esa-sceva/satcom-chunk-collection huggingface dataset:
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Protocol Specification
#### 4.2.3 Beacon-Less Routing  
The beacon-less routing (BLR) protocol [47] is a simple receiver-based scheme where the nodes on reception of data packet, based on their presence in forwarding zone decide whether or not to contend for the forwarding right of the data packet. Here, the forwarding zone set is the same as that of ILBFC. Furthermore, all the eligible nodes then assign a waiting time solely based on their geographic progress towards destination. Apart from this, an additional \(\beta\) similar to ILBFC is set to avoid redundant data packet forwarding by multiple relay nodes with similar \(\gamma\). However, BLR does not contain any winner relay management feature, and the intermediate nodes perform relay selection process (forwarding zone and waiting time criteria) for every data packet received. For the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\Gamma\) & 50 ms & \(\eta\) & 3 \\ \(\gamma_{out}\) & 50 ms & \(H_{max}\) & 6 \\ \(\alpha\) & 0.5 & \(\beta\) & 10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: ILBFC protocol parameters.  
loop protection, only the maximum hop limit (\(H_{max}\)) feature is considered in BLR. Since BLR performs relay selection for every received data packet, it will be interesting to compare its performance with ILBFC, LBF and DBD. The latter routing techniques after the contention phase, allows the selected relay node to forward all the incoming data packets for a specific time based on their link duration value.
# Aerothermoelastic Analysis of Conical Shell in Supersonic Flow
## 3 Comparison and Convergence Studies  
To verify the effectiveness of the present solution procedure, comparison and convergence studies have been carried out. First, the natural frequencies of a functionally graded conical shell under clamped-clamped boundaries at both ends are obtained and compared with the previous literature. The comparison results are shown in Table 1. It can be observed from Table 1 that with the increase in the terms of the orthogonal polynomials truncated, the dimensionless frequency parameters converge, and when \(N=10\), the present result and those from the literature [23] are in good agreement.  
Second, the correctness of the flutter stabilities derived in the present investigations are verified. The critical dynamic pressure parameter \(\lambda(\lambda=12(1-\mu^{2})\gamma p_{\infty}M_{\infty}^{2}r^{3}/Et^{3}\sqrt{ M_{\infty}^{2}-1})\) of the simply supported conical shell investigated in the literature [32] is counted, and its convergence is analyzed. It can be found from Figure 2 that the natural frequencies and dynamic pressure parameter have gradually converged with the increase in the modal truncation numbers \(m\). In addition, it can be seen that the critical dynamic pressure parameter calculated here is \(\lambda_{cr}=556\), which is in agreement with the previous result of \(\lambda_{cr}=566.4\)[32].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(\alpha\) & **Shakouri [23]** & \multicolumn{6}{c}{**Present Results**} \\ \cline{4-9}  & & \(N\) = 5 & \(N\) = 6 & \(N\) = 7 & \(N\) = 8 & \(N\) = 9 & \(N\) = 10 \\ \hline \(L/r=1\) & \(30^{\circ}\) & 0.85746 & 0.87708 & 0.86008 & 0.85790 & 0.85765 & 0.85740 & 0.85740 \\ \(60^{\circ}\) & 0.44767 & 0.45785 & 0.44823 & 0.44817 & 0.44769 & 0.44766 & 0.44766 \\ \(L/r=5\) & \(30^{\circ}\) & 0.30770 & 0.31969 & 0.31730 & 0.30935 & 0.30914 & 0.30796 & 0.30794 \\ \(60^{\circ}\) & 0.14467 & 0.16803 & 0.15799 & 0.14986 & 0.14682 & 0.14511 & 0.14493 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensionless frequency parameters (\(\overline{\omega}=\omega R\sqrt{\rho(1-\mu^{2})/E}\)) of a functionally graded conical shell under clamped–clamped boundary conditions (\(m=1\), \(n=1\), \(h/r=0.01\), \(p=1\)).  
Figure 2: Mode convergence and comparison study for the aerothermoelastic analysis with result reported in the literature [32].
## 4 State of Art about Data Availability in L2 Rollups
### On-Chain (Rollup Mode)
To overcome this challenge, different data compression techniques are used, which ultimately reduces the Gas fees for end-users. These techniques include posting only state differences and employing data compression algorithms such as Huffman coding, Run-Length Encoding (RLE), and delta encoding. Moreover, the state compression using Merkle trees and redundancy reduction using erasure coding can significantly reduce the on-chain data while ensuring data availability and integrity. Posting only the state difference instead of complete transaction data helps to achieve scalability without overburdening the underlying L1. For instance, ZKSync [19] compresses the transaction data and computes the state differences along with zk-proof to post on Ethereum L1. StarkEx [35], which employs the zk-STARK for the zk-rollup solution, is another prominent example of posting only state difference on-chain for data availability. Calldata and blob storage are the two main techniques to ensure on-chain data availability in rollups. Both solutions have their benefits and limits, as discussed below.
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 3 Results
### Selection of the Number of Incremental Prediction Samples  
To validate the effectiveness and rationality of the proposed dynamic incremental prediction model, the following comparative experiments were designed, with a focus on investigating the impact of the number of incremental prediction samples on model performance. In the experiments, the number of incremental prediction samples was set to 15, 20, 25, and 30, respectively, while R2 (coefficient of determination) and MAE (Mean Absolute Error) were retained as evaluation metrics to comprehensively assess the model's prediction accuracy and stability. The results are illustrated in Figures 10-13 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Feasible Value** & **Optimal Value** \\ \hline Convolution kernel size & (2,3,4) & 2 \\ Number of convolutional kernels & (64,128,256) & 64 \\ Pooling layer size & (2,3,4) & 2 \\ LSTM Neural Network Unit & (8,10,12,15,20,25) & 20 \\ Batch size & (5,10,15,20,25,30) & 25 \\ Number of iterations & (5,10,15,20,25,30) & 25 \\ Anti-overfitting strategy & (0.1,0.15,0.2) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameter selection performance chart.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LSTM** & **Batch Size** & **Epochs** & \multicolumn{3}{c}{**R2 (Dropout)**} \\
**Units** & **0.1** & **0.15** & **0.2** \\ \hline
8 & 5 & 5 & 0.62 & 0.60 & 0.63 \\
10 & 10 & 10 & 0.65 & 0.64 & 0.65 \\
12 & 15 & 15 & 0.65 & 0.68 & 0.69 \\
15 & 20 & 20 & 0.68 & 0.67 & 0.69 \\
20 & 25 & 25 & 0.69 & 0.65 & 0.72 \\
25 & 3 Figure 11: Results of prediction performance for 20 incremental samples.  
Figure 12: Results of prediction performance for 25 incremental samples.  
Figure 10: Results of prediction performance for 15 incremental samples.  
Based on a comprehensive evaluation using the R\({}^{2}\) and MAE metrics, the model achieves optimal performance when the number of incremental prediction samples is set to 25 (R\({}^{2}\) = 0.66, MAE = 0.0092), striking an effective balance between data fitting and prediction accuracy. Although the MAE is slightly higher when the number of incremental samples is 15, its R\({}^{2}\) is lower, indicating insufficient fitting capability. Conversely, when the number of incremental samples reaches 30, both R\({}^{2}\) and MAE fail to surpass the performance observed at 25, possibly due to an excessive sample size that prevents the model from promptly adapting to short-term data variations. Therefore, for dynamic incremental prediction, setting the number of samples to 25 is determined to be the optimal choice.
## 5 Kubernetes Capabilities for Industrial Edge Cloud
### Kubernetes Networking Aspects  
Networking requirements are about facilitating connectivity between the industrial network domain and the applications deployed in the edge domain. The requirements on connectivity typically vary between different types of edge applications. Kubernetes natively provides Layer 3 IP traffic handling within the cluster and from external hosts to services. The Kubernetes network model assigns IP addresses to pods and services. By default, a Kubernetes pod has only one network interface, and all of the traffic goes through this interface, such as communication between the Kubernetes API and the pod, the Kubernetes probes for liveness and readiness, and the user traffic. However, this default single pod network interface interconnected with the Kubernetes cluster networking is not appropriate for directly connecting to an external TSN network segment to forward the Layer 2 TSN traffic directly to a pod. Kubernetes has the option to attach multiple network interfaces to pods that can be attached to a different network. This feature is provided by the Multus container networking interface (CNI) plugin [33] Multus is a meta-plugin in the sense that it can call multiple other CNI plugins for the different interfaces. With Multus, a secondary network interface for the TSN traffic can be defined for a pod in the pod specification. For this secondary interface, another CNI plugin, the Macvlan plugin can be used. The Macvlan plugin functions like a switch already connected to the host interface of the node the pod is running on. These virtual interfaces share the physical network device of the host but have distinct MAC addresses. The nodes in the Kubernetes cluster designated for receiving TSN traffic are configured to have a secondary physical NIC for this purpose, to be used by the secondary interfaces of the pods and connect to the TSN network.  
Network service mesh [34] is another, more abstract-level initiative to extend the networking capabilities of Kubernetes. It allows for heterogeneous network configurations and on-demand, dynamic, negotiated connections with a minimal need for changes to Kubernetes. It extends the Kubernetes API with functions to facilitate connectivity between containers running services or with external endpoints, and the payload type can be Ethernet or Multiprotocol Label Switching (MPLS) in addition to IP. It also providesresiliency as it can auto-heal connections between pods and network services if various system elements restart or if the network service fails without disturbing the client pod.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Quantitative Evaluation
#### 4.3.2 Evaluation on TColor-128  
TColor-128 [49] contains a large set of 129 color sequences with ground truth and challenge factor annotations. Compared with OTB-100, TColor-128 contains more challenging sequences and is more difficult to track. We evaluated our tracker on the TColor-128 dataset and show the evaluation results between our tracker and recent trackers including Staple [54], DGT (Dynamic Graph for Visual Tracking) [9], MEEM [19], Struck [4], KCF [5], ALSA ( Adaptive Structural Local Sparse Appearance Model) [55] and LCT [50] in Figure 7.
## 4 DT Use Cases and Application in Healthcare
### Drug and Vaccine Development  
The application of technology in drug development, particularly in testing new drugs, offers significant potential in ensuring drug safety and effectiveness. Each stage of this process generates a substantial volume of data, which presents a considerable challenge in terms of data management. Digital twins (DTs) adeptly utilize this data to construct comprehensive models of the drug development process [54]. As a result, DT can notably expedite the clinical trial phase in pharmaceutical research. This acceleration is achieved by enabling trials to proceed more rapidly and efficiently, reducing the number of patients required to undergo drug testing [55].  
Expanding on this, DT's role in the pharmaceutical industry is not limited to just enhancing clinical trials. It also extends to the pivotal task of developing and producing new vaccines. By leveraging DT, scientists can more effectively select the most promising antigens for vaccine development. This aspect of DT allows for a significant portion of the development process to be conducted virtually, streamlining the research and reducing the time and resources traditionally needed for vaccine development. Such advancements in DT technology not only contribute to faster and more efficient drug and vaccine development but also promise to revolutionize the way we approach medical research and pharmaceutical innovation, leading to safer and more effective treatments being brought to market more swiftly.
# Upper Second Molar Distalization with Clear Aligners: A Finite Element Study  
[PERSON]  
1 Department of Orthodontics, University of Turin--Dental School, 10126 Turin, Italy; [PERSON]M.S.); [PERSON]SS] (S.P.); [EMAIL_ADDRESS] (A.S.)  
[PERSON]  
1 Department of Orthodontics, University of Turin--Dental School, 10126 Turin, Italy; [PERSON]M.S.); [PERSON]SS] (S.P.); [EMAIL_ADDRESS] (A.S.)  
2  
[PERSON]  
1 Department of Orthodontics, University of Turin--Dental School, 10126 Turin, Italy; [PERSON].  
the use of attachments, but just a few studies have been developed to evaluate the biomechanical performance of the aligners and their accessories [8]. The application of engineering knowledge in dentistry with the use of computational techniques has helped to understand oral biomechanics aspects. The finite element method (FEM) is a numerical technique used to perform finite element analysis (FEA) of any given physical phenomenon and is widely accepted for medical purposes. FEM can simplify the physiologic responses of the dento-alveolar complex to orthodontic forces by exhibiting quantitative data and is recently preferred by the researchers of the field [11]. The main advantage of using FEM is that many alternative designs can be tried out for their validity, safety and integrity using the computer in an in silico environment, even before the first prototype is built. Experimental validation studies of FE analyses are encouraged whenever possible [12]. Regarding aligner orthodontics, [PERSON] et al. [8] have introduced a computational design and engineering structure, which allows patient-specific simulations of the mechanical interactions between dental tissues and polymer aligners. This computational approach allows for design based on knowledge of the most suitable aligner characteristics taking into account individual needs.  
The aim of this in silico study was to evaluate, through FEM, the orthodontic distalization movement of a second maxillary molar with clear aligners, analyzing different attachment configurations.
## 5 Conclusions  
To improve dam monitoring, an automated monitoring framework is proposed in this paper. The major contributions of this study are as follows:  
Figure 11: Visualization and warning of outliers.  
Figure 10: Fem predicted and LSTM predicted data of EX2-6.  
A hybrid model integrating the LSTM and FE simulation is proposed with the variation rate of water level as the indicator. The hybrid model had a \(MAXE\) of 0.35 mm between the predicted and monitored values, which was 75% lower than the \(MAXE\) of the LSTM.  
An automated DSM platform is proposed and implemented by properly integrating web, microservices, databases, FE simulations, and deep learning. The proposed system can automatically monitor and warn dams in a real time and continuous manner, which reduces operation costs.  
Although the back analysis is optimized, the inevitable need to call GeHoMadrid several times during the inversion process to perform the simulation reduces the operational efficiency of the system. Thus, a parallel algorithm needs to be further investigated for optimization. In addition, the threshold of the rate of change in environmental variables needs further research to clarify the balance between the predicted values and the LSTM predictions.  
Conceptualization, Y.C., C.L. and T.L.; methodology, Y.C.; software, Y.C.; validation, Y.C., C.L. and H.Q.; formal analysis, Y.C.; investigation, Y.C., C.L., H.Q., D.L. and S.C.; resources, T.L.; data curation, C.L. and H.Q.; writing--original draft preparation, Y.C.; writing--review and editing, C.L.; visualization, Y.C.; supervision, T.L.; project administration, T.L.; and funding acquisition, T.L., C.L. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Key Research and Development Plan of China (No. 2022 YFC3005403), the National Nature Science Foundation of China (No. 52309151, No. 52309157), the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources (No. YK323007), the Science and Technology Project of Power Construction Corporation of China (No. DJ-ZDXM-2021-10), and the Excellent Postdoctoral Program of Jiangsu Province (2022 ZB191).  
Not applicable.  
The data are available upon request from the corresponding author. The data are not publicly available due to trade secrets.  
The authors declare no conflict of interest.
The above problems are because the conventional methods divide the original descriptors based on the geographical location. There are bound to be some limitations and irrationality for these 2D descriptors compared with the 3D Lidar [15; 16]. Although the scan context descriptor extracts and retains important features of the 3D point cloud, it is also influenced by non-important objects and the distance between the vehicles at two sampling moments, and so causes some perceptual confusion. Therefore, it is difficult to predict the set to which the data collected at the boundaries of each region belongs. In addition, some very similar descriptors appear in non-adjacent areas which can cause perceptual aliasing and misrecognition.  
To solve this problem, in this study, we proposed a Lidar place recognition system, called the Based on Global features-system (BGF-system), which is shown in Figure 1. We continue to use the improved version of the scan context and leverage it into our system. Our idea is to introduce a reclassification system based on global features. This method eliminates the influence of similar descriptors in different regions. After the global feature is extracted, it reclassifies the descriptor. Furthermore, for practical applications, we encode the region ID under the same feature set through dictionary-based coding [17; 18]. The result is a single compact region ID word, in contrast to the multi-word representation of the method named BoW [19; 20]. Thus, the performance improves compared to the improved scan context method. As the retrieval dataset has been transformed into text information, our retrieval system is lightweight and its speed is greatly enhanced under similar conditions. The contribution of our study is as follows:  
* The accuracy of Lidar place recognition has been improved according to the global features of the descriptor. This can weaken the influence of different regions but similar descriptors.
* The retrieval dataset has been transformed into text information by dictionary-based coding. We simplify the final retrieval dataset, which can significantly improve the retrieval speed.
* Four-seasons datasets (North Campus Long-Term (NCLT) datasets [21; 22; 23]) were evaluated. The proposed method was successful for place recognition using the four-seasons dataset.
## 2 Methodology  
The operating procedure of the agent-based fuzzy collaborative intelligence approach consists of several steps that will be described in the following sections:  
1. The agent-based fuzzy collaborative intelligence approach starts from the formation of the agent group. 2. Each agent automatically determines the setting of the fuzzy back propagation network (FBPN) reasoning module associated with the agent. 3. Each agent predicts the DRAM price based on the agent's view. 4. Each agent communicates its view and forecasting results to other agents with the aid of the automatic collaboration mechanism. Upon receipt, the agent adjusts its setting, so as to improve the overall performance. 5. To aggregate the forecasting results, a radial basis function (RBF) network is employed by the automatic collaboration mechanism. 6. The collaboration process is terminated if the improvement in the forecasting performance becomes negligible. Otherwise, return to step (4). In the agent-based fuzzy collaborative intelligence approach, each agent uses a FBPN to predict the DRAM price. Although there have been some more advanced artificial neural networks, such as the compositional pattern-producing network, cascading neural network, dynamic neural network, and others, a well-trained FBPN with an optimized structure can still produce very good results, which is why it is selected for this study:  
1. Inputs: \(K\) inputs, corresponding to the prices \(K\) months ago. To facilitate the search for solutions, it is strongly recommended to normalize the inputs to a range narrower than [0 1]: \[N(x)=N_{{}_{L}}+\frac{x-x_{\min}}{x_{\max}-x_{\min}}\cdot(N_{{}_{U}}-N_{{}_{L}})\] (1) where \(N(x)\) is the normalized value of \(x\); \(N_{L}\) and \(N_{U}\) indicate the lower and upper bounds of the range of the normalized value, respectively. \(x_{\min}\) and \(x_{\max}\) are the minimum and maximum of \(x\), respectively. The formula can be written as \[x=\frac{N(x)-N_{{}_{L}}}{N_{{}_{U}}-N_{{}_{L}}}\cdot(x_{\max}-x_{\min})+x_{ \min}\] (2) if the un-normalized value is to be obtained instead. 2. The FBPN has only one hidden layer, which can approximate arbitrarily any function that contains a continuous mapping from one finite space to another. The number of nodes in the hidden layer is chosen from 1 to \(2K\). 3. The output from the FBPN is the normalized price forecast. 4. The activation function used for the hidden layer is the log sigmoid function. (5) A total of 10000 epochs will be run each time. The start conditions are randomized to reduce the possibility of being stuck on local optima. The procedure for determining the parameter values is now described. After pre-classification, a portion of the adopted examples is fed as \"training examples\" into the FBPN to determine the parameter values. Two phases are involved at the training stage. At first, in the forward phase, inputs are multiplied with weights, summated, and transferred to the hidden layer.
## 4 Simulation Results and Discussions
### Randomness  
Randomness refers to the ability of a PUF to generate response bits with an equal probability of being \"1\" and \"0\" and every bit is uncorrelated with any other bits. Ideally, the probability to obtain a \"1\" or a \"0\" bit is 50%. To measure the randomness of the augmented SPUF design, one million response bits were generated and split into 100 blocks with 10,000 bits each. The National Institute of Standards and Technology (NIST) randomness test results is shown in Table 3, which shows good randomness of the PUF responses.