esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 3 Satellite IoT Edge Intelligent Computing Architecture  
The edge intelligent computing of the satellite IoT is mainly to apply edge computing and deep learning technology to the satellite IoT. Next, we will introduce the principles of the satellite IoT edge intelligent computing architecture. This chapter includes satellite IoT edge computing, distributed intelligent computing architecture in satellite IoT edge computing.
## 4 Implementation and Benchmarking of L4S in FreeBSD
### Step 3: Coupling the Probabilities of the L4S and Classic Queues  
In this section, we describe the process of coupling the drop probabilities for the low latency, low loss, and scalable throughput (L4S) and classic queues. The following code snippet initializes variables to store the base drop probabilities of each flow:  
Algorithm 6 initialized the variables to store the drop probabilities--(P\({}^{\prime}\)) and \((P_{l})\) represent the base drop probabilities from the individual queues. These variables are used to calculate the coupled drop probabilities, as shown in the following algorithm.  
```
uint32_t drop_prob_Pdash_flow_0; uint32_t drop_prob_Pdash_flow_1; uint32_t drop_prob_Pdash_flow_2; uint32_t drop_prob_Pl_flow_3; uint32_t drop_prob_Pl_flow_4; uint32_t drop_prob_Pl_flow_5; uint32_t P_Cmax;
```  
**Algorithm 6**Initializing variables for drop probabilities  
At the start of the code implementation in Algorithm 7, we compute \(P_{C}\) by utilizing Formula (2):  
\[P_{C}=(P^{\prime})^{2} \tag{16}\]  
Afterwards, we determine \(P_{CL}\) utilizing previously defined Formula (3):  
\[P_{CL}=k\cdot P^{\prime} \tag{17}\]  
We then apply the max function on \(P_{CL}\) and \(P^{\prime}_{L}\) to compute \(P_{L}\) as defined before in Equation (5):  
\[P_{L}=\max(P^{\prime}_{L},P_{CL}) \tag{18}\]  
We have a fixed 'coupling factor' that is pre-established. To determine a better 'k', it is better to understand the scenario where the algorithms are being used and either use a gradient optimization or manually test it with varying values of the 'coupling factor'. However, it is best to determine dynamically.  
```
1/*
2* Enqueue a packet in queue q, subject to space and L4S queue management policy
3* We will calculate its drop probability depending on its queue or flow index
4* Update stats for the queue and the scheduler.
5* Return 0 on success, 1 on drop.
6*/
7 static int
8 pi_enqueue(struct l4s_flow *q, struct mbuf* m, struct l4s_si *si)
9{
10....
11 int coupling_factor=2;
12....
13 int64_t prob;
14 uint32_t drop_prob_PC1_flow_3;
15 uint32_t drop_prob_PC1_flow_4;
16 uint32_t drop_prob_PC1_flow_5;
17
18 if(q->flow_index==0 || q->flow_index==1 || q->flow_index==2 )
19 prob=(pst->drop_prob*pst->drop_prob)/PIE_MAX_PROB;
20
21 if(q->flow_index==3)
22{
23 drop_prob_PC1_flow_3=drop_prob_Pdash_flow_0*coupling_factor;
24 if(drop_prob_PI_flow_3<drop_prob_PC1_flow_3)
25 prob=drop_prob_PC1_flow_3;
26 else
27 prob=drop_prob_Pl_flow_3;
28}
29
30
31
32 if(q->flow_index==4)
33{
34 drop_prob_PC1_flow_4=drop_prob_Pdash_flow_1*coupling_factor;
35 if(drop_prob_PI_flow_4<drop_prob_PC1_flow_4)
36 prob=drop_prob_PC1_flow_4;
37 else
38 prob=drop_prob_Pl_flow_4;
39}
40 if(q->flow_index==5)
41{
42 drop_prob_PC1_flow_5=drop_prob_Pdash_flow_2*coupling_factor;
43 if(drop_prob_Pl_flow_5<drop_prob_PC1_flow_5)
44 prob=drop_prob_PC1_flow_5;
45 else
46 prob=drop_prob_Pl_flow_5;
47}
48
49 if(prob < 0)
50 prob = 0;
51 elseif(prob > PIE_MAX_PROB)
52 prob = PIE_MAX_PROB;
53
54......
55}
```  
**Algorithm 7**Calculate the drop probabilities based on their queue type
## 6 Experiments
### State-of-the-Art Performance Comparison (RQ1)  
In this section, we compare our proposed method with other proposed methods in the literature for Arabic emotion recognition using benchmark datasets, namely the AETD and ArPanEmo datasets. Table 6 shows the comparison between previous studies on Arabic emotion recognition and our proposed approach on the AETD dataset.  
As seen in the results presented in Table 6, the study by [47], which proposed a hybrid model for Arabic emotion recognition, achieved a weighted average F1-score of 71.8%. Their method combines both human-engineered feature-based and deep feature-based models, as detailed in Section 2. A recent study conducted by [57] showed that employing transfer learning to simply fine-tune a BERT-based model for emotion recognition using the AETD dataset resulted in a weighted average F1-score of 78.93%. Moreover, incorporating semantic information, such as Named Entity Recognition and sentiment analysis, into the BERT-based model during the fine-tuning process yielded a higher weighted average F1-score of 81.60%. Our proposed method outperformed these aforementioned methods when tested on the AETD dataset, achieving a weighted average F1-score equal to 83.19%.  
For the ArPanEmo dataset, Table 7 shows the comparisons between our proposed method and other previous studies when tested on the ArPanEmo dataset.  
The ArPanEmo dataset was examined in the literature [62] for Arabic emotion recognition using SVM, a deep learning method (BiGRU), and transfer learning through fine-tuning a pretrained model (BERT), achieving weighted average F1-scores of 58%, 40%, and 67%, respectively. Our proposed method outperformed the previously mentioned methods, achieving the highest weighted average F1-score of 72.12%, despite starting the training process with only 10 examples per emotion class in addition to automatically annotated examples from unlabeled data during training iterations.
## 4 Experimental Results
### Human Ear Datasets
#### 4.1.2 Ustb  
USTB [37] is a human ear dataset established by the Human Ear Laboratory of the University of Science and Technology, Beijing. There are three databases in USTB, namely, database1, database2, and database3. Because database3 has more variety of categories and postures than database1 and database2, it is selected to verify the ear recognition algorithm in this paper. There are 79 categories in database3, and 70 categories are selected in this paper, namely, No. 1 to No. 70. In order to make the human ear data better simulate the real situation, the data of the human ear pictures in the database was enhanced, including the changes of angle, saturation, color, and brightness. The sample dataset is shown in Table 7. There are 7700 pictures. According to the ratio of training set: test set: verification set = 3:1:1, the training set has 4620 pictures, and the test set and verification set each have 1540 pictures, respectively.
# Task-Driven Real-World Super-Resolution of Document Scans
## 4 Experimental Validation
### Experimental Results
#### 4.3.3 Limitations  
Even though we have achieved promising results in improving OCR-related performance, the proposed task-driven SR approach has certain limitations. First, the models trained in a task-oriented way may introduce artifacts or distortions that, while beneficial for OCR, reduce the overall visual realism or general usability of the super-resolved images. This task-specific optimization limits their applicability in scenarios where perceptual quality and fidelity are crucial alongside OCR-related aspects. Furthermore, the elaborated framework has been validated in the context of SISR, which restricts the quality of the reconstruction, as genuine HR details cannot be fully recovered without additional observations, inherent to multi-image SR. Although in our experimental validation we have extensively studied the behavior of SRResNet, the proposed approach has not been evaluated considering a larger model like EDSR [20] or SwinIR [23]. However, we have managed to observe that introducing the task-oriented loss function components enhances the performance in a statistically significant way--this establishes the methodology, which may help in improving the task-oriented performance for multi-image SR. Finally, the current loss design is focused on text detection, and while recognition-related components have been included, their contribution has not yet been validated in the context of an end-to-end OCR process. This draws an interesting direction for future research, where the SR training procedure could be more tightly integrated with the full character recognition process. Furthermore, extending the task-driven framework to multi-image SR, which inherently provides richer information content, may allow for achieving improvements in both perceptual quality and OCR performance.
## 5 Experimental Setup
### Hyperparameter Setup for DRL Training  
The critical hyperparameter configurations for the DDPG, SAC, TD3, PPO, TQC, and CrossQ algorithms are outlined below. For all algorithms, an exponential decay learning rate schedule was adopted, whereby the initial learning rate was set to \(5\times 10^{-4}\)and gradually decreased throughout training to \(1\times 10^{-6}\). This approach enables rapid exploration during the early training phase while promoting stability and convergence in the later training stages.  
For the on-policy algorithm PPO, which does not utilize a replay buffer, training was conducted using newly sampled data at every iteration. Accordingly, the number of steps collected from the environment per training iteration was set to 1024, and the number of epochs, i.e., how many times the collected data is reused for policy updates, was set to 10.  
In contrast, all other algorithms (DDPG, SAC, TD3, TQC, CrossQ) are off-policy methods. These models train by sampling transitions from a replay buffer of size 300 K, which enables improved sample efficiency and policy refinement over extended training horizons.  
The model complexity and inference latency of each trained DRL algorithm are summarized in Table 7. The model size (in MB) is reported separately for the actor (policy), critic, and target networks (target policy and target critic). All algorithms employ a uniform neural network architecture denoted as [400, 300], which refers to a fully connected network with two hidden layers consisting of 400 and 300 neurons, respectively, followed by an output layer. In this configuration, each individual network with the [400, 300] architecture contains approximately 0.62 MB of parameters.  
Inference latency was measured in the Town02 scenario. For all algorithms, the average step-wise latency was computed over 4.5 k steps.  
As shown in Table 7, despite TD3 having the largest total model size, it demonstrates relatively low inference latency. In contrast, TQC has both a moderately large model size and relatively high latency. Meanwhile, DDPG and PPO incur the lowest latency among the evaluated algorithms.  
Figure 6: Visual overview of the observation pipeline. (**a**) Raw front-view RGB image perceived by the vehicle. (**b**) VAE decoder output, with segmentation-style reconstruction. (**c**) Waypoint-guided RGB image captured by a front observer camera, which is not part of the agent’s input but serves as a reference.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
###### Abstract  
Over recent years, computer-aided design (CAD) has become widely used in the dental industry. In dental CAD applications using both volumetric computed tomography (CT) images and 3D optical scanned surface data, the two data sets need to be registered. Previous works have registered volume data and surface data by segmentation. Volume data can be converted to surface data by segmentation and the registration is achieved by the iterative closest point (ICP) method. However, the segmentation needs human input and the results of registration can be poor depending on the segmented surface. Moreover, if the volume data contains metal artifacts, the segmentation process becomes more complex since post-processing is required to remove the metal artifacts, and initially positioning the registration becomes more challenging. To overcome these limitations, we propose a modified iterative closest point (MICP) process, an automatic segmentation method for volume data and surface data. The proposed method uses a bundle of edge points detected along an intensity profile defined by points and normal of surface data. Using this dynamic segmentation, volume data becomes surface data which can be applied to the ICP method. Experimentally, MICP demonstrates fine results compared to the conventional registration method. In addition, the registration can be completed within 10 s if down sampling is applied.  
Keywords:local registration; iterative closest points; multimodal medical image registration +
Footnote †: journal: Applied Sciences
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 3 System Model and Assumptions  
### Setting  
Our network topology is shown in Figure 1. We consider a WSN with nodes \(N_{i}\) for \((i=1,\ldots,m)\) and a border router (BR), which connects the WSN to the rest of the network. An NMM, as proposed in [6], is responsible for the management of the multicast groups. This NMM is considered to be more powerful and takes the role of a network gateway, often positioned in the cloud. Nodes contact the NMM and share their capabilities and interests. Based on the information given by the nodes, the NMM generates a multicast address for each match and shares the required information with the publisher, one of the members of the group, chosen by the NMM. The publisher will then be able to construct a group key with the other subscribers \(S_{s}\) for \((s=1,\ldots,n)\) of the multicast group. The NMM will not be a member of the group, but is only responsible for the initialization and verification of the authentication. There can be any number of intermediate modes (I) between the different subscribers of the same multicast group. These intermediate modes are not participating in the security protocol, resulting in a protocol that is independent of the underlying network structure.  
To guarantee that the NMM is not able to understand the multicast communication, an external trusted third party (TTP) will be responsible for the generation and distribution of key material among the nodes and the NMM. Please note that we consider an honest but curious NMM: the NMM will perform all the required steps, but might be interested in following the communication for its own purposes (e.g., selling of data to other parties) and thus should not be able to derive the group key, established by the publisher of the multicast group.
###### Abstract  
Predicting stock prices on stock markets is challenging due to the nonlinear and nonstationary nature of financial markets. This study presents a hybrid model based on integrated machine learning (ML) techniques--neural networks, support vector regression (SVR), and decision trees--that uses the stacking method to estimate the next day's maximum and minimum stock prices. The model's performance was evaluated using three data sets: Brazil's Sao Paulo Stock Exchange (iBovespa)--Companhia Energetica do Rio Grande do Norte (CSRN) and CPFL Energia (CPFE)--and one from the New York Stock Exchange (NYSE), the Dow Jones Industrial Average (DJI). The datasets covered the following time periods: CSRN and CPFE from 1 January 2008 to 30 September 2013, and DJI from 3 December 2018 to 31 August 2024. For the CSRN ensemble, the hybrid model achieved a mean absolute percentage error (MAPE) of 0.197% for maximum price and 0.224% for minimum price, outperforming results from the literature. For the CPFE set, the model showed a MAPE of 0.834% for the maximum price and 0.937% for the minimum price, demonstrating comparable accuracy. The model obtained a MAPE of 0.439% for the DJI set for maximum price and 0.474% for minimum price, evidencing its applicability across different market contexts. These results suggest that the proposed hybrid approach offers a robust alternative for stock price prediction by overcoming the limitations of using a single ML technique.  
artificial intelligence; deep learning; hybrid model; stacking; stock forecasting 202518471
## 2 Methodology
### Material Selection and Comparison  
The selection of materials for orthopedic implants requires the careful consideration of multiple factors, including biocompatibility, corrosion resistance, mechanical properties, and clinical performance. These criteria ensure that the materials not only meet the functional requirements but also exhibit long-term stability and safety within the human body. Extensive research has been conducted on the materials used for orthopedic implants. Stainless steels, cobalt-chromium alloys, and titanium alloys are among the most used due to their favorable properties.  
Figure 3: Improvise rig for simulation.  
* Stainless Steel AISI 316L (Annealed): Known for its exceptional corrosion resistance and biocompatibility, Stainless Steel AISI 316L is widely used in medical instruments and implants. Studies have shown its effectiveness and durability in various biomedical applications [9].
* Titanium Grade 4 (Annealed): This material offers a good balance of strength and corrosion resistance, making it a popular choice for implants. Its biocompatibility is superior to that of many other metals, which has been demonstrated in numerous clinical studies [10].
* Titanium Ti-6 Al-4V (Annealed): Renowned for its excellent strength-to-weight ratio, Ti-6 Al-4V is extensively used in high-stress applications, including orthopedic devices. Literature reports its successful use in implants where mechanical performance is critical [11].  
The materials chosen for this study were selected based on a thorough review of their properties and performance in biomedical applications.  
* Stainless Steel AISI 316L (annealed): Its well-documented corrosion resistance and biocompatibility make it a common choice for medical devices. The material's mechanical properties also contribute to its widespread use in implants.
* Titanium Grade 4 (Annealed): Selected for its enhanced strength and excellent corrosion resistance, Titanium Grade 4 is a favorable option for implants requiring both durability and biocompatibility.
* Titanium Ti-6 Al-4V (annealed): This alloy's superior strength-to-weight ratio and proven track record in orthopedic applications justify its inclusion in this study. Its ability to withstand high stress and maintain biocompatibility makes it ideal for use in the motorized leg-lengthening device.  
While cobalt-chromium alloys and other titanium alloys were considered, they were not selected due to specific drawbacks such as higher cost, lower biocompatibility, or their less favorable mechanical properties compared to the chosen materials. The selection of Stainless Steel AISI 316L, Titanium Grade 4, and Titanium Ti-6 Al-4V is based on their balanced properties, making them suitable for the intended application. This careful selection process is crucial for ensuring the safety, reliability, and effectiveness of the motorized leg-lengthening device.
# A Time-Series Model for Varying Worker Ability in Heterogeneous Distributed Computing Systems
## 2 Preliminaries
### System Model
#### 2.1.2 Coded Computation  
We apply an \((n,k)\) MDS code to the rows of \(\mathbf{A}_{t}\) to obtain the coded matrix \(\tilde{\mathbf{A}}_{t}\in\mathbb{R}^{n\times d}\). Afterward, the rows of \(\tilde{\mathbf{A}}_{t}\) are grouped into \(N\) submatrices as  
\[\tilde{\mathbf{A}}_{t}=[\tilde{\mathbf{A}}_{1,t};\tilde{\mathbf{A}}_{2,t};\ldots;\tilde{ \mathbf{A}}_{G,t}],\]  
where  
\[\tilde{\mathbf{A}}_{i,t}=[\tilde{\mathbf{A}}_{i,1,t},\tilde{\mathbf{A}}_{i,2,t},\ldots, \tilde{\mathbf{A}}_{i,N_{i},t}]\in\mathbb{R}^{N_{i}l_{t}\times d}\]  
Figure 1: Illustration of the time-dependent heterogeneous cluster (task assigned at time \(t\)). The master sends the input vector \(\mathbf{b}_{t}\) to the \(N\) workers, each of which stores \(\mathbf{A}_{i,j,t}\) and \(\tilde{\mathbf{A}}_{i,j,t}\) in uncoded and coded computations, respectively, for \(i\in[G]\) and \(j\in[N_{i}]\); (a) worker \(j\) in group \(i\) computes the multiplication of \(\mathbf{A}_{i,j,t}\) and \(\mathbf{b}_{t}\) with the working rate \(\mu_{i,t}\), and sends back the computation results to the master; (b) worker \(j\) in group \(i\) computes the multiplication of \(\tilde{\mathbf{A}}_{i,j,t}\) and \(\mathbf{b}_{t}\) with the working rate \(\mu_{i,t}\), and sends back the results to the master.  
is the coded data matrix allocated to the workers in group \(i\) and  
\[\sum_{i\in[G]}N_{i}l_{i,t}=n.\]  
Here, worker \(j\) in group \(i\) is assigned a subtask to compute the multiplication of \(\bar{\mathbf{A}}_{i,j,t}\in\mathbb{R}^{l_{i,t}\times d}\) and \(\mathbf{b}_{t}\) for \(i\in[G]\) and \(j\in[N_{i}]\). Worker \(j\) in group \(i\) sends the computation result \(\bar{\mathbf{A}}_{i,j,t}\mathbf{b}_{t}\) to the master after finishing the subtask multiplying \(\bar{\mathbf{A}}_{i,j,t}\) and \(\mathbf{b}_{t}\). The master can retrieve the desired computation result \(\mathbf{A}_{i}\mathbf{b}_{t}\) by combining the inner products of any \(k\)\((<n)\) coded rows with \(\mathbf{b}_{t}\) from the MDS property.  
For efficient load allocation to the distributed workers, we need to estimate the workers' ability, which is assumed to change over time. To do so, for the first \(T\) time points \(t=t_{0},t_{1},\ldots,t_{T}\), we assign the matrix-vector multiplication tasks using the uncoded computation framework to measure the runtime of all the workers. Here, every worker is assigned the same amount of the subtask, i.e., we set \(l_{i,t}=k/N\) for \(i\in[G]\), \(j\in[N_{i}]\), and \(t=t_{0},t_{1},\ldots,t_{T}\). Based on the observations on the runtime, we estimate the parameters and latent variables of the working rates. Afterward \((t>t_{T})\), the coded computation is applied using the load allocation \(l_{i,t}\) based on the estimated working rates. The latent variable is obtained continuously for each time step to properly allocate \(l_{i,t}\) to the workers. The detailed process of estimating the working rates will be shown in Section 3.