esa-sceva/satcom-chunk-collection huggingface dataset:
# Phononic Coupled-Resonator Waveguide Micro-Cavities
## 1 Introduction  
The engineering of phononic resonances enables the excitation of localized vibrations of microscopic objects at ultrasonic frequencies, forming meta-atoms or meta-molecules with precise eigenfrequencies and elastic waveforms. They have potential applications as sensor elements [1], MEMS resonators [2], elastic waveguides [3], or optomechanical systems [4; 5; 6; 7], for instance.  
Phononic resonances are naturally induced at the microscale with three-dimensional mechanical structures attached to a surface, for instance, in the form of erected pillars [8] or chains of resonators [3; 9]. One requirement is that clamping to the surface or support does not reduce too significantly the quality factor of resonances through radiation of outgoing elastic waves. Another approach that is prone to engineering relies on phononic crystals [10; 11; 12]. One of their important features is indeed the formation of complete band gaps, inside which wave propagation is forbidden and only evanescent waves can exist.  
By introducing defects in a phononic crystal, spatially-localized states are introduced with precise resonant frequencies. Further forming an infinite chain of such defects leads to the concept of the coupled-resonator elastic waveguide (CREW) [13], originally inspired by coupled-resonator optical waveguides in photonic crystals (CROW) [14]. It is known that the dispersion of waves guided along a CREW is very smooth and is determined by the coupling coefficients existing between phononic resonators. When the chain is made finite, a discrete set of resonance frequencies established along the dispersion relation for guided waves; we hence term the chain a coupled-resonator waveguide cavity.  
The resonant states are collective vibrations of the chain, as we have shown recently in the case of a chain of seventeen resonators implemented in a two-dimensional fused silica phononic crystal slab, and are rather independent of the path followed over the surface of the slab [15].  
In this paper, we experimentally explore how a set of a few phononic coupled-resonator waveguide cavities, coupled weakly through evanescent elastic waves, can form collective vibrations where the resonance modes of the individual phononic cavities are essentially preserved but can be excited at once. Experimentally, we rely on the phononic crystal slab technology developed for our previous work based on femtosecond laser writing followed by KOH etching of a fused silica plate [15], but extend our considerations to the set of three short phononic cavities shown in Figure 1. In the following, we first present the experimental results and the numerical simulations that sustain them, before discussing the observed collective vibrations in the light of a dynamical matrix model with one degree of freedom per defect and the nearest-neighbor approximation.
## 4 Experimental Test
### Experimental Results and Discussions  
The vibration isolation performance of the vibration isolator is closely related to its resonance frequency and response peak. The lower the resonant frequency, the lower the initiation frequency of vibration isolation and of the wider vibration isolation frequency band. In this experimental test, to comprehensively analyze the displacement transmissibility of the isolator with load mass m = 4.9 kg, a sweep sine excitation was applied to the base and the frequency of the exciting signal was set from 1 Hz to 20 Hz. The parameters  
Figure 10: Vibration transfer rate curves under different parameters. (**a**) Excitation amplitude (\(\zeta\) = 0.3; \(D\) = 0.02 m; \(E\) = \(3.5\times 10^{4}\) Pa); (**b**) viscous damping ratio (\(\Lambda\) = 0.05 m/s\({}^{2}\); \(D\) = 0.02 m; \(E\) = 3.5 \(\times\) 10\({}^{4}\) Pa); (**c**) [PERSON]’s modulus (\(\Lambda\) = 0.05 m/s\({}^{2}\); \(\zeta\) = 0.3; \(D\) = 0.02 m); (**d**) section diameter (\(\Lambda\) = 0.05 m/s\({}^{2}\); \(\zeta\) = 0.3; \(E\) = 3.5 \(\times\) 10\({}^{4}\) Pa).  
adopted by the theoretical calculations are listed in parentheses under the icon of Figure 12, where the reason for the large damping ratio is that the damping here is the structural damping of the vibration isolator, including friction damping of hinge joints and straight bearing, as well as damping of the silicone gel material itself.  
Figure 12 shows a comparison of experimental and theoretical results of displacement transmissibility, and the vibration of the base and bearing platform are at the excitation frequencies of 3 Hz, 9 Hz, and 15 Hz after the system reaches a steady state. It is found  
Figure 11: Experimental set-up for the vibration isolation test.  
Figure 12: Comparison of experimental and theoretical results of displacement transmissibility.  
that the experimental test results and the theoretical calculations essentially agree, and it should be noted that the experimental results in the low-frequency resonance region are slightly smaller than the theoretical results and slightly larger than the theoretical results in the high-frequency zone. This is caused by the insufficient accuracy of the installation of the physical model to produce additional damping. The effective vibration isolation frequency of the SGC vibration isolation system is about 4 Hz. As the frequency increases, the inhibitory effect of the vibration isolation system on the vibrational transmission gradually increases. When the excitation frequency increases to 15 Hz, the amplitude of the steady-state displacement has the ability to decrease to 18 dB.
## 2 Operating Principle of the Active Balancing System
### Balance Actuator  
The balance actuator, which determines the function of the total system, is the core of the balancing system, and it comprises a liquid disc and an air distributor, as shown in Figure 2. The liquid disc has two pairs of orthogonal liquid chambers, and each pair has two contra-positioned chambers that are pre-filled with balance liquid before installation. Each chamber has a corresponding gas channel along which the compressed air can enter the chamber and transfer the balance liquid, and each pair of contra-positioned chambers is connected by a liquid channel. To process these four gas channels and two liquid channels, the cover plates are divided into four pieces and are welded together with the disc using brazing welding. A strength test is also needed to check the welding quality.  
Figure 1: Flowchart of the balancing system.  
Compressed air is delivered from the standing air tubes to the liquid disc through an air distributor, which comprises a casing, a sleeve, and two bearings. There are four holes on the outer wall of the casing that are used to connect with four air pipelines. To prevent air from escaping, a middle sleeve made from polytetrafluoroethylene is needed to transfer the compressed air. More specifically, the air distributor and the balance disc are connected by a rotary shaft integrated with the cover plates of the liquid disc, and there are also four gas inlet holes on the outer wall of the shaft to form the gas channels. The air distributor and the rotary shaft are separated by a small gap. Accordingly, to guarantee the long-cycle operation of the balance actuator, two bearings are adopted between these two components.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
### Motivation and Contributions  
This subsection precisely discusses the motivation behind this research and different contributions presented in this research work. Generally, the cloud paradigm provides various IT resources virtually to users based on their requests raised to cloud providers with different SLA guarantees. To provision resources efficiently to cloud users around the world, a cloud provider needs to effectively deploy an efficient scheduler which should benefit both the cloud provider and user. The main motivation behind this research is to minimize the rate of failure of tasks which are to be computed in the cloud paradigm. This can be carried out when the scheduler chooses an appropriate VM for the task that has arrived at the cloud application console. Ineffective scheduling mechanisms cause an increase in makespan, which increases execution time, thus consuming more energy, and also leads to failure of tasks if proper assignment of tasks to a suitable VM is not performed. From this perspective, our motivation behind this research is to develop a scheduler which should minimize the rate of failure and, as such, improve quality of service. In line with this research, we also tackle different parameters, i.e., energy consumption and makespan, which are primary objectives for any task scheduler. The main contributions of our research work are as follows.  
1. A novel fault-tolerant aware task scheduling algorithm is developed by capturing priorities of tasks and VMs based on the unit electricity cost of the datacenter's location.
2. A reinforcement-learning-based deep Q-network (DQN) model is used as the methodology to design the scheduler.
3. A real-time dataset, i.e., Google Cloud Jobs, is fed as an input to the scheduler to check the efficacy of our proposed DRFTSA. An extensive set of simulations are conducted on Cloudsim using various scenarios by keeping a fixed number of VMs and varying the VMs in the simulation.
4. In this research, rate of failure, energy consumption, and makespan are evaluated by using the abovementioned dataset with different scenarios by changing the number of VMs.  
The rest of the manuscript is organized as follows. Section 2 discusses related works, Section 3 discusses problem formulation and system architecture, Section 4 discusses the methodology used in DRFTSA, Section 5 discusses the simulation and result analysis, and Section 6 discusses the conclusion and future works.
# Routing Design for Pipe System Considering Natural Frequency
## 6 Simulations and Results
### Application
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Pipe** & **Diameter (mm)** &
\begin{tabular}{c} **Extension Distance** \\ **of Each Pipe Nozzle** \\ **(mm)** \\ \end{tabular} \\ \hline
3 & P1–P6 & 5 & \\ P7–P11 & 10 & 8 \\  & P12 & 20 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pretreatment of the direction constraint of the pipe nozzles in case study 3. Figure 20: Aero engine in case study 3.  
indicates that the pipeline joint is directly connected. When the fitness value is greater than 1, this indicates that the shortest segment of the pipeline falls below the required 30 mm length, and \(\beta_{3}\) takes on a value of 0.5. Conversely, when the fitness function is less than 1, this implies that the optimization process of the pipeline has successfully increased the natural frequency by optimizing the bending positions and strategically setting clamps. It is worth noting that for pipe 12, conventional algorithms like maze, A*, and other greedy algorithms generally provide direct connection results as their main aim is to find the shortest path. However, due to the large outer diameter and considerable length of pipe 12, its natural frequency value is very small. In contrast, the proposed algorithm goes beyond simple shortest-path solutions and significantly improves the natural frequency of pipelines by iteratively optimizing pipeline path points during the layout process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case.** & **Pipe** & **Length (mm)** & \
\begin{tabular}{c} **Frequency** \\ **(Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Minimum** \\ **Segment** \\ **(mm)** \\ \end{tabular} & **Fitness Value** \\ \hline \hline \multirow{4}{*}{3} & P1–P6 & 19.1 & \(1.3\times 10^{6}\) & 19.1 & 1.18 \\  & P7 & 48.3 & \(5.30\times 10^{3}\) & 48.3 & 1 \\ \cline{1-1}  & P8 & 70.7 & \(2.08\times 10^{6}\) & 10 & 1.19 \\ \cline{1-1}  & P9 & 39.8 & \(7.97\times 10^{3}\) & 39.8 & 1 \\ \cline{1-1}  & P10 & 33.1 & \(1.17\times 10^{4}\) & 33.1 & 1 \\ \cline{1-1}  & P11 & 31.1 & \(1.33\times 10^{4}\) & 31.1 & 1 \\ \cline{1-1}  & P12 & 67.8 & \(1.47\times 10^{4}\) & 33.3 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of the pipe paths in case study 3. Figure 21: The layout of 12 pipes. Overall, the results of the experiments illustrate the robustness and efficiency of the proposed algorithms in path planning for pipelines, considering not only the shortest path but also the critical factor of natural frequency to ensure the pipelines can withstand actual engineering loads and vibrations.
## 3 Experiments and Results  
To verify the reliability and effectiveness of the performed method, an experimental setup based on the schematic diagram in Figure 1 was assembled and is shown in Figure 7. The light source is a stable He-Ne laser (HRS015B, Thorlabs, America) with a center wavelength of 632.991 nm, which can achieve frequency stability or intensity stability. The detector used to measure the straightness and roll employs a CMOS sensor (MV-CA050-20 GC, HIKROBOT, China) with a resolution of 2592 \(\times\) 2048 px and a pixel size of 4.8 \(\times\) 4.8 \(\upmu\)m. The focal length of L1 and L2 is 100 mm. The distance between the RRs is 125 mm. The distance between the light source and the moving unit is 500 mm. The distance and focal length do not affect the measurement accuracy, only the measurement resolution. A series of experiments were carried out under laboratory conditions.  
Figure 6: Measurement principle of the angular drift.
## 3 Data and Methodology
### Methodology  
The proposed method for parsing Chinese address text on the internet integrated the CHTopoNER model and dynamic FSM. First, the CHTopoNER model was used for named entity recognition (NER) of the multi-hierarchical address elements in texts from the internet. Subsequently, the hierarchical elements of each address were obtained. Furthermore, according to the hierarchical features of each address, sets of dynamic FSMs were created to parse Chinese address texts on the internet through state transitions. This method is shown in Figure 1.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Data Labeling  
In our approach, we utilized two distinct methods for data labeling:  
* **Explicit Threat Level Labeling**: Alerts were classified according to predefined threat levels to facilitate categorization and enable supervised analysis;
* **Clustering Based on Data Similarity**: We employed the KMeans algorithm [18] to identify natural clusters within the data, enabling the discovery of patterns and subtle anomalies that might not be evident through explicit labels alone.  
In explicit labeling based on the threat level, we assigned labels to each alert based on its threat level, categorizing them into three distinct categories:  
* **Unknown Threat** (low danger level;
* **Questionable Threat** (moderate danger level);
* **Dangerous Threat** (high danger level).  
To achieve this, we defined a mapping function to assign specific classes to the alerts based on their threat levels. After applying this function, we added a new \"Class\" column to the DataFrame, indicating the threat level of each alert. After applying the mapping  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **Type** & **Engineering Technique** \\ \hline Day & Numerical & MinMaxScaler normalization [0,1] \\ Hour & Numerical & MinMaxScaler normalization [0,1] \\ Minute & Numerical & MinMaxScaler normalization [0,1] \\ Event Type & Categorical & One-hot encoding \\ Event Name & Categorical & One-hot encoding \\ Source IP & Categorical & Label encoding \\ Destination IP & Categorical & Label encoding \\ Destination TCP/UDP Port & Numerical & MinMaxScaler normalization [0,1] \\ Event Severity & Numerical & MinMaxScaler normalization [0,1] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature Types and Data Preprocessing Methods.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Feature** & **Count** & **Mean** & **Std Dev** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
**Month** & 578.0 & 3.92 & 0.6 & 3 & 3.0 & 4.0 & 4.0 & 5 \\
**Day** & 578.0 & 14.7 & 4.5 & 1 & 11.0 & 14.0 & 18.0 & 28 \\
**Hour** & 578.0 & 15.3 & 2.1 & 9 & 14.0 & 15.0 & 17.0 & 21 \\
**Minute** & 578.0 & 28.5 & 12.4 & 3 & 17.0 & 27.0 & 38.0 & 59 \\
**RuleDescription** & 578.0 & 10.4 & 6.3 & 0 & 5.0 & 10.0 & 15.0 & 22 \\
**Groups** & 578.0 & 5.7 & 2.1 & 1 & 4.0 & 6.0 & 7.0 & 11 \\
**CommandLine** & 578.0 & 32.6 & 18.9 & 5 & 21.0 & 30.0 & 45.0 & 94 \\
**RuleLevel** & 578.0 & 8.9 & 3.1 & 3 & 6.0 & 9.0 & 12.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Encoded & Clean DATA Sample.  
function, the DataFrame contains a new 'Class' column indicating the threat level of each alert.
## 2 Materials and Methods
### IR Spectra Collection  
The infrared spectra were recorded on a PerkinElmer Spectrum Two(tm) (PerkinElmer, Waltham, MA, USA) FT-IR spectrometer, consisting of a deuterated triglycine sulfate (DTGS) detector and a PerkinElmer Universal Attenuated Total Reflectance (uATR) accessory equipped with a single bounce diamond crystal. Spectra were registered in the 4000-400 cm\({}^{-1}\) spectral range with 4 cm\({}^{-1}\) instrumental resolution, and sixteen scans averaged per spectral replicate. The background signal was collected with the crystal exposed to the air. ATR-FT-IR spectra were acquired after cleaning the leaves with kitchen paper to remove any soil residue. All samples were analyzed within three days of harvest, with the leaves stored in the refrigerator. The diamond was cleaned using methanol and precision wipes after each sample analysis. Care was taken to ensure complete evaporation of methanol before placing a new sample on the ATR device. A total of 60 IR spectra were acquired for Class A, 58 IR spectra for Class B, and 45 IR spectra for each of Classes C, D, E, F, G, and H.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
###### Abstract  
Previously conducted studies have established that the soil-rock mixture in the Chongqing area has the characteristics of loose structure, poor stability, strong permeability, and so on. When building a tunnel in a soil-rock mixture stratum, it is necessary to reinforce the surface rock mass and surrounding rock by grouting to improve the safety of tunnel excavation. To study the diffusion mechanism of cement slurry (Bingham fluid) in soil-rock mixtures, based on the Bingham fluid flow equation and slurry diffusion model, the Bingham fluid fracture diffusion formula was derived, and field grouting tests and indoor model tests were carried out with soil-rock mixtures in the Chongqing area as the research object. The fracture grouting diffusion formula was verified and analyzed using the test data. The research results show that the theoretical calculation results of various working conditions are close to the actual test results (the error of indoor model test results is less than 3%, and the error of field test results is less than 5%). A Bingham fluid fracture diffusion formula has been developed that applies to various working conditions of fracture grouting of soil-rock mixtures and has a good prediction effect on the value of the fracture diffusion radius.  
soil-rock mixtures; stone content; grouting test; fracture diffusion; diffusion radius Article
## 3 Results
### Sampling Design  
According to EFSA's scientific opinion, there is a lack of information on alkyl furans levels in foods [17]. Therefore, a large-scale sampling plan was designed in this study (n: 1003 samples) to provide a comprehensive database for Belgium, to fulfil the European recommendations and to initiate a risk assessment. The sampling proposed here is based on three key parameters including previous contamination data, diversity, and appearance of the food.  
First, the main food groups in the plan as well as the number of samples allocated for each group were proposed based on the level of furan contamination in foods already reported. According to the EFSA report, the group of people most exposed to furan is infants, mainly through consumption of ready-to-eat jarred or canned foods. Exposure of other population groups is mainly due to the consumption of coffee and grain-based foods, depending on age and consumer habits [17; 37]. Therefore, special emphasis was paid in this work to these food groups by allocating a relatively high number of samples for each of them in the initial proposed sampling plan. Similarly, different main groups were proposed such as composite food, snacks, bakery products, etc.  
Second, within each main group, several sub-groups were proposed according to the diversity of food products as shown in the Table 1. This parameter was evaluated by conducting a field investigation through the major food market chains. Nevertheless, due to COVID-19 constraints, those investigations were limited to coffee and baby foods sampling plans. For the remaining food groups, their sub-groups were proposed based on the number of different items present in the EFSA catalogue \"FoodEx2\" [38] as well as in the Food category system of the General Standard on Food Additives of Codex Alimentarius [39].  
Third, for each food group, special attention was given to the dietary aspect such as organic, fair trade, and special diets. In 2019, Biowallonie published a report on the evolution of the organic market share in Wallonia (southern part of Belgium) [40]. It was only 1.5% in 2009, slowly increased to 3.2% in 2018, and reached 4.8% in 2019, which proves a growing attraction for organic food products. Therefore, as the weight of organic foodstuffs increased, about 10% of the samples were allocated to organic products.  
The samples were collected randomly from the main food chains over a two-and-a-half year period, to avoid any one brand or food chain being tied to a province, but also to take into account for seasonal variations in products.  
The proposed sampling plan was subsequently submitted to a Belgian committee of food safety experts, who reviewed it and came up with the final validated sampling plan that is summarised in Table 1.