esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Subsequently, many permeability models were developed to account for the effect of effective stress and coal matrix shrinkage (17; 18; 19; 20). Most of these models assumed uniaxial strain conditions, which is inconsistent with the three-dimensional in-suit stress in coal seams. To eliminate the restriction of this assumption, a sequence of new models have been introduced that account for in the in-situ stress conditions (21; 22; 23; 24). Based on these permeability models some scholars established coupled numerical models for coal seam methane extraction. [PERSON] et al. (25) conducted a dual poroelastic model to quantify the interactions between CO\({}_{2}\) and CH\({}_{4}\) during CO\({}_{2}\)-enhance coal seam methane recovery. [PERSON] et al. (26) considered thermal transport in coal to develop a THM-coupled model for investigating coal-gas interactions. [PERSON] et al. (27) used a coupled model including equilibrium desorption model and dynamic desorption model to analyze how the non-Darcy flow influences coal seam methane extraction. [PERSON] et al. (28) explored the gas extraction effect with slotted boreholes using a THM coupled model. A hydro-mechanical model for the modeling of coalbed methane extraction is introduced by [PERSON] et al. (29). They focused on the impact of critical parameters related to coal seams on the methane production. Through a dynamic flow model, the drainage effect after using different stimulation technologies was investigated by [PERSON] et al. (30), where they found that the area of drainage zone after hydraulic fracturing was about 20 times larger than that after hydraulic slotting. However, none of these models is capable of investigating the multi-scale THM coupling behaviors of a coal seam after directional hydraulic fracturing. In this paper, a fully coupled multi-scale model was developed to investigate the effects of directional hydraulic fracturing on gas extraction and solved using the finite element method (FEM). This model correlates gas flow and heat transfer within the hydraulic fractures, matrix, and cleat system under deformation of the coal seam. Further, this model was validated and used to analyze the evolution of hydraulic fracture apertures and permeability in a coal seam during gas extraction. Finally, the impacts of some key factors on extraction completion time were investigated.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Antenna Bending Test Setup and Test  
Since the human body is a dynamic system, it is constantly moving and changing, and the tissue layers will also change with the movement of the human body. The implantable antenna may undergo a certain degree of deformation due to the movement of the surrounding muscle tissue, which may affect the performance of the antenna. It is necessary to study the structural deformation of the antenna to ensure the stability of the antenna. Therefore, we used simulation software to build a cylindrical model and then conformalized the antenna onto the cylindrical model to simulate the bending test of the antenna. As shown in Figure 13, the antenna is bent on the cylindrical model to simulate and measure the s\({}_{11}\) and radiation pattern of the antenna when bent in the X- and Y-direction.
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 3 Results  
### Design Methodology  
The design methodology includes 4 steps:  
The procedure of choosing the right energy harvesting circuit topology.  
The selection process of a suitable diode.  
The design of an appropriate impedance matching network.  
The design, the optimization, and the simulation of the whole system [7].  
Figure 3 illustrate this process.
## 3 Materials
### Feature Extraction
#### 3.2.3 Windowing  
Windowing is a key step in preparing audio signals for further analysis, and more accurate and meaningful results are obtained when using Fourier-transform-based methods. The most common window functions used in audio processing are the Hamming window, Hanning window, and Blackman window.
# Instability and Drift Phenomena in Switching RF-MEMS Microsystems
## 2 RF-MEMS Switch as a Device Based on an Electrical Instability  
Electrical actuation of a microbeam is widely used in RF-MEMS switching devices. Electrically actuated switches are usually made of a conductive microbeam that can be either single or double clamped and one or more actuation electrodes below the beam, as illustrated in Figure 1. Their frequency application range goes from DC up to 30 GHz in the most frequent cases, but RF-MEMS switches purposely designed and fabricated on special substrates can reach frequencies up to 100 GHz.  
Electrostatic actuation can be seen as a very useful instability. When the voltage applied to the actuation electrode gradually increases, the suspended beam bends progressively down, up to the point when the equilibrium between elastic and electrostatic forces cannot be maintained and the beam collapses on the electrode underneath, that is, it actuates [12]. In the case of a double-clamped beam, the actuation voltage of a rectangular beam is given by  
\[V_{act}=\sqrt{\frac{8 kd^{3}}{2\tau\epsilon_{0}A}} \tag{1}\]  
where \(k\) is the spring constant of the beam, \(d\) is the distance between the suspended beam and the electrode, \(A\) is the electrode area, and \(\epsilon_{0}\) is the vacuum permittivity.  
When the voltage decreases after actuation has been reached, the beam bends up again, detaching from the electrode at a different potential, the release voltage, which is given by  
Figure 1: Typical clamped–clamped (**a**) and cantilever (**b**) switch configurations. Both configurations use mobile suspended beams as actuators.  
\[V_{rel}=\sqrt{\frac{2 kd\cdot d_{0}^{2}}{\varepsilon_{r}\varepsilon_{0}A}} \tag{2}\]  
and is always lower than the actuation voltage. In this case, \(d_{0}\) is the thickness of the dielectric layer above the electrodes and \(\varepsilon_{r}\) is its relative permittivity. More complex formulas can be derived for rectangular cantilever beams [13; 14]. Complex beam geometries require simulation work in order to determine their actuation and release voltages, but in some special cases, useful approximate analytical formulas can be derived [2; 15].  
In an ideal device with a perfect dielectric, \(V_{act}\) and \(V_{rel}\) remain stable for an infinite time and after an infinite number of switching cycles. In the real world, instabilities and drift in both actuation and release voltages are fairly common and have different origins. The most frequent sources are charging of the dielectric, temperature variations, fabrication uncertainties, and material wear.
# A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals
## 6 Validation of the Mathematical Model  
To validate the mathematical model developed in Section 3, a simulation was carried out based on the data on technological processes collected at an inland terminal randomly selected from among the 11 European terminals. The observed inland terminal was modeled in the FlexSim CT simulation tool (Figure 4). The observed inland terminal consists of inbound and outbound container yards. The first inbound container yard had four tiers and was located along the rail track, with 648 positions for storing 40\({}^{\prime}\) containers. The second yard had three storage tiers and was located 12 m away from the first outbound container yard, with 634 positions for storing 40\({}^{\prime}\) containers.  
Containers arrived at the inland terminal by rail and were positioned within the inbound container yard according to the criteria in Table 1 and an empirically observed delivery of 24-36 full containers five times per day every weekday. Container unloading and loading each took an average of 150 min, or 300 min for both operations. A rail-mounted gantry crane is used for container loading and unloading from/to trains, while each container yard is equipped with one reach stacker that is used for container storage in the inbound and outbound container yards. Capacity for the rail-mounted gantry crane was 20-25 TEU/h, and the capacity of the reach stacker was 15 TEU/h. Containers of dimension 45\({}^{\prime}\) were stored on the edge of the container yard block; 40\({}^{\prime}\) containers, on tiers 2-3; and 20\({}^{\prime}\) containers, on tiers 2-4.
## 1 Introduction
ET: energy transfer. Figure 2: **(a)** Unit cell of bixbyite (Er\({}_{x}\)Sc\({}_{1-x}\))\({}_{2}\)O\({}_{3}\) crystal. In this schematic, Er\({}^{3+}\) at the \(C_{2}\) site are replaced by Sc\({}^{3+}\). (lower panel) the configurations of \(C_{2}\) and \(C_{si}\) sites. **(b)** Cross-sectional transmission electron microscope (TEM) image of grown (Er\({}_{x}\)Sc\({}_{1-x}\))\({}_{2}\)O\({}_{3}\). In our previous study [17], we investigated the ETs in the Er\({}_{2}\)O\({}_{3}\) single crystal under the resonant excitation of a Stark level (Y\({}_{2}\)) in a \(C_{2}\) site and estimated the large inter-site ET rate (\(\sim\)0.5 \(\upmu\)s\({}^{-1}\)) and intra-site ET rate (\(\sim\)0.1 \(\upmu\)s\({}^{-1}\)) via the excitation power dependence of the time-integrated photoluminescence (TI-PL) intensities, where the non-radiative relaxation and UC process were not explicitly taken into account. However, the energy dissipation via non-radiative relaxation and the UC process is supposed to affect the population dynamics greatly, and therefore, further detailed studies are necessary to reveal the population dynamics in a whole system. In this study, we investigate the population dynamics in a whole system which consists of not only the inter- and intra-site ETs but the non-radiative relaxation in Er\({}_{2}\)O\({}_{3}\) and (Er, Sc)\({}_{2}\)O\({}_{3}\) single crystals grown on Si(111). Since the UC process occurring in the \(C_{2}\) site makes the population dynamics complicated, the low power resonant excitation of one of Stark levels (\(\mathsf{Y}_{3}^{\prime}\)) in the \(C_{3i}\) site is employed in order to minimize the effect of UC process. After deciding the proper experimental conditions, the time-resolved PL (TR-PL) measurements and their temperature dependence have directly revealed the ETs in the same site (\(C_{3i}\), one of intra-site ET) and between the different sites (\(C_{3i}\leftrightarrow C_{2}\), inter-site ET) by using three samples with different Er-concentrations. The simple model requires the temperature sensitive and insensitive non-radiative relaxations to reproduce the TR-PL signals and their temperature dependence well.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 4 Comparison and Analysis of the Bandgap Properties  
In the engineering applications which are sensitive to vibration, such as aviation, aerospace, navigation, and vehicles, the vibration isolation performance will significantly affect the safety and reliability of the system, and one of the biggest challenges of vibration  
Figure 3: Stress–strain characteristics of the metamaterials by simulation and experiment: (**a**) experimental equipment; (**b**) IM; (**c**) SPCHM; (**d**) SPCHM with copper pins.  
isolation structures is to achieve wide and low-frequency bandgaps. Therefore, this section concentrates on the bandgap performances of the IM and SPCHM and further comparing the bandgaps of the SPCHMs with different structural parameters and, finally, establishes a design method of the bandgap.
## 1 Introduction
### Related Work
#### 1.1.4 Interoperability, Spaces, and Navigation
Since the topological relationships between building parts and spaces are available in the IFC model, this information can be used to create navigation networks. The second approach does not use the topological relationships in the IFC data and creates navigation networks from a pure building geometry. In our work, we followed the first approach. [PERSON] and [PERSON] [22] proposed an algorithm to extract a graph representing the spatial layout of a building and its topology from IFC model. The authors effectively extract information about the building from the IFC model using the topological relationships stored in the model. Besides identifying and connecting spaces, the algorithm can retrieve additional nonspatial features of the extracted paths and spaces, such as accessibility features for different types of users. This information can be efficiently used for navigation purposes, and to construct navigation networks from the IFC.
## 5 Applications of Thermal Images to Future Space Missions
### Site Selection and Safe Assessment  
TIR contributed to the Hayabusa2 mission purpose for providing information on the landing site selection for sampling and the release of landers, as well as the safe assessment during the descent operations [19]. TIR data is applicable to provide the global distribution of temperatures at any time and the highest temperature in a day for each site. After determining the thermal inertia derived from the diurnal temperature profiles, surface physical states are mapped to find the preferable sampling sites with existence of fine or small particles or the sites with base rock surfaces, or to avoid the hazardous sites with numerous boulders. In addition, the temperatures of local sites are predictable by comparison with numerical studies regarding the time of descent operations to assess safe landing.  
Figure 7: Thermal image of the Small Carry-on Impactor (SCI) released from Hayabusa2 spacecraft taken by TIR on 5 April 2019, at the altitude of 500 m. The shape and the attitude of the SCI was recognized in thermal images against the background of asteroid surface. A movement of SCI with its small nutation was observed in this image. The temperature scale is in Kelvin. (Image: hyb2_tir_20190405_015705_12.fit, ©JAXA).
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Software Architecture
#### 2.3.1 Firmware Description
To further extend the battery life, the SIM7000G is configured with its Power Saving Mode (PSM), and the luxmeter, accelerometer and CO\({}_{2}\) sensor are configured in their low-power modes, while the temperature and humidity sensor has a very low power consumption with no configuration required.
## 2 Architectural Framework of IBSN and Its Components
### Sensing Strategies  
Ongoing researches of bio-chemical and electro-mechanical sensor technologies have led to a wide range of wearable and implantable sensors, suitable for continuous monitoring. In general, bio-medical sensors can be classified into physiological and bio-chemical sensors based on the medical parameters to be monitored for specific medical conditions [3]. Some of these parameters that are monitored for different medical conditions are listed in Table 3. Important requirements for physiological and biochemical sensor types are sensitivity, selectivity, reliability, ease-of-use, sensor packaging, bio-compatibility, and power consumption. Implantable bio-sensors are often affected by noise due to bio-fouling, motion artifact, and interference. The new sensor designs are focused on reducing these artifacts, thereby increasing the implantability of the sensors.  
The IBSN system should be able to handle the diversity of the sensors without jeopardizing the quality of the sensor data which may result in the misdiagnosis. Figure 3, represents the diversity of the bio-sensors that can be used in IBSN. These medical sensors output different types of medical data at different rates. An ideal network should be self-adaptive to accommodate different kinds of sensor devices. Continuous sensing will increase the power consumption of the sensor node. However, discontinuous sensing can lead to missing life-critical data. A sensing strategy should be able to handle the trade-off between the power consumption and the reliability of the sensing system efficiently.  
Figure 3: An example of heterogeneity of nodes in IBSN.  
Figure 2: Common architecture of IBSN sensor nodes.
## 7 Conclusions  
In a smart parking, AIoT can help drivers to save searching time and automotive fuel by predicting short-term parking place availability utilizing a MLNN algorithms. To find the most suitable MLNN algorithm, this paper evaluated performance of a set of well-known MLNN algorithms (i.e., KNN, DT, RF, SVM, MLP, LSTM and SLP) as well as more than 50 combinations of them (i.e., known as Ensemble Learning) based on a real parking datasets including around five millions records of the measured parking availability in San Francisco. For evaluation, we considered the cross validation scores as well as resource requirement, simplicity and execution time (i.e., including both training and testing times) of algorithms. We saw that some ensemble learning algorithms provide the best performance in aspect of validation score. However, they consume a noticeable amount of computing and time resources. On the other hand, simple algorithms such as DT and KNN could provid a much faster execution time than ensemble learning algorithms, while we could not see a noticeable difference between their performance and the best ensemble learning algorithm (e.g., DT's accuracy is less than 1% lower than the best ensemble algorithm). In a AIoT-based smart parking, the MLNN algorithms should be run on limited-resource edge and even end device. As a result, using simple MLNN algorithms with acceptable performance such as DT can be more appropriate than complex compute-intensive ones.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **f1\_Score** & **Training (s)** & **Testing (s)** \\ \hline RF & 79.15 & 80.94 & 79.15 & 78.68 & 0.2150 & 0.0196 \\ DT,RF,CNB & 79.81 & 81.65 & 79.81 & 79.11 & 0.2205 & 0.0239 \\ KNN,DT,RF,CNB,SVM & 79.93 & 81.79 & 79.93 & 79.10 & 0.2534 & 0.0421 \\ DT & 78.96 & 81.09 & 78.96 & 78.64 & 0.0015 & 0.0009 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithms with the best performance in Part I and Part II.  
We finally simulated the proposed recommendation system using the DT Algorithm. For 1000 parking spot requests from drivers, the results showed that around 77% of drivers can not find a free spot in their selected destinations (i.e., street or segment). By predicting this issue and introducing alternative closest vacant locations to destinations to the drivers, the recommended system could save, in total, 3500 min drivers searching time. Therefore, the recommendation system can help to reduce the traffic and save a noticeable amount of fuel.  
Conceptualization: I.D., E.A. and G.M.L.; Data curation: I.D. and E.A.; Formal analysis: I.D. and E.A.; Writing--original draft: I.D., E.A. and G.M.L.; Writing--review and editing: E.A, and G.M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Literature Review
### Readability Formulas
#### 2.2.2 Dale-Chall Readability Formula  
The Dale-Chall Readability Formula is a widely used readability measure that provides an estimate of the comprehension difficulty of a given text. Developed by [PERSON] and [PERSON] in 1948 [30], this formula takes into account both the length of sentences and the familiarity of words to determine the readability level.  
The Dale-Chall readability formula calculates its final score by examining the proportion of words in a given text that do not belong to a predefined list of commonly known words. This list comprises 3000 words that are generally understood by fourth-grade students. The formula calculates the readability score by incorporating the average sentence length and the percentage of unfamiliar words.  
In summary, the Dale-Chall formula's notable advantage lies in its focus on word familiarity, enhancing its ability to assess readability, especially for less experienced readers or those with limited vocabulary. This practical and accessible approach considers both word familiarity and sentence length, offering insights into comprehension difficulty for readers of different proficiency levels. However, it is crucial to acknowledge the formula's limitations and its suitability for specific contexts.
###### Abstract  
The rapid expansion of network environments has introduced significant cybersecurity challenges, particularly in handling high-dimensional traffic and detecting sophisticated threats. This study presents a novel, scalable Hybrid Autoencoder-Extreme Learning Machine (AE-ELM) framework for Intrusion Detection Systems (IDS), specifically designed to operate effectively in dynamic, cloud-supported IoT environments. The scientific novelty lies in the integration of an Autoencoder for deep feature compression with an Extreme Learning Machine for rapid and accurate classification, enhanced through adaptive thresholding techniques. Evaluated on the CSE-CIC-IDS2018 dataset, the proposed method demonstrates a high detection accuracy of 98.52%, outperforming conventional models in terms of precision, recall, and scalability. Additionally, the framework exhibits strong adaptability to emerging threats and reduced computational overhead, making it a practical solution for real-time, scalable IDS in next-generation network infrastructures.  
Keywords: intrusion detection system; autoencoder; extreme learning machine; cloud continuum; edge-fog-cloud orchestration; scalable iot security; real-time threat detection +
Footnote †: journal:
## 4 System Architecture
### Delay Measurements  
Considering that the system will be placed in the signal path between the antennas and a GNSS receiver, it is important to measure the delay introduced, given that it may negatively affect the positioning.  
The group delay of the system was extracted using the previous S\({}_{21}\) phase measurements. Figure 7 presents the delay results obtained for the 16 channels across the E1/L1 frequency range. From the results, it is possible to verify that while not perfectly constant, the system achieves very little variations across all channels.  
Table 5 summarizes the mean and standard deviation of the group delay for the 16 channels for 1 beam. All channels have an average value of 381 ns with a maximum deviation of 1.05 ns. The delay was also calculated for the implementation using four beams, which achieves a slightly bigger average delay of 384.6 ns while keeping the variation to 5 to 6 ns. This is due to the more complex implementation of the beamforming IP core which results in a slightly bigger signal path. The influence of this delay on the positioning is yet to be validated through open-sky tests. For static scenarios, this delay should not be an issue, but in the case of dynamic scenarios in which the car can move with velocities up to 120 km/h, this means the signal reaches the receiver \(\sim\)12.7 \(\upmu\)m apart from the time it was available at the output of the antenna. Further testing needs to take place to understand the implications of this delay between the antennas' output and the receiver.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean (ns)** & **Std (ns)** & **ADC** & **Mean (ns)** & **Std (ns)** \\ \hline ADC 0 & 381.23 & 0.83 & ADC 8 & 381.27 & 0.92 \\ ADC 1 & 381.27 & 0.88 & ADC 9 & 381.28 & 1.05 \\ ADC 2 & 381.26 & 0.86 & ADC 10 & 381.26 & 0.95 \\ ADC 3 & 381.28 & 0.89 & ADC 11 & 381.28 & 0.9 \\ ADC 4 & 381.24 & 0.89 & ADC 12 & 381.27 & 0.94 \\ ADC 5 & 381.27 & 0.95 & ADC 13 & 381.3 & 0.98 \\ ADC 6 & 381.25 & 0.96 & ADC 14 & 381.26 & 0.91 \\ ADC 7 & 381.3 & 0.92 & ADC 15 & 381.3 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Group delay mean and standard deviation for each channel for 1 beam.  
Figure 7: Group delay results for L1/E1 frequency bandwidth.