esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Non-Modal Damage Sensitive Features
#### 3.3.2 Time Series Based Damage Sensitive Features
The ability of the PC-NARX to accurately predict the dynamic response of a structure under varying environmental conditions implies that it should also be able to discern damage events by monitoring the magnitude of its prediction errors, which are assumed to be Gaussian. [PERSON] et al. [169; 170] introduced the use of cointegration into SHM that can be used to remove the environmental and operational effects from non-stationary raw time series data for the purposes of damage detection. Cointegration is determined when two or more non-stationary time series form a stationary process are linearly combined, with the stipulation that both original time series must share the same degree of non-stationarity to begin with. It is commonly used in econometric analysis; however, it can also be applied to multi-sensor data in a SHM context. A maximum likelihood approach known as the Johansen Procedure [171] is used to identify the most stationary linear combination of the non-stationary variables possible. The resulting stationary process is referred to as a residual and will remain stationary, regardless of environmental conditions, until the linear relationship between the two cointegrated variables changes, which will induce a non-stationary change to the residual and may indicate the presence of damage. The methodology has recently been advanced to cater for non-linear cointegration through the use of Gaussian process regression as a cointegrating function that builds non-linear cointegrating relationships between variables [172]. The nonlinear adaptation was applied to real data from the Z-24 Bridge in Switzerland and successfully eliminated environmental and operational effects without the need to measure them, while also demonstrating considerable damage sensitivity.
## 4 Discussion
### Analysis of Wind Turbulence and Soil Water Content
In part, a reddish area and black lines represent high wavelet power levels. In part b, the red area with white contour lines demonstrates a high correlation with more than 5% significance levels. Arrows represent a multivariate phase-locked angle relationship where horizontal arrows pointing left (phase angle: 180\({}^{\circ}\)) indicates that two wavelet series are out of phase and horizontal arrows pointing right (phase angle: 0\({}^{\circ}\)) suggests that two wavelet series are in phase. A white shaded area indicates that the time series are influenced by edge effects. the canopy (35 m), soil-air interface pressure (0 m), soil-air pressure (\(-\)6 m) shows different values of magnitude, the general patterns are similar; therefore, a wavelet power analysis was employed only to the air pressure at 35 m. In contrast with the temperature (35 m) and u*, the air pressure at 35 m shows little or no daily variations. Instead, the wavelet power levels of air pressure at 35 m (Figure 10a(3)) were significant at the region of 128 (5 days) with only 1.74, which was also found in a wavelet power analysis of temperature (35 m). What is striking in the wavelet power analysis is that the pressure difference (\(\Delta\)p, 35 m and \(-\)6 m) provided in Figure 10a(4) shows strong daily fluctuation shown by dominant periods of 24 similar to temperature (35 m) and u*. However, these data should be interpreted carefully since the pressure sensor's resolution was only 0.002 bar and could not detect smaller pressure fluctuation. The pressure fluctuation in the frequency ranging from 0.01 Hz-0.1 Hz is highly dependent on the above canopy wind speed [66]. The pressure dynamics that might be generated from pressure pumping effects due to wind turbulence above the canopy must be further investigated. According to wavelet coherence analysis, the temporal correlation between the temperature at 35 m over u* (Figure 10b(1)) and \(\Delta\)p (35 m and \(-\)6 m) over u* (Figure 10b(2)) demonstrated substantial variation, especially at a daily scale with time lags of temperature (35 m) and \(\Delta\)p (35 m and \(-\)6 m) over u* ranged from 2 to 4 h. Besides the temperature, \(\Delta\)p between soil air interface and soil-air is highly likely to promote the contraction and expansion of the air stored in soil pores, cracks, and fissures, which play a significant role in regulating the vertical transport and gas storage suggested by previous works [33; 67; 68]. Nonetheless, propagation of atmospheric pressure fluctuations into the subsurface has been known to promote the air's migration in moderately permeable materials up to 10 m depth and even greater depth in a fractured rock system [69; 70; 71]. However, most works on assessing the PPE and wind turbulence CO2 focus on the top layer up to one-meter depth; our work provides empirical evidence of a strong partial relationship on a deeper soil layer (6 m).
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 3 Background
In summary, \(T_{l}\) can be interpreted as the nominal duration of the RX state when there is no channel activity, or, in other words, as the minimum amount of time that the radio is kept on (active) after each activation (the so-called DUTY_ON_TIME in TinyOS context). Note that the maximum time of activity depends on the traffic load and thus it is a priori unknown (the LPL TinyOS mechanism maintains the radio on beyond the value \(T_{l}\) when it detects channel activity). Regarding \(T_{slp}\), this is the nominal duration of the sleep period (Power Off state), that is, the time elapsed between a radio deactivation and the next radio activation. On the other hand, for the setup of the LPL mechanism, the developer has to fix a duty cycle (\(DC\)) to be used by all sensor nodes. This is a well-known MAC-layer parameter that represents the level of activity of a sensor node in communication. It is established statically according to the frequency with which the application performs environmental sensing and packet transmission. Based on \(DC\), the MAC layer configures the sleep period, whose duration can be calculated as \(T_{slp}=\frac{T_{l}(100-DC)}{DC}\). Note that, according to this expression, \(DC\) is the percentage that the minimum radio activation period represents with regard to the total period; hence, it refers to activation periods with no channel activity. Both parameters, \(T_{l}\) and \(T_{slp}\), and their relationship through \(DC\), represent a simplified view of the communication module as alternating between only two states, active (ON) and inactive (OFF), with no transitory states. This is precisely the perspective adopted in the construction of the energy consumption model in the next section. This is not only a totally acceptable approximation, but it also helps to make the analytical model less dependent on the particular radio module.
# Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of _Cannabis_ Species
###### Abstract
Another recent study has discovered that an extract of _Cannabis_ leaves was able to effectively inhibit the growth and development of _E. coli_ and _klebsiella_ spp. isolated from the _Oreochromis mossambicus_[21]. In addition, previous studies have extensively highlighted the antimicrobial properties in the literature, and _Cannabis_ has a substantially unexploited anti-viral potential against different human viral pathogens. Based on previous research studies, most medicinal plants with substantial antiviral activity, as well as those containing new plant-derived antiviral compounds, have been found to treat viral infections in people and animals [22; 23]. Recently, scientists found that the anti-inflammatory properties of cannabinoids are associated with the treatment of COVID-19, the virus infection that ravaged almost the entire world [24]. Phytocannabinoids, notably cannabidiol (CBD), tetrahydrodcannabinol (THC), and terpen, have shown the potential to reduce severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral infection by downregulating Angiotensin-converting enzyme 2 (ACE2) transcript levels [25]. These compounds have also been reported to act as the main protease inhibitors that block viral replication and exert an immunomodulatory effect by controlling the excessive release of cytokines, which is commonly associated with SARS-CoV-2 infections [26]. Apart from cannabinoids, terpenes in _Cannabis_ plants have also been widely explored for their antiviral properties, and studies including other unique secondary metabolites have been limited. Concurrently, regardless of the historical and extensive research evidence, the antiviral properties of _Cannabis_ against viruses affecting livestock animals and poultry have been understudied as an alternative, given that antiviral drugs and veterinary medicines are limited. This is due to viral mutant resistance to existing antivirals, emerging new viral pathogens, toxic side effects, and high costs [27]. Therefore, the focus of this review is to systematically review the scientific literature and establish the existing research and scientific data about the metabolomic profile of _Cannabis_ and its inhibitory effect on pathogens whilst identifying the research gaps that still need further investigation. Furthermore, this review article is intended to foster the possible groundbreaking discovery of other metabolites.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC  
While the foundational principles of underwater VLC are well established, advanced system design requires a nuanced understanding of dynamic environmental factors such as turbidity, salinity, temperature, and pressure. These factors, extensively studied in prior work [8; 50; 51], significantly influence signal propagation and must be addressed to achieve reliable high-performance communication in real-world aquatic environments.  
It is worth noting that factors such as turbidity, salinity, temperature, and pressure depend on the environment in which an underwater VLC system operates. These factors have a great bearing on light propagation, signal quality, and reliability in communication. Turbidity, brought about by suspended particles, causes scattering and absorption of light. Salinity affects the refractive index change in water due to light propagation and optical turbulence. Variation in temperature changes water density and its viscosity, consequently affecting the attenuation and scattering of light. This effect is more elaborated, as the varied pressure at depth further changes the optical properties of the water. By understanding these challenges, one shall design robust underwater VLC with a broad operative spectrum in assorted aquatic environments. The following sections examine each of these environmental factors in some detail, drawing on recent research to highlight their impact on underwater VLC performance.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 5 Conclusions  
SC-SA is a software-oriented, memory-optimized, lightweight stream cipher algorithm. It uses an 80-bit key for initialization to generate the key stream array, providing at least \(2^{80}\) encryption strength. In RC4, the number of bytes used increases linearly with the key length. To reach the set security threshold, RC4 requires a longer key, but SC-SA can avoid this problem. Although RC4 has a slight advantage in terms of memory size dependency, SC-SA has certain advantages in efficiency and the memory resource usage ratio. The S-table in RC4 and the S-box in the SC-SA cipher algorithm serve similar roles in encryption and both have array-based data structures. However, due to concerns about the security of the S-box itself, this paper provides the S-box generation process. While both are algorithms for internal state changes in the S-box, users can choose to use third-party S-boxes without worrying about backdoors.  
Conceptualization: J.Y. and Y.C.; Methods: J.Y. and Y.C.; Software: Y.C.; Verification: J.Y. and Y.C.; Formal analysis: J.Y. and Y.C.; Investigation: Y.C.; Resources: Y.C.; Data Management: Y.C.; Writing--Manuscript preparation: J.Y. and Y.C.; Writing--Review and editing: Y.C.; Visualization: Y.C.; Supervise: J.Y.; Project Management: J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62162020, and the Science Project of Hainan University, grant number KYQD(ZR)20021.  
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflicts of interest.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model
### Performance of the Anomaly Model  
Table 2 shows the results of testing the detection model, those nodes that are under jamming must detect such condition and send a notification packet. In these simulations the jammer node is located in the middle of the topology (above node 24), therefore nodes labeled as 16, 17, 18, 23, 24, 25, 30, 31, and 32 must detect the jamming attack when the detector is ideal. The simulation time for training and testing is 100 s, and we observe the nodes that have a positive detection of jamming every 10 s in the testing phase. This means that in these nodes the number of variables with values higher than the thresholds permitted by Algorithm 2 were two or three (\(n\geq 2\)) and this may be an indication of an attack condition. For these simulations, the input data is smoothed by a moving average filter of length 10, see Algorithms 1 and 2. For simplicity we have used \(\epsilon=0\), a higher value of \(\epsilon\) may reduce the number of false positives but it would degrade the detection rate. The results obtained are 100% of true positives for all the routing protocols and 8.25%, 9.0% and 1.75% of false alarms for AODV, DSR, and MPH, respectively.  
Figure 11: Average number of packet re-transmissions of the nodes from Figure 3 for DSR (Dynamic Source Routing) under normal (blue) and jamming (red) conditions.  
Figure 12: Average number of packet re-transmissions of the nodes from Figure 3 for MPH (Multi-Parent Hierarchical) under normal (blue) and jamming (red) conditions.
## 5 Conclusions  
This paper determined land use and land cover (LULC) over the period 2015-2021 for the city of Coatzacoalcos and its metropolitan area, located in the state of Veracruz, Mexico. Based on images from Landsat 8, MLC, Geographic Information Systems (GIS), NVDI spectral index and field data, the annual land use variability was produced up-to-date as reliable information for decision making and ecosystem preservation during the execution of the ongoing project called the Interoceanic Corridor. This project represents Mexico's narrowest stretch between the Pacific and Atlantic oceans and the expansion and modernization of Coatzacoalcos and Salina Cruz ports.  
The objective of this study was to provide the most recent information on land use spatial distribution that Coatzacoalcos experienced in the last six years and to improve the current available course resolution maps. The satisfactory results can be summarized in four main aspects: (1) built-up areas, including industry, residential and commercial areas, have slowed down their growth due to socio-economical drivers such as security and null monetary investments; (2) vegetation such as mixed forest and low density vegetation (bare surfaces, sparse vegetation and grassland) has been sustained and increased over time due to reforestation or migration from other classes; (3) swamps experienced considerable degradation over the past five years and (4) high and medium vegetation densities have transformed mostly to low vegetation densities due to climate drives such as low precipitation and possible high soil evaporation, which might also increase the overland flow for those areas.  
Lastly, this study demonstrated the use of free available Landsat data and their processing by open source tools. It provided an accurate approach to mapping and assessing LULC changes over time. This methodology can be applied similarly for longer periods of time and other satellite products and contributes to improving the number of applications of remote sensing and research in Mexico and other developing countries.  
Conceptualization, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; methodology, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; validation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; inversion, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; resources, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; data curation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; writing--original draft preparation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank the anonymous reviewers for their valuable comments and feedback on this article.  
The authors declare no conflict of interest.
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 4 Experiment
### Comparison Experiment and Ablation
As shown in Figure 2, we visualize the detection results on a representative challenging scene containing multiple objects with varying levels of occlusion and lighting conditions. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **VIF** & **SSIM** & **PSNR** & **Qabf** & **EN** \\ \hline Faster R-CNN [31] & 0.236 & 0.289 & 10.346 & 0.412 & 4.366 \\ GNN-based [32] & 0.250 & 0.239 & 10.899 & 0.475 & 5.342 \\ CDDFuse [33] & 0.386 & 0.363 & 12.803 & 0.645 & 6.301 \\ ProbEn [34] & 0.296 & 0.332 & 11.996 & 0.338 & 3.865 \\ MMA-UNet [37] & 0.446 & 0.478 & 13.634 & 0.702 & 6.398 \\ \hline Ours & **0.473** & **0.513** & **13.834** & **0.731** & **6.621** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of performance between our architecture and benchmark models on the MSRS dataset highlights the effectiveness of each approach in the fusion task. All experiments were identical. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **VIF** & **SSIM** & **PSNR** & **Qabf** & **EN** \\ \hline Faster R-CNN [31] & 0.436 & 0.589 & 14.546 & 0.675 & 6.376 \\ GNN-based [32] & 0.421 & 0.633 & 15.112 & 0.702 & 7.381 \\ CDDFuse [33] & 0.530 & 0.630 & 16.103 & 0.736 & 7.653 \\ ProbEn [34] & 0.492 & 0.573 & 15.102 & 0.593 & 6.784 \\ SwinF [35] & 0.502 & 0.641 & 16.330 & 0.732 & 7.801 \\ MMA-UNet [37] & **0.512** & 0.673 & 16.381 & 0.742 & **7.931** \\ \hline Ours & 0.506 & **0.692** & **17.031** & **0.781** & 7.832 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of performance between our architecture and benchmark models on the SAIC dataset highlights the effectiveness of each approach in the fusion task. All experiments were identical.
## 4 SmTS Scheme and Its Mechanical Characteristics Analysis
### Mechanical Characteristics Analysis of SmTS
#### 4.4.1 Static Analysis of SmTS  
ANSYS Workbench was used as an analysis tool to simulate the stress and deformation of key components such as the main beam of the support under different working conditions. Taking the main beam as an example, the main beam was made of two longitudinal and five transverse rectangular steel tubes. The width and height of the steel tubes were 300 mm, and its thickness was 25 mm. The length of the longitudinal and transverse steel tube was 4.5 m and 4.1 m, respectively. The transverse steel tubes were uniformly distributed on the longitudinal steel tubes. We set the material to Q550, and its parameters were set as follows: the elastic modulus was 210 GPa, Poisson's ratio was 0.3, the yield strength was 550 MPa, and the tensile strength was 670 MPa. We set the cell type as a tetrahedron mesh, and the mesh cell size was 50 mm.  
We set the boundary conditions of the main beam as follows. According to the working resistance of 720 kN of the support obtained in Section 4.3, we evenly distributed it to four columns, and the support force of each column was 180 kN. The working resistance of the support was calculated under six typical working conditions of the main beam, including uniform loading, loading at both ends, loading at both sides, lateral eccentric loading, longitudinal eccentric loading, and torsional loading. Its load is shown in Figure 17.  
Figure 17: Six typical working conditions of the main beam. (**a**) Uniform loading, (**b**) loading at both ends, (**c**) loading on both sides, (**d**) lateral eccentric loading, (**e**) longitudinal eccentric loading, and (**f**) torsional loading.  
Figure 18: Nephogram of equivalent stress and total deformation. (**a**) Stress and deformation: nephogram of main beam under uniform loading; (**b**) stress and deformation nephogram of main beam under loading at both ends; (**c**) stress and deformation nephogram of main beam under loading on both sides; (**d**) stress and deformation nephogram of main beam under lateral eccentric loading; (**e**) stress and deformation nephogram of main beam under longitudinal eccentric loading; (**f**) stress and deformation nephogram of main beam under torsional loading.  
1. The maximum stress of the main beam under each working condition was less than the material yield limit, and the material strength met the requirements.
2. When the main beam was uniformly loaded, the working condition was the best, the stress under the lateral eccentric load working condition was maximal at 210.29 MPa, and the maximum deformation was 5.81 mm. Under the longitudinal eccentric load condition, the deformation was maximal, and the corresponding maximum stress was 128.18 MPa.
3. The stress and deformation of the equipment at the forced part and the connection were larger. The reliability of the equipment could be improved by strengthening the part.
## 1 Introduction
### Background
Currently, research on ship multi-source data processing and behavior analysis based on knowledge graph technology is lacking, especially regarding the application research on ship knowledge graph construction, ship violation analysis, and identification for maritime ship data [17; 18]. Given the rapid development of shipping technology driven by new technologies such as big data, the Internet of Things, and artificial intelligence, the need for intelligent transformation and development of water traffic management and services has become more pressing. It is necessary to conduct in-depth research on the innovative application of maritime data combined with new technologies. To improve the effectiveness of ship management and services, this study proposes an innovative approach that constructs a ship static and dynamic relationship graph based on multi-source maritime ship static and dynamic data. The study analyzes the correlation relationship between ships, extracts ship behavioral features, and comprehensively reasons and identifies ship violations using these features. The proposed method is validated through a practical case study, which demonstrates the feasibility and effectiveness of the knowledge-graph-based approach for identifying ship violations. This method integrates maritime multi-source data to achieve universal ship knowledge graph modeling. Based on violation behavior rules, graph inference technology is used to efficiently determine ship violations, improving the efficiency of ship behavior judgment, and offering the potential to develop an intelligent decision-making system for maritime traffic management.