esa-sceva/satcom-chunk-collection huggingface dataset:
## A Novel Comprehensive Method for Modeling and Analysis of Mesh Stiffness of Helical Gear
###### Abstract  
The mesh stiffness of gear pairs used in aerospace applications, such as geared turbofan, has a vital influence on vibration and noise. To compensate for the deficiencies of the conventional method that does not consider slice coupling and structure coupling simultaneously, a comprehensive mathematical model for computing the mesh stiffness of helical gears is established. In this novel model, the effect of structure coupling and slice coupling between neighboring sliced gears are considered. The effect of the axial component of meshing force is also taken into account simultaneously. The results obtained by the comprehensive model are consistent with the finite element method and it proves that the novel mathematical model is sound. The influences of the helical angle and addendum modification coefficient on mesh stiffness are studied. The results show that the mesh stiffness of helical gears would be decreased in multiteeth regions caused by structure coupling. With or without consideration of the axial component, the relative mean values of mesh stiffness become larger with an increasing helical angle. The fluctuation value of mesh stiffness decreases when a positive addendum modification coefficient is adopted. The addendum modification also changes the phase of mesh stiffness. This study is helpful for a vibration analysis of gear transmission systems.  
m 2020 10 6695 22 September 2020; Published: 24 September 2020
# Deep Learning for Computational Mode Decomposition in Optical Fibers
## 1 Introduction
First, a study considered the suitability of a trained deep neural network (DNN) for this task [23]. In this work, the DNN-based measurement system is quantitatively compared to an established holographic measurement system. Furthermore, an optimized training data design is presented, which efficiently creates combinations from the mode domain of an MMF.
# Experimental Study on the Effect of Pile-End Soil on the Pile Load Transfer Law
## 3 Analysis of Test Results
### Analysis of the Skin Friction  
According to the difference between the axial force of the pile body between the two measuring points divided by the pile side surface area of the section, the skin friction \(\mathrm{f_{i}}\) of the section can be obtained. The calculation formula is:  
\[\mathrm{f_{i}}=\frac{\mathrm{P_{i-1}-P_{i}}}{\mathrm{U\cdot l_{i}}} \tag{2}\]  
where \(\mathrm{P_{i-1}}\), \(\mathrm{P_{i}}\) represents the axial force values of two adjacent measurement points above and below the pile section i. U represents the pile circumference. \(\mathrm{l_{i}}\) represents the length of the pile section i.  
From the calculation results, the skin friction distribution curves of the two test piles are obtained. The linear stage is a solid line, the local shear stage is a dashed line, and the failure stage is a dotted line, as shown in Figure 7.  
In Figure 7, it can be seen that skin friction is gradually exerted with the increase of the load. Affected by the load, the pile body is compressed and deformed, resulting in the relative displacement of the pile and the soil around the pile, thus generating skin friction to bear the upper load. Overall, skin friction tends to increase first and then decrease and increase as the load increases, and the curve is characterized by \"small on both sides and large in the middle\". Both piles decrease skin friction along the pile body after peaking with increasing load, and the rate of decrease is greater for conventional piles than for pile-end soilless compressive pile. At the ultimate load, the maximum skin friction of the pile-end soilless compressive pile is 43.46 kPa, and the maximum skin friction of the conventional compressive pile is 42.06 kPa. The soil layers corresponding to the peaks of the skin friction of the two piles are silty clay-muddy silty clay layers. The maximum skin friction of the two piles is similar in value, indicating that the skin friction performance of the two test piles in this soil layer is almost the same. At the pile-end position, the skin friction of the two piles are 35.45 kPa and 25.55 kPa, respectively. The skin friction of the conventional compressive pile is about 28% lower than that of the pile-end soilless compressive pile.  
Figure 7: Distribution curve of the skin friction: (**a**) the pile-end soilless compressive pile and (**b**) the conventional compressive pile.
## 4 Experiments  
We evaluated the proposed tracker with state-of-the-art trackers such as KCF [21], DSST [22], FRAG [15], LSHT [37], MIL [6], STRUCK [5] and TLD [9] for quantitative performance evaluation. The experiment was conducted with challenging sequences in the OTB-100 [39], and it included various attributes for natural sequences. Furthermore, we conducted experiments using one-pass evaluation (OPE). OPE is a general performance evaluation method used by the object tracking benchmark (OTB) [39].
## 2 Methodology, Fundamentals, and Materials
### Organizational Areas and Maturity Models  
Organizational functions, as described from [PERSON], can be divided into primary and support functions, which are activities that describe the value chain of an organization that are related to its competitive strength. Primary activities are directly concerned with the creation or delivery of a product or service. They can be grouped into five main areas: inbound logistics, operations, outbound logistics, marketing and sales, and service. Primary activities are linked to support activities which help to improve their effectiveness or efficiency. There are four main support activities: procurement, technology development, human resource management, and infrastructure [22].  
In the organization context, the notion of maturity is used to define, assess and form a guideline and a basis for evaluating the progress in business (i.e., the maturity of process or a technology). As the degree of maturity becomes higher, better progress is achieved in different aspects that contribute to the maturation of the entity [16]. To measure the degree of progress and advancement, maturity models were developed. Usually applied to new technologies, their goal is to provide insight into continuous process improvement and status quo analysis. As a whole, the maturity model describes an anticipated, desired or typical development trajectory [17]. There are various well-accepted generic software process capability/maturity models (SPCMMs), as well as models based on the ISO/IEC 15504 in providing a common baseline for capability assessment, and to report the assessment outcome by employing a common measurement scale [16]. [PERSON] et al. [23] have developed a maturity model of organizational structure that can be used to manage the digital transformation of companies in the context of industry 4.0. Having highly skilled workforces with a high degree of individual responsibility in a dynamic environment, collaborating within the entire value network, is of great importance [24]. A common procedure to methodically support the implementation of Industry 4.0 has not yet been established throughout the industry and, hence, maturity models are not notably common as well. Moreover, procedure models are generally applied, which mostly lack a specific assessment or measurement, as well as a goal-oriented model [17]. Maturity models or frameworks aim to assist organizations by providing comprehensive guidance for roadmap definition [16]. Industry 4.0 investments should be carried out using an integrative approach that usually involves a roadmap, which is an overall plan for the deployment of new technologies, containing organizational change, that explains the main phases that needs to be taken. This digital transformation roadmap is unique for every manufacturing company and requires the involvement of all organizational levels and decision makers [25].
## 3 Application of APD to a Soft Clay Site
### Soil Parameters
#### 3.2.2 Stress History  
Stress history is commonly characterised using the overconsolidation ratio (OCR). The selected methods for computing OCR are presented in Table 2. It is important to note that some of these methods require effective vertical stresses, which are calculated based on the initial unit weight (discussed in Section 3.2.1).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Workflow** & **Method** & & **Author** \\ \hline \multirow{6}{*}{CPT} & \(\gamma_{w}[0.27(\log R_{f})+0.36(\log q_{l}/p_{a})+1.236]\) & (1) & [16] \\  & \(19.5-2.87[\dfrac{\log(\frac{9000}{q_{l}})}{\log(\frac{20}{R_{f}})}]\) & (2) & [17] \\  & \(26-\dfrac{14}{1+[0.5\log f_{s}+1]^{2}}\) & (3) & [18] \\  & \(0.254\cdot\log(\dfrac{q_{t}-u_{2}}{p_{a}})+1.54\) & (4) & [19] \\ \hline \multirow{6}{*}{DMT} & from [PERSON]’s chart & [15] \\  & \(\gamma_{w}\cdot 1.31(\dfrac{P_{1}}{p_{a}})^{0.161}\) & (5) & [20] \\  & \(\gamma_{w}\cdot 1.35(\dfrac{P_{0}}{p_{a}})^{0.159}\) & (6) & [20] \\  & \(\gamma_{w}\cdot 1.32(\dfrac{P_{1}}{p_{a}})^{0.091}(\dfrac{P_{0}}{p_{a}})^{0.0733}\) & (7) & [20] \\  & \(\gamma_{w}\cdot 1.47(\dfrac{E_{D}}{p_{a}})^{0.045}\) & (8) & [20] \\ \hline \multirow{6}{*}{\(V_{s}\)} & \(8.31\log V_{s}-1.61\log z\) & (9) & [21] \\  & \(4.17\ln V_{s1}-4.03\) & (10) & [22] \\ \cline{1-1}  & \(6.87V_{s}^{0.227}\) & (11) & [23] \\ \cline{1-1}  & \(4.96+5.97\log V_{s}\) & (12) & [24] \\ \hline \end{tabular} Note: \(\gamma_{w}=\) unit weight of water; \(R_{f}=\) friction ratio; \(p_{a}=\) atmospheric pressure; \(E_{D}=\) dilatometer modulus; \(z=\) depth; \(\sigma_{v}^{\prime}\)= vertical effective stresses; \(V_{s1}=\) effective stress-normalised shear wave velocity (\(V_{s1}=V_{s}/(\sigma_{v}^{\prime}/p_{a})^{0.25}\)).  
\end{table}
Table 2: Selected methods for the overconsolidation ratio (\(OCR\)).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Workflow** & **Method** & & **Author** \\ \hline \multirow{6}{*}{CPT} & \(\dfrac{0.33(q_{t}-\sigma_{v})^{m^{\prime}}}{\sigma_{v}^{\prime}};m^{\prime}=1- \dfrac{0.28}{1+(\frac{I_{w}}{2.65})^{25}}\) & (13) & [25; 26] \\  & \(0.25Q_{l}^{1.25}\) & (14) & [27] \\  & \(0.2+0.39Q_{l}\) & (15) & [28] \\  & \(1.02B_{l}^{-1.077}\) & (16) & [29] \\  & \(0.63B_{l}^{-1.286}\) & (17) & [30] \\ \hline \multirow{6}{*}{DMT} & \((0.5K_{D})^{1.56}\) & (18) & [31] \\  & \(2(\dfrac{P_{0}-\sigma_{v}}{6.63\sigma_{v}^{\prime}})^{1.19}\) & (19) & [32] \\ \cline{1-1}  & \(0.24K_{D}^{1.32}\) & (20) & [33] \\ \cline{1-1}  & \(-0.0135K_{D}^{2}+0.4959K_{D}-0.0359\) & (21) & [34] \\ \hline \end{tabular}
\end{table}
Table 1: Selected methods for the unit weight (\(\gamma_{l}\)).
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 5 Quantitative Performance Analysis of Leading AIS Methods in IoT Malware Detection  
In this section, we highlight the main criteria to evaluate the performance of the most promising AIS methods in the literature for malware detection in the IoT [50; 51; 54]. The three most recent AIS solutions for securing the IoT are selected to present a quantitative performance analysis. These methods are selected because of their promising results (accuracy and false-negatives), which we were able to reproduce to enable the quantitative performance analysis. A false-negative denotes a malware that is falsely classified as benign. It follows that a better malware detection method is one that results in fewer false-negatives. There are different datasets used to evaluate IoT security solutions. The most used datasets, as listed in [61], are the NSL-KDD, the Bot-IoT, the Botnet, and the Android malware datasets. In this performance analysis, we chose to use the NSL-KDD [55] for two reasons. First, unlike the other datasets, the NSL-KDD eliminates the redundant records in the previous dataset (KDD'99), resulting in a reduction of the number of borderline records compared to any other dataset [61]. This leads to more accurate results when evaluating an AIS-based security solution. Also, by eliminating the borderline records, we reduce the total number of records (see details in Table 2), unlike the Bot-IoT [62] which has 72,000,000 records. Using a larger number of records to evaluate an IoT security solution might overwhelm the system when running the solution in an actual IoT system setup. Second, the NSL-KDD dataset is used to evaluate the NPS and NShN methods. Consequently, in order to enable a quantitative performance analysis, we reproduce the results of the MNSA using the same NSL-KSS dataset. The traffic data were captured by running 420 machines and 30 servers in 5 different departments. Although the NSL-KDD dataset is not IoT specific, it contains various malware attack types and offers different file features to test security solutions, which makes it a good fit for this experiment's purposes. In contrast to other machine learning approaches, AIS requires minimal data to create necessary detectors that are later used in the detection phase. In our case, 10% of randomly selected samples of the dataset are used in the detector generation phase, and the remaining 90% are used for testing. We compare the performance from two perspectives: in Section 5.1, we analyze the detection accuracy and F1-Score of each; in Section 5.2 we examine the complexity of each algorithm from both time and memory perspectives.
# Motion Characteristics of Self-Sensing Piezoelectric Actuator for Yarn Micro-Gripper
## 3 Performance and Self-Sensing Testing of BPBA
### Output Force Self-Sensing Test
#### 3.5.2 Comparative Analysis  
Figure 13b shows the output voltage \(V_{out}\) obtained from oscilloscope measurements. The output voltage value exhibits some offset due to the bias current of the op-amp chip.  
Figure 12: Test result of bending motion experiment. (**a**) The velocity of the end of the actuator, (**b**) the acceleration for the corresponding period.  
Figure 13: Test result of output force self-sensing experiment. (**a**) Input voltage, (**b**) output voltage, (**c**) comparison of estimated and measured output force, (**d**) the relative error of the two values.  
This is due to the fact that charge loss and bias problems may occur when the device is used for a long period of time. In this case, periodic charge resets would be used to maintain sensing accuracy. This can also be ameliorated by applying a very low rate of increase/decrease of input voltage to the driver.  
Figure 13c shows a plot comparing the estimated value F\({}_{\text{est}}\) obtained from the self-sensing circuit with the measured value \(F_{meas}\) obtained from the external force sensor. The two force curves almost overlap and the relative error between the two is represented in Figure 13d, with a maximum relative error of 1.6% and an average relative error of 0.74%. In the meantime, the relation between input voltage and output force is not zero-order hold, and the higher the input voltage, the more obvious. This is caused by the creep behavior of piezoelectric ceramic materials. As the input voltage increases, the effect produced by the leakage resistance increases.
## Theoretical Framework
### Network Training
\right]^{\mathsf{T}} \tag{15}\]  
Therefore, the gradient could be represented for the Jacobian matrix by the following expression:  
\[\mathbf{G}=\mathbf{J}^{\mathsf{T}}\boldsymbol{e}. \tag{16}\]  
Each element of the Hessian matrix can be expressed in terms of error \(e_{p\phi}\) resolving the cross partial derivative of the sum square error:  
\[h_{ij}=\frac{\partial^{2}\mathbf{E}}{\partial\mathbf{w}_{i}\partial\mathbf{w} _{j}}=\frac{\partial^{2}\Big{(}\frac{1}{2}\sum_{p=1}^{N_{p}}\sum_{\phi=1}^{N_{ o}}e_{p\phi}^{2}\Big{)}}{\partial\mathbf{w}_{i}\partial\mathbf{w}_{j}}= \sum_{p=1}^{N_{p}}\sum_{\phi=1}^{N_{o}}\Bigg{(}\frac{\partial e_{p\phi}}{ \partial\mathbf{w}_{i}}\frac{\partial e_{p\phi}}{\partial\mathbf{w}_{j}} \Bigg{)}+\overline{S}_{i,j}, \tag{17}\]  
where \(\overline{S}_{i,j}\) is:  
\[\overline{S}_{i,j}=\sum_{p=1}^{N_{p}}\sum_{\phi=1}^{N_{o}}\Bigg{(}\frac{ \partial^{2}e_{p\phi}}{\partial\mathbf{w}_{i}\partial\mathbf{w}_{j}}e_{p\phi }\Bigg{)}, \tag{18}\]  
For the Gauss-Newton method, it is assumed that \(\overline{S}_{i,j}\approx 0\); therefore, the Hessian matrix could be related with the Jacobian matrix by the following form:  
\[\mathbf{H}\approx\mathbf{J}^{\mathsf{T}}\mathbf{J}. \tag{19}\]  
The updating of the weights by Gauss-Newton method is obtained as a function of the matrix of first-order partial derivatives:  
\[\mathbf{w}+\Delta\mathbf{w}=\mathbf{w}-\Big{(}\mathbf{J}^{\mathsf{T}}\mathbf{ J}\Big{)}^{-1}\mathbf{J}^{\mathsf{T}}\boldsymbol{e}. \tag{20}\]  
The Gauss-Newton algorithm has better performance than the Newton method because it does not require the calculation of Hessian matrix. However, the Gauss-Newton algorithm could have problems of matrices not invertible. In order to resolve this problem, LM modified the Hessian matrix with an approximation:\[\mathbf{H}\approx\mathbf{J}^{\mathrm{T}}\mathbf{J}+\upmu\mathbf{I}, \tag{21}\]  
where \(\upmu\) is a value always positive such with combination to an identity matrix \(\mathbf{I}\) makes positive definite and therefore can be invertible. The updating of the weights by LM method can be presented as:  
\[\mathbf{w}+\Delta\mathbf{w}=\mathbf{w}-\left(\mathbf{J}^{\mathrm{T}}\mathbf{J }+\upmu\mathbf{I}\right)^{-1}\mathbf{J}^{\mathrm{T}}e. \tag{22}\]  
Calculation of Jacobian Matrix  
According to Equations (4) and (15), the elements of Jacobian matrix can be calculated as:  
\[\frac{\partial e_{p\dot{o}}}{\partial\mathrm{w}_{i}}=\frac{\partial\left(t_{p \dot{o}}-y_{p\dot{o}}\right)}{\partial\mathrm{w}_{i}}=-\frac{\partial y_{p \dot{o}}}{\partial\mathrm{w}_{i}}. \tag{23}\]  
Considering an ANN of one hidden layer (Figure 2), the backpropagation is used in order to resolve the Equation (23). As shown in Figure 2, the superscript denotes the position of the layer in the network while the subscript describes the position of the neuron in the layer. In the weights, the double subscript indicates the neural connection in backpropagation order. The number of input and hidden neurons are defined by \(N_{x}\) and \(N_{2}\), respectively.
## 5 Conclusions and Future Work
### Future Work  
Future research can be pursued in the following directions:  
1. Further enhance the model's robustness in extremely long-distance and dynamic time-varying channels, and improve the extraction of weak phase features by incorporating semi-supervised learning techniques. This will address the challenges of low SNR and phase ambiguity in complex underwater environments.
2. Expand the coverage of modulation types and develop a multi-task learning framework to achieve joint optimization of modulation recognition and channel estimation. This integrated approach will enhance the model's versatility across diverse communication protocols.  
Ultimately, translating the technology into practical applications will involve catering to the specific needs of both military and civil sectors, ensuring its adaptability to real-world underwater acoustic scenarios.  
Conceptualization, F.Z.; methodology, F.Z.; software, F.Z.; validation, F.Z., H.W.; formal analysis, Z.Y.; investigation, H.L.; resources, F.Z.; data curation, H.W.; writing--original draft preparation, F.Z.; writing--review and editing, F.Z. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** I apologize for the inconvenience, but the dataset used in this study is derived from a specific project. Publicly disclosing it would compromise the intellectual property rights and interests of the project due to confidentiality agreements. We appreciate your understanding of this constraint.  
The authors declare no conflicts of interest.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
###### Abstract  
The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, 5G also presents itself as a key enabler to fulfill Industry 4.0 requirements. In this article, the authors first propose a 5G-enabled architecture for Industry 4.0. Smart Networks for Industry (SN4I) is introduced, an experimental facility based on two 5G key-enabling technologies--Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)--which connects the University of the Basque Country's Aeronautics Advanced Manufacturing Center and Faculty of Engineering in Bilbao. Then, the authors present the deployment of a Wireless Sensor Network (WSN) with strong access control mechanisms into such architecture, enabling secure and flexible Industrial Internet of Things (IIoT) applications. Additionally, the authors demonstrate the implementation of a use case consisting in the monitoring of a broaching process that makes use of machine tools located in the manufacturing center, and of services from the proposed architecture. The authors finally highlight the benefits achieved regarding flexibility, efficiency, and security within the presented scenario and to the manufacturing industry overall.  
industry 4.0; 5G; NFV; SDN; IIoT; access control; manufacturing process; aeronautics advanced manufacturing center +
Footnote †: journal: Applied Sciences