esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Industrial Application  
To demonstrate the applicability of the proposed method from a practical point of view, a case that uses industrial data from a collaborating steam turbine company was considered. The steam turbine is typically an engineer-to-order product [34]. We collaborated with the Shanghai Turbine Plant (STP), which is the leading turbine company in China. STP can provide an accurate estimation of task duration based on the number of allocated workers during the engineering process. They have established the relationship between task duration and number of allocated workers based on their experience. The steam turbine assembly shop had eight work centers, and eight steam turbines could be assembled in parallel. There were eight kinds of steam turbine products to be produced, and the total number of tasks was 270. The assembly of each product was rather different in terms of tasks, due date, precedence constraints, and number of workers involved. Different projects were simultaneously executed, competing for and sharing the worker resources. Therefore, it is necessary to schedule and balance the utilization of worker resources to guarantee the due date of all products. Figure 10 depicts the sequence diagram of eight projects. The assembly shop had two skill levels of workers--nine lower level workers (junior) and six higher level workers (senior). Workers formed the assembly team to complete the task. The release dates and due dates of all products were determined by a higher level production plan. We set up the real-time visual monitoring system for steam turbine assembly and the system collected real-time assembly information. The project manager operating the real-time visual monitoring system is shown in Figure 11 and the user interface is shown in Figure 12.  
We ran our algorithm for the above case on ten independent runs and selected the best result. The Gantt chart of the GA algorithm can be seen in Figure 13a, in which the makespan was 1630 h. The Gantt chart of the PSO algorithm can be seen in Figure 13), in which the makespan was 1678 h. The Gantt chart of the TS algorithm can be seen in Figure 13c, in which the makespan was 1642 h. The Gantt chart of the plan made by the project manager can be seen in Figure 13d, in which the makespan was 1808. The Gantt chart of the PSO-TS algorithm can be seen in Figure 14, in which the makespan was 1524 h. In Figure 14, the horizontal axis represents the time horizon, the vertical axis represents the project number, the box represents the task, the number in the box represents the task number, and the length represents the duration of the task. We saw the sequence and the duration of tasks in each  
Figure 9: Mean plot and Least-significant (LSD) intervals for algorithms in relative percentage deviation (RPD) value.  
Figure 14: Gantt chart of the Particle swarm optimization-Tabu search (PSO-TS) algorithm.  
Figure 12: The user interface of the real-time visual monitoring system.  
Figure 13: Gantt chart of plans provided by Genetic algorithm (GA), Particle swarm optimization (PSO), Tabu search (TS), and the project manager.
# Gaussian Process Modeling of Specular Multipath Components
## 4 Results
### GPR of SMC Phases  
To show the applicability of the GPR for phase prediction of the SMC phases, we exemplarly analyze the estimated SMC amplitudes related to EPB and NGW.  
Figure 6 shows the results, which indicates the phase coherence of the SMCs. The respective hyperparameters obtained from the GPR are also shown in Table 1. From this Table, it is shown that the phase of the NGW component has the largest angle dependence, given that its characteristic correlation angle is the smallest.  
Predictability  
Similar to Section 4.3.1, we separate the measurements into a training set containing data from Segments 4, 5, 6 and a test set containing data from Segments 1, 2, 3, and 7. Figure 7 shows the prediction results. It can be seen that the prediction of the mean and variance is reasonably good. Table 2 shows the prediction quality via SMSE and MSLL. The SMSE values for EPB \(\mathbf{a}_{2}^{(1)}\), EPB \(\mathbf{a}_{2}^{(2)}\) and NGW \(\mathbf{a}_{5}^{(5)}\) are all significantly smaller than 1, indicating that the GPR prediction is much better  
Figure 6: GP regression of the SMC phases for (**a**) the SMC amplitude of VA at \(\mathbf{a}_{2}^{(1)}\) associated with anchor 1 (east plaster board wall), (**b**) the SMC amplitude of VA at \(\mathbf{a}_{2}^{(2)}\) associated with anchor 2 (east plaster board wall), (**c**) the SMC amplitude of VA at \(\mathbf{a}_{5}^{(1)}\) associated with anchor 1 (north glass window).  
than the one of a trivial method. Similarly, the MSLLs are all negative, also showing good prediction quality. This evaluation indicates that a consideration of the carrier phase may become possible for multipath-based tracking filters, if GPR is used for modeling the phase shifts of the SMCs.
## 2 Research Methodology
### Pose Estimation System Construction
#### 2.3.1 Feature Point Descriptor  
This study uses the fast point-feature histograms (FPFH) [15] to estimate the feature point. FPFH is the optimization method of its predecessor--point feature histograms (PFH) [16]. PFH has the theoretical computational complexity of a point cloud \(P\) with \(N\) points, \(O\big{(}N\cdot k^{2}\big{)}\), where \(k\) is the number of neighbors for each point \(p\) in \(P\). The FPFH overtakes the PFH in this aspect with the complexity of \(O(N\cdot k)\), while still retaining the advantage of the PFH [7].  
The FPFH algorithm proceeds as follows: for each query point \(p\) with its normal vector \(n\), only the relation between the point itself and its neighbors, \(p_{k}\) (the neighbors are chosen inside the sphere with the radius \(r\)), with the normal vector \(n_{k}\) (not between all of its neighbors as in PFH), is computed. Define the \(uwv\) frame (where \(u=n_{k}\), \(v=(p-p_{k})\times u\), \(w=u\times v\)) and the computation of the angular variations \(\alpha\), \(\phi\), \(\theta\) (which is called the simplified point feature histogram--SPFH) of \(n\) and \(n_{k}\) as follows:  
\[\left\{\begin{array}{c}\alpha=v\cdot n_{k}\\ \phi=(u\cdot(p-p_{k}))/p-p_{k}\\ \theta=\arctan(w\cdot n_{k},u\cdot n_{k})\end{array}\right. \tag{15}\]  
Then, the final histogram of \(p\) is weighted through re-determination of its \(k\)-neighbors and the previous SPFH values:  
\[FPFH(p)=SPFH(p)+\frac{1}{k}\sum_{i=1}^{k}\frac{1}{w_{ki}}\cdot SPFH(p_{ki}) \tag{16}\]  
where the weight, \(w_{ki}\), indicates the distance between point \(p\) and the neighbor point \(p_{ki}\). Figure 5 shows the effect area of the FPFH algorithm. In the figure, the query point \(p\) only linked with its direct neighbors \(p_{k}\) (inside the red-dashed circle). Each of the neighbors, \(p_{k}\) (in the figure, they are displayed with different colors with their own circle of region), are linked with their own neighbors. The resulted histogram is weighted together with the histogram of the point \(p\) to estimate the FPFH value. The gray connections marked with number 2 between the point \(p\) and its neighbors together have a weight twice as large as  
Figure 4: Effect of object segmentation in the study of dynamic workpiece feeding.  
other normal connections. The FPFH value of each query point \(p\) will be analyzed in the next stage of the study.
# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Resource Scheduling and Allocation
Resource allocation is considered to be an important factor in resource management. Resource allocation in the cloud is different than that in the fog. This is because the cloud is a single cohesive unit with infinite resources, so resource allocation is not a big issue in a cloud environment. Still, the fog nodes are geographically widely distributed and there are limited resources available. As a result, resource allocation in the fog environment is difficult [128] because, in addition to the fog's distribution, there are also QoS requirements for IoT devices that must be met. The approaches to resource allocation can be broadly classified into two categories: auction-based and optimization-based. The auction-based approach [129; 130; 131; 132] is similar to a real-time auction, where the IoT devices bid for the available fog nodes and the fog node is sold (allocated) to the highest bidder. A specified auction mechanism is employed to allocate the fog nodes to the IoT resources. The fog nodes listed for bidding are according to the required QoS and other constraints that specific IoT devices require. In the optimization-based technique [58; 133; 134; 135; 136; 137; 138; 139; 140], a double-matching problem is formulated wherein cloud devices and the fog nodes are coupled for the IoT devices. The problem is considered to be an NP-hard problem for finding an optimal set of fog-cloud pairs for IoT devices while fulfilling various QoS requirements.
## 2 Experimental Evaluation
### Shaking Table Test Results  
The seismic performance of the three specimen types was evaluated through shaking table tests. The MWN specimens exhibited failure at PGA of 0.4-0.5 g. Specimens retrofitted with STPU (MWP2) demonstrated improved seismic resistance, withstanding up to 0.7-0.8 g. The MWPF5 specimens, which were strengthened using both GFRP and STPU, exhibited the highest resistance, enduring seismic loads up to 0.9-1.0 g.  
Crack patterns were consistent with the intended design. In all specimen types, initial cracks developed just above the lower steel bracket, indicating that the restraining configuration effectively guided damage to the central region of the wall as shown in Figure 5. While MWN specimens showed extensive cracking and rapid failure progression, MWP2 specimens demonstrated a more ductile response. MWPF5 specimens exhibited localized cracking around the GFRP reinforcement zones, and although significant displacement was observed at high PGA levels, structural collapse did not occur until debonding between the GFRP and masonry was apparent.  
Figure 4: El Centro earthquake data.  
Figure 3: Shaking table test setup.  
These results suggest that STPU effectively enhances ductility and energy dissipation capacity under out-of-plane seismic loading. Furthermore, the addition of GFRP increases overall stiffness and strength, resulting in higher seismic load capacity. However, the increased stiffness also led to higher acceleration responses and a potential for brittle failure upon debonding. A more detailed evaluation of these results, including comparative analysis with finite element models, is presented in the subsequent sections.
## 2 Material and Methods  
### Fruit Harvest and Winemaking  
Fruits were purchased from a commercial blueberry orchard in Huangpi, Hubei, China (N 31\({}^{\circ}\)06\({}^{\prime}\), E 114\({}^{\circ}\)28\({}^{\prime}\)). Blueberries from plants of the O'Neal and Misty cultivars were randomly harvested on the same day in June of 2016. Only berries that were at their commercial maturity were selected. The two cultivars were grown under similar horticultural conditions (e.g., irrigation, fertilization, etc.). After harvest, blueberries were cooled in an air-conditioned room (-20 \({}^{\circ}\)C) for half an hour, then transported to the laboratory and frozen (\(-\)20 \({}^{\circ}\)C, 50 h) before winemaking. Three kg of blueberries of each cultivar were thawed and crushed manually in microscale fermenters (5 L). Each cultivar was well mixed before winemaking and separated into 4 replicates to avoid compositional variation. The brix of the blueberry was measured using a PAL-1 pocket refracometer (Atago USA, Inc., Bellevue, WA, USA), and was adjusted to 20 by adding sucrose. Fifty mg/L SO\({}_{2}\) (as potassium metabisulfite) was added to each ferment, and 0.02 g/kg pectinase EX (Lallemand, Montreal, QC, Canada) were added half an hour later. Fermenters were placed in a temperature-controlled rooms set at 27 \({}^{\circ}\)C, warmed to room temperature, and inoculated with _Saccharomyces cerevisiae_ D254 (Lallemand, Montreal, QC, Canada) at approximately 10\({}^{6}\) cfu/mL after rehydration, according to the manufacturer's specifications. After 24 h of fermentation, diammonium phosphate (DAP, 300 mg/L) was added to each ferment to assist the yeast growth. During fermentation, the fermenters were punched down every 24 h. After all fermentations reached dryness (<0.5 g/L reducing sugar as measured by a glucose meter, Sinocare Inc. Changsha, China), they were pressed using a cheese cloth. Wines were placed in a cold room at 14 \({}^{\circ}\)C to settle for 10 days, then racked, and an addition of SO\({}_{2}\) (30 mg/L) was added prior to being bottled in 500 mL wine bottles and stored at 4 \({}^{\circ}\)C before analysis.
## 5 Conclusions  
This work proposed a simple procedure for the characterization of 1.0 mm thick plates made of AZ31 superplastic magnesium alloy; it was based on a numerical-experimental approach. This procedure is associated with a simple equation that models the behavior of superplastic materials. The simplest mathematical equation that models the behavior of a superplastic material is a power law function between stress and strain rate. The material parameters, called m and K, were not considered as material constants (unlike the literature on the characterization of materials through the use of the free-forming test), but as parameters depending on the equivalent strain. The procedure adopted in this work to determine these parameters required a numerical-experimental approach. The experimental test consisted of a free-forming process at constant pressure whose output was represented by the H-t curve, i.e., the variation over time of the dimensionless displacement measured at the apex of the dome. Through the experimental tests, it was first possible to identify the variation of the parameter m with the dimensionless displacement. Then, using an inverse analysis based on the finite element method, it was possible to draw the variation of the K parameter with the same dimensionless displacement. Once the m and K parameters were determined, numerical simulations of the free-forming process were carried out at constant pressure, and the pressure values belonged to the range adopted for the characterization of the material. The results of the numerical simulations, in terms of the H-t curve, were compared with the experimental results of the same free-forming tests. The recorded errors were lower than 9%, thus increasing the reliability of the values of the m and K parameters determined using the simple material characterization procedure proposed in this work.  
Conceptualization, G.G. and W.P.; Methodology, G.G.; Writing--original draft, W.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: Comparison of H–t curves obtained numerically and experimentally for \(p\) = 0.3 MPa.
###### Abstract  
The biochemical degradation of abundant cellulosic biomass for industrial use and energy production has been extensively researched in recent years. Some elaborate cellulose digestion approaches have been developed based on specialized bacteria, which possess sophisticated mechanisms to efficiently degrade recalcitrant natural carbohydrates. In this study, we assembled catalytic domains from multiple cellulolytic enzymes onto a scaffold along with a cellulose-binding module (CBM), specifically targeting crystalline cellulose. The catalytic domains of endoglucanase and cellobiohydrolase from _Acetivibrio thermocellus_ were linked to a heterotrimeric protein scaffold that assembles in a specific order. The bicatalytic complex failed to show the anticipated synergistic effect in cooperative cellulolysis, presumably because the catalytic domains only serve as weak anchors for each other in binding to the substrate. On the other hand, cellulose digestion was remarkably promoted by incorporating a CBM into a stable complex with a catalytic domain. Interestingly, the reversible association of catalytic domains and excess CBM proved more advantageous than fixed association. This suggests that the dynamic incorporation of CBM units enhances the accessibility of cellulose-degrading catalytic modules to the polysaccharide strand by preventing overly strong binding. This finding could have interdisciplinary applications for enzymes converting polymeric substrates other than cellulose.  
carbohydrate-binding modules; catalytic domain assembly; cellulose hydrolysis; multienzyme complex; PCNA; reversible substrate binding Article
## 4 Conclusions  
PEF could efficiently inactivate _E. coli_ and _S. cerevisiae_, with energy inputs of 100 and 150 kJ/L at a 20 kV/cm field strength. The _E. coli_ was reduced by a 5.6 decimal reduction at an energy of 100 kJ/L and _S. cerevisiae_ was completely inactivated. No microorganisms were observed in the model solution at an energy input of 150 kJ/L. Compared to the thermal treatment, the PEF treatment better preserved the compounds responsible for the characteristic fresh orange juice aroma, such as dl-limonene, \(\beta\)-myrcene, \(\alpha\)-pinene, and valencene. The area% of unfavorable volatile compounds, such as decanal, was higher in the thermally treated orange juice than in PEF-treated orange juice. The sensory evaluation revealed that the PEF-treated orange juice had a stronger fresh orange aroma and a weaker unfavorable cooked orange aroma than the autoclaved orange juice. However, there were no significant differences in orange aroma between PEF-treated and pasteurized orange juices. Based on PCA and PLS regression analyses, both PEF-1 (20 kV/cm, 100 kJ/L) and PEF-2 (20 kV/cm, 150 kJ/L) treated samples were clustered together with the untreated or thermally pasteurized samples, whereas sterilized juice was negatively correlated with the untreated sample. The PEF-treated juice was strongly positively correlated with the sour and fresh sensory attributes and volatiles related to fresh orange juice aroma. Therefore, PEF may be an alternative to thermal treatment as a pasteurization technique that can preserve quality characteristics, such as the fresh flavor and taste of freshly squeezed orange juice.  
Figure 3: Partial least squares (PLS) regression map showing the relationship among the instrumental data correlated with sensory attribute data in all orange juice samples. The sensory parameters are explained in Table 1 and the compound codes are given in Table 2.  
**Author Contributions:** Conceptualization, D.-U.L. and J.L.; investigation, H.L. and E.K.; writing--original draft preparation, Y.-N.K.; writing--review and editing, S.C. and J.L.; supervision, D.-U.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by High Value-Added Food Technology Development Program (2019), Ministry for Food, Agriculture, Forestry and Fisheries, Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article or supplementary material.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
###### Abstract
Our semi-supervised methods, by only utilizing 40% labeled data, almost reach the same Intersection-over-Union (IoU) values as fully supervised methods achieve with 100% labeled data. Compared with other semi-supervised methods, ours significantly outperforms other methods on IoU values and FPS values. The major contributions of our work can be summarized as follows:  
* To the best of our knowledge, our work is the first to introduce semi-supervised deep learning methods for drivable area segmentation. We propose a semi-supervised drivable area segmentation method based on expanded feature cross-consistency. The method is able to effectively utilize the information hidden in unlabeled data, which achieves a performance close to that of a fully supervised model with all labeled data by using only parts of labeled data. * We innovatively design a series of encoder-level feature perturbations and verify their effectiveness in our semi-supervised methods through a series of ablation study experiments. Figure 1: The overall flow of semi-supervised training: Main modules first generate pseudo labels on original unlabeled data, which is shown in black arrows. Then, auxiliary modules make predictions on perturbed data, and unsupervised loss is designed to measure the discrepancy between pseudo labels and perturbed prediction, which are shown in light blue arrows (predictions perturbed by auxiliary encoders) and orange arrows (predictions perturbed by auxiliary decoders). * We conducted a wide range of experiments on changing proto-segmentation models and comparing our semi-supervised method with others on road segmentation. Results show that our method has good generalizability and robustness in the field of drivable road segmentation. The rest of this paper is organized as follows: Previous work related to drivable road area segmentation and semi-supervised methods is reviewed in Section 2. Our proposed semi-supervised segmentation methods are elaborated on in Section 3. The experimental settings are provided in Section 4. The experimental results including those of ablation experiments and comparisons with other semi-supervised methods are specified in Section 5. The discussion on the generalizability and extensibility of our semi-supervised method is illustrated in Section 6. We conclude this work and delineate the potential future directions for improvement in Section 7.
## 5 Discussion
### Discussion of Relative Tectonic Activity
The basins were classified into horizontal basins or tilting basins. Tilting basins were classified into three classes due to their tilting degrees. A\({}_{\text{f}}\) values provided A\({}_{\text{f}}\)-50, which is the difference between a neutral value (50) and the calculated values [31]. In order to evaluate the RTAM, several researchers assigned four classes of tilting or asymmetry: symmetrical (A\({}_{\text{f}}\) < 5), low degree of asymmetry (A\({}_{\text{f}}\) between 5 and 10), moderate tilting (A\({}_{\text{f}}\) between 5 and 10), and high degree of tilting (A\({}_{\text{f}}\) > 15) (Figure 4). Generally, the elongated drainage basins are more likely to be found in areas characterized by high tectonic activity, and they tend to lose their shape, becoming more circular as the activity decreases [13]. Therefore, high values of the Bs parameter are directly correlated with the basin elongation rates. Accordingly, in this study, class 2 Bs values were represented by five sub-basins (1, 2, 5, 6, and 10) at the southern, eastern, and northern parts of the study area (Figure 5). Class 2 occupied the majority of the study area, while class 1 was recorded only for three sub-basins (3, 4, and 15). The hypysometric integral (Hi) is a factor for the distribution of elevations of the study basin where high values of this parameter might point to young, uplifting, active tectonic regions, and low values indicate possibly eroded areas that are characterized by low tectonic activity [31]. The majority of the Wadi Al-Lith study area is mainly represented by low values of hypysometric integral presenting class 1 tectonic activity. In this study, class 1 was recorded for sub-basins 2, 4, 5, 6, 9, 10, 12, 13, and 15. The highest value of the Hi was recorded for sub-basin 14, presenting low tectonic activity signals (Figure 6). The RTAM results showed that the majority of the sub-basins were controlled by medium to high tectonic activity, while the low class of tectonic activity was represented by just three small sub-basins. The RTAM analysis suggested that class 1 included four sub-basins, and class 2 comprised eight sub-basins (Figure 8). Thus, more than two-thirds of the studied sub-basin area demonstrated moderate to high tectonic activity due to the apparent morphological response.