esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Long-Term Field Evaluation  
Afterward, to verify the reliability of the proposed multi-sensory information fusion system for the living trees, commercial equipment is adopted to calibrate the true values of MC level, the KT-80, a dual-function wood moisture measuring instrument from KLORTNER Technology, Italy [42]. We have carried out a measurement campaign on four typical tree species in Nanjing Forestry University (metasequoa, lodgepole pine, hovenia acerba, and a cherry-apple tree) in a monthly cycle from October 2021 to November 2022. The collected data includes the AE parameters, the root MC value, air humidity, and temperature. Because of the numerical fluctuation caused by seasonal variation, the FA-ISSA-SVM and the other six models (GA-SVM, GS-SVM, SVM, RF, KNN, Bayes) were used to analyze the feature parameters and compare identification performance. The final recognition accuracies were 96.4%, 96.7%, 96.3%, and 95.5%, respectively. Figure 13 shows the diagnostic process of the metasequoa with an accuracy of 96.4% as a demonstration, where the solid line represents the average MC of seven algorithms for metasequoa at the very month, the shaded part is the error band area, and the dotted line represents the average of the MC for the quarter. So, we can see and compare both MC variations during the months and quarters.  
According to the practical testing data and predicted value, the average MC reached a maximum of 42.3% in summer, while the average MC in spring and autumn decreased to 39.7% and 38.2%, respectively. Of course, the lowest value was 36.2% in winter. Due to the influence of long-term light conditions, high temperature, CO\({}_{2}\), and other environmental factors in summertime, the effective photosynthesis and transpiration rate of living trees have increased to a certain extent; thus, the MC level of living tree trunks also increases.  
Compared with other recognition models, the final results of the FA-ISSA-SVM in one year are closer to the calibrating data. In addition, the FA-ISSA-SVM has an excellent performance in terms of error band interval, and its error band area is far smaller compared to other algorithms, which also means that the data obtained by the FA-ISSA-SVM fluctuates within a credible range.  
Next, to further verify the prediction performance of these models, multiple indicators of the RMSE, mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness-of-fit (\(R^{2}\)) were employed for the evaluation. Compared with Bayes, RF, KNN, SVM, GS-SVM, GA-SVM, and the FA-ISSA-SVM model show optimal diagnostic accuracy with high stability; specific data are shown in Figure 14.
## 3 Materials and Methods
### BorderlineSMOTE  
BorderlineSMOTE is an advanced oversampling technique developed to improve upon the traditional SMOTE method, which generates synthetic samples for minority classes using their nearest neighbors [42]. A key limitation of SMOTE is its tendency to cause overlap between minority and majority class samples, leading to potential misclassification. To address this, [PERSON] et al. introduced BorderlineSMOTE, which focuses specifically on minority class samples near the decision boundary--those most susceptible to misclassification [45].  
The BorderlineSMOTE algorithm identifies the K-Nearest Neighbors of each minority class sample. If a sample is surrounded predominantly by majority class neighbors, it is placed in a DANGER set, as it is more likely to be misclassified. Synthetic samples are then generated for these DANGER set instances by interpolating between the original sample  
Figure 4: Minority class member generation diagram based on SMOTE algorithm [44].  
(\(P_{d}\)) and its nearest neighbors (\(P_{k}\)). The new synthetic sample (\(P_{new}\)) is calculated using the following formula [46;47]:  
\[P_{new}=P_{d}+rand(0,1)x(P_{k}-P_{d}) \tag{3}\]  
Here, \(rand(0,\,1)\) is a random number between 0 and 1, ensuring that the synthetic sample is located along the line segment between the original sample and its nearest neighbor.  
By focusing on generating synthetic samples near the decision boundary, BorderlineSMOTE reduces the risk of class overlap and improves the classifier's ability to distinguish between classes, particularly in datasets with significant class imbalance.
## 7 Case Studies
### Analysis of the Effectiveness of the Proposed Distributed Cooperative Dispatch Method  
In order to verify the effectiveness of the proposed distributed cooperative dispatch method, the centralized dispatch method is used as a comparison [34]. Table 1 shows the comparison of the system costs of the two methods. Table 2 shows the comparison of the calculation times of the two methods.  
The comparison in Table 1 shows that the operation costs of DN, DHN, and BESS of the two methods have a very small difference, and the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, which proves that the proposed distributed cooperative dispatch method does not fall into the local optimum during the solution process and has the same solution accuracy as the centralized dispatch method. The comparison in Table 2 shows that although the solution time of the proposed distributed cooperative dispatch method is much more than that of the centralized dispatch method, it is still within the acceptable range.  
In summary, it can be concluded that the proposed distributed cooperative dispatch method can achieve the same solution accuracy as the centralized dispatch method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cost (10\({}^{3}\) CNY)** & **Proposed Method** & **Centralized Dispatch Method** \\ \hline \(\mathrm{C}^{\mathrm{DN}}\) & 9.45 & 9.38 \\ \(\mathrm{C}^{\mathrm{DHN}}\) & 13.76 & 13.72 \\ \(\mathrm{C}^{\mathrm{BESS}}\) & \(-0.105\) & \(-0.117\) \\ Total & 23.10 & 22.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of system cost of the proposed method and centralized dispatch method.  
Figure 3: Test topology containing a 33-node DN and a 44-node DHN.
## 3 Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithm  
In this section, the IFPFCM clustering algorithm has been briefly reviewed. It considers rewarding more crisp membership degrees. By adding the second term, when the degree of membership becomes fuzzier, the increase in the second term can minimize Equation (3) and use this to reward the degree of membership, making it more clearly divided. From Equation (3), a second term was evaluated as \(-e_{k}/4\); if the membership degrees become fuzzier, the second term increases the \"reward\" term for membership degrees near 0 and 1 in order to force a more crispy assignment. Since we sought to minimize Equation (4), this modification rewards crisp membership degrees. The IFPFCM clustering algorithm is used in Lagrangian theory to minimize the objective function \(J_{ifpfem}\), as shown in Equation (3), and the constraints are in Equation (4). The IFPFCM clustering algorithm has two updated equations for \(u_{ik}\) and \(v_{i}\), which are outlined in Equations (5) and (6), respectively [29]:  
\[J_{ifpfem}(\mathrm{V},\ U,\ X)=\sum\limits_{i=1}^{c}\sum\limits_{k=1}^{n}u_{ik} ^{2}d_{ik}^{2}-\sum\limits_{k=1}^{n}e_{k}\sum\limits_{i=1}^{c}\left(u_{ik}- \frac{1}{2}\right)^{2}. \tag{3}\]  
\[\mathrm{subject\ to\ }\sum\limits_{i=1}^{c}u_{ik}=1. \tag{4}\]  
\[u_{ik}=\frac{1}{\sum\limits_{s=1}^{c}\left(\frac{d_{ik}^{2}-e_{k}}{d_{ik}^{2}- e_{k}}\right)}, \tag{5}\]  
\[v_{i}=\frac{\sum\limits_{k=1}^{n}u_{ik}^{2}x_{k}}{\sum\limits_{k=1}^{n}u_{ik}^ {2}}. \tag{6}\]  
where \(u_{ik}\) denotes the fuzzy membership degree, \(U=[u_{ik}]\) is a \(c\times n\) matrix, \(x_{k}\) is \(k\)th data of the \(X\) dataset, \(v_{i}\) is the \(i\)th prototype of the \(V\) prototype matrix, and \(d_{ik}\) is the distance measure between \(x_{k}\) and \(v_{i}\). As \(d_{ik}^{2}-e_{k}\) always remains positive, the maximal reward following is denoted in the following equation:  
\[e_{k}=\min\left\{d_{ik}^{2}-\eta\middle|i\in\{1,\ 2,\ \ldots,\ c\}\right\},\ \eta\in(0,\ \infty). \tag{7}\]
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Collaborative Localization Correction  
Particles are corrected using the encountered user's position and the comparison model's output. The two users are close to each other when an encounter is detected. Therefore, we can correct the localization of both encountered users by replacing the position of some particles \((x,y)\) with the position of the collaborator \((x_{c},y_{c})\). Here, we assume that both devices use the same map for localization. Equation (4) is used to determine the number of particles to be corrected according to the comparison output \(o\), which represents the collaborator's localization quality against their own localization. \(N_{\text{total}}\) is the total number of particles in their own device. For example, when \(o=0.2\), 20% of the position of particles \((x,y)\) are replaced with the collaborator's position \((x_{c},y_{c})\). \(\sigma_{\text{pos}}=1.5\) m is used to consider the deviation of the distance between users during the encounter (and positioning errors). Accordingly, the position of the particle is corrected by \((x,y)=(x_{c}+N(0,\sigma_{\text{pos}}^{2}),y_{c}+N(0,\sigma_{\text{pos}}^{2}))\).  
\[N_{\text{replace}}=N_{\text{total}}*o \tag{4}\]  
The offset \(\theta_{o}\) of particles is corrected, since the heading direction can deviate when a localization has significant errors. This offset is determined according to the initial heading direction on the map. Hence, we correct the offset using the offsets of previous particles. Specifically, the offset is updated by Equation (5). We update 50 % of particles at time \(i\). The function med represents the median function. \(\tilde{\theta}_{o,i}\) is the average offset in particles at time \(i\). The average offset \(\tilde{\theta}_{o,i}\) is used to suppress a drastic update. \(\sigma_{noise}=2.0\) denotes the standard deviation.  
\[\theta_{o,i}=\text{med}(\tilde{\theta}_{o,1},\cdots,\tilde{\theta}_{o,i-1})+N( 0,\sigma_{\text{noise}}) \tag{5}\]
# High-Precision Chip Detection Using YOLO-Based Methods
## 2 Materials and Methods
### Model Architecture
#### 2.1.2 Ghost Module  
The ghost module is a model compression technique [27]. Compared to traditional convolutional modules, the ghost module reduces the overall computational costs by controlling the number of filters in the first part of the standard convolution and generating additional feature maps using low-cost linear operations. This approach reduces the number of parameters and the computational complexity without altering the size of the output feature maps. The ghost module consists of three steps: primary convolution, ghost generation, and feature map concatenation. The structure of the ghost module is illustrated in Figure 2.  
Figure 2: CNN and ghost module: (**a**) operation of CNN, (**b**) operation of ghost module. Figure 2a illustrates the computational approach of a standard convolutional network. The relationship of input \(X\in\mathbb{R}^{c\times h\times w}\) and output \(Y\in\mathbb{R}^{h^{\prime}\times w^{\prime}\times n}\) can be expressed as  
\[Y=X*f+b \tag{1}\]  
where * is the convolution operation, \(b\) is the bias term, \(c\) is the number of input channels, and \(h\) and \(w\) are the height and width of the input data. \(h^{\prime}\times w^{\prime}\) is the spatial size of the output feature map. \(n\) is the number of filters. \(f\in\mathbb{R}^{c\times h\times c\times n}\) is the convolution filter in this layer. \(k\times k\) is the kernel size of the convolution filters. Compared to the standard convolutional network, the ghost module reduces the number of required parameters and the computational complexity. Its implementation involves a primary convolution process and a cheap operation process, as illustrated in Figure 2b. In the primary convolution, \(m\) (where \(m\leq n\)) intrinsic feature maps \(Y^{\prime}\in\mathbb{R}^{h^{\prime}\times w^{\prime}\times m}\) are generated, as formulated in Equation (2):  
\[Y^{\prime}=X*f^{\prime} \tag{2}\]  
where \(f^{\prime}\in\mathbb{R}^{c\times k\times k\times m}\) is the utilized filter. As formulated in Equation (3), next, a series of cheap linear operations is applied on each intrinsic feature in \(Y^{\prime}\) to generate s ghost features. \[y_{i,j}=\Phi_{i,j}\big{(}y^{\prime}_{i}\big{)},\ \forall\ i=1,\ldots m,j=1,\ldots s \tag{3}\]  
where \(y^{\prime}_{i}\) is the intrinsic feature map in \(Y^{\prime}\), and \(\Phi_{i,j}\) is the j-th linear operation. As specified in [27], \(d\times d\) linear convolution kernels of \(\Phi_{i,j}\) are required to maintain consistent dimensions for distinct indices i and j. \(s\) can be obtained by \(n=m\cdot s\). By consolidating the \(y_{i,j}\) terms in Equation (3), we derive the aggregated output tensor \(Y^{\text{g}}=[y_{1,1},y_{1,2}\ldots y_{m,s}]\). This demonstrates that the standard convolution output \(Y\) and the ghost module output \(Y^{\text{g}}\) exhibit identical dimensionality. Consequently, the ghost module achieves plug-and-play compatibility, serving as a drop-in replacement for conventional convolutional layers.
###### Abstract  
Augmented reality applications involving human interaction with virtual objects often rely on segmentation-based hand detection techniques. Semantic segmentation can then be enhanced with instance-specific information to model complex interactions between objects, but extracting such information typically increases the computational load significantly. This study proposes a training strategy that enables conventional semantic segmentation networks to preserve some instance information during inference. This is accomplished by introducing pixel weight maps into the loss calculation, increasing the importance of boundary pixels between instances. We compare two common fully convolutional network (FCN) architectures, U-Net and ResNet, and fine-tune the fittest to improve segmentation results. Although the resulting model does not reach state-of-the-art segmentation performance on the EgoHands dataset, it preserves some instance information with no computational overhead. As expected, degraded segmentations are a necessary trade-off to preserve boundaries when instances are close together. This strategy allows approximating instance segmentation in real-time using non-specialized hardware, obtaining a unique blob for an instance with an intersection over union greater than 50% in 79% of the instances in our test set. A simple FCN, typically used for semantic segmentation, has shown promising instance segmentation results by introducing per-pixel weight maps during training for light-weight applications.  
computer vision; convolutional neural networks; deep learning; hand segmentation; semantic segmentation +
Footnote †: journal: Applied sciences
## 3 Fuzzy MPC Algorithm with Advanced Generation of Prediction
### Formulation of the Optimization Problem  
Now the dynamic matrix (22) will be used to predict the influence of control corrections on the control plant outputs. Therefore, after using the free response (30) and the dynamic matrix (22) one obtains the following prediction:  
\[\mathbf{y}=\widetilde{\mathbf{y}}+\mathbf{A}_{k}\cdot\Delta\mathbf{\tilde{u}}. \tag{31}\]  
Due to the change of decision variables, optimization task ((1)-(4)), which is solved by the control algorithm in each iteration can be written as:  
\[\arg\min_{\Delta\mathbf{\tilde{u}}}\left\{J_{MPC+}=(\overline{\mathbf{y}}-\widetilde {\mathbf{y}}-\mathbf{A}_{k}\cdot\Delta\mathbf{\tilde{u}})^{T}\cdot\mathbf{\kappa}\cdot( \overline{\mathbf{y}}-\widetilde{\mathbf{y}}-\mathbf{A}_{k}\cdot\Delta\mathbf{\tilde{u}})+ \Delta\mathbf{u}^{T}\cdot\mathbf{\Lambda}\cdot\Delta\mathbf{u}\right\} \tag{32}\]  
subject to:  
\[\Delta\mathbf{u}_{\min}\leq\Delta\mathbf{u}\leq\Delta\mathbf{u}_{\max}\, \tag{33}\]  
\[\mathbf{u}_{\min}\leq\mathbf{u}\leq\mathbf{u}_{\max}\, \tag{34}\]  
\[\mathbf{y}_{\min}\leq\widetilde{\mathbf{y}}+\mathbf{A}_{k}\cdot\Delta\mathbf{\tilde{u}}\leq \mathbf{y}_{\max}\, \tag{35}\]  
where  
\[\Delta\mathbf{\tilde{u}}=\left[\Delta\mathbf{\tilde{u}}^{1},\ldots,\Delta\mathbf{\tilde{u }}^{n_{u}}\right]^{T},\,\Delta\mathbf{\tilde{u}}^{m}=\left[\Delta\mathbf{\tilde{u}}^{ m}_{k+1|k},\ldots,\Delta\mathbf{\tilde{u}}^{m}_{k+s-1|k}\right]. \tag{36}\]  
Note that the performance function in (32) depends quadratically on decision variables \(\Delta\mathbf{\tilde{u}}\) and all constraints depend linearly on decision variables. Thus, a standard, easy-to-solve linear-quadratic optimization problem was obtained.  
**Remark 1**: _In the proposed algorithm, one can also use a slightly modified form of the performance index, in which the second component depends only on the corrections \(\Delta\mathbf{\tilde{u}}\) of the control signals determined in the previous iteration of the algorithm, that is,_  
\[J_{MPC+v2}=(\overline{\mathbf{y}}-\widetilde{\mathbf{y}}-\mathbf{A}_{k}\cdot\Delta\mathbf{ \tilde{u}})^{T}\cdot\mathbf{\kappa}\cdot(\overline{\mathbf{y}}-\widetilde{\mathbf{y}}-\bm {A}_{k}\cdot\Delta\mathbf{\tilde{u}})+\Delta\mathbf{\tilde{u}}^{T}\cdot\mathbf{\Lambda} \cdot\Delta\mathbf{\tilde{u}}. \tag{37}\]  
It should be emphasized, however, that such modification causes a change in the meaning of the \(\lambda\) parameter in relation to the classical formulation. Consequently, algorithms with a modified form of the performance function generate faster responses, but influence of the \(\lambda\) parameter on system robustness becomes significantly limited.
## 4 Simulation Example  
To check the accuracy of the theoretical findings, we employ a simulation example in this section. Consider a multi-agent system with eight followers and four leaders to demonstrate the use of the control protocol. The dynamics of each agent are described by Equations (3) and (4), where \(x_{f}(t),x_{i}(t)\in\mathbb{R}^{2}\), \(f=1,\ldots,8\), \(l=1,2,3,4\). The initial states of each follower and leader are set to \(x_{fj}(0)\in(-2;2)\), \(x_{l1}(0)=(3;-2)\), \(x_{l2}(0)=(-4;4)\)\(x_{i3}(0)=(1;-1)\), \(x_{i4}(0)=(2;-2)\). It is possible to choose the parameter \(\theta\), system matrices \(A\) and \(B\), and matrix \(Q\) with positive definiteness, as follows:  
\(\theta=0.5\).  
\(A=\begin{bmatrix}-1&0\\ 1&0\end{bmatrix},B=\begin{bmatrix}0.1\\ 1\end{bmatrix},Q=\begin{bmatrix}0.3&0\\ 0&0.3\end{bmatrix}\).  
According to inequality (19), the conclusion can be obtained as  
\(P=\begin{bmatrix}4.4448&4.1448\\ 4.1448&4.1448\end{bmatrix}\).  
Figure 2 depicts the topology of the communication. While the other nodes represent the followers, nodes 1, 2, and 3 represent the leaders.  
Clearly, Assumption 1 is satisfied. Corresponding to Equation (3),  
\(L1=\begin{bmatrix}2&0&0&-1&0&0&-1&0\\ -1&3&0&0&0&0&-1&-1\\ 0&0&1&0&0&0&-1&0\\ 0&-1&0&2&0&0&0\\ 0&0&-1&0&2&0&0&0\\ 0&0&0&0&-1&2&-1&0\\ 0&0&0&0&0&-1&2&0\\ 0&-1&0&-1&0&0&-1&4\end{bmatrix}\)  
Figure 2: Communication topology.  
\[L2=\left[\begin{array}{cccc}0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ -1&0&0&0\\ 0&0&-1&0\\ 0&0&0&0\\ 0&-1&0&0\\ 0&0&0&-1\\ \end{array}\right]\]  
According to Definition 2, TBG \(\xi(t)\) can be chosen as  
\[\xi(t)=\left\{\begin{array}{ll}\frac{10}{4^{6}}t^{6}-\frac{24}{4^{5}}t^{5}+ \frac{24}{4^{5}}t^{4},&0\leq t\leq\ t_{f}\\ 1,&t>t_{f}\\ \end{array}\right.\]  
\(t_{f}=4\) s in this case.  
Figure 3 shows the change in the state trajectory of the multi-agent system. It can be seen that all followers gradually converge to the convex hull formed by the leader state, indicating that the control method is effective. The control input is shown in Figure 4. When \(t=4\) s, the control input tends to be stable, indicating that the controller remains stable in the subsequent period, which further proves that the containment control problem of the system is solvable (3). Figure 5 shows the instants and intervals of event triggering. The line indicates whether the event is triggered (if triggered, there are line circles), and the vertical distance of a single circle is the interval time between the triggering and the previous moment.It can be seen that the followers have no Zeno phenomenon (i.e., continuous points) and the trigger interval is long, indicating that the control scheme effectively reduces the communication.  
Figure 3: State trajectory under the control protocol.  
Figure 4: Evolution of the control input under the control protocol.  
Figure 5: Moments and intervals triggered by events.
Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties  
[PERSON]  
1 Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; [EMAIL_ADDRESS] (R.P.); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; [EMAIL_ADDRESS] (R.P.); [EMAIL_ADDRESS] (S.S.)  
2 [PERSON]  
for non-destructive evaluation of peach fruit. In another study conducted by [PERSON] et al. [22], a fast method for quality evaluation was proposed for passion fruit using NIR and mid-infrared spectroscopy methods.  
Color is one of the most critical factors in the quality of fruits [23; 24; 25]. [PERSON] et al. [26] demonstrated that Vis-NIR could be used to detect chlorophyll and lycopene levels in tomato accurately. In another study conducted by [PERSON] et al. [27], lycopene and beta-carotene were estimated by chromatography and Vis-NIR spectra on tomatoes. [PERSON] et al. [28] described a portable application that uses color segmentation and a probabilistic approach based on histograms in the optimum color space to optimize the water needs calculation.  
As observed, researchers have focused on the non-destructive estimation of the physicochemical properties of different fruits due to the high speed of monitoring operation during growth to consumption. Previous research uses statistical methods to estimate properties, which are linear methods and often mistaken for complex data or use a simple artificial neural network (ANN) without optimal adjustment. In addition, in most research, the prediction of properties is performed only once, making it impossible to check the methods' reliability.  
This paper aims to determine the most effective wavelengths for non-destructive prediction of total soluble solids and starch of Fuji apples at different stages of their growth using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). This may help create portable tools that can be used in the field to optimize orchard management during harvest time and postharvest operations. Optimal adjustment of the parameters of the artificial neural network (including the number of layers, number of neurons, transfer function, backpropagation network training function, and backpropagation weight/bias learning function using an algorithm based on biogeography) ensures the method's maximum performance for predicting starch properties and soluble solid content. The selection of the most effective wavelengths among whole wavelengths using a simulated annealing algorithm selects the effective spectra based on the thinking behind the metal annealing operation. Repeating the non-destructive estimation operation 100 times measures the reliability of the proposed algorithm.