esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The results obtained from the experiments in the industrial plant have shown that combining density segmentation in multiple directions allows an effective detection of conveyor idlers. In general, this approach may also be applied to other non-elongated but periodically appearing objects. Density segmentation, as a single stage of the overall process, is fast and does not require significant computational power, making it suitable for mobile robots.  
The overall performance of the idler frame detection process is reliable, with more than 83% of correct detection in the clipped regions and the ability to detect all idlers for at least part of the time they are visible.  
The obtained efficiency of the algorithm confirms the validity of our approach. As a result, we plan two directions of further development: the integration of the proposed workflow with the navigation system of a mobile robot and further improvement of the workflow.  
The future work toward incorporating the workflow into a mobile robot navigation system includes integration with a simultaneous localization and mapping (SLAM) algorithm, such as the particle filter-based FastSLAM, using detected idler frames as map features. Reliable navigation in rough terrain will allow further integration with the measurement unit dedicated to idler roller monitoring.  
The short processing time, even in less favorable areas like Path B, provides room for further improvements to the algorithm by incorporating additional processing steps. These enhancements could include combining data from multiple steps, applying filtering techniques such as an extended Kalman filter or similar methods, and fusion with data from other sensors.  
Other areas of possible improvement include the detection range and the algorithm's processing time. The experiments showed that efficient performance is limited to regions where idler frames are within the theoretical field of view of at least six LIDAR channels. Expanding this area could be achieved by using a LIDAR with a higher number of channels or tuning the placement of the sensor. Although the processing time is well below the required threshold, data on the processing duration indicate that the main contribution to the overall time comes from ground detection in the cropping and alignment stage. It is expected that optimization of that stage, particularly by tuning the RANSAC algorithm, can further reduce processing time.  
Future work will focus on improving classification by dynamically tracking idlers across consecutive scans, incorporating 3D shape matching, and sensor fusion with image processing from an RGB camera.  
Conceptualization, J.J.; methodology, J.D. and J.J.; software, J.D.; validation, J.D. and J.J.; writing--original draft preparation, J.J.; writing--review and editing, J.J and J.D.; visualization, J.D.; supervision, J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 System Model
### JML Detection  
A JML detection rule is formulated to enhance detection performance and fully exploit the diversity potential of the system.  
The relay protocol operation is illustrated in Figure 1, where the source, relay, and destination nodes are denoted by \(S\), \(R\), and \(D\), respectively. During the first transmission phase (time slot 1), the source broadcasts a signal vector \(\mathbf{x}_{S}\) received by both the relay and destination nodes. The transmitted signal can be expressed as  
\[\mathbf{q}_{SR}=\text{sign}(\mathbf{H}_{SR}\mathbf{x}_{S}+\mathbf{n}_{SR}),\; \mathbf{q}_{SD}=\text{sign}(\mathbf{H}_{SD}\mathbf{x}_{S}+\mathbf{n}_{SD}). \tag{11}\]  
In the direct transmission path, the destination node attempts to recover the transmitted vector \(\mathbf{x}_{S}\) based on the quantized observation \(\mathbf{q}_{SD}\). Meanwhile, on receiving the quantized signal \(\mathbf{q}_{SR}\), the relay node performs signal detection by applying the ML decision rule to estimate \(\mathbf{x}_{S}\).  
\[\mathbf{x}_{R}=\underset{\mathbf{x}_{S}\in\mathcal{A}^{2N_{S}}}{\text{argmin} }\,d_{w}(\mathbf{q}_{SR},\mathbf{c}_{SR}(\mathbf{x}_{S});\mathbf{w}_{SR}( \mathbf{x}_{R}),\mathbf{\tilde{w}}_{SR}(\mathbf{x}_{S})). \tag{12}\]  
During the second transmission phase (time slot 2), the relay forwards the previously detected signal \(\mathbf{x}_{R}\) to the destination node. The signal transmitted from the relay can be represented as  
\[\mathbf{q}_{RD}=\text{sign}(\mathbf{H}_{RD}\mathbf{x}_{R}+\mathbf{w}_{RD}). \tag{13}\]  
At the destination node, joint detection is performed by utilizing the quantized signals received from both transmission phases, namely \(\mathbf{q}_{SD}\) and \(\mathbf{q}_{RD}\), to recover the original source signal \(\mathbf{x}_{S}\). A widely adopted approach for maximizing diversity in such cooperative systems is the JML detection method, originally proposed by [10], which formulates the decision rule as  
\[\mathbf{\hat{x}}_{S}=\underset{\mathbf{x}_{S}\in\mathcal{A}^{2N_{S}}}{\text{ argmax}}\,\mathbb{P}\{\mathbf{q}_{SRD}|\mathbf{x}_{S}\}=\underset{\mathbf{x}_{S} \in\mathcal{A}^{2N_{S}}}{\text{argmax}}\,\mathbb{P}\{\mathbf{q}_{SD}|\mathbf{ x}_{S}\}\times\sum_{\mathbf{x}_{R}\in\mathcal{A}^{2N_{S}}}\mathbb{P}\{\mathbf{x}_{R}| \mathbf{x}_{S}\}\mathbb{P}\{\mathbf{q}_{RD}|\mathbf{x}_{R}\}, \tag{14}\]  
Figure 1: One-way relay protocol for cooperative DF MIMO systems using one-bit ADCs.  
where the combined quantized observation at the destination is denoted by \(\mathbf{q}_{SRD}=[\mathbf{q}_{SD}^{T},\ \mathbf{q}_{RD}^{T}]^{T}\), which aggregates the information received over both time slots. The probability that the relay decodes \((\mathbf{x}_{S})\) into an incorrect symbol \(\mathbf{x}_{R}\) is represented by the conditional error probability \(P\{\mathbf{x}_{R}|\mathbf{x}_{S}\}\).
## 1 Introduction  
The wet multidisc clutch plays an important role in power-shift transmission, determining stability and reliability during the process of starting and shifting [1, 2]. The engagement properties of friction components have an immediate impact on the torque capacity of wet clutch [3, 4, 5]. With the aim to improve the torque efficiency and service life of the wet clutch, numerous theoretical and experimental researches were conducted regarding the thermodynamic characteristics. Based on the transient temperature model, [PERSON] et al. [6] and [PERSON] et al. [7] revealed that the grooved regions of the friction component could reduce the temperature gradient and deformation effectively. [PERSON] et al. [8] proposed a torque transmission model to predict the friction torque (FT) and surface temperature of the friction pair. In addition, an algorithm to estimate the FT and lifetime of the clutch was derived with the application of [PERSON]'s theory and [PERSON]'s law [9].  
As shown in Figure 1, the spline teeth of the friction components have obvious wear marks after the experiments, indicating that the spline teeth were subjected to friction during the axial movement of friction components. However, relatively limited studies were devoted to the comprehensive analysis of the clutch structure and taking the spline friction into account. The paper [10] offers a detailed mathematical analysis, given the spline friction, to investigate the various consequences on the FT of clutch. [PERSON] et al. [11] developed a contact pressure model of the friction pair to analyze the interface temperature distribution in view of the spline friction. Moreover, by using the thermal model of multidisc clutch, [PERSON] et al. [12] investigated the influence of spline friction on the temperature difference between different friction pairs.  
This paper is devoted to investigating the effect of spline friction on the contact pressure and FT of friction components. The mathematical model regarding the spline friction of components is established first. Then, the numerical simulation and bench test are carried out by changing the number of friction pairs to verify the existence of spline friction. Finally, the effect of spline friction is summarized in detail, and the measures to improve the torque capacity and lifetime of multidisc clutch are proposed.
## 1 Introduction
* The problem of content placement and user association is investigated jointly in large-scale cache-enabled coordinated ultra dense networks. We formulate the problem as a constrained non-convex combinatorial programming problem to maximize network throughput of cell edge UEs under the consideration of the backhaul load;
* A two-step heuristic algorithm based on the cross-entropy (CE) method is proposed to solve the problem: A content placement strategy is first proposed based on cross entropy under the assumption of the conventional \(N\)-Best scheme; given the proposed content placement strategy, a user association algorithm is then proposed based on the cross-entropy method. Extensive simulations are conducted to evaluate the performance of the proposed approach. Simulations are conducted to validate the performance of the proposed cross-entropy based schemes in terms of network throughput and backhaul load. Simulation results show that the proposed caching and user association algorithms can reduce backhaul load and improve network throughput of cell edge UEs simultaneously. The rest of this paper is organized as follows. The system model is established in Section 2 with some basic assumptions. In Section 3, we formulate the problem and propose the algorithms. In Section 4, simulation results are presented and the system performance is evaluated. Section 5 concludes this paper.
## 4 Protocol Framework
### Methods  
Different from those methods for detecting cloned nodes in networks, in which the relevant detection information is transferred to the designed witness node for detection, here a novel proposed protocol which is called encounter-based sequential hypothesis testing protocol (ESHT) does not require related routing algorithms and routing messages for the path-finding of witness nodes. The mobility feature of sensor nodes is utilized to realize the protocol. When the tracer, that is witness node, obtains time-location information of one-hop neighbor which is just in its tracking set, the Sequential Tests of Statistical Hypotheses are applied for detecting cloned nodes in the mobile network. As soon as two witness nodes which have the same tracked node encounter each other, the detection information is transferred to one of the meeting witness nodes for detection. Table 2 shows correlative notations used in the ESHT.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Denotation** \\ \hline \(v_{\max}\) & The predefined maximum speed. \\ \hline \(a_{0}\) & The possibility of the event that a normal node is misjudged as replica due to temporal \\  & synchronization and positioning errors. \\ \hline \(a_{1}\) & The possibility of the event that the measured speed of a replica exceeds the predefined maximum \\  & value \(v_{\max}\). \\ \hline \(\eta\) & The maximum false positive rate. \\ \hline \(\gamma\) & The maximum false negative rate. \\ \hline \(l_{a}^{L}\) & The detection information denoted as \((n_{a}^{L},\omega_{a}^{L})\). \\ \hline \(v_{L}^{t}\) & The measured speed at a time \(T_{i+1}\) to be trial sample in Bernoulli trial. \\ \hline \(n_{p}\) & The length of the queue for each tracked node. \\ \hline \(n^{x}\) & The total number of speed samples of the tracked node \(x\). \\ \hline \(\omega^{x}\) & The cumulative total of the amount of times a specific event that \(S_{i}^{i}=1\) occurs in the \(n^{x}\) samples \\  & of the tracked node \(x\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations used in the proposed protocol.  
There are three stages in the ESHT protocol as follows:
# Special Issue on \"Nanotech for Oil and Gas\"
## 2 Nanotechnology and Nanomaterials Applications in the Oil and Gas Industry
When the loading of NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) reached 30% the maximum pressure and the maximum pressure rise rate of methane (9.5% premixed methane-air) explosions were decreased by 35.1% and 95.8%, respectively, and the time to reach the pressure peak was extended from 0.07 s to 0.50 s.  
Three papers focus on the experimental study of pore structure characteristics, permeability and gas diffusivity. The paper by [PERSON] et al. [8] applied mercury intrusion porosimetry (MIP) to study the pore size, pore volume and pore structures for different ranks of coal. The effective diffusion coefficients and permeability of differently ranked coals were theoretically deduced from MIP data. They showed that CH\({}_{4}\) diffusivity exhibits an increasing trend with the increase of permeability and porosity. The paper by [PERSON] et al. [9] quantitatively analyzed the pore size distribution and pore throat coordination number of tight sandstone by scanning electron microscopy (SEM) in combination with high-pressure MIP experiments. They reported that the permeability of tight sandstone increases with the average pore throat radius, sorting coefficient, median pore throat radius, and average pore throat coordination number, but decreases with the increase of the pore throat ratio. The porosity is positively correlated with the average pore radius and pore throat coordination number, but negatively correlated with the pore throat radius. The paper by [PERSON] et al. [10] applied a combination of sophisticated characterization techniques, i.e., low-field nuclear magnetic resonance (NMR), SEM, MIP, and pulse-decay-permeability measurement to study the pore structure and permeability of different ranks of coal. The permeability was found to be related to the NMR porosity and movable fluid porosity. These studies provide fundamental basis for investigating the gas/oil reserves in tight sandstone and other unconventional reservoirs, and could have important implications for gas sequestration or gas production from enhanced coalbed methane recovery. Lastly, the paper by [PERSON] et al. [11] is a numerical study. They proposed a fully coupled model for describing gas flow in multiscale shale reservoirs by combining the effects of pore size distribution, stress-sensitivity, and adsorption/desorption. The proposed model was validated using traditional models as well as field data on gas production from Marcellus Shale. The model showed promising applications for analyzing various gas flow regimes in multiscale pores/fractures, and accurately evaluating in situ apparent permeability and the gas flow capacities of shale reservoirs.
# Vibration Response Law of Aircraft Taxing under Random Roughness Excitation
###### Abstract  
Understanding the vibration response patterns of aircraft taxiing under runway roughness excitation is crucial for aircraft design and runway performance evaluation. In this paper, we establish pavement roughness models for both asphalt and concrete surfaces, taking into account their unique structural characteristics. We construct a six-degrees-of-freedom aircraft model using multi-rigid-body system dynamics theory and employ the pseudo-excitation method to examine the influence of pavement roughness types on the steady vibration response of aircraft taxiing at constant speeds. Furthermore, we analyze the non-stationary vibration response patterns of aircraft during takeoff and landing taxiing using the method of instantaneous frequency response function with space frequency. Lastly, we explore the effect of stochastic structural parameters on aircraft vibration response using the Monte Carlo method. Our findings reveal that the roughness power spectrum differs between asphalt and concrete pavements, and the established roughness models in this paper demonstrate a strong fit (R2 > 0.95). The type of pavement roughness has a relatively minor impact on the power spectral density distribution of the aircraft vibration response, suggesting that the same roughness model can be used for both asphalt and concrete pavements when high accuracy is not required. The power spectral density distribution of aircraft vibration response varies across different motion attitudes, with the vibration response during landing being significantly larger than that during takeoff. Among the aircraft structural parameters, the randomness of the sprung mass has the most substantial effect on the aircraft vibration response, potentially causing the variation coefficient of dynamic load on the front landing gear to exceed 0.11. Tire stiffness comes next, which can lead to the variation coefficient of dynamic load on the main landing gear reaching 0.07. The results have a guiding role in optimizing aircraft structure design and ensuring pavement performance.  
aircraft vibration; random roughness excitation; roughness model; non-stationary vibration; stochastic structural parameters +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Array Metamaterial Results  
Different types of array combinations are also simulated to test the coupling effect and to verify the consistency of the results, which is the best way to achieve the expected electromagnetic features. Arrays of the proposed MM with dimensions of 1 \(\times\) 2 and 2 \(\times\) 2 are shown in Figure 17. These two designs are simulated by the CST and the reflection coefficient (S\({}_{11}\)), and transmission coefficient (S\({}_{21}\)) results are presented in Figure 18. The variations of resonance frequencies among the unit cell and the 1 \(\times\) 2 and 2 \(\times\) 2 arrays are given in Table 4, which confirms the consistency of the results.  
Figure 16: Graph of real and imaginary magnitudes of refractivity for the unit cell.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author Name \& Ref.** & **Shape of MM** & **Size (mm2)** & **Substrate Material** & **Metamaterial Type** & **EMR (\(\lambda\)/L)** \\ \hline [PERSON] et al. [46] & H & \(30\times 30\) & FR-4 & LHM & 3.65 \\ [PERSON] et al. [47] & Double Z & \(8.5\times 8.5\) & FR-4 & LHM & 4.83 \\ [PERSON] et al. [48] & SRR & \(16\times 12\) & FR-4 & NRI & 5.35 \\ [PERSON] et al. [49] & Double dumbbell & \(9\times 9\) & FR-4 & ENG & 11.5 \\ Proposed MM & Power tiller wheel & \(11\times 11\) & RT6002 & ENG & 11.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of previously published MMs with the MM proposed in the present study.  
Figure 15: Graph of real and imaginary magnitudes of permeability (\(\upmu\)) for the unit cell.
## 2 Magnetizer Modeling and Simulation Analysis
### Preliminary Experiment  
Considering the MFL signal is weak at a distance of 30 mm, a preliminary experiment was done to verify the feasibility of large-lift-off detection using the existing equipment, which detects the signal at a distance of about 20 mm to 25 mm.  
At the start, the existing sensor head used at a distance of 5 mm or less was improved. As shown in Figure 6, machining a groove whose deepness is 5 mm and width is 30 mm in the middle of the armature, as shown in Figure 6, aims to install magnetic sensors.  
The second change in the existing sensor head is to use wear-resistant sleeves of different sizes, as shown in Figure 7. To add up lift-off distance, different sizes of sleeves combination can make the sensor head eccentric to the detected wire rope.  
Select the 24 mm diameter wire rope as the test object, make three different defects on it and collect the signal at the distance of 20 mm and 25 mm shown in Figure 8. The signals are shown in Figure 9a,b, and the spikes represent the MFL signal of three defects collected by coil sensors. Specifically, the detector was moved from left to right and returned. Consequently, the spikes from left to right represent the defects of a single broken wire, binding broken wire, and two broken wires, respectively. The results show that a large lift-off distance in collecting wire rope defect signals is feasible. Meanwhile, the size of the large-lift-off-distance sensor head is determined according to the previous experiment. The inner diameter, outer diameter, and width of the ring magnets are, respectively, 85 mm, 120 mm, and 10 mm. The actual sensor head is shown in Figure 10.  
Figure 6: (a) 3D model armature; (b) The real armature.  
Figure 7: Wear-resistant sleeves.
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Reconstruction
#### 3.3.2 Challenges and Future Directions  
Challenges in DL-based image reconstruction include the need for large-scale training data, the limited generalizability of the models across different imaging devices and acquisition protocols, and the potential for introducing new artifacts or biases into the reconstructed images [126]. Future research may focus on developing techniques to leverage limited training data, such as unsupervised or self-supervised learning methods [127], and designing more robust models that can generalize across different imaging conditions [128]. Furthermore, ensuring the safety and reliability of DL-based reconstruction methods by quantifying their uncertainties and validating their performance on large, diverse datasets will be crucial for their clinical adoption. Figure 8 is a typical example of DL-based medical image registration.
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 4 Feature Generation
### Results
\begin{table}
\begin{tabular}{l l c c c} \hline \hline \multicolumn{1}{c}{**Dataset**} & **Classifier** & **Original** & **AutoLearn** & **Proposed** \\ \hline \multirow{8}{*}{Waveform} & Average & 82.34 & 81.77 & \(\mathbf{-82.71}\) \\ \cline{2-5}  & KNN & 80.00 & 82.20 & **83.60** \\  & LR & **86.80** & 85.00 & 85.20 \\  & SVM-L & **87.20** & 83.20 & 83.80 \\  & SVM-P & **85.80** & 84.40 & 85.40 \\  & RF & 81.60 & 81.20 & **84.60** \\  & AB & 80.00 & 80.60 & **83.00** \\  & DT & 75.00 & **75.80** & 73.40 \\ \hline \multirow{8}{*}{Abalone} & Average & 22.32 & **22.80** & **22.80** \\ \cline{2-5}  & KNN & 20.57 & **22.49** & **22.49** \\ \cline{1-1}  & LR & 22.73 & **23.44** & **23.44** \\ \cline{1-1}  & SVM-L & **24.40** & 24.16 & 24.16 \\ \cline{1-1}  & SVM-P & 23.92 & **24.64** & **24.64** \\ \cline{1-1}  & RF & **22.73** & 21.05 & 21.05 \\ \cline{1-1}  & AB & 22.01 & **21.29** & **21.29** \\ \cline{1-1}  & DT & 19.86 & **22.49** & **22.49** \\ \hline \multirow{8}{*}{Eucalyptus} & Average & 59.85 & 59.27 & **62.16** \\ \cline{1-1} \cline{2-5}  & KNN & 58.11 & **60.81** & **60.81** \\ \cline{1-1} \cline{2-5}  & LR & 58.11 & **64.86** & 58.11 \\ \cline{1-1} \cline{2-5}  & SVM-L & 60.81 & **63.51** & 62.16 \\ \cline{1-1}  & SVM-P & **62.16** & 55.41 & **62.16** \\ \cline{1-1}  & RF & **70.27** & 62.16 & 68.92 \\ \cline{1-1}  & AB & 51.35 & 45.95 & **59.46** \\ \cline{1-1}  & DT & 58.11 & 62.16 & **63.51** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison in terms of classification accuracy (2/3). \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{8}{*}{Sonar} & Average & 76.19 & 79.59 & **80.95** \\ \cline{2-5}  & KNN & 80.95 & **90.48** & **90.48** \\  & LR & 71.43 & **80.95** & 71.43 \\  & SVM-L & 66.67 & **76.19** & 71.43 \\  & SVM-P & 80.95 & 80.95 & **85.71** \\  & RF & 66.67 & 76.19 & **80.95** \\  & AB & 76.19 & 76.19 & **80.95** \\ \cline{1-1}  & DT & **90.48** & 76.19 & 85.71 \\ \hline \multirow{8}{*}{Vehicle} & Average & 74.29 & 76.30 & **76.47** \\ \cline{1-1} \cline{2-5}  & KNN & 70.59 & 69.41 & **71.76** \\ \cline{1-1}  & LR & 80.00 & 83.53 & **84.71** \\ \cline{1-1}  & SVM-L & 81.18 & **85.88** & **85.88** \\ \cline{1-1}  & SVM-P & 75.29 & 75.29 & **76.47** \\ \cline{1-1}  & RF & 78.82 & **83.53** & 77.65 \\ \cline{1-1}  & AB & 64.71 & **65.88** & 61.18 \\ \cline{1-1}  & DT & 69.41 & 70.59 & **77.65** \\ \hline \multirow{8}{*}{Ionosphere} & Average & 92.86 & 96.83 & **98.02** \\ \cline{1-1} \cline{2-5}  & KNN & 91.67 & 94.44 & **100.0** \\ \cline{1-1}  & LR & 88.89 & **100.0** & 97.22 \\ \cline{1-1}  & SVM-L & 88.89 & 94.44 & **100.0** \\ \cline{1-1}  & SVM-P & 83.33 & 91.67 & **94.44** \\ \cline{1-1}  & RF & **100.0** & **100.0** & **100.0** \\ \cline{1-1}  & AB & 97.22 & **100.0** & 97.22 \\ \cline{1-1}  & DT & **100.0** & 97.22 & 97.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._