esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Design of Rotary Drilling Rig Mast Based on Multi-Condition Analysis
## 1 Introduction
Safety assessment involves analyzing potential risk factors to evaluate the safety of systems or structures, identify hazards, and ensure the safety of personnel and the environment. It typically combines quantitative analysis with qualitative judgment. [PERSON] et al. [11] developed a crane safety assessment method combining the cloud model and ICWGT, balancing expert experience with data. Applied to a shipyard portal crane, it accurately reflects safety levels. [PERSON] et al. [12] reviewed crane safety literature using 59 risk factors from CORD, identifying eight key gaps critical for improving crane management and guiding future research. [PERSON] et al. [13] proposed a crane safety assessment method integrating entropy and cumulative prospect theory, effectively addressing inaccuracies. The method evaluates four crane samples, demonstrating its precision and feasibility. [PERSON] et al. [14] introduced an improved Variable Fuzzy Sets (IVFS) method with normal membership functions to simplify crane safety evaluation when criteria are interval-based. Applied to numerical cases, the method demonstrated its effectiveness and practicality. [PERSON] et al. [15] proposed an alternative methodology for assessing old timber structures, moving beyond traditional concepts like \"Knowledge Level\" and \"Confidence Factor\". Experimental data from timber joists were used to support the approach. [PERSON] et al. [16] applied the IMO's formal safety assessment (FSA) to develop risk-based rules for offshore crane systems, proposing a cost-benefit method for target reliability and a framework for risk-basedrules. [PERSON] et al. [17] developed a safety evaluation system for quayside container crane structures, introducing the \"0-4\" scoring method combined with fuzzy comprehensive evaluation (04-FCE). The approach assesses safety levels, offers risk reduction suggestions, and is validated through case analysis. Existing optimization designs primarily focus on ultimate load conditions, with limited attention given to safety evaluation, resulting in lower safety levels in optimization outcomes. This deficiency could lead to structural failures, posing risks to personnel safety. This study proposes a multi-condition safety design methodology for rotary drilling rig masts. The approach employs cross-sectional parameters as optimization variables and aims to minimize cross-sectional area while considering constraints such as strength, stiffness, stability, fatigue, and safety evaluation. Safety evaluation is conducted by constructing a fuzzy analytic hierarchy model to comprehensively assess safety performance under multiple working conditions, considering safety evaluation indicators such as strength, stiffness, stability, and fatigue strength. The proposed method enables a thorough quantitative analysis of the mast's safety performance and is validated through practical case studies, demonstrating the effectiveness of the evaluation system.
## 5 Discussions and Conclusions
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Fuzzy enhancement and improved Canny's leaf edge extraction. _J. Orteolectron. Laser_**2018**, _29_, 1251-1258. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] A New Multiscale Analysis Algorithm for Image Edge Extraction of Strawberry Leaves in Natural Light Greenhouse. _Agric. Eng._**2018**, _30_, 8-16. * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Grape leaf segmentation for disease identification through adaptive Snake algorithm model. _Multimed. Tools Appl._**2020**, _80_, 8861-8879. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] A method for cotton canopy image segmentation based on improved full convolution network. _Trans. Chin. Soc. Agric. Eng._**2019**, _34_, 193-201. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Panicle-SEG: A robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. _Plant Methods_**2018**, _13_, 104-111. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Grape disease leaf segmentation based on full convolution network. _J. Nanjing Agric. Univ._**2019**, _41_, 752-759. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation. _Agriculture_**2021**, _11_, 420. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Lightweight Multi-Scale Dilated U-Net for Crop Disease Leaf Image Segmentation. _Electronics_**2022**, _11_, 3947. [CrossRef]  
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# A Lightweight Barcode Detection Algorithm Based on Deep Learning
## 1 Introduction  
As an exceptionally efficient method for information storage and transmission, one-dimensional codes and two-dimensional codes have profoundly simplified the procedures involved in logistics tracking, inventory management, and commodity certification, thereby bringing unprecedented convenience and operational efficiency to various sectors and industries across diverse fields [1; 2; 3; 4; 5; 6; 7]. These advanced coding technologies not only achieve rapid data entry and automatic identification with a remarkably high degree of accuracy and reliability, but they also have the capacity to encapsulate a vast array of content, ranging from straightforward commodity information to intricate data links, through their innovative and unique coding methodologies. This demonstrates their impressive information processing capabilities. Relevant and in-depth research has clearly shown that the advancement of barcode recognition technology holds immense significance for improving the efficiency and accuracy of information processing, accelerating the digital transformation of various industries, optimizing supply chain management processes, fostering the application and development of emerging technologies, and expanding the realm of potential application fields.  
In recent times, an increasing number of researchers have started to direct their attention towards the practical applications of barcodes in real-world scenarios. Identification technology must not only possess the capability to detect barcodes in real-time with precision and accuracy, but it must also ensure that the applications utilizing this technology consume minimal power. With this in mind, this paper introduces a novel barcode recognition algorithm specifically designed to address the challenges associated with barcodedetection and analysis in real-life situations. The primary contributions of this paper can be summarized as follows:  
1. This paper proposes a high-speed and accurate barcode recognition algorithm based on a lightweight barcode detection model. The model achieves real-time barcode detection with a small number of parameters, low computational complexity, high accuracy, and rapid inference capabilities.
2. While focusing on minimizing both parameter count and computational complexity of the model, this paper also enhances the model's ability to dynamically adjust contextual feature relationships. To achieve this, we introduce a linear attention mechanism and efficient convolutional operations into the network structure, thereby improving the feature extraction capabilities of the feature fusion convolutional blocks and pooling layers, as well as the operational efficiency of the convolutional modules. Furthermore, we reconstruct the detection head and adjust the model's loss function to enhance the convergence speed and training quality of the model.
3. The effectiveness of the proposed algorithm and model is verified in this paper. Comparative experimental results demonstrate that our model outperforms other models in terms of detection accuracy, model size, and detection speed.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 3 Method
### Signal Processing  
In vibration signal processing, in addition to directly extracting the instantaneous amplitude, phase, and frequency of the vibration signal, the Hilbert transform can also demodulate the signal to obtain information about the interference and distortion of the signal's instantaneous attributes. Moreover, it can indicate the changes in frequency amplitude over time and extract some time-frequency characteristics that cannot be obtained using other methods [22].  
Figure 1: Vibration signals of (**a**) healthy (H), (**b**) ball fault (BF), (**c**) inner race fault (IRF), and (**d**) outer race fault (ORF).  
Figure 2: Flowchart of the proposed bearing fault detection method.  
When a bearing is faulty, the vibration signal spectrum often contains a lot of low-frequency noise interference. Through demodulation, the fault characteristic components modulated at high frequencies are demodulated to low frequencies, reducing low-frequency interference and improving the signal-to-noise ratio. In this work, the Hilbert transform is used to implement envelope demodulation of the signal, obtaining the envelope signal after filtering. The envelope signal retains the oscillating parts of the original signal and includes harmonics at the fault frequency.  
The principal steps are as follows:  
Step 1: Perform the Hilbert transform on the vibration signal \(y(t)\):  
\[\begin{array}{l}\overset{\wedge}{y}(t)=\frac{1}{\pi}\int_{-\infty}^{a}\frac{ y(\tau)}{t-\tau}d\tau\\ =\frac{1}{\pi}\int_{-\infty}^{a}\frac{y(t-\tau)}{\tau}d\tau{=}y(t)\times\frac{1 }{nt}\end{array} \tag{1}\]  
Here, \(\overset{\wedge}{y}(t)\) is the envelope-demodulated signal obtained through convolution integration, and \(1/\pi t\) is the impulse response of the convolution integration.  
Step 2: Construct the analytic signal \(s(t)\) of \(y(t)\):  
\[s(t)=y(t)+\overset{\wedge}{y}(t) \tag{2}\]  
Here, \(\overset{\wedge}{y}(t)\) is the imaginary part of the analytic signal.  
Step 3: The envelope signal \(Y(t)\) of the Hilbert transform of the signal \(y(t)\):  
\[Y(t)=\sqrt{\left[y(t)\right]^{2}+\left[\overset{\wedge}{y}(t)\right]^{2}} \tag{3}\]  
Step 4: After filtering the signal \(Y(t)\) and performing a fast Fourier transform (FFT), the envelope spectrum of the original signal \(y(t)\) can be obtained, from which characteristic information can be extracted.  
Figure 3a-d shows the envelope spectra of vibration signals in different operating states with a speed of 1797 rpm, a fault diameter of 0.007 inches, and 0 hp in the CWRU Dataset. It can be observed from Figure 3a-d that there are significant differences in the peak values and corresponding frequencies of the envelope spectra for different operating states.  
Figure 3: Envelope spectrum of the vibration signal of **(a)** H, **(b)** BF, **(c)** IRF, and **(d)** ORF.
## 3 Results and Discussion
### Geometrical Parameters of Fabric Face Layer
#### 3.1.3 Influence of Weft Yarn Linear Density on Fabric Geometrical Parameters  
The effect of weft yarn count on the fabric geometrical parameters is presented in Figure 8. Table 7 shows pictures of the surface structure of some fabric samples, including A5/D5, A6/D6, and E1/H1. As shown in Figure 8a, it is easy to see that the fabric samples A (1-7) and E (1-4) have similar width of repeat unit to that of fabric samples D (1-7) and H (1-4), respectively, because they have the same warp density. Figure 8b shows the comparison of length of weave repeat between each pair of fabric samples A/B and E/H. It can be seen that the difference is significant wherein the reduction of L from fabric samples A to D and E to H reach 32.76% and 25.12%, respectively. This can be ascribed to the use of finer weft yarns of 75 D in the fabric samples D (1-7) and H (1-4) compared to the samples A (1-7) and E (1-4) that used thicker weft yarns of 150 D.  
The same situation is observed from Figure 8c, which shows the comparison of length of one segment (parameter P) between fabric samples using finer and thicker weft yarns. The average difference between two sets of fabric samples A/D is 0.158 mm wherein the decrease reaches 37.09%, while the difference between the comparison pair of E/H is 0.165 mm with the reduction of 28.25% from samples E to H. Figure 8d shows the difference between the comparison pairs of A/D and E/H in terms of the spacing between two adjacent face weft yarns in different segments. It can be seen that fabric samples D1, D2, H2, and H3 have smaller spacing than that of corresponding comparison samples A1, A2, E2, and E3. In addition, it is easy to observe that the spacing S of the blue-red-black samples E (1-4) and H (1-4) is much smaller compared to the fabric sample A (1-7) and D (1-7), which may indicate that the mixing of the component of black yarn leads to the decrease of the spacing between face yarn and thus lessen the exposing of white warp on the fabric face. The comparison of each pair of samples were conducted by using statistical analysis, and the results are listed in Table 9.  
Figure 8: Effect of weft yarn count on the geometrical parameters: (**a**) width of repeating unit; (**b**) length of repeating unit; (**c**) length of one segment; (**d**) spacing between two adjacent face weft yarns in different segments.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 1 Introduction
The classification operator groups the final generation based on train sequence indicators, configuring the voting entities for the subsequent voting process. 3. **Condorcet Voting Mechanism for Collaboration**: A Condorcet voting mechanism is employed as a collaborative approach within the MAS, enabling the selection of the most acceptable solution across the entire network and ensuring global feasibility. 4. **Case Study and Comparative Analysis**: We conduct a case study that demonstrates the benefits of the MAS-based approach compared to First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing rules. The analysis highlights the strengths and weaknesses of the MAS approach in the context of train rescheduling. The remainder of this paper is organized as follows: Section 2 provides a review of the state of the art in railway traffic management, focusing on formulation approaches and search algorithms, along with a brief introduction to MAS and its recent applications in the transportation industry. Section 3 describes the train rescheduling problem, which is modeled as a mixed-integer linear programming (MILP) problem. In Section 4, we propose a hierarchical MAS for train rescheduling, discussing network decomposition and the allocation of the optimization problem within the MAS. The section also details the local searching algorithm and introduces the Condorcet voting mechanism. Section 5 presents a performance evaluation of the MAS using a British railway network, comparing the MAS-generated solutions with those from FCFS and TTOE approaches and discussing the advantages and disadvantages of applying the MAS to train rescheduling.
## 4 Cooperative Reinforcement Learning Algorithm for Resource Allocation
)}=L_{dB}(d)+log_{10}(X_{u})-A_{dB}(\theta)\)
3 Gain between the BS and a user, \(G_{Bu}=10^{\frac{-PL_{dB,B,u}}{10}}\)
4 Gain between two users, \(G_{uv}=k_{uv}d_{uv}^{-\alpha}\)
5 SINR of the D2D users on the \(r\)th RB, \(\gamma_{r}^{D_{u}}=\frac{p_{r}^{D_{u}}G_{Du,r}^{E_{uv}}}{\sigma^{2}+p_{r}.G_{ u}^{E_{u}}+\sum_{v\in D_{r}}^{r}p_{r}^{E_{v}}G_{Du,r}^{E_{v}}}\)
6 SINR of the cellular users on the RB, \(\gamma_{r}^{c}=\frac{p_{r}^{c}.G_{tr}}{\sigma^{2}+\sum_{v\in D_{r}}p_{r}^{E_{ v}}G_{v,r}}\)
7 if\((\gamma_{r}^{c}\geq\tau_{0}\), \(G_{Bu}\geq\tau_{1}\) and \(G_{uv}\geq\tau_{2})\)then
8\(\Re=\log_{2}(1+SINR(u))\);
9 else
10\(\Re=-1\);
11
12 Apply Algorithm 2 for the power allocation
13 endloop
```  
**Algorithm 1**Proposed resource allocation method  
Algorithm 2: Cooperative SARSA(\(\lambda\)) reinforcement learning algorithm over number of iterations. ```
1 Initialize \(Q(s,a)=0\), \(e(s,a)=0\), \(e_{max}=0.3\), \(e_{min}=0.1\), \(k=0.25\), \(\rho=1\), \(\gamma=0.9\), \(\gamma_{1}=0.5\), \(\lambda=0.5\)[17; 29]
2 loop
3 Determine the current \(s\) based on \(\gamma_{r}^{c}\), \(G_{Bu}\) and \(G_{uv}\)
4 Select a particular action \(a\) based on the policy, \(e=\min(e_{max},e_{min}+k*(S_{max}-S)/S_{max})\)
5 Execute the selected action
6 Update learning rate by \(\alpha=\frac{\rho}{visited(s,a)}\)
7 Determine the temporal difference error by \(\delta_{t}=\Re+\tau_{1}fQ_{t+1}(s_{t+1},a_{t+1})-Q_{t}(s_{t},a_{t})\)
8 Update eligibility traces
9 Update the Q-value, \(Q_{t+1}(s_{t+1},a_{t+1})\gets Q_{t}(s_{t},a_{t})+\alpha\delta_{t}e_{t}(s_ {t},a_{t})\)
10 Update the value function and share with neighbors
11 Shift to the next based on the executed action
12 endloop
```  
**Algorithm 2**Cooperative SARSA(\(\lambda\)) reinforcement learning algorithm over number of iterations. Algorithm 1 depicts the overall proposed resource allocation method. After setting the initial input parameters, the system oriented parameters, i.e., pathloss, channel gains, SINR of the D2D users and cellular users on the \(r\)th RB are calculated (Step 2-6 in Algorithm 1). Then the reward function is calculated (Step 8) and is assigned when the state values satisfy the constraint in step 7. After that Algorithm 2 is applied for the adaptive resource allocation. Algorithm 2 shows our proposed reinforcement learning algorithm execution steps for resource allocation over number of iterations.
## 3 Results and Discussion
### Assessment on the Phase Formation
Therefore, the decrease in the value of \(\Omega\) contributed to the formation of eutectic. Rules for solid solution formation in HEAs have been proposed by [PERSON] et al. [25], TCP phase will emerge when \(\Delta H_{mix}<-15\) kJ mol\({}^{-1}\) and \(\delta>6.6\%\). In case of CrCoNiTa\({}_{\rm x}\), the values have been modified to set as: \(-7.06<\Delta H_{mix}<-15.52\), \(2.76<\delta<\)5.39. Unfortunately, the rule proposed by [PERSON] et al. cannot accurately predict the formation of Laves phase in the CrCoNiTa\({}_{\rm x}\) alloy. The influence of Ta addition in CrCoNiTa\({}_{\rm x}\) on the changes of \(\Omega\) and \(\delta\) in CrCoNiTa\({}_{\rm x}\) alloys, single-phase FCC (x = FCC), and Laves phase (x = Laves) are presented in Figure 4. As the Ta content varies from x = 0.1 to x = 0.7, the value of \(\delta\) increases from 2.7% to 5.39%, and \(\Omega\) reduces from 1.42 to 0.74. Therefore, the stability of FCC phase is compromised with the increasing \(\delta\). The FCC phase is in the occupation of the left portion (\(\delta<\) 2.2%) and the Laves phase forms as the Ta content exceeds x = FCC and \(\delta>\) 2.2%. Therefore, according to the previous discussion, Ta reduces the stability of FCC and promotes the formation of Laves, which is due to its large lattice distortion caused by the large difference in atomic size. It has been found that FCC phase is stable with VEC \(>\) 8 and BCC phase is stable with VEC \(<\) 6.87 [27]. CoCrMnFeNi (VEC = 8.27), CoCrFeNi (VEC = 8.25) and CrCoNi (VEC = 8.33) [13; 28; 29]. As for BCC phase, MoNbTaVW (VEC = 5.4) and MoNbTaW (VEC = 5.5) at VEC \(<\) 6.87 [30]. Thus, VEC is a parameter that can effectively predict phase formation. Figure 5 illustrates that CrCoNi and x = FCC single-phase locate at VEC \(>\) 8.25, while two phases (FCC + Laves) exist in the range of \(7.5<\) VEC \(<\) 8.25, and Laves phase (x = Laves) lies at VEC \(<\) 7.5.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 4 Formulation of Power Loss of the Proposed Studied Topology
### Power Losses Calculation in Power Diodes  
The RMS current following through the diode is represented as \(i_{Dx_{rms}}\). The power loss due to ESR of the diodes is denoted as \(P_{rDx}\) and another loss due to the forward voltage drop is denoted as \(P_{FwDx}\). So, the power loss in each diode is represented as  
\[P_{Dx}=V_{FwD1}{<i_{Dx}>}+i_{Dx_{rms}}{}^{2}r_{Dx} \tag{47}\]  
\[\left\{\begin{array}{c}i_{D1_{rms}}=\frac{2<i_{0}>}{\left(1-d\right)\sqrt{( d)}}\\ P_{D1}=V_{FwD2}\frac{2<i_{0}>}{\left(1-d\right)}+\frac{4<i_{0}>}{d\left(1-d \right)^{2}}r_{D2}\end{array}\right. \tag{48}\]  
\[\left\{\begin{array}{c}i_{D2_{rms}}=\frac{2\left(1+d\right)<i_{0}>}{\left(1 -d\right)^{2}\sqrt{(d)}}\\ P_{D2}=V_{FwD2}\frac{2\left(1+d\right)<i_{0}>}{\left(1-d\right)^{2}}+\frac{4 \left(1+d\right)^{2}<i_{0}>}{d\left(1-d\right)^{4}}r_{D2}\end{array}\right. \tag{49}\]  
\[\left\{\begin{array}{c}i_{D3_{rms}}=\frac{<i_{0}>}{\sqrt{(d)}}\\ P_{D3}=V_{FwD3}{<i_{0}>}+\frac{<i_{0}>}{\left(d\right)}r_{D3}\end{array}\right. \tag{50}\]  
\[\left\{\begin{array}{c}i_{D3_{rms}}=\frac{<i_{0}>}{\sqrt{(1-d)}}\\ P_{D4}=V_{FwD4}{<i_{0}>}+\frac{<i_{0}>}{\left(1-d\right)}^{2}r_{D4}\end{array}\right. \tag{51}\]  
Figure 10: Equivalent circuit diagram of the studied converter with parasitic elements in each component.  
\[\left\{\begin{array}{c}i_{D5_{rms}}=\frac{2<i_{0}>}{(1-d)\sqrt{(1-d)}}\\ P_{D5}=V_{FwD5}\frac{2<i_{0}>}{(1-d)}+\frac{4<i_{0}>^{2}}{(1-d)^{3}}{}^{2}r_{D5} \\ \left\{\begin{array}{c}i_{D6_{rms}}=\frac{<i_{0}>}{\sqrt{(1-d)}}\\ P_{D6}=V_{FwD6}<i_{0}>+\frac{<i_{0}>^{2}}{(1-d)}{}^{2}r_{D6}\\ \end{array}\right.\end{array}\right. \tag{52}\]  
The total loss by the diode can be added together and represented as  
\[P_{Dtotal}=\sum_{i=1}^{6}P_{Di}\]
## 5 Conclusions  
This paper developed an algorithm for estimating the NMF rank in music signal processing systems. As has been mentioned in previous studies, music signal processing systems based on NMF algorithms perform well when the order is the same as the number of independent notes or chords. Thus, for NMF-based music signal processing systems, estimating the proper NMF rank is important.  
The proposed NMF rank estimation method was based on the SURE method. To use the SURE method, an estimate of the noise variance is required because it is assumed to be known. The conventional method estimates the noise variance using RMT, but the SURE-RMT algorithm estimates the NMF rank for general signal analysis and not merely for music signal processing systems. The proposed algorithm uses the onset information to estimate the number of basis vectors. The input data from the beginning to the second onset is required to have one basis; thus, the noise variance was estimated accurately using the eigenvalues of the correlation matrix of the partial data. We proposed two rank estimation algorithms, i.e., RESUREv1 and RESUREv2, which use the maximum and median values, respectively, of the noise eigenvalues.  
Simulations for estimating the NMF order were performed to evaluate the proposed algorithm. The simulations were performed using 30 test signals taken from the MAPS database. The evaluation results demonstrated that the proposed algorithm estimated the NMF rank to be closer to the number of musical notes compared to the conventional NMF rank estimation algorithms such as VAR-NMF, ARD-NMF, and GaP-NMF. Furthermore, music transcription evaluations were performed for the proposed rank estimation method combined with the conventional NMF algorithm, with results showing that the proposed method is effective as compared to the conventional-NMF-based music transcription algorithm. Though the F1-score performance advantages are not significantly large compared to to the ARD-NMF and GaP-NMF performances, the results are still meaningful, because the proposed algorithms are lightweight, are easy to combine with any other NMF methods that require the rank parameter as a priori, and lack setting parameters that affect the performance substantially.  
Figure 14: Chart comparing the elapsed times of the parts of various algorithms related to model order selection. The elapsed times were measured using MATLAB on a laptop with a 3.5-GHz dual-core CPU and 16-GB memory.