esa-sceva/satcom-chunk-collection huggingface dataset:
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 4 Experiment
### Experimental Results  
Table 1 shows the results of these methods on Carrier600. It is evident that our proposed framework CarrDet achieves the highest mAP value of 94.32%, which is 10.73% higher than Sparse R-CNN, 8.85% higher than ConvNeXt, 7.9% higher than Deformable  
Figure 3: The distribution of carrier number on Carrier600.  
DETR, 6.96% higher than DINO, 3.42% higher than TOOD, and 3.41% higher than ViT. Moreover, our method also performs similarly well at processing speed. CarrDet achieves the highest IPS value of 4.93, which means processing 4.93 images per second. Notably, it shows enhancements compared to other models, surpassing Sparse R-CNN by 1.25 IPS, ConvNeXt by 3.48 IPS, Deformable DETR by 1.85 IPS, DINO by 2.41 IPS, TOOD by 0.85 IPS, and ViT by 1.46 IPS.  
The superiority of our proposed method CarrDet over general object detection methods can be attributed to these key factors. Firstly, general object detection methods tend to overlook carriers with shallow edges, as depicted in the red bounding boxes in Figure 3(b). In contrast, CarrDet uses the CFBA module to specifically focus on carriers' edge features, effectively distinguishing carriers with shallow edges from the background and reducing the unrecognized rate. Secondly, as shown in the blue bounding boxes in Figure 3(c), general object detection methods may erroneously recognize defective carriers. To address this, CarrDet implements the SDCD module to judge whether the shape of the carrier is a complete circle. This process efficiently filters out defective carriers, which leads to a noteworthy enhancement in detection accuracy. Finally, CarrDet uses components with a relatively simple architecture as the feature extractor and detection head, resulting in a faster image processing speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **mAP(\%)** & **IPS** \\ \hline Sparse R-CNN [31] & ResNet50 & 83.59 & 3.68 \\ ConvNeXt [32] & ResNet50 & 85.47 & 1.45 \\ Deformable & ResNet50 & 86.42 & 3.08 \\ DETR [33] & ResNet50 & 87.36 & 2.52 \\ DINO [34] & ResNet50 & 90.90 & 4.08 \\ TOOD [35] & VisionTransformer & 90.91 & 3.47 \\
**CarrDet** & **ResNet50** & **94.32** & **4.93** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection results on Carrier600. “Method” refers to specific algorithmic implementations, whereas “Backbone” refers to the core network architecture in deep learning models.  
Figure 4: _Cont._
## 3 Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization  
This section proposes a DTG-VPO algorithm based on twin-layer elastic optical satellite network virtualization. First, an enhanced model based on path optimization is constructed in the model introduced in the previous section. A traffic grooming algorithm based on LFA is proposed, and finally, a DTG-VPO algorithm is designed. The remainder of this section describes the proposed algorithm in detail.
## 4 MAMZI with Phase Noise Stabilization
### Operation of the Control Algorithm  
We used an unrestricted direct search algorithm to stabilize the studied system, specifically, the \(P\&O\) algorithm, which is recognized for its simplicity and robustness in optimizing systems with multiple degrees of freedom [24]. In the MAMZI system, the optical intensity of one of the outputs (\(I_{k}^{out}[n]\)) will be optimized by perturbing a control phase in each arm (\(\phi_{k}^{C}[n]\)) as follows:  
1. The algorithm evaluates the error defined as \(e[n]=|I_{k}^{out}[n]-I_{ref}[n]|\), where \(I_{k}^{out}[n]\) is the measured intensity at \(k\)-th output and \(I_{ref}[n]\) is a reference value. If the error exceeds a predefined threshold (usually less than 0.001 to reach high visibility [22]), the optimization process starts in the initial work cycle (\"\(wc=1\)\").
2. The controller perturbs the \(wc\)-th phase control as \(\phi_{\mathrm{inc}}^{C}[n]=\phi_{\mathrm{inc}}^{C}[n-1]+0.01\pi\cdot\delta\cdot sig (\Delta I)\), where the step \(\delta\) is a manually adjustable value. The factor \(0.01\pi\) considers the amplitude of the simulated noise and allows optimization of the system response in the presence of fluctuations. However, the algorithm does not rely solely on this dependency; we can adjust the factor to be larger or smaller, which will only modify the response based on variations in the parameter \(\delta\).
3. The algorithm then proceeds to perturb the next phase (\"\(wc=wc+1\)\") of the other arm of the MAMZI, repeating step (ii).
4. Once all phases have been processed, the system restarts the cycle from step (i), continuing the iterative optimization process.
## 6 Conclusions and Recommendations  
The telecommunication industry has been hit the hardest and at a high stake, with an average annual churn rate of 30%, resulting in a tremendous waste of money and effort. It is essential to understand why customers leave to minimize the damage to the bottom line of the business.  
Since churn is certain to result in lost revenue, and the cost of customer acquisition is equivalent to 5-10 times the cost spent on customer retention or satisfaction, churn forecasting has become a major task for companies to remain competitive. While aiming to help companies reduce customer churn to survive or grow, customer churn forecasting can be used in the long term to obtain continuously updated, real-time customer data. Therefore, the principal aim of this study was to build a prediction system that can help to identify customers who are likely to leave, specifically in the telecommunication industry. Various data mining methods like Decision Trees, Random Forests, and Logistic Regression were utilized by experts to build the predictive model. This study created a comparison system, since it is a more interpretable and intelligible approach to visualizing several machine learning algorithms. The performance of the models was evaluated using the accuracy measures, area under the curve, and sensitivity and specificity measures. The Random Forest classifier is the best classifier in this study, with an accuracy of around 90%, and it also performed well in other evaluation metrics.  
Moreover, this paper contributes to the field by introducing the concept of explainable ensemble learning. By integrating various tools, such as LIME and SHAP, this study provides decision-makers with clear, actionable insights into the factors driving customer churn. This advancement is particularly significant in addressing the opaque nature of machine learning algorithms, making advanced analytics accessible and applicable for strategic decision-making in business settings.  
Conceptualization, V.C. and F.O.A.; methodology, V.C. and F.O.A.; software, V.C., F.O.A. and M.A.G.; validation, V.C. and Q.A.X.; formal analysis, V.C. and K.H.; investigation, V.C.; resources, V.C.; data curation, F.O.A. and M.A.G.; writing--original draft preparation, V.C. and F.O.A.; writing--review and editing, V.C., K.H., Q.A.X. and V.B.; visualization, K.H., Q.A.X., F.O.A. and M.A.G.; supervision, V.C.; project administration, V.C.; funding acquisition, V.C. All authors have read and agreed to the published version of the manuscript.  
This research is partly supported by VC Research (VCR 0000183) for Prof. [PERSON].  
The authors do not own the data.  
Thank you to reviewers and editor for their reviews.  
The authors declare no conflicts of interest.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Modulation Recognition of Radio Signals by CNN
Finally, the training time is about 28 min and a batch takes 24 s, much less than the training time of CNN. The training time is similar to ResNet, Inception and CLDNN, but the recognition effect is better than them. Therefore, in terms of online learning, our proposed method has higher recognition correlation and optimal performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **ResNet** & **Inception** & **CLDNN** & **CNN** &
\begin{tabular}{c} **Model** \\ **(This Paper)** \\ \end{tabular} \\ \hline Highest & & & & & \\ recognition & 87.75\% & 93.60\% & 92.82\% & 92.34\% & 98.47\% \\ accuracy & & & & & \\ \hline Average & & & & & \\ recognition & 60.40\% & 62.43\% & 65.40\% & 64.29\% & 68.25\% \\ accuracy & & & & & \\ \hline Number of & & & & & \\ parameters & 3,425,233 & 10,142,983 & 164,233 & 278,299 & 3,306,075 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Recognition accuracy and number of parameters of five models. Figure 9: Classification accuracy over different SNRs. Figure 10: Confusion matrices for different models when SNR = 18 dB.
## 1 Introduction
Additionally, although the peak ankle plantar flexion moment and power are significantly smaller in high collar, compared to low collar basketball shoes during landing jumps [13], knowledge of the effects of football shoe collar types on ankle dorsiflexion/plantar flexion movement is currently limited. Furthermore, according to previous studies, around 31% to 46% of football injuries, especially for the knee and ankle, are induced by losing balance or inducing a strain after landing [19; 20]. Hence, for football shoes, questions remain regarding how ankle kinematics and kinetics behave in both dorsiflexion-plantarflexion and inversion-eversion dynamic movements when performing jumping and landing maneuvers. It should be noted that postural stability has been used to examine the risk of ankle sprain [21; 22], and a deficiency in postural stability could play a significant role in increasing ankle sprain risk [20]. A study has found that high collar boots have smaller postural sway, compared to low collar boots, and thereby collar height might have a positive effect on postural control [23]. In a recent study, however, a high collar football shoe did not enhance static postural stability, compared to a low collar shoe [18]. Thus, limited research is available regarding the effects of shoe collars on postural stability. Evidence from a psychological study shows that elastic ankle taping or stiff ankle bracing provides beneficial effects by increasing the feeling of confidence and stability during dynamic-balance tasks [24]. However, direct evidence is conflicting on the beneficial impact on dynamic balance [25; 26; 27; 28]. The lack of consistent findings may be due to a lack of measuring more sensitive parameters. The dynamic postural stability index (DPSI) measures three directional components of the ground reaction force during single-leg jump landings. Furthermore, DPSI and its directional components can detect differences in dynamic stability in different football collar types [29]. Therefore, DPSI provides a measure of dynamic stability that has high precision and reliability [30]. Determining the effect of high collar football shoes on ankle biomechanics and DPSI during single-leg jump landings might provide further insight into the biomechanics and dynamic stability of playing football. The purpose of the study aims to determine differences in shoe collar types (i.e., low collar, elastic collar, and high collar) on ankle biomechanics and DPSI during anterior and lateral single-leg jump landings. Our first hypothesis was that smaller ankle ROM, moment, and joint stiffness would result from the high collar football shoe, compared to the elastic or low collar shoes, in both tasks. Our second hypothesis was that dynamic stability would improve when wearing a high collar football shoe, compared to an elastic or low collar shoe, in both tasks.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 2 Preliminaries
### Polar Codes Encoder  
Polar code is a linear block code subspace of the binary vector space \(\mathbb{F}_{2}^{N}\), which is an \((N,K)\) block code of length \(N\) over the binary Galois field \(\mathbb{F}_{2}\), and forms a \(K\)-dimensional vector subspace of \(\mathbb{F}_{2}^{N}\). Polar codes encoder adds frozen bits and transforms the initial information massage vector \(M\) of length \(K\) into a codeword vector \(C\) of length \(N\), where the fraction \(R=K/N\) is called the code rate.  
On the transmitter side, the codeword is created by inserting the information bits in the positions corresponding to noiseless channels, while frozen bits are allocated at the positions corresponding to pure-noisy channels. These bits are transformed using generator matrix \(G\) and transmitted over the communication channel. The location selection for frozen bits in the codeword depends on polar code construction. Therefore, the encoder of the polar codes can be mathematically represented as follows:  
\[\mathbf{C}=\mathbf{M}^{\prime}\mathbf{G} \tag{7}\]  
where \(\mathbf{M}^{\prime}\) is the message vector with length \(N\) bits consisting of \(K\) information bits and \(N-K\) frozen bits, and the generator matrix \(\mathbf{G}\) is defined as follows:  
\[\mathbf{G}=\mathbf{G}_{m}^{\otimes*} \tag{8}\]  
which has been constructed using Kronecker power denoted by \(\otimes^{n}\) for \(m\times m\) binary kernel matrix \(G_{m}\), where \(n=log_{m}N,\ m\) is the base number. For \(2\times 2\) polarizing binary kernel matrix \(\mathbf{G}_{2}=\left[\begin{array}{rrrrrr}1&0&0\\ 1&1\end{array}\right]\) proposed by [PERSON], the generator matrix \(\mathbf{G}\) is as follows:  
\[\mathbf{G}=\mathbf{G}_{2}^{\otimes^{n}}=\left[\begin{array}{rrrrrrrr}1&0&0&0\\ 1&1&0&0\\ 1&0&1&0\\ 1&1&1&1\end{array}&\begin{array}{rrrrrrrr}&0&0&0&0\\ \cdots&\cdots&0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ \vdots&&&\ddots&\ddots&&\vdots\\ 1&0&0\\ 1&1&0&0&0\\ 1&1&0&0\\ 1&0&1&0\\ 1&1&1&1\end{array}&\begin{array}{rrrrrrrr}&0&0&0\\ \cdots&\cdots&0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ \vdots&\ddots&\ddots&&\vdots\\ 1&0&0&0\\ 1&1&0&0\\ 1&0&1&0\\ 1&1&1&1\end{array}\right] \tag{9}\]
## 2 Materials and Methods
### Lesion Classification  
This stage of our pipeline involved three main steps: feature extraction, feature selection, and model fitting. Feature extraction was performed using SimpleITK [53] and PyRadiomics [54], while machine learning models were implemented through the scikit-learn package [55]. To optimize the performance of our ML model, we conducted a grid search to explore hyperparameter combinations and employed LASSO regression and Principal Component Analysis (PCA) for feature selection and reduction. Additionally, a random search of the internal parameters of each model paired with cross-validation was used for model fitting. The grid search evaluated variations in the number of selected features, the proportion of excluded lesion slices (avoiding slices closer to the lesion center), and the number of cross-validation folds. LASSO regression was also applied with cross-validation. The final classification model was a voting classifier comprising three base learners: random forest, logistic regression, and XGBoost, with weighted voting.  
Given that we used cross-validation to select our best-performing classifier, we merged the training and validation datasets into a single training set. This approach allowed usto maximize the available data for model training. During cross-validation, the model's performance was evaluated using the recall score, ensuring a robust assessment of its ability to identify positive cases correctly. At the testing phase, we computed a traditional binary--positive class is adenoma, negative class is non-adenoma--confusion matrix and its related metrics:  
* Accuracy: The proportion of correctly classified instances out of the total number of instances.
* Precision: The ratio of true positives to the total predicted positives: \[\text{Precision (\%)}=\frac{\text{True Positives (TP)}}{\text{True Positives (TP)}+\text{False Positives (FP)}}\times 100\]
* Recall (Sensitivity): The ratio of true positives to the actual positives: \[\text{Recall (\%)}=\frac{\text{True Positives (TP)}}{\text{True Positives (TP)}+\text{False Negatives (FN)}}\times 100\]
* Specificity (%): The ratio of true negatives to the actual negatives: \[\text{Specificity (\%)}=\frac{\text{True Negatives (TN)}}{\text{True Negatives (TN)}+\text{False Positives (FP)}}\times 100\]
* F1-Score: The harmonic mean of precision and recall: \[\text{F1-Score}(\%)=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}\times 100\]
## 3 Derivation of the 10\(N-9\) Phase-Shifting Algorithm
### Design Process of the \(10n-9\) Algorithm  
For achieving insensitivity to the \(k\)th harmonic component in the intensity signal, a single root should be located in a characteristic diagram except for the fundamental signal \(m=1\), based on [PERSON]'s theory. However, the synchronous detection algorithm [43] cannot compensate for the phase shift errors and coupling errors. To be insensitive to the phase shift miscalibration, the complex number \(x=\exp(-i\alpha)\) should be double roots of characteristic polynomial, which is the \(N+1\) algorithm derived by [PERSON] [44]. By placing double roots at the complex number \(\exp(im\alpha)\) (\(m=2,3,4\)) on the characteristic diagram, the generated \(2N-1\) algorithm is insensitive to both phase shift miscalibration and the coupling errors.  
To obtain high-precision measurement results, it is very important to suppress the harmonics while compensating for the nonlinearity in phase shift error [24]. By placing 10 multiple roots on the characteristic diagram, a novel \(10N-9\) algorithm was generated, as shown in Figure 3. The new \(10N-9\) algorithm can compensate for up to eighth-order nonlinearity of phase shift errors \(o(\varepsilon_{8})\) and coupling errors \(o(A_{m}\varepsilon_{8})\).  
The characteristic polynomial of the \(10N-9\) algorithm can be described as  
\[\begin{array}{ll}P(x)&=\left[P_{sync}(x)\right]^{10}\\ &=\left(1+x+x^{2}+x^{3}+\cdots+x^{N-1}\right)^{10}\\ &=\sum\limits_{k=1}^{10N-9}{w_{k}x^{k}}.\end{array} \tag{10}\]  
where \(P_{sync}\) is the characteristic polynomial of synchronous detection.  
The sampling amplitudes of the \(10N-9\) algorithm are defined as  
\[a_{k}=\frac{2}{N}w_{k}\cos\frac{2\pi}{N}[\ k-(5N-4)], \tag{11}\]  
Figure 3: Characteristic diagram of the \(10N-9\) algorithm.  
\[b_{k}=\frac{2}{N}w_{k}\sin\frac{2\pi}{N}[\ k-(5N-4)]. \tag{12}\]  
where \(w_{k}\) represents the polynomial window functions, which are described in Appendix A. Figure 4 represents the shape of the window function.
## 5 Case Analysis
### Commonality Indexes
#### 5.1.2 The Calculation of Weights of Commonality Indexes  
According to Equation (6), AHP is used to calculate the weights of benefits, resource development and development potential of the commonality indexes, so are the weights of their respective subindexes. The calculation results are shown in Table 10.  
As can be seen from Table 10, the weight of benefits in the commonality indexes is the largest, followed by the weight of development potential, and the weight of resource development is the least. For the subindexes, under the benefit index, the economic benefit accounts for the largest proportion, and under the development potential index, the sources of funds account for the largest proportion.
###### Abstract  
An effective integrated design optimization method is developed to reduce the maximum von Mises stress around vent holes of a high-pressure turbine sealing disk. It mainly includes four different shape designs (circular, elliptical, race-track, and four-arc) for holes, an updated self-developed modelling and meshing tool, an APDL-based strength analysis, and a self-proposed efficient switching delayed particle swarm optimization (SDPSO) algorithm. The main idea of SDPSO is: (1) by evaluating an evolutionary factor and utilizing a probability transition matrix, a non-homogeneous Markov chain is determined and auto-updated in each generation; (2) the evolutionary factor and the Markov chain are used to adaptively select the inertia weight, acceleration coefficients, and delayed information to adjust the particle's velocity. The performance of SDPSO is evaluated through two benchmark optimization problems with constraints. The results show that SDPSO is superior to two well-known PSO algorithms in optimization capability, numerical robustness, and convergence speed. Furthermore, SDPSO is used for the stress optimization of vent holes with four different shapes. The results show that: (1) SDPSO is suitable and valuable for practical engineering optimization problems with constraints; (2) the developed integrated design optimization method is effective and advanced for reducing the maximum von Mises stress around the vent holes; and (3) the four-arc hole has more tremendous advantages in reducing the maximum von Mises stress, followed by the elliptical hole, the race-track hole, and the circular hole.  
particle swarm optimization (PSO); switching delayed particle swarm optimization (SDPSO); stress concentration; stress optimization; vent hole; four-arc hole; elliptical hole; race-track hole; circular hole; turbine sealing disk
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 1 Introduction  
China has gradually transformed from traditional agriculture to modern agriculture and is gradually moving towards intelligent agriculture with the development of computer technology. A greenhouse is a typical scene of the application of modern agricultural technology in that its internal environment is operable. A greenhouse can form a small, closed ecosystem suitable for plant growth and improve the yield and quality of agricultural products, and it is thus used widely in agricultural production. Among the environmental factors of a greenhouse, crops are most sensitive to temperature. Temperature affects the enzyme activity of plant cells and thus the growth rate, yield, and quality of crops, i.e., temperature strongly affects the growth and development of crops. In ensuring the yield and quality of agricultural products, we should ensure the normal growth of crops by accurately controlling the greenhouse temperature.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### New Model
#### 2.4.2 Mkelm
However, the kernel function in KELM is a single, static function, which limits its ability to handle complex data structures and its adaptability to different data types and features [33; 34; 35; 36]. Therefore, this paper introduces multiple kernel functions, which are combined linearly to form a final kernel function. The weight of each kernel function is optimized through an algorithm. This enables the model to adapt to data characteristics from multiple dimensions and perspectives, improving the model's learning and generalization abilities. As a result, the model demonstrates more stable performance across various scenarios. This study selects the following kernel functions to construct the multiple kernel function: RBF (Radial Basis Function) kernel, linear kernel, and polynomial kernel. The linear kernel can capture the linearly separable parts of the data well without increasing the model's complexity. The RBF kernel maps the data into an infinite-dimensional space, effectively handling linearly non-separable data structures in the original space. The polynomial kernel is capable of handling high-dimensional polynomial structures in the data. These kernels are combined, and their weights are optimized. The parameters that need to be optimized in the MKELM include the width of the Gaussian kernel (\(\sigma\)), the degree of the polynomial kernel (d), the regularization parameter C, and the weights of the three kernel functions, namely c1, c2, and c3, where c1 + c2 + c3 = 1.