esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Uncertainty Analysis  
The complexity of an UQ process increases drastically with the number of uncertainty sources in a setup (an effect known as the curse of dimensionality). Therefore, it is a good practice to exclude potential uncertainty sources from the UQ process that are likely to be irrelevant or have a negligible effect on the output uncertainty. This is typically done with sensitivity analysis (SA), but in cases like the presented application case, expert assessment can suffice to reduce the UQ complexity.  
First of all, uncertainty of the MAS can be excluded from the consideration since this is the tested algorithm that does not represent a physical real-world system. This leaves potential uncertainty in the load schedules and the wind power feed-in. However, load development can typically be predicted very well. Furthermore, a number of the loads are highly flexible and their schedules may be changed by the MAS. Thus, their uncertainty is not considered to have a significant effect on the outcome. Uncertainty in the wind power data, on the other hand, is considered relevant since it is reasonable to assume that power feed-in data that is transmitted to the MAS may be afflicted with a measurement error that is additionally linked to a prediction error.  
In this case, a simple worst case analysis is conducted for demonstration purposes by assuming a maximal data error of \(\pm 10\%\). Therefore, the UQ can be conducted with an interval representation of the uncertainty. The results (Figure 8) show that stronger wind power feed-in can cause the MAS to not fully meet the target schedule - although the resulting congestion is still far less severe than in the case without control.  
Note that the current ISAAC implementation does not include robust optimization features that work with uncertainty information. In such a case, it would have been reasonable to employ probabilistic uncertainty models to provide the optimization with a better basis for decision making. In the current setup, however, the uncertainty assumptions suffice to demonstrate the importance  
Figure 8: Uncertainty of the aggregated schedule in comparison with load limits imposed by the transformer.  
of UQ even in such simple cases. While UQ with interval uncertainty and a small number of simulators can likely be conducted with simple adjustment of the code, more complex co-simulation scenarios and consideration of probabilistic uncertainty quickly illustrate the need for dedicated tools like MoReSQUE.
## 4 Methods and Experiment
### Keyframe Extraction
#### 4.5.1 Student Behavior Dataset

As detailed in Section 3, regarding student behavior data collection, we have compiled a dataset of student behavior and divided it into five distinct classes: hand-raising, interaction, sitting, turning around, and writing. Hand-raising represents students raising their hands to express opinions or ask questions. Interactions denote students engaged in group discussions and problem-solving. Sitting indicates students are listening to the teacher's explanations during lectures. Turning around represents students observing their friends' activities, and writing captures students solving math problems and recording their answers on paper. The student behavior dataset comprises 578 video clips with a 30 frames per second (fps) frame rate. The videos encompass various durations, with a minimum of 0.7 s (21 frames) and a maximum of 83.87 s (2516 frames). Each clip contains a total of 40 keyframes, which amounts to a total of 23,120 keyframes. The dataset is divided into training, validation, and testing subsets with 70, 15, and 15% of the data, respectively. Each video clip has an original keyframe of ten RGB, ten geometric transformations, ten color transformations, and ten optical flow keyframes. To ensure that frame overlap is considered, we use the SADLK algorithm to select ten keyframes from each video clip at a consistent 30 fps frame rate, regardless of the frame length. #### 4.5.2 Experimental Environment

To implement our proposed framework, the software and hardware specifications are as follows: Python version 3.11, TensorFlow and Keras version 2.12, OpenCV 4.7 on an Ubuntu operating system version 22.04 LTS, server processor Intel Xeon Gold 6230R of 4.0 GHz with 128 GB RAM, and GPU Nvidia Quadro RTX 8000. We perform experiments on multiple pretrained models with the SADLK algorithm keyframe selection methods and BiLSTM-AT to classify student behavior in the classroom. #### 4.5.3 Model Hyperparameters Tuning

The model is compiled with sparse categorical cross-entropy loss, the Adam optimizer with a learning rate of 0.001, and accuracy as the evaluation metric. The model is trained for 50 epochs, and the layer configuration of the proposed multiple pretrained and BiLSTM-AT is used for classifying student behavior. To load the data into the model, we choose ten keyframes and then extract features using the multiple pretrained models. The image size of the keyframes is 224 \(\times\) 224 with three channels of RGB. All of the features are fused, and the number of features is 7168. The complete layer configuration is listed in Figure 5.0 Our model uses all the parameters of the multiple pretrained models during training, and none are non-trainable. #### 4.5.4 Evaluation Metrics

When assessing the efficacy of multiclass student behavior classification models, performance is evaluated by computing accuracy, precision, recall, and the f1 score based on confusion matrices. In precision metric measures, TP and FP represent the number of true-positive and false-positive samples in the positive class, respectively, while recall
## 5 Conclusions, Challenges, and Future Research Directions
### Challenges  
Despite the promising results, several challenges were identified throughout the study:  
1. Data availability and continuity: not all variables were consistently recorded over the entire study period, limiting the use of some predictors in long-term modelling.
2. Temporal resolution: hourly data introduce substantial variability, leading to relatively lower correlation coefficients between PM levels and some influencing factors.
3. Model interpretability: although DL models achieve high accuracy, they often function as black boxes, making it challenging to derive causal relationships.
4. Overfitting risk: high R\({}^{2}\) values in training phases for some models indicate potential overfitting, necessitating careful model validation.
5. Computational complexity: some FS techniques and hybrid models, like Deep-NARMAX, require significant computational resources, which can limit their use in real-time or large-scale applications.
6. It is necessary to understand how much data are missing in the official network to use complementary data from other reliable sources. The used dataset was provided by the network of stations for air quality monitoring that belongs to the National Environmental Agency (Romania), which is part of the European Environmental Agency (EEA)
7. No long-term and hybrid ML-DL approach study of PMs exists in Romania.
8. While the proposed models demonstrated strong predictive and forecasting capabilities, this study is limited by the availability and completeness of historical data, particularly before 2022. Future work will explore real-time forecasting frameworks and integrate additional spatial or satellite data sources to enhance model generalizability.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 2 Demand Management and Direct Control of Electric Water Heaters
### Load Shifting by Direct Control of Electric Water Heaters  
In most countries, the profile of total electricity consumption shows two pronounced peaks: early in the morning, before starting the working day, and near two hours after people return to their homes from work. Usually, the power consumption of electric water heaters has a high correlation with the total consumption: both present coincident peaks, as shown in Figure 1, for a representative analysis using data from the _electric water heater consumption_ subset, part of the ECD-UY dataset. This correlation is explained by the high impact of water heaters compared with other domestic appliances. For example, in the case study analyzed in this article (Uruguay), water heaters account for 27% of the total residential electricity consumption, on average, peaking at 35%, according to the subset data. A specific feature of water heaters is that they have the ability to accumulate energy in the form of heat inside the water tank. This thermal inertia allows proper planning of switch on/switch off periods to help the grid operation while trying to affect the thermal comfort of users as little as possible.  
According to the correlation between total energy consumption and water heaters' energy consumption, the amount of energy associated with electric water heaters within a demand peak can be deferred by switching off the devices in a proper moment and switching them on in the future. This strategy allows implementing a load shifting on the demand curve, since the total amount of energy consumed in the period remains equal but the load profile is modified. Several studies have addressed the load shifting problem using direct control of devices [11; 12], but few articles have focused on quantifying the thermal discomfort generated by the real application of this strategy.
## 5 Performance Analysis
### Performance Analysis on Solid Waste Monitoring Application  
The results of the existing WPAN and Cloud-Assisted Architecture [47], SWM Technique [48], Real-Time Smart Garbage Bin Apparatus [49], PortiK System [50], Solid Waste Management Architecture [51], and ICT-based SWCCS [52] are discussed with a Waste Global Dataset collected from Kaggle. The URL of the dataset is given as [72]. In total, 500 sensor nodes are deployed to conduct the simulation in solid waste monitoring.  
Figure 15 illustrates the Execution Time (\(ET\)) in Equation (6) of six different methods evaluated with varying solid waste data points from 100 to 1000. For 300 data points, the \(ET\)s recorded are 33 ms for WPAN and Cloud-Assisted Architecture, 39 ms for SWM Technique, 43 ms for the Real-Time Smart Garbage Bin Apparatus, 48 ms for the PortiK System, 20 ms for Solid Waste Management Architecture, and 53 ms for ICT-based SWCCS. As the number of data points increases, the \(ET\) also tends to rise. Notably, the Solid Waste Management Architecture exhibits the shortest \(ET\) among the methods, largely due to  
Figure 14: Overall performance analysis of accuracy.  
Figure 13: Performance analysis of accuracy.  
its integration of Global System for Mobile Communications (GSM) tracking sensors and Bluetooth options for data tracking. Furthermore, waste sorting is enhanced through the use of Convolutional Neural Networks (CNNs) within smart trash bins and sensors, leading to efficient waste management with reduced time consumption. Specifically, the \(ET\) of the Solid Waste Management Architecture is decreased by 35%, 45%, 48%, 52%, and 53% compared to other approaches.  
Figure 16 presents the \(ET\) performance outcomes for six existing methods in solid waste management. The experimental results indicate that the Solid Waste Management Architecture achieves the lowest \(ET\) among the methods analyzed. Specifically, the \(ET\) recorded for the existing methods are as follows: 39 ms for the WPAN and Cloud-Assisted Architecture, 46 ms for the SWM Technique, 49 ms for the Real-Time Smart Garbage Bin Apparatus, 54 ms for the PortiK System, 26 ms for the Solid Waste Management Architecture, and 56 ms for the ICT-based SWCCS. This performance analysis highlights the efficiency of the Solid Waste Management Architecture, demonstrating its effectiveness in reducing \(ET\) compared to the other frameworks.
# 3D DNA Nanostructures: The Nanoscale Architect
## 2 3D DNA Nanostructure Design
### History
#### 2.1.2 DNA Origami  
The shift into the current paradigm of DNA nanostructure design occurred when [PERSON] developed a technique now known as _DNA origami_[23]. A long piece of ssDNA, known as the scaffold strand, winds through a raster of the shape while hundreds of short ssDNA span and hybridize to spatially adjacent, non-consecutive regions of the scaffold strand according to Watson-Crick base pairing rules, thus tying them spatially together. As the staple strands bind, duplexes are formed which stiffen the structure to form the shape, forming a series of coplanar, anti-parallel helices bound by crossovers. This concept was a very important conceptual breakthrough in the field of DNA nanotechnology and significantly improved the size, yield, and shape complexity of DNA nanostructures. Although initially demonstrated for designing only 2D shapes, the DNA origami technique has since been extensively developed as the underlying strategy for many subsequent derived techniques for designing increasingly complex 3D shapes.
## 4 Comparisons of Results for DFE, MLD Algorithms
### Equalization by Maximum Likelihood Detection (MLD) Algorithm  
The MLD algorithm is applied to the distorted received data after the photodiode in the OFDM analyzer. MLD incorporates the entire sequence of received symbols for the channel memory length. In the case of QPSK, there are four sequences; for 16-QAM,  
\begin{table}
\begin{tabular}{l l} \hline
**Link Design Components** & **Values** \\ \hline Bits per Symbol & 2, 4, 6, 7 \\ \hline SSMF Length & Up to 120 km \\ \hline Dispersion & 16 ps/nm/km \\ \hline Dispersion Slope & \(0.08\times 10^{3}\) s/m\({}^{3}\) \\ \hline SSMF Attenuation Coefficient & 0.2 dB/km \\ \hline Photo Diode Model & PIN \\ \hline Responsivity & 0.8 A/W \\ \hline Thermal Noise & \(10^{-12}\) A/Hz\({}^{1/2}\) \\ \hline Shot Noise & ON \\ \hline Cyclic Prefix & 0.125 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._16 possible symbol sequences; for 64-QAM, 64 symbol sequences; and for 128-QAM, 128 symbol sequences.  
The distorted OFDM subcarrier signals with the QPSK, 16-QAM, 64-QAM, and 128-QAM modulation formats result in the constellation diagram and EVM calculations that are shown in Figure 3 before (a, b, c, and d) and after equalization (e, f, g, and h) for the respective modulation formats and at the maximum attained fiber lengths for each case.  
Maintaining the performance criteria of modern communication systems across different modulation schemes requires keeping the error vector magnitude (EVM) of the received signal below the proposed 3 GPP thresholds, ensuring the quality and reliability of the signal, particularly as the complexity of the modulation format increases. The implementation of the proposed maximum likelihood detection (MLD) algorithm is tested across different fiber lengths. For QPSK modulation on OFDM subcarriers, an EVM of 17.06% is achieved after equalization at a maximum fiber length of 120 km. When using 16-QAM modulation, the fiber length is reduced to 100 km, and the EVM to 12.40% after equalization. For higher-order modulations, such as 64-QAM and 128-QAM, the fiber lengths are 50 km and 40 km, respectively, to maintain compliance with the 3 GPP threshold limits, and EVM values of 7.35% for 64-QAM and 4.85% for 128-QAM are obtained.  
All equalized EVM values successfully meet the stringent 3 GPP threshold limits (17.5% for QPSK, 12.5% for 16-QAM, 8% for 64-QAM, and 5% for 128-QAM), demonstrating the effectiveness of the proposed MLD in maintaining signal integrity across various modulation formats and fiber lengths.
## 3 Time and Frequency Domain Analysis of Track Dynamic Response  
In this section, the dynamics of the tracks with and without the ballast mat are studied in the time and frequency domains. In the time domain, the wheel displacements and the rail, sleeper and ballast accelerations are compared. In the frequency domain, the power spectra of the rail, sleeper and ballast vibrations are compared. Finally, the insertion loss generated by the installation of the ballast mat is analysed for each track component. For an ideal wheelset drop test, it can be assumed that both sides of the wheels come into contact with the rails simultaneously. Thus, although the model is capable of simulating the track dynamics in all directions, the system shows a symmetrical behaviour under the scenarios studied here due to the absence of excitations in the lateral direction. Hence, only the vertical response of the structure is analysed in the following.
## 3 Results and Discussion
### Accumulated Coverage  
The accumulated coverage of both the SSO and lower inclination orbits for a period of 7 days is shown in Figure 12. It is obvious that the total coverage percentage of the satellite with a 24-degree inclination angle is higher than the satellite with SSO. The satisfied coverage percentage was more than 95% for lower inclination orbits and 67.5% for SSO.
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.1 Services  
It is envisioned that many services will emerge in the near future to meet the demands of the 21 st Century. We mention here a few of the most famous services on the rise. _Mixed reality_ (XR) is foreseen to be a new way of interacting with the environment around us. Both of its forms, AR and VR, are very promising for many applications such as filtering the view ahead of the driver with warning signs and important instructions to help while driving. Another application of XR is to provide new ways of controlling our surrounding environments such as smart houses and work offices. _Holographic communication_ is expected to reinforce the immersive experience and offer new ways of interaction with the environment. For instance, _holographic communication_ can be used to add more authenticity to a conversation between humans, and this is manifested in scenarios related to telepresence or translating spoken words into descriptive virtual objects [53]. Collectively, XR and _holographic communication_ are anticipated to have a powerful impact on the future of education, and this is exemplified by establishing educational institutions that are entirely based on virtual real-time remote learning. In Figure 5, we display some of the 6G system's applications.  
Another technology worth looking at is human chip implants. It is projected that micro-chips will enable a whole new class of immersive experiences such as remote healthcare and monitoring and sharing human sensory data to allow for more personalized network experiences. Moreover, autonomous robots and systems are expected to play a huge role in the near future, some of which involve building smart cities, which will be tightly related to factory automation and self-sustaining networks. One of the challenges that is expected to be faced in the near future for communication between brain implants and the global Internet is establishing secure communication channels against eavesdropping and hacker attacks [54]. All of these services are expected to be deployed on a large scale, therefore under massive network deployments, such as 3D heterogeneous networks, multi-tier base stations, and _cyber-physical systems_ (CPS), and there will be the need for high-precision communication to offer seamless and integrated connectivity.
# Optimization of Transmission X-ray Target for Intense Pulsed Electron Beam Accelerators
## 1 Introduction
However, when using a dual-layer target, with a combination of a high atomic number material for X-ray conversion and titanium membrane for vacuum sealing and electron dumping, the X-ray conversion targets in these schemes are thicker than optimal, as the highest X-ray fluence is typically achieved with a thickness much smaller than the electron range [25]. Thus, further research on the optimization of the convertor structure is still required, especially when considering more IPEB characteristics, such as electron energy spectrum, to match dual-layer converters with a combination of a heavy metal X-ray converter layer, such as tungsten or molybdenum, and a titanium window for vacuum sealing, for as high photon fluence as possible with complete absorption of electrons. Further, the corresponding output features such as the spatial distribution of X-ray fluence and dose by per pulse should be determined for the reasonable usage of the radiation. In this work, the energy spectrum of IPEBs was analyzed by the space-charge effect in their emission and was used for the sources of a Monte Carlo (MC) simulation. The photon emission distribution from tungsten and molybdenum targets was studied, and the design and optimization of a dual-layer structure converter system with a heavy element X-ray conversion layer and an extra titanium membrane were made.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 2 Steel Damper Experiment
### Loading Schemes  
The loading configurations for the cylindrical steel damper and E-shaped steel damper are illustrated in Figure 3. The loading rule follows the reference of Chinese standards, which specifies that analyzing the mechanical behavior of dampers requires loading at four displacement levels, which are 0.25 Ad, 0.5 Ad, 1\(\Delta\)Ad, and 1.2\(\Delta\)Ad. The corresponding number of cycles for each displacement level is 5, 5, 10, and 1 cycle, respectively. And then, under the displacement of 1\(\Delta\)Ad, the dampers are cyclically loaded until failure to analyze their fatigue performance. The average speed of all loading steps should not be less than 2 mm/s. For the E-shaped specimens, loading is performed according to the requirements of the standard. Considering the rapid deformation rate of steel dampers under an earthquake, the loading speed is increased in the 5 th loading step. The difference between Specim #1 and Specim #2 lies in the loading rate of the 5 th loading step, aiming to analyze the effect of the loading rate on fatigue performance. As for the cylindrical specimens, there is a slight deviation in the loading displacement compared to the standard, but it generally achieves the objective of analyzing the fatigue performance of the dampers. After increasing the loading rate in the 4 th loading step, it is found that the loading device has the remaining capacity, so the loading speed is further increased in the 5 th loading step. The loading scheme is presented in Table 1.  
Figure 1: Connection device and mechanical diagram. (**a**) Cylindrical steel damper (**b**) E-shaped steel damper.  
Figure 2: The steel damper dimensions.