esa-sceva/satcom-chunk-collection huggingface dataset:
# Simply Applicable Method for Microplastics Determination in Environmental Samples
## 3 Materials and Methods
### Gas Chromatography and Mass Spectroscopy Analysis  
After HS-SPME application, desorbed compounds were identified using gas chromatograph 6890 (GC; Agilent Technologies Inc., Santa Clara, CA, USA) coupled with a series of quadrupole mass spectrometer 5973 Network (MS; Agilent Technologies Inc., Santa Clara, CA, USA) operated in the electron impact ionization (EI) mode. Chromatographic separations were carried out on a fused silica 15 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m Rxi-35 Sil MS capillary column (Restek, Centre County, PA, USA). GC operating conditions were as follows: splitless mode; programmed oven temperature from 50 \({}^{\circ}\)C (2 min hold) to 270 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\). Helium (Messer, Bad Soden, Germany) was used as a carrier gas at a constant flow of 1.0 mL min\({}^{-1}\). EI occurred at electron multiplier voltage of 1200 V. MS scanned from \(m/z\) 50 to 550 automatically, with an ion source and quadrupole temperature of 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively.  
The identification of MP type was based on the structure specific produced compounds after the thermal decomposition and subsequent characteristic fragment ions from MS. Selected structure specific characteristic compounds, adsorbed on SPME, were first tentatively identified using NIST mass spectral reference library (United States National Institute of Standards and Technology, Gaithersburg, MD, USA) for GC-MS by EI and then confirmed with standard reference polymers and standard reference compounds, where possible (see detailed description in Section 3.4).
## 4 MADDPG-Based Integrated Space-Air-Sea Network System Cost Minimization Algorithm
### MADDPG Algorithm Framework  
The MADDPG is a multi-agent reinforcement learning algorithm in which each intelligent agent collaborates with other agents through training two evaluation networks to achieve coordinated decision-making. Specifically, when an intelligent agent's Actor network outputs an action, the states of the other agents will also be affected, impacting the next learning step's strategy. Therefore, each agent's decision is no longer independent. Additionally, by sharing a common experience replay buffer, each intelligent agent can fully consider the environment and the states of the other agents to make more rational decisions. This algorithm can solve the issue where traditional reinforcement learning algorithms struggle to converge in dynamic multi-agent environments.  
The framework of the MADDPG algorithm is shown in Figure 2, where each DDPG intelligent agent comprises Actor and Critic evaluation networks, along with the corresponding target networks. Additionally, all the agents share a common experience replay buffer, which stores the experiences encountered by each intelligent agent during training to update the network parameters. The target networks use a soft update method to update the parameters with a very small ratio to ensure the stability of the target networks, which accelerates convergence and enhances the algorithm's stability.  
The MADDPG algorithm adopts a combination of centralized training and decentralized execution. During the training phase, each intelligent agent sends its local observation \(o_{i}(t)\) and action \(a_{i}(t)\), and then all the agents' observations and actions are combined into a state \(s(t)\) and sent to each agent. In this way, all the agents can exchange their local information simultaneously. Additionally, each agent's Critic network is trained on the state and action of all the agents, which include the observations and actions of all the agents. During the testing phase, each agent only relies on its local observation \(o_{i}(t)\) to execute the action that maximizes the cumulative rewards.  
Figure 2: MADDPG framework description.
## 2 Data Description  
### Sea Surface Wind Data  
A microwave scatterometer operates in the C-band or Ku-band, with different swath widths and resolutions. For example, the AMI-SCAT worked at -5.3 GHz (C-band) but had a narrow swath (500 km), often not covering entire TCs [29]. NASA's QuikSCAT SeaWinds (launched in 1999) had a -1800 km swath, covering approximately 90% of tropical oceans daily [30]. The advanced scatterometer (ASCAT) onboard MetOp satellites has a center frequency near 5.255 GHz (C-band) and -550 km swath width [31]. China's first experimental HY-2A satellite (16 August 2011) carried a microwave scatterometer (HSCAT-A) similar to SeaWinds (Ku band, wide 1800/1400 km swath) [32]. HY-2B (2018), HY-2C (2020), and HY-2D (2021) carried improved HSCAT instruments with better signal-to-noise ratio and refined geophysical model functions [33]. Operating in different orbits, HY-2B/2C/2D form a multi-satellite network covering over 80% of global oceans four times daily [34].  
FY-3E (launched on 5 July 2021) is China's second-generation POES equipped with Wind Radar (WindRAD), using dual C- and Ku-band frequencies [35], thus supplementing HY-2B/2C/2D coverage. Altogether, these scatterometers provide 10-m sea surface wind vectors multiple times per day [36].  
Our study uses the 10-m sea surface wind vector products from HY-2B/2C/2D HSCAT, MetOp-B/C ASCAT, and FY-3E WindRAD. With different local equator crossing times, these instruments collectively observe TCs several times daily. Figure 1 shows an example of MetOp-B ASCAT vs. HY-2B HSCAT coverage/time on 2 August 2023, illustrating the narrower ASCAT swath (~550 km) compared to HSCAT (~1800/1400 km).  
Additionally, we use the MTCSWA dataset from NOAA/NESDIS/STAR [37] to obtain QuickSCAT derived R\({}_{34\text{kt}}\). MTCSWA combines multiple POES and GOES data at 3-h intervals [38]. We use the version from 7 September 2022 onward, covering a ~900-km radius around the TC center at 4.5 km and 10\({}^{\circ}\) azimuth resolution. We also use the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) [39] to calculate \(P_{\infty}\). ICOADS is the most comprehensive global ocean surface dataset, containing multiple variables (SST, SLP, wind speed/direction, etc.) from ships, buoys, and other ocean platforms [39]. We use its near real-time version 3.0.2, which was last updated in February 2022.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 4 Discussion
### Queueing Problems  
The classification of our ticketing system gives context as to where the identified problems lie within our enterprise queueing application. The 27% unclassified and another 25% made up of non-queue issues, such as installation, migration, APARs and documentation, equate to over 50% of the total problem tickets. The bulk of these problems may have easy fixes. Communication channels, security and transport layer security make up another 28%. Further investigations may be warranted in the detailed classification of communication channels, security issues and unclassified messages.  
We investigated our dataset for evidence of throttling. Within the logs analysed, throttling was always set to zero, meaning that throttling did not occur during the period analysed. We also saw that the frequency of bad messages which cause re-tries was less than 1%. Similarly, when looking at the malformation of messages during the EDI transformation stage, we found less than 1% had issues.  
Overall, we saw few problems with EDI transformations and bad message formats in the enterprise dataset analysed.
## 4 Discussion
### Transcription Factors Related to Terpenoid Synthesis  
Terpenoids encompass a diverse range of compounds with intricate metabolic regulation. The synthesis of plant terpenoids is a complex process involving various enzymes encoded by structural genes that catalyze the formation of multiple intermediate and final products in the terpenoid biosynthesis pathways. Regulatory genes are another crucial factor influencing terpenoid accumulation, forming complexes either individually or through interactions, and specifically binding to cis-elements in the promoters of structural genes. These regulatory complexes modulate terpenoid synthesis by upregulating or downregulating the expression of structural genes.  
Transcription factors play a critical role in regulating multiple key genes involved in terpenoid metabolism [47]. Identifying these transcription factors contributes to our understanding of plant terpenoid synthetic biology. Six transcription factor families are currently known to be involved in terpenoid production, including AP2/ERF, bHLH, MYB, NAC, WRKY, and bZIP [6,46,48,49]. For example, AaERF1 and AaERF2 bind to CBF2 and RAA motifs in the promoters of ADS and CYP-71 AV1 to regulate artemisinin synthesis [50]; in _Citrus sinensis_, citERF1 activates CitIPS16 geraniol synthase expression by binding to the CitIPS16 promoter [51]. WRKY regulates plant physiological and biochemical processes by modulating signaling pathways such as SA, ABA, JA, and ETH, thereby participating in plant growth, development, and stress responses. In _Catharantlus roseus, Gossypium arboretum_, and _Artemisia annua_, _WRKY1_ has been shown to regulate monoterpene and sesquiterpene synthesis. For instance, CrWRKY1 inhibits CrMYC2 transcription to suppress monoterpene synthase, thereby regulating monoterpene production [52]. _GaWRKY1_ promotes sesquiterpene synthesis in cotton by upregulating _CAD1-A_ expression [53]; AaWRKY1, induced by MeJA, binds to the W-box cis-acting element of the ADS promoter to enhance artemisinin synthesis in _Artemisia annua_[54].  
Research on HMGR-related transcription factors remains limited, and current knowledge primarily derives from _Arabidopsis thaliana_. Studies indicate that HY5 and PIF3 act antagonistically to regulate _HMGR_ expression and sterol biosynthesis by physically binding to the AtHMGR2 promoter [55]. In this study, WGCNA analysis suggests that ERF118 and WRKY12 may act as transcription factors that modulate HMGR-related pathways affecting fruit quality.
## 7 Deploying EEG within IoT 5G Environment  
The integration of EEG technology within the IoT 5G framework offers the potential to seamlessly incorporate real-time brain activity and behavioral data into existing digital health networks. Utilizing advancements in wearable and mobile computing technology, EEG in the 5G era can facilitate real-time monitoring of a patient's brain waves. This integration allows for immediate access to a vast array of personal digital health data and insights [71; 112]. Furthermore, the advancements in low-power 5G networks, coupled with energy-efficient 'Always-On' 5G-enabled devices, are poised to meet the emerging demands for cost-effective and long-term EEG monitoring. These technological innovations have the potential to reduce operational costs for healthcare providers and enhance patient outcomes by enabling remote access and monitoring of EEG data from any internet-connected location [113]. In summary, the advent of the IoT 5G era indicates a transformative phase in the monitoring, analysis, and application of EEG data, heralding new opportunities in digital health and personalized medicine. IoT-based EEG monitoring systems are typically comprised of three interconnected components: the EEG sensing network, the IoT cloud, and the graphical user interface (GUI). This structure is depicted in the representative example shown in Figure 8.
## 3 Results and Discussion
### Changes in Dominant Nitriifying Bacteria According to FA Concentration  
At the genus level, the changes in the proportion of nitriifying bacteria differed between PN1 and PN2 (Table 4). Representative genera of AOB, which oxidize NH4+ to NO2-, include _Nitrosomonas_, _Nitrosospira_, and _Nitrosococcus_. Representative genera of NOB, which oxidize NO2- to NO3-, include _Nitrospira_, _Nitroducter_, _Nitrogosa_, and _Nitrooccus_[28; 29; 30]. In the initial inoculum of PN1 and PN2, _Nitrosomonas_ was below the detection limit, and _Nitrospira_ accounted for 1.29%, with no other known AOB and NOB genera detected. _Nitrosomonas_ and _Nitrospira_ are known to be the most common nitrifying bacteria in wastewater treatment plants that perform biological nitrogen removal [35].  
In PN1, nitritation was induced but inhibited by FA at 10 mg/L. On day 6, _Nitrosomonas_ increased to 0.24%, and _Nitrospira_ to 3.23%. After 50 days, _Nitrosomonas_ decreased to 2.82%,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Inoculum**} & \multicolumn{2}{c}{**PN1**} & \multicolumn{4}{c}{**PN2**} \\ \cline{2-10}
**Day** & **0** & **6** & **33** & **50** & **6** & **13** & **22** & **33** & **50** \\ \hline _Nitrosomonas_ & 0.00 & 0.24 & 6.45 & 2.82 & 3.04 & 3.64 & 14.75 & 21.78 & 23.82 \\ _Nitrospira_ & 1.29 & 3.23 & 7.94 & 28.48 & 0.87 & 0.58 & 0.19 & 0.09 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative abundance of nitrifying bacteria at the genus level in PN1 and PN2.  
Figure 4: Non–metric multidimensional scaling results based on the genus–level microbial communities in PN1 and PN2.  
while _Nitrospira_ significantly increased to 28.45%. It was difficult to suppress NOB at the mainstream NH4+-N concentration of 50 mg/L, making this strategy unsuitable for initiating nitritation.  
In contrast, PN2 induced nitritation, inhibited by FA at 50 mg/L. On day 6, _Nitrosomonas_ increased to 3.04%, while _Nitrospira_ decreased to 0.87%. As the influent NH4+-N concentration gradually decreased, the proportion of _Nitrosomonas_ continued to increase, while _Nitrospira_ gradually reduced. Finally, at an influent NH4+-N concentration of 50 mg/L (FA 10 mg/L), _Nitrosomonas_ significantly increased to 23.82%, and _Nitrospira_ decreased to 0.03%. Therefore, starting the operation of a nitritation reactor at a high FA concentration and gradually adapting it to the mainstream wastewater NH4+-N concentration can effectively suppress NOB and stably enrich AOB.
## 1 Introduction
Using geopolymer concrete infill in the steel-concrete composite flooring system is advantageous, providing better rigidity to the composite slabs with increased stiffness. Compared to conventional reinforced concrete slabs, it reduces mid-span deflection and the cost of geopolymer concrete preparation [18]. To address the issue of increased self-weight, researchers have also focussed on the use of lightweight concrete using lightweight aggregates or laminated fibres, which reduces the cracks and exhibits a similar structural performance [21]. Lightweight fibres such as Rimix and other polymer fibres offer better interlocking and friction between the sheet-concrete interfaces. Furthermore, the placement of infill material is challenging in closed deck slabs, which creates the need for high-performing composites made of infills such as self-compacting concrete, reducing the labour involvement and cost of compaction [22; 23; 24]. Composite slabs made of high-performance engineered composites exhibit better performances than conventional concrete by reducing the number of cracks. The flowable self-compacting infill improves the tensile ductility and strain capacity while maintaining low crack widths [24]. A limited number of studies have been reported in this field of using flowable sustainable concrete despite the advantages of an infill that can fill under its self-weight, reducing time, cost, and labour components [20]. Additionally, in most of the cases, the slab decks have a denser reinforcement which requires the additional use of compacting techniques; however, this study adopts a self-compacting infill material which brings cost savings [20]. A thorough literature review highlights the urgent need to include lightweight, high-strength, and durable materials to maximise the advantages of modular construction. Thus, in this experimental program, an innovative modular composite floor slab is jointly developed by Deakin University with L2U Pty Ltd., Perth, and FormFlow, Geelong [25]. The study assesses the structural performance and feasibility of using sandwich decks of simple square corrugated sheets [26] with basalt FRP-reinforced self-compacting geopolymer concrete (SCGC) as infill material. This paper presents the test results of the flexural behaviour of various configurations of the sandwich floor slabs and a detailed analysis and comparison of the load-displacement response, failure modes and load-strain data of the slabs. The performance is then assessed for suitability in modular construction against their hollow counterparts.
## 3 Thermographic Techniques for Skin Cancer Diagnosis  
Steady-state and dynamic infrared thermography were both suggested for detecting skin cancers. The steady-state thermography requires that the subject reach a sufficient thermal equilibrium with the ambient air contained in the examination room, which should be maintained at a constant temperature during the test. Dynamic thermography usually involves applying thermal stress to the skin region under investigation before acquiring thermal images. Cold stress is often used rather than a hot stimulus. One of the reasons, according to [PERSON] et al. [23], is that a hot stress can lead to the denaturizing process of proteins when skin temperature exceeds 42 \({}^{\circ}\)C.  
In 2014, [PERSON] et al. [24] applied and compared three different techniques (high-frequency ultrasound, positron emission tomography/computed tomography, and infrared thermography) to examine 15 patients affected by advanced-stage melanoma who presented a total of 52 lesions. The results showed that the ultrasound technique was able to detect all the lesions, while tomography and IR thermography were only able to detect 24 and 15 lesions, respectively. In particular, the thermographic technique results varied according to the lesion size, and they were better for lesions greater than 7 mm. According to these authors, the ultrasound technique integrated with an accurate clinical examination could not be replaced by the other two techniques. However, no thermogram post-processing method was mentioned in their work. The lack of these post-processing techniques, along with the sole visual evaluation of the thermograms, could be the cause of the poor results obtained with IR imaging.  
The possibility of distinguishing between melanomas and melanocytic nevi in both steady-state and dynamic conditions was demonstrated by [PERSON] et al. [25] using different machine learning classifiers to separately test the collected thermal images. Thermal acquisitions were performed in a room at 21 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C and relative humidity less than or equal to 50% using an FLIR E60 sc IR camera with a 320 \(\times\) 240 focal plane array, a noise equivalent temperature difference less than 50 mK at 30 \({}^{\circ}\)C, and a measurement uncertainty of \(\pm\)2%. Also, the quality of the recorded images was ensured by calibrating the camera with a black body source. A period of acclimatization of 10 min was considered satisfactory to reach a good thermal equilibrium to perform the IR imaging in steady-state conditions. In dynamic conditions, a 5 mm diameter aluminum disk with a thickness of 20 mm put in contact for 1 min with the skin lesion was used as a cold stimulus. In the steady-state mode, only one infrared image was acquired, while in the dynamic mode, an image was recorded immediately after the removal of the cold stimulus and the other five at each following minute. Distinctive thermal parameters to characterize the skin lesion were retrieved from the recorded images in both static and dynamic modes.
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Data Acquisition and Processing
#### 3.1.1 Type of Acquisition Interface  
Several solutions are possible when designing the acquisition part of interfaces (see Table 1), each with its own pros and cons. Smart glasses are frequently chosen as the interface type because of their many advantages, including being able to carry several acquisition tools, or adopting the eyes' point of view. They are also easy to wear and can be found quite easily (several models with built-in sensors, cameras and headsets are currently available). Their main drawback is high cost, making them unaffordable to many. Acquisition devices can also be mounted on a white cane, with the main advantage being the use of a tool that is already used by most BVIP. The problem is that electronic tools on the canes tend to make them heavy and uncomfortable to carry. The most common type of interface in the analyzed articles is the smartphone, a tool that is both easy to find and already used by most people. Smartphones are becoming increasingly technologically advanced, and some models are now well equipped with sensors and high-resolution cameras. Despite these advances, many older or cheaper models do not hold sufficient data-acquisition tools, and replacing them is sometimes a problem for some users. Some research teams have decided to create their own wearable interfaces to deal with the issues mentioned above. For example, [PERSON] et al. [21] designed an acquisition interface worn on a headband, while [PERSON] et al. [22] and [PERSON] et al. [23] opted for small bags worn across the chest.