esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Impact of Changes in GPP and ET on WUE  
WUE is a comprehensive indicator that reflects the carbon fixation ability and water consumption of plants. Trend monitoring of GPP, ET, and WUE in Chinese terrestrial ecosystems over the past 20 years has revealed a significant increasing trend in these variables, which is consistent with previous research findings [51; 52; 53]. In different climatic regions, there are obvious differences in GPP, ET, and WUE. GPP and ET in humid and semihumid areas, as well as in humid areas, are higher than those in arid and semiarid areas, as well as in arid regions. In the warm temperate semihumid region (V) (Figure 3), the increases in GPP, ET, and WUE were found to be the greatest among all the climate types. This region, which is also the distribution area of the Loess Plateau in China and an important region for the implementation of the Grain-for-Green program, has shown significant improvement in vegetation coverage in the past 20 years [54; 55]. As a result, GPP in this region has increased significantly. Moreover, the warming and humidification of the regional climate, along with increased vegetation transpiration [56], have led to a significant increase in ET. Because the growth rate of GPP is greater than that of ET, WUE in this region has also shown a significant increasing trend. Compared with the warm temperate semihumid region (V), the subtropical humid region (VI) has also presented high values for GPP and ET. However, owing to the ample water vapor in humid regions, the increase in ET exceeds that in GPP, resulting in a lower increase in WUE. Notably, there has also been a significant increase in WUE in the Qinghai-Tibet Plateau region of China.  
Figure 6: The relative contributions of different driving factors to changes in GPP, ET, and WUE from 2001 to 2020 ((**a–f**) shows the relative contributions between GPP, with TEM, PRE, RAD, SM, VPD and LAI; (**g–l**) shows the relative contributions between ET with above variables; (**m–r**) shows the relative contributions between WUE with above variables, while GPP; gross primary productivity, ET: evapotranspiration, WUE: water-use efficiency, TEM: temperature, PRE: precipitation, RAD: solar radiation, VPD: vapor pressure deficit, SM: soil moisture).  
This is mainly due to the recent warming and environmental protection efforts, such as ecological conservation [57], which have resulted in notable vegetation recovery in the Qinghai-Tibet Plateau region. Consequently, WUE has also shown a marked increase.
## 3 Modeling the VSG Control Strategy
### Synchronization Mechanism  
Except for some isolated synchronous generators that supply their own loads, such as generators for emergency or remote regions, conventional power grids are composed of hundreds or thousands of generators that share the entire load of the system. Their parallel operation is realized on the basis of synchronization mechanisms. In conventional power systems, before paralleling, the oncoming SM must compare and adjust its electric voltage output so that they match the amplitude, phase angle, phase sequence, and frequency of the running system. The typical parallel operation of an oncoming generator that is paralleling to the running system is shown in Figure 12. After paralleling, the paralleled machine regulates its output power for energy transmission. Under the unified synchronization mechanism, all of the generators in the entire system operate under the same frequency synchronously, and jointly participate in regulating the frequency and voltage through their inner SM droop characteristics.  
Figure 13: Alternating current (AC) side circuit of synchronous generator.  
Note that the output power (P, Q) of the oncoming synchronous generator is zero before parallel operation, as the parallel switch is off. It can be certified that the SM performs as an enhanced phase lock loop (PLL) called the sinusoid locked loop (SLL) during the parallel operation, which has a frequency channel to synchronize the frequency, and a phase and amplitude channel to synchronize the voltage with the running system [18]. In such a way, synchronous machines have the ability to self-synchronize and plug-and-play.
## 4 Multi-Angle Analysis of RGN Predicted Results
### RGN Prediction Process and Data Acquisition
#### 4.1.1 Calculation Stage of RGN  
In the calculation stage, the main task is to process the training data into the format required by the network and process it into the torsion angle data in the RGN calculation stage to prepare for the generation of the tertiary structure in the next stage. First, the training data we take is the processed ProteinNet test set [32] in CASP7 [12]. Second, in the data preprocessing stage, the amino acid sequence and PSSM sequence in the ProteinNet data set are processed into RGN interface data. The main fields in the data set are shown in Table 1.  
Third, integrate the above information into the RGN calculation stage. The calculation stage is based on the calculation unit of long short-term memory ((LongShort-TermMemory, LSTM) to calculate the internal state of the integration of the current residue and adjacent units. The twist angle between residues is output by each unit. The format is 3 \(\times\) 1 matrix (\(\mathbf{A}^{\mathrm{T}}\), \(\mathbf{A}=(\,\phi,\psi,\omega\,)\)), which will be used as the input data of the geometric stage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field Name** & **Field Meaning** \\ \hline ID & Predicted dataset protein ID \\ Classification & Prediction method classification: TBM and FM \\ Primary & Protein amino acid chain \\ Evolutionary & Location specific scoring matrix \\ Tertiary & Three-dimensional atomic representation of protein \\ Mask & Position indicator, presence or absence of residue atom \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main fields of RGN input data.  
Figure 1: RGN prediction process.
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 1 Introduction  
A regular analysis of machining equipment reliability not only reduces production maintenance costs, but also ensures the machining quality of the workpiece. The traditional reliability analysis methods use a large number of similar device failure time information to obtain their overall characteristics, which is based on the experimental data of a lot of samples, and the result is only an average property under the current conditions. The reliability analysis method based on a degradation model is a breakthrough in the development of reliability engineering technology. But there are still some weaknesses, such as the degraded data in practical is difficult and costly to obtain. In recent studies, state monitoring methods have been used to obtain dynamic signals, which reflect the health state of the equipment and can be used for reliability analysis without disturbing the normal operation of the equipment.  
For machining equipment, high productivity and required product quality ultimately need to be achieved. Therefore, the reliability analysis of machining equipment should consider the mission reliability based on equipment status identification, process design analysis and product quality. All of the above methods focus on the operational reliability of the equipment, while ignoring the characteristics of the mechanical machining equipment that is different from the general mechanical equipment. In recent years, some scholars have studied the task reliability of machining equipment by applied the multi-source data of the processing process.  
In this paper, the reliability analysis is summarized from three aspects: Multi-source information during cutting process, failure physical analysis for signal selection and reliability assessment based on condition information. The technical method of condition monitoring is combed according to the framework of reliability analysis. This paper focuses on how to extract relevant features from the cutting process information and how to use the multi-source data to perform real-time reliability assessments of machining equipment.  
Based on the above content, a literature search was carried out. The databases referenced in this paper mainly include \"Web of Science\", \"Google scholar\", and \"China National Knowledge Infrastructure\". The number of keywords to be searched in Web of Science and the number of documents in past five years are shown in Table 1. It can be seen that \"Condition Monitoring Technology\" and \"Quality Control\" have been the hot topics of current research, and related researches of other keywords also show an upward trend.  
According to the search results, the rest of this article is organized as follows: Section 2 reviews the multi-source information during cutting process. The failure physical analysis for signal selection is described in Section 3. In Section 4, the detail of reliability assessment based on condition information are presented. Finally, the conclusions of this work and outlook on future challenges are provided in Section 5.
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 1 Introduction  
Marine environments represent the last frontier of biodiversity. Even though marine habitats account for more than 70% of the Earth's surface, supporting large numbers of living organisms, they remain biologically unexplored. Marine fungi have been largely overlooked in comparison to terrestrial fungi. They represent less than 1% of the known fungal species [1, 2], remaining a poorly characterized and poorly understood group. In this respect, marine fungi only recently have attracted attention and are yet to find a prominent place in biotechnology [3, 4, 5].  
Under extreme conditions, such as high salinity, ultraviolet light exposition, low temperature, limited access to nutrients and substrates for growth, and even extreme hydrostatic pressure [6], marine fungi have developed unique physiological and chemical capabilities to thrive and survive [6, 7]. They have been isolated from a variety of habitats including mangrove plants, macroalgae, seagrass, other marine plants or macro-phytes, coral reefs, drift- and intertidal wood, vertebrate and invertebrate marine animals, phytoplankton, sea ice, sea garbage, and coastal and open-ocean water columns [8, 9].  
Studies have shown that marine fungi are a biochemically diverse group of organisms which provide a plentiful and diverse source of unique novel bioactive natural compounds with a wide range of industrial, agricultural, cosmetic, and pharmaceutical applications [4; 10; 11; 12; 13]. Recently, many studies focused on the identification of compounds produced by marine fungi [9]. Marine fungi produce a wide diversity of enzymes with biotechnological relevance, such as amylase, chitinase, ficoidanase, galactosidase, glucosidase, inulinase, keratinase, lactose, lipase, lignin peroxidase, protease, polygalacturonase, and xylanase [14]. Additionally, they are known to produce secondary metabolites including terpen, steroids, polyketides, peptides, and alkaloids [3; 4]. These secondary metabolites are associated with a wide range of activities, such as antibacterial [15; 16], antifungal [12], antiviral [13; 17], anticancer [18; 19], cytotoxic [20], and antioxidant activities [21]. In addition, recent studies recognized marine fungi as being useful in the biodegradation of polyethylene microplastics [22; 23].  
While searching for biotechnology relevant activities produced by marine fungi, we screened fungal species isolated from marine and estuarine environments for enzymatic, antimicrobial, antioxidant, and cytotoxic activities. We also investigated whether sea salt can have pronounced effects on bioactivities from marine fungi.
## 2 Literature Review
### Impact of Virtual Reality on the Cognitive Load, Task Performance, and Situational Awareness  
According to research, cognition arose in tandem with the advancement of computers and artificial intelligence (AI) [14]. The term cognition is associated with computing and analyzing data information. Researchers define the ability to acquire knowledge that involves rich information through reasoning and perception. Human cognition involves gathering information and developing experiences from their interactions with the environment, as shown in Figure 1[49]. Every human perceives, processes, and creates a mental portrayal of their particular reality. According to the author [50], the designer thinks about what he is doing, calling it the \"reflection-in-action\" process.  
Virtual headsets are consumer-grade products that are scarce; thus, measuring the TP of these commercial VR is difficult. Further, these VR systems are composed of various components, including VR headsets, desktop monitors, smartphones, and VR applications. Each of these elements has a direct impact on the user's performance. TP measures assume that an individual's mental workload while interacting with the system during a task is a good indicator of CL [51]. Task completion time and error identification rate are examples of CL and TP metrics [52].  
The NASA Task Load Index (TLX) is a subjective workload assessment tool that lets users perform subjective workload assessments on participants working with different human-machine interface systems. In 1988, [PERSON] and [PERSON] developed the NASA TLX questionnaire to quantify the physical and mental load associated with performing a given task [53]. NASA TLX uses a six-dimensional rating system to calculate an overall CL. This score is based on the weighted average of ratings on six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration. NASA TLX has measured CL in physical, virtual, simulations, and lab tests [15; 19; 54; 55].  
In the examination of SA measurement, [PERSON] et al. [56; 57] categorized existing techniques into five categories: (1) the physiological method [58] corresponds to heart rate, electroencephalography (EEG) [59; 60] and now most recent electrodermal activity  
Figure 1: Human factors, cognitive aspects, and knowledge representation in building design.  
(EDA) [61]; (2) the performance method such as task success or failure, detection of hazards; (3) the observer rating technique such as the situational awareness behavioral rating technique [62]; (4) the self-rating technique such as the crew and mission awareness scale and situational awareness review technique (SART) [23]; (5) the freeze rating technique that is a situational awareness global assessment technique (SAGAT). All these above techniques have some benefits and drawbacks. According to researchers, SART is generally acknowledged as low cost, simple to perform, and easy to analyze [63; 64]. This technique has three dimensions: (1) demand on the attentional resource (D), (2) supply of attentional resource (S), and (3) understanding of the situation (U).
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 5 System Testing
### Pattern-Recognition Accuracy at Different Test Voltages  
For the collection of a dataset, the influence of different voltage ranges on the accuracy of the neural network needs to be considered. In this paper, discharge data under 6 kV, 7 kV, 8 kV, and 9 kV voltage conditions were mainly collected; the amplification of the amplification circuit was fixed; and the thickness of the insulation layer was fixed at 4.5 mm. The recognition accuracy under different voltages was obtained through 100 instances of actual tests, as shown in Table 3.  
The datasets collected at these different voltages were trained, and the results showed that the local discharge signal induced by the insulation layer defects when the discharge voltage was 8 kV had distinct characteristics and that the trained neural network had a higher accuracy rate of 98.3%.
# Development of a Natural Matrix Hybrid Hydrogel Patch and Evaluation of Its Efficacy against
## 4 Conclusions  
AD is a genetic disease involving immunological abnormalities. Studies suggest that a functional deficit due to a hypersensitivity response caused by increased IgE antibody levels, disproportionate T lymphocyte differentiation resulting from decreased cell-mediated immune function, and skin adrenaline receptor blockages may cause AD. Moisturizing the surface of the skin and applying topical corticosteroids (a class of steroid hormones) are common approaches for treating or managing atopic diseases. However, the long-term use of hormonal agents causes various side effects including atrophied skin, vasodilation, pigmentation, and scarring [18]. Therefore, atopic treatments with anti-inflammatory properties that lack these side effects are needed.  
This study successfully developed a CAE-loaded HA-Dex hybrid hydrogel patch that could control CAE release by fusing HA and DEX through a crosslinking agent (BDDE). FE-SEM showed that increased CAE content was associated with increasingly distorted pore sizes and shapes. In addition, the gelation rate, swelling ratio, and migration rate tended to increase as the CAE content increased. The highest level of cellular activity and the most stable release profile were obtained using a 0.4 wt% CAE-loaded HA-Dex hybrid hydrogel patch.  
In conclusion, this enabled moisturization with minimal skin irritation even after a long period of use and the slow, stable release of CAE to the skin. The safety and efficacy of the patch for AD treatment were also evaluated in vitro. We found that patches containing 0.4 wt% CAE produced the most stable release profile and the highest level of cellular activity. This type of hydrogel patch could potentially be used as a delivery system for other therapeutic agents for AD.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by the Government-wide R&D Fund Project for Infectious Disease Research (GFID) (grant number: HG19C0707), Republic of Korea. Also this research was supported by the Ministry of SMEs and Startups(MSS), Korea, under the \"Regional Specialized Industry Development Program (R&D, Project number P0013693)\" supervised by the Korea Institute for Advancement of Technology (KIAT). This research was supported by the Government-wide R&D Fund Project for Infectious Disease Research (GFID) (HG19C0707), Republic of Korea. Additionally, this research was supported by the Korea Institute of Industrial Technology (KITECH) under the \"AI platform for vision systems and applications Project\" (JA200003).  
Figure 9: Wound closures rates for _Centella asiatica_ extract (CAE)-loaded hyaluronic acid-dextran (HA-Dex) hybrid hydrogel patches. (a) 9.8:0.2, (b) 9.6:0.4, (c) 9.4:0.6, (d) 9.2:0.8, and (e) 9:1 wt% HA-Dex: CAE.
###### Abstract  
As one of the most common forms of corrosion in the marine environment, pitting corrosion can have a detrimental impact on the ultimate strength of steel columns. Pitting pits are usually covered by corrosion products, and the detection of pitting is very difficult, so how to effectively identify random pitting corrosion on steel columns has become a very vital issue. In this paper, a deep-learning-based pitting damage determination method for steel columns is investigated by combining numerical simulation and theoretical analysis, which was validated by experimental results. First, a multi-parameter localized pitting corrosion model was proposed that considered the pitting corrosion randomness in time and space distribution. Second, the relationship between the ultimate strength and corrosion rate of steel columns was analyzed. Finally, a steel column damage determination framework was constructed based on the convolutional neural network. Results showed that the ultimate strength and corrosion rate developed different trends in various corrosion regions, and a damage determination accuracy of 90.2% could be achieved by the neural network after training, which satisfied the practical engineering requirements. This study lays the groundwork for further application of deep learning to the research on the pitting damage to steel structures.  
pitting corrosion; corrosion damage determination; deep learning; CNN; steel column
# Video Games in Rehabilitation Programs for People with Parkinson's Disease: A Systematic Review
## 3 Results
### Summary of Results
[31; 36] compared the effects of VR, motor imagery, and conventional physical therapy on balance, motor function, and activities of daily living in PD patients. VR was found to be more effective than motor imagery and conventional physical therapy, leading to significant improvements in motor function and daily activities. Furthermore, the study concluded that the combination of VR and motor imagery provided superior outcomes compared to either intervention alone. [PERSON] et al. [35] conducted a feasibility study using Leap Motion-controlled video game-based therapy for upper limb rehabilitation in PD patients. The results indicated that this approach has significant improvements in upper limb motor function. [PERSON] et al. [37] examined the efficacy of Wii Sports-based strategy training in reducing fall risk and falls and improving quality of life in adults with PD. The study showed that participants in the Wii Sports group experienced fewer falls and reported better quality of life compared to the control group (Table 2). There also does not seem to be a consensus on the number of sessions or the protocol for each intervention. [PERSON] et al. [29] developed a protocol in which they conducted three sessions per week, for 8 weeks, each lasting 60 min. [PERSON] et al. [33] administered three sessions per week of 90 min for 6 weeks. [PERSON] et al. [35] implemented 30 min sessions, twice a week, for 12 weeks. [PERSON] et al. [31; 36] conducted a protocol with three sessions per week for 12 weeks, with a duration of 60 min per session. [PERSON] et al. [32] administered two sessions per week of 50 min for 5 weeks. [PERSON] et al. [37] developed a 12-week protocol, with three sessions per week, each lasting 30-40-min. [PERSON] et al. [30] carried out a 30 min intervention three times a week for 8 weeks. Finally, [PERSON] et al. [34] conducted four sessions per week, for 5 weeks, with 45 min sessions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
All the studies reviewed agree that VR is a good complement to conventional rehabilitation and that its use must be supervised by a professional (physical therapist or occupational therapist) [29; 30; 31; 32; 33; 34; 35; 36; 37]. In most of the studies reviewed [29; 30; 32; 33; 34; 35; 37], no follow-up evaluation was administered to determine if the benefits obtained through VR were maintained over time. [PERSON] et al. [31; 36] evaluated patient outcomes 6 months post-intervention and confirmed that the benefits were maintained in this elapsed time.