esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Monitoring Procedure for Equipment Status
### Monitoring for Equipment Abnormality  
In this paper, we propose a procedure for monitoring and diagnosing changes in equipment status using a supervised learning algorithm that collects vibration data from unlabeled gearboxes, as shown in Figure 2. First, we select an appropriate window size (w) for equipment vibration data collection to monitor equipment status and extract images using continuous wavelet transform (CWT). The image dataset does not yet have teaching labels for normal and abnormal states, so a supervised learning algorithm cannot be used. Using the Euler method, the data are divided into PN and PA datasets. The interval parameter required in the Euler method is related to the latency period of equipment faults and is monitored when interval parameter \(d=1\) and \(d=4\). After assigning pseudo-labels and configuring training and test datasets, it is possible to obtain accuracy and \(F1-score\) image classification performance measures using the CNN image classifier. If the classification performance metric(_Accuracy_, _Precision_, _Sensitivity_, _F1 - score_) is above 0.80, it generally means there is a significant difference between the two datasets.  
Figure 1: Proposed Pseudo-Labeling method; \(T_{n}\): Data time stamp, \(t_{n}\): Classification sequence.  
Figure 2: Flowchart of the proposed procedure (1, 2: Sensor positions/A: Non drive side, B: Drive side).
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 2 Balancing Circuit
### Operation Principle  
MC2 MC is the balancing approach of this circuit, and it can be divided into two modes: n-cell-to-n-cell (n \(\geq\) 1) and n-cell-to-(n\(-\)1)-cell (n \(\geq\) 2). In this section, the operation principle of the balancing main circuit is introduced using a six-cell battery pack as an example.  
Mode-1 (n-cell-to-n-cell): This mode is presented as an example of a three-cell-to-three-cell balancing. Assuming B\({}_{4}\), B\({}_{5}\), B\({}_{6}\) are high-energy cells and B\({}_{1}\), B\({}_{2}\), B\({}_{3}\) are low-energy cells, as shown in Figure 2, currently, the balancing process is as follows: S\({}_{16}\) and S\({}_{24}\) are closed to connect the energy-sending side. S\({}_{31}\) and S\({}_{43}\) are closed to connect the energy-receiving side. Currently, a voltage difference is produced on the energy transmission line of the balancing main circuit by voltage mapping of two primary side inductors of the forward converters. The transferred current will transmit energy from high-energy cells to low-energy cells.  
Figure 2: Three-cell-to-three-cell balancing.  
Mode-2 (n-cell-to-(n-1)-cell): This mode is described by three-cell-to-two-cell balancing as an example. Assume that B4, B5, and B6 are high-energy cells and B1 and B2 are low-energy cells, as shown in Figure 3. The balancing process currently is as follows: S16 and S24 are closed to connect the energy-sending side. S31 and S42 are closed to connect the energy-receiving side. For now, a voltage difference is produced on the energy transmission line of the balancing main circuit by voltage mapping of two primary side inductors of the forward converters. The transferred current will transmit energy from high-energy cells to low-energy cells.
# Evaluation of Resilient Modulus and Rutting for Warm Asphalt Mixtures: A Local Study in Iraq
## 2 Materials and Methods
### Preparation of Specimens  
Cylindrical samples were prepared for HMA and WMA specimens with Marshall specimen dimension, which is (100 mm \(\times\) 63.5 mm) (diameter \(\times\) height) The device of Marshall compactor was used for compacting the samples by applying 75 blows on each face of the specimen according to the ASTM D6926-10 specification. The temperature of mixing and compacting ranges of HMA samples was specified by measuring the viscosity of asphalt binder at 135 \({}^{\circ}\)C and 165 \({}^{\circ}\)C based on ASTM D2493 using the method of Equiviscous [29]. The temperatures for mixing and compacting were set to be equivalent to the viscosities of the asphalt binder, which were 0.17 \(\pm\) 0.02 Pas. sec. and 0.28 \(\pm\) 0.03 Pas. sec., which were (155 and 145) respectively.  
In this investigation, a mixture of WMA was compacted at 120 \({}^{\circ}\)C after mixing at 135 \({}^{\circ}\)C to maintain homogeneity. Based on the literature, experts, and the relevant recommendations of the WMA technology makers, these temperatures were chosen [16; 17; 29; 30; 31; 32; 33]. This method is consistent with the WMA design approach outlined in NCHRP Report 714 [34]. This drop in temperature causes the bitumen's viscosity to decrease, and after crystallization, the bitumen becomes more rigid, which improves the asphalt's resistance to deformation.  
The amount of the optimum asphalt binder content is determined using the volumetric properties of the WMA blends. This will increase the efficiency of the additives of warm mix and lessen the effect of aging [17; 35; 36].
## 4 Proposed Orchestrator
### Solution Considerations
#### 4.2.2 Multi-Provider Network Slice Modification  
Once slices are created, they may be modified. Figure 7 shows the sequence of requests and interactions for NS modification.  
Once the _Multi-Provider Slice Manager_ is configured, the Intelligent Service Leader provides the corresponding service decomposition details of the slice request. The _Multi-Provider Slice Manager_ relies on jobs from the _United Distributed Cloud Mediator_ for guidance on heterogeneous platforms. Cross-domain connectivity is established through the _United Connectivity Resource Manager_. After that, the _Multi-Provider Slice Manager_ establishes  
Figure 6: Multi-provider network slicing creation sequence.  
secure communication with each _Service Manager_ in the relative administrative domain. It then provides service-type specifics (e.g., SLA and policy) related to the corresponding slice request.  
The _Service Manager_, in turn, performs a mapping analysis to identify the network resources, that is, network functions, value-added service, and connectivity, that correspond to certain technology subdomains, and then informs the _Slice Life-Cycle Manager_.  
The _Slice Life-Cycle Manager_ selects the appropriate slice template and creates the desired _slice resource graph_. It then carries out the resource configuration toward the corresponding subdomain by issuing a request toward the respective _Specific-provider NFV-MANO_ and/or _Specific-provider SDN Controller_, which, in turn, needs to create the desired NFV, computing, and connectivity slate. There are two major options when configuring an NFV or computing slate: the _Specific-provider NFVO_ forwards the request directly to the corresponding VIM, or it communicates the request to the relevant VNFM.  
When the request directly reaches the VIM, it represents a situation of resource scaling related to a shared VNF resource. However, requests for instantiating VNFs are handled by the Subdomain VNFM. For the connectivity slate, the _Specific-provider SDN Controller_ performs the necessary network configurations to establish the transport layer and related service chain. A multi-domain NSI becomes operational when all domain-specific NS Subnet Instances (NSSIs) and cross-domain connectivity are configured successfully. Once the resources are granted, an Acknowledgment is returned to the tenant, which also updates the _Multi-Provider Network Slice Selector_.
## 2 Overview of Supplementary Damping Systems
### Principles of Energy Dissipation Mechanisms
#### 2.2.5 Tuned Mass Damping  
Tuned mass dampers (TMDs) operate on the principle of dynamic vibration absorption. By adding an auxiliary mass-spring-damper system tuned to the fundamental frequency of the structure, vibrational energy is transferred from the main structure to the TMD.  
The optimal tuning ratio for a TMD is given by:  
\[\text{f}\_\text{opt}=1/(1+\upmu) \tag{4}\]  
where f_opt is the optimal frequency ratio and \(\upmu\) is the mass ratio of the TMD to the structure. Key characteristics of tuned mass damping include:  
Frequency-tuned behavior.  
Effectiveness in a narrow frequency band.  
Potential for off-tuning due to structural changes.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 5 Discussion  
The results indicate that time-series decomposition techniques can improve the predictive performance. The prediction errors shown in the trends and residuals were mitigated by each other in the additive model. Consequently, the proposed model has a positive effect not only on the predictive performance but also on the followability of trends (downward, steady, or upward). Thus, by predicting trends and residuals individually with external variables, we obtained better results.  
However, besides the advantages of time-series decomposition, the problem of seasonality should always be considered in a timely manner. In this study, repetitive seasonality values were derived within a fixed period, 2003-2020; however, a longer time period with the addition of new data requires different seasonality values. Therefore, new seasonality values must be considered for a longer time period because there is a higher possibility of obtaining different values for seasonality.  
In addition, the data used in our research were collected from a system called Port-MIS. However, the actual volume of the containers may not be the same as that of the system. The actual volume can be determined based on the terminal's working history. This means that the information received in the system differs from the actual volume processed in the terminal. Thus, an error is generated from the data. Therefore, precise volume prediction may be possible based on the information of the actual container volume. Furthermore, the unit of measurement used in the experiment is a twenty-foot equivalent unit (TEU); however, the container has various TEU units that do not assure a precise index while aggregating or unifying the actual container volume from the system.
## 2 Materials and Methods
### Statistical Analysis  
Reliability and level of agreement between the force variables within (intra-session) and between (test-retest) sessions were determined by the intraclass correlation coefficient (ICC), standard error of the measurement (SEM) and coefficient of variation (CV). Two-way mixed-effects, absolute agreement model in ICC was conducted according to guidelines for test-retest and intrarater reliability [28]. The SEM was calculated from the square root of the mean square error term in a repeated-measures ANOVA to determine the measurement error and between-participant variability [29]. The CV was calculated relative to the SEM as a percentage (CV = 100 SEM/mean) [29]. Criteria for acceptable reliability were set for very high (CV \(\leq\) 5%, ICC \(\geq\) 0.90), high (CV \(\leq\) 10%, ICC \(>\) 0.90) and moderate (CV \(\leq\) 15%, ICC \(>\) 0.80) according to previous studies testing the reliability of RFD and impulse during isometric exercises [19; 20; 21]. Inter-limb asymmetries \(>\)15% were considered as high [30]. Normal distribution was verified by Kolmogorov-Smirnov tests. Student's \(t\)-test for paired samples was performed to identify significant differences (\(p<\) 0.05) between the test and retest conditions. Effect size (ES) was calculated [31] to estimate the magnitude of the differences using the Hedges' \(g\) and interpreted as low (0.20) medium (0.50) and high (0.80).  
Figure 2: Example (screen-capture obtained from the force sensor software) of some parameters for the force-time curve during the maximal isometric knee extension test. Raw data (original signal) are in black. Fitted data (inverse monoexponentially function that better fits the raw data) are in grey. Note: Fmax is named as maximal voluntary concentric force (MVC) in the present manuscript.
## 4 Results and Discussion
### 2D vs 3D  
Analysing the performance of the RF and the ResNet-34 architectures presented in Table 7, it is clear that the 3D approach yielded better overall results than the 2D approach for both pipelines. For the RF model in the internal test set, the AUC value for the 3D model was 0.836 compared with 0.806 for the 2D model. In addition, for the ResNet-34, the 3D CNN presented an AUC of 0.841 compared with 0.783 for the 2D CNN. The 3D models also increased the balanced accuracy.  
These results are supported by the fact that the 3D models take into account the spatial context of the complete CT volume, capturing spatial relationships across consecutive slices. Consequently, a 3D input allows the recognition of tumour characteristics, patterns, and structures along the whole volume that could not be fully perceived with the 2D inputs. Given the proven advantages of utilising 3D inputs, the 3D version of the more complex models within each pipeline (XGBoost and Hybrid ViT) was selected.  
To better understand the differences in the decision-making process between the classifiers based on 2D and 3D ROISs, the ten most important features were extracted for the classical machine learning classifiers, the results are shown in Figure 8. In addition, the importance of the features was computed according to the three types of radiomic features considered in this work, the results are present in Table 9. Looking at the results, it is possible to see that the textural features have a higher impact on the classifiers' decisions and that in the sets of the most important features are present both first-order statistical and textural features. In both RF classifiers, the importance of shape-based features is low and the impact of textural features is higher in the 3D RF.  
Figure 8: Feature importance for the 10 most important features for the (**a**) 2D Random Forest, (**b**) 3D Random Forest and (**c**) 3D XGBoost Classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Input & Classifier & Test Set & AUC &
\begin{tabular}{c} Balanced \\ Accuracy \\ \end{tabular} & Precision & Recall & Specificity \\ \hline \multirow{4}{*}{3D} & \multirow{2}{*}{XGBoost} & Lung-PET-CT-Dx & 0.853 & 0.801 & 0.909 & **0.909** & 0.692 \\  & & NSCLC-Radiomics & 0.570 & 0.533 & 0.176 & **0.872** & 0.193 \\  & & NSCLC-Radiogenomics & **0.745** & 0.550 & 0.820 & **0.980** & 0.120 \\ \cline{2-7}  & \multirow{2}{*}{Hybrid ViT} & Lung-PET-CT-Dx & **0.869** & **0.816** & **0.927** & 0.864 & **0.769** \\  & & NSCLC-Radiomics & **0.617** & **0.580** & **0.208** & 0.638 & **0.521** \\  & & NSCLC-Radiogenomics & 0.613 & **0.567** & **0.827** & 0.892 & **0.240** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test results (AUC, balanced accuracy, precision, recall and specificity) of XGBoost and Hybrid ViT on three different test sets (Lung-PET-CT-Dx, NSCLC-Radiomics, and NSCLC-Radiogenomics) for 3D input type. The highest values within each metric are highlighted in bold for each test set.
## 3 The Proposed Sensor Deployment Algorithms
### Problem Analysis
The selection criteria for the four optimization objectives above are as follows: on the one hand, they must be dimensionless; on the other hand, the values of the objective functions should lie within the range [0, 1], requiring normalization. In the objectives mentioned above, Equation (7) represents the deployment cost, which is negated and normalized; Equation (8) represents the sum of the sensing probabilities for each monitored target, normalized; Equation (9) represents connectivity, summed and normalized; and Equation (10) represents energy consumption within the monitoring area, also normalized. Since there are conflicts among these four optimization objectives when optimizing them simultaneously, generally speaking, a higher value of the Equation (7) function indicates lower deployment costs, implying fewer sensors used or the use of lower-cost sensor types. Consequently, this often results in a lower overall sensing probability in Equation (8), reduced connectivity in Equation (9), and increased energy consumption in Equation (10). We aim for low costs and energy consumption while achieving high comprehensive sensing probability and connectivity. In this study, we convert the multi-objective optimization problem into a single-objective optimization problem using a weighted sub-objective approach, mathematically described as follows:  
\[ \begin{split} F&=\alpha_{1}f_{1}+\alpha_{2}f_{2}+ \alpha_{3}f_{3}+\alpha_{4}f_{4}\\ &=\alpha_{1}(1-\frac{\sum\limits_{i=1}^{M}d_{i}P_{i,\alpha}b_{i, \mu}}{\sum\limits_{i=1}^{M}\max(d_{i}P_{i,\mu}\max(b_{i,\mu}))})+\alpha_{2} \frac{1}{W}\sum\limits_{i=1}^{W}PR(s_{i},t_{i})\\ &\quad+\alpha_{3}\frac{1}{S}\sum\limits_{i=1}^{S}y(s_{i,\mu})+ \alpha_{4}(1-\frac{\sum\limits_{i=1}^{T}P_{i,\mu}s_{i,\mu}^{2}}{\sum\limits_{ i=1}^{T}P_{i,\mu}s_{i,\mu}^{2}})\end{split}  \tag{11}\]  
where \(\alpha_{1}+\alpha_{2}+\alpha_{3}+\alpha_{4}=1\) and \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), \(\alpha_{4}\) are the corresponding weight of the objective function. \(\alpha_{1}\) stands for the weights of deployment costs function, \(\alpha_{2}\) is the weights of sensing probabilities function, \(\alpha_{3}\) stands for weights of connectivity function, and \(\alpha_{4}\) is the weights of energy consumption. For each function, we assign different weight values to focus on different application scenarios. Therefore, in this study, we propose a new concept: the comprehensive network performance of a wireless sensor network. The formula is shown above, and the comprehensive network performance can provide an evaluation of the wireless sensor network's performance.
## 6 Conclusions  
This study proposes a method using the LM-OBF approach in response to the limited accuracy and robustness of magnetic source parameter estimation under strong background noise conditions. This method combines magnetic anomaly detection techniques with conventional parameter optimization algorithms, establishing a novel forward modeling relationship. It transforms magnetic anomaly data into a two-dimensional orthogonal basis function space through the Gram-Schmidt orthogonalization process. An objective function is then constructed within a least squares framework and optimized using the LM algorithm, achieving a stable estimation of magnetic source parameters. Results from synthetic models and field experiments demonstrated that the LM-OBF algorithm can accurately and stably estimate the position, magnitude of magnetic moment, and directional parameters of magnetic dipoles, even under a minimum signal-to-noise ratio of \(-10\) dB. However, this method still encounters several challenges in practical applications: (1) The OBFs method shows limited adaptability to non-Gaussian noise, potentially reducing the effectiveness of LM-OBF parameter estimation in complex real-world noise environments. (2) The parameter estimation speed of the LM-OBF algorithm is slow for large-scale data, making it unsuitable for scenarios requiring real-time estimation. (3) Effectively isolating the magnetic target source from the background field to ensure that the inversion data contains only the target magnetic anomalies is crucial for accurate parameter estimation. Addressing these issues is critical to further advancing magnetic source parameter estimation under strong noise interference.  
Methodology, J.Z.; Software, Z.Y.; Resources, J.Z.; Writing--original draft, Z.Y.; Writing--review & editing, Z.Y.; Supervision, Z.Z.; Funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Ministry of Science and Technology of the People's Republic of China under Grant 2022 YFC3003402, and the National Natural Science Foundation of China under Grant 42074119.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.
# The Role of Advanced Manufacturing Technologies in Production Process Performance: A Causal Model
## 5 Discussion, Conclusions and Industrial Implications  
### The Most Important Effects and Their Industrial Implications  
* The largest direct effect between the latent variables is from _Stand-Alone Systems_ on _Intermediate Systems_ with a value of 0.636, which indicates that having an appropriate implementation of technologies, such as CAD, CAPP, CNC, and MWL will ease the implementation of automated material handling and inspection, as well as testing systems, consequently. This finding is essential for managers, because it indicates that _Stand-Alone Systems_ support the _Intermediate Systems_ implementation that have a higher automation and integration level, taking advantage of the experience obtained by operators, as is indicated by [PERSON] and [PERSON] [73]. * The second biggest direct effect is between _Intermediate Systems_ and _Integrated Systems_ with \(\beta=0.613\), which indicates that technologies as AS/RS, AMHS, and AITE will support the FMC/FMS, CIM, JIT, MRP, and MRP II. This finding is common sense because they are related to material flow or production organization. For instance, it is reported that AS/RS and AMHS facilitate JIT, because they support the material flow [62, 74]. * In the same way, _Stand-Alone Systems_ are those that have the greatest total effect on the _Production Benefits_ (0.521); as a result, their appropriate implementation will improve productivity indexes such as low cost production and processing time. However, with _Stand Alone Systems_ managers can also obtain better quality, plant capacity, and distribution. Also, this relationship has the greatest indirect effect with a value of 0.390 through _Intermediate Systems_, and it suggests that _Stand-Alone Systems_ are the most relevant AMT in the maquiladora industry, because it is a result of the support of other more integrated technologies. * Finally, the direct effect from _Integrated Systems_ on _Production Benefits_ have a value of 0.439, which indicates an AMT integration flow, because taking care of the biggest direct effects, the following route seems to be crucial among variables, indicating that the AMT implementation process is gradual: _Stand-Alone Systems_\(\rightarrow\)_Intermediate Systems_\(\rightarrow\)_Integrated Systems_\(\rightarrow\)_Production Benefits_