esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup  
This section details the considered datasets and performance metrics for the evaluation of the proposed approach. For performance validation, the proposed method (DFG-A-DFC) is compared against four state-of-the-art unsupervised feature selection methods namely, unsupervised feature selection using feature similarity measure (FSFS) [27], densest subgraph finding with feature clustering (DSEFC) [12], multi-cluster feature selection (MCFS) [19] and Laplacian score for feature selection (LSFS) [26]. Moreover, it has been observed in the literature that the number of considered features is half of the original features for fair comparison [12]. To achieve the same, LSFS considers features on the basis of their ranking, while DFG-A-DFC and DSEFC keep half of their original feature size at least.
## 3 Measurement Uncertainty Analysis of the Linear Scan Method
### Measurement Uncertainty Evaluation
\begin{split}& u(\Delta r_{i})=\sqrt{\left(\frac{x_{i}^{2}+x_{i}^{2}}{2 \mathbb{R}^{2}}\right)^{2}u^{2}(\overline{R})+\left(-\frac{x_{i}}{\overline{R }}\right)^{2}u^{2}(x_{i})+\left(1-\frac{x_{i}}{\overline{R}}\right)^{2}u^{2}(z _{i})}\\ &=\sqrt{\left(\frac{0.50575^{2}+0.52802}{2\times 0.74940^{2}} \right)^{2}\times 14.24^{2}+\left(-\frac{0.50575}{0.74940}\right)^{2}\times 42.3 7^{2}+\left(1-\frac{0.55280}{0.74940}\right)^{2}\times 38.45^{2}}\\ &=34.84\;\text{nm}\end{split}  \tag{28}\]  
Using the same assumptions as in Equations (6) and (29), the combined standard uncertainty \(u(\Delta\text{zq})\) of roundness can be expressed as follows. \[u(\Delta zq)=2u(\Delta r(\theta)) \tag{29}\]  
\[u\big{(}\Delta z_{q}\big{)}=2u(\Delta r_{i}) \tag{30}\]  
Substituting the uncertainty calculated in Equation (26), the combined standard uncertainty \(u(\Delta\text{zq})\) is obtained from Equation (27), as shown in Equation (31) below. \[u\big{(}\Delta z_{q}\big{)}=2u(\Delta r_{i})=2\times 34.84=69.68\;\text{nm} \tag{31}\]  
The expanded uncertainty can be obtained when k = 2 shown in Equation (32). \[U\big{(}\Delta z_{q}\big{)}=ku\big{(}\Delta z_{q}\big{)}=2\times 69.68=139.36\; \text{nm} \tag{32}\]  
As can be seen above, the uncertainty in the roundness measurement of a small cylindrical workpiece with a diameter of 1.5 mm was estimated to be \(\pm\)95.44 nm using the linear scanning method, achieving a target measurement uncertainty of \(\pm\)0.1 \(\upmu\)m or less. Table 3 summarizes the results of the standard uncertainty.
## 6 Conclusions  
In this paper, we analyze the failure mode of the rotor under multi-physical loads and construct an accelerated reliability test load spectrum for the magnetic bridge, which is prone to monolithic protrusion and bridge fracture during the actual operation of electric vehicle drive motors. Our main conclusions are as follows.  
(1) We propose a method for analyzing the failure mode of the magnetic bridge and constructing an accelerated reliability test cycle under multi-physics fields. This method includes establishing a physical model of the rotor failure, simulating and verifying multi-physics field loads, analyzing magnetic bridge damage, establishing life models, determining whole life cycle damage targets, selecting reliability test conditions, optimizing the number of cycles, and compiling an accelerated test load spectrum.  
(2) In the multi-physics field load simulations, we identify the stress hazard points in the rotor at the magnetic bridges. The maximum point of electromagnetic stress on the rotor surface occurs at the intersection of the permanent magnet and the rotor slot. Under severe operating conditions, the stress magnitude at the spacer bridge is less than 2 MPa, rendering the effect of electromagnetic forces negligible. Additionally, the highest rotor temperatures occur near the magnets in the simulation analysis by thermal field loading. The rotor temperature decreases from the inside to the outside along the permanent magnet region, and the temperature rise of the stator has less influence on the rotor due to the air gap.  
(3) We define the whole life cycle damage targets of the rotor magnetic bridge under fatigue failure and fatigue aging coupled failure. We construct the accelerated test cycle conditions, which include steady-state conditions and alternating conditions. We use a multi-objective optimization algorithm to determine the optimal number of cycles of working conditions and to construct a load spectrum for accelerated tests of magnetic bridges considering multiple failure modes. Ultimately, we develop an accelerated test load spectrum operating for 716 h, which can reproduce the user's operating time of 11,571 h at 300,000 km, and the acceleration factor is 16.16.  
Conceptualization, S.W., J.S., L.Z. and L.L.; methodology, S.W., J.S., L.Z., L.L., Z.W. and X.W.; validation, S.W., J.S. and D.W.; formal analysis, S.W. and L.Z.; investigation, J.S., L.Z. and Z.W.; resources, D.W and X.W.; data curation, S.W. and L.L.; writing--original draft preparation, S.W.; writing--review & editing, S.W., J.S. and L.L.; visualization, S.W.; supervision, J.S.; project administration, S.W. and L.Z.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 31: Damage recovery ratios for both failure modes.
## 5 Simulation and Results Analysis
Figure 16 depicts the comparison of energy consumption per packet in different protocols. From the simulation results, we can find that the energy consumption per packet of all methods monotonously increases with the traffic load. Also, it can be observed that CPCSS does not always show the better performance than the other protocols. As the traffic load is relatively low, CPCSS consumes more energy for each packet due to excessive overhead for node classification and redundancy calculation. Along with the traffic load of network increases, this type of impact turns out to be negligible in contrast of communication overhead and CPCSS can demonstrate high energy efficiency. Figure 14: The total energy consumed over rounds. Figure 8:0 The alive nodes over rounds.
## 2 Method
### Calibration Model
#### 2.3.1 Linear Method  
The CoM of the end tool can be obtained using Equation (7). From the relation \(\boldsymbol{\tau}=\mathbf{r}\times m\mathbf{g}=[-m\mathbf{g}]_{\times}\mathbf{r}\), we derive a linear equation in the form \(\mathbf{r}=[-m\mathbf{g}]_{\times}^{+}\boldsymbol{\tau}\) which can be solved by the least squares method. By constructing a matrix from the predicted values obtained across \(n\) postures, we can determine the CoM that minimizes the torque error in each posture through the pseudo-inverse.  
\[\begin{bmatrix}d_{x}\\ d_{y}\\ d_{z}\end{bmatrix}=\frac{1}{m}\begin{bmatrix}[-\mathbf{g}_{/1}]_{\times}\\ -\mathbf{g}_{/2}]_{\times}\\ \vdots\\ \begin{bmatrix}-\mathbf{g}_{/n}]_{\times}\end{bmatrix}^{+}\begin{bmatrix} \boldsymbol{\tau}_{/1}^{ft}\\ \boldsymbol{\tau}_{/2}^{ft}\\ \vdots\\ \boldsymbol{\tau}_{/n}^{ft}\end{bmatrix} \tag{7}\]  
The elements of the crosstalk matrix can be obtained using linear Equation (8). Since \(\mathbf{C}_{12f}\boldsymbol{\tau}_{f}^{st}=\boldsymbol{\mathbf{f}}^{ft}- \boldsymbol{\mathbf{f}}_{f}^{st}\), we can vectorize \(\mathbf{C}\) and convert \(\boldsymbol{\tau}\) into matrix form by following the definition of Equation (8) to achieve a least squares method structure, allowing the elements to be solved through the pseudo-inverse.  
\[\begin{split} vec(\mathbf{C}_{12f})=\begin{bmatrix}\left\langle \boldsymbol{\tau}_{1}^{ft}\right\rangle\\ \left\langle\boldsymbol{\tau}_{2}^{ft}\right\rangle\\ \vdots\\ \left\langle\boldsymbol{\tau}_{n}^{ft}\right\rangle\end{bmatrix}^{+}& \begin{bmatrix}\boldsymbol{\mathbf{f}}_{1}^{ft}-\boldsymbol{\mathbf{f}}_{/1}^{st }\\ \boldsymbol{\mathbf{f}}_{2}^{ft}-\boldsymbol{\mathbf{f}}_{/2}^{st}\\ \vdots\\ \boldsymbol{\mathbf{f}}_{n}^{ft}-\boldsymbol{\mathbf{f}}_{/n}^{st}\end{bmatrix},\\ \text{where}\quad vec(\mathbf{C}_{12f})=\begin{bmatrix}0&c_{1}&c_{2}&c_{3}&0&c_{4}&c_{5}&c_{6}&0 \end{bmatrix}^{\intercal}\\ \left\langle\boldsymbol{\tau}_{i}^{ft}\right\rangle=\begin{bmatrix}0&\tau_{y}& \tau_{z}&0&0&0&0&0&0\\ 0&0&0&\tau_{x}&0&\tau_{z}&0&0&0\\ 0&0&0&0&0&0&\tau_{x}&\tau_{y}&0\end{bmatrix}\end{split} \tag{8}\]  
The tilt information of the base obtained from Equation (2) is non-linear, making it impossible to determine the parameters using a linear equation. Therefore, while compensation is possible with a linear equation on a flat surface, it is insufficient on an inclined surface. The cases where CoM and crosstalk are calibrated using Equations (7) and (8) with the LSM are labeled as _LSM Calibration_ for the following experiments.
## 2 Materials and Methods
### Statistical Analysis and Data Processing  
We subjected the data to statistical analysis utilizing JMP Pro 18 (Statistical Discovery LLC, Cary, NC, USA) and Microsoft(r) Excel(r) for Microsoft 365 MSO (Version 2503 Build 16.0.18623.20266, 64-bit) to investigate the relationships between truck traffic, topography, and UFP concentration. Multiple sources were used in obtaining these datasets, including a video-based traffic monitoring system and the NanoScan. We evaluated the concentrations and distributions among study locations using descriptive statistical analysis after data collection and preparation.  
Descriptive statistics, including mean, mode, standard deviation, scatterplots and dual-axis time series plots, were used to examine UFP concentration distribution over several sites. This set the standard for the comparison of UFP values and the finding of notable differences. Traffic data were combined with UFP concentrations using descriptive statistics to enable a complete understanding of normal traffic patterns at every site and their effects on UFP exposure.  
We utilized linear regression modeling to evaluate the impact of topographic elevation on UFP concentrations. This study involved modeling UFP concentration as the dependent variable and truck count as the independent variable. The coefficient of determination (R\({}^{2}\)  
Figure 3: Particle number size distributions simultaneously collected during sampling from NanoScan data for UFPs that are generally smaller than 0.1 μm [42]. The particle distribution was bimodal for both locations and similar at both locations, with geometric mean count diameters of 0.07 μm (geometric standard deviation of 2.0) and 0.016 μm (geometric standard deviation of 1.52) for Brockway and geometric mean count diameters of 0.06 μm (geometric standard deviation of 1.34) and 0.015 μm or 15 nm (geometric standard deviation of 1.9) for Beechurst.  
and \(p\)-values were employed to evaluate the strength and significance of the relationship. Additionally, dual-axis time series plots were developed to visualize the relationship between truck traffic and UFP concentrations over the study period. As evidenced in Figure 1, the \(x\)-axis represents the number of trucks recorded during the study period while the \(y\)-axis represents computed changes in UFP concentration. This graphic depiction helped to clearly spot trends and patterns that might not have been obvious from raw data. A \(p\)-value < 0.05 was considered statistically significant.
## 2 Materials and Methods
### Micro X-ray Computed Tomography (\(\upmu\)XCT)  
Fast phase-contrast \(\upmu\)XCT experiments were carried out at the Advanced Photon Source beamline 2-BM-B at the Argonne National Laboratory (Argonne, IL, USA). Details of the beamline setup have been described elsewhere [30; 31]. Imaging was performed using a filtered polychromatic X-ray illumination beam with a peak energy of about 25 keV. A 20-\(\upmu\)m thick Ce-doped Lutetium Aluminum Garnet (LuAG:Ce) scintillator was positioned at a distance of 10 cm from the sample and used to convert the transmitted X-rays to visible light. The images were magnified by a 10\(\times\) Mitutoyo (Kawasaki, Japan) long working-distance objective lens and recorded by a PCO.edge(tm) (Kelheim, Lower Bavaria, Germany) camera with a 2560 by 2160-pixel count. The experimental setup resulted in a 0.65 \(\upmu\)m pixel size and produced a field-of-view of 1.664 mm by 1.404 mm. Each tomogram data set consisted of 1500 projections collected over a 180\({}^{\circ}\) rotation at 0.12\({}^{\circ}\) angular increments with 5 ms exposure time for each projection. Each 1500 projection tomogram data set took approximately 7.5 s to collect. White-field and dark-field images were also collected for each tomogram data set and used for intensity corrections. A white-field image was collected while the detector was irradiated without the sample's obstruction, and the dark-field image was collected while the X-ray beam was blocked from the detector.  
XCT imaging was performed on each wood specimen conditioned in absorption at RH values of 0% (dry air), 33%, 75%, and 95%. Before imaging, the specimen was placed into an external chamber for at least 24 h to condition at the given RH, which was controlled using desiccant or aqueous salt solutions (Table 1). The wood specimen was then transferred quickly from the external chamber to the in situ beamline RH chamber (Figure 2) and conditioned for 10 min at the corresponding RH step before imaging. The RH in the beamline chamber was controlled with an IntruQuest HumiSys(tm) HF (Coconut Creek, FL, USA) RH generator. The RH and temperature were measured inside the beamline chamber during imaging using a Sensirion (Staefa, Switzerland) SHT1x humidity sensor. The calibration of the temperature and RH sensor inside of the beamline chamber was verified using a Control Company (Webster, TX, USA) 4085 Traceable(r) Hygrometer Thermometer Dew Point Meter. The temperature ranged from 27 to 28 \({}^{\circ}\)C. After imaging, the sample was transferred to the external humidity chamber that was conditioned for the next higher RH step. Imaging for each RH step occurred on successive days to allow at least 24 h of conditioning. The process was repeated until all the wood specimens were imaged at each RH step.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 6 Discussion  
With respect the local filter, the sensor subsystem is a linear system. For the nonlinear system, the extending Kalman is one of the solutions. The robustness and other research need to be further improved for complex scenarios.  
The \(H-1\) dimension optimization is inevitable for BICI. The fast calculation method of the CI method can be used as a reference [35], which reduces the computational complexity of the BICI algorithm and fits the real-time positioning and navigation for the embedded platform with computing and power consumption limitation.  
In multi-model algorithm, BICI-IMM utilizes a fixed model set. The size of the model set will be very large in the actual scene. It will boost the computational complexity and positioning error due to the conflicts among the motion models. The variable-structure multi-model algorithm can achieve a balanced positioning accuracy, model set size and computational complexity. Therefore, it is necessary for the further study of a fusion positioning algorithm based on a variable-structure multiple-model, in order to enhance positioning accuracy in challenging scenes.
## 2 Background
### Previous Work
#### 2.3.5 General Approach to Key Recovery
More generally, let \(L^{i}=[c^{i}_{0},c^{i}_{2},\ldots,c^{i}_{m_{i}-1}]\) be the list of chunk candidates for chunk \(i\), with \(0<m_{i}\leq 2^{w}\), and let \(c^{i_{0}}_{j_{0}},\ldots,c^{i_{n}}_{j_{n}}\) be chunk candidates, with \(0\leq i_{0}<\cdots<i_{n}<\mathcal{N},0\leq j_{i}<m_{i}\). Let us define the function \(\texttt{combine}(c^{i_{0}}_{j_{0}},\ldots,c^{i_{n}}_{j_{n}})\) to return a new chunk candidate \(c\) constructed by calculating \(c=(c^{i_{0}}_{j_{0}}score+\ldots+c^{i_{n}}_{j_{n}}score,c^{i_{0}}_{j_{0}}.value \parallel\ldots\parallel c^{i_{n}}_{j_{n}}.value)\). Note that, when \(i_{0}=0,i_{1}=1,\ldots,i_{N-1}=\mathcal{N}-1\), \(c\) will be a full key candidate. Therefore, the key enumeration problem asks for selecting a chunk candidate \(c^{i}_{j_{i}}\) from each list \(L^{i}\), \(0\leq i<\mathcal{N}\), in order to construct full key candidates \(c=\texttt{combine}(c^{i_{0}}_{j_{0}},\ldots,c^{i_{n}}_{j_{n}})\) subjected to a condition on their scores [17]. Typically, an algorithm that generates full key candidates \(c\) is called key enumeration algorithm (KEA). For our key recovery problem, we adapt a technique combining several key enumeration algorithms that has been recently introduced in [5] and exploit the in-memory representation of the secret key of the LUOV scheme. This adaptation is described in Section 4.
## 5 Feature Extraction
### Statistical Features  
A set of classical statistical features was considered for comparing to the performance of the proposed CCM and SD features. The set of statistical features has been studied in [39] for detection of multi-fault in roller bearings and gearboxes. The features are the mean value, root mean square value, standard deviation, kurtosis, maximum value, crest factor, rectified mean value, shape factor, impulse factor, variance, minimum value and skewness. This set corresponds to a 12-dimensional array.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Rubber Industry  
In the rubber manufacturing sector, kaolin is a valued material due to its ability to reinforce and stiffen rubber compounds while being more cost-effective than alternative pigments [106]. Although carbon black dominates as the pigment for black rubber products, kaolin is widely utilized for its advantages in non-black rubber [107].  
Kaolin's role is categorized based on particle size, distinguishing between hard and soft clays. Hard clays, which are characterized by their fine particles, are essential in products demanding high durability and wear resistance, such as in the production of shoe heels, tires, conveyor belts, and bicycle tires. These clays provide the necessary stiffness to uncured rubber, thereby preventing deformation during manufacturing. They are also crucial in mitigating mechanical molding challenges in hard rubber goods, including household items, toys, and various novelties [108]. Moreover, they find application in gloves, adhesives, butyl inner tubes, reclaimed rubber, and neoprene compounds.  
On the other hand, soft clays are preferred in rubber formulations in which cost efficiency is a primary concern and wear resistance is of lesser importance [109]. These clays are incorporated into a range of products, including tire bead insulation, household items, blown sponges, and rubber toys. They allow for higher filler loadings and enable more rapid extrusion than hard clays. High-aspect ratio, delaminated kaolins are specifically used in white sidewall tires to effectively mitigate air leakage. Additionally, kaolins that have been surface-modified are employed to improve dispersion and reinforcement within rubber compounds [110].
## 4 Charging Method
### Pulse Charging (PC) Method  
Pulse charging (PC) for rechargeable batteries did not become widely used in industry until the 1970s. Pulse charging uses a series of high-current pulses to charge a battery. Periods of inactivity are typically interspersed between pulses to give the battery time to recharge before the next pulse is sent. The battery is charged using periodic pulses of current; during this time, the battery converts the electric energy into chemical energy and the chemicals on the plates redistribute themselves equally, improving the charge's efficiency [73]. The schematic for a pulse charging system is depicted in Figure 16.  
The following are typical steps in the pulse charging process:  
* Pulse Phase: The battery is subjected to a short (milliseconds to seconds) high-current pulse. The normal current used to charge the battery is far less than the pulse current.
* Rest Phase: After each pulse, the battery is given a rest and the charging current is stopped. During this time, sulphate crystals dissolve and chemical processes take place.
* Repeated Cycle: Multiple cycles of pulse and rest may be required, depending on battery life and the extent of rejuvenation.  
Pulse charging can be performed either manually using dedicated chargers or automatically through the utilization of battery management systems. The regulation of pulse frequency, duration, and amplitude is typically implemented to ensure optimal outcomes while avoiding any potential harm to the battery.  
Figure 16: Pulse charging method for EV battery.  
Figure 15: Multistage constant current charging method for EV battery with battery current (red), and battery voltage (green).
###### Abstract  
The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.  
VANET; spider monkey; leader; machine learning; optimization; relay; NS2.35 +
Footnote †: journal: _Sensors_**2024**, 24, 2334. [[https://doi.org/10.3390/s24072334](https://doi.org/10.3390/s24072334)]([https://doi.org/10.3390/s24072334](https://doi.org/10.3390/s24072334))
## 2 Image Processing in Remote Sensing
### Image Classification
First the object based classification of the image is done. This technique is important for efficiently developing the object based image classification and helps in the further research process for obtaining more accurate results.