 & 8.7 \\ \hline
1 & 60 & 15.8 & 15.7 \\ \hline
0 & 60 & 15.6 & 15.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMS horizontal position error over all partial GNSS outages.
# A Modified Pressure-Sinkage Model for Studying the Effect of a Hard Layer in Sandy Loam Soil
## 3 Deformation under Pressure Plate as a Function of Depth
In this part of the curve, the plate acts upon a homogenous soil, where the effect of the hard layer does not exist and the increasing soil strength is not yet significant. The soil deformation increases constantly, and the soil under the plate behaves similarly to when it is in the infinite thickness (without a hard layer or a specific depth). The pressure continuously intensifies with the relative sinkage, and the relationship between them is almost linear. The second part is following the breaking point that indicates the compaction zone or the zone of the soil bin bottom (the hard layer in a field) effect. Here, the compaction, as well as the deformation, increases rapidly. Further, above the rigid layer, the pressure almost does not decrease, because this layer modifies the pressure distribution in the soil body. This extended portion from the transition zone to the specific depth has a varied load-bearing capacity. Equation (1) can be used for the first part of the pressure-sinkage curve. A power regression analysis was carried out to determine the relationship between the applied pressure (p) and the relative sinkage (z\({}_{\rm o}\)/D) for both soil thickness levels with diverse densities, as per Equation (1). Table 3 presents the results. The data were fitted to the power model with a high coefficient of determination (R\({}^{2}\)). The exponent (n) represents the soil's deformation and compaction behavior, considering vertical loading. The soil's moisture content and particle size distribution have a major effect on this. The sinkage modulus (k) is primarily dependent on the soil type, moisture content, and density. As per [24], the average value of exponent (n) at the optimum moisture content level is 0.8, which reduces as moisture content increases. The typical value of exponent (n) in the present study ranges from 0.7 to 0.9, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Thickness** & **Plate Diameter** & **H/D** & **z\({}_{\rm o}\)/D =** & **z\({}_{\rm o}\)/D =** & **z\({}_{\rm o}\)/D =** \\
**H [cm]** & **D [cm]** & **H/D\(-\)1.05** & **H/D\(-\)1.06** & **H/D\(-\)1.07** \\ \hline
30 & 20 & 1.5 & 0.45 & 0.44 & 0.43 \\
40 & 20 & 2 & 0.95 & 0.94 Figure 4: Pressure–sinkage curves for a soil thickness level of 30 cm at bulk densities of (**a**) 1.3 g/cm\({}^{3}\), (b) 1.4 g/cm\({}^{3}\), and (c) 1.5 g/cm\({}^{3}\). Figure 5: Pressure–sinkage curves for a soil thickness level of 40 cm at bulk densities of (**a**) 1.3 g/cm\({}^{3}\), (**b**) 1.4 g/cm\({}^{3}\), and (**c**) 1.5 g/cm\({}^{3}\).
## 3 Comparison of Mutually Exclusive Alternatives
### Economic Efficiency Indicators
#### 3.3.3 Economic Rent (\(Er\))  
An increase in land-use efficiency due to reduced flooding is a benefit of flood management. Economic rent (\(ER\)) considers the efficiency of land uses in a floodplain. \(ER\) is defined as the net annual revenue associated with a resource that can be land use for a floodplain [51; 52]. \(ER\) is an indicator that combines the effectiveness of alternatives to achieve the economic objectives of floodplain management.  
The average annual net return, discounted to its present value, can be considered the \(ER\) of land use in a floodplain. If \"\(R_{n}\)\" is the annual net return of a unit of land area in year \"\(n\)\", assuming a constant discount rate \"\(r\)\", then the \"\(ER\)\" in time \"\(t\)\" will be the average discounted annual net return. This relationship can be expressed using the following formula:  
\[ER=\sum_{n=0}^{n=t}\frac{R_{n}}{t(1+r)^{n}} \tag{1}\]  
It is also possible to define the net annual return per unit of land as the difference of annual gross returns to total costs (excluding land rent), which represents economic efficiency [51]. In the case of a floodplain, the annual cost of the measure, including capital and maintenance costs, and the expected residual damage reduce the ER; these are called combined deductions. Therefore, ORP can also be defined as a state of flood management when the risk of \(ER\) reduction is minimal.  
\"Location benefits\" (\(R_{loc}\)) and \"intensifying benefits\" (\(R_{int}\)) are the two parts of \(ER\) (please refer to Equation (2)). \(R_{loc}\) is connected with the availability of land for intensive economic uses, e.g., shifting from agriculture to industrial land use [53], whereas, \(R_{int}\) is the value of increasing land use such as changing from lower- to higher-value crop cultivation or, alternatively, producing higher-yield crops or intensifying agricultural economic activities [53].  
\[\Delta R_{n}=\Delta R_{loc}+\Delta R_{int} \tag{2}\]  
The negative impacts of floods are reduced with improved flood management, which results in an increase in both components by higher flows of investments. Although several factors depend on an increase in location benefits and intensification benefits, the availability of investments is the most important factor by itself. Investment project planning in the floodplain is subject to a precise evaluation of \(ER\).
## 3 Results and Discussion
### Destruction Process and Mechanical Characteristics
#### 3.3.1 Destruction Phase  
During the pre-peak phase (point A), the stress-strain curve forms an approximately straight line, signifying the model's presence in the elastic phase. The formation of cracks is relatively limited, and those that do appear tend to align with the direction of maximum compression. These cracks are distributed across the specimen and appear to be non-interacting. A non-linear curve (point B) emerges as the strain increases before reaching the peak, signifying the model's partial entry into the plastic phase with rapidly developing cracks. One or more macroscopic failure planes develop, cutting across the specimen. As the stress-strain curve transitions into the post-peak phase (point C), cracks continue evolving and join to form through cracks, leading to a rapid drop in the compressive stress and damage to the model. Figure 11 presents the resultant stress-strain curve, along with the entire crack development process across different stages.
## 3 Control Algorithm Description  
It is further assumed that an image to be demonstrated by the satellites consists of several English alphabet letters. However, it could be any symbol or drawing. All letters or symbols can be represented as a composition of points with defined relative position. Each point of a letter is a satellite with a sunlight reflector in our case. For image demonstration using sunlight reflectors, the required relative motion of the satellites should be achieved with an acceptable margin. The relative configuration is based on the closed trajectories with zero relative drift between all the satellites. The projected circular orbits are used for image construction to preserve the relative distances in projection onto the horizontal plane, which is suitable for the Earth observer. Instead of Cartesian coordinates, the curvilinear relative coordinates are utilized for the reference relative trajectories construction, which improves the accuracy of the relative motion model. Since the satellites are in low Earth orbits with an altitude of 300-400 km, the difference of aerodynamic forces acting on the satellites is considered as the control source. By changing the attitude of the reflector relative to the incoming airflow, it is possible to obtain the required value and the direction of the aerodynamic drag and lift. The linear-quadratic regulator is applied to the control problem for converging to zero mean deviation of the current trajectory from the reference one for each satellite in the formation. This section provides the details of the control scheme designed to achieve the required relative configuration.