esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 4 Challenge: Data Secrecy  
Industrial data usually do not contain personal information that would call for privacy, yet sensitive business information or even trade secrets, such as production process or supply chain details, often still call for stringent data secrecy.  
Due to price and convenience considerations, training and inference are usually also performed in a centralized way in the cloud--either public cloud services or third-party servers. Thus, it is possible for the data and deep learning models to be revealed during transmission, sharing, and processing. The data can be revealed directly during transmission and processing and indirectly through model inversion, in which the training data can be inferred from the model stored in the cloud. Hence, privacy-preserving machine learning techniques have been proposed in order to protect data after it leaves the hands of the owners. Direct data reveal can be prevented through elimination approaches, encryption techniques, and data sanitation, i.e., differential privacy, whereas indirect data reveal is usually reduced through gradient noising. In addition to eliminating, encrypting or sanitizing raw data, new deep learning architectures have been proposed to prevent data leakage during training or inference. For this, the most representative approach is federated learning. However, it should be emphasized that the privacy-preserving methods can be applied to decentralized systems as well in order to reduce the potential risks of data revealing. In this section, the main techniques for privacy-preserving machine learning (PPML) will be introduced, including elimination, data encryption, differential privacy and deep model architectural approaches. Some of these techniques can be used either for protecting raw data or to prevent data revealing during training and inference, as summarized in Table 3. For a more comprehensive review on security for smart manufacturing, refer to [103].
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 3 Background
### Gap Filling  
However, gaps can appear in datasets due to communication errors or faulty devices and can significantly degrade the data quality. Therefore, various algorithms have been developed for filling the gaps such as Papoulis-Gerchberg, Wiener, Spatio-Temporal, Envelope, and Empirical Mode Decomposition Fillings.
## 3 Results  
### Walking Speed and Stance-Swing Phase Duration Ratio  
The preferred walking speed of the amputees (0.92 \(\pm\) 0.08 m/s) was significantly lower than that of the controls (1.37 \(\pm\) 0.13 m/s, \(p\) \(<\) 0.001). The duration of the stance phase for the control group accounted for 60.5% \(\pm\) 1.7% of the gait cycle, as shown in Figure 3. The ratio for the non-amputated limb group was significantly larger (\(p\) \(<\) 0.001): 66.1% \(\pm\) 2.4%. The amputated limb group exhibited a significantly smaller stance ratio: 58.6% \(\pm\) 1.2% (\(p\) \(<\) 0.001).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & **Joint Pairs** & **Phase** & **Able-Bodied Controls** & **Amputated Limbs** & **Non-Amputated Limbs** \\ \hline \multirow{4}{*}{RMS} & Hip–knee & Stance & 0.24 & 0.65 & 0.78 \\  & & Swing & **0.003** & 0.86 & 0.53 \\ \cline{2-6}  & Knee–ankle & Stance & 0.16 & 0.09 & 0.55 \\  & & Swing & 0.08 & 0.78 & 0.63 \\ \cline{2-6}  & Hip–ankle & Stance & 0.08 & 0.81 & 0.78 \\  & & Swing & **0.02** & 0.39 & 0.28 \\ \hline \multirow{4}{*}{DP} & Hip–knee & Stance & 0.48 & 0.40 & 0.75 \\  & & Swing & **<0.001** & 0.86 & 0.57 \\ \cline{2-6}  & Knee–ankle & Stance & 0.22 & 0.07 & 0.49 \\  & & Swing & **0.02** & 0.80 & 0.76 \\ \cline{2-6}  & Hip–ankle & Stance & 0.10 & 0.81 & 0.86 \\  & & Swing & **0.02** & 0.36 & 0.31 \\ \hline \multirow{4}{*}{DI} & Hip–knee & Stance & 0.32 & 0.23 & 0.27 \\  & & Swing & 0.67 & 0.20 & 0.28 \\ \cline{1-1} \cline{2-6}  & Knee–ankle & Stance & 0.87 & 0.15 & 0.92 \\ \cline{1-1} \cline{2-6}  & Wing–ankle & Stance & 0.79 & 0.87 & 0.44 \\ \cline{1-1} \cline{2-6}  & Hip–ankle & Stance & 0.11 & **<0.001** & 0.28 \\ \cline{1-1} \cline{2-6}  & Swing & 0.71 & 0.57 & 0.19 \\ \hline \hline \end{tabular} RMS: root-mean-square; DP: deviation phase; DI: decomposition index. Bold values indicate \(p\)\(<\) 0.05.  
\end{table}
Table 2: Significance level of the Shapiro–Wilk test to check for data normality.  
As shown in Figure 4, for the non-amputated limb group, the hip joint was nearly motionless during the terminal swing, with an average angular velocity of \(0.6\pm 1.6^{\circ}\)/s (flexion) from approximately 91% to 100% of the gait cycle. This was significantly lower than the extension velocity for the controls (\(37.0\pm 6.2^{\circ}\)/s, \(p<0.001\)). An extreme point was observed in the hip angular velocity curve for the amputated limb group. The hip flexion velocity increased after the decline at the frame of \(80.8\%\pm 2.4\%\) of the gait cycle, which was not observed for the non-amputated limb and control groups. During the mid-stance and terminal stance (22% to 36% of the gait cycle), the average dorsiflexion velocities of the ankle for the non-amputated limb group were \(1.9\pm 24.1^{\circ}\)/s--significantly lower than those for the able-bodied controls (\(34.8\pm 30.7^{\circ}\)/s, \(p=0.04\)). The source data of joint angles and joint angular velocities were provided in Supplementary Materials of Table S4 and Table S5, respectively.
## 7 Classification of Mobile Sink based Data Collection Schemes
### Controlled Sink Mobility-Based Schemes  
In this category, those data collection schemes where the sink mobility is under the control of the network and/or observer of the network to achieve some specific goal are discussed. The goal highly depends on applications where in most of the cases it is improving the network lifetime, while some focus on improving packet delivery ratio or reducing data delivery latency to meet real-time communication requirements. In the rest of this section, an overview of the various proposed schemes based on controlled sink mobility is given, including their aims, methodologies, strengths and weaknesses. [PERSON] _et al._ proposed a virtual structure-based data dissemination scheme for efficient data collection [45] using a mobile sink. The proposed scheme aims to reduce message delivery latency thereby optimizing trade-off between network lifetime and delivery latency. To do so, it adopts optimization algorithms (ICRP [46] and ILS [47]) to define optimal density control policies, sensor clustering and sink routes. It imposes topologic constraints (sensor clustering) to reduce energy consumption and to avoid collisions, thereby allowing only CH nodes to communicate with mobile sinks when the sinks arrive at the CH positions. It integrates the clustering and routing problems together, thereby providing a virtual infrastructure (all CH positions) for the subsequent visits of the mobile sinks. The idea behind clusters formation is to minimize the set of nodes that need to be visited by the mobile sinks, thus resulting in reduced message delivery latency along with less energy consumption due to the shorter paths to the sinks. After clustering, it next determines a set of routes for mobile sinks such that every single sensor node is either being covered by that route, or lies within the communication range of another CH in another route. During sink movement, each sink broadcasts a message every second to alert the nearby nodes to be prepared for communication. Sensor nodes upon receiving the alert and coming in contact with mobile sinks forward their stored data (if any). Figure 11: Data dissemination tree model in SEAD.
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 2 Methods
The following flowchart describes the details of the proposed method and how to filter incoherent noise:  
1. Normalize each trace in the processed seismic data and define a rough interval around the initial guess of the first-arrival time. In this method, there will be a rough offset-time window \((W)\), which can be used to isolate the early-arrival waveform we want. 2. Apply the rough window \((W)\) to the seismic data \((d)\) to obtain the muted early-arrival waveform \((WD)\). 3. Apply the proposed interferometric denoising filter (IDF) on the muted waveform \((WD)\) to focus the information with regular and symmetrical patterns on both time and offset domains; in the meantime, the incoherent noise will be suppressed. 4. Normalize each trace in the processed seismic data again. 5. Check the S/R ratio; if it does not improve enough, repeat steps 3 and 4. During this workflow, the incoherent noise will be eliminated iteratively. However, more iterations will destroy useful information. Hence, we can stop processing once the seismic data S/N ratio is acceptable. Usually, 3-4 iterations are enough to give good results. Figure 1: (**a**) A set of noiseless data. (**b**) One trace (green line in (**a**)). (**c**) The space profile (red line in (**a**)). (**d**) A set of pure random noise without a signal. (**e**) One trace (green line in (**d**)). (**f**) The space profile (red line in (**d**)).
## 5 Conclusions and Future Work  
### Conclusions  
This study proposed a modeling method for predicting the tamping maintenance cycle of railway tracks by characterizing the deterioration pattern of track longitudinal level. The following conclusions can be drawn from the study findings.  
* The proposed method employed a power-time-transformed Wiener process to construct the prediction model of each 200 m track segment, allowing simultaneous consideration of heterogeneity and uncertainty in the track geometry deterioration process.
* Both the deterioration parameters and the prediction results of the tamping maintenance cycle for the studied 200 m track segments (\(n=2171\)) were inconsistent, indicating spatial heterogeneity in the deterioration pattern of track geometry.
* For the problem scenario considered in this study, the accuracy and solving efficiency of the MLE method were superior to those of the adaptive MCMC method, and the results obtained using the higher-performance optimization algorithm solver MPA when using the MLE method were more accurate.
* The overall prediction performance of all the prediction models for all segments was robust, meeting the management requirements for tamping maintenance planning over an annual or even longer time span.
* This study is helpful in assisting railway management in shifting the maintenance strategy from period-based preventive maintenance to condition-based predictive maintenance.
## 4 Results and Discussion
### Performance Evaluation  
The confusion matrix also showed promising results, with 192 observed as normal and around 372 pneumonia cases (refer to Figure 10). The ROC graph also shows a positive result with a score of 96.70%, as shown in Figure 11.  
Figure 11: ROC curve.  
Figure 10: Confusion matrix for the proposed approach.  
Table 3 presents the performance analysis of three methods: DCGAN, DCGAN+inceptionV3, and DCGAN+XGradientBoost. Based on their accuracy, precision, recall, and F1 Score, the result shows that DCGAN+XgradientBoost outperformed the other two methods by gaining an accuracy score of 98.88%, precision score of 99.1%, recall rate of 98.70%, and F1 Score of 99.3%.  
Figure 12 compares the proposed approach with the other two techniques. It is observed visually that the overall value of DCGAN+Xboost is exceedingly high compared to other techniques with significant scores.
## 6 Conclusions  
This paper proposes a new terrain entropy calculation method, the Euclidean distance terrain entropy, by calculating the distance between points with the same and different values, followed by appropriate classification. Three experiments were conducted to verify its effectiveness, leading to the following conclusions:  
1. The existing methods exhibit significant computational biases in the presence of special terrain, while the proposed method effectively resolves this issue.
2. The proposed method can effectively capture the increase in the spatial disorder of grid DEMs caused solely by point location exchange, and it shows a significant improvement in measurement ability compared to slope terrain entropy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Window Size** & **Time/s** \\ \hline
3 \(\times\) 3 & 0.039 \\
5 \(\times\) 5 & 0.040 \\
7 \(\times\) 7 & 0.060 \\
9 \(\times\) 9 & 0.107 \\
11 \(\times\) 11 & 0.343 \\
13 \(\times\) 13 & 0.856 \\
15 \(\times\) 15 & 2.317 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation time of Euclidean distance terrain entropy for grid DEMs with different window sizes.  
* Euclidean Distance Terrain Entropy can more accurately reflect the terrain complexity in grid DEMs, especially in simple and extremely complex terrains, where its entropy values match terrain complexity better than other terrain entropy methods.  
Generally speaking, Euclidean distance terrain entropy provides a more precise tool for DTA under special terrain conditions. The main limitation at present is computational efficiency. Future research will focus on optimizing the classification algorithm, in order to enhance computational efficiency. To achieve this, advanced computational methods, such as parallel and cloud computing, may prove to be useful. Further experiments will be conducted to validate the applicability of this method across different DEM datasets and explore the impact of various window sizes on terrain entropy calculations, thereby improving its reliability and applicability in complex terrain and diverse application scenarios.  
Conceptualization, F.Z.; methodology, T.Y.; software, T.Y.; validation, T.Y.; formal analysis, F.Z.; investigation, T.Y.; resources, Y.X. and J.B.; data curation, T.Y.; writing--original draft preparation, T.Y. and J.B.; writing--review and editing, F.Z.; visualization, T.Y.; supervision, F.Z.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China [grant numbers 41671446], Provincial National Nature Science Foundation of Hunan [grant numbers 2021J]J30702], Changsha Municipal Natural Science Foundation [grant numbers kq2402013].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank the anonymous reviewers for their constructive comments.  
The authors declare no conflicts of interest.
## 3 Parameter Assessment for Optimization
### Sensitivity Analysis of Structural Parameters  
Combining the selected BPMSM characteristics and the structural parameters to be optimized, we define the sensitivity (\(f\)) to quantify the effects of the structural parameters. A larger \(f\) means that the optimized performance is more sensitive to the structural parameters. The sensitivity \(f\) is calculated as follows:  
\[f=\left|\frac{\left(y_{jo}-y_{j}\right)/y_{j}}{\left(x_{io}-x_{i}\right)/x_{i}}\right| \tag{4}\]  
where \(y_{j}\) and \(y_{i0}\) is the \(j^{\text{th}}\) performance, before and after optimization, respectively; and \(x_{i}\) and \(x_{i0}\) is the \(i^{\text{th}}\) structural parameter, before and after optimization, respectively.  
According to electromechanics, the length of air gap \(\delta_{0}\) is defined as the difference between the radius of the armature and the outer radius of the rotor:  
\[\delta_{0}=\frac{D}{2}-r \tag{5}\]  
Due to the coupling geometrical relationship between the optimized mechanical structural parameters, when a single structural parameter changes, other structural parameters will change accordingly. The change principle of the BPMSM structural parameters is defined as follows:  
Figure 4: The electromagnetic torque and motor losses.  
When \(b_{t}\), \(h_{t}\), \(r_{3}\), \(N_{1}\), and \(N_{2}\) change individually, all other parameters remain unchanged;  
When \(r\), \(\delta_{0}\), and \(h\) change individually, \(r_{4}\), \(d_{2}\), and \(h_{t}\) will change accordingly.  
According to the above principle, the univariate simulation for the influence study of \(r\), \(\delta_{0}\), \(h\), \(h_{1}\), \(b_{t}\), \(r_{3}\), \(N_{1}\), \(N_{2}\) to \(k_{z}\), \(k_{x}\), \(k\), \(T\), \(P_{Cu}\), and \(P_{Fe}\) is carried out. For each condition of the structural parameters to meet a specified optimized performance, five groups of simulation are performed. The average sensitivity of the individual structural parameters to a specified optimized performance is determined and shown in Figure 5.  
The purpose of sensitivity analyses is to screen out structural parameters with a greater impact on the overall performance. According to Figure 5, the sensitivity of different structural parameters to a specified optimized performance are concentrated as values smaller than 0.3. A larger \(f\) means that the optimized performance is more sensitive to the structural parameters. To reflect the influence of structural parameters on optimized performance with centralized characteristics, we define the threshold of 0.3 as the dominant index for sensitivity evaluations. The sensitivity evaluation index of structural parameters of BPMSM performance is shown in Table 2 using the + symbol. It can be seen from Table 2 that \(h\) and \(r\) appear five times with the highest frequency, \(\delta_{0}\) and \(N_{1}\) appear three times, \(b_{t}\) and \(N_{2}\) appear twice, and the others did not appear. Thus, \(\delta_{0}\), \(h\), \(r\), \(N_{1}\), and \(N_{2}\) are selected as the BPMSM mechanical structural parameters.
## 1 Introduction
The experimental results show that the proposed algorithm performs best in the many scenarios and is comparable in other conditions to the best alternative algorithms, moreover with lower complexity. The rest of the paper is organized as follows: Section 2 reviews AOA modelling in both linear and circular arrays. The mathematical model of the proposed method and the computational complexity between PDDA and other techniques are presented in Section 3. Simulation results, discussion and comparisons between PDDA and other AOA methods appear in Section 4. Finally, Section 5 summarizes the results and sets out conclusions. The following notation is used in the remainder of this paper: lower-case and upper-case bold characters denote vectors and matrices, respectively, whereas the lower-case characters refers to scalar values. E \(\{\cdot\}\) represents the statistical expectation. \((\cdot)^{\mathrm{T}}\) and \((\cdot)^{\mathrm{H}}\) indicates to the transposition and conjugate transposition of a vector or a matrix. \((\cdot)\) represents the estimated value.
## 3 Results and Discussion
### Moisture Content and Water Activity (\(a_{\mathrm{w}}\))  
Table 2 shows the Moisture Content and Water Activity (\(a_{\mathrm{w}}\)). Moisture content is an important parameter to determine the shelf-life of microcapsules and indicates the residual water value on it. Values between 1 and 6% are desirable to preserve the stability during their storage [46]. Powders obtained by spray drying and spout-fluid bed drying had a moisture content of 3.95 \(\pm\) 0.01% and 2.25 \(\pm\) 0.43%, respectively. No significant difference (\(p<0.05\)) was observed. Similar values were obtained by [PERSON] et al. [17] in spray drying of Mexican plum pulp extract using maltodextrin as wall material.  
The water activity (\(a_{\mathrm{w}}\)) is a parameter that indicates stability of a food product. Several authors have reported that \(a_{\mathrm{w}}\) values below 0.6 indicate that the product is stable at the microbiological and chemical level because of the low amount of free water available for biochemical reactions to occur [47]. The values of \(a_{\mathrm{w}}\) obtained by spray drying and spout-fluid bed drying were of 0.25 \(\pm\) 0.01 and 0.26 \(\pm\) 0.03, respectively (Table 2). According to the statistical analysis, no significant difference (\(p<0.05\)) was observed. In this study, considering the low values of \(a_{\mathrm{w}}\) and moisture content of the powder obtained by spray drying and spout-fluid bed drying, good stability of these could be expected.
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.1 Hydrocarbons: Lineal, Branched and Cyclic  
Table 3 shows the influence of breed and finishing diet on hydrocarbon compounds of _longissimus thoracis et lumborum_ muscle of foals. The thirteen substances pertaining to this volatile family were detected in all treatments and were distributed as follows: five lineal hydrocarbons, three branched hydrocarbons, and five cyclic hydrocarbons. As can be seen, breed significantly (_p_ < 0.01) affects the total hydrocarbon content, being higher in BU foals compared with JN foals (2919 _vs._ 2462 AU \(\times\) 10\({}^{4}\)/g fresh meat, respectively). Similarly, the BU group also presented (though not significantly) the highest concentrations of the total lineal (3.97 _vs._ 3.92 AU \(\times\) 10\({}^{4}\)/g fresh meat, respectively), branched (_p_ < 0.001; 4.12 _vs._ 2.96 AU \(\times\) 10\({}^{4}\)/g fresh meat, respectively) and cyclic (_p_ < 0.01; 2911 _vs._ 2455 AU \(\times\) 10\({}^{4}\)/g fresh meat, respectively) hydrocarbons. Nevertheless, our results were lower than those reported by other authors, who studied the volatile profile of raw meat from GM \(\times\) HB [25; 27] and GM [26] foals. However, it is worth highlighting that in these studies [25; 27; 26], although SPME methodology was employed, a different data processing methodology (total ion chromatogram--TIC) was used, while in our study, the extracted ion chromatogram (EIC) is considered for peak detection and quantification. Therefore, our results are not exactly comparable with these works. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Volatile Compounds**} & \multirow{2}{*}{**LRI**} & \multirow{2}{*}{\(m/z\)} & \multicolumn{2}{c}{**JN**} & \multicolumn{2}{c}{**BU**} & \multicolumn{2}{c}{**Sig.
###### Abstract  
Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models' behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explainability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.  
breast cancer; deep learning; explainable AI; Grad-CAM; LIME; t-SNE; UMAP; tomosynthesis; mammography; DBT; CNN; shape classification +
Footnote †: journal: Applied Sciences