esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Structured Pimary Methodology for Effective AI Adoption in SMEs  
This section introduce a primary roadmap structured methodology for effective AI Adoption as a way of stages and key activities. It addresses the organizational challenge of lacking a clear methodology for AI adoption, a barrier within the organizational dimension of the TOE framework. Many SMEs begin the adoption of AI without a structured approach, leading to fragmented efforts, inefficient resource allocation, and misalignment between business objectives and AI capabilities. We propose an initial structured methodology designed to guide SMEs through effective AI adoption, as a formal step-by-step approach to reduce risk, manage change and ensure strategic coherence. Without such a roadmap, SMEs can underestimate the complexity of AI integration, resulting in slow adoption, failed projects, or wasted investment. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**TOE Dimension** & **Challenge** & **Key Actionable Solutions** \\ \hline \multirow{8}{*}{**Technological**} & Lack of data quality and access & Data governance, real-time data collection, cleaning, integration with APIs & \multirow{8}{*  
Actionable strategies to build a methodology-driven approach include the following six steps:  
1. **Conduct a Comprehensive Readiness Assessment** Evaluate the organizational technological infrastructure, digital maturity, workforce competencies, leadership alignment, and financial capacity to establish a realistic starting point for AI adoption. 2. **Define Strategic Objectives and Use Cases** Set measurable AI-related goals related to business priorities, such as improving efficiency, improving customer experience, or enabling data-driven decisions. 3. **Select Appropriate AI Solutions and Deployment Models** Choose between off-the-shelf, customized, or cloud-based AI tools based on internal needs, available resources, and scalability requirements. 4. **Implement in Phases with Pilot Projects** Begin with small-scale pilots to validate the feasibility and value of AI, make necessary adjustments and build internal trust and capacity before greater deployment. 5. **Integrate Training and Change Management** Ensure ongoing staff training and transparent communication to address resistance, clarify roles and reinforce the value of AI as an enhancement tool. 6. **Measure, Adjust, and Scale Strategically** Establish key performance indicators (KPI) to track progress. Use insights from early phases to iterate and scale AI solutions in broader operations. Table 5 summarizes this methodology in six actionable phases, offering a practical guide to align technological adoption with organizational and strategic readiness. While this represents an initial framework, future research can refine it into diagnostic and maturity-assessment tools. Figure 2 visually complements Table 5 by illustrating the phased AI adoption methodology tailored for SMEs. Each stage--ranging from readiness assessment to full-scale deployment--maps directly to the structured process outlined in the table. The diagram
# Lunar Satellite Constellations in Frozen Low Orbits
## 2 Dynamical Model
The orbits are propagated using the variable-order Adams method (a Fortran routine similar to ode113 routine in MATLAB R2021b) in rectangular coordinates of the selenocentric celestial reference frame (SCRF): SCRF axes are parallel to those of the International Celestial Reference Frame [11], but the origin is at the Moon's center of mass. The error of the truncated lunar gravity field model is estimated based on the final differences in orbital elements (semimajor axis, eccentricity, inclination, right ascension of the ascending node, and true longitude) when propagating each orbit with either the truncated or full (\(50\times 50\)) model of Moon's gravity field. The minimum, median, and maximum differences in the final values of orbital elements over each of two sets of orbits are summarized in Table 1. They are quite small, which proves the quality of the truncated model.
# A Sensor Drift Compensation Method with a Masked Autoencoder Module
## 2 Materials and Methods  
### Dataset  
For our experiments, we utilized the publicly available dataset that was constructed by [PERSON] et al. [27], which is a well-known dataset used in drift compensation research [7; 12; 14; 15; 28; 29; 30]. This dataset comprises 13,910 measurements gathered from an E-nose device with 16 chemical sensors that were exposed to the 6 gases of ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene at various concentrations. The data were accumulated over the course of three years and were divided into 10 batches, which were chronologically arranged as shown in Table 1. Each sample in the dataset is represented by a 128-dimensional feature vector, which was extracted from the E-nose signal along with the corresponding gas type and concentration. A detailed explanation of the dataset and the feature vector can be found in the paper by [PERSON] et al. [27].  
We categorized the dataset into three groups based on the elapsed time. Dataset-A includes samples from months 1 to 20 which represent past samples, and dataset-B and dataset-C include samples from months 21 to 23 and months 24 to 36, respectively. Dataset-B and -C were used to analyze the drift compensation ability in addition to fine-tuning in later experiments for comparison purposes.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 3 Clinical Applications
### Periodontal Tissue Regeneration
#### 3.1.2 Scaffolds for Periodontal Tissue Regeneration  
Nanostructured biomimetic materials, for periodontal regeneration, may offer higher performance due to a greater surface area to volume ratio and to chemical/ electrical synergistic effects. However, these materials may be eliminated from periodontal defects by phagocytosis [28].  
Nanohydroxapatite (nHA) is an alloplastic material that is chemically similar to the inorganic building blocks of a bone matrix. It is widely used as a bone repair material because it is biocompatible, osteeconductive, and resorbable. There are some limitations when it comes to the use of nHA, including insufficient toughness and poor mechanical properties. To overcome these limitations, material scientists have developed composite materials containing nHA and synthetic polymers, such as polylactic acid (PLA), poly lactic-co-glycolic acid (PLGA), polyamide, chitosan, and PCL [7]. One group showed that a composite graft of nHA and microsized beta-tricalcium phosphate (B-TCP) helped to enhance the binding and retention of nHA to periodontal defects by minimizing its dissemination and phagocytosis [29]. Nanomaterials such as nHA may be used in combination with biologics such as enamel matrix derivative (EMD) to enhance regenerative effects [30]. In fact, amelogenin, the main active ingredient of EMD, induces the formation of acellular cement and stimulates the proliferation and differentiation of periodontal fibroblasts and osteoblasts [31].  
Several polymers may be used for periodontal regeneration, including natural and synthetic materials, such as collagen, chitosan, dextran, alginate, PLA, poly-glycolic acid (PGA), and polyethylene glycol (PEG). In addition to acting as scaffolds, hydrogels, micro/nanoparticles, and membranes may also be used as drug/cell delivery systems to improve periodontal regeneration [32; 33].
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 2 Materials and Methods
### Development of a Standalone Application
Testing and optimization  
Figure 8: HF OFDM RECEIVER—graphical user interface (GUI). Figure 9: HF OFDM TRANSMIITER/RECEIVER—settings tab. The application has undergone rigorous field testing. The application has been tested in both laboratory conditions and real outdoor HF communication scenarios. This was done to ensure that the OFDM protocol and channel aggregation work efficiently under a variety of conditions, including different signal-to-noise ratios, interference, and propagation environments. Performance tuning is a process designed to improve the efficiency and effectiveness of a system or component. Depending on the results of performance testing, the application may require additional optimization procedures, including refining modulation parameters, adjusting channel aggregation logic, and improving the responsiveness of the graphical user interface.
## 2 Materials and Methods
### Data Collection
#### 2.2.3 Satellite Data  
The MODIS Surface Reflectance (MOD09Q1) instrument was used in this study to detect the growth stages of rice crops through a time-series profile of vegetation indices. These data were derived from the TERRA MODIS satellite and downloaded from the Land Processes Distributed Active Archive Center ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov))) on a REVERB interface with h27v07 tile between 2006 and 2016 in 10-year periods. These data provided an eight-day composite at a 250-metre spatial resolution as a gridded level-3 product in a sinusoidal projection.
## 2 DAB DC/DC Resonant Converter TPS Close Loop Control Scheme
### Mathematical Formulation of DAB TPS Control Method  
#### 2.2.1 Charging Power Regulation with Inverter Phase Shift Control  
Using Fourier series expansion of the inverter AC pulsing voltage as explained in [18; 19; 20], the inverter steady state AC RMS voltage fundamental frequency component can be expressed as (1) as a function of the inverter phase shift angle \(\beta\)p and DC voltage. When phase shift angle \(\beta\)p = 0.5 (\(\beta\)p (deg) = 180\({}^{\circ}\)), the inverter voltage waveform becomes a square waveform with maximum inverter AC RMS voltage, as shown in (2).  
\[\mathrm{v_{ac\_inv}}=\frac{2\sqrt{2}}{\pi}\sin(\pi\beta_{\mathrm{p}})*\mathrm{ V_{dc\_inv}} \tag{1}\]  
\[\mathrm{v_{ac\_inv}}\ (\beta_{\mathrm{p}}\ =\ 0.5)\ =\frac{2\sqrt{2}}{\pi} \mathrm{V_{dc\_inv}} \tag{2}\]  
The fundamental resonant frequency component of inverter and rectifier AC RMS voltage and AC RMS current can be decomposed into real and imaginary components in the phasor diagram. With power loss from switching devices ignored, the inverter's DC power equals its AC power.  
The averaged DC input power can be expressed as a product of the inverter's AC RMS voltage, AC RMS current, and the inverter power factor, as shown in (3), where \((\varphi(PF)_{inv})\) is the power factor angle of the inverter AC output.  
\[\mathrm{I_{dc\_inv}}*\mathrm{V_{dc\_inv}}=\mathrm{v_{ac\_inv}}*\mathrm{I_{ac\_ inv}}*\mathrm{cos}(\varphi(PF)_{inv}) \tag{3}\]  
Substituting (1) into (3), the averaged inverter DC input current can be calculated from (4). If the resonant compensation circuit is designed to operate the inverter at ZPA condition with \(\varphi(PF)_{inv}=0\), the relationship of the inverter averaged DC input current and its AC output current is simplified as (5).  
\[\mathrm{I_{dc\_inv}}=\frac{2\sqrt{2}}{\pi}\sin(\pi\beta_{\mathrm{p}})*\mathrm{ I_{ac\_inv}}*\mathrm{cos}(\varphi(PF)_{inv}) \tag{4}\]  
\[\mathrm{I_{dc\_inv}}=\frac{2\sqrt{2}}{\pi}\sin(\pi\beta_{\mathrm{p}})*\mathrm{ I_{ac\_inv}} \tag{5}\]  
Equation (1) means that inverter output voltage can be regulated through inverter phase shift angle for a given fixed inverter DC input voltage. The inverter AC current reflects the battery charging current, the rectifier operation condition, and the resonant circuit operation condition.  
Equations (4) and (5) show that DAB input DC current or power is decided by the inverter AC current loading and inverter shift angle. Equations (4) and (5) establish the foundation for battery charging power regulation in constant current (CC) mode or constant voltage (CV) mode using inverter phase shift control.
## 3 JPRL-Based Joint Resource Allocation Approach
### Time Analysis of the Proposed JPRL Method  
We analyzed the time complexity of our proposed JPRL method. In Algorithm 1, let \(I\) be the total number of training episodes and \(T_{i}\) be the training steps in the episode \(i\). Therefore, the total amount of training iterations implies the time complexity, that is \(\mathcal{O}\Big{(}\sum_{i=1}^{I}iT_{i}\Big{)}\). For each iteration, the computational efficiency is subjected to the the size of the neural network, i.e., the number of parameters. According to [44], the time complexity for a fully connected layer is \(\mathcal{O}\left(\sum_{l=1}^{L}c_{l}c_{l-1}\right)\), where \(l\) is the fully connected layer and \(c_{l}\) denotes the number of neural units in layer \(l\). In the JPRL method, each agent utilizes two algorithms to output the bandwidth and power actions. Note that the two algorithms are run simultaneously. Thus, the time complexity is \(c=\mathcal{O}\Bigg{(}\max\Bigg{(}\sum_{l=1}^{L}c_{l}c_{l-1}\Bigg{)}\Bigg{)}\). The total time complexity of the JRPL method is \(\mathcal{O}\Big{(}c\sum_{i=1}^{I}iT_{i}\Big{)}\).
# Effect of Wetting Conditions on the In Situ Density of Soil Using the Sand-Cone Method
## 1 Introduction  
Various field-density testing methods have been developed to determine the in situ density of compacted soils. They include the sand-cone, rubber-balloon, and nuclear methods. These methods are of the utmost importance to ensure that the specified dry density of soil is achieved during compaction. The method is usually chosen based on several factors that are considered important during construction, including the availability of specialized equipment and material, logistics, the complexity of the method, and the time required to perform the test [1]. The sand-cone is the most commonly used method, since it is relatively simple and inexpensive compared to other methods.  
[PERSON] [2] showed that errors in the sand-cone method could be minimized by preparing a test hole with a flat base and a depth of at least 20 cm, as well as by using rounded sand with a coefficient of uniformity (Cu) of less than 1.5 and mean grain size (D\({}_{50}\)) of approximately 0.5 mm. He used artificial test holes of various shapes, which were possibly made in the in-situ test holes. During field compaction, temporary rainfall or an increase in the groundwater level can increase the moisture content of the compacted ground. However, the wetness of the field ground has not been considered in previous studies. Furthermore, though dry sand must be used to fill the test holes, the stored dry sand can be wetted by rain or atmospheric humidity, even if it has been dried before, resulting in increased moisture content. Therefore, this study examined the effects of wetting conditionsof the ground and the moist filling sand on the dry density, as determined using the sandstone method in a laboratory. A small model of the ground containing in situ soil with different water content was used for simulating moist ground conditions in the laboratory. Different proportions of kaolinite were added to the in situ soil to simulate the various soils. In addition, an artificial, cylindrical test hole with a known volume was used to determine the effect of moist sand filling.
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 4 Advance Forecasting and Verification of Fracture Zones in Liangwangshan Tunnel
The identification results for each survey line are shown in Figure 13, as well as the discontinuity in phase axes. The results indicate fracture zones within the three regions. Figure 12: (**a**) Tunnel face. (**b**) The survey line arrangement for GPR detection. The excavation site photo of the Liangwangshan Tunnel is shown in Figure 14. In the detected abnormal area, there are fracture zones in front of the tunnel excavation, which is consistent with the identification results of the seismic wave data and GPR data, verifying the accuracy of this method. Figure 13: Results of recognition of fracture zones. (**a**) Abnormal interface 1, survey line 1. (**b**) Abnormal interface 1, survey line 2. (**c**) Abnormal interface 2, survey line 1. (**d**) Abnormal interface 2, survey line 2. (**e**) Abnormal interface 3, survey line 1. (**f**) Abnormal interface 3, survey line 2.
## 2 Materials and Methods
### Scanning Electron Microscopy and Raman Spectroscopy  
Morphology and chemical properties of selected sensors were studied by scanning electron microscopy (SEM, JEOL, Tokyo, Japan), JEOL JSM'7001F (JEOL, Tokyo, Japan) with energy-dispersive X-ray analysis (EDX). To study the phase of thin-layer metal oxides, Raman spectroscopy (Renishaw micro-Raman spectrometer, Renishaw, Wotton-under-Edge, England) was used. The Raman spectra were excited by a 633 nm laser with a power 0.5 mW.
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 2 Technical Foundation
### Design of QKP
#### 2.3.1 Definition of Parameters  
The dynamically resizable QKP can generate and store keys and then resize them according to actual demand, thus avoiding the problem of wasted and insufficient keys. If there is currently more demand, then the size of the QKP needs to be increased, with more connections being transferred in parallel to satisfy more requests. Conversely, if demand decreases, then the size of the QKP can be reduced.  
**Definition 1:**_Link key generation rate. The link key generation rate is the number of links \(i\) to link \(j\) keys established through the key negotiation protocol in a given period using quantum communication methods. Its equation can be expressed as._  
\[R_{ij}=\frac{N_{num}}{T_{i}} \tag{1}\]  
_where \(R_{ij}\) denotes the link key generation rate, \(N_{num}\) denotes the number of successfully negotiated link keys, and \(T_{i}\) denotes the total time required to negotiate the link for the key._  
**Definition 2:**_Q\({}_{ij}\) denotes the rate of key consumption from link \(i\) to link \(j\), i.e., the number of keys consumed per unit of time, which can be calculated by Equation (2). Refers to the number of keys generated in the QKD network, and \(t\) denotes the real time used to create these keys._  
\[Q_{ij}=\frac{F_{t}}{t_{i}} \tag{2}\]
## 2 Theoretical Analysis
### Duffing Equation  
Duffing equation can be written as follows:  
\[m\ddot{x}+\mu\dot{x}\pm kx\pm\beta x^{3}=F\cos(\omega t) \tag{1}\]  
where \(m\) is the mass, \(\mu\) is the damping coefficient, and \(k\) and \(\beta\) are the linear and nonlinear spring constant, respectively. \(F\cos(\omega t)\) is the external force, \((\cdot)\) represents differentiation with respect to time, and \(\omega\) is the vibration frequency. The undamped free vibration form of the Duffing equation is  
\[m\ddot{x}+kx+\beta x^{3}=0 \tag{2}\]  
Equation (2) can be divided into two forms, \(k>0\) and \(k<0\); when \(k<0\), the solution of Equation (2) contains two forms \(\pm 1\). However, when \(k>0\), Equation (2) can be obtained by \"Dimensional Analysis method\" to obtain a high-order approximate solution [1]. Once the analytical solution is obtained, it does not need to be estimated by machine learning methods for this case.
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Receptors and Intracellular Signaling
#### 2.2.2 IL-37  
In 2002, for the first time, it had been proposed that IL-37 could bind IL-18R\(\alpha\)[18] with low affinity and, in turn, antagonize the effects mediated by this receptor. At the same time, a different group demonstrated that IL-37 could also bind IL-18 BP and recruit IL-18R\(\beta\) to prevent the formation of the active complex IL-18R [28]. In addition to these possible mechanisms of action, further studies confirmed that IL-37 is a nonclassical inhibitor that can bind IL-18R\(\alpha\) and subsequently favor the recruitment of IL-1R8 instead of the usual IL-18R\(\beta\)[29]. IL-1R8 TIR domain lacks the Ser447 and Tyr536 residues; therefore, its binding to MyD88 results in poor signal transduction and triggers multiple intracellular switches that block the inflammation [29] and regulate inflammatory and immune processes in various pathologic conditions [30]. Noteworthy, at high concentration, IL-37b tends to form homodimers, which limit its bioactivity by either reducing the affinity for IL-18R\(\alpha\) or restricting the recruitment of IL-1R8. IL-37 homodimerization represents a possible autoregulatory mechanism to hinder further immunosuppression [31].  
As mentioned above (2.1.2), and similarly to IL-1\(\alpha\) or IL-33, IL-37 is also a \"dual function\" cytokine able to translocate into the nucleus and bind nuclear DNA to exert regulatory functions on gene transcription. This process is caspase-1-dependent and relies on the formation of a Smad3-IL-37 complex in the perinuclear space, followed by its subsequent translocation into the nucleus. The role of Smad3 is demonstrated by the lost ability of IL-37 to suppress cytokine-induced inflammation in response to Smad3 inhibition [19].
# Zebrafish Larval Melanophores Respond to Electromagnetic Fields Exposure
## 2 Materials and Methods
### Very-High-Frequency (VHF) Electromagnetic Field Irradiation  
The range of frequency of hundreds MHz is mainly used for radio transmission and telephone communication, so it is widespread in the environment. To explore its effect on biological samples, suitable devices are necessary such as transmission lines. For our purpose, the geometry of the line we built was flat and was designed with a height of \(h=1.35\) cm, width of \(a=9\) cm, and length of 20 cm. The length of the line does not affect the line efficiency, and it allows the processing of multiple samples simultaneously. The \(RF\) generator was a ROHDE & SCHWARZ SM 300 with an output impedance of 50 \(\Omega\) (Rohde & Schwarz Italia, Rome, Italy). Its maximum output power was 20 mW. The characteristic impedance of a flat line, neglecting the radiation that escapes laterally, is looked at in the following formula [7]:  
\[R_{\text{o}}=\sqrt{\frac{L}{C}}=\sqrt{\frac{\mu_{\text{o}}}{\varepsilon_{0}}} \frac{h}{a} \tag{2}\]  
where \(L\) and \(C\) are the inductance and capacitance for length unit, respectively, and \(\varepsilon_{0}\) and \(\mu_{0}\) are the electrical permittivity and magnetic permeability, respectively. To avoid signal reflections, the characteristic impedance of the line must approximate that of the generator. From Equation (2), the line has an impedance of 56.6 \(\Omega\) in vacuum, which involves a reflection of the signal of 6.2%. This value is rather small, but if we concentrate on the growth medium made mostly of water, the characteristic impedance again decreases, as well as the reflection coefficient of the field.  
Consequently, a high-frequency 50 \(\Omega\) cable connected the input line to the generator, while the line output was closed to a load of 50 \(\Omega\) using 4 \(\parallel\) 200 \(\Omega\) resistors (Supplementary Figure S5). These last are non-inductive resistors.  
The wavelength at 100 MHz in vacuum is \(\sim\)3 m, and such a value makes secure the transversal uniformity of the fields inside the line. The applied input signal was 1 V, and the correspondent electric field was \(\sim\)71 V/m, while the magnetic field that resulted was of \(\sim\)240 nT.  
The measurements at 100 MHz this time were made with a LeCroy Wavepro 7100 fast oscilloscope, 200 GS/s with 1 GHz band limitation (LeCroy Corporation, New York, NY, USA).