esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Architectures
#### 3.3.1 Model Size  
The first strategy to reduce computational complexity is limiting the network size in the sense of several layers and neurons while taking care of model performance. In more complex computer vision tasks, it would lead to significantly worse performance. Still, it is shown in [44; 52] that deep multilayer perceptron (MLP) with just three layers can be successfully used in EM shower/PU classification. Unlike in the previous approaches, the new classification cases are added, with background represented as QCD events and a mixed background scenario with both QCD and neutrino PU events.  
Two simple NN models are considered: MLP and CNN, but because of space constraints, just the last classification case for both models is analyzed in detail since it includes both types of backgrounds. The hyperparameter optimization software framework Optuna [53] is used to determine the models' structure. Optuna uses a tree-structured Parzen estimator sampler, a Bayesian optimization technique that models the search space. It uses the history of previously evaluated hyperparameter configurations to sample the following ones.  
For the CNN, the search space contains 6-16 kernels for the Conv2D layer and 8-18 nodes in the following 1-2 dense layers, so far resulting in 1452 possible network combinations. For the MLP, the search space contains 2-3 layers, each with 4-16 nodes, generating 2366 combinations. The proposed activation functions for intermediate layers for both models are ReLu and sigmoid, while the output layer uses softmax for the activation function. The search space also contains optimizers, namely, Adam, RMSprop, and SGD, each with a range of learning rates, greatly increasing the number of possible combinations in the search space to 8712 for CNN and 14,196 for MLP. The HGCAL images, which are in 3D tensor format, require reformatting to serve as suitable inputs for MLP, i.e., to ensure compatibility with the MLP architecture, the tensor needs to be reshaped into a 1-dimensional vector.  
When more architectures with similar accuracies are highly ranked, the one that minimizes the number of trainable parameters is chosen.
# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 2 Background
[23] further expanded LSTM to create Bidirectional Long Short-term Memory (BiLSTM). The structure of BiLSTM closely resembles that of Bi-RNN, incorporating two independent LSTM units concatenated together. By doing so, the BiLSTM model effectively addresses the limitation of LSTM's inability to incorporate future information, enabling feature data obtained at time t to encompass both past and future information [24]. [PERSON] et al. [25] introduced the Transformer in 2017 as an innovative deep learning framework, distinct from the conventional structures of CNNs or RNNs. The Transformer relies entirely on the attentional mechanism to capture global dependencies between model inputs and outputs. This remarkable ability to handle long-term dependencies and interactions renders the Transformer well suited for time-series modeling tasks, leading to high performance in various time series-related endeavors [26]. To address specific limitations of the Transformer in long time-series prediction, [PERSON] et al. [27] proposed the Informer model in 2021. Building upon the classical Transformer encoder-decoder structure, the Informer model aims to tackle challenges encountered in long time-series prediction tasks. In the same year, [PERSON] et al. [28] presentedTemporal Fusion Transformers (TFTs), implementing a multiscale prediction model with a static covariate encoder, a gated feature selection module, and a temporally self-attentive decoder. TFTs not only deliver accurate predictions but also retain interpretability, considering global, temporal dependencies, and events.
## 8 Conclusions and Future Work  
Robust acquisition of spatial distortion maps represents the first step towards building a real-time ATRIS 3D vision system. An innovative approach to acquisition of projected patterns has been proposed and its performance evaluation has been presented in the paper. By introducing cyclic orthogonal codes as modulation sequences for sequential frame acquisition, the proposed computer vision system becomes robust to the environmental factors such as background noise, deliberate jamming and mutual interference of similar systems. The approach guarantees collision-free functionality for a limited number of users, which satisfies the requirements for multiple sensors operating in the same area. The system robustness to background light and co-located systems, its sensitivity and its dynamic properties can be adapted to application requirements by adjusting the length of the modulation sequence. The experimental setup of the system with 32-bit long sequence demonstrates the system's excellent performance in indoor environment and proves the suggested concept for outdoors scenes in direct sunlight.  
In order to further improve the proposed approach and adapt it to a particular operating environment and system requirements, further work will mainly focus on finding the most appropriate pseudo random modulation sequences for a particular application. The length of the pseudorandom sequence is strongly correlated to the dynamic properties of the system, _i.e._, the number of frames per second and the system robustness. The next challenge is in automatic adjustment of the system parameters to time-varying environment, _i.e._, external illumination and number of interferers. This challenge includes methods for environment detection, quantitative evaluation of image quality and methods for adaption of the sequence length in order to find a tradeoff between the required frame rate and satisfying processing gain. In some applications a high density of proactive sensors in an area is expected, for example in automotive applications, where the number of users may exceed the number of available orthogonal pseudorandom sequences, which results in high mutual interference. Thus, an efficient technique should be found to decrease the mutual interference between systems. Among methods the pseudo random sequence hopping is a promising one, which has to be looked at. Finally, the introduction of the proposed approach to other proactive range detection methods can be studied.  
This research was supported by the European Union, European Social Fund, within the scope of the framework of the Operational Programme for Development of Human Resources in the Period 2007-2013 contract No. 3211-11-000492 (ATRIS).
## 2 Related Work
The ORR has been experimentally compared to RRSA and five other improved versions of RRSA. The results show that ORR outperforms in terms of performance optimization in time-sharing operating systems. The proposed ORR algorithm gives a new research direction for fair job scheduling in computer systems. The authors in [25] introduced a novel CPU scheduling algorithm called Adjustable Time Slice (ATS) based on RR scheduling. ATS improves performance and reduces time costs by clustering processes according to their execution time, weight, quantum, and context switches. Unlike traditional RR scheduling that uses a fixed time slice, ATS uses a dynamic approach to allocate time quantum by evaluating the resemblances among the processes within each group, where preference is given to shorter processes over longer ones. The works above analyzed various Round Robin scheduling algorithms for improving system performance compared to traditional algorithms. Therefore, more efforts are required to improve performance in specific scenarios and develop new variants. This work proposes a unique approach for improving task execution in real-time systems by incorporating dynamic time quantum and priorities into the Round Robin scheduling algorithm. The key novelty of our approach lies in the dynamic adjustment of time slices based on the execution time and priority of each task. This results in more efficient resource utilization and ensures that critical tasks are completed on time. Additionally, we prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, which is also a unique feature in this field. Another unique feature of our approach is that when a high-priority process is executed and does not finish, its priority is decreased by one to prevent low-priority processes from starvation. To the best of our knowledge, no previous research has combined these elements of dynamic time quantum, priorities, and decreasing priority in the Round Robin scheduling algorithm. Our proposed approach offers a promising solution for optimizing task execution in real-time systems and provides an original contribution to the existing literature in this field.
## 2 Methods
### The MIMO Signal Detection Problem  
The MIMO signal detection problem involves estimating the transmitted symbol \(\widehat{\mathbf{s}}_{k}\) of \(\mathbf{s}_{k}\) in relation to the known received signal vector \(\mathbf{y}_{k}\) and channel matrix \(\mathbf{H}_{k}\). This detection is formulated as  
\[\widehat{\mathbf{s}}_{k}=\underset{\widehat{\mathbf{s}}_{k}\in\chi^{N_{k}}}{ \arg\min}\big{\|}\mathbf{y}_{k}-\mathbf{H}_{k}\mathbf{s}_{k}\big{\|}_{2}. \tag{2}\]  
However, solving the optimization problem in Equation (2) using the optimal ML detector is computationally challenging due to the finite alphabet constraint \(\mathbf{s}_{k}\in\chi^{N_{k}}\). The ML detector exhibits a prohibitively high complexity, and thus various other detectors with different complexities and characteristics have been explored.  
In the MIMO signal detection problem, it is typically assumed that the wireless channel \(\mathbf{H}_{k}\) for the \(k\)-th subcarrier (\(k=1,2,\ldots,N_{\text{c}}\)) is known through perfect channel estimation. The impact of imperfect channel estimation will also be discussed in the following Results and Discussion section.
## 2 Mathematical Modeling
### Muschelknautz Model
#### 2.2.1 Prediction of the Pressure Drop  
The pressure drop across the cyclone occurs mainly due to friction between the fluid and the walls, irreversible losses within the vortex core, and inlet acceleration losses. The latter often dominates the total pressure drop. The losses developed inside the body are determined by the following:  
\[\Delta P_{body}=f\frac{A_{R}}{0.9Q}\frac{\rho}{2}(v_{\theta w}v_{\theta cs})^{ 1.5} \tag{1}\]  
where \(f\) denotes the friction factor, \(\rho\) represents the gas density, the volume flow rate is given by \(Q\), \(v_{\theta w}\) is the velocity at the wall, \(v_{\theta cs}\) is the tangential velocity of the rotating fluid in the inner core radius, and \(A_{R}\) represents the total inside surface area (contributing to the frictional drag).  
The pressure drop in the inner surface of the outlet tube is given as follows:  
\[\Delta P_{c}=\Bigg{[}2+\bigg{(}\frac{v_{\theta cs}}{v_{c}}\bigg{)}^{2}+3 \bigg{(}\frac{v_{\theta cs}}{v_{c}}\bigg{)}^{4/3}\Bigg{]}\frac{1}{2}\rho v_{c }^{2} \tag{2}\]  
where \(v_{c}\) is the average axial velocity of the gas exiting via the vortex finder. Hence, the total pressure loss,  
\[\Delta P_{total}=\Delta P_{body}+\Delta P_{c} \tag{3}\]  
In non-dimensional form, the above expression is written as follows:  
\[Eu=\frac{1}{\frac{1}{2}\rho v_{in}^{2}}(\ \Delta P_{body}+\Delta P_{c}) \tag{4}\]  
Here, \(Eu\) denotes Euler's number, and the gas velocity at the inlet is \(v_{in}\).
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 7 Hadron Beam Lines
### Momentum Selection & Final Focusing Stages  
In low intensity charged particle beams, the accepted particles from the first stage cross through a momentum-selection station, consisting of a magnetic spectrometer. In most cases, it includes two or more dipole magnets, in combination with a quadrupole magnet and a collimator slit. The momentum selection comes with a (sometimes quite important) reduction of the overall rate. In case of slowly extracted or low rate beams, a momentum spectrometer made of tracking devices (such as fast wires or scintillating fiber monitors [126]) can offer a particle-by-particle momentum reconstruction with a resolution down to 2.5%, depending on the spatial resolution of the tracking devices employed. These techniques are discussed, for example, in Refs. [127; 128]. After the momentum selection stage, it is usual to recombine the dispersed rays so that the particles exiting the momentum selection have, at first-order, trajectories independent of their momentum within the accepted momentum band. For this reason, we either implement a field-lens quadrupole or a \"geometrical\" recombination of the dispersion, depending on the exact configuration of the magnetic elements that generate the dispersion. After the momentum selection and recombination stages, a quadrupole multiplet (usually a doublet or triplet) is used to make the beam parallel towards the end of the transfer line (the decay volume in neutrino beams). The strength of this stage must be tuned according to the transverse and the longitudinal dimensions of the decay volume. In-between or downstream of these quadrupoles we place the particle identification instrumentation.  
This class of designs can be applied also for narrow-band neutrino beams, adding the horn (Section 6.1) or working with radiation hard, large aperture magnets (Section 6.2). Given the large intensity, single-particle diagnostics is not a viable option and can be used only in low-rate calibration runs.  
The transfer line between the target and the decay volume is highly tunable. Depending on the chosen target and the specific particle production, we can design a beamline that selects, accepts and transports a variety of momenta. This is usually achieved by scaling the strengths of the magnetic elements of the beam, i.e., re-optimizing the currents. In this case, the particle identification instrumentation must be able to operate efficiently in a wide range of momenta. The power supplies used e.g., in the CERN North Area have an uncertainty of \(\sim\)0.15 A and, for large deflection angles, may give uncertainties on the hadron momentum up to \(\sim\)3%.  
Clearly, high-intensity wide-band neutrino beams cannot afford a hadron, momentum-selective beamline due to the strong reduction of fluxes due to acceptance limitations. In these cases, the decay volume is located just after the horns as in Figure 1 bottom.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Energy Consumption Measurement Results and Discussion
Due to that process time constraint, we had only used small input data sizes (up to 512 \(\times\) 512) that can be processed by most algorithms within one processing period. The energy consumption of the hardware platform after running for 1-h is displayed in Table 3, and the corresponding bar chart is shown in Figure 7. The bolded data in that table are notable outcomes for each input sets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Matrix’s Order** & **MM-2 DFT** & **MV-2 FFTm** & **MV-2 FFTv** & **FFT\_CPU** \\ \hline
64 & **11.8 wh** & 12.0 wh & 12.0 wh & **11.8 wh** \\
128 & **11.9 wh** & 12.1 wh & 12.0 wh & **11.9 wh** \\
256 & **11.9 wh** & 12.2 wh & 12.2 wh & 12.4 wh \\
512 & **12.3 wh** & 12.9 wh & 12.8 wh & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Entire board of energy consumption during continuous tests. Figure 6: Energy consumption of single execution in continuous test. Figure 7 illustrates that the energy consumption of various algorithms generally increased as the order of input matrices increased, yet with different growth rates. Among these algorithms, MM-2 DFT was the most energy-efficient overall. FFT_CPU showed low power consumption comparable to MM-2 FFT when the input data size was equal to or lower than 128 \(\times\) 128. However, its power consumption soared to the highest among all the evaluated algorithms when the input data size reached 256 \(\times\) 256 or higher. Regarding the two implementations of MV-2 FFT, MV-2 FFTv was more energy-efficient compared to MV-2 FFTm but still consumed more time than MM-2 DFT on all tested input data sizes.
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 3 Prototype of 5G Integrated with TSN
### Setup of the TSN Network
Deterministic delivery of data can only be achieved with IEEE 802.1 Qbv time scheduling if all integrated components are time synchronized. Synchronization via Ethernet is offered by the IEEE 802.1 AS, gPTP [27] or IEEE 1588 Precision Time Protocol [28]. For the TSN network, two industrial switches with FPGA-based Ethernet ports (1 Gbps) supporting IEEE 1588, IEEE 802.1 AS and IEEE 802.1 Qbv were installed with the two endpoints. This can be seen in Figure 2. With TSN switches synchronized to a common time, the IEEE 802.1 Qbv time schedules of the different TSN switches can be configured in a coordinated manner so that prioritized traffic can be transmitted with deterministic performance through the TSN network. For the planning of the time schedules of the TSN switches, the minimum and maximum latencies of every TSN switch need to be considered. If large jitter is introduced by nodes in the network, the calculation of feasible time schedules for the TSN switches becomes difficult and even infeasible if the jitter becomes too large. Figure 3: Examples of IEEE 802.1 Qbv scheduled traffic with two time slots for high priority and best effort traffic. Figure 2: Architecture of the TSN setup with connection points for the PLC and the factory cloud. Therefore, it is important to maintain low jitter throughout the network for applying TSN time scheduling according to IEEE 802.1 Qbv.
# Determination of Geometrical REVs Based on Volumetric Fracture Intensity and Statistical Tests
## 4 Determination of the Geometrical REV
### REV Size Based on Statistical Tests
The Wald-Wolfowitz runs test demonstrated that the null hypothesis could not be rejected when the sample size is larger than or equal to 13 m, but the likelihood ratio test demonstrated that the null hypothesis was accepted when the sample size was larger than or equal to 12 m. Therefore, the geometrical REV size was determined to be 13 m using the Wald-Wolfowitz runs test, but the geometrical REV size was determined to be 12 m using the likelihood ratio test. The REV size based on the Wald-Wolfowitz runs test was larger than that of the likelihood ratio test. Different results were obtained for these two statistical test methods that consider different aspects. The likelihood ratio test focuses on the likelihood ratio, which expresses how many times more likely the data are under one model than the other, whereas the Wald-Wolfowitz runs test focuses on the randomness of the sequence for two independent sample data. Therefore, it is necessary to determine a more reasonable REV size by considering the comprehensive difference between different methods. Finally, considering  
Figure 10: The error bar chart with confidence interval for different datasets. Figure 9: The REV values varied with the study regions. a result that both the likelihood ratio test and Wald-Wolfowitz runs test accept, the larger cube size, 13 m, is selected as the geometrical REV size of the fractured rock mass at the Maji dam site in China.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Model Assessment and Implementation
#### 3.2.4 Model Implementation  
Following the RAE\({}_{t}\) model [10], the recurrent layers of our models implement GRU over the widely known long short-term memory (LSTM, [58]) as it presents roughly the same performance [59], but has fewer parameters and needs to store less information per time (i.e., it is faster). The encoder and decoder recurrent blocks of the models (i.e., \(E^{1}(\cdot)\) and \(g^{1}(\cdot)\) on Algorithms 1 and 2) are a stack of two bidirectional RNN layers of 64 units in order to increase representation complexity as [10] presented. The models were trained for 300 epochs with a batch size of 64 and an _Adam_ optimizer [60]. The implementation was performed using the _Keras_ library ([[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)), accessed on 10 July 2021).
# Steel Corrosion Behavior in Light Weight Fly-Ash Based Alkali Activated Mortars
###### Abstract  
Alkali activated materials as possible sustainable alternative to cementitious binders showed competitive performances in terms of mechanical and durability properties and high temperature stability. For this reason, light weight fly-ash based mortars have already been optimized as passive fire protective coating for steel structures. However, a lack of information about the durability of these innovative systems in terms of steel corrosion resistance is still present. Thus, this study aims at investigating the durability of steel coated with a 20-mm thick light weight mortar layer in a neutral environment (tap water) and in presence of chloride-containing solution (0.2 M NaCl). In addition, the influence of pore solution chemistry and pH was discussed through electrochemical testing in leachate pore solution and NaOH aqueous solutions at different concentrations. It was found that almost complete protection ability of light weight mortar was obtained when coated steel is exposed to neutral solution for 60 days, while in presence of chlorides, steel is more susceptible to corrosion already after 40 days of exposure. In addition, the developed open porosity of the light weight mortars, it was found that pH and the chemistry of the pore solution in contact with steel strongly influenced the steel corrosion resistance.  
alkali activated materials; steel corrosion; light weight mortars; electrochemical testing; durability; chlorides; coating +
Footnote †: journal: Applied Sciences  
15 January 2021  
Accepted: 18 February 2021  
Published: 22 February 2021
## 5 Synergistic Effects of Integrating IoT, LLMs, and Federated Learning  
The previous sections have laid the groundwork by introducing the core concepts and individual capabilities of IoT, LLM and FL. While pairwise integrations--such as applying LLMs to IoT data analytics [105], using FL for privacy-preserving IoT applications [20,21], or employing FL to train LLMs [23]--offer significant advancements, they often encounter inherent limitations [10]. Centralized LLM processing of IoT data raises critical privacy and communication bottlenecks [13]; traditional FL models struggle with the complexity and scale of raw IoT data [14]; and federated LLMs without direct access to real-world IoT streams lack crucial grounding and context [7].  
This section argues that the true transformative potential lies in the synergistic convergence of all three technologies, IoT, LLMs, and FL, explicitly enhanced by Privacy-Enhancing Technologies [49]. This three-way integration creates a powerful ecosystem where the strengths of each component compensate for the weaknesses of the others, enabling capabilities and solutions that are fundamentally unattainable or significantly less effective otherwise [45]. We posit that this synergy is not merely additive but multiplicative, paving the way for a new generation of advanced, privacy-preserving, context-aware distributed intelligence operating directly at the network edge [15]. We will explore this \"\(1+1+1>3\)\" effect through three core synergistic themes, building upon the motivations discussed in works like [56].