esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Dataset and Metrics  
All experiments involving our simple yet efficient SAM-based Copy-Paste data generation were conducted using the publicly available AGAR dataset [4]. This dataset consists of high-resolution images (approximately 4000 \(\times\) 4000 pixels) featuring five bacterial species, _S. aureus_, _B. subtilis_, _P. aeruginosa_, _E. coli_, and _C. albicans_, cultured on agar plates with two distinct background types (_bright_ and _dark_). The dataset includes approximately 5241 training images and 1747 validation images. To evaluate our model on a limited number of real images, we further divided the dataset into a smaller subset of 1000 images for fine-tuning the neural network. To analyze the effect of varying dataset sizes for network fine-tuning, additional subsets containing [10, 25, 50, 75, 100, 200, 500] real images were also created.  
For the evaluation of the colony detection tasks, we used mean Average Precision (mAP) across the ten standard IoU thresholds between 0.5 and 0.95, following the convention of the COCO Challenge [49] for object detection. Additionally, we report mAP at an IoU threshold of 0.5 (AP\({}^{50}\)), as precise localization is less critical in the context of colony detection. The key goal is to ensure all colonies are detected without any omission.
## 2 Background Literature
### Complexity and Strategy in Industrial SCs
Lastly, the technology industry typically requires agile SCs due to the highly unpredictable nature of both local markets and the supply of new, fast-evolving technological materials [41]. Several structural factors can contribute to the transformation of an SC from a simple and complicated system to a complex one [42]. This transformation hinges on the creation of interrelationships and mutual dependencies between factors such as, but not limited to the following: (i) The globalization of operations: when international and domestic operations become intertwined, the SC landscape becomes more intricate [43]; (ii) Product variety with shared components: extensive product variety, particularly when leveraging common parts across products, fosters mutual dependencies within the SC [44; 45]; (iii) Technological integration: the implementation of shared control systems across diverse operations introduces a layer of complexity, as these systems become interdependent [46]; and (iv) Climate and geopolitical disruptions [47]: events like political instability or climate change have the potential to destabilize SCs. Regarding climate change, the Fukushima disaster in 2011 had a significant impact on Toyota's operations. An earthquake, followed by a tsunami, damaged the company's SC infrastructure, disrupting production and delivery schedules [48]. The crisis highlighted the importance of ensuring the resilience of the SC in the face of external disruptions, leading Toyota to re-evaluate its risk mitigation strategies. Ref. [49] explored the disruptions before and after the disaster, with a particular focus on the complex relationship between SC complexity and resilience. The convergence of such multiple factors creates a more intricate and interdependent array, boosting complexity. Uncertainty emerges as a recurring element in both SC strategies and manufacturing priorities. This recognition underscores the need for adaptive and dynamic approaches within industrial SCM to effectively navigate the complexities posed by fluctuating demand and uncertain supply scenarios [47].
## 2 Methods
### Experimental Setup
#### 2.3.3 Dataset Partitioning  
Before training the model, the dataset is partitioned as follows: The training set comprises three groups of data with added noise at load levels of 20%, 40%, and 60%, as well as \(E_{\text{a}}(n)\), \(E_{\text{b}}(n)\), and \(E_{\text{c}}(n)\), along with the first 70% of the data under 80% load conditions. The remaining 30% of the data under 80% load conditions are used as a validation set to assess model's predictive performance. Data at the 100% load level are not involved in the initial training and validation of the model; instead, they are evenly split into a training set and a test set. The training set is used for incremental training on the model that has already been trained on the previous four data groups, whereas the test set is used to assess the model's predictive performance on entirely new data. This design allows for assessing the model's generalization on previously unseen 100% load level data, ensuring that the model demonstrates robust predictive ability when faced with new load conditions in practical applications.
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
### How to Confer Biodegradability to Polymeric Architectures  
To confer biodegradability to polymers, they have to be designed with intra-linkers that can be cleaved by either physiological substances (e.g., glutathione) or enzymatic catalysis [29; 30].  
Figure 1: (**a**) Generally accepted mechanism for a RAFT polymerization. Copyright 2009, American Chemical Society; (**b**) The mechanism for NMRP polymerization. Copyright 2001, American Chemical Society; (**c**) Transition-metal-catalyzed ATRP. Copyright 2001, American Chemical Society.  
The biodegradable linkages can be tailored on the polymer backbones, on the side chains, on the cross-linking agents, etc. Several covalent linkages are biodegradable, e.g., the acetal linkage is acid labile [31]; the ester linkage is degradable upon hydrolysis [32; 33]; disulfide bond is cleavable in the presence of glutathione (GSH), the most abundant intracellular thiol (0.2-10 mM) in most mammalian and many prokaryotic cells [34; 35; 36]; and polymers such as polycaprolactone (PCL) [37] and poly(amino acid)s [38] with polypeptide backbone can be degraded in biological environments by enzymes such as proteinases and peptidases.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 2 System Overview and Problem Statement
### Problem Statement  
According to the previous description, we can find that in the entire service delegation process, the most important part lies in the rules for mutual trust evaluation between objects, and how to use the trust evaluation information to make decisions. The first important problem is the structuralization of the interactions and the calculation of the direct trust.  
Problem Statement 1: Based on historical interaction records between object A and object B, how does object A determine the direct trust of object B?  
The recommended trust opinions from intermediate objects can be great references for object A to evaluate the trust of object B. However, trust opinions from different sources should have different degrees of confidence. For example, we usually believe in information from reliable sources. Therefore, how to effectively quantify the confidence of information from different sources will be the second important problem.  
Problem Statement 2: When intermediate object C provides A with trust opinions about object B, how will A integrate the opinions of C?  
The success of task execution is seriously related to the delegation decision of the SR, so in the process of service delegation, the SR must carefully evaluate the reliability of SPs. Establishing trust is a suitable way to evaluate the reliability of an object. However, the SR will not only consider trust, but also its own benefits in the delegation process. Therefore, how to comprehensively consider both trust and utility so as to ensure that a relatively reliable SP is selected and optimize the utility of SR is the third important problem.  
Problem Statement 3: According to the trust of the candidate objects, how does object A delegate the task?In summary, _Problem 1_ corresponds to the quantitative calculation of \(T_{p_{j}\gets u_{i}}^{d}(\varphi)\). _Problem 2_ corresponds to the formulation of Equation (1). In addition, _Problem 3_ corresponds to the formulation of Equation (4).
###### Abstract
Focusing on the concept of meta-scheduling, i.e., the ability to exploit different algorithms for the solution of a scheduling problem, this paper extends the current state of the art with a generic description model, able to define the agent's capabilities and different algorithms acquired. This research study aims to support this challenge with the provision of an AAS-based information model for the description of scheduling agents. The proposed modeling methodology describes aspects related to interoperability with other agents or assets and enables abstraction on scheduling operations. The agent software that was created to be aligned with the model is able to encapsulate multiple optimization methods within a toolbox of schedulers, enabling applicability in various industrial cases and scalability. Each of the scheduling methods is parameterized within an AAS model in a way that allows the selection of the corresponding scheduler to be based on the capabilities of the scheduling method. Communication is also established within the AAS based on different operations and events that other agents or assets can access. This enables more abstract interaction across industrial software entities by calling higher-level behaviors of the agent, regardless of the lower-level implementation details. The agent implementation was performed in the SARL agent-oriented programming (AOP) language and deployed in an open-source platform for MASs. During the validation of the scheduling agent, three optimization methods, namely deep reinforcement learning (DRL), mathematical programming, and heuristics optimization, were integrated. The industrial installation took place on a bicycle production system where the scheduling agent was accessed via a shopfloor UI to perform scheduling operations in different problems of the factory. The rest of the paper is organized as follows: Section 2 presents the state-of-the-art related works of the method that was used in this scientific work. Section 3 presents the proposed AAS model for modeling a Production Scheduling Agent. Moreover, Section 4 presents the industrial implementation in which the proposed model was used. Finally, Section 5 is the conclusion of this work and the future directions of this study.
# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
## 3 Controller Design
### Stability Analysis  
To demonstrate the stability of adaptive integral backstepping control of neural networks, a new Lyapunov function is designed, and the Lyapunov function \(V_{3}\) is defined as:  
\[V_{3}=V_{2}+\frac{1}{2\lambda}\big{(}\hat{W}-W^{*}\big{)}^{2}\geq 0 \tag{38}\]  
The derivative of \(V_{3}\) yields:  
\[\begin{split}&\dot{V}_{3}=\dot{V}_{2}+\frac{1}{\lambda}\big{(} \hat{W}-W^{*}\big{)}\frac{d\hat{W}}{dt}\\ &=-k_{1}{e_{1}}^{2}+e_{1}e_{2}+e_{2}\bigg{[}k_{1}e_{1}+\ddot{ \phi}_{d}-\frac{\dot{\phi}\dot{\phi}\big{(}I_{y}-I_{z}\big{)}}{I_{x}}-\frac{l}{ I_{x}}U_{2}-d_{\phi}\bigg{]}+\frac{1}{\lambda}\big{(}\hat{W}-W^{*}\big{)}\frac{d \hat{W}}{dt}\end{split} \tag{39}\]  
Substituting Equations (36) and (37) into Equation (39), we obtain:  
\[\begin{split}&\dot{V}_{3}=-k_{1}{e_{1}}^{2}-k_{2}{e_{2}}^{2}+e_{2} \Big{(}\dot{d}_{\phi}-d_{\phi}\Big{)}-\frac{1}{\lambda}\big{(}\hat{W}-W^{*} \big{)}\hat{W}\\ &=-k_{1}{e_{1}}^{2}-k_{2}{e_{2}}^{2}+e_{2}\big{(}\hat{W}-W^{*} \big{)}h_{1}(x)-\frac{1}{\lambda}\big{(}\hat{W}-W^{*}\big{)}\hat{W}\\ &=-k_{1}{e_{1}}^{2}-k_{2}{e_{2}}^{2}+\big{(}\hat{W}-W^{*}\big{)} \bigg{(}e_{2}h_{1}(x)-\frac{1}{\lambda}\hat{W}\bigg{)}\end{split} \tag{40}\]  
At this point, the neural network is adjusted mainly by adjusting the weights of the neural network. Therefore, the neural network weight adaptive method is designed as \(\hat{W}=\lambda{e_{2}}h_{1}(x)\). Then, we can obtain:  
\[\dot{V}_{3}=-k_{1}{e_{1}}^{2}-k_{2}{e_{2}}^{2}\leq 0 \tag{41}\]  
Therefore, the designed roll angle control system is progressively stable.  
In the same way, we can obtain the pitch angle controller and the yaw angle controller:  
\[U_{3}=\frac{I_{y}}{l}\bigg{[}\Big{(}1-{k_{3}}^{2}+\mu_{2}\Big{)}e_{3}+(k_{3}+ k_{4})e_{4}-k_{3}\mu_{2}\gamma_{2}+\ddot{\theta}_{d}-\frac{\dot{\phi}\dot{ \phi}(I_{z}-I_{x})}{I_{y}}-\dot{d}_{\phi}\bigg{]} \tag{42}\]  
\[U_{4}=I_{z}\bigg{[}\Big{(}1-{k_{5}}^{2}+\mu_{3}\Big{)}e_{5}+(k_{5}+k_{6})e_{6} -k_{5}\mu_{3}\gamma_{3}+\ddot{\phi}_{d}-\frac{\dot{\phi}\dot{\phi}\big{(}I_{x }-I_{y}\big{)}}{I_{z}}-\dot{d}_{\phi}\bigg{]} \tag{43}\]  
where \(k_{i}\), \(\mu_{i}\) are constants greater than 0, and  
\[\left\{\begin{array}{l}e_{3}=\theta_{d}-\theta,\gamma_{2}=\int_{0}^{\tau}e_{ 3}(\tau)d\tau\\ e_{4}=k_{3}e_{3}+\dot{\theta}_{d}+\mu_{2}\gamma_{2}-\dot{\theta}\\ e_{5}=\varphi_{d}-\varphi,\tau_{3}=\int_{0}^{\tau}e_{5}(\tau)d\tau\\ e_{6}=k_{5}e_{5}+\dot{\varphi}_{d}+\mu_{3}\gamma_{3}-\dot{\varphi}\end{array}\right. \tag{44}\]
## 3 Numerical Studies
### iFEM Studies Involving Uniaxial Strain Data
#### 3.2.2 Results for Damage Case-2  
The iFEM results for Damage Case-2 are shown in Figure 16. Despite the smaller damage size, the plots clearly showed a \(I_{D}\) peak near the actual damage site. Compared to the benchmark results (Figure 6), the strain concentration is more diffuse; nevertheless, the plots illustrated the presence of damage near the corner of the plate. An interesting point to be noted is that even for Unitcell-2, the strain distribution far from the damage site is more prominent. This is not the case in the benchmark results where the far-field strains are virtually unaffected by the damage. The leakage of the strain field and subsequent contamination of the far-field strains are limitations of the Unitcell strategy. This illustrated potential difficulties in using the present strategy for detecting small-sized damages.  
The iFEM results obtained using strain data contaminated with 5% noise are shown in Figure 17.  
The plots of Figure 17 showed numerous \(I_{D}\) peaks at various locations of the plate. None of the peaks coincided with the actual damage site, leading to erroneous conclusions when used for damage detection. These results are compared with benchmark iFEM results for Damage Case-2 using contaminated strain data (5% noise). These benchmark results are shown in Figure 18, and they also indicated a drop in damage detection performance due to the addition of noise. Compared to Unitcell-2, the benchmark results showed a prominent \(I_{D}\) peak near the damage site (Figure 18b). The benchmark results also had the same limitations seen for Unitcell-2, where minor \(I_{D}\) peaks are seen at far-field locations. However, these results provided one significant inference: there is a lower limit to damage  
Figure 16: iFEM reconstructed \(I_{D}\) distribution for Damage Case-2 using: (**a**) Unitcell-1, and (**b**) Unitcell-2.  
Figure 15: Benchmark iFEM results of \(I_{D}\) for Damage Case-1 (5% noise) using: (**a**) no threshold, and (**b**) threshold = 0.5.  
size that could be successfully detected using the proposed methodology. Successful damage detection is not possible as the strain perturbations due to the damage, measured by the strain-sensors, are smaller than the strain perturbations due to the added noise. It also pointed to possible alternative scenarios where similar-sized damages could be detected, e.g., when the damage is closer to one of the sensors and measures a higher strain perturbation.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 4 Results and Discussion
### Flow Behavior
The secondary flow developed close to the valve disc made the downstream flow considerably smoother. The two secondary flows flowing along both sides of the valve disc mixed and developed with a vortex in the downstream direction. A vortex region with eddies behind the valve disc was observed as valve size increased to the DN 400 valve. In sum, among the turbulence models, the largest pressure drop was observed in \(k-\varepsilon\) turbulence models showing higher turbulence kinetic energy in Figure 5. Large pressure drops had an effect on the estimation of flow coefficient, \(C_{\omega}\), with large value. On the other hand, relatively smaller pressure drop can be obtained in \(k-\omega\), and SST turbulence models. This effect of turbulence model can be attributed to the amount of discrepancy with turbulence models, therefore, the largest discrepancy between calculation and experimental value for the flow coefficient can be observed in \(k-\varepsilon\) turbulence models, which is mentioned in Section 4.1. Furthermore, the overall comparison between turbulence models yielded the value of differences: the difference between turbulence models increased with decreasing valve size and disc opening percentage. As the valve size and disc opening decrease, a smaller flow region with a relatively small area between the disc and the valve wall caused a large pressure drop, which increases the turbulence as seen in the increasing behavior of turbulent kinetic energy. Therefore, among two-equation turbulence model, priority should be given to \(k-\omega\), and SST turbulence models. This is especially considered for the design of valve facilities that are relatively small sized and have large pressure drops.
# Mechanical and Heterogeneous Properties of Coal and Rock Quantified and Mapped at the Microscale
## 1 Introduction  
The mechanical properties of coal and rock are of key importance in coal mining and exploration [1; 2; 3], rock strata control [4; 5; 6], hydraulic fracturing [7; 8; 9], rock burst [10; 11], (enhanced) coal bed methane recovery (CBM/ECBM) [12], water hazard control [13; 14], geo-sequestrian [15; 16], and underground coal gasification in coal mines and deep unmineable coal seams [17].  
Traditionally, the mechanical properties of intact coal and rock were mainly measured by standard centimeter-scale tests, mainly including uniaxial compression, triaxial compression, tension, bending, cutting, and polyaxial tests [18; 19; 20; 21; 22]. However, for soft and broken coal and rock obtained from deep underground coal mines, this experimental method is not feasible due to the impossibility of obtaining intact standard samples. As a result, it is necessary to find an innovative and advanced technique, which only requires a small sample volume, to measure geo-mechanical properties of coal and rock in coal mines.  
In this study, to measure the mechanical parameters of soft and broken coal and rock, firstly, nanoindentation technique was briefly introduced, including its calculation method and experimental procedures. Secondly, mechanical parameters were calculated by averaging method. Thirdly, the heterogeneous properties of coal and rock were also quantified and mapped by using their mechanical parameters. This study provides a new technique to obtain the mechanical properties of geomaterials at the microscale.
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 6 Conclusions  
Based on the background of the existing deterioration tunnel blasting project, this paper concludes the following via numerical simulation, field experiment, and data wavelet packet analysis:  
1. When the full section detonator explodes, the stress wave spreads in an arc shape and has a large range. In the blasting of the mechanical cutting method, the second layer hole is connected with the mechanical cutting area, and the blasting stress wave propagates along the four directions of the cut hole to form a petal-shaped stress ring, and the diffusion range is obviously reduced.
2. The use of mechanical cutting instead of traditional cutting hole blasting can effectively reduce the blasting vibration. The vibration velocity of the existing deterioration tunnel is reduced by 38% maximum, and the vibration velocity of the other points is reduced by 20-30%. Mechanical cutting provides several free surfaces for the blasting of the surrounding holes of the tunnel, which can realize the vibration control of the blasting adjacent existing deterioration tunnel.
3. The energy of the blasting vibration signal is mainly concentrated near the frequency band where the main frequency of the original signal is located. The main excitation frequency range of the existing deterioration tunnel is 31.25-46.875 Hz, and the energy of the blasting vibration signal is not evenly distributed in the frequency band but is mainly concentrated in the low frequency band. The damage degree of surrounding rock decreases with the increase of distance. When the blasting seismic wave passes through the surrounding rock, the damping of the rock mass will filter out the vibration signal of higher frequency, which plays an effect similar to the low-pass filter.  
Data curation, W.X.; Formal analysis, H.Z.; Investigation, W.X.; Project administration, W.X.; Resources, J.S.; Supervision, J.S.; Writing--original draft, W.X.; Writing--review and editing, W.X. and H.Z.; Conceptualization, W.X. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (NSFC) 51208036.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Classification Accuracy  
As can be seen from Figure 6, the classification results were consistent with the previous expectations. The multiscale and multi-feature MSMF-PointNet structure proposed in this paper had the best classification effect, as can also be clearly seen from the quantitative evaluation results in the table in Figure 5. On a whole, the final classification result of MM was almost the same as the real value, but the classification effect was especially prominent in the roof, tree, and impervious surface categories. As can be seen from the SS, features were classified as roof or tree much more accurately when added to the SM based on the local characteristics of the spherical neighborhood. Figure 6 shows the two places with ground features that the SS, according to the topography and elevation, simply classified as tree, roof, and shrub, while the SM refined the classification and was able to distinguish roof, tree, shrub, impervious surface, fence, and car.  
Although MM had the highest classification accuracy and the best segmentation effect of various ground objects, some details were still incorrectly classified for various reasons. Therefore, we selected several typical regions to further investigate these reasons, as shown in Figure 8a-c.  
When the height of a shrub was too low, i.e., too close in height to the low vegetation, it was misclassified as low vegetation, as shown in Figure 8a. When tall and short trees were mixed, the MSMF-PointNet misclassified some smaller trees as shrubs, as shown in Figure 8b. Similarly, in Figure 8c, some shrubs were also misclassified as trees.  
Overlap in a strip can make the point cloud's density uneven. In overlapping strips, the local density is large, whereas the density of a single strip density is small. As shown in Figure 9, a significant fault appeared in the single-strip area, where a bump can clearly be seen. Moreover, in the field, there was a large difference between the heights of the left and right sides of a narrow groove, R = 0.8. The ground on the right side of this groove was higher and was inappropriately classified as a roof. When the local feature R = 1.2 was added, the impervious ground was accurately classified.  
As can be seen from the results in Figure 7 and Table 5, the classification accuracy of the Shashi city dataset was higher than that of the Vaihingen city dataset because the distribution of the former was more encrypted and more uniform than that of the latter; this also proves the generalizability of the proposed network.