esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
The simple assembly line balancing problems are fundamental versions of the general ALBP, which has attracted the attention of practitioners and researchers of OR. With respect to the objective functions, the SALBP was classified into SALBP-1, SALBP-2 and SALBP-E. These deterministic problems are not always applicable for real assembly and production lines, since in practice the durations of the assembly operations and other parameters may depend on many factors and are not constant values throughout the lifecycle of the assembly and production lines.  
This survey covered most of the papers dealing with the assembly and disassembly line design and balancing under uncertainty. Deterministic models for assembly lines have also been discussed, provided that they are subject to some deviations from normally fixed manufacture conditions. The survey showed that for non-deterministic assembly line design and balancing, most of the referenced papers modeled the assembly operation durations as independent with known probability distributions. Most frequently, such optimization problems have been addressed by heuristic and meta-heuristic approaches. Since the design of assembly and production lines is a strategic problem of high importance, defining the optimal line balances over a large planning horizon, more effort needs to be devoted to the development of efficient exact methods. In the case of the disassembly lines, more attention needs to be paid to the high uncertainty of end-of-life product quantity and quality, as well as to the environmental impact of such lines.  
This review focused on assembly and production lines with stochastic, fuzzy and uncertain parameters. We surveyed both the progress in academic knowledge and the current needs of the practitioners. Reviewing the previous studies and studying the needs of the industry led us to propose new settings for the SALBP and highlight the research areas that are worth further investigation. To reduce the financial costs associated with the modification of the existing assembly line, a new formulation, SALBP-\(E_{\alpha}^{\beta}\), of the SALBP, balancing the assembly line with three variants of the objective Functions (2)-(4), was proposed for further research. These problems, SALBP-\(E_{\alpha}^{\beta}\), with various objective functions, are intended for frequent modifications of existing assembly lines and production lines due to the accelerated development of technologies and frequent changes in the consumer market, which necessitates the assembly of new product models instead of obsolete assembly products.  
The research was funded by Belarusian Republican Foundation for Fundamental Research, grant number \(\Phi\)21-010 and grant number \(\Phi\)23 PH-017.  
Not applicable.  
The author declares no conflict of interest.
## Literature Review
### Vision-Based Retail  
Preliminary efforts related to the classification of fruit and vegetables at supermarket self-checkouts have been reported recently [36; 37; 38; 39; 40]. A MobileNet-based fruit classification system for a supermarket has been presented in [41]. A dataset of images of different fruit was obtained and used for transfer learning the MobileNet. The MobileNet architecture is selected to reduce the computational cost. To improve the overall effectiveness of MobileNet, new features are proposed as an input to MobileNet. A unique RGB code is defined for each fruit which is considered as a feature vector along with an RGB histogram and K-means centroid. An accuracy of 95% has been reported, however the number of varieties of fruit considered are significantly low. Considering the large number of fruit and vegetables sold at a supermarket, the proposed idea of a unique RGB code can be a limitation. The concept of using multiple patches of local features of a supermarket object was used in [42]. A Local Concepts Accumulation (LCA) layer is defined as a penultimate layer on CNN architecture. Entropy maximisation is used as a loss function for the classification of supermarket produce, where an accuracy of 100% has been achieved for ResNet with LCA. Recently, an attention fusion network has been proposed for image-based nutrition estimation of cooked food in [43]. A progressive weighted average of CNN weights is presented for the classification of fruit and vegetable images in [44]. Only colour and texture were considered as a feature for classification where a patch of 640 \(\times\) 640 pixels was cropped from the images taken in a real supermarket environment. A more detailed discussion on utilisation of machine learning techniques for different applications including fruit and vegetables classification can be found in [41; 45; 46; 47].  
Current supermarket self-checkout systems require an unassisted selection from a LUT for billing of fruit and vegetables. This selection from the LUT can require good knowledge about the various species and kinds of fruit and vegetables, which increases the chances of an incorrect selection. The addition of a vision sensor can significantly improve the process of LUT-based selection. There can be many methods to realise for this application, for example a threshold can be set on the classification accuracy to consider it as a final selection. In the case where the classification accuracy is less than the threshold, the customer can be directed to a subset of the LUT with selections that are limited based on the classification results. The limited selection will be populated with a subset of similar fruit or vegetables varieties. This can significantly reduce the chances of incorrect selection and will improve the billing experience even if the systems cannot achieve 100% accuracy.
## 3 Detection Natural Cracks by Using PECT
### Experiment Testing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & \multicolumn{3}{c}{\(\Delta V_{R}\) **(mV)**} & \multicolumn{3}{c}{\(\Delta V_{RA}\) **(mV)**} & \multicolumn{3}{c}{\(\Delta V_{F}\) **(mV)**} & \multicolumn{3}{c}{\(\Delta V_{FA}\) **(mV)**} \\ \cline{2-9}
**Number** & **P\({}_{1}\)** & **P\({}_{2}\)** & **P\({}_{3}\)** & **P\({}_{1}\)** & **P\({}_{2}\)** & **P\({}_{3}\)** & **\\ \hline
1 & 115.038 & 147.428 & 249.413 & 170.626 & 22.0371 & 62.149 & 161.172 & 81.786 \\
2 & 205.304 & 171.510 & 196.430 & 191.081 & 159.262 & 132.025 & 149.158 & 146.815 \\
3 & 230.247 & 212.390 & 204.637 & 215.758 & 183.632 & 162.388 & 159.813 & 168.611 \\
4 & 242.097 & 201.811 & 205.246 & 216.385 & 176.704 & 145.231 & 211.473 & 177.803 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Peak value of rising edge and falling edge. Figure 8: Top view of the detection probe and specimen position. Figure 9: Relationship between \(\Delta V_{R}\)/\(\Delta V_{F}\) and crack depth. By performing linear regression on both the average peak voltage values of the rising edge and the falling edge, the linearity mapping relationship between the natural depth and the rising edge or the falling edge can be summarized as Equation (1), where \(d\) is the crack depth. \[\left\{ \begin{array}{l}\Delta V_{RA}=41.83d+110.6\\ \Delta V_{FA}=79.14d-20.56\end{array} \right. \tag{1}\]  
Table 5 lists the inversion results. The actual crack depth is the result that is observed using the optical method, which can be thought of as the true depth of the crack. Corresponding inversion errors for specimens 1#, 2#, 3#, and 4# are based on the mapping model shown in Equation (1). According to Table 5, the error range for the four specimens lies between 0.50% and 11.77% generally. Especially when utilizing the falling edge inversion model, the relative errors for the four specimens are in the range of 0.5-11.77%, while when utilizing the rising edge inversion model, the inversion error is in the range of 1.11-2.34% for four specimens. For specimen 1#, 2#, and #3, the measured accuracy is higher according the rising edge inversion model; and for specimen 4#, the reverse case is shown. Therefore, it is necessary to seek a generalized approach for the characterization all specimens with natural crack depth which has satisfactory accuracy. Figure 11: Relationship between average voltage and crack depth. Figure 10: The difference signal between \(V_{average}\) and \(V_{ref}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Data Type** & **Voltage** & **Measured Crack** & **Actual Crack** & **Error** \\
**Number** & **Data Type** & **(mV)** & **Depth (mm)** & **Depth (mm)** & **(\%)** \\ \hline \multirow{3}{*}{\(1\)} & \(\Delta V_{RA}\) & 170.6262 & 1.4435 & & 2.34 \\  & \(\Delta V_{FA}\) & 81.786 & 1.3011 & 1.4105 & 7.76 \\ \cline{1-1}  & Average & 126.2061 & 1.3723 & & 5.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of fitting curve errors for each specimen.
## 5 Conclusions  
In this study, a hybrid model based on the HFACS and FFTA was used in the EMAs. The HOFs of EMAs were classified using the HFACS structure, and the formation process of accidents was revealed through the application of FFTA. According to the results, the following issue can be concluded:  
1. The roots of human and organizational errors in EMAs were identified and predicted based on the form of four levels of the HFACS method. The number of fundamental causes (more detailed level) detected at the level of the unsafe acts is 26, the preconditions for unsafe acts level is 62, the level of unsafe supervision is 12, and the level of organizational influences is 19.
2. Using the FTA, a logical relationship between identified causes and human and organizational errors was constructed. 20 roots were identified as the BEs for the TE.
3. The results of calculating and analyzing the probability of occurrence of EMAs using FFTA reach 1.0414 \(\times\) 10\({}^{-2}\).
4. The ranking of the MCSs showed that by controlling the occurrence of the BEs that constitute the MCSs, the occurrence of EMAs would be reduced.  
As for the method itself, it has the following two advantages:  
Figure 6: Percentage of risk reduction.  
When quantitative historical fault data is scarce or unavailable, fuzzy logic can process qualitative data. 2. The use of linguistic values in system reliability assessment can express expert opinions intuitively and conveniently. Although FFTA-HFACS has the above merits, it also has the following limitations: 1. FTA is the conversion of a physical system into a structured logical diagram [72] and, therefore, also requires certain incident scenarios for the construction of the incident tree structure. 2. The successful and exact application of this method still relies on the estimation of the probabilities of the BEs. Therefore, experts with extensive knowledge and experience should be consulted [18]. In light of above, further research can be conducted: * The FFTA-HFACS hybrid approach can quantitatively assess the probability of failure and significant accident risk or a combination of their accident risks, but it lacks an explanation of how accident risk affects the occurrence of an accident. This is an issue that is well worth investigating. * In this study, the MCS was artificially identified as an accident critical risk and its elimination would improve the reliability of the system. However, it has not yet been considered what measures can be taken to eliminate the MCS, and in this regard, the use of multi-criteria decision making (MCDM) may be a good idea in the future [64].  
[PERSON] and [PERSON] contributed to paper writing and the whole revision process; [PERSON] contributed to paper editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (51977038).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Problem Description and Assumptions
### Quality-Cintement Demand  
With the progress of production, the equipment state change affects the quality of products. When the equipment state is worse, the defective rate of the product is higher, and the relationship between them is as shown in Equation (3) [36]. When the defective rate increases, considering product repair, the ratio of low-quality products increases, and the demand rate decreases. The relationship between the demand rate and the ratio of low-quality products is shown in Equation (4) [31].  
\[p(x)=p_{0}+\eta(1-\exp(-ax^{\theta})) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maintenance Group Number** & **Probability of Maintenance Group** & **Deterioration Probability of Component State** \\ \hline (1) \(M\) (0) & \(\int_{0}^{\min(x_{1},D_{p}^{(i)})}\int_{0}^{\infty}\int_{0}^{\min(x_{3},D_{p}^ {(j)})}s(Y)dy_{1}dy_{2}dy_{3}\) & \(f_{1}(x_{1}-y_{1})f_{2}(x_{2}-y_{2})f_{3}(x_{3}-y_{3})\) \\  & \(+\int_{0}^{\min(x_{1},D_{p}^{(i)})}\int_{0}^{\min(x_{2},D_{p}^{(2)})}\int_{D_{ p}^{(3)}}^{\infty}s(Y)dy_{1}dy_{2}dy_{3}\) & \(f_{1}(x_{1})f_{2}(x_{2}-y_{\[d_{r}=d_{\max}\times(1-\mu\times\rho) \tag{4}\]  
where \(p_{0}\) is the defective rate when the equipment is completely new, \(\eta\) is the boundary of quality deterioration, \(\alpha\) and \(\beta\) are parameters obtained according to historical data, \(d_{\max}\) represents the demand rate when all products are high-quality products, \(\mu\) is the mediation coefficient, \(0<\mu\leq 1\), and \(\rho\) is the low-quality qualified product rate in a production cycle, that is, the proportion of low-quality qualified products in the total number of products.  
There are two types of low-quality products in a cycle; one is a certain proportion of low-quality products in qualified products, and the other is that unqualified products can be repaired and become low-quality products. The quantity calculation of these two kinds of low-quality products is shown in Equations (5) and (6). Since the proposed equipment is three-unit, \(\int_{0}^{t_{n}}p(x)dt\) represents the defective rate of the system from 0 to \(t_{n}\).  
* The number of low-quality qualified products in qualified products is: \[N_{1}=\theta_{1}\times Q\times(1-\int_{0}^{t_{n}}p(x)dt)\] (5)
* The number of low-quality qualified products repaired by unqualified products is: \[N_{2}=\theta_{2}\times Q\times\int_{0}^{t_{n}}p(x)dt\] (6)  
According to Equations (5) and (6), the ratio of low-quality qualified products in a production cycle is:  
\[\rho=\frac{N_{1}+N_{2}}{Q} \tag{7}\]  
Combining Equations (4) and (7), the demand rate can be obtained as follows:  
\[d_{r}=d_{\max}\times(1-\mu\times(\theta_{1}\times(1-\int_{0}^{t_{n}}p(x)dt)+ \theta_{2}\times\int_{0}^{t_{n}}p(x)dt)) \tag{8}\]
# Doppler-Based Acoustic Gyrator
###### Abstract  
Non-reciprocal phase shifters have been attracting a great deal of attention due to their important applications in filtering, isolation, modulation, and mode locking. Here, we demonstrate a non-reciprocal acoustic phase shifter using a simple acoustic waveguide. We show, both analytically and numerically, that when the fluid within the waveguide is biased by a time-independent velocity, the sound waves travelling in forward and backward directions experience different amounts of phase shifts. We further show that the differential phase shift between the forward and backward waves can be conveniently adjusted by changing the imparted bias velocity. Setting the corresponding differential phase shift to 180 degrees, we then realize an acoustic gyrator, which is of paramount importance not only for the network realization of two port components, but also as the building block for the construction of different non-reciprocal devices like isolators and circulators.  
non-reciprocal acoustics; gyrators; phase shifters; Doppler effect +
Footnote †: journal: Applied Physics Letters
## 3 First Generation: Virtual Inertia Topologies
### Synchrotroners: A Synchronous Generator Model Based Topology
The synchronverter topology has also inspired the operations of rectifiers as synchronous motors [39] which helps in obtaining inertial response from the load side of the power system. Moreover, the voltage-source based implementation means that synchronverters can be operated as grid forming units, and ideally suited for inertia emulation from DGs that are not connected with the main grid. The fact that the frequency derivative is not required for the implementation, is a major advantage as derivative terms often induce noise in the system. Although the synchronverter is able to replicate the exact dynamics of a SG, the complexity of the differentialequations used can result in numerical instability. Moreover, a voltage-source based implementation means there is no inherent protection against severe grid transients, which may result in need of external protection systems for safe operation.
## 3 System Model  
### Mammal-Friendly Communication Mechanism  
In the marine environment, artificial acoustic systems and marine mammals share spectrum resources ranging from tens to hundreds of kilohertz, as shown in Figure 1. Within the communication frequency range of 1 kHz to 40 kHz, significant overlap exists between the frequency bands used by artificial underwater sensors and various marine protected animals such as dolphins and whales. The impact of artificial noise sources, like ships, sonar, and underwater sensors on marine mammals, primarily manifests in hearing impairment, abnormal behavior, and organ damage [18; 19]. For instance, Reference [20] analyzed the naval sonar military exercises held in Spain in 2002, which resulted in chronic tissue damage to beaked whales near the Canary Islands. Relevant evidence was provided, indicating that the cause of the disease was the formation of bubbles within their bodies due to interference from naval sonar signals.  
To sum up, the impact on Marine life should be considered in underwater acoustic communication. Therefore, this paper proposes an MFPC algorithm. By controlling the  
Figure 1: Diagram of signal overlapping frequency bands.  
power and energy of sensor nodes, the algorithm can avoid the interference of marine mammals and improve the utilization rate of the spectrum.  
The flow chart of the algorithm in this paper is shown in Figure 2. The steps of the algorithm are as follows:  
1. Set underwater acoustic channel parameters.
2. The optimal power \(P_{lk}\) of the secondary user is obtained.
3. The channel allocation ratio \(a_{k}\) is obtained.
4. Determine whether the benefit of the secondary user continues to increase. If so, the secondary user reduces its power according to the step \(\lambda\), returns to Step 2, and starts the algorithm process again until the utility function of the secondary user no longer increases.
5. The optimal Nash equilibrium solution is obtained and the algorithm is terminated.
# Statistical Tolerance Analysis--A Survey on Awareness, Use and Need in German Industry
## 5 Awareness of Deviations and Tolerances
This expectation may be strengthened by the survey results of 95 participants (7 abstentions) that are detailed in Figure 4: while only 13 of these 95 respondents did not inspect incoming components from external suppliers, the remaining vast majority of 82 participants had already established procedures to inspect incoming components. This figure comprises those who inspected every single component delivered (19 of 95) as well as those who inspected a set of samples taken either from every individual batch (40 of 95) or from a larger number of batches (23 of 95). In consequence, components were identified whose actual geometries did not meet the tolerances specified. Fortunately, 81% (=81 of 100, 2 abstentions) of the participants sorted out these parts and sent them for reworking (either in-house or at the supplier). However, this is obviously a cost-intensive procedure and thus not optimal for components of low cost. In the end, even this rework can still be in vain. Therefore, it is not surprising  
Figure 4: Response of 95 participants to the (single-choice) question: “Do you inspect incoming components from external suppliers to evaluate conformity with given requirements? Figure 3: (**a**) Response of 99 participants to the (single–choice) question: “What percentage of problems that arise during production, assembly and use of products are caused by deviations and are tolerance-related issues?”; (**b**) response of participants to selected (single–choice) questions. that 68 of 100 respondents (2 abstentions) finally considered these components as scrap. Moreover, three participants stated that they discussed with the customer to achieve a so-called deviation approval of the components or to coordinate expedient actions and plan further steps. The results discussed reveal a great awareness of deviations and the relevance of proper tolerance engineering among the survey participants. Nevertheless, successful tolerance engineering is still a challenging field for engineers from all domains. This reaffirms the use of tolerance analyses (as early as possible) in order to specify valid tolerances--following the \"first time right\" philosophy of engineering design.
## 2 Materials and Methods
### Identification of Collected LM Canola Samples  
An immunochemical strip kit (Envirologix Inc., Portland, ME, USA) was utilized to analyze the monitoring samples, enabling the detection of CP4-EPSPS and PAT proteins, which confer tolerance to glyphosate and glufosinate herbicides. The collected samples were processed as follows: an extraction buffer was added to the ground sample tissue, and the testing strip was inserted into the extract [44].  
Event-specific PCR was conducted to identify the LMO gene cassette within the canola genome of the collected samples. Certified reference materials (CRMs) from the American Oil Chemists' Society (Urbana, IL, USA) and the Institute for Reference Materials and Measurements (Geel, Belgium) were used as controls during the PCR analysis. Genomic DNA from leaf tissues was extracted using the Nucleic Acid Extraction kit (TIANLONG, Xi'an, China), following the manufacturer's protocol. The primer set for the \(CruA\) gene was employed as a control in the PCR reactions. Specific PCR analysis, using primer sets for eight LM canola events, was performed to characterize the suspicious samples, as described in Table 1.  
The PCR was conducted using the 2X Lamp Taq PCR Pre-Mix (Biofact Inc., Daejeon, Korea) in a 30 \(\mu\)L reaction volume, containing 50 ng of genomic DNA and 1 \(\mu\)L of each primer (10 pmol/\(\mu\)L). The PCR amplification was performed on the ProFlex PCR System (Applied Biosystems, Waltham, MA, USA) with the following program: initial denaturation at 95 \({}^{\circ}\)C for 5 min, followed by 33 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 59 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s. A final extension was carried out at 72 \({}^{\circ}\)C for 7 min, followed by a hold at 4 \({}^{\circ}\)C [45]. The PCR products were electrophoresed on a 2.5% agarose gel and identified using the ChemiDoc XRS\({}^{+}\) Imaging System (Bio-Rad, Hercules, CA, USA). Sequencing of the PCR products was performed by Biofact Inc. (Daejeon, Republic of Korea) to confirm event-specific amplification. The sequences were aligned using the BioEdit v7.2.6.0 program [46].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target Event** & **Primer Name** & **Sequence (5’-3’)** & **Product Size (bp)** & **Reference** \\
**(Or Gene)** & & & & \\ \hline Topas 19/2 & Topas 19/2-F & CGACCCGGCCTGATATATGA & & \\  & Topas 19/2-R & GTTGCGGTTCGTGCAGT TCC & & \\ \hline Rf3 & Rf3-F & AGCATTTAGCATACCATCAGACA & & [48] \\  & Rf3-R & ATCAATATCATTGCAACGGAAAAGG & & This study \\ \hline \end{tabular}
\end{table}
Table 1: Oligonucleotide primers were used in this study.