esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Zeolites are a class of crystalline materials formed by a skeleton based on tetrahedral silicon and aluminum (and others, such as P, Ge, Ga, B, S, and Fe), which form microporous (<2 nm) channels and cavities. Due to their microporous structure, these materials are extremely versatile and are widely used as adsorbents, ion exchangers, detergents, and catalysts [1; 2; 3]. However, it is in the catalysis field that zeolites play an essential role in refining, processing, and organic synthesis for fine chemistry. In fact, zeolites make up more than 40% of the solid catalysts used in the chemical industry [4].  
In the last two decades, two-dimensional lamellar zeolitic precursors (LZPs) have been found for some types of zeolites. These LZPs show the same basic structure as the tridimensional form with separated lamellae approximately 1 to 2 nm thick along one direction, and these precursors condense topotactically, producing three-dimensional structures. According to the International Zeolite Association (IZA), there are more than 200 framework topologies, and less than 10% of these structures have an LZP or exist in a two-dimensional form [5]. MWW, FER, NSI, OKO, RRO, CAS, CDO, PCR, RWR, and AFO are some examples of zeolite framework topologies that exist with a lamellar form. Readers can find the list of lamellar zeolites and their references in excellent reviews [6; 7; 8; 9; 10].  
Among these framework topologies, LZP with MWW topology, known as (P)MCM-22 (MCM-22 precursor), is remarkably the most studied LZP. Moreover, its modification with postsynaptic procedures yields engineered materials with different pore architectures and lamellae organizations, such as hybrid organic-inorganic, pillared, misaligned, disordered, delaminated, and desilicated structures [11]. These modifications open avenues to obtaining elegant and designed solids with hierarchical pore structures that could facilitate reactants in reaching active sites to increase the conversion and yield of the desired products. Considering the importance and the versatility of this class of nanoporous materials, this work will focus on MWW-type zeolites, showing their general aspects of synthesis and recent advances.
## 7 Conclusions  
This study systematically investigated the damage and failure evolution law and frequency-domain distribution characteristics of AE signals of deep granite under tri-axial loading tests. Further, the conditions for the prediction signals before peak stress were defined. The important conclusions are summarized as follows:  
(1) The AE IET function could effectively identify the deformation and failure degree of rocks, and the loading process was divided into five stages. In the early stage of loading, the function fluctuated in a low region; however, the rapid increase in its value in the later stage indicates the generation of macroscopic cracks and rock fracture.  
(2) The AE signals with amplitude greater than 85 dB, peak frequency greater than 350 kHz, and frequency centroid greater than 275 kHz were defined as the precursor signals before the occurrence of the peak stress. The signals are applicable to all samples under different confining pressures and have good universality and applicability.  
(3) Through wavelet packet analysis, the frequency domain was divided into 16 sub-frequency bands of 31.25 kHz. The energy proportion of the characteristic signal was mainly concentrated in the 343.75-375 kHz frequency band, followed by the 281.25-312.5 kHz frequency band, and these were related to the distribution of the primary peak frequency and the secondary peak frequency of all signals. These results agree well with the selection standard of the precursor signals.  
Conceptualization, H.J.; methodology, L.Z.; software, L.Z. and L.L.; validation, J.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.L.; supervision, H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (No. 2204084), the National Key Research Development Program of China (No. 2016 YFC0600801), and the National Natural Science Foundation of China (Nos. 51534002 and 52004015).  
The authors declare no conflict of interest.
## 4 AI-Driven Concepts in MCNs  
While previous sections discussed the architectural evolution and enabling communication technologies for MCNs, this section focuses on AI/ML methodologies that are increasingly central to network intelligence. Unlike traditional communication enablers, AI-driven techniques empower maritime networks to autonomously learn, adapt, and  
Figure 2: High-level Multi-layered 6G Architecture.  
optimize resource allocation, fault recovery, traffic management, and security under the unique constraints of the maritime environment.  
AI/ML techniques are becoming fundamental components of next-generation communication networks, including 6G-enabled MCNs. In maritime environments, where operations are often remote, dynamic, data-rich, and safety-critical, the ability to autonomously learn, predict, and adapt is crucial [2; 15]. This section introduces the essential AI/ML paradigms, starting from foundational concepts, and explains their application potential in MCNs, addressing use cases such as routing, anomaly detection, predictive maintenance, and decision support.  
In principle, AI/ML refers to a set of algorithms that learn from data to make predictions or decisions without being explicitly programmed. ML can be broadly categorized into _Supervised Learning_ (i.e., algorithms that learn from labeled data with known input-output pairs), _Unsupervised Learning_ (i.e., models discover patterns in unlabeled data such as clustering), _Semi-Supervised Learning_ (i.e., algorithms that combines small amounts of labeled data with large unlabeled datasets), _Reinforcement Learning_ (i.e., agents that learn through interactions with an environment via rewards/punishments), _Self-Supervised Learning_ (i.e., models that use the structure within the data itself to generate pseudo-labels) [56]. There might also be multi-model techniques, such as _Federated Learning_, which involves training multiple models across decentralized devices while preserving data privacy [57]. In the context of MCNs, these approaches can be used to optimize routing, manage scarce spectrum, detect faults, predict maintenance, or make decentralized control decisions across a large network of ships, UAVs, buoys, and satellites.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 3 Problem Description  
Spectrum sensing can be modeled as a binary hypothesis testing problem as shown in Equation (1):  
\[y(n)=\left\{\begin{array}{l}u(n)\quad\quad H_{0}\\ x(n)+u(n)\quad\quad H_{1}\end{array}\right.\quad n=0,1,2,\ \ldots\,\mathrm{N}-1, \tag{1}\]  
where \(H_{0}\) represents the assumption that there is no primary user signal on the frequency band; \(H_{1}\) represents the assumption that the frequency band is occupied by a primary user; \(y(n)\) represents the received baseband signal; \(u(n)\) is independent identical distributed Gaussian white noise with mean 0 and variance \(\sigma_{u}^{2}\); \(x(n)=h(n)s(n)\) is the primary user signal, and \(x(n)\) and \(u(n)\) are independent of each other, where \(h(n)\) represents the channel gain between the primary user and the secondary user and \(N\) indicates the signal length. Two indexes to measure the spectrum sensing performance of the algorithm are detection probability \(P_{d}\) and false alarm probability \(P_{f}\), which are defined as shown in Equation (2).  
\[P_{\mathrm{f}}=P(H_{1}|H_{0})\quad\quad\mathrm{P_{d}}=\mathrm{P}(H_{1}|H_{1}). \tag{2}\]  
Single-node primary user adversarial attack is to add a perturbation to the received clean signal \(y(n)\), making the spectrum sensing classifier unable to classify correctly. Its mathematical expression is shown in Equation (3),  
\[F_{\beta}(y(n))\
eq F_{\beta}(y(n)+\delta(n)), \tag{3}\]  
where \(F_{\beta}\) represents the attacked spectrum sensing classifier, that is, the target model; \(\delta(n)\) represents the perturbation. The USDAA algorithm proposed in this paper improves the robustness of the model against adversarial samples through unsupervised adversarial pre-training, and the mathematical expression of the defense algorithm is shown in Equation (4), where R\({}_{\theta}\) represents the defense model trained by the USDAA algorithm.  
\[R_{\theta}(y(n)){=}\mathrm{R}_{\theta}(y(n)+\delta(n)). \tag{4}\]
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 2 Materials and Methods  
This study employed a comprehensive suite of hardware and software tools to develop and evaluate an advanced underwater acoustic modem system leveraging an SVM for signal classification and the CWT for feature extraction. These tools, as well as the methodology followed for the construction and test of the prototype, are described next. The experimental results presented in this paper were obtained under controlled laboratory conditions designed to emulate the underwater acoustic environment.
## 3 Analysis and Calculation Parameters
### Earthquake Excitation Model
\[S_{g}(\omega)=\left[\frac{\omega_{g}^{4}+4\zeta_{g}^{4}\omega_{g}^{2}\omega^{2}}{ \left(\omega_{g}^{2}-\omega^{2}\right)^{2}+4\zeta_{g}^{2}\omega_{g}^{2}\omega^ {2}}\right]\times\left[\frac{\omega^{4}}{\left(\omega_{f}^{2}-\omega^{2} \right)^{2}+4\zeta_{f}^{2}\omega_{f}^{2}\omega^{2}}\right]S_{0} \tag{14}\]  
where \(\omega_{g},\,\,\zeta_{g}\) denote the site excellence frequency and site damping ratio, respectively, \(\omega_{f},\,\,\zeta_{f}\) denote the ground filtering parameters for controlling low-frequency components of ground motion, and \(S_{0}\) denotes the spectral intensity factor, as shown in Table 5.  
The site categories are divided into four classes, I, II, III and IV, according to the soil layer where the structure is located. 1. Class I site soil: rocky, compact gravelly soil. 2. Class II site soil: medium-dense, loose gravel soil, dense, medium-dense gravel, coarse and medium sand; clayey soil with foundation soil with a permissible bearing capacity \([\sigma_{0}]>150\) kPa. 3. Class III site soil: loose gravel, coarse and medium sand, dense and medium-dense fine and silty sand, clayey soil with a permissible bearing capacity \([\sigma 0]\leq 150\) kPa and fill soil with \([\sigma_{0}]\geq 130\) kPa. 4. Class IV site soil: silty soil, loose fine and chalky sand, recently deposited clayey soil; fill with foundation soil with an allowable bearing capacity \([\sigma_{0}]<130\) kPa. Taking a Class II site with an eight-degree fortification and a 0.1 g seismic acceleration as an example, the design earthquake group is set as the second group. The seismic excitation adopts a combination of longitudinal, transverse, and vertical earthquakes \(E_{x}+E_{y}+0.65E_{x}\). Figure 5: Strength envelope function diagram. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Site Classification**} \\ \cline{2-5}  & **I** & **II** & **III** & **IV** \\ \hline \(\zeta_{g}\) & 0.728 & 0.775 & 0.822 & 0.868 \\ \(\zeta_{f}\) & 0.411 & 0.557 & 1.140 & 2.208 \\ \(\omega_{g}(\text{rad}\cdot\text{s}^{-1})\) & 24.763 & 18.656 & 13.491 & 9.848 \\ \(\omega_{f}(\text{rad}\cdot\text{s}^{-1})\) & 0.453 & 0.355 & 0.154 & 0.082 \\ \(S_{0}(\text{cm}^{2}/\text{s}^{2})\) & 11.241 & 15.546 & 22.370 & 31.201 \\ \(S_{0}(\text{cm}^{2}/\text{s}^{2})\) & 43.028 & 59.512 & 85.598 & 119.304 \\ \(S_{0}(\text{cm}^{2}/\text{s}^{2})\) & 172.103 & 238.028 & 342.905 & 478.009 \\ (PGA = 0.4 g) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Seismic PSD parameter values.
## 5 Numerical Experiments and Analysis of Results  
The proposed methodology was developed entirely in Python 3.12.7, using key libraries such as NumPy (2.2.3) for numerical operations, PyTorch (2.6.0) for neural network implementation, scikit-learn (1.6.1) for evaluation metrics, and Matplotlib (3.10.1) for visualizations. These tools supported the implementation of the A* algorithm, the feedforward neural network for travel time estimation, and the simulation of UAV routing scenarios with obstacles and environmental factors, resulting in a fully Python-based solution without the need for external software. The computational experiments were conducted on a computer with a 12 th Generation Intel(r) CoreTM 17-1280P processor at 1.80 GHz, 32 GB RAM, and running Windows 11 Pro (version 24H2) 64-bit. Due to the absence of standardized benchmarks for the dynamic TOP with obstacles, a diverse set of synthetic test instances was developed. These instances were designed to reflect the complexities inherent in realistic UAV routing scenarios, integrating various operational and environmental constraints. All experiments were conducted on a 30 \(\times\) 30 km grid-based environment.  
Two primary instance sets were created to evaluate the proposed approach: one comprising 21 nodes and another with 31 nodes. For each instance set, scenarios with and without obstacles were considered to assess the impact of obstacle avoidance on route optimization. Figure 8 illustrates the spatial distribution of nodes, obstacles, and depots for both instance sets.  
The experimental framework was structured to systematically evaluate the influence of critical parameters:  
* Obstacle configuration: Two scenarios were tested, obstacle-free environments and environments with strategically placed obstacles that UAVs must avoid.
* Wind conditions: Wind speed was varied between 0 km/min (no wind) and 0.3 km/min, with directional variations at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\).  
* Payload weight: Tests were conducted with payloads of 0 kg (no payload) and 1 kg.
* UAV travel speed: Two different UAV speeds were examined, 0.8 km/min and 1.1 km/min.
* Route duration limitations: Maximum route durations of 36 min and 46 min were imposed, reflecting typical operational constraints in real-world UAV deployments.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 4 Empirical Tests in Software
### Interleaver Implementation in MatLab/Simulink Software  
The implementation of the interleaver in MatLab/Simulink is conducted on a script, where the instructions of the pseudocode presented in Figure 3 are programmed. With this purpose, the same functions described in Section 3.7.5 are employed, as well as the control routines and sentences that enable the application of the criteria. The result of this implementation, according to the described algorithm, generates a vector with permutated indexes, which is inserted in the turbo coder and decoder of the communication system simulation, as shown in Figure 4.  
Figure 4: Simulation for the communication system in Simulink.
# LoRa 2.4 GHz Communication Link and Range
## 4 Path Loss Modeling
### Free Space Environment  
The first scenario can be described as a free space environment in which there is a line of sight (LoS, i.e., the primary Fresnel zone is to be at least 60% clear.) between the TX and RX locations. In this case, we can use the widely used Free Space Path Loss (FPSL) model to evaluate the maximum communication range. This model calculates the loss between two isotropic radiators in free space, without considering any obstacles, reflections or interference. The model solely relies on the frequency and distance between the transmitter and receiver to calculate the path loss:  
\[L_{p,LoS}(d)=32.44+20\log_{10}(f)+20\log_{10}(d), \tag{10}\]  
where \(f\) is in MHz and \(d\) is in km. The combination of a given SF and BW yields a certain sensitivity \(P_{RX}\). Consequently, given the maximum path loss obtained from (9), we can calculate the maximum distance as  
\[d=10^{(L_{p,LoS}(d)-32.44-20\log_{10}(f))/20}. \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**BW (kHz)**} \\  & & **203** & **406** & & **812** & & **1625** \\ \hline SF & \(P_{RX}\) & \(R_{D}\) & \(P_{RX}\) & \(R_{D}\) & \(P_{RX}\) & \(R_{D}\) & \(P_{RX}\) & \(R_{D}\) \\
5 & \(-109\) & \(31.72\) & \(-107\) & \(63.44\) & \(-105\) & \(126.88\) & \(-99\) & \(253.91\) \\
6 & \(-111\) & \(19.03\) & \(-110\) & \(38.06\) & \(-108\) & \(76.13\) & \(-103\) & \(152.34\) \\
7 & \(-115\) & \(11.1\) & \(-113\) & \(22.2\) & \(-112\) & \(44.41\) & \(-106\) & \(88.87\) \\
8 & \(-118\) & \(6.34\) & \(-116\) & \(12.69\) & \(-115\) & \(25.38\) & \(-109\) & \(50.78\) \\
9 & \(-121\) & \(3.57\) & \(-119\) & \(7.14\) & \(-117\) & \(14.27\) & \(-111\) & \(28.56\) \\
10 & \(-124\) & \(1.98\) & \(-122\) & \(3.96\) & \(-120\) & \(7.93\) & \(-114\) & \(15.87\) \\
11 & \(-127\) & \(1.09\) & \(-125\) & \(2.18\) & \(-123\) & \(4.36\) & \(-117\) & \(8.73\) \\
12 & \(-130\) & \(0.595\) & \(-128\) & \(1.19\) & \(-126\) & \(2.38\) & \(-120\) & \(4.76\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Receiver sensitivities (\(P_{RX}\) in dBm) and corresponding data rates (\(R_{D}\) in kbit/s) of the SX1280 LoRa module for every combination of spreading factors (SFs) and bandwidths (BWs).
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 3 Signal Model and Time-Frequency Transformation  
The dual-component radar signals under different SNRs are adopted, and the time-frequency transformation technology is implemented to obtain the detailed features and classify the radar signal types.
# Multi-Omics Approaches to Study Molecular Mechanisms in _Camanabis sativa_
## 2 Studying the Metabolomic Profile of Cannabis  
### Key Metabolites: An Overview  
Cannabis is a polymorphic plant species producing a diverse profile of bioactive metabolites which have unique chemical structures and physiochemical properties [20]. Among them, the main compound class are the cannabinoids, accounting for ~20% of the total secondary metabolites in cannabis, and terpenoids are also highly abundant, of which isoprenes, monoterpenes, and sesquiterpenes are predominant [32].  
Cannabinoids are primarily synthesized in the glandular trichomes of female flowers, while trichomes of male flowers are generally very low in cannabinoids [33]. Cannabis trichomes are classified as stalked, sessile, or bulbous, where bulbous trichomes produce limited cannabinoidss compared to the other types [34]. Trichomes contain resin storage cells and, during the flower and seed maturation stage, the composition of cannabinoids within the resin changes, reaching the highest level at flower maturity [33]. The concentration of cannabinoids increases in warmer temperatures but is negatively correlated with the mineral content of soil [35]. Cannabinoid yield is also affected by UV-radiation and an increase was observed in cannabis flowers after UV-B-induced stress [36].  
Figure 1 illustrates the main steps of THC and CBD biosynthesis. THC and CBD are synthesised from two distinct metabolic pathways: the polykeletide and the methyleryth-thiritol phosphate (MEP), producing olivetolic acid (OA) and geranyl diphosphate (GPP), respectively [5; 37]. Specifically, OA and GPP synthesize the cannabigerolic acid (CBGA), containing a pentyl side chain, which produces the acidic precursors of THC (THCA) and CBD (CBDA) [38]. The cannabichromenic acid (CBCA) is also produced [39]. Synthesis of THCA, CBDA, and CBCA proceeds through the appropriate oxidocylases: THCA synthase, CBDA synthase, and CBCA synthase, respectively.  
These acidic cannabinoidss are thermally unstable and can be decarboxylated when exposed to light or heat via smoking [40]. Terpenoids are produced by dimethylallyl diphosphate (DMAPP) and isopentenyl diphosphate (IPP) metabolic pathways, which share a GPP precursor with cannabinoids [41].  
However, the biosynthesis of cannabinoidss and terpenes is still far from being fully understood at the molecular level [34]. Further and innovative investigations are crucial for many upcoming medicinal cannabis applications, where novel bioactive compounds or less abundant cannabinoids may be of great interest [42].
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 6 Conclusions  
The receiver with the digital RC calibration, AGC, and DCOC presented in this work is used for processing navigation signals from BDS-3 and GPS. The receiver is fabricated in 55-nm CMOS technology, occupying an active die area of 0.345 mm\({}^{2}\) with RX only. The current consumes 4.8 mA with a supply of 1.2 V. The noise figure performance is excellent (only 1.74 dB). The current consumption and area are competitive. Compared with the advanced navigation modules in the market, the positioning performance of the receiver is verified, and the comparison result provides convincing evidence that the proposed receiver can be well-suited for navigation applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **This Work** & **Ref. [28]720** & **Ref. [7]722** & **Ref. [29]723** & **Ref. [30]720** & **Ref. [31]718** \\ \hline Tech. (nm CMOS) & **55** & 110 & 65 & 28 & 180 & 180 \\ \hline Integration Level & **Fully** & Fully & Fully & Fully & Fully & Fully \\ \hline Frequency Bands & **L1/E1** & L1/E1/E5/E5 & L1/L2/L5/S & L1/L2/B1/E1 & L1/B1/E1 & L1/B1/E1 \\ \hline Navigation & GPS/BD53/ & Gailleo/ & NavIC/GPS/ & & & \\ Systems & Galileo & GLONASS/ & Galileo/BD52 & GPS/BD2/Galileo & GPS/BD2/Galileo & GPS/BD2/Galileo \\  & QZSS/IRNS & & & & \\ \hline NF (dB) & **1.74** & 2.3 & 3.8\(\sim\)4.4 & 3.1 & 1.79 & 1.8 \\ \hline Bandwidth (MHz) & **2/4** & 2\(\sim\)52 & 2/4/17/20/24 & 2/4 & 2/4 & 2/4 \\ \hline Max gain (dB) & **113.2** & 131 [1]  
**Author Contributions:** Conceptualization, S.H.; methodology, S.H. and J.L.; software, S.H. and M.L.; validation, S.H.; formal analysis, S.H. and J.L.; investigation, S.H.; resources, S.H. and J.L.; writing--original draft preparation, S.H.; writing--review and editing, J.L. and M.L.; visualization, S.H.; supervision, F.T.; project administration, F.T.; funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partly funded by the Natural Science Foundation of Chongqing, China, no. cstc2019 jcyj-zdxmX0014.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.