esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 5 Classification
### Neural Network Architecture
Resize convolutions were used instead of the more common transpose convolutions to avoid checkerboard artefacts, as discussed in [15]. The resize layers of these convolutions use bilinear up-sampling to double the image dimensions along both axes. An additional, final convolution layer merges the activations the last eight filters into a grayscale image. A sigmoid activation function in the last layer limits the images to a zero to one range. The general architecture was modeled to reflect common practices in the field of deep learning. The kernel counts are a power of two, doubling with every encoder layer, similar to SegNet and U-Net. Likewise, the decoder is roughly mirroring the encoder part of the network. The filter kernels were chosen to be relatively big at \(7\times 7\), to give the network a big receptive field of r = 127 px, as shown in the following calculation:  
\[\text{r}=\text{k}_{1}+\sum_{n=2}^{N}\left[\left(\prod_{m=1}^{n-1}s_{m}\right) \cdot(k_{n}-1)\right] \tag{1}\]  
where \(N\) is the total number of layers, \(s_{m}\) is the stride of the \(m\)-th layer and \(k_{n}\) is the kernel size of the \(n\)-th layer. The large receptive field allows the network to put individual output pixel into the context of a large input image region. This allows the network to accurately detect defects of low severity but big expanse. The hyper-parameter configuration of the model was empirically arrived at, after the model was continuously fine-tuned over the course of several training runs. It was chosen to maximize accuracy while keeping inference time acceptable.
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.2.3 Business Buildings (Offices)  
Office buildings were ranked third in the feasibility of implementing CE principles, with a total score of 20. Only one respondent rated business buildings with a low potential. The respondents identified several factors that enhance the potential of business buildings for CE implementation. For instance, A1 pointed out that \"_Office buildings are feasible for circular strategies because they typically feature open spaces or light separations. Instead of concrete walls, offices often use glass partitions or movable wall separations, which are easier to manage, adapt, or disassemble. This is why I chose office buildings as the second most feasible option\"_. Similarly, C3 commented that \"_Business office can be renovoted on a regular basis. Therefore, it would be easy to implement CE principals through refurbishment programmes and reuse of various materials\"_. C1, CT1, and CT2 shared a similar perspective, stating that there is strong potential for incorporating CE in business buildings due to the compelling business case. This view aligns with the priorities of stakeholders who focus on financial returns. On the other hand, A2, who rated the feasibility of implementing CE principles as low, believed that the upfront costs could pose a significant barrier. A2 suggested that some owners in business may prioritize short-term gains and favor lower initial expenses, potentially overlooking the long-term benefits that CE practices could provide.
###### Abstract  
The aim of the present study was to comprehensively analyze and identify the metabolites of different varieties of raw peanut, as well as provide a reference for the utilization of different varieties of peanuts. In this study, three varieties of peanuts, namely ZKH1H, ZKH13H, and CFD, were investigated via ultrahigh-performance liquid chromatography (UPLC) and widely targeted metabolomics methods based on tandem mass spectrometry (MS) and solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). In total, 417 nonvolatile and 55 volatile substances were detected. The nonvolatile substances were classified into the following 10 categories: organic acids and derivatives (28.9%); organic oxygen compounds (21.9%); lipids and lipid-like molecules (12.6%); organotherocyclic compounds (9.9%); nucleotides, nucleotides, and analogues (9.4%); benzenoids (7.8%); phenylpropanols and polykeptides (6.1%); organic nitrogen compounds (2.7%); lignans, neolignans, and related compounds (0.5%); and alkaloids and their derivatives (0.3%). The volatile compounds (VOCs) were classified into the following eight categories: organic oxygen compounds (24.1%); organic cyclic compounds (20.4%); organic nitrogen compounds (13%); organic acids and their derivatives (13%); lipids and lipid-like molecules (11.2%); benzenoids (11.1%); hydrocarbons (3.7%); and homogeneous non-metallic compounds (3.7%). Differentially abundant metabolites among the different peanut varieties (ZKH13H vs. CFD, ZKH1H vs. CFD, and ZKH1H vs. ZKH13H) were investigated via multivariate statistical analyses, which identified 213, 204, and 157 nonvolatile differentially abundant metabolites, respectively, and 12, 11, and 10 volatile differentially abundant metabolites, respectively. KEGG metabolic pathway analyses of the differential non-VOCs revealed that the most significant metabolic pathways among ZKH13H vs. CFD, ZKH1H vs. CFD, and ZKH1H vs. ZKH13H were galactose metabolism, purine metabolism, and aminoacyltRNA, while the nitrogen metabolism pathway was identified as a significant metabolic pathway for the VOCs. The present findings provide a theoretical foundation for the development and utilization of these three peanut species, as well as for the breeding of new peanut varieties.  
raw peanut; variety; widely targeted metabolome; volatile metabolites; nonvolatile metabolites +
Footnote †: journal: _Molecules_  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 7 Future Prospects of Multi-Source Data Fusion and Analysis Algorithms in Smart Cities
### The Coordinated Development of Interdisciplinary Research and Privacy Protection  
The application of multi-source data fusion and analysis algorithms in smart city construction involves multiple disciplines, but interdisciplinary research is not yet in-depth, and the synergy between disciplines has not been fully utilized. There is a disconnect between urban planning, sociology, computer science, and other disciplines in data understanding, application needs, and technical implementation, which makes it difficult to perfectly match algorithm design with actual urban management needs. At the same time, with the increasing popularity of data collection and use, privacy protection and data security issues have become increasingly prominent, becoming an important factor restricting the promotion and application of algorithms.  
In the future, interdisciplinary research will become a key driving force for the development of this field. Experts and scholars from different disciplines are encouraged to work closely to conduct research projects and break down disciplinary barriers. Urban planners and data analysts should work together to optimize algorithm design based on urban development goals and spatial layout needs to better serve urban planning and resource allocation. Sociologists should participate in algorithm evaluation to ensure that algorithm applications positively impact society from the perspectives of social fairness and resident satisfaction. In terms of privacy protection, laws, regulations, boundaries, and responsibilities of data use must be strengthened and clarified. At the same time, we should develop innovative privacy protection technologies, such as encryption algorithms, anonymization processing technologies, and data desensitization technologies, to ensure that personal privacy is not violated during the data fusion and analysis process and to achieve a balance between data utilization and privacy protection [70].
## 2 Literature Review
### Multi-Sensor Data Fusion
The aforementioned studies indicate that, despite significant research focused on fusing multiple data sources for ship target perception, two unresolved issues remain. First, existing studies often fail to fully consider the limitations of onboard computational resources, leading to bottlenecks in inference speed, which affect system real-time performance and efficiency. Second, most research focuses on fusion perception methods for shore-based or fixed-angle cameras, while less attention is given to the challenges posed by onboard cameras in real-world navigation environments, such as the effects of fog interference and AIS data instability. Therefore, from the perspective of practical ship applications, optimizing the inference speed of perception models and the data fusion mechanism to improve the accuracy and usability of multi-source perception methods is an urgent problem that needs to be addressed.
###### Abstract  
Treated sewage source heat pump systems can reuse the waste energy in the treated sewage. However, biofilms in the heat exchangers decrease the system efficiency. This work investigates the feasibility of thermal shock at accessible temperatures in heat exchangers for biofilm inhibition. _Bacillus subtilis_ biofilms were formed on coupons and in a miniaturized plate heat exchanger. Thermal shocks at different temperatures (50-80 \({}^{\circ}\)C) for different exposure times (1-60 min) were used to treat the biofilms. The results showed that thermal shock had a significant bactericidal and biofilm inhibition effect, and the effect was enhanced as the temperature and the exposure time increased. Data fitting of the biomass showed that temperature had a more significant influence on the biofilm inhibition effect than exposure time. The results of the heat exchanging experiments showed that high temperature thermal shock could significantly mitigate the heat transfer deterioration caused by the biofilms, indicating that thermal shock could be used as a viable biofilm inhibition approach for heat exchangers.  
treated sewage source heat pump systems; thermal shock; biofilm inhibition; temperature; exposure time +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
A novel CPF loop for stable \(V/f\) control of PMSM is proposed in this paper. The proposed sensorless control strategy is robust against machine parameters variation and offers a low-cost and simple-to-implement solution. It also allows controlling the motor power factor without dependence on machine parameters with the new CPF loop in the \(d_{\varphi}q_{v}\) reference frame. The main advantages of such a structure are speed control accuracy, parameter dependence, and computational time. The proposed control strategy was also compared against the traditional sensorless FOC with a Luenberger-type back-EMF observer within the paper. The back-EMF observer depends on the PMSM model, so it is necessary to know the machine parameters. The strategy with a back-EMF observer can be used just in the mid- and high-speed regions but with more than satisfactory control performance. However, due to the low-speed performance disadvantage, the stable \(V/f\) was also used as a start-up control strategy for sensorless FOC with the Luenberger-type back-EMF observer, providing satisfactory results. The experimental verification on a 1.41 kW PMSM machine designed for golf cart traction applications shows that stable \(V/f\) with a CPF loop seems to be more attractive than the back-EMF-based method regarding sensorless speed control.  
Conceptualization, M.V.; methodology, M.V. and P.M.; software, M.V. and L.G.; validation, M.V. and P.M.; formal analysis, M.V.; investigation, M.V.; resources, M.V.; data curation, M.V.; writing--original draft preparation, M.V.; writing--review and editing, L.G. and P.M.; visualization, M.V.; supervision, L.G. and P.M.; project administration, P.M.; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Stable \(V/f\) with CP** & \multicolumn{2}{c}{**Sensorless FOC with**} \\  & & **Luenberger-Type Back-EMF Observer** \\ \hline Accuracy & High & High \\ Dynamics & Good & Very good \\ Parameters dependence & Low & Medium \\ Computational time & Low & High \\ Load disturbance response & Very good & Good \\ Start-up & Good & - \\ Structure tuning & Easy & Hard \\ Low-speed operation & Bad & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between stable \(V/f\) with CPF and sensorless FOC with Luenberger-type back-EMF observer.
## 3 Materials and Methods
### Ablation Study of the Proposed Model (Optimized ANN)  
Four tests were run by altering different components of the suggested optimized ANN model, as part of an ablation investigation. It is possible to produce a more reliable design with increased classification accuracy, by altering different components. The following elements conducted an ablation study: changing dense layer, changing activation function, and changing kernel_initializer and optimizer.
## 4 Results and Discussion
### Ore-Forming Simulation Results
Notably, the areas of JJY and DBZZ occur the strike changes of the Zhaoping fault, displaying an eastward-bending feature (Figure 7c). Given that the Zhaoping fault underwent a dextral strike-slip deformation during the mineralization period, such variations in the strike would accelerate shear strain, resulting in a local, high strain accumulation. This may influence the distinct ore-shooting features at JJY (NE-plunging) and DBZZ (SE-plunging). Temperature variation exhibits a noteworthy correlation with gold mineralization that most ore-bearing units occur the high temperature variations (Figure 10e) and the No. 7 orebody occurs slightly higher temperature changes than others. This suggests the gold deposition is at least partly influenced by temperature variation. However, we noticed that non-ore units can also undergo similar, high temperature changes (Figure 10e,f). Thus, gold-mineralization is most likely controlled by multiple factors at Xiadian, of which pressure fluctuation and water-rock interaction (i.e., sulfidation) have been recognized as important ore-forming mechanisms [57; 63; 65; 66; 101]. The fluid divergence records the fluid flow process in individual voxels. The voxels in the Xiadian gold deposits exhibit a wide range of fluid divergence and most of them (approximately 82%, 73 t) occur the ingress fluid flow (negative fluid divergence) (Figure 10g,h). Nevertheless, there are still some voxels with high gold contents and positive fluid divergence that appear to be irregularly distributed in space. We tentatively infer that while the fluid divergence indicates fluid flux characteristic, the orogenic gold deposit formed in an open structural-hydrothermal system in which fluid ingress and egress are common but not directly equivalent to gold precipitation or dissolution. Consequently, the relationship between fluid divergence and gold mineralization is complex. In summary, the geodynamic indices of mineralization demonstrate a significant correlation with gold distribution and provide different controls on mineralization compared to fault geometry. Therefore, these indicators can be reasonably utilized as predictor variables for exploration.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 3 Test Results
### Attenuation Law of Lasers with Different Wavelengths in the Same Suspended Media  
#### 3.2.1 CaCO\({}_{3}\)  
Suspended Medium  
In CaCO\({}_{3}\) medium, comparing the lasers with wavelengths of 405 nm, 450 nm, 532 nm, 650 nm, and 808 nm, it can be seen from Figure 3a that the increasing rate of the laser attenuation coefficient is the lowest when the laser wavelength is 450 nm, and all of the attenuation coefficients are lower than those of other wavelengths. The cloud diagram of the laser attenuation coefficient with different wavelengths and different turbidity grades is shown in Figure 3b. When the laser wavelengths are 450 nm and 650 nm, there are two \"ravines\" with low attenuation coefficients. In order to compare the attenuation characteristics of 450 nm and 650 nm in depth, we adopted the above-mentioned fitting formula and solved the following two simultaneous equations:  
\[Y=0.1784X+0.2174 \tag{7}\]  
Figure 2: Attenuation coefficient—turbidity curves using different wavelength lasers: (**a**) 405 nm; (**b**) 405 nm; (**c**) 532 nm; (**d**) 650 nm; (**e**) 808 nm.  
\[Y=0.2508X-0.0796 \tag{8}\]  
When the turbidity is 4 NTU, the attenuation coefficients of the two lasers are equal. Thus, when the turbidity is lower than 4 NTU, the 650 nm laser is suggested, and when the turbidity is higher than 4 NTU, the 450 nm laser is suggested. However, considering that the turbidity of purified water is about 5 NTU and that the water environment in engineering projects is generally more turbid than purified water, the laser wavelength of water-filled cave detection should be 450 nm, requiring a blue wavelength laser.
## Results
### Coping Strategies
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Online Contacts (_n = 87_)** & \begin{tabular}{c} **Others** \\ **(_n = 93_)** \\ **M (SD)** \\ \end{tabular} & **F** & \(p\) & \(\eta^{2}\) \\ \hline Proactive coping & 1.25 (1.07) & 0.63 (0.86) & 15.17 & 0.000 & 0.09 \\ \hline Avoidance coping & 1.38 (0.84) & 1.13 (0.84) & 3.29 & n.s. & 0.02 \\ \hline Passivity coping & 0.24 (0.60) & 0.13 (0.48) & 1.54 & n.s. & 0.01 \\ \hline \multirow{4}{*}{Proactive coping} & **Online harassment (_n = 61_)** & \begin{tabular}{c} **Others** \\ **(_n = 119_)** \\ **M (SD)** \\ \end{tabular} & **F** & \(p\) & \(\eta^{2}\) \\ \cline{1-1} \cline{2-6} Proactive coping & 1.13 (1.24) & 0.88 (0.85) & 2.17 & n.s. & 0.01 \\ \cline{1-1} \cline{2-6} Avoidance coping & 1.34 (0.87) & 1.22 (0.83) & 0.75 & n.s. & 0.005 \\ \cline{1-1} \cline{2-6} Passivity coping & 0.22(0.57) & 0.17 (0.54) & 0.29 & n.s. & 0.002 \\ \cline{1-1} \cline{2-6}  & **Online Advances (_n = 64_)** & \begin{tabular}{c} **Others** \\ **(_n = 116_)** \\ **M (SD)** \\ \end{tabular} & **F** & \(p\) & \(\eta^{2}\) \\ \cline{1-1} \cline{2-6} Proactive coping & 1.14 (1.02) & 0.85 (1.00) & 2.98 & n.s. & 0.02 \\ \cline{1-1} \cline{2-6} Avoidance coping & 1.42 (0.74) & 1.18 (0.91) & 3.05 & n.s. & 0.02 \\ \cline{1-1} \cline{2-6} Passivity coping & 0.17 (0.48) & 0.20 (0.60) & 0.14 & n.s. & 0.001 \\ \cline{1-1} \cline{2-6}  & **Online Threats (_n = 37_)** & \
\begin{tabular}{c} **Others** \\ **(_n = 143_)** \\ **M (SD)** \\ \end{tabular} & **F** & \(p\) & \(\eta^{2}\) \\ \cline{1-1} \cline{2-6} Proactive coping & 1.14 (1.29) & 0.91 (0.91) & 1.32 & n.s. & 0.009 \\ \cline{1-1} \cline{2-6} Avoidance coping & 1.27 (0.90) & 1.27 (0.84) & 0.000 & n.s. & 0.000 \\ \cline{1-1} \cline{2-6} Passivity coping & 0.41 (0.76) & 0.12 (0.45) & 8.07 & 0.005 & 0.05 \\ \cline{1-1} \cline{2-6}  & **Online Identity Fraud (_n = 37_)** &
\begin{tabular}{c} **Others** \\ **(_n = 143_)** \\ **M (SD)** \\ \end{tabular} & **F** & \(p\) & \(\eta^{2}\) \\ \cline{1-1} \cline{2-6} Proactive coping & 1.27 (1.24) & 0.89 (0.94) & 3.93 & 0.04 & 0.03 \\ \cline{1-1} \cline{2-6} Avoidance coping & 1.29 (0.87) & 1.26 (0.84) & 0.02 & n.s. & 0.000 \\ \cline{1-1} \cline{2-6} Passivity coping & 0.23 (0.58) & 0.18 (0.55) & 0.28 & n.s. & 0.002 \\ \hline \hline \end{tabular}  
Notes: M, mean; SD, standard deviation (Range: 0–4).  
\end{table} 
Table 4: Coping strategies: comparison on the basis of different forms of cyberstalking (one-way ANOVA).
## 2 Background
### Ensemble Learning  
Ensemble learning [9] is a machine learning technique that combines the prediction of multiple machine learning models to obtain a better prediction than predictions produced from any single model. Each machine learning model (base learner) is trained on the data separately. Then, the prediction results of the base learners are combined using any combination method (e.g., averaging and voting) [9]. Ensemble learning can be divided into two categories: heterogeneous and homogeneous, depending on the base learners. If the base learners are of the same type, it is homogeneous; if not, it is heterogeneous [9]. Several ensemble learning approaches were proposed, such as boosting, bagging, and stacking ensembles. In this study, we will focus on boosting ensemble learning, specifically on the most popular boosting algorithm, AdaBoost.  
Adaptive boosting (AdaBoost) is the most common boosting algorithm. It is an iterative process that combines multiple base learners sequentially to improve the prediction performance. It assigns higher weights to the misclassified samples in order to focus on these samples in the next iteration. Thus, the input of each iteration is based on the results of the previous iteration [10]. Boosting ensembles have been proven to be suitable in classifying imbalanced datasets in other fields [11; 12]. In this study, we utilized the Stagewise Additive Modeling (SAMME.R) algorithm presented in Algorithm 1 to build our AdaBoost ensemble. SAMME.R proved to be more effective, converge faster, and achieve lower errors in fewer iterations [13].  
```
0: Dataset \(D_{n}(x_{1},c_{1}),...,(x_{n},c_{n})\)
0: AdaBoost.SAMME.R
1: Initialize samples weights \(w_{i}=1/n\), \(i=1,...,n\)
2:for\(m\gets 1\) to M do
3: Train a weak learner \(T^{(m)}(x)\) using weights \(w_{i}\)
4: Obtain the weighted class probability estimates: \(p_{k}^{(m)}(x)=Prob_{w}(c=k|x),k=1,...,K\)
5: Set: \(h_{k}^{(m)}(x)\leftarrow(K-1)\left(log\,p_{k}^{(m)}(x)-\frac{1}{K}\sum_{k^{ \prime}}log\,p_{k^{\prime}}^{(m)}(x)\right)\), \(k=1,...,K\)
6: Set: \(w_{i}\gets w_{i}\cdot exp\left(-\frac{K-1}{K}\,y_{i}^{T}log\,p^{(m)}(x_{i })\right),i=1,...,n\)
7: Re-normalize \(w_{i}\)
8:endfor
9: Output: \(C(x)=argmax\sum_{m=1}^{M}h_{k}^{(m)}(x)\)
```  
**Algorithm 1** SAMME.R algorithm
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 2 Model Principles and Methods  
### Pca  
In practice, the principal component analysis (PCA) method represents a frequently used linear dimensionality reduction analysis, which converts a set of potentially correlated variables into a group of linearly unrelated variables through orthogonal transformation. Hence, the transformed set of variables can be defined as the principal component [34]. The principle can be presented as follows.  
The original sample X is assumed to contain n indicators, namely, \(X=[x_{1},x_{2},\ldots,x_{n}]^{T}\). Among them, \(x_{1},x_{2},\ldots,x_{n}\) signify the original sample data set, and \(n\) denotes the dimensions of the variables. The new variable \(Y\), obtained after the dimensionality reduction, can be expressed as \(Y=UX=[y_{1},y_{2},\ldots,y_{m}]^{T}\). Meanwhile, \(U\) depicts the coefficient matrix, where \(U=\begin{pmatrix}u_{11}&\cdots&u_{1n}\\ \vdots&\ddots&\vdots\\ u_{m1}&\cdots&u_{mn}\end{pmatrix}\), while \(y_{1},y_{2},\ldots,y_{m}\) indicate the generated new variables, and \(m\) refers to the dimensions of the new variables. Hence, the calculation formula for \(y_{1},y_{2},\ldots,y_{m}\) can be realized as follows.  
\[\left\{\begin{array}{l}y_{1}=u_{11}\ x_{1}+u_{12}\ x_{1}+\ldots+u_{1n}\ x_{n}\\ y_{2}=u_{21}\ x_{1}+u_{22}\ x_{1}+\ldots+u_{2n}\ x_{n}\\ \vdots\\ y_{m}=u_{m1}\ x_{1}+u_{m2}\ x_{1}+\ldots+u_{mn}\ x_{n}\end{array}\right. \tag{1}\]  
The major steps of the PCA method can be summarized as follows.  
* Standardize the sample data and calculate the correlation matrix.
* Solve the eigenvalues and sort the principal components according to their magnitudes.
* Calculate the variance contribution rate of each principal component and select m (\(m<n\)) principal components based on the principle of the cumulative variance contribution rate reaching 85%.
* Calculate the correlation coefficient matrix and list the calculation formula of the principal components so that the dimensionality reduction of the original data can be achieved.