esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trajectory Optimization Design  
### Kinematics Modeling  
Figure 2 shows the overall structure of the selected pickup manipulator with five-Degree-of-Freedom. The selected pickup manipulator is mainly composed of a base, links, joints, and an end gripper. The coordinate system of the selected pickup manipulator linkage, which is established by the D-H method [18; 19; 20], is shown in Figure 3. The kinematics modeling analysis is performed as follows.  
Figure 1: Trajectory optimization of the pickup manipulator in the obstacle environment.  
Figure 3: Linkage coordinate system of the pickup manipulator.  
In the formula, \(k=d_{1}+d_{5}a_{z}-a_{4}\sqrt{1-a_{z}^{2}}-p_{z}\); \(A=c_{1}p_{x}+s_{1}p_{y}+a_{4}a_{z}\pm d_{5}\sqrt{1-a_{z}^{2}}\); \(B=p_{z}-d_{1}-d_{5}a_{z}\pm a_{4}\sqrt{1-a_{z}^{2}}\).  
Both sides of formula (3) left multiply the inverse matrix \({}_{2}^{0}T^{-1}\), then:  
\[\left[\begin{array}{cccc}c_{1}c_{2}&c_{2}s_{1}&-s_{2}&d_{1}s_{2}\\ -c_{1}s_{2}&-s_{1}s_{2}&-c_{2}&d_{1}c_{2}\\ -s_{1}&c_{1}&0&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{cccc}\begin{array}{cccc}n_{x} &o_{x}&a_{x}&p_{x}\\ n_{y}&o_{y}&a_{y}&p_{y}\\ n_{z}&o_{z}&a_{z}&p_{z}\\ 0&0&0&1\end{array}\right]={}_{2}^{0}T^{-10}5T\text{,} \tag{10}\]  
\[{}_{2}^{0}T^{-10}5T=\left[\begin{array}{cccc}c_{34}c_{5}&-c_{34}s_{5}&-s_{3 4}&a_{2}+a_{4}c_{34}-d_{5}s_{34}+a_{3}c_{3}\\ s_{34}c_{5}&-s_{34}s_{5}&c_{34}&d_{5}c_{34}+a_{4}s_{34}+a_{3}s_{3}\\ -s_{5}&-c_{5}&0&0\\ 0&0&0&1\end{array}\right]\text{.} \tag{11}\]  
According to formula (10) and formula (11), take L (1,3) = R (1,3), L(2,3) = R(2,3), then:  
\[\left\{\begin{array}{l}\theta_{34}=a\tan 2\left(\begin{array}{c}-\cos \theta_{1}\cos\theta_{2}a_{x}-\cos\theta_{2}\sin\theta_{1}a_{y}+\sin\theta_{2}a _{z},\\ -\cos\theta_{1}\sin\theta_{2}a_{x}-\sin\theta_{1}\sin\theta_{2}a_{y}-\cos \theta_{2}a_{z}\end{array}\right)\text{,}\\ \theta_{4}=\theta_{34}-\theta_{3}\end{array}\right. \tag{12}\]  
The inverse kinematics of the pickup manipulator is completed with the above computing. According to the position of the small ball detected by the binocular camera, the joint angle of the pickup manipulator can be calculated by using the inverse kinematics mode and realize the motion control.
## 2 Materials and Methods
### Previous Work
#### 2.2.2 Knowledge Distillation  
Knowledge distillation (KD) is a widely adopted model compression technique that enables small models to learn from larger, high-performing ones. Proposed by [PERSON] et al. [2], it involves training a student model to match the softened outputs of a teacher model, thereby retaining knowledge while reducing computational cost.  
KD techniques are generally categorized into three types: response-based [14], feature-based [15], and relation-based [16]. Response-based methods match the teacher's output distributions, while feature-based approaches align intermediate representations. Relation-based methods go further to capture the structural relationships among features.  
In terms of training dynamics, distillation can be conducted in offline, online, or self-distillation modes. Offline distillation uses a pre-trained teacher model, while online distillation allows teacher and student to be trained simultaneously, enabling continual adaptation. Self-distillation reuses the same model architecture to guide itself.  
Existing KD methods face limitations in agricultural applications, particularly the requirement for separate training phases in offline distillation and the lack of domain-specific regularization in standard online distillation. Our approach innovates by implementing real-time mutual learning with parameter exchange every 50 iterations and introducing agricultural-specific regularization that penalizes false positives on healthy leaves, combined with adaptive loss weighting that prioritizes rare disease classes.  
This work adopts online knowledge distillation using YOLOv10l as the teacher model (see Figure 5). The overall loss function for distillation is a combination of cross-entropy loss and Kullback-Leibler (KL) divergence loss:  
\[\mathcal{L}_{\text{total}}=\alpha\mathcal{L}_{\text{CE}}+\beta\mathcal{L}_{ \text{KL}} \tag{1}\]  
where \(\mathcal{L}_{\text{CE}}\) is the cross-entropy loss against ground truth and \(\mathcal{L}_{\text{KL}}\) measures divergence between the soft outputs of teacher and student. The loss is minimized via stochastic gradient descent (SGD):  
\[\theta_{t+1}=\theta_{t}-\eta\
abla_{\theta}\mathcal{L}_{\text{total}} \tag{2}\]  
Figure 4: Structural diagram of YOLOv10.
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 5 Conclusions  
A resilience index based on system availability was adapted and applied. Through that, it was possible to measure the impact of different planned maintenance strategies. In addition, the effect of the incorporation of additional pieces of equipment in order to increment redundancies was analyzed.  
Regarding the proposed methodology, it was possible to identify where or how it is possible to positively influence the system resilience applying different preventive maintenance strategies. Moreover, it is feasible to prevent excessive maintenance activities that will not produce significant improvements in one of the system's overall characteristics, such as resilience. In other words, a balance can be established between the maintenance strategy, and its resources, and the impact on the system's resilience, based on the overall availability. Such balance can be affected over time, hence the importance of developing simulations or projections using Monte Carlo experiments to simulate the behavior of each reliability and maintainability parameter over time. This allows the incorporation of a useful element into the decision-making process with regard to the physical assets management.  
[PERSON] designed the research, [PERSON] contributed to the construction of the model and result analysis and [PERSON] contributed to the simulation experiment, writing and editing of the manuscript. [PERSON] contributed to writing and editing and the results analysis. All authors have read and agreed to the published version of the manuscript.  
No funding is present.  
This work utilized R-MES suite by R-MES analytics, Santiago, Chile.  
The authors declare no conflict of interest.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 3 System Model
### Graph Model  
The key to improve the system performance for real-time, non-real-time and stretch optimal models is to disseminate a greater number of requested data items per broadcast tick. From the  
Figure 1: System architecture. network coding perspective, it needs to make the encoding decision in such a way so that more number of clients will be satisfied per coded packet and eliminates the redundant encoding. For this, assume that \(G(V,E)\) is a CR-graph (Cache-Request-Graph) which is formed from the clients' cached and requested data items. The next step is to find a maximum clique which contains the maximum priority vertex based on the used scheduling algorithm. In on-demand broadcast, CR-graph construction is a practical assumption because clients can piggyback their updated cache information in the requests submitted to the server. With the knowledge of clients' cached and requested items, a graph \(G(V,E)\) is constructed at the server to guide the encoding decision. A brief overview of graph construction is given below. The summary of used notations in this paper is shown in Table 1.  
\(C\) denotes the set of clients in the system, which is, \(C~{}=~{}\{c_{1},\,c_{2},\,\cdots,\,c_{n}\}\), where \(n\) is total number of clients. The set of data items requested by client \(c_{i}\) is denoted by \(R(c_{i})\), and the set of data items cached at \(c_{i}\) is denoted by \(CH(c_{i})\) (\(1\leq i\leq n\)). The server has a database \(D\) with \(m\) data items, where \(d_{j}\) is the \(j\)th data item (\(1\leq j\leq m\)). **Definition 1**. : _Based on the clients' requested and cached data item, a graph \(G(V,E)\) is constructed by the following rules: \(V=\{v_{ij}|\) client \(c_{i}\) requests for item \(d_{j}\), \(1\leq i\leq n,\ 1\leq j\leq m\}\) \(E=\{(v_{i_{1}j_{1}},v_{i_{2}j_{2}})\,|\,j_{1}=j_{2};\,\text{or}\,\,j_{1}\
eq j _{2},\,d_{j_{2}}\in CH(c_{i_{1}}),\,d_{j_{1}}\in CH(c_{i_{2}})\}\) Accordingly, the rules for constructing edges are specified below: \(\bullet\)\(\epsilon(v_{i_{1}j_{1}},v_{i_{2}j_{2}})\) with \(j_{1}=j_{2}\) means that if clients \(c_{i_{1}}\) and \(c_{i_{2}}\) request the same data item, there will be an edge between the two vertices \(v_{i_{1}j_{1}}\) and \(v_{i_{2}j_{2}}\). \(\bullet\)\(\epsilon(v_{i_{1}j_{1}},v_{i_{2}j_{2}})\) with \(j_{1}\
eq j_{2},\,d_{j_{2}}\in CH(c_{i_{1}})\), and \(d_{j_{1}}\in CH(c_{i_{2}})\) means that if client \(c_{i_{1}}\)'s cache contains the data item being requested by client \(c_{i_{2}}\) and vice versa, there will be an edge between vertices \(v_{i_{1}j_{1}}\) and \(v_{i_{2}j_{2}}\)._  
A clique, denoted by \(\delta\), is a subset of the vertices in the CR-graph \(G\), such that every two vertices are connected by an edge, i.e., \(\delta\subseteq V(G)\).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline _Natural terrestrial_ & \multicolumn{3}{c}{_Components_} & \multicolumn{3}{c}{_Seasonal Indices_} & \multicolumn{3}{c}{_Accuracy measurements_} \\ _ecosystem types_ & Trend & Seasonal & W & Sp & Su & A & MAPE & MAD & MSD \\ \hline (Sub)nival snow/ice & 0.0879 + & A & -0.0799 & -0.0810 & 0.1307 & 0.0301 & 22.14 & 0.017 & 0.0004 \\ Alpine barren/sparsely & 0.1691 - & A & -0.0320 & -0.0842 & 0.1130 & 0.0032 & 14.35 & 0.024 & 0.0015 \\ vegetated & 0.00016*t & A & 0.0004 & -0.0535 & 0.0682 & -0.0150 & 15.03 & 0.020 & 0.0007 \\ Boreal barren/sparsely & 0.2234 + & M & 1.0438 & 0.8409 & 1.2988 & 0.8163 & 6.47 & 0.014 & 0.0002 \\ Boreal forest & 0.17046 - & A & -0.0336 & 0.0364 & 0.0307 & -0.0334 & 5.57 & 0.009 & 0.0001 \\ barren/sparsely vegetated & 0.00026*t & M & 0.8195 & 1.1118 & 1.2276 & 0.8409 & 5.32 & 0.009 & 0.0001 \\ Cool Temperate grassland & 0.00040*t & M & 0.8757 & 1.0178 & 1.2485 & 0.8578 & 4.50 & 0.008 & 0.0001 \\ Cool Temperate & 0.24422 - & A & -0.0232 & -0.0101 & 0.0621 & -0.0288 & 3.59 & 0.008 & 0.0001 \\ Warm Temperate & 0.1775 + & A & -0.0347 & 0.0503 & 0.0172 & -0.0329 & 4.09 & 0.007 & 0.0001 \\ barren/sparsely vegetated & 0.00011*t & M & 0.9042 & 1.2371 & 1.0143 & 0.8442 & 3.64 & 0.008 & 0.0001 \\ Warm Temperate & 0.2596 - & M & 0.8574 & 1.1917 & 1.1045 & 0.8462 & 3.20 & 0.008 & 0.0001 \\ shrubland/woodland & 0.00036*t & M & 0.7713 & 1.0351 & 1.2301 & 0.9633 & 2.44 & 0.007 & 0.0001 \\ Warm Temperate forest & 0.00033*t & A & -0.0288 & 0.0773 & -0.0161 & -0.0324 & 3.74 & 0.008 & 0.0001 \\ Mediterranean forest & 0.2687 + & A & -0.0174 & 0.0668 & -0.0050 & -0.0444 & 3.93 & 0.010 & 0.0001 \\ Mediterranean forest & 0.2768 - & M & 0.9280 & 1.2723 & 0.9481 & 0.8514 & 3.88 & 0.010 & 0.0002 \\ Mediterranean forest & 0.2935 - & A & -0.0156 & 0.0339 & -0.0005 & -0.0176 & 2.12 & 0.006 & 0.00006 \\ \hline \end{tabular}  
* A: Additive; M: Multiplicative; t: temporal index of 1 to 28 for the period of 2000 to 2007 in the recurrent seasonal order of Winter (W), Spring (Sp), Summer (Su), and Autumn (A), respectively.  
\end{table} 
Table 6: Decomposition of MODIS EVI time series into trend and seasonal components for natural terrestrial ecosystems in Turkey. **Figure 3.** Fourier-filtered (FF) MODIS NDVI time series and their noises for natural terrestrial ecosystems in Turkey. The recurrent seasonal order between years on X-axis is Winter, Spring, Summer, and Autumn, respectively; (a) (Sub)nival snow/ice; (b) Alpine barren/sparsely vegetated; (c) Boreal barren/sparsely vegetated; (d) Boreal forest; (e) Cool Temperate barren/sparsely vegetated; (f) Cool Temperate grassland; (g) Cool Temperate shrubland/woodland; (h) Cool Temperate forest; (i) Warm Temperate barren/sparsely vegetated; (j) Warm Temperate grassland; (k) Warm Temperate shrubland/woodland; (l) Warm Temperate forest; (m) Mediterranean barren/sparsely vegetated; (n) Mediterranean grassland; (o) Mediterranean shrubland/woodland; and (p) Mediterranean forest. Figure 3: cont. +++ ==ERROR: No output for this page==  
Figure 4: cont.
## 5 Conclusions  
In this paper, a vortex-induced flutter composite piezoelectric energy harvester is designed. The structure is a symmetrical arrangement of airfoils on both sides of the cylindrical bluff body, which can simultaneously realize both vortex-induced vibration energy harvest and flutter energy harvest. An experimental study on the influence of key parameters on its output performance was carried out, coupled with experimental research on the effect of vortex-induced vibration and flutter energy harvest. The main results are as follows:  
(1) The piezoelectric energy harvester VFPEH-30 with a symmetrical arrangement of airfoils and cylindrical blunt body can play a role in reducing the starting wind velocity, which is 4.8 m/s, much lower than the classical cylindrical blunt body vortex excited piezoelectric energy harvester VEH-30 and the airfoil flutter piezoelectric energy harvester FEH, by 32.40% and 43.53%, respectively.  
(2) The symmetrical arrangement of the airfoils on both sides of the cylindrical blunt body enables the cantilever beam of the energy harvester to produce bending and torsional compound vibrations, realizing energy harvest in both bending and torsional directions. It effectively increases its output voltage and output power. The effect of energy harvest varies from one cylindrical blunt body to another, between 20-35 mm. The energy harvest is better when the blunt body diameter is 30 mm, the output voltage of the VFPEH-30 first increases and then decreases with increasing air velocity. The results show that the maximum output power is 6.47 mW at a wind speed of 9.0 m/s and an optimum load of 140 k, which is much higher than that of the VEH-30 and FEH.  
(3) Compared with the classical vortex-induced piezoelectric energy harvester and the flutter piezoelectric energy harvester, the vortex-induced and flutter composite piezoelectric energy harvester has a higher power output at a lower wind speed. This work provides an effective experimental basis for a combined vortex-flutter energy harvester with two mechanisms.  
[PERSON] and [PERSON] designed the model and carried out the experiment; [PERSON] and [PERSON] provided guidance; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; [PERSON] provided experimental assistance. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Research Development and Promotion Project in Henan Province (Grant No. 22210221005).  
Not applicable.  
The authors declare no conflict of interest.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 6 Open Research IIoT Application Domains, Fog Computing as an Enabler
### Device to Device (D2D) Communication  
Researchers have to find a new way in which devices can independently work without using the existing cellular infrastructure. The demand for new infrastructure development is due to the increase in the number of smart devices that causes scarcity in spectrum and resources. This idea gives rise to D2D communication technology in which devices are part of the Internet and can communicate with each other without the involvement of internet infrastructure. This direct communication will result in less usage of the wireless spectrum. A device is itself constrained-bounded in terms of parameters, such as energy or resources, hence cloud and fog computing technologies support them. These emerging technologies enhance processing capabilities and result in efficient system formation. Cloud services for D2D communications have performance bottleneck as devices use direct communication, hence an alternative fog service is required. Fog with the same set of services, provide better performance. Fog networking act as an alternative of cloud, as it provides desired requirements in the vicinity of edge devices. Researchers have proposed D2D fogging to address energy-efficient task offloading in D2D communication [137]. The proposed method includes a framework for task offloading and uses assistance from the network for D2D. Mobile users use the communication and computation resources of each other dynamically. After discussing several security issues in fog computing, authors have proposed three lightweight anonymous authentication protocols (LAAPs) [108].  
The proposed scheme with the aid of D2D communication, is feasible for IoT devices which are resource-limited. [PERSON] et al. [109] proposed a F-community architecture for F-RAN followed by a data caching scheme. The caching scheme for UEs in D2D aided F-RAN helps in reduction of delays. Nodes in the system with higher chances of being selected as the central nodes store the most popular content in their cache. For access to any data, a user receives data from the cache stored previously. [PERSON] et al. [138] have proposed cachingMobile, which, compared to the cloud, can meet the low latency and energy efficiency requirements by using the elastic services provided by the nodes at the edge. Energy efficiency and low latency requirements is a major issue in this technique as the resources at the edge and the devices that are mobile. This approach has proposed to use D2D communication to carry out communication at a short distance and save network resources. CachinMobile not only improves energy efficiency but also maintains QoS.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
## 3 Effect of Cut-Out Zone Structural Parameters  
### Design of Experiments (DOE)  
Methods to enhance the mechanical performance of bolts primarily include three approaches: improvement of material mechanical properties, increase in preload force [29], and structural optimization. In addition to traditional metal heat treatment methods for enhancing bolt mechanical performance [32], recent studies have successfully demonstrated the application of composite materials in bolts [33]. Therefore, the mechanical performance improvement approaches using composite materials [34; 35] also represent effective alternatives. While material property improvements exhibit certain uncertainties and preload  
Figure 15: Comparison of bolt changes between simulation and test.  
force increases offer limited performance enhancements, this study therefore focuses on optimizing the notch groove structure. Before exploring the effects of the depth and width of the cut-out zone on the fracture threshold of bolts, it is first necessary to test a system of four bolts in a coupler yoke. Therefore, the corresponding experimental design needs to be carried out. The experimental design scheme employs the Latin Hypercube method to generate samples. The reason for using the Latin Hypercube method to discretize sample points is that it not only ensures that the sampling values are evenly distributed throughout the sample space but also prevents the sampling values from being overly clustered. A total of 78 sets of structural parameters were generated, and the resulting test groups are shown in Appendix A Table 11. The distribution of sample points is illustrated in Figure 16. Based on the structural parameters provided in Appendix A Table 11, the finite elements in the cut-out zone of the four cut-out induced bolts (grouped as one set) were modified in the finite element model. The preload force of each cut-out induced bolt was maintained consistently at 400 N.
## 5 Experimental Results and Discussion
### Estimated State Transition Probabilities
#### 5.3.2 Probability of Error  
Another validation criterion that is used to determine the precision of the FMMs and to check the accuracy of the estimated models is the probability of error. Table 6 shows the error probabilities for both the measured error sequences and the model-generated error sequences for each of the four scenarios that were taken into account for this study.  
A closer look at the error probabilities for the measured error sequences in Table 6 reveals that 16-QAM systems are noisier than 8-QAM systems and 8-QAM systems are noisier than 4-QAM systems. This is because, as the constellation size increases (from 4-QAM to 8-QAM or higher), the symbols are forced closer together and thus more susceptible to errors caused by noise and channel impairments. Furthermore, Table 6 shows the relative consistency between \(P_{o}\) for experimentally measured error sequences and \(P_{m}\) for FMM-generated error sequences for the four considered cases. This validates the accuracy of the estimated or derived models. However, there was a small difference in the model-generated probabilities (\(P_{m}\)) for 16-QAM and 8-QAM in Case 2, with the model-generated probabilities (\(P_{m}\)) for 8-QAM being higher than for 16-QAM. The statistical nature of the model-generated error sequence could explain this disparity.  
Figure 14: LLR plot for Case 3 (combination of a laptop charger and computer monitor).  
Figure 15: LLR plot for Case 4 (combination of CFL bulb and hairdryer).
## 3 Results
### EAOC vs. NEOC Classification
In the Xception-based architecture, GB achieves the highest value of 0.75, while KNN and SVM perform similarly at 0.66, and Majority Vote reaches 0.72. In the DenseNet121-based architecture, SVM shows the best performance with 0.77, whereas GB has the lowest value at 0.59, and Majority Vote records 0.58. ResNet50 demonstrates strong results, with GB achieving the highest score of 0.83, followed closely by Majority Vote at 0.81. The VGG19-based architecture shows the best overall performance, with Majority Vote reaching 0.84 and GB and RF performing equally at 0.78. ResNet50 and VGG19 appear to be the most effective architectures, particularly when using GB and Majority Vote classification strategies. Figure 6 shows the confusion matrices for the different ML algorithms combined with different CNN architectures, compared with the majority voting ensemble, too. Majority voting generally outperforms individual models, especially in reducing non-tumoral ovary misclassification. SVM shows a strong classification performance, particularly for the tumoral ovary class, while GB and RF have variable results depending on the CNN used. Figure 5: MLcps metric values are reported in a histogram for each CNN + ML architecture (i.e., KNN, RF, GB, SVM and major voting system based on KNN, RF, and GB) for EAOC vs. NEOC classification. (**a**) Xception, (**b**) DenseNet-121, (**c**) ResNet50, and (**d**) VGG19-based architecture. * Based on KNN, RF, and GB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{\(\boldsymbol{p}\)**-Values**} \\ \hline
**CNN** & **KNN** & **RF** & **GB** & **SVM** & **Majority Voting * \\ \hline ResNet50 & 0.45 & 1.00 & 0.24 & - & 0.53 \\ VGG19 & 0.46 & 0.47 & 0.47 & - & 0.57 \\ DenseNet121 & 0.93 & 0.66 & 0.66 & - & 0.35 \\ Xception & 0.93 & 0.53 & 0.41 & - & 0.73 \\ \hline \hline \end{tabular}  
* Based on KNN, RF, and GB. The symbol *-.7 indicates the reference model for the DeLong test comparison.  
\end{table} 
Table 5: Results of DeLong test comparing the CNN + ML algorithm versus each CNN + SVM approach, assumed as a reference, for the EAOC vs. NEOC classification tasks. These findings reinforce the advantage of ensemble learning in improving robustness and reducing model-specific biases.