esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Application of the Methodology  
This section outlines how the described methodology was employed, from keyword identification to the analysis of the results. We commenced with a precise definition of the primary research question, which, in turn, naturally steered our selection of keywords by pinpointing the essential concepts relevant to the query. As described in the introductory paragraph, the aim of this paper is to analyse the current scenario in the predictive energy- and carbon-related analyses and machine learning tools for data-informed building design.  
A preliminary review of the existing literature provided a baseline understanding of the dialogue within the field, identifying the terms frequently used by authors on the subject. This initial scan was instrumental in building a foundation of commonly used language and jargon in the domain of energy-efficient building design. These selected keywords were categorised into three sub-topics:  
1. Advanced digital tools for data-informed design: including terms like Artificial Neural Networks, machine learning, genetic algorithm, sensitivity analysis, multi-objective optimisation, and Metamodel.
2. Performance-driven design: encompassing performance, energy consumption prediction, and building performance simulation, among others.
3. Early stage energy and operational carbon assessment: featuring early stage life-cycle assessment and operational carbon.  
Bibliography databases, such as Web of Science and Scopus, along with the European Commission research project platform CORDIS, were utilised. Searches were conducted considering the intersection of different keywords to evaluate both the quantity and quality of the results. The filters applied to refine the search included open access results, specific subject areas (engineering, energy, environmental science, etc.), all document types, time periods, and affiliations.  
The results from the database searches were analysed to understand the significance and temporal impact of each sub-topic. Tools like VosViewer and Bibliometrix [20] facilitated a deep semantic analysis. Preliminary findings indicate a rising interest in the confluence of these sub-topics. While performance-driven design for energy optimisation is a prevalent theme, words such as \"framework\", \"integration\", and \"requirements\" integrated with carbon assessment elements remains sparse, indicating a potential gap in the literature.
## 3 Results and Discussion
### Biotin and Thiamine Impact on the Wine Metabolome  
An untargeted UPLC-qTOF-MS analysis of the wines collected at the endpoint of the alcoholic fermentation was performed in order to investigate the influence held by both biotin and thiamine on the _S. cerevisiae_ metabolome.  
The retrieved features were filtered to only retain those existing in at least two of the three biological replicates of all modalities, to ensure significance in their presence in the final wines. Subsequently to those treatments, 3870 features have been detected in both positive and negative ionization modes and analyzed using both PCA at a significance level of \(\alpha\) = 0.05 and one-way ANOVA at a significance level of \(\alpha\) = 0.01.  
In order to investigate possible differences between the metabolomes of _S. cerevisiae_ when grown in different initial concentrations of either biotin or thiamine, a principal component analysis (PCA) was performed on the intensities obtained for all 3870 extracted features, subsequently leading to planar representations in which components PC1, PC2, and PC3 accounted for, respectively, 20.4%, 13.5%, and 5.6% of the observed variations (Figure 3A,D).  
No clear discrimination between groups appeared here, and especially no differentiation could be found between the intermediary and lowest vitamin doses. As such, for all further metabolomic investigations, the wines obtained from _S. cerevisiae_ grown in 3 \(\upmu\)g/L of biotin (\"high biotin\") were separated from those grown in both 0.5 and 0 \(\upmu\)g/L of biotin (\"low biotin\"), for which data were taken into account together, as one unique condition. Similarly, wine resulting from alcoholic fermentation in 250 \(\upmu\)g/L of thiamine (\"high thiamine\") was considered separately from those obtained in both 50 and 0 \(\upmu\)g/L of thiamine (\"low thiamine\").
# Mining of Potential Gene Resources for Breeding Nutritionally Improved Maize
## 9 Discussion
Another issue that should be addressed is that many genes have multiple functions and are expressed in various plant tissues and organs. Therefore, applying these genes for nutrient quality improvement using a knockout strategy may also cause serious plant growth and development defects, and the knockdown strategy may be more suitable for such cases. Additionally, protein engineering to generate amino acid substitution mutants instead of knockout may provide another option to solve this problem. This requires knowledge of the working mechanism of the protein and the specific mutation technology. The mechanisms of vitamin synthesis, mineral absorption and transport are still not fully clear. Many vitamins need to work together, so multivitamins are now advocated. Vitamin absorption also has a great relationship with the cooking method. Studies have shown that the cooking temperature was the decisive factor in the cooking loss of carotenoids in corn, and the boiling and steaming of corn caused it to retain the most nutrients [144]. Using exogenous fertilization seems more straightforward for mineral replenishment, but the cost and problems associated with soil and groundwater contamination make genetic manipulation a better choice. However, the effect of genetic manipulation for mineral content improvement is also related to the cultivar. Although phytic acid is harmful to the absorption of metal ions, it is also the storage form of phosphorus in plants. In order to avoid the effect on phosphorus uptake, the regulation of phytic acid content should be carefully considered. In addition to modifying plant genes, genetic engineering allows the possibility of introducing genes with special effects from other species such as bacteria into maize. The use of zein promoters that specifically express bacterial \(crtB\) and \(crtI\) genes in maize endosperm resulted in a thirty-four times increase in total caroten [145]. The bacterial _lysC_ gene encodes an AK, but unlike AK in plants, it is not inhibited by lysine feedback, so when _lysC_ was ectopically expressed in tobacco seeds, lysine content was increasingly detected [146]. Similar strategies could also be applied to maize. Many genes show synergistic effects on a specific biological process. Therefore, overexpression of a series of synthases along the same synthetic pathway may cause more substantial effects than overexpression of one gene alone. Some genes may regulate a synthetic pathway coordinately. For example, when \(HGGT\) is co-expressed with caroten synthesis genes in sorghum, increased vitamin E can reduce the oxidative degradation of caroten, increase the stability and half-life of caroten and thus increase the caroten content [89].
## 3 Results and Discussion
### Analysis of the Grid Frequency Values Measured by the Inverters of PV Systems
Figure 32 shows a colour map reflecting the value of the frequency over the entire monitoring period measured by the inverter connected to phase 1 in installation 4. It can be seen that the small oscillation of values of this parameter was not related to the production of the PV plants, which was shown in Figure 8. In the case of plants with three-phase inverters, the measurements of this parameter, as in the case of single-phase inverters, only take place during the hours when they are in operation, unlike the grid voltage, which was recorded by the three-phase inverters 24 h a day. Therefore, it is not possible to compare the value of the grid frequency during the hours of sunshine, with PV production, with those during the hours of the night, when the PV plants are not producing electricity. It would be interesting if inverters had the possibility to record this parameter throughout the day, to have more information on what happens to it when there is no production from the PV plant. Still, though, the use of inverter measurements enables a first assessment to be made of the influence of the production of the PV installations and the variability of their production on the grid frequency values, without observing in this work that it is directly having a negative impact. Even if the presence of such non-synchronous generation power plants reduces the inertia of the system in terms of frequency control, at least the presence of production does not seem to contribute to changing this grid parameter. When these renewable installations are small, they have a negligible influence on the frequency. However, when a larger number of such installations are connected, their aggregate impact could be significant. In the future, with the increasing penetration of such installations in the grid, it will also be necessary for these installations to be able to participate in the frequency regulation of the grid, which would require, e.g., not working at full capacity, in order to have a reserve for continuous or occasional frequency control [28]. In addition, inverters with power electronics that can respond to increases or variations in power flows would be  
Figure 31: Relationship between the variations of the grid frequency in one of the phases versus the values of the variations of the \(P_{AC}\) power produced in the PV 4 installation measured during month 11 (November). required, and this response could take place more quickly than is currently the case with conventional generators, which could even be an advantage [46,52].
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 2 Background
### Unmanned and Autonomous Vessels  
The concept of unmanned vessels relies on the land-based shore control center (SCC) [6], where the crew will monitor the vessel using crucial information transferred by satellite communication at short time intervals. If the autonomous system registers a problem--in addition to in several other situations--the crew at the SCC will take remote control of the vessel [7]. [PERSON] et al. [8] explain unmanned ships as:  
_\"An unmanned ship is a ship with no humans onboard. An unmanned ship does not have to be autonomous; it can be under autonomous control but it can also be under remote control from a [Shore Control Center], or from other places (e.g., a pilot or tugboat or a mooring supervisor).\"_  
An autonomous ship is defined as a ship:  
_\"with modular control systems and communication technology to enable wireless monitoring and control, including advanced decision support systems and the capabilities for remote and autonomous operation\"_[9].  
[PERSON] et al. [8], explain that autonomous vessels have an automated software system that navigates and maneuvers the vessel. The system and the ship are under monitoring from the SCC. An autonomous ship has no crew in the engine room or bridge, but it may have a maintenance or service crew. The navigation and engine rooms have a programmed automatic system that typically reaches a particular level of artificial intelligence [7].  
The MUNIN project describes the concept of an autonomous ship as a ship which is operated autonomously within specific limits by new systems onboard the ship. In this scenario, the functions of control and monitoring could be performed by operators in the SCC [10]. The crew continuously monitor the operation of the vessel from the SCC, implementing direct remote control in exceptional situations.  
The operation of unmanned and autonomous vessels can be defined in four main states: autonomous execution, autonomous control (autonomous problem solving), remote control, and fail-to-safe mode [5]. Autonomous execution means that the onboard autonomous system constantly controls and measures the vessel's status to determine if the vessel can continue with its predefined plan. This involves providing regular data to the SCC so that the offshore crew can monitor the functioning of the vessel, while the SCC is not required to intervene, except in certain special cases. The vessel will enter into the autonomous control or autonomous problem solving states when a deviation from the predefined plan is required (e.g., crossing other vessels or harsh weather) [10]. At any time, the SCC can take remote control over the vessel and completely override the onboard autonomous ship controller (ASC) [5]. Fail-to-safe mode is the vessel's status if communication is lost and the autonomous system cannot resolve the problem [7].
## 4 Case Study--Numerical Experiments
### Inter-Facility Material Transfer Process  
SM is a specialised steel manufacturing firm that produces steel rebars for the domestic market. To manufacture steel billets, which serve as the primary inputs for producing steel rebars, SM employs an electric arc furnace (EAF) system. The billet production process involves utilising various materials, including scrap materials, hot briquette iron (HBI), and specific additives such as carbon, chromium, and deoxidisers such as aluminium or silicon. Daily, SM acquires scrap materials from the domestic market, which are stored in different forms and purities in the SM storage yard. These forms include heavy metal scrap and shredded scrap. Additionally, SM incorporates HBI and direct reduced iron (DRI) as raw materials in the billet production process. The selection of billet grades is determined by evaluating the quality and percentage of the scrap materials. To simplify our discussions  
Figure 4: Steps of optimisation approach used in Anylogic software.  
in subsequent sections, we will utilise the notations A, B, and C to represent heavy metal scrap, shredded scrap, and HBI/DRI materials.  
In the context of SM, the production of steel billets takes place on a daily basis, using materials A, B, and C. These materials are transported each day by a fleet of homogeneous trucks to the facility situated within the billet plant. Figure 5 illustrates the inter-facility material transfer operations undertaken to meet the demand requirements of the billet plant. Figure 6 presents the layout of the storage area and the billet plant, visually representing their spatial arrangement.  
As depicted in Figure 6, except for the loading and unloading service stations, all other stations are shared among the three types of material trucks. Truck servicing follows a first-come, first-served basis. The node details and average service time for each service station are presented in Table 1, denoted in minutes. Additionally, Table 2 provides the weight of a full truckload of each material.  
Figure 5: Process flowchart for the inter-facility material transfer operations.  
Figure 6: Layout plan of SM’s raw material storages and billet plant.  
Each service station within the system is managed by a single server, implying that each station has the capacity to serve only one truck at any given time.  
In order to evaluate and optimise inter-facility material transfer operations, this study conducted performance measurements and buffer allocation optimisation exercises. The experiments were designed based on scenarios identified in Table 3. Additionally, various parameters were considered, including the cost of buffer space (C\({}_{i}\)) and the maximum allotted budget for buffer space allocation (\(\beta\)), with values of EUR 200 and EUR 5000, respectively. All experiments were conducted to simulate a 24 h time-period operation.
# Exploiting Data Similarity to Improve SSD Read Performance
## 2 Background and Research Motivation
### Motivation
#### 2.3.3 Key Idea of the Proposed Solution  
To address the aforementioned issues, designing an effective page allocation scheme emerged as the most direct and efficient approach to enhance channel and chip utilization, harness data similarity in workloads, and thus improve SSD performance. However, mainstream allocation schemes, including static and dynamic schemes, possess limitations. Static allocation schemes fail to evenly distribute the read and write loads, whereas dynamic allocation schemes address the bus resource underutilization and imbalance during data writes but impede the exploitation of rich parallelism during data reads from flash chips, thereby impacting read performance. As a result, the channel and chip utilization imbalance problem remains unsolved [16]. Consequently, based on the observed high data similarity in workloads, we propose a novel page allocation scheme that combines the strengths of static and dynamic allocation while accounting for the need for simplicity and lightweightness. This scheme aims to improve the channel and chip balance for read operations by leveraging the feature of data content similarity. In this paper, we present DS-FTL, which facilitates the allocation of page-size sub-requests to different parallel units to maximize channel and chip utilization and enhance overall write performance. Additionally, we adopt a multi-path read scheme based on data similarity to boost overall read performance.
## 3 Tracking Control Laws
### Adjusting \(\Delta\) as the Vehicle Follows the Target Depth  
Consider the case where the vehicle follows a target depth under our control laws. Since the input of the vehicle switches among (5)-(7), the vehicle depth does not converge to the target depth exactly. Instead, the vehicle fluctuates up and down the target depth. However, it is desirable that we reduce the fluctuation as the vehicle follows the target depth.  
We introduce a fuzzy method to reduce the fluctuation of the vehicle, as it follows the target depth. At each time step k, we store the error between the vehicle's depth and the target depth: \(e(k)=z(k)-z_{target}(k)\). Then, we check the variance of the error, which is given as follows.  
\[var(e)=1/w\times\sum_{t=k-w+1}^{t=k}e^{2}(t) \tag{9}\]  
where \(w>0\) is a window size to derive the variance. In the case where \(var(e)\) is less than a certain threshold, say \(Th\), then we assume that the vehicle is following a target depth. In this case, we set \(\Delta\) in both (5) and (7) to \(\Delta_{2}<\Delta\). In this way, we can reduce the torque to rotate the vehicle. Since the torque is reduced, we can reduce the fluctuation as the vehicle follows the target depth.
## 1 Introduction
In addition, there is a big gap between the local structure flow of the liquid-solid-gas three-phase interaction, transient motion characteristics, velocity, and pressure field distribution of slurry sewage sludge flow characteristic parameters. The actual situation and the numerical simulation aspects of segment slug flow need to be further studied. Therefore, it is necessary to carry out a numerical simulation study of slurry biomass flow and different aspects, especially in situations to avoid clogging. The related multiphase flow modeling and solution methods also need to be further explored [21; 22; 23; 24]. The behavior of solid particles of varying sizes within an accelerated toroidal volume filled with fluid has been analyzed using computational fluid dynamics-discrete-phase model (CFD-DPM) simulations by [PERSON] and [PERSON] [25; 26] and [PERSON] and [PERSON] [27]. During the flow of sewage sludge suspension, a critical factor is its heat uptake behavior under external heating. The hydrothermal liquefaction (HTL) process operates at temperatures exceeding 350 K, inducing rheological variability in the sewage sludge suspension. This variability significantly influences the flow field, consequently impacting the efficiency and composition of the resultant bio-oil. Notably, simulations can incorporate the temperature-dependent rheokinetics of the suspension, which plays a crucial role in the reactor's heat transfer convection. This article presents a multiphase CFD simulation of the HTL process in a three-dimensional reactor. The influence of individual process parameters on the course of the reaction was determined. The simulation was conducted using ANSYS FLUENT software version 15.0, offering versatility in adjusting various parameters, such as temperature, pressure, residence time, flow rate of feedstock, as well as properties of operational units. This process simulation was applied to multiple feedstocks and reaction conditions, and the results were compared with published experimental data [16; 22]. In this study, an integrated CFD model with HTL process kinetics was proposed to simulate the effects of slurry flow rate, temperature, residence time, and convective heat transfer on the biocrude oil yield of the resulting product in a pilot-scale tubular reactor. The obtained results can be practically used to optimize the structure and determine intensive mixing zones in the HTL process.
###### Abstract  
The application of shaking during the withering process has been shown to significantly enhance the floral aroma of black tea. However, prior to this study, there was limited research on the effects of shaking combined with other withering treatments on the aroma components of black tea. In this study, the aroma attributes of black teas processed with shaking combinations of yellow-light or high-temperature withering (YLS, HTS, and HYS) were evaluated through sensory evaluation, and the volatile composition and identification of key aroma compounds in black teas and in-process withered tea leaves were analyzed by gas chromatography-mass spectrometry (GC-MS). The results indicated that black teas subjected to different shaking combinations exhibited a distinct floral aroma with higher volatile compound content, with the YLS treatment showing the most significant aroma improvement. Eight volatile compounds with relative odor activity values (rOAV) > 1 were identified from 53 differential volatile compounds in black tea under different shaking combinations as the most important contributors to aroma quality. Linalool, trans-\(\beta\)-Ionone, \(\alpha\)-ecdrene, and nonanal were identified as key floral volatiles with high rOAVs. Their concentrations in YLS were notably higher compared to those in S, both in withered tea leaves (with the exception of trans-\(\beta\)-Ionone) and in the final dry black teas, suggesting that there may be a synergistic effect between the yellow-light withering and the shaking process in enhancing these key floral compounds. Overall, this study found that shaking combined with yellow-light withering can improve the aroma composition and quality of black tea, providing a theoretical basis and practical guidance for the production and optimization of high-aroma black tea.  
black tea; shaking combinations; yellow-light withering; volatile substance; aroma improvement +
Footnote †: journal: _foods_  
0000-0002-0002-7880][PERSON]  
1 Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Leisure Agriculture, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; [EMAIL_ADDRESS] (Z.A.); [EMAIL_ADDRESS] (L.J.)
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Anomaly Detection  
Anomaly detection is a technique used in machine learning for identifying the unusual behavior or malicious activity in the IoMT network by an intruder [27]. It is also used to predict any future malfunction of IoMT networks. The overall goal of using anomaly detection is to determine the potential security, privacy, and performance issues [28].