esa-sceva/satcom-chunk-collection huggingface dataset:
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 2 Transportation Problem (TP)  
The transportation problem (TP) is a classic optimization problem in the field of operation research and logistics. It deals with the optimal allocation of goods or resources from multiple suppliers to multiple consumers, in an effort to minimize the overall cost while satisfying supply and demand constraints. The TP is formulated as a linear programming problem.  
Let us denote the following:  
* \(m=number\ of\ suppliers\);
* \(n=number\ of\ consumers\);
* \(s_{i}=the\ capacity\ of\ supplier\ i,\ i=1,2,\ldots m\);
* \(d_{j}=the\ capacity\ of\ consumer\ j,\ j=1,2\ldots n\);
* \(x_{ij}=number\ of\ units\ to\ transported\ from\ supplier\ i\ to\ consumer\ j\);
* \(c_{ij}=cost\ of\ transporting\).  
The mathematical model of the TP can be formulated as follows:  
\[minZ=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}\cdot x_{ij} \tag{1}\]  
\[\sum_{i=1}^{m}x_{ij}\geq d_{j}\ \ for\ j=1,\ 2,\ \ldots,\ n \tag{2}\]  
\[\sum_{j=1}^{n}x_{ij}\leq s_{i}\ \ for\ i=1,\ 2,\ldots,\ m \tag{3}\]  
\[x_{ij}\geq 0\ \ for\ i=1,\ 2,\ \ldots,m\,\ j=1,\ 2,\ldots,n \tag{4}\]  
Equation (1) outlines the objective function that needs to be minimized. Equation (2) encompasses the supply constraints, ensuring that the available quantity at the source points is greater than or equal to the demanded quantity at the destination points. Similarly, Equation (3) ensures that the sum of the quantities transferred from source \(s_{i}\) to destination \(d_{j}\) does not exceed the available quantity. Equation (4) introduces a necessary condition, specifying that the units \(x_{ij}\) must be positive integers. For the sake of simplicity, we assume a balanced condition model, where supply and demand are equal in this paper.  
As already mentioned, various algorithms and methods, such as the northwest corner method, the least-cost method and [PERSON]'s approximation method have been developed for finding a basic feasible solution. In addition, extensions and variations of the TP have emerged to enhance the model's relevance across diverse scenarios.
## 1 Introduction
On the other hand, research where audio processing is carried out are based on medium cost platforms, such as Raspberry or GPU, or using cloud services. Hence, the aim of this research is to solve these deficiencies designing a low cost acoustic sensor to do audio processing at the edge of network. There is no doubt that significant progress has been made in the field of wireless acoustic sensor networks. However, an improvement to the actual sensors is needed because the main drawback of these recent WASN is that their power consumption and cost do not fit some of the critical requirements of AAL applications: power consumption, mobility, size and cost. In addition, humans are most sensitive to frequencies between 2 kHz and 5 kHz, and the speech and environmental sounds are often less than 5 kHz bandwidth. Therefore, the sensor has to be characterized by a spectrum with these frequencies. In this paper, a novel wireless acoustic sensor is proposed. The main novelty of this work comes from the fact that the proposed wireless acoustic sensor is able to record audio samples at least at 10 kHz sampling frequency (5 kHz bandwidth) and 12-bit resolution, and audio signal processing can be carried out at node without compromising the sample rate and the energy consumption. Furthermore, this sensor can be applied in AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, wireless network connectivity, audio sampling and computation capabilities for audio processing. Thus, the identification of sounds for an AAL context, such as fall detection or health related symptoms, could be carried out at the edge of a WASN reducing network latency and improving response time and battery life of proposed sensor, enhancing quality of life and safety of older people. The remainder of this paper is organized as follows. Section 2 describes the low cost proposed wireless acoustic sensor. Section 3 describes the used methods to validate and evaluate the proposed sensor. Section 4 shows the results of some experiments carried out to validate the designed sensor in this study. Finally, in Section 5 draws some conclusions and discusses some possible directions for future research.
## 6 Conclusions  
The complex electromagnetic environment affects the device performance of the electrical system in the generator, such as set-signal acquisition and conversion, which generates signal interference in the signal system. This study analyzed load-mutation faults in generator sets caused by signal interference in an electrical system. By extracting the fault characteristics based on the load-mutation fault data in the ST set, a CCPP model was built to analyze the impact of load mutation on the CCPP. Finally, a corresponding fault diagnosis and isolation method was proposed to ensure the safe and smooth regulation of the CCPP.  
Drawing on electrical fault data from coal-fired units, the fault characteristics of the load mutation during the open-loop regulation of a single-shaft CCPP were refined. Compared with oscillating load mutation, pulsing load mutation has a more serious impact on the load regulation of CCPP. It is also a type of fault that needs to be isolated.  
A fault isolation scheme for a single-shaft CCPP was proposed. For pulsing load mutation, replacing the load command with the power output of the CCPP temporarily as a feedback signal to its load control system when a pulsing load mutation occurs can ensure the fuel quantity command and unit operation stability.  
Conceptualization, methodology, formal analysis, supervision and writing J.W. and Y.C.; software, investigation, validation, and writing K.Y., Y.W., Z.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the China Postdoctoral Science Foundation (Grant No. 2021T140154) and National Science and Technology Major Project (2017-I-0007-0008).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 11: Simulation with different control methods. (**a**) Simulation with fault diagnosis isolation method. (**b**) Simulation without fault diagnosis isolation method.
## 5 Further Discussion on the Tracking Control Algorithm
### The Proposed Controller versus Other Control Schemes
Similarly, any complicated control algorithm makes difficult the dynamics analysis proposed in this work. The reason for this is that to show the changes in vehicle dynamics while tracking the desired trajectory, a velocity transformation that diagonalizes the inertia matrix is sufficient. However, without the velocity transformation, no insight into the dynamics of the system will be obtained so as to estimate the impact of mechanical couplings in for the assumed model and trajectory type. Methods containing observers are also not useful for analyzing dynamics for coupling estimation because the additional system (observer) introduces its own dynamics into the original model. Control algorithms with prescribed performance are supposed to lead to signals within prescribed limits. In contrast, the presented scheme is concerned not only with achieving the control objective, but also with studying the impact of coupling during performing the primary task. Comparative simulations were performed only with the input-output feedback linearization method developed in [56] to show the differences resulting from the application of the classical asymmetric vehicle model and the model obtained by means of the velocity transformation. In the original method, the accelerations are coupled in such a way that the inertial forces cannot be attributed to only one variable. On the contrary, when the dynamics is described in quasi-velocities the mechanical couplings occurring in the system as their effect on the vehicle motion can be estimated. The original control algorithm is used for tracking while the one presented in this paper, in addition to accomplishing the control task, also provides insight into vehicle dynamics. Since the symmetric inertia matrix plays a key role for the proposed controller, from this point of view the control methods can be divided into:  
* Methods for the model with a diagonal inertia matrix [18; 27; 28; 31; 35; 36; 37; 38; 40; 41; 42; 46; 71];
* Methods for the model with a symmetric inertia matrix [49; 50; 52; 56; 57; 58; 59; 60; 72]. Control methods designed for the dynamics model with diagonal inertia matrix cannot be compared with the proposed algorithm because it is suitable for estimating the effect of dynamic couplings. If a diagonal inertia matrix is adopted in the model, it is assumed that any inaccuracies due to inertial forces will be compensate by the control algorithm. The proposed scheme can also be applied to a model with a diagonal matrix, but then it will only be used for control purposes and not to estimate the effect of couplings on the vehicle behavior.
## 4 Directed Search for NDSS Members  
The analysis performed in the previous section has induced an idea of the suggested directed search algorithm. Similar to the approach presented in [22], the newly suggested algorithm starts with an approximation of the leftmost and rightmost members of the Pareto front. These approximations are obtained by applying the algorithm _Swap_ (\(\mathbf{y}\), \(\mathbf{a}\), \(NDSS\)) to the starting solution \(\mathbf{y}\) mentioned in [22] for \(\mathbf{a}=[0,1]\) and \(\mathbf{a}=[1,0]\), respectively. The starting step of the algorithm consists of the determination of \(noT\) of triangulation points, where the number of points \(noT\) is an input parameter of the approach. Two triangular points are the above-mentioned leftmost and rightmost members. Then, other \(noT-2\) approximations of the Pareto front members are obtained by application of the swap algorithm for \(\mathbf{a}^{k}=[k^{\alpha}\Delta,\,1-k^{\alpha}\Delta]\) for \(\Delta=1/(noT-1)\) and \(k=1,\,\ldots,\,noT-2\). The \(noT\) obtained approximations are ordered increasingly according to the values of \(f^{2}\) and form a list \(sT\) of so-called \"triangulation\" points or milestones. The list \(sT\) and starting set \(NDSS\) of non-dominated designs represent the inputs of the _DirectedSearch_ algorithm.  
The basic step of the algorithm consists of the determination of the direction \(\mathbf{d}\) from the \(k\)-th member of the list \(sT\) to the \((k+1)\)-th member in the space given by coordinates \(f^{2}\). and \(f^{1}\). Expression (7) determines the vector specifying this direction in the two-dimensional Euclidean space.  
\[\mathbf{d}=\left[f^{2}\left(\mathbf{y}^{k+1}\right)-f^{2}\left(\mathbf{y}^{k}\right),f^{ 1}\left(\mathbf{y}^{k+1}\right)-f^{1}\left(\mathbf{y}^{k}\right)\right] \tag{7}\]  
Figure 2: The black circles correspond to the Pareto front members, and the empty circles denote the solutions met during the minimization process.  
The search in the direction \(\mathbf{d}\) from the point determined by the solution \(\mathbf{y}^{k}\) is performed by the algorithm _Swap_ (\(\mathbf{y}^{k}\), \(\mathbf{a}^{k}\), \(NDSS\)), where components of \(\mathbf{a}^{k}\) are computed according to (8).  
\[\mathbf{a}^{k}=\left[\mathbf{a}^{k}_{1},\mathbf{a}^{k}_{2}\right]=\left[f^{1}\left(\mathbf{y}^{ k}\right)-f^{1}\left(\mathbf{y}^{k+1}\right),f^{2}\left(\mathbf{y}^{k}\right)-f^{2} \left(\mathbf{y}^{k+1}\right)\right] \tag{8}\]  
If the algorithm _Swap_ is performed for specified \(\mathbf{y}^{k}\) and \(\mathbf{a}^{k}\) according to (8), then the direction of its progress is depicted in Figure 3.  
The chance of obtaining more approximations of the Pareto front members is bigger. The _DirectedSearch_ algorithm performs according to the following steps:  
_DirectedSearch_ (_noT_, \(T\), _NDSS_)  
Set \(k=0\).  
While \(k<noT-1\), perform Step 2; otherwise, terminate and return the current _NDSS_.  
For the \(k\)-th and \((k+1)\)-th members \(\mathbf{y}^{k}\) and \(\mathbf{y}^{k+1}\) of the list \(T\), determine \(\mathbf{a}^{k}\) according to (8) and perform _Swap_(\(\mathbf{y}^{k}\), \(\mathbf{a}^{k}\), \(NDSS\)). Set \(k=k+1\).
## 6 Conclusions  
In order to achieve both corrosion detection and corrosion level determination for hydraulic metal structures, while simplifying the annotation task, this paper proposes  
Figure 10: Detection results in real-world scenarios.  
a serial architecture-based hydraulic metal structure corrosion detection method based on the improved YOLOv10. The detection process is divided into two steps: first, the improved YOLOv10-vit is used for corrosion location detection, and then the proposed YOLOv10-vit-cls is used for corrosion level determination. The research findings and advantages of this paper are as follows:  
(1) By integrating MobileViTv3 into the third C2f layer of the YOLOv10n backbone, the YOLOv10-vit is proposed. In the hydraulic metal corrosion detection task, the proposed YOLOv10-vit achieves higher precision compared to previous YOLO series networks, performing optimally in metrics such as F1 and mAP.  
(2) YOLOv10-vit-cls is derived from YOLOv10-vit by optimizing the backbone and replacing the detection head with a classification head. In the corrosion level determination task for hydraulic metal structures, the proposed YOLOv10-vit-cls can leverage the pre-trained weights of YOLOv10-vit for transfer learning, reducing training costs while achieving higher accuracy in corrosion level determination.  
(3) The proposed serial architecture simplifies the corrosion annotation task. Through the corrosion level determination network, the model can automatically learn features of different corrosion levels. Therefore, only the corrosion target detection dataset needs to be annotated, without the need to annotate corrosion level data simultaneously, thus improving the efficiency of corrosion data annotation for hydraulic metal structures.  
Although the proposed YOLOv10-vit and YOLOv10-vit-cls perform well on the dataset, there are still some misdetections when validating images collected from real-world scenarios using the serial architecture. Future research should analyze the causes of misdetections, optimize the network structure, and further improve model accuracy.  
Conceptualization, H.C. and F.K.; methodology, H.C. and F.K.; software, H.C. and F.K.; validation, H.C. and F.K.; formal analysis, F.K.; investigation, F.K.; resources, F.K.; data curation, F.K.; writing--original draft preparation, H.C. and F.K.; writing--review and editing, H.C. and F.K.; visualization, F.K.; supervision, F.K.; project administration, F.K.; funding acquisition, F.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2022 YFB4703404), and the Scientific Research Project of China Yangtze Power Co., Ltd.(Z232402014).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.
## 4 Experimental Results
### Data Preprocessing  
The learning performance prediction data set had 4375 first-year students enrolled in 12 departments of a Taiwanese university during the first semester of the academic year 2020-2021. These departments were selected randomly. However, after data cleaning, only 2407 usable numbers of students were selected for the experimental sample data since all variables were fulfilled in students' profiles, resulting in a return rate of 55%. The remaining 1968 students (45%) who had missing variables in their profiles, dropped out, and/or were suspended, were excluded in this study.  
After relevant data sets were processed, a total number of 18 factors, which were predicted to influence the learning performance of freshmen students, were used as input (independent) variables for the prediction model (Table 2). These proposed factors included \"Department\", \"Gender\", \"Address\", \"Admission status\", \"Aboriginal\", \"Child of new residents\", \"Family children ranking\", \"Parent average income per month\", \"On-campus accommodation\", \"Main source of living expenses\", \"Students' loan\", \"Tuition waiver\", \"Father live or not\", \"Father's occupations\", \"Father's education\", \"Mother live or not\", \"Mother's occupations\", and \"Mother's education\". The factor \"Average scores\" of all the subjects' grades in the first semester recorded in the school grading system was used as output (dependent) variable for the model (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Feature Name** & **Feature Description** & **No.** & **Feature Name** & **Feature Description** \\ \hline
1 & Department & Students’ majored department & 10 & Main source of living expenses & Students’ living expenses support \\
2 & Gender & Students’ sex & 11 & Student loan & Students borrow money in- and out-school or from friends/relatives \\
3 & Address & Students’ home address type & 12 & Tuition waiver & Free or reduce tuition fee \\
4 & Admission status & School admission offered to student & 13 & Father live with or not & Students’ father lives in the family \\
5 & Aboriginal & Students’ origin & 14 & Father’s occupation & Careers of students’ father \\
6 & Child of new residents & Immigration status of students’ family & 15 & Father’s education & Father’s highest education status \\
7 & Family children & & & \\ ranking & Student’s born time in the family & 16 & Mother live with or not & Students’ mother lives in the family \\
8 & Parent average & The average income of student’s income per month & parents in each month & 17 & Mother’s occupations & Careers of students’ father \\
9 & On-campus & Students live in- or out of the accommodation & 18 & Mother’s education & Mother’s highest education status \\ \hline \hline \end{tabular}
\end{table}
Table 2: The input variables.
## 2 Algorithm Principle
### Introduction to UKF Algorithm and Its Implementation Steps
\] (22)  
\( \begin{array}{ll}\text{\textcircled{2} State mean weighted processing:}\\ &\\ &\\ &\\ &\\ &\\ &\end{array} \] (23)  
where, \(\kappa_{i}(i=0,1,\cdots,2L)\) is the weight corresponding to the Sigma point set \(X_{i}\):  
\[\left\{ \begin{array}{ll}\kappa_{0}^{(n)}=\mu/(L+\mu)\\ \kappa_{0}^{(j)}=\mu/(L+\mu)+(1-\alpha^{2}+\beta)\\ \kappa_{i}^{(n)}=\kappa_{i}^{(j)}=1/(2(L+1)),&i=1,2,\cdots,2L\end{array} \right.. \tag{24}\]  
where \(\beta\) describes the system distribution information. To determine the mean and variance of \(Y\), we can transform \(Y=g(X)\) to get point \(Y_{i}=g(X_{i})\), and calculate the mean and variance of \(Y_{i}\) according to the obtained point \(Y\) and the corresponding weights:  
\[\left\{ \begin{array}{l}\overline{Y}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(n)}Y_{i }\\ D_{Y}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(j)}(Y_{i}-\overline{Y})(Y_{i}- \overline{Y})^{\mathsf{T}}\end{array} \right.. \tag{25}\]
3. State variance weighted processing: \[D_{k}^{-}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(j)}(X_{i,k/k-1}^{l}-\hat{X}_{k}^ {-})(X_{i,k/k-1}^{l}-\hat{X}_{k}^{-})^{\mathsf{T}}.\] (26)
4. One-step observation equation of state: \[Y_{i,k/k-1}=h(X_{i,k/k-1}^{l}),\hskip 14.226378 pti=0,1,\cdots,2L\enspace.\] (27)
5. Observation weighting processing: \[\hat{Y}_{k}^{-}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(n)}Y_{i,k/k-1}.\] (28)  
3. Measurement update (1) Covariance matrix weighting processing of the observations: \[D_{Y_{k}Y_{k}}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(j)}(Y_{i,k/k-1}-\hat{Y}_{k} ^{-})(Y_{i,k/k-1}-\hat{Y}_{k}^{-})^{\mathsf{T}}.\] (29)
2. Covariance matrix weighting processing of the state value and observation value: \[D_{X_{k}Y_{k}}=\sum\limits_{i=0}^{2L}\kappa_{i}^{(j)}(X_{i,k/k-1}^{l}-\hat{X}_ {k}^{-})(Y_{i,k/k-1}-\hat{Y}_{k}^{-})^{\mathsf{T}}.\] (30)
3. Filter gain matrix: \[K_{UKF}=D_{X_{k}Y_{k}}D_{Y_{k}Y_{k}}^{-1}.\] (31)
4. State estimation equation: \[\hat{X}_{k}=\hat{X}_{k}^{-}+K_{UKF}(Y_{k}-\hat{Y}_{k}^{-}).\] (32)
5. Estimated error variance matrix: \[D_{k}=D_{k}^{-}-K_{UKF}D_{Y_{k}Y_{k}}K_{UKF}^{\mathsf{T}}.\] (33)  
We can see from the calculation process of the UKF filter that the UKF filter does not need to be linearized, which avoids the introduction of linearization errors, thereby improving the estimation accuracy. In addition, the UKF filter simplifies greatly the implementation effort; it is precisely because of this that the UKF filter is widely used in the field of navigation and positioning [28].
# SecureVision: An Open-Source User-Customizable Image Encryption Program
## 3 Results and Discussion  
### Decryption and Merging  
Decryption of the encrypted images should be relatively simple, considering that the same set of keys used for encryption should be presented to the program. In fact, the decryption step essentially returned the shuffled pixels back to their original positions and restored their original values. However, as mentioned above, due to changes between floating-point and integer variables, as well as random position shuffling, there could be pixel overlaps during encryption. This pixel overlap led to a loss of data in some parts of the encrypted image, which subsequently caused a loss of pixels in the restored image. For example, decryption of the image shown in Figure 6 with \(A=1.0\) is shown in Figure 7, where loss of pixels can be clearly seen.  
Figure 6: Original and encrypted images with their respective histograms showing the distribution of greyscale values of pixels. The \(x\)-axis in the histograms represents greyscale values ranging from 0 (black) to 255 (white).  
To tackle this problem, we developed a module in our program that performed multi-layer merging of decrypted images with different pixel offsets. In this module, multiple encrypted images were produced with different _deno_ and \(b\) values (see Section 2), which led to small offsets for the shuffled pixel positions. The offset value was defined by the user, which generated a small range of variations in the random numbers so that those missing pixel positions could be reconstructed when different images with different offsets were merged together. The offset value was one of the keys for image encryption and decryption. The evolution of decryption and multi-layer merging is shown in Figure 8, which demonstrates that our program can successfully decrypt and recover the original image (see Figure 5 for the original image).  
Figure 8: Evolution of image decryption with multi-layer merging. The associated histogram showing the distribution of greyscale values of pixels for each decrypted image is also shown. The \(x\)-axis in the histograms represents greyscale values ranging from 0 (black) to 255 (white).  
Figure 7: Restored image after decryption of the image shown in Figure 6 with \(A=1.0\). The black lines in the image represent pixel loss regions.  
The requirement of the set of keys as well as the multiple encrypted image layers to fully recover the original image in fact added an extra layer of security to the current method. The multiple encrypted images could effectively be treated as public keys, while the values used by the user to encrypt the image could be treated as private keys.