esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 3 Methodologies
### GA-SPO Hybrid Algorithm  
#### 3.3.1 Probability Selection Model and Random Disturbance  
In the basic PSO, the speed update is composed of three parts: the inertia maintenance part, the particle best direction update part, and the global best direction update part. In Formula (15), \(w,c_{1}r_{1},c_{2}r_{2}\) represent the influence of the three parts of the update on the final speed update. For continuous problems, the final particle speed can be obtained by vector calculation and parameter comprehensive reference. However, the TTSA has discrete characteristics. Inertia retention, particle best, and global best represent different task allocation schemes. The schemes are discrete from each other, and there is no correlation between them. Therefore, the vector superposition method cannot be used for calculation. The probability model is used to select one of the three parts as the final selected particle motion direction and target; that is, the algorithm parameters \(w,c_{1}r_{1},c_{2}r_{2}\) are set according to the probability model, which respectively represent the probability value of selecting the inertia retention, particle best, and global best, and the final direction and target are selected according to the set probability value proportion. The \(w,c_{1}r_{1},c_{2}r_{2}\) total probability is 100%, and the \(w\) probability is inertia retention operation. The probability \(c_{1}r_{1}\) is updated according to the particle best direction and target, and the \(c_{2}r_{2}\) probability is updated according to the global best direction and target.  
According to the definition of the PSO, inertia is maintained as the particles continue to move forward along the direction and displacement of the last iteration. The selection of the last iteration has been replaced and updated according to the rules. Inertia maintenance has no practical operational significance.  
In order to avoid the particle position update falling into the local optimal solution trap, a random perturbation operation is proposed: when the particle motion direction and target calculated in this iteration are completely consistent with the motion direction and target calculated last time, the algorithm randomly selects the task nodes to exchange, thus increasing the diversity of the particle position update and avoiding the algorithm falling into the local optimal solution trap.
## 3 Results
### Relationships between Inorganic and Organic Pollutants  
#### 3.2.1 Polycyclic Aromatic Hydrocarbons (PAH)  
There are more or less close relationships between the levels of inorganic and organic pollutants in the humus layer, which suggest common emission sources (Table 2, Figure 3). For the mineral soil (0-5 cm depth) these relationships either hardly exist or are not present at all. Here the deposited heavy metal quantities are obviously influenced to a large extent by the current soil chemical properties and their effects on mobilization processes (see Section 3.1).  
The closest relationship between the PAH\({}_{16}\) values in the humus layer is to the As content (Figure 3). As can also be seen for the element Pb, the scatter diagram for As indicates that the best-fit curve runs almost through the origin of the XY-axis, so that for these trace elements (and for PAH) only anthropogenic input sources can be assumed. With regard to the correlation coefficients, the relationships to the heavy metal concentrations of the NFSI-1 are similar to those of the NFSI-2. With a coefficient of determination of R\({}^{2}\) = 0.64, PAH\({}_{16}\) can be estimated based on the As and Cr concentrations in the humus layer. The corresponding regression equation is:  
\[PAH_{16}[mg/g]=367.3*As[mg/kg]+291.0*Cr[mg/kg]-382.4 \tag{3}\]  
Looking at the correlation matrix of the PAH\({}_{16}\) individual analytes and the inorganic pollutants in the humus layer, it becomes evident that As and Cr are closely associated especially to the low-molecular weight PAH with two or three aromatic rings (Table 3). The correlation coefficients for As and Cr decrease significantly from the low molecular weight substances with a maximum of R for naphthalene (2 aromatic rings) and fluoren (3 aromatic rings) to the high molecular weight substances with a minimum of R for benzo(a)pyren (5 aromatic rings). In contrast, for the elements Pb and Cu, there are significantly closer relationships to the higher molecular weight PAHs than to the lower molecular weight ones.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**PAH\({}_{16}\)** & **As** & **Cd** & **Cr** & **Cu** & **Ni** & **Pb** & **Zn** \\ \hline
**NFSI-1** & & 0.65 ** & 0.69 ** & 0.69 ** & 0.74 ** & 0.70 ** & 0.54 ** \\
**NFSI-2** & 0.81 ** & 0.52 ** & 0.75 ** & 0.71 ** & 0.64 ** & 0.71 ** & 0.12 \\ \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients between PAH\({}_{16}\) concentration and the As, Cd, Cr, Cu, Ni, Pb and Zn concentrations in the humus layers of Brandenburg NFSI sites differentiated for data from NFSI-1 (n = 36) and NFSI-2 (n = 50); significance: ** \(p\) < 0.01.  
Figure 3: Scatter diagrams of the PAH\({}_{16}\) concentrations and As, Cd, Cr, Cu, Ni, Pb and Zn concentrations in the humus layer of NFSI sites in Brandenburg (NFSI-2), and regression function.
# High-Performance Speed Control of PMSM Using Fuzzy Sliding Mode with Load Torque Observer
## 3 Controller Design
### Current Feedback Decoupling  
According to the voltage Equation (1) under the synchronous rotation axis coordinate system, there are coupling terms \(L_{q}\omega_{r}i_{q}\) and \(L_{d}\omega_{r}i_{d}\) for current and velocity in the  
Figure 1: System control block diagram.  
voltage equation. In addition, \(u_{q}\) includes disturbance terms related to flux linkage and velocity; these cross-coupling can be offset by feedforward compensation [1; 22] to realize the decoupling control, the current feedback decoupling control block diagram as shown in Figure 2.  
With the introduction of feedback compensation, the following Equations are obtained  
\[\left\{\begin{array}{l}u_{d}^{*}=G_{1}(s)(\vec{t}_{d}-i_{d})-\hat{L}_{q} \omega_{r}i_{q}\\ u_{q}^{*}=G_{1}(s)(\vec{t}_{q}-i_{q})+\hat{L}_{q}\omega_{r}i_{d}+\omega_{r} \psi_{f}\end{array}\right. \tag{4}\]  
where \(\hat{L}_{q}\) and \(\hat{L}_{d}\) are the estimates of \(L_{q}\) and \(L_{d}\); the parameter values on the motor nameplate are selected as \(\hat{L_{d}}\) and \(\hat{L_{q}}\); if real-time and accurate decoupling is desired, the least-squares method and the Kalman filtering method can be used for online identification of \(\hat{L_{d}}\) and \(\hat{L_{q}}\). \(\hat{L_{q}}\omega_{r}i_{q}\) and \(\hat{L_{d}}\omega_{r}i_{d}\) are introduced to cancel the coupling terms in the \(d-q\) axis voltage equations of PMSM; \(\omega_{r}\varphi_{f}\) is introduced as the compensation term of anti-potential. If \(\hat{L_{q}}=L_{q}\), selecting \(G_{1}(s)=k_{c}(R_{s}+L_{q}s)\), \(G_{2}(s)=k_{c}(R_{s}+L_{q}s)\), then  
\[\frac{i_{d}(s)}{\hat{r}_{d}^{*}(s)}=\frac{i_{q}(s)}{\hat{r}_{q}^{*}(s)}=\frac{ k_{c}}{1+k_{c}} \tag{5}\]  
From Equation (5) it can be seen that the equation is a linear equation, there is no coupling term in the voltage equation of \(d\) and \(q\) axis, and the control is completely decoupled, and the decoupling is a proportional link. When the control strategy \(i_{d}=0\) is adopted, the electromagnetic torque does not include the magneto resistive torque component and is linear with the q-axis current. The process of decoupling restores a kind of symmetry to the control system. By eliminating the cross-coupling terms, it makes the control of the d and q axes more independent and symmetric, which simplifies the control process and improves the system's control ability.
## 6 Discussion
### Complexity Analysis of the Proposed CAD  
The computational effectiveness, feature dimensions, and classification complexity of the suggested CAD system in various configurations are all thoroughly examined in the complexity analysis shown in Table 10. This table demonstrates the influence of features extracted from different layers of multiple CNNs, along with feature reduction techniques like DWT, NNMF, and multi-layer fusion strategies, on overall model complexity and decision-making speed. The quantity of parameters, layer depth, input feature size, and classification duration collectively provide insights into the trade-offs among accuracy, interpretability, and computational feasibility in the deployment of these models for thermographic breast image classification. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multirow{2}{*}{**CNN Layer**} & \multicolumn{2}{c}{**Dimensionality Reduction**} & \multirow{2}{*}{**LSVM**} & \multirow{2}{*}{**QSVM**} & \multirow{2}{*}{**CSVM**} & \multirow{2}{*}{**MGSVM**} & \multirow{2}{*}{**CGSVM**} \\ \hline \multirow{4}{*}{**EfficientNetB0**} & **Layer 1** & **NNMF** & 98.1 & 97.9 & 97.7 & 98.0 & 98.0 \\  & **Features** & **PCA** & 97.2 & 97.8 & 97.3 & 97.1 & 97.6 \\  & **Autoencoders** & 97.9 & 97.8 & 98.0 & 98.3 & 97.6 \\ \cline{2-8}  & **Layer 2** & **NNMF** & 98.9 & 99.1 & 99.0 & 99.0 & 98.3 \\  & **Features** & **PCA** & 98.7 & 98.5 & 98.1 & 98.7 & 99.1 \\  & **Autoencoders** & 98.7 & 99.0 & 98.7 & 98.9 & 98.7 \\ \hline \multirow{4}{*}{**MobileNet**} & **Layer 1** & **NNMF** & 98.3 & 98.6 & 98.1 & 98.3 & 98.2 \\  & **Features** & **PCA** & 98.1 & 97.8 & 97.2 & 97.9 & 97.9 \\  & **Autoencoders** & 98.1 & 97.9 & 97.7 & 98.1 & 98.2 \\ \cline{2-8}  & **Layer 2** & **NNMF** & 97.6 & 97.5 & 97.6 & 97.2 & 97.0 \\  & **Features** & **PCA** & 97.6 & 97.1 & 96.9 & 97.3 & 97.0 \\  & **Autoencoders** & 97.1 & 96.8 & 96.8 & 97.0 & 95.8 \\ \hline \multirow{4}{*}{**ShuffleNet**} & **Layer 1** & **NNMF** & 96.3 & 96.4 & 96.1 & 96.3 & 94.5 \\  & **Features** & **PCA** & 94.9 & 94.7 & 93.5 & 95.1 & 94.6 \\  & **Autoencoders** & 95.7 & 95.2 & 95.7 & 96.2 & 94.9 \\ \cline{2-8}  & **Layer 2** & **NNMF** & 96.7 & 97.0 & 96.8 & 96.9 & 95.8 \\  & **Features** & **PCA** & 95.9 & 96.0 & 95.8 & 96.6 & 96.1 \\  & **Autoencoders** & 96.8 & 96.8 & 96.6 & 96.9 & 96.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: A comparative performance evaluation with dimensionality reduction approaches including NNMF, PCA, and Autoencoders.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 2 Method
### Deep Learning for Tool Wear Monitoring  
#### 2.3.1 Structure of the Deep Learning Network  
The deep learning network for tool wear monitoring in variable cutting depth milling is shown in Figure 4. It consists of two parts, a deep autoencoder and a deep multi-layer perceptron. The deep autoencoder is used to reduce the dimension of the input vectors (the pre-selected features) and learn the highly correlated features. Then, the following deep multi-layer perceptron is used to learn the effect of the extracted features on the tool wear and predict the tool wear value.
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 3 Datasets
### Music-Speech Detection  
Music-speech detection aims to detect the boundaries of music and speech in audio signals such as radio and TV programs. The neural network performs multi-output detection to allow the simultaneous occurrence of music and speech. The number of output neurons at each time step is six because we are detecting two acoustic classes. We obtained 5 h of audio from the MuSpeak dataset [38]. In addition, we collected 18 h of audio from BBC Radio Devon, which was manually annotated by the authors. Both datasets were roughly split into 50% for training, 30% for validation, and 20% for testing.  
There are many openly available datasets with separate files of music and speech, such as MUSAN [39], GTZAN [40; 41], Scheirer and Slaney dataset [42], and Instrument Recognition in Musical Audio Signals (IRMAS) [43], to name a few. However, the problem with such datasets is that they are not mixed in the style of TV or radio programmes. Broadcast audio is generally well-mixed with instances of speech over background music, one song fading out and a new song fading in, and so on. In a previous study [17], we presented an approach that artificially synthesises large training sets for music-speech detection. This technique automatically mixes separate files of music and speech in the style of a radio DJ. Various parameters such as audio fade curves and audio ducking are randomised to obtain a variety of synthetic examples. In the current paper, we included 46 h of synthetic examples in the training set. Table 4 shows a brief overview of the contents of each split in the dataset. For a detailed explanation of the training sets and experimental setup, please refer to this study [17].  
All the audio files were resampled to 16 kHz. They were converted to mono by averaging the channels before pre-processing. Subsequently, we extracted 64 log-mel bins with a hop size of 10 ms and a window size of 25 ms. The frequencies for the mel spectrogram ranged from 125 Hz to 7.5 kHz. We adopted audio features similar to those used by YamNet [31]. Note that we did not use any regularization such as L2 normalization, spatial dropout, or SpecAugment for this dataset because the training set is large.  
We evaluate the model on two different test sets. The first one being our in-house test set, which contains approximately 4.5 h of audio from BBC Radio Devon and MuSpeak [38]. The second one was the MIREX music-speech detection dataset, which contains 27 h of audio from various TV programs.
## 3 Results
### Results
#### 3.2.3 Outex Database  
Table 5 shows the classification results of our method applied to the Outex database (68 images). Each image in the training set was partitioned randomly, varying the number of windows per side from two to four in each training image, and the number of random images from \(5\times\text{n}^{2}\) to \(18\times\text{n}^{2}\). The first column shows the best results reached by our method, and the second column shows the results after the post-processing stage. The best results are highlighted by bold text.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Method** & **Result** \\ \hline [PERSON] et al., 2004 [9] & Multispectral & 97.9\% \\ [PERSON] et al., 2004 [27] & Color histogram \(\text{I}_{1}\text{I}_{2}\text{I}_{3}\text{3}\text{2}^{3}\) & 100\% \\ [PERSON] et al., 2011 [28] & Improved hue, luminance, and saturation color & 100\% \\  & space (IHLS) B = 16 & \\ [PERSON] et al., 2013 [4] & Color filter array - chromatic & 98.61\% \\ [PERSON] et al., 2013 [4] & Chromatic co-occurrence matrices & 99.07\% \\ [PERSON] et al., 2017 [50] & Deterministic walks’ direction histogram & 99.65\% \\ [PERSON] et al., 2017 [30] & Parallel vectors & 99.5\% \\ [PERSON] et al., 2018 [23] & Smoothed morphological operators (SMO) & 99.54\% \\ [PERSON] et al., 2018 [38] & Adapted Laplace score & 94.9\% \\ \hline Global–local Haralick BQMP method 1 & 97.13\% \\ KNN global–local Haralick BQMP method + post-processing 1 & 97.25\% \\ Global–local Haralick BQMP method + post-processing 1 & 100\% \\ \hline \hline \end{tabular}  
* The last three methods are our results.  
\end{table}
Table 4: Best results of global–local Haralick–BQMP classification for the Vistex database with 54 images and best results published previously on the same database.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**\(\text{r}\times\text{n}^{2}\) Random Images 1 in Training**} \\ \hline \multicolumn{6}{c}{**Without Post-Processing**} & \multicolumn{3}{c}{**With Post-Processing**} \\ \hline \multirow{3}{*}{n = 4} & r = 2 & r = 3 & r = 4 & r = 2 & r = 3 & r = 4 \\  & 95.52\% & 94.83\% & 94.13\% & 99.54\% & 100\% & 100\% \\  & 95.68\% & 95.29\% & 94.64\% & 99.54\% & 100\% & 100\% \\  & 96.13\% & 95.88\% & 95.22\% & 100\% & 100\% & 100\% \\  & 96.45\% & 96.26\% & 95.36\% & 100\% & 100\% & 100\% \\  & 96.72\% & 96.42\% & 96.42\% & 100\% & 100\% & 100\% \\  & 95.67\% & 97.13\% & 95.88\% & 100\% & 100\% & 100\% \\  & 96.96\% & 96.32\% & 95.62\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}  
* n is the number of images per side in the training stage; r is the times we iterated the method in each image.  
\end{table}
Table 3: Classification results of the experiment on the Vistex database for the set of 54 images. The best results reached with and without post-processing are highlighted by bold text.  
Table 6 compares the results published previously in the literature and our results for the Outex database with 68 images. It can be observed in Table 6 that our post-processed method reached the highest results with 98.97%.
## 3 Review of PLC Systems
### Evolution of PLC Standardization
#### 3.2.1 UNB and NB PLC Standardization
ITU-T initiated in January 2010 the G.hnem working group (hnem stands for \"home networking aspects of energy management\") with the objective of defining a NB PLC standard for energy management applications. In March 2010, the P1901.2 working group in IEEE, began to work on a NB PLC standard for Smart Grid Applications (LV and MV). Both ITU and IEEE have completed their standards, according to references [69, 70].
# Image Reconstruction Using Autofocus in Single-Lens System
## 2 Methodology  
The experimental layout of distance scanning and computational imaging is given in Figure 1. The system is mainly composed of four parts: fiber-optic laser with collimating lens, diffuser, single lens, and scientific CCD. The scanning operation of object distance \(z_{1}\) is achieved by changing the position of the lens.  
The scattered light through D irradiates the sample and travels a distance \(z_{0}\) in the free space. The beam is further modulated by the lens L\({}_{2}\), and then the diffraction images are recorded by CCD at the distance \(z_{2}\). For autofocus, a set of speckle images are recorded and used to scan the distance \(z_{1}\) by CEFs. Here the first recorded position is the back focal plane of the lens L\({}_{2}\). For sample reconstruction, coherent illumination is applied for recording diffraction patterns. The first image \(I_{0}\) is recorded at the focal plane of L\({}_{2}\), and the others are recorded outside the back focal plane. The images recorded by coherent illumination will be used for image reconstruction after the auto-focusing task is completed.
# Optimization of Optical Networks Based on CDC-ROADM Technology
###### Abstract  
New generation of optical nodes in dense wavelength division multiplexed networks enables operators to improve service flexibility and make significant savings, both in operational and capital expenditures. Thus the main objective of the study is to minimize optical node resources, such as transponders, multiplexers and wavelength selective switches, needed to provide and maintain high quality dense wavelength division multiplexed network services using new generation of optical nodes. A model based on integer programming is proposed, which includes a detailed description of an optical network node. The impact on the network performance of conventional reconfigurable optical add drop multiplexer technology is compared with colorless, directionless and contentionless approaches. The main focus of the presented study is the analysis of the network congestion problem arising in the context of both reconfigurable optical add drop multiplexer technologies. The analysis is supported by results of numerical experiments carried out for realistic networks of different dimensions and traffic demand sets.  
Keywords:DWDM network design; optical network optimization; CDC-ROADM; optical node model; network congestion; linear programming (LP); integer programming (IP) +
Footnote †: journal: Applied Sciences
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 4 Annotation Scheme
### Therapist Utterance Attributes
#### 4.1.4 Reflection  
Reflection is an essential means of listening. In using reflections, the therapist shows that they are listening to and understanding the client, which is effective in helping people to change. Following MISC, we consider two reflection types: simple and complex. A simple reflection shows an understanding of the client's words but contains little additional meaning--for example, by repeating the client's statement. In comparison, a complex reflection conveys a deeper level of understanding of the client's point of view and adds  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Utterance** & **Input Type** \\ \hline You’re not alone in feeling that way. Binge drinking can feel normal to some people. & Information \\ \hline So that’s a hormone that allows you to utilise sugar in your body. & Information \\ \hline I want you to be healthy. And I don’t want to see you coming back in here for something else. So I’m really gonna recommend that you try to cut down to that amount. & Advice \\ \hline That’s why I recommend that all my adolescent patients not drink at all. & Advice \\ \hline So, what have you looked into about, um, you know, advocacy in that area or expungement or anything like that? & Options \\ \hline Okay. So, exploring some yoga classes. Is doing yoga in your living room appealing to you at all? & Options \\ \hline So for you being in your class, when that bell rings, then you know, this is the goal. & Negotiation/goal setting \\ \hline Do you think you could go two months without drinking? & Negotiation/goal setting \\ \hline \hline \end{tabular}
\end{table}
Table 6: Example labelling for therapist Input from the dataset.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Utterance** & **Question Type** \\ \hline Do you have children in your house? & Closed (Yes/No answer) \\ \hline How much does it actually cost you a week? & Closed (Number) \\ \hline Okay. What kind of alcohol do you drink at parties? & Closed (Specific fact) \\ \hline So what is a typical week for you as far as your alcohol use is concerned? & Open (Seek information) \\ \hline Okay. So how do you feel about being here today? & Open (Invite client’s perspective) \\ \hline So, when you think about what you like and don’t like about your drinking, where do you wanna go from here? & Open (Encourage self-exploration) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example labelling for therapist Question from the dataset.  
substantial meaning to the client's statement, using techniques such as metaphors and exaggeration [1]. Two pairs of contrasting simple and complex reflections to the same client statement are presented in Table 7. Clearly, the simple reflections identify the client's emotion/situation but do not go beyond the overt content of the client's statement, while the complex reflections \"continue the paragraph\" by interpreting the client's words and anticipating what they might reasonably say next.