esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Urbanization and Sunlight Exposure on Cataract Incidence
### Sunlight Exposure  
Most of the previous studies have demonstrated that sunlight exposure is a significant risk factor for cataracts. For examples, a population-based cross-sectional study on the prevalence of cataract and its association with sun exposure in three different geographically diverse populations of India during 2010-2016 has reported that cataract is associated with an increased sun exposure level [13].  
Another case-control study in Australia between 1992 and 1996 found a strong positive association of occupational sun exposure with nuclear cataracts (odds ratio = 5.9; 95% confidence interval = 2.1-17.1) [14].  
However, results indicated no association between years of outdoor exposure and the risk of cataracts in a frequency-matched case-control study of 343 cases and 334 controls in Spain from 1994 to 1995 [15].
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Proactive Strategy
#### 4.2.1 Ad-hoc on-Demand Distance Vector (AODV) Routing Protocol  
When a node has data to send to the sink and its neighborhood table entry is either outdated or has no source to the sink, it generates and broadcasts a route request (RREQ) packet, which is forwarded using various metrics (according to the modified method employed) to the sink. At each hop, every node searches its neighborhood table for a valid route to the sink and will only rebroadcast the RREQ when none is found. The node then sets a backward pointer to the neighbor from which it received the RREQ packet. When the RREQ packet reaches the sink, the sink will generate a route reply (RREP) packet and will unicast it along the found path to the source; then, the data transfer will begin. If the source receives more than one RREP packet, it selects the final route based on the minimal hop count or the best link quality. In the advent of a route failure during data transfer, a route error (RERR) packet is generated and is unicast back to the node from which the message initiated. This packet flushes the corresponding routing table entries in the intermediate nodes. The version implemented for this evaluation employs cross-layer techniques to avoid paths characterized by high packet losses.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 5 Conclusions  
Recently, FANETs have evolved into an emerging area of research. Such networks are characterized by high mobility, frequent topology changes, and 3D-spatial movement of the UAVs,  
Figure 6: Network load simulation results (**a**) 30 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**b**) 30 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**c**) 100 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**d**) 100 nodes with 25 m/s and 1 km \(\times\) 1 km speed and area, respectively; speed and area, respectively; (**e**) 100 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**f**) 100 nodes with 60 m/s and 1 km \(\times\) 1 km speed and area, respectively.  
which constitutes a challenge to the routing protocol. For this reason, choosing suitable and reliable routing protocols is essential to authenticate robust communication amongst the UAVs.  
In this paper, we investigated various topology-based routing protocols in the context of FANETs. We discussed them along with their method of working and limitations. We also provided a brief qualitative review of the aforementioned routing protocols on the basis of important parameters such as mobility, traffic density, routing overhead and the packet forwarding mechanism. All of these factors affect the performance of the flying ad-hoc networks. We also provided a simulation-based study of the various most-significant topology-based routing protocols in terms of throughput, end-to-end delay, and network load. We hope this study will help network engineers when choosing appropriate routing protocols in different kinds of scenarios for FANET deployment. We also believe that FANETs will be a ubiquitous technology in the future.  
Conceptualization, M.A.K.; Methodology and Implementation, M.A.K and A.S.; Simulation, I.U.K.; Validation, M.A.K.; Data Curation, I.M.K. and A.S.; Writing-Original Draft Preparation, I.M.Q and M.A.K.; Writing-Review & Editing, I.M.Q and M.A.K.; Supervision, I.M.Q.  
This research received no external funding.  
The basic idea of this article is based on the conference version named \"Flying Ad-Hoc Networks (FANETs): A Review of Communication architectures, and Routing protocols\". The paper was presented by the same authors in proceedings of the 2017 First International Conference on Latest Trends in Electrical Engineering and Computing Technologies (INTELLECT), Karachi, Pakistan, 15-16 November 2017.  
The authors declare no conflict of interest.
# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 4 Algorithm Design
### Model Training  
We use a simple, fully connected neural network as the brain. Algorithm 1 presents the decision-making process of a batch task. As mentioned above, the dimension of our network input vector is fixed, and the number of task-VM pairs in the environment is dynamic. Calculating the fitness scores of all of the task-VM pairs, the agent uses the value of \(\epsilon([0,1])\) as a probability to decide whether to randomly select an action for exploration or the task-VM with the highest adaptability as a scheduling decision. A number is randomly selected from a Poisson distribution in units of batches, tasks generated in each batch are denoted as \(task\_list\), and \(vms\_with(t)\) represents the set of VMs that can execute task \(t\) in the cluster. The relevant information of each decision is saved; \(r\) is calculated until all of the tasks of a batch are completed.  
```
0:\(task\_list\): list of tasks waiting to be scheduled; \(vm\_list\): list of VMs in cluster; \(memory\_list\): store decision-making information at each step
1:for each \(t\) in \(task\_list\)do
2:if\(random()<\epsilon\)then
3:\(a=randomInt(len(vmswith(t)))\)
4:else
5:\(S=\{S_{ik}|vm_{k}\in vms\_with(t)\}\)
6:\(a=argmaxQ(S)\)
7:endif
8: Schedule task \(t\) to VM with number \(a\)
9: Store transition \((S,a,r,S_{next},done)\) in \(memory\_list\)
10:end
11: After \(task\_list\) is executed, calculate reward \(r\) according to Equation (11)
12:return\(memory\_list\)
```  
**Algorithm 1** Agent Scheduling Process  
Algorithm 2 is the process of model training, in which we randomly sample the decision information record of \(memory\_list\) according to the size of \(mini\_batch\) to obtain training samples. We then use \(Q\) and \(TQ\) to calculate the expected reward with states \(S\) and \(S_{next}\). We use smooth_L1 as the loss function to calculate the TD-error for back gradient propagation, update the target Q network (TQ) after every C times of training, and perform partial updates at a rate of 20%.  
```
0:\(memory\_list\): Stored transition \((S,a,r,S_{next})\); \(minibatch\): number of samples used for training each time; \(Q\): Q value model; \(TQ\): Target Q value model;
1:\((S,A,R,S_{next})\): Random sampling \(mini-batch\) from \(memory\_list\)
2:\(q\_list=Q(S)\)
3:\(tq\_list=TQ(S_{next})\)
4: select \(q\) from \(q\_list\) with \(A\)
5: select \(tq\) from \(tq\_list\) with \(argmax(\)\(tq\_list)\)
6:\(y=r+\gamma*tq\)
7:\(L=smooth_{t}1_{1}loss(q,y)\)
8: Perform a gradient descent step on \(L\) with respect to the Q network parameters; calculate gradient by \(L\) and then update Q model
9: Every C steps reset \(TQ=0.8*TQ+0.2*Q\)
```  
**Algorithm 2** Training Algorithm
## 4 Classification of Experimental Results and Analysis
### The Magnitude Classification Model of Transfer Learning  
In this study, transfer learning was compared with different neural network models. This experiment transformed the magnitude prediction task into a classification problem. Figure 7 presents the evaluation metrics for transfer learning using different strategies. This study compared the training process curves of various models to verify the effectiveness of the proposed method.  
First, various neural network models were trained, and their classification performance was compared using evaluation metrics specific to classification tasks. In this study, comparisons were conducted with traditional magnitude classification models, CNN models, and the ResNet18 model proposed herein, with additional comparative training results  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Precision\%** & **Accuracy\%** & **Recall\%** \\ \hline TL-M1 & 84.5 & 82.9 & 81.1 \\ TL-M2 & 90.1 & 90.0 & 90.8 \\ TL-M3 & 92.7 & 91.2 & 90.8 \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation indicators of different strategy models for transfer learning.  
Figure 7: Evaluation index plots of different network classification models.  
from the AlexNet and VGG16 models included. The respective classification evaluation metrics on the test set for these five models are presented in Figure 7.  
As indicated in the figure, the magnitude prediction network utilizing residual networks for transfer learning reached a stable state more rapidly and achieved the highest accuracy. This demonstrates that under the configured settings, training with residual networks yields superior performance. Both the training set accuracy and test set accuracy improve as the number of training iterations increases, while the time required to converge to a high level is shorter. This addresses issues such as vanishing gradients and exploding gradients caused by network deepening. The residual mapping ensures that network performance does not degrade, enabling the model to retain shallow features while learning deep features.  
Notably, the mapping mechanism of ResNet18 does not introduce additional parameters or computational overhead but significantly enhances the effectiveness of network training. Consequently, ResNet18 was selected as the basic framework for network design in transfer learning, with the third fine-tuning strategy adopted. The detailed model results are presented in Table 4.
## 2 Literature Review
### Addressing Data Imbalance with SMOTE  
Imbalanced datasets, wherein one class significantly outweighs the others, pose a major challenge for intrusion detection systems, particularly in IoT scenarios. The prevalence of normal network traffic compared to actual attacks can lead to biased model performance. To overcome this limitation, [PERSON] et al. [PERSON] et al. (2018) proposed SMOTE, a synthetic data generation technique that rebalances class distribution by oversampling the minority class. SMOTE has gained widespread adoption in intrusion detection research, with notable studies by [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2019). showcasing its efficacy in enhancing the detection capabilities of IDS in IoT networks and mitigating the impact of class imbalance.  
DLNID is an end-to-end model, eliminating the need for manual feature extraction. Experimental results on the NSL-KDD public benchmark dataset demonstrate that DLNID outperforms other comparison methods in terms of accuracy and F1 score, achieving 90.73% and 89.65%, respectively [20].
## 2 Methodology
### Novel Constructive Solutions
The study was conducted for models with wall thickness \(g\in\{18,\,19,\,20,\,21,\,22\}\) mm and for equidistant ribs spaced at \(50,\,75\) or \(100\) mm, respectively, as well as those spaced unevenly. Only these combinations generated 40 constructive variants. Figure 6: 3D model of the PAI 25 press frame, with ribs right tilted at \(130^{\circ}\). Figure 7: 3D model of the PAI 25 press frame, with intersected ribs tilted by \(45^{\circ}\) and \(135^{\circ}\), respectively. The large number of solutions (and variants) required their identification by codes. This was achieved by an alpha-numerical system that would include the specific relevant aspects of the studied solutions, namely:  
- the type of press corresponding to the frame;  
- the lateral wall thickness;  
- the distance between the equidistant ribs;  
- the tilting angle of the identically oriented ribs in relation to the horizontal, measured clockwise. Table 1 features these identifiers. The frame identifier codes for the examples in the table above read as follows:  
1. B-PAI25_g20_e75_\(\alpha 10\)\(\Leftrightarrow\) \"PAI 25 press frame, with lateral walls of thickness \(g=20\) mm, with lateral walls ribbed at the interior, equidistant parallel ribs spaced at 75 mm, tilted at an angle \(\alpha=10^{\circ}\) measured clockwise in relation to the horizontal\";
2. B-PAI25_g19_e100 + e100_\(\alpha 10+\beta 100\)\(\Leftrightarrow\) \"PAI 25 press frame, with lateral walls of thickness \(g=19\) mm, with lateral walls ribbed at the interior, equidistant parallel ribs spaced at 100 mm, one set of ribs tilted at an angle \(\alpha=10^{\circ}\) and another set of ribs tilted at an angle \(\beta=100^{\circ}\) (perpendicular ribs), both measured clockwise in relation to the horizontal\";
3. B-PAI25_g21_var_curv \(\Leftrightarrow\) \"PAI 25 press frame, with lateral walls of thickness \(g=21\) mm, with lateral walls ribbed at the interior, non-equidistant ribs displayed by a curve\". Where applicable, one or more identifier code sequences can be left out. For example, identifier code B-PAI25_g20 corresponds to a \"PAI 25 press frame with lateral walls of thickness \(g=20\) mm, with unribbed lateral walls\", which is exactly the reference construction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Press Type** & \
\begin{tabular}{c} **Lateral Wall** \\ **Thickness** \\ \end{tabular} &
\begin{tabular}{c} **Spacing between the** \\ **Equidistant Ribs** \\ \end{tabular} & **Rib Tilting Angle** \\ \hline B-PAI25 & g20 & e75 & \(\alpha 10\) \\ B-PAI25 & g19 & e100 + e100 & \(\alpha 10+\beta 100\) \\ B-PAI25 & g21 & var & curvv \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Code identifier fields proposed for the new devised frames and examples. Figure 8: 3D model of the PAI 25 press frame, with curved ribs.
## 5 Evaluation Results
### Convergence Analysis  
The convergence analysis of the HG-multi-agent reinforcement learning method comprises two layers: the upper layer involves the game among the VPP aggregator, household connection, and EST, while the lower layer features the multi-agent Q-learning model represented by the household agents. This study selected HEMS training data from household user 1 for analysis. The overall convergence of this algorithm is illustrated in Figures 10 and 11.  
As shown in Figure 10, it is evident that the upper-layer multi-agent game model converges by the 44 th iteration, with computational errors for all agents remaining within  
Figure 11: Convergence curve of the multi-agent Q-learning model training for a single household user.  
Figure 10: Convergence curves for the HG-multi-agent reinforcement learning method.  
Figure 9: Comparison results of different methods. (**a**) Computational time. (**b**) User costs after DR compensation.  
the predefined threshold. Figure 10 illustrates the training results of HEMS for household user 1, indicating that in the multi-agent Q-learning model, each appliance agent gradually achieves convergence after 200 training iterations. During the training convergence of the equipment agent, a small number of data points that do not align with the convergence trend are removed to provide a clearer visualization of the agent's convergence. These data points do not affect the final convergence outcome.
# Virtually Connected in a Multiverse of Madness?--Perceptions of Gaming, Animation, and Metaverse
## 2 Background
### Metaverse  
After our dive through the gaming area and the technological development (virtual reality and animation concepts), we arrive at the main concept of this investigation, the metaverse.  
The metaverse concept definition appeared for the first time by the author [PERSON] in his book Snow Crash in 1992. It was defined as a virtual world that could reach, interact, and affect human existence [31]. However, until today, there has yet to be a consensus about the definition of the metaverse, but there will be definitions near agreement in the future. The metaverse can be defined as a massive dimension network of interconnected 3D virtual worlds rendered in real time that can be experienced synchronously and persistently by an unlimited number of users with a unique sense of presence and data continuity, who have identity, history, rights, objects communication and payments [31]. It is also a 3D experience where we can interact with virtual and augmented reality through headsets, sensory gloves, cameras, and sensors registering our bodily movements [8].  
The metaverse has its inner world that continues to exist even if we are not connected [8]. It can be described as the layer between us and the reality [32], where a 3D virtual world is shared, and the experiences can be experienced through virtual and augmented reality [33]. It is based on the real world but without physical limitations [34]. The users can involve themselves socially, economically, and culturally through their avatars [35] because the metaverse unites platforms of socially immersive virtual realities compatible with video games with massive online multiplayers, open gaming worlds, and collaborative spaces of augmented reality [36]. It is also seen as a digital universe that mixes online gaming elements with social networks and virtual reality, allowing users to engage digitally [37].  
The metaverse social application will transform social networks [18], and we can see that the gaming world is the founder of this concept because gamers could tie it to the screen and envelop it with alternative worlds [6]. The gaming experience has increasingly become a lived experience, and the limits between the metaverse and what is gaming and what is not have disappeared [8]. The metaverse can be achieved via the internet through augmented reality devices, game consoles, computers, tablets, or mobile phones [4]. In this way, the metaverse concept is present consciously or unconsciously in our lives.
## 4 Discussions  
### The Topographic Effects on GPP Inconsistency at Two Resolutions  
The time series GPP estimation is very important for monitoring vegetation growth conditions. Mountainous regions typically present high spatial heterogeneity, which is characteristic of steep slopes, altitude variations and notably biological diversity [11; 39; 40]. The results in this study showed the topography had a significant relationship with MODIS GPP inconsistency at two resolutions, as the topography influences the surrounding environment of the carbon assimilation procedure, such as air humidity, air temperature, water redistribution and solar radiation. Generally, air temperature increases linearly with the decrease of altitude. However, in most carbon cycle models, air temperature is assumed to be homogeneous within each modeling grid. This assumption neglects the impact of inner altitude variations in each grid, bringing uncertainties to the simulation over complex terrain. Besides air temperature, solar radiation, air humidity and precipitation also face the same problems. Related studies have illustrated that the spatial variations of altitude and slope greatly influenced hydrologic procedure [13; 14; 41], and hence affect the carbon assimilation procedure [42; 43; 44]. In general, the patchy ecosystem structures in mountain areas usually follow topographic and climatic gradients, and the remarkably spatial heterogeneity has considerable effects on the spatial distributions of GPP estimation.
## 3 Experiments
### Transfer Learning
This proves that even if the node are not individually trained, the model trained with the Node 7 data can be directly used for data compression of other nodes with a small compression performance  
Figure 8: The reconstruction error of models with different model parameters for all nodes. The red line shows the reconstruction error of the model which testing by use the parameters of Node 7. The blue line shows the reconstruction error of the model which testing by use the parameters of node self. The reconstruction error is the average value of all sample points in node. Figure 7: The average SNR values of models with different model parameters for all nodes. The blue box denotes the average SNR value of the model which testing by use the parameters of node self. The upper and lower boundaries of the blue box correspond to the plus or minus 1 dB range of the result value. The ’*’ symbol denotes the average SNR value of the model which testing by use the parameters of Node 7.  
reduction. The hidden mathematical features learned by the proposed model are common to the same categories of data in the vicinity. For the reconstruction error of all nodes, most of the model reconstruction errors which using the parameters of node self are below 0.1 \({}^{\circ}\)C. For the model that uses the parameters of node self, its compression performance is generally better than using the model parameters of Node 7, but it does not improve too much. For all nodes, the minimum and maximum errors of the digital difference between the data reconstruction of red line and blue line in Figure 8 are 0.0003 \({}^{\circ}\)C and 0.067 \({}^{\circ}\)C, respectively. For those nodes located near Node 7, such as Nodes 4-10, the reconstruction errors are not significantly reduced by use the model parameters that do not correspond to the node self. This results prove that our algorithm has good transfer learning ability. When the model is applied, we can train only one node's model and then apply the model to all nodes. This can further reduce the computational consumption of the node model training.