esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting COVID-19 Effectively with Transformers and CNN-Based Deep Learning Mechanisms
## 3 Results  
During the fine-tuning process, we performed extensive experiments to determine the optimal hyperparameters for our Vision Transformer models. These hyperparameters included training batch size, evaluation batch size, learning rate, weight decay, gradient accumulation steps, and warmup ratio. To achieve the best results, we systematically varied these hyperparameters and evaluated the performance of the model using the validation set. Through this process, we found that the best hyperparameters for our models were a training batch size of 8, evaluation batch size of 4, weight decay of 0.01, gradient accumulation step of 4, and warmup ratio of 0.1. Using these optimal hyperparameters, we evaluated the performance of our models on our curated dataset. We trained each model using a 5-fold cross-validation approach, and the results of our evaluation are shown in Table 1.  
The VGG-19 model achieved an F1-score of 0.7439 and an average accuracy of 0.7660, while the DenseNet-121 model had an F1-score of 0.9093 and an average accuracy of 0.9228. The Maftouni Ensemble model with FC had an F1-score of 0.9386 and an average accuracy of 0.9508, and the Maftouni Ensemble model with FC+ SVM had an F1-score of 0.9420 and an average accuracy of 0.9529. The Vision Transformer model with a one-step classifier had an F1-score of 0.9618 and an average accuracy of 0.9693, and the Vision Transformer model with a two-step classifier achieved the highest performance, with an F1-score of 0.9767 and an average accuracy of 0.9736. Overall, the results of our experiments demonstrate that the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Mean(Acc)** \\ \hline VGG-19 & 0.8001 \(\pm\) 0.0097 & 0.7684 \(\pm\) 0.0094 & 0.7439 \(\pm\) 0.0188 & 0.7660 \(\pm\) 0.0111 \\ \hline DenseNet-121 & 0.8777 \(\pm\) 0.0071 & 0.9409 \(\pm\) 0.0098 & 0.9093 \(\pm\) 0.0110 & 0.9228 \(\pm\) 0.0035 \\ \hline Maftouni Ensemble Model with FC & 0.8984 \(\pm\) 0.0071 & 0.9830 \(\pm\) 0.0071 & 0.9386 \(\pm\) 0.0082 & 0.9508 \(\pm\) 0.0012 \\ \hline Maftouni Ensemble Model with FC+SVM & 0.9078 \(\pm\) 0.0110 & 0.9789 \(\pm\) 0.0103 & 0.9420 \(\pm\) 0.0097 & 0.9529 \(\pm\) 0.0043 \\ \hline Vision Transformer with 1 step Classifier Model & 0.9698 \(\pm\) 0.0054 & 0.9666 \(\pm\) 0.0075 & 0.9618 \(\pm\) 0.0119 & 0.9693 \(\pm\) 0.0037 \\ \hline Vision Transformer with 2-step Classifier Model & 0.9766 \(\pm\) 0.0081 & 0.9761 \(\pm\) 0.0037 & 0.9767 \(\pm\) 0.0075 & 0.9736 \(\pm\) 0.0051 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representation of average model performance with standard deviation  
Figure 4: Two-step Transfer Learning approach.  
Vision Transformer model with a two-step classifier is the most effective approach for the multi-class classification of COVID-19 chest X-ray images. However, we also showed that other models, such as DenseNet-121 and Maftouni Ensemble models, can also achieve high performance with our curated dataset.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 5 Conclusions  
In this paper, we proposed an IEEE 802.15.4 MAC layer-based cross-layer protocol for mesh routing. The TSMR can provide robust connectivity and efficient route discovery procedures from the base station to mobile and static leaves, and vice versa. Additionally, the proposed scheme provides fast and efficient association/reassociation procedures for the mobile nodes while limiting the impact of the signaling overhead to support the device's mobility. Finally, the TSMR can provide an efficient and distributed address-allocation algorithm and improve the network reliability. We evaluated the performance of the IEEE 802.15.4 standard, RPL, MeshMAC, and TSMR through simulation using realistic radio models. Simulation results indicate that the TSMR can improve reliability and reduce end-to-end delays. The proposed protocol could be directly applied with a small overhead to the current IEEE 802.15.4 systems.  
Conceptualization, J.-W.K.; methodology, J.-W.K., J.K., and J.L.; software, J.-W.K. and J.L.; vali-dation, J.-W.K. and J.L.; formal analysis, J.-W.K., J.K., and J.L.; investigation, J.-W.K. and J.L.; re-sources, J.-W.K.; data curation, J.-W.K., J.K., and J.L.; writing\(-\)original draft preparation, J.-W.K. and J.K.; writing\(-\)review and editing, J.L.; visualization, J.-W.K., J.K., and J.L.; supervision, J.K. and J.L.; project administration, J.K. and J.L.; funding acquisition, J.K. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the MSIT, grant number 2021R1A4A5028907 and 2022R1A2C1009951.  
Not applicable.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number: 2021R1A4A5028907 and 2022R1A2C1009951).  
The authors declare that they have no conflict of interest.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.4 Analytical Hierarchical Process (AHP)-Based Multimetric Geographical-Routing Protocol (AMGRP)  
AMGRP [35] is an efficient routing protocol that considers multiple metrics, such as link lifetime, mobility, density, and node status under an AHP to obtain good protocol performance.  
The forwarding decision in this proposal is very dependent on the use of mobility metrics such as distance, speed, and moving direction. This protocol assumes that each node knows its position and the position of the destination. This is important to prevent making wrong forwarding decisions by sending packets to vehicles that are moving against the destination direction. Suppose source vehicle \(s\) is at (\(x_{0}\), \(y_{0}\)), the destination node is at (\(x_{d}\), \(y_{d}\)), and neighbor vehicle \(i\) is at (\(x_{i}\), \(y_{i}\)); then, the moving angle between source and neighbor \(i\) toward destination \(d\) can be obtained as shown in Equation (2):  
\[A_{s,i}^{(d)}=arccos\frac{(x_{d}-x_{0})(x_{i}-x_{0})+(y_{d}-y_{0})(y_{i}-y_{0}) }{\sqrt{(x_{d}-x_{0})^{2}+y_{d}-y_{0}^{2}}\sqrt{(x_{i}-x_{0})^{2}+y_{i}-y_{0} ^{2}}}. \tag{2}\]  
The second metric (link lifetime) is calculated as in Equation (3), where \(R\) is the radio range, (\(x_{s}\), \(y_{s}\)) and (\(x_{i}\), \(y_{i}\)) are the source and neighbor location, respectively, and \(v\) are the velocities.  
\[L_{s,i}=\frac{\sqrt{(x_{i}-x_{s})^{2}+(y_{i}-y_{s})^{2}}}{v_{s}-v_{i}}. \tag{3}\]  
If the queue length is small, the node could be congested because more data packets need to be processed. Before selecting the next hop, node status is obtained by calculating the buffer capacity (\(Q_{i}(t)\)) and can be calculated as follows:  
\[Q_{i}^{(t)}=\frac{Q_{max}-Q_{i}^{(t)}}{Q_{max}}, \tag{4}\]  
where \(Q_{max}\) gives the maximum buffer size. \(Q_{i}^{(t)}\) is defined as the number of packets in the buffer queue at time \(t\)[35]. Density is also computed to minimize the local maximum; using the hello packet, each node calculates node density to select paths with a high number of vehicles and avoid intermittent connectivity.  
Between each metric, there is a relative ranking, with the mobility metric as the most important criterion, node density as the second most important, node status as the third, and finally link lifetimeas the least important criterion, as we can see in the values of Table 3. It is important to observe that the mobility metric has higher priority and an important role in the decision of the next-hop selection, in contrast with link lifetime that is least important among the four metrics included in decision criteria. Finally, a total score is assigned to each neighbor from the neighbor list considering the relative weights for the deciding factors to establish an optimal multihop routing path selecting an efficient forwarding node; a difference between path selection in AMGRP and GPRS is shown in Figure 8.
## 1 Introduction
MJI-HI enhances spectral resolution by indirectly extending the maximum optical path difference (OPD), imposing no constraints on FTIS modulation schemes, sampling intervals, or spectral ranges. This demonstrates strong compatibility with FTIS architectures while delivering benefits including large optical throughput, structural simplicity, cost-effectiveness, and high robustness. The method is applicable to both SMFTIS and TSMFTIS. Furthermore, the system employs a dual-beam configuration to enhance spectral super-resolution accuracy while minimizing the potential system-level impacts introduced by FPI integration. We have proposed methods for twofold and multifold spectral super-resolution, and simulation analyses have verified the feasibility of achieving twofold and threefold spectral resolution enhancement. The future work will experimentally pursue higher super-resolution factors by holistically addressing FTIS stability, sampling strategies, inversion algorithm accuracy/efficiency, and FPI modulation techniques. However, there remains a lack of analysis regarding potential optical errors in MJI-HI and their impacts on system performance. Such investigations would establish a theoretical foundation for assessing the feasibility of higher-fold super-resolution schemes while providing critical guidance for MJI-HI's further development and practical implementation. Both FPI and FTIS are well-established interferometric systems, and their individual optical errors have been extensively studied [27; 28; 29; 30; 31; 32; 33; 34]. However, despite prior research on combining FPI and FTIS, there remains a lack of systematic analysis of the optical errors in such integrated systems. Potential errors in the FPI include cavity spacing offsets, non-parallelism, surface irregularities, and reflectivity variations [27; 28; 29; 30], while errors in the FTIS may involve mirror tilt, beam collimation accuracy, and beam splitter reflectivity variations [31; 32; 33; 34]. This paper analyzes the impact of these errors on interferograms and spectral outputs, as well as their effects on the super-resolution results of MJI-HI. Based on the analysis, we propose algorithmic improvements to enhance system robustness. Additionally, the paper provides a detailed examination of interdependent errors between the FPI and FTIS, such as how FPI cavity spacing offsets interact with FTIS mirror tilt. The analysis is validated through simulation experiments. This work summarizes the critical technical considerations for developing MJI-HI and serves as a reliable reference for future studies integrating FPI and FTIS technologies.
# Adhesion Performance between Solid Waste and Bitumen Based on Surface Energy Theory
## 5 Discussion
### Laboratory Test Verification  
The boiling test [27] was used to study the adhesion performance between three aggregates and bitumen, and the conclusion indicates that the adhesion performance between glass and ceramic aggregates and bitumen is actually better than limestone aggregate. The first step of a boiling test is to lift the heated aggregate with thread and immerse it in hot bitumen to form a bitumen membrane on the whole surface of aggregate, then use thread to keep the cooled aggregate suspended in the boiling water, put the aggregate into the normal temperature water after 3 min and observe the spalling degree of the bitumen  
Figure 13: ER value between bitumen and aggregates.  
The evaluation results of adhesion performance grade between various aggregates and bitumen are shown in Table 12.  
However, the evaluation results of the adhesion performance grade between bitumen and aggregate, according to Table 11, are easily affected by subjective factors. Due to the irregular shape of aggregate, it is easy to generate large errors when estimating the spalling area, and it is difficult to distinguish the adhesion performance differences between various aggregates and bitumen. Therefore, the test will also weigh the mass of the bitumen on the aggregate surface before and after water boiling, and calculate the bitumen spalling rate during the water boiling process (the mass of spalling bitumen/the initial mass of bitumen \(\times\) 100%) to evaluate the adhesion performance between various aggregates and bitumen accurately. The calculation results of the bitumen spalling rate during the water boiling process are shown in Table 13.  
Tables 12 and 13 both show that the adhesion performance between glass or ceramics and bitumen is better than that between limestone and bitumen, which verifies the above conclusion based on surface energy theory.  
Other relevant tests with municipal solid waste incineration (MSWI) and bottom ash aggregate (abbreviation BAA), which contains plenty of glass and ceramics, were also conducted. The conclusion indicates that the water damage resistance of asphalt mixture added with BAA is better than that of ordinary asphalt mixture [28]. Because BAA is a mixture mainly composed of glass and ceramics, the conclusion can also prove that adding glass aggregate or ceramic aggregate to an asphalt mixture can improve its water damage resistance, verifying the above conclusion made on the basis of surface energy theory.
## 3 Test Results and Analysis  
### Damage Phenomenon  
During initial loading, the specimen is in an elastic stage, and there is no change on the surface. When the specimen reaches peak stress, the bearing capacity begins to decrease, and cracks begin to appear and gradually extend, as shown in Figure 3a. As stress further increases, the cracks' width gradually expands, as shown in Figure 3b. During further loading, the central part of the specimen expands slowly, the surface of the specimen develops cracks, and the surface mortar and aggregate gradually fall off, as shown in Figure 3c. The specimen is completely destroyed, forming two concave cylinders with wide ends and narrow middles, as shown in Figure 3d. This test shows that the dosage of AOD slag has little effect on the failure of the mixture.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Specimen** & **Inorganic** & & & & & **Optimum** & **Maximum Dry** \\
**Identification** & **Binder Blend** & **Cement/(g)** & **Fly Ash/(g)** & **Slag/(g)** & **RA 1/(g)** & **Dosage/(\%)** & **Density/(g/cm\({}^{3}\))** \\ \hline \(R=0\%\) & 1:4:0 & 162.3 & 649.2 & 0.0 & 4597.2 & 7.7 & 2.082 \\ \(R=3\%\) & 1:3:1 & 161.0 & 483.0 & 161.0 & 4562.0 & 8.7 & 2.066 \\ \(R=6\%\) & 1:2:2 & 160.0 & 320.0 & 320.0 & 4533.1 & 9.6 & 2.053 \\ \(R=9\%\) & 1:1:3 & 159.1 & 159.1 & 477.2 & 4506.6 & 10.2 & 2.041 \\ \(R=12\%\) & 1:0:4 & 158.2 & 0.0 & 632.8 & 4482.3 & 10.9 & 2.030 \\ \hline \end{tabular}  
* RA: recycled aggregate.  
\end{table}
Table 2: Optimum moisture dosage and maximum dry density of AOD slag mixtures.
# A Promising Technological Approach to Improve Indoor Air Quality
###### Abstract  
Indoor Air quality (IAQ) in private or public environments is progressively recognized as a critical issue for human health. For that purpose the poor IAQ needs to be mitigated and immediate drastic measures must be taken. In environmental science and especially in advanced oxidation processes and technologies (AOPs-AOTs), photocatalysis has gained considerable interest among scientists as a tool for IAQ improvement. In the current study an innovative paint material was developed which exhibits intense photocatalytic activity under direct and diffused visible light for the degradation of air pollutants, suitable for indoor use. A laboratory and a real scale study were performed using the above innovative photo-paint. The lab test was performed in a special design photo-reactor while the real scale in a military's medical building. Nitrogen Oxide (NO) and Toluene concentration was monitored between \"reference\" rooms (without photo paint) and \"green\" rooms (with photo-paint) in order to estimate the photocatalytic efficiency of the photo-paint to degrade the above pollutants. Results of the study showed a decrease up to 60% and 16% for NO and toluene respectively under lab scale tests while an improvement of air quality up to 19% and 5% under real world conditions was achieved.  
IAQ improvement; photo-paint; NO; Toluene degradation Article
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Machine Learning in WSNs
#### 1.2.3 Reinforcement Learning  
Reinforcement learning learns optimal actions through trial and error, and it is used in WSNs to select optimal routes for messages. In WSNs, RL algorithms are applied to optimise routing protocols, allocate resources such as energy and bandwidth, and adaptively control network operations in response to environmental changes. The capacity of RL to autonomously improve decision-making over time underscores its significance in the management and optimisation of WSNs [18; 19; 20]. For example, Q-learning has been used to select energy-efficient routes or to dynamically adjust node transmission power. While RL can theoretically be used for clustering (e.g., learning optimal CH selection policies), it typically requires a large number of interactions to converge and may introduce significant computational and communication overhead. This makes RL less practical for real-time clustering in large-scale, energy-constrained WSNs.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Artificial Intelligence (AI)  
AI [69] is not part of the earlier generation networks (from 1G to 4G) [64]. It is part of the 5G network, officially from the latest addition in the 3 rd Generation Partnership Project (3 GPP) Release 18 specifications [35], which is expected to benefit the telecommunications sector with the development of various notable applications [70; 71; 72; 73]. As part of the new 6G network, AI plays an essential role in revolutionizing communication and automation, such as in handover process [74; 75; 76; 77; 78], resources allocation [79; 80], and network selection. Additionally, integrating AI to the 6G networks leads to higher performance, particularly when delay-sensitive applications are involved. After all, both AI and ML are the key technologies for 6G [49]. Taking the case of ML, its algorithms can be efficiently used for the allocation of BS resources and close-to-optimum performance. Besides that, intelligent materials used in public settings (e.g., buildings or streetlights) can make use of the network with the customized changes (to radio waves). Moreover, deep learning techniques can help to enhance the accuracy of indoor positioning [81].
## 2 Results  
### AsA Concentration in Leaves and during Fruit Ripening  
The concentration of total ascorbic acid (tAsA) and reduced AsA (dAsA) in leaves of the two cultivars differed significantly. Specifically, the variety PEP1 had a significantly higher content of tAsA and dAsA compared to PEP10 (Figure 1). Moreover, differences were also evident in the percentage of reduced ascorbate relative tAsA, which was 61% for PEP1 and 4% for PEP10.  
For the analysis of AsA in fruits, peppers were harvested at identical phenological stages throughout the course of the experiment, mainly considering the development (e.g., color and size) (Supplementary Materials, Figure S1). The concentrations of tAsA and dASA varied significantly between the two cultivars and in relation to the ripening stage (Figure 2).  
While at the different stages under investigation (i.e., immature green, IG, mature green, MG, and mature, fully red, R) PEP1 always accumulated more tASA than PEP10, the difference in total ascorbic acid between the two varieties increased during fruit development. PEP11 accumulated more tAsA following MG, where fruits reached their final size, while it did not significantly increase in PEP 10. In this variety, a similar trend was also present for the dAsA, whose concentration did not significantly change at end of fruit cycle. While the accumulation of tASA and dAsA during maturation followed the same trend in PEP10, in PEP1 the increase of tAsA was accompanied by a decrease in dAsA and vice versa.  
This is also illustrated by the proportion of reduced AsA present in the total AsA, a broad indicator of the cellular oxidative stress, which showed an opposite trend in the two varieties (Supplementary Materials, Figure S2). In PEP1 this ratio declined from IG to MG, while it increased in PEP10. The difference between the two varieties slightly varied from MG to R stage.  
Collectively, the data indicated that the higher accumulation of AsA in the PEP1 variety is not organ specific, being also evident in leaves, and that the two varieties differ not only in the concentration, but also in the accumulation trend of AsA during fruit development and maturation. The largest difference was present at the mature stage, with PEP1 accumulating 3.5x tASA than PEP10.  
Figure 1: Total ascorbic acid (tAsA) and reduced ascorbic acid (dAsA) concentration (mg/kg FW) in leaves of the two pepper varieties. Data are mean \(\pm\) s.d. of three biological replicates. Asterisks indicate a statistically significant difference between varieties (\({}^{**}p<0.01\); \(t\)-test).  
Figure 2: Total ascorbic acid (tAsA) (**a**) and reduced ascorbic acid (dAsA) (**b**) concentration (mg/kg FW) during fruit development. Data are mean \(\pm\) s.d. of three biological replicates. IG: immature green; MG: mature green; R: red. At each stage, asterisks indicate a statistically significant difference between varieties (\({}^{**}p<0.01\); \(t\)-test).
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 3 Methods and Proposed Approach
### Machine-Learning Algorithms
#### 3.5.2 Random Forest  
RF falls under the category of supervised machine-learning algorithms and has been used for both classification and regression problems [27]. In the case of classification, RF handles categorical data, while in the case of regression, it handles continuous data. RF is based on a large number of decision trees, where each DT gives a class prediction and the class with a maximum number of predictions becomes the model's prediction. RF uses a method called bagging that allows each DT to randomly select a sample dataset to have different types of trees for better accuracy and minimum variance. Moreover, it uses feature randomness (a method in the ensemble), which increases diversification and decreases correlation among trees. RF can be represented as:  
\[RFfi_{i}=\frac{\sum_{j\in alltrees\
ormfi_{ij}}}{T} \tag{5}\]  
where \(RFfi_{i}\) is the importance of feature \(i\) that is calculated from all trees, \(normfi_{ij}\) is feature \(i\) that is normalized in a tree \(j\), and \(T\) is the number of trees. RF is used with two hyper-parameters as shown in Table 11. The n_estimator hyper-parameter defines how many decision trees will be in the RF voting procedure, and the max_depth hyper-parameter defines the depth of each DT. We used 300 decision trees, and each tree was restricted to 300 level depth.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyper-Parameters** & **Hyper-Parameter Tuning** \\ \hline DT & max\_depth = 300 & max\_depth = [50 to 500) \\ \hline RF & n\_estimators = 300, max\_depth = 300 & max\_depth = [50 to 500), n\_estimators = [50 to 500] \\ \hline XGBost & n\_estimators = 300, max\_depth = 300, & max\_depth = [50 to 500], n\_estimators = [50 to 500], learning\_rate = [0.0 to 1.0) \\ \hline KNN & n\_neighbors = 3 & n\_neighbors = [2 to 10] \\ \hline SVM & Kernel = poly, C = 2.0 & Kernel = [poly, linear, sigmoid], C = [1.0 to 5.0] \\ \hline LR & solver = liblinear, C = 2.0, & Kernel = [iblinear, sag, sagal), C = [1.0 to 5.0], \\  & multi\_class = multinomial & multi\_class = multinomial \\ \hline \hline \end{tabular}
\end{table}
Table 11: Machine-learning model hyper-parameter settings.