esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a flexible assembly job shop scheduling problem with uncertain processing times is considered, where the uncertain processing time can be represented by interval numbers. To solve this problem, a robust optimization model based on chance constraints is formulated, and an efficient master-apprentice evolutionary algorithm is proposed. The case study validates the effectiveness of the robust optimization model and demonstrates the superior performance of the MAE compared to the two competitive methods. The main contributions of this study are summarized as follows:  
1. This study introduces the FAJSP with uncertain interval processing times for the first time and establishes a robust optimization model based on chance constraints to address the uncertainty.
2. Using statistical analysis techniques, interval numbers are represented by confidence levels to describe processing times, and the relationship between confidence levels and the robustness of the scheduling solutions is analyzed.
3. A dual-individual-based MAE is developed specifically for the FAJSP. First, two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Next, path relinking and tabu search operators are integrated to enhance solution quality. Finally, a decoding method based on interval scheduling is proposed to accurately handle processing times in the form of interval numbers.  
This paper discusses the impact of uncertain processing times on the scheduling process; however, in actual workshop production, there are often numerous uncertain factors. Sudden events such as equipment failures and delivery delays can affect the final scheduling results. Therefore, in future research, it is necessary to incorporate such uncertainties into consideration, bringing the theoretical study of scheduling problems closer to practical applications. This will enable the scheduling methods to better adapt to more complex production environments, providing manufacturing enterprises with more valuable guidance.  
Conceptualization, P.Z. ([PERSON]); methodology, P.Z. ([PERSON]) and P.Z. ([PERSON]); software, S.X.; validation, S.X. and P.Z. ([PERSON]); formal analysis, P.Z. ([PERSON]); investigation, Y.L.; resources, P.Z. ([PERSON]); data curation, S.X.; writing--original draft preparation, P.Z. ([PERSON]); writing--review and editing, P.Z. ([PERSON]), P.Z. ([PERSON]), and S.X.; visualization, P.Z. ([PERSON]); supervision, P.Z. ([PERSON]); project administration, P.Z.  
Figure 14: Gantt chart of the optimal solution for Mk05A obtained by MAE.  
([PERSON]); funding acquisition, P.Z. ([PERSON]) and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shanghai Science and Technology Innovation Action Plan (No. 23692121400 and No. 22511101903).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
### Rheological Characterization
#### 4.2.2 Viscoelastic Energy Characterization
As can be seen from Figure 12, with the increase in shear time, the apparent viscosity of CNTs greases first increases and then gradually decreases, while the other three greases gradually decrease with the increase in shear time. At 300 s shear, the viscosity of PTFE grease is 1.09 Pa-s, and the minimum viscosity of WS\({}_{2}\) grease is 0.74 Pa-s, 32% lower than PTFE. The maximum viscosity of graphene grease is 1.90 Pa-s, which is 74% higher than that of PTFE. The viscosity of CNTs grease is 1.56 Pa-s, 43% higher than that of PTFE. This indicates that the properties of graphene and CNTs are better than those of PTFE, among which graphene is the best, and WS\({}_{2}\) is inferior to PTFE under freeze-thaw conditions. The main reason why the apparent viscosity of grease initially decreases with time and then tends to be stable is as follows: grease is a colloidal structure composed of soap fiber and base oil. The apparent viscosity of grease is related to the thin consistency of grease, which in turn is related to the size of soap fiber. The larger the size of soap fiber, the more obvious the shear-thinning phenomenon. Under high-speed shear, the size of soap fiber is constantly reduced, while the size and direction of soap fiber become more and more stable, so the apparent viscosity of grease tends to be constant. The abnormal phenomenon that the apparent viscosity of CNTs first increases and then decreases is due to structural adjustment, and no stable colloidal structure is formed. The experimental results show  
Figure 11: Change of apparent viscosity of grease with time under shear condition. Figure 12: Changes of apparent viscosity with time of various freeze–thaw greases under shear conditions. that graphene grease has better shear resistance and fluidity and is more suitable for harsh construction conditions of rotary bridges.
## 6 Conclusions  
This paper presents a one-bit hexagonal meander-shaped unit cell, an electronically reconfigurable wideband transmitarray with beam-scanning and polarization selector characteristics for the Ku band. The unit cell is implemented as a cascade transmitter and receiver phenomenon with an impedance-matching receiving layer to enhance the bandwidth of the unit cell. The polarization rotation feathers are obtained, as the transmitter and receiver are orthogonal. The full-wave simulations of the 20 \(\times\) 20 arrays were performed for the Ku band's wideband reconfigurable transmitarray. The presented array contains 400 PIN diodes in the proposed configuration to control the phase shift distribution of each element of the transmitarray. The given design exhibits a 3 dB bandwidth of 26.6% at 16.5 GHz, an aperture efficiency of 34.4%, and beam-scanning characteristics available  
Figure 16: A 20 \(\times\) 20 gain radiation pattern for 20 \(\times\) 20 array at 17.5 GHz. (**a**) E-plane max gain (0–60 degrees). (**b**) E-plane co-pol and cross-pol patterns.  
for \(\pm\)60 degrees for both the E plane and H plane. This polarization selector transmitarray maintains a high gain with low cross-polarization that is applicable for many applications.  
Conceptualization, Q.A. and Y.X.; methodology, S.S. and Q.A.; software, Q.A.; validation, W.T. and S.S.; formal analysis, W.S.; resources, W.T.; writing, W.S. and Q.A.; original draft preparation, Q.A., W.T., and S.M.A.; review and editing, S.M.A. and Y.X.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62201038, in part by the China Postdoctoral Science Foundation under Project 2022M710366, and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars under Project XSQD-202206003.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Hybrid Business System Model
### Analysis for Throughput in Mixed Services  
The traditional channel capacity is calculated by [PERSON]'s theorem based on infinite code and the bit error rate \(\varepsilon\approx 0\). However, the bit error rate is non-zero and the code length of the channel transmission is limited in the actual environment. A longer code length can effectively improve the transmission rate [22]. However, there are many small data packets that need to be transmitted for AIoT services, and adding a significant amount of redundant information to each AIoT service to ensure high-reliability transmission would cause a considerable amount of useless information to be transmitted in the network. Therefore, the channel rate (bits/s/Hz) is obtained as follows:  
\[\frac{1}{n}\text{log}_{2}M^{*}(n,\varepsilon)\approx C-\sqrt{\frac{V}{n}}Q^{-1 }(\varepsilon), \tag{3}\]  
where \(n\) is the length of the code. \(C=\text{log}_{2}(1+\gamma)\) is the unit channel capacity in the infinite code. \(V=1-\left(1+\gamma\right)^{-2}\) is the channel dispersion, which is used to measure the random variability of the channel relative to the deterministic channel with the same capacity. \(Q^{-1}(\cdot)\) is the inverse of the function \(Q(x)=\int\limits_{x}^{\infty}\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{\tau}}dt\).  
In the OFDM system, \(n_{x}\) is the length of the code, and the number of occupied symbols is positively correlated with the duration of the signal. When the total occupied bandwidth is \(B\), the given channel code length for a transmission task can be seen as \(N_{x}=BTx\in\{h,j\}\). \(T\) is the duration of one symbol during the service transmission. The system bandwidth is divided into multiple bandwidths with a size of \(B_{0}\). In the system of URLLC service and mMTC service, when the number of symbols occupied by the URLLC service is \(n_{x_{1}}\), the total traffic transmitted by the URLLC service can be approximately calculated as follows:  
\[C_{URLLC}\approx n_{x_{1}}B_{0}TC-\sqrt{n_{x_{1}}B_{0}TV}Q^{-1}(\varepsilon), \tag{4}\]  
where mMTC service does not have high requirements for the delay; the transmission traffic can be increased by increasing the length of the code. When the number of symbols occupied by the mMTC service is \(n_{x_{2}}\), the mMTC traffic transmitted during the signal duration can be calculated as follows:  
\[C_{mMTC}=n_{x_{2}}B_{0}TC, \tag{5}\]
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 3 Proposed Method
### Object Association  
To determine the unique IDs to detect objects consistently across frames, we adopted a strategy inspired by deepSORT, which leverages two types of information to verify the identity of objects detected in consecutive frames. Figure 4 illustrates the purpose of the data association stage.  
Figure 4: Structure of the data association stage. The red boxes represent the result detected from the object detector.  
Figure 3: Flowchart of the proposed method.  
The first is the object's predicted position, obtained by applying a Kalman filter to the detection results. The second is the object's appearance features, extracted using a deep learning model. By combining these two sources of information, we generated a score matrix that provides a more reliable estimate of object identities across frames as follows:  
\[c(i,j)=\lambda IoU(B_{i},B_{j})+(1-\lambda)Triplet(i,j), \tag{1}\]  
where \(\lambda\) denotes a weighting factor. The details of each information function \(IoU(B_{i},B_{j})\) and \(Triplet(i,j)\) are described in the sections below. Once the cost matrix \(c(i,j)\) between the current track and the detection results is calculated, an ID is assigned to the object with the maximum value via the Hungarian algorithm [22]. Unmatched objects among the detection results are provided a new ID. Objects that do not appear for a certain period during the tracking are deleted to ensure a high computational speed.
## 3 Results and Discussion
### Comparison with Other Algorithms  
In this section, the case where the QP value is uniformly 32 is selected for comparison of the effect of the improved method with the other two common GAN types of network utilization (ESRGAN and DRRN). The results are presented below in Table 7.  
As evidenced by the data presented in the preceding table, the PSNR value of the enhanced methodology has exhibited an average increase of 8.3816% in comparison to ESRGAN and an average increase of 14.4% in comparison to DRRN. The LPIPS value has been reduced by an average of 4.8817% as compared to ESRGAN and by an average of 11.9500% as compared to DRRN. In comparison to DRRN, the LPIPS value has been reduced by an average of 654% as compared to ESRGAN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Sequences** & **Pre-Improved** & **Improved** \\ \hline Akiyo & 25.0139/0.2971 & 28.4538/0.2643 \\ \hline Bus & 29.6670/0.2958 & 30.4636/0.2654 \\ \hline Bridge & 23.7735/0.2998 & 27.9192/0.2693 \\ \hline Foreman & 29.7798/0.3018 & 31.2972/0.2683 \\ \hline Flower & 26.5820/0.3051 & 30.6072/0.2729 \\ \hline Container & 27.0128/0.2969 & 28.7121/0.2669 \\ \hline Tempete & 22.8407/0.2918 & 24.5577/0.2585 \\ \hline Basketball & 26.9732/0.2990 & 29.6312/0.2667 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of PSNR/LPIPS values (QP = 35).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Sequences** & **Pre-Improved** & **Improved** \\ \hline Akiyo & 21.1844/0.3104 & 23.8498/0.2768 \\ \hline Bus & 26.6978/0.3155 & 28.4123/0.3130 \\ \hline Bridge & 21.2134/0.3129 & 21.7755/0.2804 \\ \hline Foreman & 28.7527/0.3205 & 29.6062/0.3115 \\ \hline Flower & 26.3201/0.3250 & 26.9888/0.3120 \\ \hline Container & 23.4114/0.3109 & 25.5351/0.2776 \\ \hline Tempete & 20.1939/0.2996 & 21.4672/0.2715 \\ \hline Basketball & 25.4732/0.3285 & 27.7612/0.3180 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of PSNR/LPIPS values (QP = 37).
## 4 The Study on Gas Control Technology of High Gas-Drainage Roadway
### Result and Discussion
#### 4.3.3 Extraction Effects at Different Vertical Positions  
Figure 13 illustrates the variation in gas concentration within the high gas-drainage roadway and return airflow when the horizontal distance from the return air roadway is fixed at 30 m, and the vertical distances from the coal roof are 20 m, 28 m, 35 m, and 40 m. As shown in Figure 13, it can be seen that the gas concentration in the high gas-drainage roadway initially increases and then decreases, with increasing vertical height. This trend is attributed to the fact that at a vertical height of 20 m, the high gas-drainage roadway is located in the lower part of the compaction area within the 'O'-shaped ring formed by mining-induced fractures. At this position, less gas can be extracted from the upper part of the fracture zone, but due to its proximity to the working face, it is capable of extracting more gas directly from the working face. However, its closeness to the return air roadway may result in air leakage from the return air roadway, which reduces both the gas concentration and the net gas flow in the high gas-drainage roadway.  
At a vertical height of 28 m, the high gas-drainage roadway is located in the middle of the 'O'-shaped ring fracture zone, enabling efficient gas extraction from this region and resulting in the highest gas concentration of 4.32%. As the vertical height increases to 35 m and 40 m, the high gas-drainage roadway moves farther from the working face and shifts into the upper portion of the 'O'-shaped ring fracture zone. Although the lower density of gas promotes upward migration, enabling the extraction of gas rising from the goal, the increased distance from the working face introduces greater resistance to upward flow. This hinders gas migration and makes extraction more difficult, resulting in a reduced gas concentration in the high gas-drainage roadway.  
Figure 13: The gas extraction effects of the high gas-drainage roadway at different vertical positions.  
As shown in Figure 13, when the vertical distance varies from 20 m to 45 m, the gas concentration in the return airflow of the gob-side entry retaining initially decreases and then increases, exhibiting a trend opposite to that of the extraction concentration. The maximum gas concentration in the return airflow reaches 0.75%, while the minimum value of 0.64% occurs when the high gas-drainage roadway is located 28 m above the coal roof.  
When the high gas-drainage roadway is located 28 m from the coal roof, the gas extraction concentration exceeds 3.6%, while the gas concentration in the return airflow remains below 0.64%. This configuration yields the optimal extraction performance. Therefore, it is recommended that the high gas-drainage roadway be positioned at a vertical distance of 28 m from the coal roof.
## 5 Future challenges  
Energy consumption forecasting is an important instrument that can help in the recognition, measurement and management of demand flexibility. In this aspect, some challenges need to be overcome in the future.  
* Future lines of research should encourage the use of current methods (physical, data-driven and hybrid), allowing them to be relevant for the representation of the energy of buildings at various scales and in different environmental conditions [122]. Current methods need to address challenges such as forecast error compensation, dynamic model selection issues, adaptive predictive model design and data integrity [123].
* In some forecasting methods based on machine learning, noise-free simulation data are embraced. In this way the variety of energy performance is more predictable than that of energy simulation results [124], which lead to an overfitting or even wrong-fitting issues [125].
* Achieving high accuracy in forecasting energy use is critical to improving energy management. In any case, this requires the determination of appropriate estimation models, ready to capture the individual attributes of the array to be anticipated, which is a task that includes a lot of uncertainties [126].
* Since the energy frameworks of buildings have the essential function of meeting the needs of tenants, numerous investigations accepted a consistent schedule. For residential buildings, tenant patterns are more erratic and irregular, and the assumption of a coherent schedule is less reasonable [127]. The main causes of such inconsistencies are unrealistic inputs regarding tenant behavior and existing forecasting methods [128].
* Forecasting methods need the ability to accurately predict when space is occupied [129]. Some research is still needed on building management systems in terms of tenant behavior and choices, e.g., a collaboration between the system and tenant preferences in terms of comfort and energy use, the adaptability of these systems to tenant behavior and the extent to which they can adjust to that behavior [130].  
Considering the above challenges, it would not be possible to use a single forecasting method that could be applied to any building to meet all the requirements of the different application scenarios, taking also into account the preference of the occupants. Therefore, one should think about maximizing the potential of each of the methods by using criteria to select the best one for a specific application. Building prediction methods benefit economically and environmentally by creating energy flexibility strategies such as strategic conservation, flexible load shape, peak trimming and load shifting. These strategies are highly dependent on the behavior of the occupants, so it is necessary to create models that consider the preferences and decisions of the building's occupants, which would change depending on the nature of the building.
# Spatial Modes of Laser-Induced Mass Transfer in Micro-Gaps
## 2 Experimental Setup Details and General Procedure
### Laser System  
The material transfer from the donor slide to the acceptor slide was actuated by a laser beam from a 1 W 532 nm continuous wave diode-pumped solid-state laser (FSDL-532-1000T, Frankfurt Laser Company, Friedrichsdorf, Germany) that was modulated by an acousto-optic modulator (002 AF1, Polytec GmbH, Waldbronn, Germany) into pulses on the order of milliseconds and deflected in a scan head (hurrySCAN 10, Scanlab AG, Puchheim, Germany) with a 100-mm focusing f-\(\theta\)-lens. The laser radiation was absorbed by the polyimide film, and heated the polymer layer with the embedded amino acids and induced the material transfer across the gap, probably by evaporating solvent residues and creating an aerosol.
## 3 Theory and Methodology
### The Proposed Tsallis-Entropy-Based Method
#### 3.2.1 Definition of Classical Tsallis Entropy  
The mathematical form of classical Tsallis entropy in image thresholding was initially introduced in [25]. For an image **I** with **L** gray levels, the entropy can be defined as follows:  
\[S_{q}=\frac{1-\sum_{i=0}^{L-1}p_{i}^{q}}{q-1} \tag{4}\]  
where \(\mathbf{q}\) in the formula represents the entropy exponent, which indicates the degree of non-additivity. When \(\mathbf{q}>1\), it represents stronger interdependence or collective behavior among the system's components. p\({}_{i}\) represents the probability of intensity i.  
The pseudo-additivity principle applies to Tsallis entropy in independent statistical models:  
\[S_{q}(X+Y)=S_{q}(X)+S_{q}(Y)+(1-q)S_{q}(X)S_{q}(Y) \tag{5}\]  
For a given threshold t, the image is segmented into object and background regions, corresponding to the gray-level intervals 0, 1, 2, 3,..., t and t + 1, t + 2,..., L - 2, L - 1, respectively. Assuming that p\({}_{i}\) = p\({}_{0}\), p\({}_{1}\),..., p\({}_{L-1}\) represents the gray-level probability distribution, the background and object distributions can be expressed as  
\[P_{B}=\frac{p_{0}}{p_{b}},\frac{p_{1}}{p_{b}},\ldots,\frac{p_{t}}{p_{b}} \tag{6}\]  
\[P_{O}=\frac{p_{t+1}}{p_{o}},\ldots,\frac{p_{L-1}}{p_{o}} \tag{7}\]  
where \(p_{b}=\sum_{i=0}^{t}p_{i}\) and \(p_{o}=\sum_{i=t+1}^{L-1}p_{i}\). The Tsallis entropy can be formulated as  
\[S_{q}^{B}(t)=\frac{1-\sum_{i=0}^{t}(\frac{p_{i}}{p_{b}})^{q}}{q-1} \tag{8}\]  
\[S_{q}^{O}(t)=\frac{1-\sum_{i=t+1}^{L-1}(\frac{p_{i}}{p_{o}})^{q}}{q-1} \tag{9}\]  
The objective function for determining the optimal threshold seeks to maximize the information measure between the two classes of gray levels:  
\[t_{opt}=\operatorname*{arg\,max}_{t}\left[S_{q}^{B}(t)+S_{q}^{O}(t)+(1-q)S_{q} ^{B}(t)S_{q}^{O}(t)\right] \tag{10}\]
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.3 Onion  
The volatile profile of both (red and yellow) onion varieties is very distinct. Red onions are characterized by several chemical groups, namely aldehydes (26.7%), organosulfur compounds (19.6%) and carboxylic acids (15.3%), while yellow onions are characterized essentially by organosulfur compounds, which account for 73.8% of its total volatile fraction (Figure 2).  
The characteristic flavor and the bioactive properties of onions have been mainly attributed to the organosulfur volatile compounds [41], which are biosynthesized essentially after the rupture of the cell structure, when the precursor compounds, S-alk(en)yl cysteine sulfoxides, come into contact with the alliinase enzyme to produce alkenyl sulfenic acids [42]. Many of these compounds have been shown to have antidiabetic and insulinotropic activity in in vivo animal studies [43]. Some studies have demonstrated that the antimicrobial activity of sulfides is affected by the number of carbon atoms of the alk(en)yl groups and sulfur atoms and by the concentration of the diverse sulfur compounds in the volatile fraction of onion, thus emphasizing the importance of characterizing the volatile profile of the onion in the different forms used [41; 44; 45].  
Figure 1: Distribution of chemical families identified in orange and white carrot.  
Among the main organosulfur compounds identified in the studied onion varieties, dipropyl disulfide (47.2%, yellow onion), dimethyl trisulfide (8.8% red onion, and 9.4% yellow onion) and dipropyl trisulfide (4.3%, yellow onion) are predominant.  
Another class of volatile compounds commonly detected in fresh onion is volatile aldehydes [41], with 2-methyl-2-pentenal (55) produced by the sequential transformation of 1-propenylsulfenic acid to thiopropanal S-oxide, which subsequently originates 2-methyl-2-pentenal [46], the major compound of this family (22.3% in red onion and 9.8% in yellow onion).