esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pre-Identification Method for Continuous Low Thrust Parameters
### Pre-Identification Method of Continuous Normal Thrust Parameters for Changing RAAN  
Neglect small periodic variations in orbital inclination if \(a_{\rm T}=0\). The semi-major axis a is unchanged and the RAAN will vary linearly under the continuous normal thrust.  
\[\Omega=\Omega_{0}+\frac{2}{\pi na\sin i}a_{\rm N}t \tag{27}\]  
If \(a_{\rm T}\
eq 0\), the semi-major axis a varies under the action of the tangential thrust, and the same process of derivation as in Section 5.3 leads to  
\[\Omega=\Omega_{0}-\frac{2a_{\rm N}}{3\pi n\mu a_{\rm T}\sin i}\left(\sqrt{ \frac{\mu}{a_{0}}}-a_{\rm T}t\right)^{3}+\frac{2a_{\rm N}}{3\pi a_{\rm T}} \tag{28}\]  
Equation (28) describes the amount of change in RAAN ratios due to the combined effect of normal and tangential acceleration. In fact, the variation in the RAAN is strongly influenced by the perturbation, the dominant of which is the \(j_{2}\) term arising from the Earth's non-spherical gravity. Long-term uptake effects only consider the effect of the \(j_{2}\) term. The first-order long-term rate of change of RAAN under the influence of \(j_{2}\) terms of perturbation is  
\[\dot{\Omega}=-\frac{3J_{2}}{2p^{2}}n\cos i \tag{29}\]  
The units of calculation are in normalized units and the corresponding central celestial gravitational coefficient is \(\mu=GM=1\). Let \(\Omega_{\rm c}\) be the corresponding value of the mean root of the ascending RAAN chi via moment \(t\), then  
\[\Omega_{\rm c}=\Omega_{0}-\frac{2a_{\rm N}}{3\pi n\mu a_{\rm T}\sin i}\left( \sqrt{\frac{\mu}{a_{0}}}-a_{\rm T}t\right)^{3}+\frac{2a_{\rm N}}{3\pi a_{\rm T }}+\dot{\Omega}t \tag{30}\]  
According to Equation (30), the normal acceleration can be solved from the amount of change in RAAN, the magnitude of tangential acceleration and the time interval between the two arc segments.
## 3 Discussion
### Morphological Features and Phenotyping  
The populations under study differed in morphological features including total height (tall, mid, or dwarf), thin or squat growth, leaf shape, phyllotaxy, number of nodes, number of lateral infloresances, internode length, compact or non-compact inflorescence, flowering time (fast, mid, or late), seeding time (fast, mid, or late), seed features, etc., and although not reported here, this is in line with earlier research reported that chemical phenotypes can be characterized by different morphological features [8]. [PERSON] and colleagues (2021) reported architecture of cannabis plant may considerably affect the cannabinoids profile, which has significant pharmaceutical and economic importance [45]. Additionally, phenotype markers that can facilitate preliminary identification and selection as a supplement to chemical and genetic analysis developed in 2021 [46]. They showed significant morphological differences in terms of leaf color, leaflet shape, large and compact infloresances, and dense and resinous trichomes, which were identified between 21 cultivars covering three chemical phenotypes (THC dominant, CBD dominant and intermediate cultivars). They also reported that modern cannabis cultivars are morphologically distinguished by a morphological feature (leaflet shapes) by users and breeders [46].  
Among the studied populations, three dwarf populations including Saq-01, Pir-01, and Naq-01 distributed in the north-west along with 10 other populations were located in the group of Type I (Figure 3a). These three unique populations were assumed to be marijuana, as they have similar features to those reported recently with thinner stems, more branches, and a higher density of floral tissues than industrial hemp plants [8]. In addition, the plants of population of Ard-01 were morphologically (tall, branched with space between branches, branches out and few leaves, early seeds mature, and drop) and chemically (located in the group of Type II) distinct.
## 2 System Design
### Data Releasing Module
#### 2.5.2 WEB Releasing  
The real-time water-level data can be acquired through Internet on the WEB releasing module. In view of the application and analysis of the historical data, three web releasing methods are designed, including map releasing, database releasing and the water-level variation trend chart.  
(1). Map Releasing: The Web electronic map is introduced in the RWMS to show the detailed locations, descriptions, and the maximum/minimum of water-level by adding the labels in the map. The map of  
Figure 7: Flow chart of the data releasing module.  
China will be the default map, but the map can be customized through revising the scale and the sites in the background configuration file.  
(2). Database Releasing: The data obtained from RWMS is saved as specified data format in the designated database. The real-time database inquiry can be achieved through the database development, such as the site inquiry, the period inquiry and the matching inquiry.  
(3). Water-level Variation Trend Chart: The water-level variation trend chart of the designated site can be drawn in RWMS through choosing the site and the time period. The user can follow the water-level trend by appointing the last time as the collection time of the last piece of data.
# Xanthone Derivatives and Their Potential Usage in the Treatment of Telangiectasia and Rosacea
## 2 Xanthone Derivatives and Their Vascular Activity
### Antioxidant Activity  
Several studies have investigated the antioxidant activity of xanthone derivatives and have found promising results:  
* Free radical scavenging: Both 1,3,7-hydroxyxanthone and other hydroxy xanthone derivatives were shown to effectively scavenge free radicals, thereby reducing oxidative stress and preventing cellular damage as well as protecting against ultraviolet (UV) radiation and pollution [42].
* Anti-inflammatory effects: Inflammation is closely linked to oxidative stress, and xanthone derivatives like \(\alpha\)-mangostin, cowanol, and cowanin have demonstrated anti-inflammatory properties, which may contribute to their overall antioxidant activity.
* Neuroprotective effects: Some studies suggest that some xanthone derivatives, like mangiferin, may have neuroprotective effects by scavenging free radicals and reducing oxidative damage in the brain, which could potentially help with the prevention or treatment of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease [43; 44; 45; 46].  
[PERSON] et al. [47] provided novel scientific evidence demonstrating the protective effects of mangostanin (Figure 8), a xanthone derivative isolated from _Garcinia mangostana_ fruit, on epidermal keratinocytes against oxidative stress-induced damage. Pretreatment with mangostanin significantly attenuated H\({}_{2}\)O\({}_{2}\)-induced cytotoxicity and ROS production. Mechanistically, mangostanin inhibited the activation of key signaling pathways, including p53, p38 MAPK, ERK, and AKT, as well as the cleavage of Caspase-9 and Caspase-3 while preserving critical cell survival signals such as EGFR and signal transducer and activator of transcription 3 (STAT-3). These findings suggest a potential pharmaceutical application for mangostanin in skin protection and aging. However, further in vivo and clinical studies are warranted to validate its efficacy and safety for potential osmeceutical, pharmacological, or nutraceutical formulations [47].  
As mentioned above, xanthone derivatives demonstrate potent activity toward targets and pathways associated with vascular disorders. This activity warrants further investigation, specifically for potential usage of xanthone derivatives as active pharmaceutical ingredients in topical formulations.  
Figure 8: Structure of mangostanin ((R)-4,8-dihydroxy-2-(2-hydroxypropan-2-yl)-7-methoxy-6-(3-methylbut-2-en-1-yl)-2,3-dihydro-5H-furo[3,2-b]xanthen-5-one).
## 2 Typical Scheme and Disadvantages of Sublevel Caving Method
### Disadvantages of Sublevel Caving Method  
1. Covering rocks to put ore, high loss rate, and dilution rate increase the cost of mine beneficiation [26; 27; 28].  
The high loss rate and dilution rate brought about by overburden rock decentralization directly lead to the rising cost of mine beneficiation. At the same time, under the action of frequent blasting vibration and ore release and unloading load, it will also further aggravate the secondary dilution of ore, resulting in a lower grade of ore, and then increase the cost of mine beneficiation.  
2. Causing collapse and subsidence of the earth's surface [29; 30].  
Mining by the sublevel caving method may cause subsidence and settlement of the ground surface, which is potentially damaging to surface buildings and facilities, and may also adversely affect the surrounding environment. In addition, the deformation of the surface may require additional work to repair or stabilize, which will increase the environmental management costs of mining, as shown in Figure 1(a), which shows the impacts on the surface of a mine using the sublevel caving method.  
3. Causing massive accumulation of tailings and increasing the safety risk of tailing ponds [31].  
First, tailings ponds may be destabilized due to improper design, mismanagement, or natural factors (such as earthquakes, rainfall, etc.), resulting in landslides, mudslides, and other disasters; second, tailings may contain heavy metals, toxic and hazardous substances, etc., and if the tailings pond leaks or breaches the dam, these hazardous substances may enter the water, soil, and atmosphere, polluting the surrounding environment and affecting the ecosystem; lastly, the tailings piling up will also occupy a large amount of land, destroying the original topography and geomorphology, and affecting the rational use of land resources. Figure 1(b) shows the waste tailings storage of a mine.  
The above drawbacks limit the scope of application of the sublevel caving method in some mines, and it is difficult to adopt this mining method in some mines with more complicated geological conditions or higher requirements for environmental protection and safety. At the same time, these limitations have given rise to the demand and research for more advanced, safer, more efficient, and more environmentally friendly mining methods in mines.  
Figure 2: Influence of caving mining method on the surface of Qibaoshan Mine.
## 2 Background
### Why Autonomous Shipping  
The possibility of reducing human error-caused maritime accidents and the potential to reduce operating costs are the main driving forces behind research and development in autonomous shipping technology [11]. Traditional technical and operational solutions to reduce costs, such as building larger ships and reducing ship speed, have reached their limitation in their ability to overcome economic, social, and environmental challenges [9]. Autonomous vessels are a linchpin to future competitiveness and sustainability within the European shipping industry; they are a potential cure for the current difficulties facing the maritime sector [12].  
A study conducted by the German international financial services company Allianz determined that 75-96% of maritime related accidents from 1912 to 2012 were caused by human error [13]. Autonomous vessels may positively affect the safety of ship navigation and relieve the burden of fatigue, which is a major contributing factor to human-error-caused accidents [14]. Autonomous vessels may also have cost savings. A cost benefit analysis conducted by Maritime Unmanned Navigation through Intelligence in Networks (MUNIN) found that the cost of owning and operating an autonomous bulk carrier versus a conventional manned bulk carrier would save a total of $4.3 million USD over a 25-year period [12].  
Societal advantages may also be achieved by autonomous shipping. Flexibility of operations brought on by autonomous technologies could improve service coverage for goods distribution [9]. The supply chain could be optimized, leading to an increase in cost efficiency; environmental preservation could increase due to a reduction in fuel usage, food waste, garbage disposal, and sewage; and infrastructure could be improved for areas of larger volumes of shipping traffic [9].  
It is not certain to what extent the maritime industry will adopt automation [7]. A 2019 study conducted by the World Maritime University predicted that human supervised autonomous vessels will reach between 11-17% of global shipping by 2040 [15], and MASS trials continue to gain speed around the globe.
## 2 Macroscopic Characteristics of Samples  
The surface morphology of LPBF parts is related to the laser scanning process. In the work conducted by [PERSON] et al. [55], the surface of the as-built IN625 sample presented a typical \"V\" shape morphology (Figure 2a), which is similar to the situation in the welding process. However, the shape of the Y-Z section is \"scales\" as shown in Figure 2b. The width of the melt track is about 100 \(\upmu\)m, and the adjacent tracks overlap closely, resulting in nearly 100% density and almost no gaps. The \"V\" angle increases as the laser scanning speed decreases.  
[PERSON] et al. [56; 30] used LPBF pulse shaping IN625 alloy to study the effect of process parameters on the roughness (\(R_{\text{a}}\)) of top and side of thin-walled parts and prepared the best sample with 9 \(\upmu\)m roughness on the top surface and 10 \(\upmu\)m roughness on the side face. They concluded that higher peak power could reduce balling formation by increasing melt wettability, thereby reducing the top and side \(R_{\text{a}}\). Large laser repetition rate, low scanning speed, high spot overlap rate, and high energy density decrease the top \(R_{\text{a}}\) but increase the side \(R_{\text{a}}\). By controlling these factors reasonably, low \(R_{\text{a}}\) on the top surface and side can be obtained simultaneously [56; 57].  
By melting the IN625 bare plate, the melt pool geometry (width and depth) of IN625 alloy during LPBF process can be capably reflected, in which typical chevron features (Figure 3) of solidified materials appear due to fluid flow and free surface evolution [58]. Specifically, the increase of scanning speed leads to an increase of chevron spacing, but the angle of the chevrons decreases. To eliminate the thermal stress concentration caused by the chevron feature of LPBF sample surfaces, [PERSON] et al. [59] used M280 laser forming equipment to print the IN625 metallographic sample, and the laser scanning path was superposed with different angles. Although a few irregular microcracks appear on the sample surface, there is no obvious transition region between the laser beam action region, and the single track melting layer perpendicular to the scanning direction of the laser beam shows an arc distribution.  
Overall, IN625 has good LPBF forming ability with a wide limit range of process parameters [60]. High density (e.g., 99.99% [61] and 99.74% [62]) and smooth surfaces can be obtained by carefully controlling the processing parameters. The macroscopic characteristics of the as-built IN625 samples are closely related to the process parameters. It is obvious that the chevron feature of the track can be seen in almost all previous studies [55; 56; 57; 58; 62].  
Figure 2: Macroscopic morphology characteristics of as-built Inconel 625 (IN625) samples: (**a**) top surface morphology, (**b**) “scales” shape of Y-Z section, and (**c**) illustration of the coordinate system and laser scanning mode [55].
# Elastic Slow Dynamics in Polycrystalline Metal Alloys
## 4 Multi-Relaxation Theory and Results
### Results and Discussion  
#### 4.2.1 Distribution Properties  
In order to compare properties of the obtained distributions as a function of conditioning amplitude \(A_{cnd}\), we introduce the following indicators:  
* Area, defined as \(\int F(\tau)d\tau\). It is equal to the velocity variation at time \(t=0\), i.e., it quantifies the amount of conditioning;
* Peak position, defined as the relaxation time corresponding to the maximum of the distribution. It defines the most relevant relaxation time in the process and, as shown above, it is linked to the grain size;
* Width, defined as the width at half height of the distribution;
* Centroid, defined as \(\tau_{c}=\frac{\int_{0}^{\infty}\tau F(\tau)d\tau}{\int_{0}^{\infty}F(\tau)d \tau}\). It allows to quantify the asymmetry of the function.  
The four indicators were calculated for the tested samples at increasing conditioning amplitude \(A_{cnd}\) in successive experiments and are shown in Figure 8. The area (subplot (a)) for all samples increases linearly with increasing conditioning amplitude. Even though an exact comparison between samples is critical (given that, for the same input, the strain in the sample could be different), still data allow to appreciate that mortar exhibits stronger conditioning than metal alloys, as expected: The area is much larger at lower conditioning amplitude levels. All metal alloys behave similarly, also from a quantitative point of view. Conditioning is weaker in the Magnesium sample. Remarkably, for all samples a threshold for activation of conditioning is present: The fitting straight line intersects the x-axes at a positive value of \(A_{cnd}\). The existence of a threshold for conditioning, shown elsewhere for granular samples [24], is thus present also in metal alloys.  
Figure 7: Best fitting distributions \(F(\tau)\) for different samples. (**a**) One peak distributions. The first distribution for the titanium alloy is also reported for reference. (**b**) Two peaks distribution. The two distributions are reported separately and \(F(\tau)=F_{1}(\tau)+F_{2}(\tau)\).  
The other three indicators are independent from \(A_{cmd}\), which suggests that, once the activation threshold is reached, features corresponding to all relaxation times are activated and the number of them participating to relaxation is proportional to the level of conditioning (linear dependence of the area on \(A_{cmd}\)). As already discussed, the peak position increases with the grain size. Width and centroid have almost the same value for all metal alloys samples considered, except for the centroid in the case of the composite Magnesium sample, which is slightly larger (i.e., stronger asymmetry) and close to that of mortar.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 2 Methodology  
The overall architecture of the spatiotemporal aggregation network is illustrated in Figure 1. The input data is first processed by the point cloud input unit and the spatiotemporal clue-gated encoder, after which the processed data is passed through the U-Net network, which embeds multiple spatiotemporal feature select state-space modules. The final results are output through the semantic segmentation head.  
Specifically, the point cloud input unit divides the input flow into two branches: one for serialized historical frame data and the other for current frame data. The sequence data consists of multiple frames of point clouds, providing an input format that is well-suited for subsequent feature extraction and learning.  
The spatiotemporal clue-gated encoder first performs preliminary feature encoding on the serialized sequence data and current frame data using 3D sparse convolution layers. For the encoded sequence features, the spatiotemporal clue-embedding layer captures the spatiotemporal clues hidden within the multiple historical frame features, resulting in long-term spatiotemporal features. These long-term features are then dynamically balanced with the current features through the gating unit, which aggregates the features to produce effectively combined spatiotemporal representations.  
Finally, the spatiotemporal feature select state-space module takes the aggregated spatiotemporal features as input and processes them through multiple 3D sparse convolution layers and Mamba blocks to perform global context modeling of the spatiotemporal features. Simultaneously, it leverages the typical encoder-decoder structure of U-Net to generate feature representations that are beneficial for semantic segmentation.  
The following sections (Sections 2.1-2.3) provide a detailed introduction to the three components mentioned above.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 3 Results and Discussion
### Inhibition of the Biofilm Development
#### 3.2.2 Biofilm Inhibition by ZnO-NPs on the Glass Surface
This makes the biofilms an obvious target for the management of bacterial infections. A study has found that ZnO-NPs synthesized using leaf extract of _Oclradenus bacactus_ inhibited the biofilms development of _C. violaceum_, _P. aeruginosa_, _E. coli_, _K. pneumoniae_, _S. marcescens_, etc. at the sub-MICs [28]. Zinc oxide-silver nanocomposites have also been reported to selectively target and reduce  
Figure 5: Confocal microscopic images of the _E. coli_ and _P. aeruginosa_ biofilms in the absence and presence of ZnO-NPs. (**A**) Untreated control _E. coli_; (**B**) _E. coli_ treated with 1/2 \(\times\) MIC of ZnO-NPs; (**C**) untreated control _P. aeruginosa_; (**D**) _P. aeruginosa_ treated with 1/2 \(\times\) MIC of ZnO-NPs. Figure 6: Scanning electron microscopic images of the _E. coli_, _P. aeruginosa_, and _S. aureus_ biofilms in the absence and presence of ZnO-NPs. (**A**) Untreated control _E. coli_; (**B**) _E. coli_ treated with 1/2 \(\times\) MIC of ZnO-NPs; (**C**) untreated control _P. aeruginosa_; (**D**) _P. aeruginosa_ treated with 1/2 \(\times\) MIC of ZnO-NPs; (**E**) untreated control _S. aureus_; (**F**) _S. aureus_ treated with 1/2 \(\times\) MIC of ZnO-NPs. the biofilms of _S. aureus_, _E. coli_, and _C. albicans_[39]. Moreover, green synthesized ZnO-NPs using seaweed were found to highly inhibit the biofilms of _B. licheniformis_, _E. coli_, _B. pumilus_, and _P. vulgaris_[40]. The findings indicate the possible application of ZnO-NPs to target the biofilms of bacterial pathogens, especially for surface coating and topical applications.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 4 Performance Evaluation
### Simulation Results
It is evident that as there are more vehicles, the throughput increases. This is because more vehicles mean that UEs changed. We compared the system throughput between two different scenarios (highway and urban) as well as when the number of vehicles changing in a certain area. It is observed that for highway scenarios, the system throughput improves by 10%, which is easy to understand because the highway scenario has few obstacles. Figure 8a,b illustrates how the distance between vehicles affects the throughput of the system for two scenarios (highway and urban). It is clear that as the distance between  
Figure 7: Throughput for different NR-V2X configurations with varying vehicle speeds without retransmissions and varying vehicle numbers. (**a**) Highway and (**b**) Urban. vehicles increases, the system throughput decreases. The system throughput drops by roughly 10 to 20% as vehicles' distances fall between 50 and 100 m.  
It is evident that the network is greatly impacted by the distance between the vehicles. When vehicles are closer, the sidelink network has an advantage over the network. The speed of the network also affects communication in similar ways as the other cases. It can be observed that lower speeds result in better system performance. Figure 9a,b illustrates how the distance between vehicles decreases the latency of the system. The result is shown for both highway and urban scenarios. Additionally, it is observed that as average vehicle speed increases, the system latency increases. While transmitting a packet in sidelink, the PSCCH carries SCI messages, which describe the dynamic properties of transmission for the PSSCH. Thus, when the vehicles have increased speed, the PSCCH takes more time to send the SCI message to the PSSCH channel. Figure 8: Throughput for different NR-V2X configurations with varying vehicle speeds without retransmissions and varying inter-vehicle distances. (**a**) Highway and (**b**) Urban.