esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 4 The Secure Auditable Registry Based On Message-Oriented Middleware (SARMOM) Pattern
### Solution  
The solution consists in using a Secure Auditable Registry (SAR) service that is supported by Message-Oriented Middleware (MOM). The architecture of the system after applying the solution is depicted in Figure 5 below. Please note that the Machine Controller node will be replicated.  
A Machine Controller node can manage one machine (i.e., an IoT device), but can also manage a group of machines. In our specific case study, we use a Machine Controller node for a group of slot machines called a \"block\" that can consist of up to 128 machines, but the pattern can be used with any arbitrary number of machines per controller. In fact, for other domains, such as autonomous drones, there is one Machine Controller per machine (drone).  
SUD Client nodes represent the SUD microservices that need to access the data in the registry to provide their functions. Examples of these nodes are Reporting, Accounting, Business Analytics and Anomaly Detection. It is possible to have more than one Auditing Authority node.  
The solution entails several steps that can be taken incrementally:  
1. In the first place, a Secure Auditable Registry (SAR) service should be integrated with the SUD, using an independent component (e.g., microservice). This component implementing SAR must also implement security mechanisms to protect the integrity and the ordering of the records it contains. See implementation section for details.  
Figure 4: Secure Tropos diagram describing the goals of a typical SUD that may use the \(\mathrm{SARMOM}\) pattern.  
2. Then, to free the SAR component from dealing with other requirements, such as concurrent access, scalability and resilience, the access to the SAR must be conveyed through an MOM service. In our case, the ideal solution is to use a pull-based MOM (such as Kafka) so that the registry can retrieve the messages at its own pace, knowing that there is no risk of messages being lost because of the lower performance of the SAR component. In this way, we externalize the management of topics and we have guarantees for the ordering of messages (at least, at the topic level). Additionally, dealing with issues like concurrency, high input throughput and differences in production and consumption speeds is also solved by the MOM. The MOM also helps with error recovery, since it can provide persistence of the records received.
# Cycle Generative Adversarial Network Based on Gradient Normalization for Infrared Image Generation
## 4 Analysis of Experimental Results
### Experimental Results
The following visualization results of the loss function obtained during model training are shown in Figures 6 and 7: the cycle loss of generator A and generator B shows that the cycle loss value of the CycleGAN method is high and low until 400 step, which has poor stability; the CBAM_CycleGAN method has less local fluctuations, which has average stability; the cycle loss value of the generator in the proposed method shows a regular gradient downward trend, converges faster and the overall process is smoother. Between 400 step and 1000 step, all three methods tend to be smooth, but the CycleGAN method and CBAM_CycleGAN are more volatile with poor model generation ability. After 1000 steps, compared with CycleGAN and CBAM_CycleGAN methods, the loss value of the proposed gradient cycle generation adversarial network (GN_CycleGAN) tends to stabilize as the gradient decreases, and the cycle loss is lower than that of cycleGAN and CBAM_CycleGAN methods, laterally reflecting that the model has a better generative abil  
Figure 4: Comparison of CycleGAN and our generation results on the Flir dataset. ity. On the whole, the gradient-based cyclic adversarial network method (GN_CycleGAN) has an obvious gradient descent pattern and converges quickly, which reflects the excellent stability of the generator network, and, therefore, can prove the effectiveness of the proposed method. Through the experimental data of image evaluation metrics on several datasets and the visualization results of the training model, it can be concluded that the gradient normalization method proposed in this paper has better generative performance in infrared image generation. To address the problem of poor quality and texture details loss in the infrared images obtained by infrared devices in harsh environments, this paper uses a network with stronger feature extraction capability in the generator network, and introduces channel attention and spatial attention mechanisms into the network structure, which have better effects on the extraction of texture details in image generation. To prevent the problem of easy model collapse during the training process, we added a gradient normalization module into the discriminator to further strengthen the convergence speed of model train  
Figure 5: Comparison of CycleGAN and our generation results on the OSU coloured hot dataset. Figure 6: Visualisation of the cycle loss gradient results for generator_A. ing. Through experiments, it is shown that the model's convergence speed is faster after introducing this module, and the model training presents gradient descent with better stability. Meanwhile, the experimental data in Table 2 shows that, for the image data obtained from the introduced residual network and attention mechanism, the effect is significantly improved, indicating that the effect of image generation is very satisfactory.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 2 Vehicular NDN
In Reference [19], the authors leverage the GPS technology on-board of vehicles and extend VNDN forwarding with the concept of geo-location. Content name prefixes are mapped to the geographic areas of their producers and Interests are forwarded based on that geographical information. Vice versa, in Reference [20], vehicles maintain a table that tracks information about the neighbor nodes, like their speed and direction. When an Interest must be forwarded, vehicles access the table to select the best forwarder among their neighbors. Notwithstanding the different implementation details of the forwarding strategy, all VNDN designs benefit from the native in-network caching function provided by NDN. The simplest and fast caching scheme, implemented in the vanilla NDN implementation, is CEE, where all incoming packets are cached. If the CS is full, a replacement operation is performed which can be based on traditional policies, like LRU. Many other caching solutions were proposed in the literature that outperform CEE. They range from simple random caching [21] to topology-based and popularity-based schemes [14]. However, the majority of them refer to traditional static contents [22]. Figure 2: Reference Vehicular Named Data Networking (VNDN) scenario.
## 3 Results
### Model Performance Evaluation
#### 3.2.2 Performance Results
Optimizing both metrics ensures that the models accurately identify pollution occurrences while minimizing false alarms, thereby facilitating effective environmental monitoring and management efforts. Model refinement efforts, as future work, will focus on adjusting the classification thresholds, feature engineering, and exploring ensemble methods to optimize sensitivity and specificity simultaneously, enhancing the overall performance and reliability of the pollution detection models. The findings of this comparative analysis validate the utility of machine learning algorithms, particularly the Random Forest model, for robust and reliable pollution detection in river ecosystems. The high accuracy, precision, recall, and AUC values indicate the potential practical deployment of these models in real-time monitoring systems, contributing significantly to environmental conservation efforts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Class** & **Sensitivity** & **Specificity** \\ \hline SVM & Wastewater & 0.903 & 0.111 \\ SVM & Debris & 0.962 & 0.200 \\ k-NN & Debris & 0.801 & 0.444 \\ RF & Oil spill & 0.864 & 0.333 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity and specificity values for SVM, k-NN, and Random Forest.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 5 Performance Evaluation  
The SS-MAC improves many techniques defined by the IEEE 802.15.4 standard, such as the cluster tree topology network, the hybrid CSMA/TDMA technique and the channel-hopping mechanism. It focuses on improving the IEEE 802.15.4 standard and draws on the sharable slot algorithm of SSMA (Slotted Sense Multiple Access) [8]. Therefore, in this section, we compare SS-MAC with three other protocols, namely, the IEEE 802.15.4 standard, SSMA and LELLMAC (Lifetime Extension Low Latency MAC) [9]. In terms of the topology and distributed transmission mechanism, LELLMAC has a similar design to SS-MAC. Based on the operation mechanism, we build the SS-MAC and IEEE 802.15.4 MAC models on the MATLAB platform and evaluate the performance of SS-MAC under different network scales. Using the formulas given in the above references and the parameter values in Table 2, we conduct numerical calculations on the MATLAB platform to compare the packet delay and throughput performance of the four protocols. Table 2 lists the values of the simulation parameters.
## 4 Results and Discussion
### Influence of the Distance Between Isolation Piles and Pit  
The vertical control efficiency for various distance between isolation piles and foundation pit is plotted in Figure 23. Figure 23 demonstrates that when the pile toe depth was less than 25 m, the vertical control efficiency of the isolation pile either decreased or initially increased and then decreased as the distance between the isolation pile and the foundation pit increased. However, when the pile toe depth exceeded 25 m, the vertical control efficiency generally showed an increasing trend or followed a pattern of first decreasing and then increasing. This behavior could be primarily attributed to the changes in the soil displacement field caused by the blocking effect of the isolation pile. The underlying explanations are as follows:  
For shallow pile depths, as the distance between the isolation pile and the foundation pit increased, the blocking effect altered the soil displacement field. This led to an increase in the vertical displacement of the tunnel. In contrast, for deeper pile depths, the influence of the isolation pile was more directly linked to the soil displacement at the pile location. According to the mirror image method [27], the soil displacement at the tunnel location was  
Figure 23: Relationships between vertical control efficiency and distance between the foundation pit and isolation piles for varying pile lengths: 10 m (**a**), 15 m (**b**), 20 m (**c**), 25 m (**d**), 30 m (**e**), 35 m (**f**), and 40 m (**g**).  
inversely proportional to the distance between the tunnel and the isolation pile. Therefore, assuming that the ratio of the maximum soil displacement at the isolation pile to that without the isolation pile remains constant for varying distances between the isolation piles and the foundation pit, the reduction in tunnel deformation became more pronounced as the distance between the isolation piles and the tunnel decreased. This results in greater effectiveness of the isolation piles in controlling deformation.  
The horizontal control efficiency for various distances between isolation piles and foundation pit is plotted in Figure 24. The results in Figure 24 indicate that the trends in horizontal and vertical control rates were generally consistent. Specifically, the horizontal control rate exhibited one of the following trends as the distance between the isolation piles and the foundation pit increased: it either initially decreased and then increased, initially increased and then decreased, or consistently either decreased or increased.
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 2 Materials and Methods
### Design of Top Seedling Mechanism  
When the plug tray is inserted into the seedling feeding mechanism, it initially passes through the seedling top area. To facilitate the subsequent seedling removal operation and to ensure both the efficiency and success rate of this process, it is essential to loosen the entire row of seedlings in the plug tray. This article utilizes an electromagnetic seedling lifting method for seedling lifting operations, which primarily consists of six electromagnetic push rods housed within a fixed push rod box. The electromagnetic push rods are installed inside the push rod box, with the spacing between adjacent push rods aligned with the specifications of the tray. For the 72-hole tray selected in this study, the distance between adjacent cells is 42 mm. Consequently, the center distance between adjacent electromagnetic ejector pins is also set to 42 mm to correspond with the tray dimensions. The fixed ejector box is installed and fixed under the seedbed, and the seedling top area of the seedbed is provided with a rectangular opening slot. The electromagnetic ejector selected in this article is a push-pull long-stroke electromagnet produced by Jiexiang Electric. By using a push-pull force tester, it was measured that the plug seedlings overcame the adhesion force between the seedlings and the inner wall of the plug grid and reached the maximum value when they were in a loose state. If the displacement is 14.34 mm, the length of the ejector rod should be greater than or equal to the maximum displacement. Taking into account factors such as the thickness of the mounting plate on the displacement, the strike of the electromagnetic ejector rod is set to 20 mm, as shown in Figure 2d.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Cerium Dioxide Nanoparticles  
Cerium is one of the lanthanides, the elements having 4f electrons, which contribute to peculiar physicochemical properties which can be beneficial also for biomedical applications. Cerium dioxide (CeO\({}_{2}\)) is widely used as a catalyzer due to its redox properties based on the Ce\({}^{3+}\) to Ce\({}^{4+}\) valence conversion. Cerium dioxide nanoparticles (CeO\({}_{2}\)-NPs) compared to conventional CeO\({}_{2}\) bulk lead to significant improvement of catalytic activity. CeO\({}_{2}\)-NPs are multifunctional, unique atomic lattice structures with oxygen vacancies, making them free radical scavenging agents [164].  
In vitro experiments have shown that CeO\({}_{2}\)-NPs exhibited low cytotoxicity in macrophages and enhanced proliferation and osteogenic differentiation of MSCs [165]. [PERSON] et al. reported that CeO\({}_{2}\)-NPs enhanced endochondral ossification-based bone regeneration on a femur defect model in mice and described the molecular mechanism behind this effect via stimulation of DEAH-box polypeptide 15-p38 mitogen-activated protein kinase axis [166]. The clinical potential has been addressed in several ways. CeO\({}_{2}\)-NPs can be used for titanium implant surface modification. There are studies showing antibacterial and anti-inflammatory properties of such coatings [167]. Another direction of research is the reinforcement of scaffolds with CeO\({}_{2}\)-NPs. [PERSON] et al. fabricated and tested the gelatin-alginate-cerium oxide nanocomposite scaffold with promising results indicating that incorporation of CeO\({}_{2}\)-NPs into the scaffold matrix changed its properties strongly with the enhancement of cell attachment, proliferation, and viability and free radical scavenging ability [168].  
[PERSON] et al. studied the effects of the use of nanocrystalline CeO\({}_{2}\) in the early stages of bone regeneration. Rabbits were used as the animal model. Artificially formed defects in the lower jaw were filled with a suspension containing calcium hydroxide, barium sulphate, and nanocrystalline CeO\({}_{2}\). Suspension of calcium hydroxide was used as the control. This assay showed that nanocrystalline CeO\({}_{2}\) could improve osteogenesis and decrease inflammatory response [169].
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method
#### 2.2.1. Adapter for Adaptive Fusion of Multiple Meteorological Elements  
To facilitate the training of the network and improve its accuracy, the proposed model in this study incorporates an adapter, which primarily consists of four components: a normalization layer, a weight layer, a fusion layer, and a data mapping layer. The normalization layer is employed to eliminate the dimensional differences among multi-meteorological-element data, thereby enhancing the model's convergence speed and precision. This study adopts the Z-score normalization method, with the calculation formula as follows:  
\[Z=\frac{X-\mu}{\sigma}, \tag{1}\]  
Here, Z is the normalized value, \(X\) is the original meteorological element data, \(\mu\) is the mean of the original meteorological element data, and \(\sigma\) is the standard deviation of the original meteorological element data.  
The weight layer in the adapter can adjust the importance scale among different elements, thereby effectively resolving feature conflicts caused by special weather conditions and geographical locations among multiple meteorological elements. It assigns a weight value to each meteorological element at every grid point within the identification area. During network training, these weight values are continuously adjusted using labels to filter out conflicts and modulate the strength of the correlation between multi-meteorological element features and frontal systems.  
Considering that multiple meteorological elements collectively form a systematic representation relative to frontal systems, the fusion layer in the adapter is employed to extract the composite features of these elements. This facilitates the subsequent network in extracting highly abstract features, thereby improving training efficiency and recognition accuracy. The input data shape is [256; 512; 6], where 256 and 512 represent the dimensions of the 2D array in the identification area (the size and dimensions are determined based on practical application requirements; the data shape selected in this paper is chosen merely for computational convenience). The third dimension, 6, indicates that the fusion layer has six input channels, corresponding to six weight matrices. The output data shape of the fusion layer is [256; 512; 3].  
Since instance segmentation networks primarily process image data, the 3-channel fused feature data are mapped to the range [0; 255] before being input into the instance segmentation network to ensure better compatibility.  
\[Y=a+\frac{b-a}{X_{max}-X_{min}}.(X-X_{min}), \tag{2}\]  
Among them, a and b are the minimum and maximum values of the input data mapping interval, respectively. In this paper, a is set to 0 and b is set to 255. \(X_{max}\) is the maximum value in the dataset \(X\), and \(X_{min}\) is the minimum value in the dataset \(X\). \(Y\) represents the mapped data.
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 5 Conclusions  
This paper employed panel structures with stiffening characteristics as the research object. Firstly, dynamic characteristic analysis and linear buckling analysis were conducted, and their relationship was analyzed. Then, the topology optimization of the panel with ribs and frames was presented based on the MMC method. Finally, the optimization results were analyzed and the following conclusions were drawn:  
1. Adding stiffening characteristics to a standard panel will not reduce the natural frequency of the panel, but can improve the structural buckling resistance. There is a coupling relationship between the first-order natural mode and the buckling mode of the panel with ribs and frames. With the increase in the prestress, the natural frequency of the panel with ribs and frames decreases until the load increases and the buckling load decreases to zero.
2. After topology optimization using different numbers of components, different results were obtained. As the number of components increases, the topology of the structure gradually becomes more complex. But they are all close to similar force transmission paths.
3. Compared to the results before optimization, the first-order natural frequency and buckling factor of the optimized panel with stiffening characteristics have been improved. The linear stage of the optimized panel with ribs and frames' displacement load curve is extended, indicating that the critical buckling load of the panel with ribs and frames has been effectively increased by nonlinear buckling analysis. This result further proves the coupling relationship between the first-order natural mode and the buckling mode of the panel with ribs and frames. It also proves the feasibility of improving the buckling resistance performance of the structures by optimizing the dynamic characteristics.  
Overall, this article optimized the buckling resistance performance of the structure based on a developed MMC method. Starting from the coupling relationship between structural dynamics and buckling, the goal of increasing the buckling load of the structure was achieved by optimizing the first-order natural frequency of the structure. This method has positive reference value for further optimizing the overall mechanical performance of panel structures.  
Conceptualization, Y.B.; methodology, Y.B. and D.Z.; software, X.H. and Q.D.; validation, Y.B. and X.H.; formal analysis, Y.B.; investigation, Q.D.; data curation, D.Z.; writing--original draft preparation, X.H.; writing--review and editing, Y.B.; supervision, Y.B. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 4 Experiment and Analysis
### Benchmark Model  
To demonstrate the validity of the proposed model, two types of representative recommendation algorithms are selected as comparison models in this experiment. They are collaborative filtering-based methods (ItemKNN, BPR, MF, NeuMF) that only consider implicit feedback and HIN-based methods (FMG, MCRec). The comparison models are given below:  
* ItemKNN [35], a classical collaborative filtering recommendation algorithm that makes recommendations based on the historical interaction behavior of users and items.
* BPR [36], a Bayesian-based personalized ranking model, which is a typical pairwise learning personalized ranking method based on implicit feedback.
* MF [37], a standard matrix decomposition algorithm that uses the cross-entropy loss function instead of the original loss function for TOP-N recommendation.
* NeuMF [17], a neural network-based ranking recommendation algorithm consisting of matrix decomposition and a multilayer perceptron.
* FMG [25], an advanced recommendation algorithm for heterogeneous information networks. It uses a two-step process: information extraction (matrix decomposition of individual meta-path connection matrices) and information utilization (recommendation using FM).
* MCRec [24], a deep network model with a cooperative attention mechanism for TOP-N recommendation in multi-source information networks.