esa-sceva/satcom-chunk-collection huggingface dataset:
# Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM
###### Abstract  
Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.  
Keywords:computer-aided diagnosis; mammography; Contourlet; adaptive support vector machine; classifier +
Footnote †: journal:
# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 2 A Ranging Error Mitigation Algorithm for CIR Signal Characteristics Based on Fuzzy Inference
### Implementation of the Fuzzy Inference System  
#### 2.2.1 Selection of Structure and Membership Function of the Fuzzy Inference System  
In this paper, the Mamdani fuzzy system model [26] shown in Figure 1 was adopted. Firstly, the RSSI, FPPL and RT characteristic data of the CIR signals collected by the UWB hardware were converted into fuzzy variables available for fuzzy inference through fuzzification, and then, the necessary variable of ranging errors was obtained via fuzzy inference. Finally, the fuzzy variable in the form of discourse domain was transformed by de-fuzzification into an estimated value of the ranging errors. The deviation probability distribution of the RSSI, FPPL, RT and ranging errors of CIR signals were basically in line with the Gaussian distribution. Here, the Gaussian membership function was selected for inputting the RSSI, FPPL and RT and outputting the ranging errors.
## 4 Prediction Model Establishment
### Evaluation Index Selection  
In order to test the predictive performance of the LSSVM model and the LSSVM model optimized by different algorithms, this paper uses common metrics such as root mean square error (_RMSE_), standard deviation (_STD_), and correlation coefficient (\(R^{2}\)) to evaluate the model's performance. The specific calculation formula is as follows:  
Root mean square error (_RMSE_):  
\[RMSE=\sqrt{\frac{1}{n}\frac{1}{i=1}\left(y_{true}^{i}-y_{pred}^{i}\right)^{2}} \tag{16}\]  
where \(n\) represents the number of samples, \(y_{true}^{i}\) represents the true value, and \(y_{pred}^{i}\) is the predicted value.  
Standard deviation (_STD_):  
\[STD=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\mu\right)^{2}}{n-1}} \tag{17}\]  
where \(\mu\) represents the sample mean value.  
Correlation coefficient (\(R^{2}\)):  
\[R^{2}=1-\frac{\frac{\sum\limits_{i=1}^{n}\left(y_{true}^{i}-y_{pred}^{i}\right) ^{2}}{\sum\limits_{i=1}^{n}\left(y_{true}^{i}-\overline{y}\right)^{2}}}{\sum \limits_{i=1}^{n}\left(y_{true}^{i}-\overline{y}\right)^{2}} \tag{18}\]  
where \(\overline{y}\) represents the average value.  
The root mean square error (_RMSE_) is the square root of the average of the squared differences between the predicted and true values in the sample. The metric is highly sensitive to large errors and can directly reflect the average error between the true value and the predicted value. This makes it more suitable for evaluating the stability of the model. The standard deviation represents the extent of dispersion within the dataset.  
Figure 8: GA-LSSVM dust concentration prediction model process.  
The larger the value, the more distinct the data. The correlation coefficient represents the proportion of the variation between the actual value and the predicted value that can be explained by the model. The value is between 0 and 1. It is generally believed that the correlation is greater than 0.80, indicating that the model fits well.
## 3 Eigenvalue Fusion and other Fusion Methods for Detecting General Wideband Signals
### Decision Fusion for General Wideband Signals  
In Section 1 we have mentioned that centralized cooperative spectrum sensing can be made on a decision fusion basis, in which CR decisions are combined at the FC. Commonly used decision fusion combining rules are AND, OR and majority-voting [2,24], which are collectively classified under the general term \(z\)_-out-of-\(M\)_. If \(z\) is \(1\) and \(M\), the \(z\)_-out-of-\(M\)_ becomes the OR and AND rules, respectively. If \(z\geq M/2\), the \(z\)_-out-of-\(M\)_ becomes the majority-voting rule.  
The final decision at the FC related to a given band is arrived at as follows: let \(u_{i}\) represent the decision made by the \(i\)-th CR upon a given sub-band and \(u\) represent the final decision made at FC for that sub-band, with \(u_{i}\), \(u\in\{0,1\}\), where \"0\" (\"1\") indicates the absence (presence) of a primary signal in the sensed sub-band. For the AND rule, the FC determines \(u=1\) if \(u_{i}=1\) for \(i=1,2,\ldots,m\). For the OR rule, \(u=1\) if \(u_{i}=1\) for any \(i\). For the majority-voting rule, the FC declares \(u=1\) if \(\sum u_{i}>m/2\) and \(u=0\) if \(\sum u_{i}<m/2\); an arbitrary decision is made when \(\sum u_{i}=m/2\)[2,24].  
Individual CR decisions can be made by applying any of the detection techniques discussed so far. The test statistics for the \(i\)-th CR decision upon the \(k\)-th sub-band of a general wideband signal can be determined from minor modifications in Equations (14)-(17), leading to:  
\[T_{\text{GLRT},k,i}=\frac{\lambda_{1,k,i}}{\frac{1}{JK}\sum\limits_{j=1}^{J} \sum\limits_{z=1}^{K}\lambda_{j,z,i}} \tag{18}\]  
\[T_{\text{MMED},k,i}=\frac{\lambda_{1,k,i}}{\frac{1}{K}\sum\limits_{z=1}^{K} \lambda_{J,z,i}} \tag{19}\]  
\[T_{\text{MED},k,i}=\frac{\lambda_{1,k,i}}{\sigma^{2}} \tag{20}\]  
\[T_{\text{ED},k,i}=\frac{1}{J\sigma^{2}}\sum\limits_{j=1}^{J}\lambda_{j,k,i} \tag{21}\]  
where, again, \(\{\lambda_{1,k,i}\geq\lambda_{2,k,i}\geq\cdots\lambda_{J,k,i}\}\) are the \(J\) eigenvalues associated with the \(k\)-th sub-band and \(i\)-th CR.
## 3 Vibration Analysis and Experiment at Operating Speed Range
### Electromagnetic Force Analysis for Operating Speed Range  
The electromagnetic force, one of the sources of motor vibration, is calculated using the radial magnetic flux density of the air gap and the Maxwell stress tensor theory, as expressed in Equations (1)-(4) [28]. Previous studies have shown that the tangential electromagnetic force can be neglected because it is negligible compared to the radial electromagnetic force [31].  
\[b(\alpha,t)=b_{1}(\alpha,t)+b_{2}(\alpha,t), \tag{1}\]  
\[b_{1}(\alpha,t)=\sum_{\
u=1}^{\infty}B_{mu\
u}\cos(\
u p\alpha\mp\omega t), \tag{2}\]  
\[b_{2}(\alpha,t)=\sum_{\mu=1}^{\infty}B_{m\mu}\cos(\mu p\alpha\mp\omega t), \tag{3}\]  
\[p_{r}(\alpha,t)=\frac{1}{2\mu_{0}}[b^{2}(\alpha,t)-b_{t}^{2}(\alpha,t)]\approx \frac{b^{2}(\alpha,t)}{2\mu_{0}}, \tag{4}\]  
where \(b_{1}\) is the magnetic flux density wave form of the stator, \(b_{2}\) is the magnetic flux density wave form of the rotor, \(\alpha\) is the electrical angle, and \(t\) is the time. Furthermore, \(B_{mu\
u}\) and \(B_{m\mu}\) are the magnitude of the magnetic flux density of the stator and rotor for the stator space harmonic \(\
u\) and rotor space harmonic \(\mu\), respectively. \(\omega\) is the angular frequency, \(p_{r}\) is the radial magnetic force per unit area at any point of the air gap, and \(\mu_{0}\) is the magnetic permeability of free space.  
In this section, a 2-D electromagnetic field FE analysis is performed to calculate the electromagnetic force at an operating speed range of 200 to 2000 rpm. As shown in Figure 8,  
Figure 7: Electromagnetic-structural vibration coupled analysis process.  
a 2-D FE model is constructed using 25,360 s-order 6-node triangular elements. A transient analysis is performed with a time step of \(7.8\times 10^{-5}\) s for 0.64 s, because of the need to consider the subsequent structural analysis and vibration experiment. These analysis conditions are able to represent a bandwidth of 5120 Hz and the frequency resolution of 1.56 Hz when performing FFT to analyze the electromagnetic force in the frequency domain.  
Figure 9. shows the distribution of magnetic flux density on air gap according to time at a constant speed of 2000 rpm. As shown in Figure 10, the distribution of magnetic flux density of the 4-pole permanent magnet motors mainly rotate to the shape of second spatial order, which is the fundamental spatial order, with a current frequency of 66.6 Hz. Furthermore, by using a waterfall graph analyzing the electromagnetic force applied on a tooth in the operating speed range as shown in Figure 11, it is possible to confirm the electromagnetic force component consisting of time frequencies that are four times larger than the mechanical rotation frequency occurrence.  
Figure 8. Finite element model to calculate electromagnetic force.  
Figure 9. Distribution of magnetic flux density at 2000 rpm. (**a**) 3-D distribution of magnetic flux density. (**b**) 2-D distribution of magnetic flux density.
## 3 Results
### Cell Culture  
The thorough design of our new fluidic device provides uniform shear stress to help the BMEC differentiation for a better human BBB model. hCMEC/D3 cells were seeded through the upper channel coated with type IV collagen. Cells adhered on the porous membrane and grew for three days until they formed a fully confluent cell monolayer. Then, cells were exposed to the flow for four additional days for an optimal differentiation. In parallel, control conditions were maintained in static culture with medium change. Physiological CO\({}_{2}\) levels and temperature were provided and monitored directly from the incubator controls. The pH of the media was measured with standard laboratory equipment after seven days of culture in static or dynamic conditions. It indicated a relatively constant physiological value around 7.2 and 7.4, showing no medium deterioration. Viability was assessed with a live/dead assay and depicted a satisfactory average rate of 90.3 \(\pm\) 6.6% in static and 94.5 \(\pm\) 3.7% in dynamic condition after seven days of culture. These results show that the device is suitable for cell culture.  
Phase contrast microscopy was used daily to monitor morphology, viability, and growth. An example of our field of vision is presented in Figure 3, after seven days of dynamic culture at 10 dyn\(\cdot\)cm\({}^{-2}\). Cells are clearly visible above the membrane pores.  
Both in static and dynamic conditions, images exhibited confluent cell monolayers. hCMEC/D3 cells exposed to shear stress appear more elongated from the first hours of flow. The regions in the same image show various cellular alignments. We investigated this point by a cell orientation analysis detailed in Supplementary Data S3. It reveals that cells in static condition do not have any preferential alignment, whereas those in dynamic condition orientate particularly in the channel axis (around 0\({}^{\circ}\), corresponding to the direction of flow). This significant change in orientation is an indicator of an effective exposure to the flow, as previously observed with hCMEC/D3 cells [42]. It confirms that cells are able to sense and respond to flow in our device.  
This environment is relevant for the culture of BMEC until at least eight days, by retaining viability and typical morphology.
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 1 Introduction
The saliency network was used to integrate the feature information of foreground and background in the image to achieve data amplification, and then the intraclass and interclass hybrid schemes were used to make the expanded data set play a role in guiding and training the network. Literature [16] proposed a two-stage fine-tuning approach (TFA), which achieved better performance in both the source domain and target domain. They first used the Faster R-CNN model as the detection framework, utilizing base-class image samples for pre-training the detection network and freezing the class-irrelevant part of the network. Then, they used new class samples of few-shot to fine-tune the model in the second stage. In the literature [17], the few-shot object detection via feature reweighting (FSRW) method was proposed for few-shot detection. This approach employed transfer learning and a reweighting strategy. The feature reweighting vector, generated by the support set and query set, guided the model to learn the discriminant ability and quickly generalized it for the detection of new classes. In addition, there are metric learning methods [18; 19] and graph convolutional network-based methods [20; 21]. However, as shown in Figure 1, insulators have their own characteristics. For example, the scale of the insulator target shown in (a) is slightly smaller in the image, and the defective part of the insulator shown in (b) and the insulator blocked by other equipment shown in (c) appear. It can be seen that the current few-shot object detection methods on the targeted insulators are difficult to achieve. Therefore, based on the FSRW model, an object detection method for few-shot insulators is proposed in this paper. The main contributions are as follows:  
* The article uses YOLOv5 as the main detection network of the whole model, the feature extraction network of YOLOv5 is replaced by a corrected self-calibrated convolutional (SCconv) network [22] to develop the ability of the model to detect insulators with different scale targets;The article embeds the selective kernel network (SKNet) attention mechanism [23] before the input of the reweighting module, to generate a detailed mask and enhance the capacity of the network to obtain key detail meta-feature information of the support set;
* The article proposes an improved multi-stage non-maximum suppression (NMS) algorithm to avoid the wrong deletion of candidate boxes with insulator targets and reduce the missing detection of occluding insulators.
## 3 Results
### Analysis of One-Layer Absorber  
Simulated results obtained considering a one-layer of thickness d = 2 mm and normal incidence are shown in Figure 8 (solid line) along with the measured data (dashed line) and the bare Epilox (dotted line). Practically no absorption occurs in the case of the resin alone. Obviously, the bare resin cannot benefit from any dielectric loss associated with either interfacial polarization or free-carrier conduction. On the other hand, the increase of the relative permittivity values upon the introduction of the MWCNTs into the polymer matrix produces a minimum in the reflection coefficient because of the increase in the interfacial polarization [24]. For a 4 wt.%, MWCNT concentration, a peak of \(-\)17 dB at 6 GHz is observed.  
Figure 6: FESEM cryo-fractured surface of the composite with different magnification (**a**) 1.0 kX, (**b**) 10.0 kX \(\upmu\)m, (**c**) 200 kX  
Figure 7: Real part of permittivity (top) and conductivity (bottom) versus frequency for 4 wt% MWCNT-loaded epoxy resin: experimental measurements (dashed line) and simulations (stars); Pure epoxy (Epilox) resin (dash-dotted line).
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 7 Future Directions in AIoT for Aquaculture
The development of digital twins in aquaculture is gaining momentum, with initial implementations focusing on modelling water quality dynamics and fish growth. These virtual models are used to optimise feeding strategies, predict environmental impacts, and improve overall farm management. As the technology matures, digital twins are expected to play an increasingly important role in decision support and automation in aquaculture. Mobile and offline-compatible AloT solutions can extend disease detection capabilities to remote and resource-limited aquaculture facilities. Advanced automation and robotics are also poised to revolutionise aquaculture operations.
# A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
###### Abstract  
Complex products (CPs) modeling design has a long development cycle and high cost, and it is difficult to accurately meet the needs of enterprises and users. At present, the Kansei Engineering (KE) method based on back-propagated (BP) neural networks is applied to solve the modeling design problem that meets users' affective preferences for simple products quickly and effectively. However, the modeling feature data of CPs have a wide range of dimensions, long parameter codes, and the characteristics of time series. As a result, it is difficult for BP neural networks to recognize the affective preferences of CPs from an overall visual perception level as humans do. To address the problems above and assist designers with efficient and high-quality design, a CP modeling design method based on Long Short-Term Memory (LSTM) neural network and KE (CP-KEDL) was proposed. Firstly, the improved MA method was carried out to transform the product modeling features into feature codes with sequence characteristics. Secondly, the mapping model between perceptual images and modeling features was established based on the LSTM neural network to predict the evaluation value of the product's perceptual images. Finally, the optimal feature sets were calculated by a Genetic Algorithm (GA). The experimental results show that the MSE of the LSTM model is only 0.02, whereas the MSE of the traditional Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) neural network models are 0.30 and 0.23, respectively. The results verified that the proposed method can effectively grapple with the CP modeling design problem with the timing factor, improve design satisfaction and shorten the R&D cycle of CP industrial design.  
CPs; modeling design; Kansei Engineering; LSTM neural network; GA; truck crane 2023  
22
## 2 Materials and Methods
### Method
#### 2.3.5 Evaluation Indices  
Pearson's correlation coefficient (R), relative bias (RB), and root mean square error (RMSE) were selected as the evaluation indices to assess the performance of VHIopt in enhancing drought monitoring enhancement across different vegetation types. R measures the consistency between VHIopt and SPEI03 or SMA, with an optimal value of 1 indicating a perfect correlation. RB evaluates the systematic bias between VHIopt and SPEI03 or SMA, with an optimal value of 0. RMSE assesses the overall error and precision of VHIopt, SPEI03, and SMA, with an optimal value of 0. The formulas for calculating RB and RMSE are as follows [73]:  
\[RB=\frac{\sum_{i=1}^{N}{(R_{R}-R_{V})}}{\sum_{i=1}^{N}{\overline{R_{V}}}} \tag{15}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{(R_{R}-R_{V})^{2}}}} \tag{16}\]  
where \(N\) represents the number of data; \(i\) represents the \(i\)-th data; \(R_{V}\) represents VHI; \(R_{R}\) represents SPEI03 or SMA.
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.4 Extrapolation Error Reduction in Single-Satellite Ephemeris  
We simplify the orbit extrapolation error data as \(S_{orbit}(t)\). Based on the previous analysis, since the ephemeris data exhibit certain long-term correlation characteristics, short-term correlation characteristics, and random characteristics, they can be decomposed into the RE component \(U(t)\), PE component \(S(t)\), and the TE component \(R(t)\):  
\[S_{orbit}(t)=U(t)+S(t)+R(t) \tag{14}\]  
* Extraction and Processing of PE \(S(t)\)  
For PE \(S(t)\), due to its periodic characteristics, spectral analysis can be used to extract the PE from the ephemeris error \(S_{orbit}(t)\). First, the fast Fourier transform (FFT) algorithm is used to convert the ephemeris extrapolation error from the time domain to the frequency domain, with the Fourier series expression of \(\Delta eph\):  
\[S_{orbit}(t)=A_{0}+\sum_{i=1}^{k}\left(A_{i}\cos(2\pi\omega_{i}t)+B_{i}\sin(2 \pi\omega_{i}t)\right) \tag{15}\]  
where \(A_{0}\) is the mean value of the extrapolation error over the observation period, \(A_{i}\) and \(B_{i}\) are the amplitudes of different PEs of the extrapolation error, and \(\omega_{i}\) is the frequency of the corresponding PE. By utilizing the orthogonality of different PEs, it can be derived that  
\[A_{0}=\frac{1}{T}\sum_{i=1}^{T}S_{orbit}(t) \tag{16}\]\[A_{i} = \frac{2}{T}\sum_{t=1}^{T}S_{orbit}(t)\cos(2\pi\omega_{i}t) \tag{17}\] \[B_{i} = \frac{2}{T}\sum_{t=1}^{T}S_{orbit}(t)\sin(2\pi\omega_{i}t) \tag{18}\]  
where \(T\) represents the sampling period.  
* Extraction and Processing of TE \(R(t)\)  
TE \(R(t)\) is caused by the characteristics of the SGP4 algorithm, which will gradually increase as the extrapolation time increases. Therefore, a TSF algorithm is exploited to learn the characteristics of the part of the data with small errors and predict subsequent data based on these characteristics.  
We fit the upper and lower envelopes \(ul(t)\) and \(dl(t)\) of \(S_{orbit}(t)\) based on all extrema of \(S_{orbit}(t)\) and calculate their mean \(ml(t)\). Then, we solve the trend characteristic function \(g(t)=S_{orbit}(t)-ml(t)\). Depending on whether \(g(t)\) is an intrinsic function, we iterate on \(X_{i}(t)\) to obtain the intrinsic function \(G_{1}(t)\). Next, we calculate the residual sequence \(r_{1}(t)=S_{orbit}(t)-G_{1}(t)\). We repeat the above steps until \(r_{n+1}(t)\) no longer meets the iterative conditions, forming the extracted trend component of \(X_{i}(t)\):  
\[S_{orbit}(t)=\sum_{i=1}^{n}G_{i}(t)+r_{n+1}(t) \tag{19}\]
# A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks
## 3 Proposed Scheme
#### 3.1.3 Step Three: Outlier Classification Using the Box-Whisker Plot  
In the proposed scheme, we utilized the box-whisker plot to find both the OMU and the ROMU as outliers in the result of Equation (11). A box-whisker plot divides Equation (11) into four parts. First, the results are made in order form and they are divided into an upper and lower half by the median. The median of the lower half is named as the lower quartile, while the median of the upper half is stated as the upper quartile. The lower and upper extremes are marked as the least and greatest values of the results. All the SUs' results of step two were arranged in ascending order from the lowest to the highest. The median value of the results can be calculated as \(Med=median(C)\). The first quartile is denoted as \(Q1_{Lower}\), which implies the value at the 25 th percentile of \(C\). The third quartile is also calculated as \(Q3_{Lower}\), which implies the value at the 75 th percentile of \(C\), and thus, the inter-quartile value is determined by \(IQR=Q3_{Lower}-Q1_{Lower}\). The upper and the lower limits of the box-whisker plot were measured and marked for detection of the outlier values as follows:  
\[Lower\ Limit=Q1_{Lower}-1.5*IQR. \tag{12}\]  
\[Upper\ Limit=Q3_{Lower}+1.5*IQR. \tag{13}\]  
Once the lower and the upper quartile limits were defined by the box-whisker plot by Equation (12) and Equation (13), an SU was declared as one of the outliers, i.e., OMU or ROMU, on the basis of the following criteria:  
\[SU_{j}=\left\{\begin{array}{c}MU\,\ ;ifUpper\ limit\ \leq Data\leq Lower \ Limit\\ Normal\ SU\ ;Otherwise\end{array}\right., \tag{14}\]  
Since the outliers have different responses in Equation (11) than the normal SUs, they are classified as outliers with Equation (14). The overall flow chart of the proposed scheme is shown in Figure 2.  
Figure 2: Flow chart of the proposed scheme.