esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Cyclic fatigue experiments were performed on Al7075-T6 to investigate potential damage indicators from AE signals. Rather than accepting AE summary statistics such as counts and energy as fully informative damage measures, information entropy was calculated based on the raw voltage data from each recorded AE signal. Three different methods of estimating the information content and the disorder from AE signals were proposed. After developing methods to reduce AE noise, several conclusions were made regarding estimating damage with novel information entropy measures of AE signals. Results support that the proposed instantaneous entropy is a more sensitive statistic and may better correlate to fatigue damage than the AE counts and energy, while the proposed average and weighted average entropy methods provide unique damage trends that could differentiate between small and large cracks. These conclusions are drawn based on coupon-scale fatigue experiments with one high-strength aluminum alloy and therefore the effectiveness of monitoring information entropy  
Figure 14: Weighted average entropy trends versus fatigue cycles through two damage levels: (**a**) weighted average entropy until a 1-mm crack; and (**b**) weighted average entropy until fracture.  
Figure 13: Average entropy trends versus fatigue cycles through two damage levels: (**a**) average entropy until a 1-mm crack; and (**b**) average entropy until fracture.  
metrics over traditional AE summary statistics would need to be verified for other test conditions and materials for the purpose of the model generalization. Even still, when AE is used as an NDE technique to assess fatigue damage and supplement remaining useful life models for aircraft, the information entropy of the signals should be considered to be an important damage metric in addition to the traditionally used signal features.  
We have repeated the experiments eight times and the trend of evolution of the information entropy is relatively similar for test of different conditions. Considering the inherent scatter in the results of fatigue test we expect to have relatively similar trends if the tests are repeated.  
Supplementary materials are available online at [[http://www.mdpi.com/2076-3417/7/6/562/s1](http://www.mdpi.com/2076-3417/7/6/562/s1)]([http://www.mdpi.com/2076-3417/7/6/562/s1](http://www.mdpi.com/2076-3417/7/6/562/s1)).  
This research was supported by the Office of Naval Research under grant number #N00014140453. The authors are grateful for their support.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the manuscript; [PERSON], [PERSON], and [PERSON] revised the manuscript, and [PERSON] supervised the research.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Results and Discussion
### XPS Measurements of the Spent Adsorbents
Specifically, both the binding energies of Fe 2\(p_{3/2}\) and Fe 2\(p_{1/2}\) relative to that of the FeC sample after dye adsorption show similarities with that of Fe sample. Also, the BE are due to the Fe\({}^{3+}\) and Fe\({}^{2+}\) species present on the surface. Reasons for this phenomenon can be clearly related to the fact of the stability of the Fe\({}_{3}\)O\({}_{4}\) (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)) phase in the spent FeC composite. Accordingly, the BE at 529.9, 531.4 and 532.8 eV from O 1\(s\) can be ascribed to the lattice oxygen of Fe species and oxygen surface hydroxyl groups and from the adsorbed dye. Moreover, the C 1\(s\) and N 1\(s\) binding energies are similar to those of their Fe oxide counterpart, implying that the FeC and the dye interaction are relatively strong. The surface atomic concentration is C: 52.33%, N: 10.94%, O: 30.33%, Fe: 6.05% and S: 0.35%. Such composition indicates that the amount of carbon and nitrogen are much higher than that in the Fe sample after dye adsorption due to the fact that the dye adsorption is greater on the FeC composite, as shown in the adsorption studies results. However, the % of N on the FeC surface is higher than that that of Fe sample, which indicates the probably dye adsorption takes on the later sample. As for the FeC sample, Fe/FeC sample after dye adsorption has similar values of C 1\(s\), O 1\(s\) and N 1\(s\) binding energies range to FeC. On the other hand, the composition is slightly different, e.g., C: 47.77%, N: 8.26%, O: 35.58%, Fe: 8.218% and traces of ca. 0.17% from sulfur, which shows a smaller amount of the C, N, S and O from the dye on the surface of the adsorbent. This lower surface content of the elements from the dye is due to the higher surface area of this adsorbent (202 m\({}^{2}\cdot\)g\({}^{-1}\)). In this way part of the organic dye is into the mesopores of the solid and cannot be detected by XPS. Thus, the availability of the Fe species could explain the better performance of Fe/FeC towards the adsorption of acid Red 66, compared with Fe and FeC analogues.
## 3 Methodology
### Multi-Scale Attention  
For the trend component, sub-trends with different receptive fields often have a significant impact on future trends. The existing research is often limited to the learning of fixed-length trends. Under this condition, choosing an appropriate lookback window often becomes a critical issue. A small window can lead to underfitting, while a large model can lead to overfitting problems. A direct solution is to optimize this hyperparameter through the grid search method [33], but this method is computationally expensive. Therefore, a multi-scale autoregressive mixture is used to adaptively capture sub-trends with different receptive fields. The size of the j-th convolutional kernel is represented as \(g_{j}\). The inputs to MSC-Attention, queries, keys, and values, are denoted as \(q_{t}\in\mathbb{R}^{T_{t}\times D}\), \(k_{t}\in\mathbb{R}^{T_{t}\times D}\), and \(v_{t}\in\mathbb{R}^{T_{t}\times D}\), respectively. Unlike traditional self-attention mechanisms, a mean convolutional kernel \(g_{q}=\frac{1}{\int\limits_{j=1}^{I}g_{j}}\) is used to obtain the query vector \(Q_{t}\):  
\[Q_{t}=CausalConv(q_{t},g_{q}). \tag{9}\]  
Further, \(K_{t}\) and \(V_{t}\) can be represented as  
\[K_{t}^{j} =CausalConv_{k}(k_{t},g_{j}), \tag{10}\] \[V_{t}^{j} =CausalConv_{v}(v_{t},g_{j}).\]  
After capturing sub-trends with different receptive fields, softmax is used for activation. Finally, the trend representation is generated as follows:  
\[MSC\text{-}Attention(q_{t},k_{t},v_{t})=softmax(Q\cdot K^{\top})\cdot V, \tag{11}\]  
where softmax is used for activation.  
The entire process of the algorithm is summarized in Algorithm 1.
## 3. Gear Manufacturing
### Effects of Manufacturing Parameters  
The mLaB from Concept Laser, Germany, was used as the SLM machine to manufacture the part. The SLM machine was integrated with a Yb:glass fiber supplied by IPG, Germany. The machining chamber was filled with argon to keep the oxygen content below 0.4% by volume. After investigating various metallurgical properties, such as density, pore properties, tensile properties, and the hardness of the Ti-6 Al-4V samples manufactured by SLM, the best manufacturing parameters for fabricating the gear were determined [42].  
The scanning speed has the greatest influence on the thermal gradient and the properties of the melt pool in the SLM process, especially for Ti-6 Al-4V [43] Therefore, the scanning speed was varied in a wide range between 150 mm/s and 1000 mm/s to determine the best possible manufacturing parameters for the fabrication of such gears with a large scanning area per powder layer. High scanning speeds led to an insufficient volume of the melt pools and, thus, to a higher number of unmelted zones. On the other hand, a lower scanning speed led to a high energy density in the powder bed, which reduced the viscosity and evaporation of the melt [44]. As a result, a high number of melt splashes occurred, causing material loss in the scanning zones. Therefore, both high and low scanning speeds resulted in a lower product density.  
In addition, the high scanning speed caused a thermal shock due to rapid heating and cooling, which led to a high thermal gradient in the fabricated part during SLM. On the other hand, a high ED also led to a high thermal gradient and residual stress in the layers that were already fabricated during SLM. However, the moderate scanning speed, i.e., 600 mm/s, led not only to considerable density but also to the highest strength of the samples [43]. Therefore, the best combination of manufacturing parameters was a 75 W laser power, 600 mm/s scanning speed, and 0.077 mm hatch spacing for a 52 um layer thickness. This combination of fabrication parameters resulted in a density of 4.405 g/cm\({}^{3}\), which corresponds to a relative density of 99.45, and the parts contained nearly spherical pores with a size of less than 20 um. Therefore, this sample also exhibited improved tensile properties, including a tensile strength of 1250 MPa and a yield strength of 1030 MPa. The hardness of the part is also quite high, at 390 HV, to ensure high wear resistance in the gear tooth.
# Two Medoid-Based Algorithms for Clustering Sets
## 5 Clustering Results
#### Time Efficiency  
The time complexity of the implemented K-medoids algorithm is \(O\big{(}N^{2}KTl\big{)}\), where \(N\) is the number of the data objects (sets) of the dataset, \(K\) is the number of medoids, \(T\) is the number of iterations, and \(l\) is the average length of a set.  
In the following, some information is provided to determine the time computational benefits that can be gathered from using Java parallel streams. The experimental results reported in Tables 4-11 were all achieved by executing the proposed K-medoids or random swap algorithms in parallel (parameter \(PARALLEL=true\)).  
Table 12 reports the results of 2000 repetitions of K-medoids, with G-K-means++ seeding and modified Jaccard distance, on the data_1200_200_32\(5\)1 dataset, separately, in sequential, and in parallel mode. The elapsed time \(ET\) (in msec) and the total number of completed iterations (\(IT\)) were also measured. Then, the average elapsed time per iteration, \(aET^{i}=\frac{ET}{IT}\), was computed.  
Then, the speedup was estimated as follows:  
\[speedup=\frac{Sequential\big{(}aET^{i}\big{)}}{Parallel\big{(}aET^{i}\big{)}}.\]  
From the results in Table 12, we can calculate a speedup of \(442/63=7.02\). This corresponds to a parallel efficiency (eight physical cores) of \(7.02/8=87.8\%\).  
Note that the clustering algorithm executes the same computational steps in sequential and parallel modes. Despite the small value of the dataset size, and then of the clusters' size, the speedup value reflects a good exploitation of the parallelism in the partitioning and the medoids update steps of K-medoids (see Algorithm 1), as well as in the recurrent calculations of the \(SDM\) and of the Silhouette clustering index \(SI\).
## 3 Results
#### 3.1.1 The Regular Part of the Asymptotic Representation  
The functions of the regular part, \(\bar{u}_{k}^{(\mp)}(\mathbf{x})\) and \(\bar{v}_{k}^{(\mp)}(\mathbf{x}),k=0,1,\ldots\), are defined in such a way that the following equalities are satisfied:\[\Delta\left(\sum_{i=0}^{n}e^{i+4}\bar{u}_{i}^{(\mp)}(\mathbf{x})\right) -f^{(\mp)}\left(\sum_{i=0}^{n}e^{i}\bar{u}_{i}^{(\mp)}(\mathbf{x}),\sum_{i=0}^{ n}e^{i}\bar{\varphi}_{i}^{(\mp)}(\mathbf{x}),\mathbf{x},\varepsilon\right) =O\Big{(}\varepsilon^{n+1}\Big{)},\] \[\Delta\left(\sum_{i=0}^{n}e^{i+2}\bar{\varphi}_{i}^{(\mp)}( \mathbf{x})\right) -g^{(\mp)}\left(\sum_{i=0}^{n}e^{i}\bar{u}_{i}^{(\mp)}(\mathbf{x}), \sum_{i=0}^{n}e^{i}\bar{\varphi}_{i}^{(\mp)}(\mathbf{x}),\mathbf{x},\varepsilon\right) =O\Big{(}\varepsilon^{n+1}\Big{)}.\]  
In order to obtain the equations from which the regular part functions are determined, it is necessary to expand the functions on the left side of these equalities according to the [PERSON] formula and equate the coefficients at the same powers of epsilon.  
In particular, in the zeroth order, Propositions 1 and 2 imply  
\[\bar{\varphi}_{0}^{(\mp)}(\mathbf{x})=\psi^{(\mp)}(\mathbf{x}),\ \ \bar{u}_{0}^{(\mp)}(\mathbf{x})=\varphi^{(\mp)}(\psi^{(\mp)}(\mathbf{x}), \mathbf{x}).\]  
The \(k\)th-order functions \(\bar{u}_{k}^{(\mp)}(\mathbf{x})\) and \(\bar{\varphi}_{k}^{(\mp)}(\mathbf{x})\), \(k=\overline{1,n}\), are the solutions to systems of equations  
\[\begin{cases}\bar{f}_{u}^{(\mp)}(\mathbf{x})\bar{u}_{k}^{(\mp)}(\mathbf{x})+ \bar{f}_{0}^{(\mp)}(\mathbf{x})\bar{\sigma}_{k}^{(\mp)}(\mathbf{x})=\bar{F}_{ k}^{(\mp)}(\mathbf{x}),\\ \bar{g}_{u}^{(\mp)}(\mathbf{x})\bar{u}_{k}^{(\mp)}(\mathbf{x})+\bar{g}_{v}^{( \mp)}(\mathbf{x})\bar{\sigma}_{k}^{(\mp)}(\mathbf{x})=\mathcal{G}_{k}^{(\mp)} (\mathbf{x}),\end{cases} \tag{19}\]  
where the notation in Equation (17) is used and \(\bar{F}_{k}^{(\mp)}(\mathbf{x})\), \(\bar{G}_{k}^{(\mp)}(\mathbf{x})\) are the known functions, particularly  
\[\bar{F}_{1}^{(\mp)}(\mathbf{x}):=-\bar{f}_{\varepsilon}^{(\mp)}(\mathbf{x}), \ \ \bar{G}_{1}^{(\mp)}(\mathbf{x}):=-\bar{g}_{\varepsilon}^{(\mp)}(\mathbf{x}).\]  
The systems in Equation (19) are solvable due to Propositions 1-3.
# Implications of Metamaterial on Ultra-Wide Band Microstrip Antenna Performance
## 4 Antennas Implemented with Metamaterials
### Implementation of Square-Srr  
The S-SRR has been utilized in many antenna and microwave applications; such that, to design multi-band and single frequency notch filters and reduce the UWB antenna interference [41]. In this section, we study the effect of the array size on the antenna performance. The analysis starts with \(2\times 1\) array and increases up to \(2\times 5\) array size as shown in Figure 39a--e. The return loss results and  
Figure 36: Antennas maximum gain.  
Figure 37: Inner ring rotation.  
Figure 38: Simulated and measured S\({}_{11}\).  
geometric configuration for all arrays \(2\times 1\)-\(2\times 5\) are presented in Figure 40 and Table 5. Maximum gain for the circular antenna with the S-SRR implementation of the studied arrays is around 5 dB; the gain is 4.897 dB for \(2\times 1\) and increased up to 5.053 dB for \(2\times 5\), Figure 41. The simulated radiation pattern of the antenna with the \(2\times 4\) array of the double S-SRR, Figure 42, shows that the antenna exhibits an omnidirectional radiation pattern and low gain at the rejection frequency 5.6 GHz.  
The previous array size increased to \(3\times 4\), \(4\times 4\) and \(4\times 5\) as illustrated in Figure 39f-h. The three arrays have an improved frequency bandwidth from 2.2 GHz to 10 GHz with a notched band from 4.85 GHz to 5.8 GHz; with almost 1 GHz rejection band, Figure 43. The antenna maximum gain for all 3 arrays is around 5.2 dB as shown in Figure 44. The proposed antenna of \(3\times 4\) array has an omnidirectional radiation pattern at 4, 5, 5.2, 6, 8 and 10 GHz except for 5.6 GHz as this frequency located in the rejection band.  
Figure 41: Antennas maximum gain with different S-SRR arrays.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **Array** & **Bandwidth (GHz)** & **Rejected Bands/Freq. (GHz)** \\ \hline
**a** & \(2\times 1\) & 2.2–9 & 5.6 \\
**b** & \(2\times 2\) & 2.2–9 & - \\
**c** & \(2\times 3\) & 2.2–8 & 5.6 \\
**d** & \(2\times 4\) & 2.2–8 & 5.6 \\
**e** & \(2\times 5\) & 2.2–8 & 5.6 \\
**f** & \(3\times 4\) & 2.2–10 & 4.85–5.8 \\
**g** & \(4\times 4\) & 2.2–10 & 4.85–5.8 \\
**h** & \(4\times 5\) & 2.2–10 & 4.85–5.8 \\ \hline \end{tabular}
\end{table}
Table 5: Results of S-SRR arrays implementations with the antenna.  
Figure 40: S\({}_{11}\) for different S-SRR arrays.  
Figure 42: Radiation pattern for \(2\times 4\) array, (**a**) Phi = 0\({}^{\circ}\) (**b**) Phi = 90\({}^{\circ}\).
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Methodology
#### 2.2.6 Performance Evaluation and Comparison  
The predictive modeling task in this study is a binary classification problem, and the performance of the models is evaluated using several key metrics. The models produce four possible outcomes. True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). These outcomes help assess the model's strengths and weaknesses, ensuring that the selected algorithm is well suited for the classification task. The effectiveness of the model in distinguishing survival outcomes is measured using the following evaluation metrics: accuracy, precision, recall (Equation (5)), and F1 score (Equations (1)-(4)). Additionally, the Receiver Operating Characteristic (ROC) curve and Precision-Recall (P-R) curve are used to provide further insights into model performance [21].  
To interpret prediction error and optimize performance, the mean absolute error (MAE) is calculated as the average difference between the predicted and actual values (Equation (6)). A combinatorial approach is employed to create a weighted combination of multiple models or features, allowing the system to leverage the strengths of different models for superior predictive accuracy [22].  
These evaluation metrics and techniques ensure that model optimization is achieved by fine-tuning the algorithm's parameters, thereby maximizing classification accuracy and improving generalization.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
\[TPR=\frac{TP}{TP+FN},\ \ FPR=\frac{FP}{FP+TN} \tag{5}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\mid\text{y}_{i}-\hat{y}_{i}\mid \tag{6}\]
# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 1 Introduction
Therefore, the fusion of multi-source sensors for positioning is a prevalent trend. Visual positioning [11] is currently a popular research direction; [PERSON] et al. [12] proposed an efficient visual Simultaneous Localization and Mapping (SLAM) method that directly estimates the robot's orientation using line features and vanishing points (VP). In monocular visual SLAM, the lack of direct depth measurement results in scale uncertainty, which is a significant limitation for positioning. [PERSON] et al. [13] proposed a robust fusion framework based on an extended Kalman filter, integrating low-cost IMU and visual sensors, and further improving the accuracy by introducing ultra-wideband (UWB) radio ranging technology [14]. The experimental results showed that the system was able to maintain sub-meter level positioning accuracy even in the presence of multiple interruptions in signal reception. The remainder of this paper is structured as follows. First, in Section 2, the fusion algorithm used in the experiments is introduced. The fusion algorithm consists of two parts: the first part is the visual odometry algorithm, and all the visual experiments in this paper are based on ORB-SLAM2. ORB-SLAM2 contains three main threads: tracking, local mapping and loop closing. The keyframes captured by the camera are processed through the three main threads and optimized to obtain the keyframes with the minimum error. The second part is the multi-sensor fusion positioning algorithm research. Based on the adaptive Kalman filter [15], the camera odometry data output by Xtion and the GNSS/IMU integrated navigation data output by the smartphone are fused. Section 3 includes experimental analysis and results. The experimental analysis introduces the experimental hardware, sensor parameters, test sites, and experimental methods. The experiments were divided into obstructed and unobstructed groups, with multiple trials conducted within each group. The experimental results of satellite positioning, GNSS/IMU integrated positioning, and GNSS/IMU/visual fusion positioning were compared against the ground truth. Furthermore, the positioning accuracy of the different methods was analyzed in the various environmental conditions. Section 4 summarizes the entire experiment, analyzes the improvements in the positioning accuracy achieved by the GNSS/IMU/visual fusion approach, discusses the problems encountered during the experiments, and provides an outlook on the future prospects.
## 3 VRDeepSafety Simulation Environment
### V2X Communication Integration
#### 3.7.5 Impact on VRFormer Accident Prediction  
By integrating V2X communication, VRDeepSafety improves VRFormer's capacity to predict accidents. This expanded situational awareness allows VRFormer to detect objects or vehicles that may be hidden from its sensors, minimizing blind spots and increasing risk prediction. VRFormer can avoid collisions by proactively detecting hazards in real-time using messages such as DENMs, which provide alerts about road conditions or emergencies.  
Furthermore, by combining V2X data with onboard sensor inputs, VRFormer can conduct enhanced predictive analysis, assess collision risks, and take preemptive safety actions, such as braking or steering adjustments. Moreover, VRFormer can synchronize its maneuvers with those of nearby vehicles through coordinated driving strategies facilitated by CAMs and SPaT data. This coordinated decision-making is particularly critical in complex or high-traffic areas where the ability to predict and respond proactively can prevent accidents.  
To evaluate VRFormer's performance in accident prediction, we use the accident prediction accuracy (APA) metric. APA is calculated as the ratio of true positive accident predictions to the total number of actual accidents in the GT data. This metric provides a direct, quantifiable measure of prediction accuracy. The APA is mathematically defined as follows:  
\[\text{APA}=\frac{\text{Number of True Positive Accident Predictions}}{\text{Total Number of Accidents in Ground Truth}} \tag{21}\]  
A high APA score indicates that the integration of V2X data significantly improves VRFormer's ability to predict accidents accurately, thereby enhancing overall safety. This improvement is a direct result of the enhanced situational awareness and predictive capabilities enabled by VRDeepSafety's V2X communication infrastructure.  
In summary, the combination of V2X within the VRDeepSafety framework facilitates the real-time exchange of data, which significantly improves accident prediction and prevention. By expanding situational awareness and enabling coordinated decision-making, VRDeepSafety contributes to safer and more efficient road systems and is therefore a key component of future AVs safety.  
Figure 3: V2X communication data flow in VRDeepSafety.