esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 6 Conclusions  
In this paper, we studied the problem of evaluating two categories of robustness for the reliable schedule to be obtained by inserting PM activities. We transform the problem into the calculation of the expected completion time of operation and derive an accurate analytical expression. Due to the high burden of practical calculation, we also proposed an analytical approximation based on the internal relationships among the scheduling structure, the number and sequence of PM activities, the probability and downtime of failures, and the completion time of operation. Compared with Monte Carlo simulations, the proposed method has almost the same accuracy and lower computation burden. Through a comparison with the state-of-the-art surrogate robustness measures, the experimental results show that the proposed measure is far superior to the other three surrogate measures in all cases. Additional experiments were carried out to demonstrate the importance of PM activities, especially in the case of high failure probability and long CM time.  
Several lines of future work might be explored. First, machine failure probability and repair time are considered to be known, but this information is often unknown in actual production; the robustness evaluation model can be established from the perspective of fuzzy theory or data mining. Additionally, more uncertain factors in production (such as uncertain processing time, uncertain job arrival time, and rework) can be incorporated into the evaluation model. Finally, based on the robustness evaluation model, we hope  
Figure 9: Quartile graph of \(SRIR\),\(PIR\) and \(QRIR\) for every benchmark problem. (**a**) Quartile graph of at different fault levels. (**b**) Quartile graph of at different fault levels. (**c**) Quartile graph of \(QRIR\) at different fault levels.  
to further propose a new algorithm to optimize the insertion order of PM activities to significantly improve the stability and performance of scheduling.  
For Conceptualization, Z.B.; methodology, Z.B.; software, Z.B.; validation, Z.B.; writing--original draft preparation, Z.B.; writing--review and editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [grant number 71961029] and the Key Research and Development project of Xinjiang Uygur Autonomous Region [grant number 2020B02013].  
Not applicable.  
The data that support the findings of this study are openly available at [[http://optimizizer.com/jobshop.php](http://optimizizer.com/jobshop.php)]([http://optimizizer.com/jobshop.php](http://optimizizer.com/jobshop.php)) (accessed on 1 January 2016).  
The authors declare no conflict of interest.
## 5 Results  
According to the proposed method, both the S-band and X-band signals are processed using the recorded interferometry data from the CDSN to verify the combined efficacy of the pseudo-code ranging and DOR signal in this section. The S-band measured data are transmitted from China's Queqiao-1 lunar relay satellite, and the X-band data are recoded during the static tests of a typical deep space probes.
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
###### Abstract  
Exosomal nanoparticles (exosomes or nanovesicles) are biogenic membrane vesicles secreted by various cell types and represent a conservative mechanism of intercellular and interspecies communication in pro- and eukaryotic organisms. By transporting specific proteins, nucleic acids, and low molecular weight metabolites, the exosomes are involved in the regulation of developmental processes, activation of the immune system, and the development of a protective response to stress. Recently, the plant nanovesicles, due to an economical and affordable source of their production, have attracted a lot of attention in the biomedical field. Being a natural transport system, the plant exosomes represent a promising platform in biomedicine for the delivery of molecules of both endogenous and exogenous origin. This review presents current data on the biogenesis of plant exosomes and their composition, as well as mechanisms of their loading with various therapeutic compounds, which are determining factors for their possible practical use. We believe that further research in this area will significantly expand the potential of targeted therapy, particularly targeted gene regulation via the small RNAs, due to the use of plant exosomes in clinical practice.  
exosomes; targeted drug delivery; multivesicular bodies; membrane transport +
Footnote †: journal: Applied sciences
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME  
#### 3.3.1 Gingival Recession (REC)  
The incidence of REC was examined in seven studies [7; 19; 21; 28; 34; 36; 41], while only two studies recorded this finding pre-operatively [7; 21]. Four studies [19; 28; 34; 36] reported REC on a patient-based level, while four reported on a site-specific level [7; 21; 28; 41]. Most studies found that SARME resulted in increased REC among posterior teeth and mesial to the central incisors after sutural separation [7; 19; 21; 34]. Interestingly, in a three-arm study comparing NSRME, SARME, and control patients, a notable increase in REC was reported in posterior teeth after NSRME (8.4%) as compared to SARME (3.6%) [28].
## 4 CEL Modelling
### Mesh Convergence Study  
The mesh convergences study is necessary to investigate the suitable mesh size for accurate results and computational efficiency. For the mesh convergences study, the forced replacement method is simulated. The input parameters for the mesh convergence study are summarized in Table 2. The height of embankment of 2.0 m is adopted in mesh convergence studies, and the distance from surface to replacement is identified in terms of mesh sizes sm. In this study, five different mesh sizes are assumed with the ratios between the mesh size and the width of embankment (\(s_{m}/B_{0}\)) of 0.0833, 0.0417, 0.0167, 0.0083 and 0.0042. Here, the mesh size increases from Mesh 5 to Mesh 1, in other words, the mesh density increases from Mesh 1 to Mesh 5. Table 3 indicates the summary of the mesh convergence studies. Figure 7 shows the relationship between replacement depth and computation time with mesh sizes. The replacement depths of Mesh 4 are identical with that of Mesh 5, also the  
Figure 6: Deformation of a continuum in analysis: (**a**) Lagrangian analysis; (**b**) Eulerian analysis.  
computation time of Mesh 5 is dramatically increased. It is demonstrated that the mesh size of Mesh 4 reaches the convergence, therefore, Mesh 4 is used for all of CEL analyses.
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 5 Discussion  
Our proposed MMAPA framework is designed with potential real-time deployment in mind. During validation, we observed that the average inference speed for the fNIRS-only model was approximately 3620 samples per second, while video-based models achieved only around seven samples per second. These results suggest that the fNIRS-based MMAPA variant is highly capable of supporting real-time pain assessment even on resource-constrained devices. In contrast, the large computational overhead of video-based models highlights the need for the further optimization of video embeddings. Future work should explore more lightweight yet effective video feature extraction methods to enhance real-time applicability without sacrificing accuracy. Additionally, deployment on lower-power edge devices could benefit from techniques such as model pruning, quantization, or knowledge distillation, which we leave as future directions.  
From an ethical standpoint, we acknowledge the risk of bias when applying MMAPA to underrepresented groups such as elderly or non-Western populations, particularly given the demographic limitations of the AI4 Pain and BioVid datasets. Broader validation across diverse cohorts is necessary before clinical adoption to ensure fairness and generalizability.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **Parameters** & **Inference Speed (Samples/s)** \\ \hline MMAPA (fNIRS) & 58.67\% & **0.49 M** & **3620** \\ MMAPA (Video) & 61.67\% & 86.23 M & 7.5 \\ MMAPA (Video + fNIRS) & **62.67\%** & 86.73 M & 7.35 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model complexity and inference speed (measured on an **NVIDIA RTX 2080 Ti**).  
Figure 9: Validation accuracy comparison of baseline and proposed fusion methods using various data modalities, highlighting improvements through our fusion techniques. The orange bar represents “B-1”, and the blue bar represents “B-6”.  
Regarding model failure analysis, MMAPA consistently struggled in detecting the \"No Pain\" category, often misclassifying these instances as \"Low Pain\" or \"High Pain\". This challenge likely arises from the more subtle facial and physiological cues present when no pain is experienced, which is a phenomenon similarly noted in prior studies. Due to dataset restrictions, we could not publicly share qualitative examples, but internal error analysis confirmed the difficulty in distinguishing the \"No Pain\" class reliably.
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
## 3 Method
### Study Procedure  
This study introduces a data assimilation technique for time series reconstruction, uses the data assimilation technique to connect remote sensing parameters and ground observations, and calibrates errors caused by the time trajectories of the remote sensing parameters and errors in the output of the simulation continuously. In combination with the Kalman filter, this study uses the daily average land surface temperature from the ground observation stations to filter and optimize the remote sensing-inverted land surface temperature time series and divides the area into zones according to DEM elevation data to build a corresponding fitting function and method to optimize the land surface temperature of the entire study area.  
At the pixel scale, we obtained the daily average surface temperature of the ground observation stations at different sites and the GMS-5-based remote sensing inversion data for the surface temperature, applied the Kalman filter to reconstruct a short-period time series of the land surface temperature from the retrieved remote sensing inversion data, and analyzed the accuracy of the reconstruction.
2. For the post-reconstruction land surface temperature of each station, we built a linear fitting function using the \"sub-regional and per-seasonal\" method and developed a technical scheme for reconstructing the land surface temperature time series. We assumed that the conditions in each zone, including the elevation, precipitation, dryness, and topography, were generally consistent and divided the study area according to its seasonal characteristics (spring, summer, autumn, and winter). According to the land surface temperature inverted by means of remote sensing during each season in 2002 in each zone and the Kalman filter reconstruction values, we applied the least squares method to build linear fitting functions to obtain the empirical coefficient for each season and to then build a first-order linear regression function based on the fitting functions between the remote sensing inversion data and refined value of each sub-region and season in 2002.
3. We optimized the original remote sensing inversion of the land surface temperature in each sub-region of the study area, analyzed and compared the accuracy and rationality of the land surface temperatures of the representative stations before and after the reconstruction, and drew conclusions. We used the established fitting function of each zone and season to calculate the remote sensing inversion data using a raster calculator, obtaining the spatial distribution of the optimized land surface temperature in the study area and the refined values for the other stations. Regarding the optimized land surface temperature, we selected the representative stations in the area for validation to analyze and compare the accuracy and rationality of the land surface temperature before and after reconstruction and conducted an error analysis.  
The full procedure is explained in the flowchart of Figure 2.  
Figure 2: Flowchart of the operation of the study.
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 2 Theoretical Background
### Weibull Statistics Analysis  
The two-parameter Weibull distribution is used to statistically analyze fatigue behaviors [22]. It has the capability to perform accurate fatigue failure analysis. The probability density function \(f(t)\) and cumulative distribution function \(F(t)\) are described by the Equations (5) and (6), respectively [23].  
\[f(t)=\frac{\beta}{\eta}\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta-1}exp\Bigg{\{}- \bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{5}\]  
\[F(t)=1-exp\Bigg{\{}-\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{6}\]  
where \(t\) is the selected random variable (stress load). The corresponding reliability function \(R(t)\)[23] is given as  
\[R(t)=exp\Bigg{\{}-\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{7}\]  
From [24], the Weibull fatigue damage \(\beta\) and \(\eta\) parameters are determined as  
\[\beta=\frac{-4\mu_{y}}{0.995\times In\bigg{(}\frac{\alpha}{\alpha_{2}}\bigg{)}} \tag{8}\]  
\[\eta=exp(\mu_{x}) \tag{9}\]  
where \(\mu_{y}\) represents the mean of the \(Y\) vector (see Equation (13)), and \(\mu_{x}\) represents the log-mean of the failure-time data [24], which here is determined directly from the monitoring variable (insertion loss measured in dB). Thus, \(\mu_{x}\) is determined as  
\[\mu_{x}=ln(\sigma_{1}\sigma_{2})^{\frac{1}{2}} \tag{10}\]  
Here, note the efficiency of the Weibull parameters \(\beta\) and \(\eta\) depends only on the accuracy by which the values are determined by Equation (3). In this paper, they were determined using an Agilent N7745A instrument (Keysight, Kimballton, IA, USA) with an 8-channel optical multiport power meter. Once the \(\beta\) and \(\eta\) values were known, the Weibull stress random analysis was performed as shown in the next section.
## 5 Experiments
### Results and Discussion
The vibration frequency is suppressed successfully similar to that in Groups A, C, and D.  
In addition to the command tracking performance, another significant indicator for SISPs is the isolation used to measure the ability of disturbance isolation of the SISP. For a constant value disturbance or a periodic disturbance with constant amplitude, the isolation describes the system's ability to restrain the disturbance under this frequency. If the disturbance is in a wide frequency range, the isolation can also be obtained by using the frequency characteristics. The smaller the isolation is, the stronger the system's isolation ability and the better the LOS stability accuracy are. For this system, the inertial angular velocity after compensating is \(\dot{\theta}_{b}(t)+\dot{\theta}_{e}(t)\), and the external disturbance inertial angular velocity is \(\dot{\theta}_{b}(t)\), which is measured by the gyroscope. The amplitude-frequency curve from \(\dot{\theta}_{b}(t)+\dot{\theta}_{e}(t)\) to \(\dot{\theta}_{b}(t)\) is presented in Figure 16.  
Figure 16: Frequency isolation curve. Figure 10:0 Single-sided dimensionless amplitude spectrum of the relative angular velocity. (**a**) Group A; (**b**) Group B; (**c**) Group C; (**d**) Group D; (**e**) Group E.  
From Figure 16, the proposed method frequency isolation is lower than the PI control method in the SISP. The method proposed in this study has a positive external low-frequency disturbance effect. The isolation at a typical frequency of 1 Hz is taken as a quantitative comparison. The isolation of the PI feedback control is 3.37%, while the isolation of the proposed method is 1.54%.
## 4 CNN Architectures
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Description** & **Output** & **Iteration** \\ \hline
**input (224 \(\times\) 224 \(\times\) 3)** & Cochleogram or Mel Spectrogram & & - \\ \hline
**ZeroPadding2D** & Size = 3 \(\times\) 3 & (None, 70, 70, 3) \\ \hline
**Conv2D** & f = 64, k = 7 \(\times\) 7, strides = 2 \(\times\) 2, a = ReLu & (None, 32, 32, 128) \\ \hline
**BatchNormalization** & - & (None, 32, 32, 128) \\ \hline
**MaxPooling2D** & Pool size = 3 \(\times\) 3, strides = 2 \(\times\) 2 & (None, 15, 15, 128) \\ \hline
**Conv2D** & f = 64, k = 1 \(\times\) 1, s = 1 \(\times\) 1, \(p\) = valid, a = ReLu & (None, 15, 15, 64) \\ \hline
**BatchNormalization** & - & (None, 15, 15, 64) \\ \hline
**Conv2D** & f = 64, k = 3 \(\times\) 3, s = 1 \(\times\) 1, \(p\) = same, a = ReLu & (None, 15, 15, 64) \\ \hline
**BatchNormalization** & - & (None, 15, 15, 64) \\ \hline
**Conv2D** & f = 256, k = 1 \(\times\) 1, s = 1 \(\times\) 1, \(p\) = valid, a = ReLu & (None, 15, 15, 256) \\ \hline
**BatchNormalization** & - & (None, 15, 15, 256) \\ \hline
**Conv2D** & f = 128, k = 1 \(\times\) 1, s = 2 \(\times\) 2, \(p\) = valid, a = ReLu & (None, 8, 8, 128) \\ \hline
**BatchNormalization** & - & (None, 8, 8, 128) \\ \hline
**Conv2D** & f = 128, k = 3 \(\times\) 3, s = 1 \(\times\) 1, \(p\) = same, a = ReLu & (None, 8, 8, 128) \\ \hline
**BatchNormalization** & - & (None, 8, 8, 128) \\ \hline
**Conv2D** & f = 512, k = 1 \(\times\) 1, s = 1 \(\times\) 1, \(p\) = valid, a = ReLu & (None, 8, 8, 512) \\ \hline
**BatchNormalization** & - & (None, 8, 8, 512) \\ \hline
**Conv2D** & f = 256, k = 1 \(\times\) 1, s = 2 \(\times\) 2, \(p\) = valid, a = ReLu & (None, 4, 4, 256) \\ \hline
**BatchNormalization** & - & (None, 4, 4, 256) \\ \hline
**Conv2D** & f = 256, k = 3 \(\times\) 3, s = 1 \(\times\) 1, \(p\) = same, a = ReLu & (None, 4, 4, 256) \\ \hline
**BatchNormalization** & - & (None, 4, 4, 256) \\ \hline
**Conv2D** & f = 1024, k = 1 \(\times\) 1, s = 1 \(\times\) 1, \(p\) = valid, a = ReLu & (None, 4, 4, 1024) \\ \hline
**BatchNormalization** & - & (None, 4, 4, 1024) \\ \hline
**Conv2D** & f = 512, k = 1 \(\times\) 1, s = 2 \(\times\) 2, \(p\) = valid, a = ReLu & (None, 2, 2, 512) \\ \hline
**BatchNormalization** & - & (None, 2, 2, 512) \\ \hline
**Conv2D** & f = 512, k = 3 \(\times\) 3, s = 1 \(\times\) 1, \(p\) = same, a = ReLu & (None, 2, 2, 512) \\ \hline
**BatchNormalization** & - & (None, 2, 2, 512) \\ \hline
**Conv2D** & f = 2048, k = 1 \(\times\) 1, s = 1 \(\times\) 1, \(p\) = valid, a = ReLu & (None, 2, 2, 2048) \\ \hline
**BatchNormalization** & - & (None, 2, 2, 2048) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ResNet-50 Architecture. Figure 4: SE-Res2 Block Architecture [9]. Figure 3: ECAPA-TDNN Architecture [9]. \begin{table} \begin{tabular}{c c c} \hline
**Layer** & **Description** & **Output** & **Iteration** \\ \hline \multicolumn{3}{c}{**AveragePooling**} \\ \multicolumn{3}{c}{**Flatten**} \\ \hline \multicolumn{3}{c}{**Fully Connected**} \\ \hline \multicolumn{3}{c}{**Softmax**} \\ \hline \end{tabular} 
\begin{tabular}{c} \\ \hline Conv1D+ReLu+BN (k=5. d=1) \\ \
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
## 2 Methodology
### SimAM Attention Mechanism  
The SimAM attention mechanism introduces a parameter-free 3D attention framework that synergistically integrates the spatial-channel interdependencies. This biologically inspired mechanism enables dynamic feature recalibration by quantifying the neuronal importance through energy minimization principles, effectively amplifying the discriminative features without introducing additional learnable parameters. Given an input feature map \(X\in\mathbb{R}^{C\times H\times W}\), the module evaluates the significance of each target neuron \(\mathfrak{t}\) through an energy function defined as:  
\[{e_{t}}^{*}=\frac{4\big{(}\hat{\sigma}^{2}+\lambda\big{)}}{\left(t-\hat{ \mu}\right)^{2}+2\hat{\sigma}^{2}+2\lambda} \tag{6}\]  
where \(\lambda\) denotes a regularization term to prevent numerical instability, and \(\hat{\mu}\) and \(\hat{\sigma}^{2}\) represent the mean and variance of the neurons within the corresponding channel, respectively. The energy metric \({e_{t}}^{*}\) inversely correlates with the neuronal significance: lower energy values indicate stronger statistical divergence between the target neuron \(t\) and its spatial neighbors \(\{x_{i}\}\), implying higher saliency. Consequently, the attention weight for neuron \(t\) is derived as \(\frac{1}{\hat{\sigma}^{2}}\), establishing an adaptive feature enhancement mechanism that prioritizes distinctive patterns while suppressing redundant activations.  
Figure 6 illustrates the attention mechanism structure of SimAM. SimAM employs a neural energy minimization principle to provide physics-aware feature selection without learnable parameters. For defects such as stomatal and slag inclusion with pixel values smaller than 30 px, it quantifies the significance of each neuron through the energy metric \({e_{t}}^{*}\), where low-energy neurons \(\frac{1}{{e_{t}}^{*}}\) correspond to statistically divergent defect features relative to the background. This dynamic feature recalibration suppresses the redundant activations while amplifying the discriminative defect patterns, thereby enhancing the small target detection and improving the performance on small target defects.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Communication Overhead  
The nodes in the network have a fixed bandwidth for data transmission. Hence, extra parameters needed to ensure the identity and security of data are considered overheads and should always be minimized. In the GS method, the main task is to distribute group keys to the clients and clients to transfer messages to the server. Distributing group keys are of linear complexity which occurs when any member is being added to or leaving the group. When there is any change in the group members, group keys are updated. This may increase the complexity of the group signature algorithm by m (number of clients in a group) communication because the updated group keys need to be transferred to each group member. However, it is still linear and less complex when compared with the exponential complexity of the plain SA algorithm.  
Our algorithm uses four types of communication: administrator-to-client communication, client-to-server communication, administrator-to-server communication, and server-to-client communication. Each communication adopts a different message format, as discussed below.
# Industrial Dust Explosions. A Brief Review
## 4 Review of Some Current Issues Related to Fighting of Dust Explosions  
### National and International Standardization  
There is a number of national and international standards specifying measures for preventing and mitigating industrial dust explosions, and methods for the testing of ignitability and explosibility of dusts. The central standardization organizations include CEN (European Union), IEC (international), NFPA, and ASTM (USA). New standards are introduced, and old ones continuously updated by all of these organizations.