esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Recognition Mechanisms Between Imprinted Polymers with Biomarkers
### Bacterial and Fungal Cells  
Direct bacteria imprinting and generation of recognition sites on polymeric matrices have demonstrated to be practicable [26]. It belongs to one type of cell-imprinted polymers. The recognition mechanism can give credit to the diversity of bacteria cells in shape (e.g., round-shaped _Staphylococcus aureus_ and the rod-shaped _Escherichia coli_ (_E. coli_)), the uniform size of the same bacteria and the relatively rigid cell wall, which enable size and shape-dependent physical space matching. More importantly, chemical recognition based on the multiple interactions between the cell surface and MIPs/SIPs is essential to recognizing bacteria cells. [PERSON] and [PERSON] [27] developed the bacteria cell-imprinted polydimethylsiloxane (PDMS) to investigate the role of chemical recognition (Figure 4B). The results showed that cell imprinted PDMS with methylsilane groups results in a cavity, thus losing much of its ability to capture the imprinted bacteria, although the shapes of the imprints were shown to be hardly affected which was proved by atomic force field microscopy. Hence, employing suitable  
Figure 3: (**A**) Structural formulas of the d-arabitol template, 2,2\({}^{\prime}\)-bitiophene-5-boronic acid functional monomer, 3,3\({}^{\prime}\)-bitiophene crosslinker, and b-arabitol esterificated with three molecules of functional monomer. Reproduced from [18]—Published by the American Chemical Society. (**B**) Structural formulas of methacrylic acid (MAA) and 2,5-dimethyl-4-hydroxy-3(2H)-furanone (DMHF). Reproduced from [21]—Published by Elsevier B.V.  
functional groups or monomers to form efficient chemical interactions between MIPs/SIPs and the bacterial cell surface is a more important factor for cell-imprinting. Other studies also revealed that chemical recognition plays a dominant role in bacteria recognition. Phenylboronic acid (PBA) groups can significantly improve bacteria affinity of the MIPs with controllable bacteria recognition due to the reversibility between PBA and cis-diol groups of glycan chains presented on the bacterial surface [28]. Besides PBA groups, carbohydrate polymers like chitosan, which exhibits affinity for various bacteria strains, were also applied to create excellent MIPs matrices with high bacteria affinity [29].  
The factors influencing yeast cells (_Saccharomyces cerevisiae_) recognition by SIPs were studied by means of spectroscopic and microscopy techniques. The results indicated that cell imprinting creates selective binding sites on the surface of the SIPs layer in the form of binding cavities that match the cells in shape and size. Furthermore, it demonstrated that the incorporated phospholipids significantly enhance cell adhesion to the SIPs. The role of phospholipids in the SIP recognition mechanism is mediated by long-range hydrophobic forces [7].
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Boost Converter with Clamped Capacitors  
This converter (Figure 12(a)) incorporates two clamped capacitors (\(C_{1}\) and \(C_{2}\)) [5]. The capacitors work as a filter with low impedance when the converter needs to operate in high frequency, which is used for CM noise mitigation. The current ripple may increase up to two times in comparison with a converter with clamped diodes; in addition, this converter presents a lower efficiency than the SBBC with clamped diodes and switches in similar applications. In this topology, the clamped capacitors must have the same values; otherwise, the converter has imbalances, increasing CM noise. In this case, the converter could need a different controller for each switch. Figure 12(b) presents a modification for this converter [6]; the modification includes two coupled inductors (\(L_{3}\)) in series with \(l_{1}\) and \(L_{2}\) for mitigating CM noise. In this case, the capacitors are also used to reduce the magnetization current from \(L_{3}\) (they present low impedance); additionally, the choke inductors present high impedance in their terminals. Then, this topology has a wide frequency range for CM noise mitigation.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 4 Bearing Capacity under Eccentric Load
### Parameter Analysis of Bearing Capacity
#### 4.2.5 Influence of Steel Tube Wall Thickness  
The relationship of steel tube wall thickness with the bearing capacity is shown in Figure 15a. Similarly, the DC joint is affected by the steel tube wall thickness weakly, but the bearing capacity increases with steel tube wall thickness. Figure 15b shows the curves of the steel tube wall thickness with the initial compression stiffness and yield load. The relation between the bearing capacity and the steel tube wall thickness is a positive linear correlation. When the wall thickness increases by 2 mm in each step, the initial compression stiffness and yield load increase by about 59.8 kN/mm and 45 kN, and the increase rate is about 1.5% and 1.1%, respectively.
## Literature Review
### Sentiment Analysis and Opinion Mining  
Topic discovery is somewhat close to sentiment analysis and opinion mining. Various approaches to perform sentiment analysis and opinion mining on micro-blogs (and Twitter in particular) have been proposed. Their application context is very different with respect to the context and the goal considered in this paper. Nevertheless, it is useful to give an overview of these techniques.  
[PERSON] et al. [11] proposed an algorithm to exploit the emotions of Twitter users by considering a very large data-set of tweets for sentiment analysis. They proposed a distributed framework to perform sentiment classification. They used Apache Hadoop and Apache Spark to take the benefits of big data technology. They partitioned tweets into three classes, i.e., positive, negative and neutral tweets. The proposed framework is composed of four stages: (i) feature extraction (ii) feature vector construction (iii) distance computation, and (iv) sentiment classification. They utilized hashtags and emoticons as sentiment labels, while they performed classification by adopting the AkNN method (specifically designed for Map-Reduce frameworks).  
The study [12] by [PERSON] et al. evaluated the impact of research articles on individuals, based on the sentiments expressed on them within tweets citing scientific papers. The authors defined three categories of tweets, i.e., positive, negative, and neutral. They observed that articles which were cited in positive or neutral tweets have more impact if compared to articles cited in negative tweets or not cited at all. To perform sentiment analysis, a data-set of 6,482,260 tweets linking to 1,083,535 publications was used.  
Twitter data are also very important for companies, so as to exploit them to improve their understanding about the perception by customers of the quality of their products. In [13] authors proposed an approach to process the comments of the customers about a popular food brand, by using tweets from customers. A Binary Tree Classifier was used for discovering the polarity lexicon of English tweets, i.e., positive or negative. To group similar words in tweets, a K-means clustering algorithm was employed.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method  
To verify the feasibility of image transferring over the LoRa physical layer, we conducted a practical measurement experiment in the Sabak Bernam mangrove forest, northwest of the Selangor state, Malaysia. For this experiment, the LoRa transmission node was fixed to the top of a Skylift crane, at a height of approximately 40 m, and the receiver node was installed to the back of a four-wheel drive vehicle, which travelled one kilometer following each measurement, to allow for the assessment of image quality at different distances, as illustrated in Figure 4.
## 4 The Role of YchF/Ola1 in Translation
This further supports the hypothesis that translation of lmRNAs is initiated on 70S ribosomes. Absence of YchF promotes translation of lmRNAs and reduces the translation of canonical mRNAs [42]. This is in line with reduced global protein synthesis of the \(\Delta ychF\) strain and the observation that \(\Delta ychF\) cells are outcompeted by wild type cells under non-stress conditions [42]. It was recently shown that IF3 transiently occupies a second binding site on the 50S ribosomal subunit, which might be important for the translation initiation of lmRNAs [125]. IF3 and YchF occupy overlapping binding sites on the 30S subunit [42; 126] and both also interact with the 50S subunit [42; 125]. A direct contact between YchF and IF3 was furthermore demonstrated by cross-linking in vivo [42]. Thus, one attractive model is that YchF controls lmRNA translation by regulating IF3 binding to either the 30S or 50S ribosomal subunit (Figure 4B). Under normal growth conditions, YchF supports IF3 binding to the 30S subunit and thus favours canonical translation. When cells enter the stationary phase or encounter stress conditions, the YchF levels drop [42], while IF3 levels increase [127]. This enhances the interaction of IF3 with the 50S subunit and promotes lmRNA translation. This model is in line with recent data showing enhanced lmRNAA translation under oxidative stress conditions [128], which are associated with reduced YchF levels [20]. In essence, a decrease in YchF and an increase in IF3 during the stationary phase or stress conditions would prepare the translation machinery for the increased lmRNAs accumulation that occurs due to increased MazF-dependent cleavage of the Shine-Dalgarno sequence under these conditions [111; 112] (Figure 4B). Increased lmRNA translation in the absence of YchF possibly also explains the cold-sensitive phenotype of the _E. coli_\(\Delta ychF\) strain [20]. During cold shock, IF2 levels increase and IF2 accumulation stimulates lmRNA translation at the expense of canonical translation [63; 127]. Further stimulation of lmRNA translation when YchF is absent, potentially reduces canonical translation to a level that impairs cell viability at low temperatures.
###### Abstract  
In recent years, with the steady growth of load demand in distribution networks, the fluctuation and uncertainty of power loads have significantly increased. Meanwhile, the rising penetration of photovoltaic generation has further exacerbated the challenges of power system accommodation capability. To enhance photovoltaic accommodation capability and realize the secure and economic operation of distribution networks, a multi-time scale hierarchical coordinated optimization operation strategy for distribution networks with aggregated distributed energy storage has been proposed. First, the regulation requirements of aggregated distributed energy storage are analyzed, and a distributed energy storage aggregation model is established based on an inner approximate Minkowski Sum. Subsequently, a multi-time scale optimization operation model considering source-load uncertainties for day-ahead, intra-day, and real-time stages is developed based on a stepped carbon emission cost model. Then, a power allocation method within the aggregated distributed energy storage based on the water-filling algorithm is presented. Finally, a practical distribution network in a demonstration county in China is used as a case study to validate the proposed method. The results demonstrate that the proposed strategy effectively reduces system operation costs while improving photovoltaic accommodation capacity and enhancing the reliability of system operation.  
optimal operation; multi-time scale; uncertainty; distributed energy storage; Minkowski Sum +
Footnote †: journal: Applied sciences
## 1 Introduction
The three best known are according to [PERSON][11]: an A-scan, which is only measured and evaluated at one point, where the time curve of the pressure or the sound amplitude at this position is evaluated; a B-scan, in which a line of measuring points is measured with an A-scan and with a defined distance, thus obtaining 2D information that can then be displayed either at a fixed time as a line graph or for a defined period of time as an area plot [12]; and a C-scan, which needs the highest effort because a surface that is discretized by a grid is measured one after the other, point by point, with single A-scans [13]. If the scanning plane is far away from the panel surface, then a volume scan can be done. At defined distances, planes are measured with a C-scan, and then those are combined to volumetric data to evaluate. This technique is very time-consuming [14]. Receiver arrays [15] can be used to shorten the measurement time, but such arrays have a great influence on the sound field. A detailed description of the common procedures of air-coupled ultrasound for the examination of wood materials can be found in [PERSON] and [PERSON] [7; 9]. However, in order to obtain the information of a volume sound field and to shorten the measuring time, one possibility is to measure only one plane in a C-scan and to calculate the volume sound field or the sound field of the air layer directly above the panel surface via post-processing methods. This can be done by the re-radiation method [16; 17; 18; 19; 20; 21; 22]. In this way, very well interpretable amplitude images of the air layer directly above the panel surface are generated. These allow the identification and localization of flaws or the heterogeneities in the materials [17; 18; 22]. To create a C-scan image with a high detectability of flaws by using a typical measurement grid with a grid point distance of \(\leq\frac{\lambda}{3}\)[16; 17; 18; 22], many measurements have to be made, and this is comparatively time-consuming. Nevertheless, with the re-radiation method of Marhenke2019 [22] we were able to identify flaws which are smaller than \(\lambda\). Therefore, there is a need for techniques that can be applied with the re-radiation method that provide high detectability of flaws in a C-scan image, while requiring less measurement time due to a coarser measurement grid (\(>\frac{\lambda}{3}\)). In this publication, we propose a novel way of data post-processing before the use of the re-radiation method. This makes it possible to obtain images with a high detectability of flawsfrom a very coarse measuring grid (>\(\lambda\)) of a C-scan and to identify flaws which are smaller than \(\lambda\). For this, the novel idea of virtual measuring grid points is used, which are filled with time data via different interpolation methods. This opens up the possibility of reducing the number of measuring grid points significantly, thus reducing the experiment time of up to 98%.
## 3 Methodology
### Ranking Strategies  
In this phase, the identified 16 strategies are ranked with respect to four strategy selection criteria (SSCs), which were identified by reviewing the current literature around strategy selection [34; 41; 42; 43; 44] and are described as follows:  
* Impact on CLP (IC) is the impact of a strategy on CLP improvement in a project under study.
* Implementation feasibility (IF) is the degree to which a strategy can be implemented in a project with respect to economic, technical, and scheduling constraints, including required time and cost of implementation.
* Workers' adaption (WA) is the ease with which workers can adapt to a strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
* Implementation risk (IR) is the potential for a strategy to encounter development or deployment failure. (The term describes risks related to strategy launch.)  
As stated in the introduction section, two widely used fuzzy MCDM methods--fuzzy AHP and fuzzy TOPSIS--are integrated to rank the CLP improvement strategies meeting the above criteria. The main advantages of these methods are that they mathematically represent uncertainty and vagueness in the decision-making process without involving cumbersome mathematics ([PERSON] et al., 2018). According to [PERSON] et al. (2018), the combination of fuzzy AHP and fuzzy TOPSIS shows better performance compared to using each method separately. Accordingly, fuzzy AHP is used to determine the relative weights of SSCs based on fuzzy pairwise comparison, and fuzzy TOPSIS is applied to determine the relative importance of each strategy and rank the strategies. Figure 2 presents the hierarchical structure of the decision-making process for CLP improvement strategy selection. Inputs of the proposed decision-making methods are provided by two questionnaire surveys, described in the next section. The following sections demonstrate the development of fuzzy AHP and fuzzy TOPSIS to identify the top-ranked CLP improvement strategies.
## 2 Definition and Main Legal Issues of ASWQE  
### Definition of ASWQE Projects  
ASQWE generally refers to \"an aquifer and a series of facilities used to fill up the aquifer with surface water using an artificial method like a well or a pond, store it for a certain amount of time or let it flow and then pump it up\" [13]. ASWQE projects involve injection/recharge and pumping facilities, pipe network facilities, pre- and post-treatment facilities, system detection facilities, and operating systems, but large artificial structures, such as tunnels, underground retaining facilities, or underground dams, are not required [7; 13].  
As shown in Figure 1, the ASWQE project involves highly complicated procedures and processes, such as the collection and pretreatment of source water, artificial injection into the aquifer, storage/filling/recharge, recovery/pumping, and water treatment/supply, all of which require highly intricate and complex legal legislation to be developed based on overseas and domestic legal and regulatory system benchmarking. Some examples include the legal status and water rights of the source water, i.e., surface water and stored groundwater; the legal status of the aquifer serving as the underground space for storage; the licensing basis and procedures for each phase of the project; subjects of the project; the purpose of using groundwater; a legitimate reward and compensation system; and the health and environmental standards in all phases of the project. Figure 1 represents that the method and procedure treating the source water, the water quality/quantity, and the environmental/health standards can be determined differently depending on the end use of the cleaned groundwater.  
The principles of ASWQE are fundamentally equivalent to those of aquifer storage and recovery (ASR) or aquifer storage transfer and recovery (ASTR) technologies [14]. ASR and ASTR generally rely on the abundance of surface water during the rainy season. Surface water is injected into an aquifer and stored and recharged to form clean groundwater resources, which then can be pumped out for use mostly during the dry season [16]. The key difference between ASWQE and ASR/ASTR is that the former is used at all times to provide a constant supply of drinking water, while the latter is mainly used for storing water during rainy seasons and recovering the stored water during periods of water shortage for various purposes, such as emergency water provision during saltwater intrusion.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Limitations of Current Studies  
One problem that plagues most studies based on field data (not only those based on data fusion) is that the results are not realistically weighed against the limitations of the dataset used in the experiments. In particular, in the case of agriculture, it is very unlikely that a single dataset will cover all the variability that can be found in practice [1]. As emphatically put by [PERSON] et al. [89], \"how well a model fits a calibration data set does not reveal any information on how good the model performance will be for other data, i.e., for real predictions\". In their work, the model was tested using data collected in a different year, under the justification that validating a model with data captured under very similar conditions to those used for training will almost invariably lead to unrealistic results, which unfortunately is usually the case. Indeed, when this approach was applied by [PERSON] et al. [71], the results obtained for different years were strongly disparate, thus revealing that the data used for training was not representative enough. [PERSON] et al. [36] added that it is difficult to find the ideal sample distribution due to too much homogeneity and presence of extraneous factors. Additionally, even if the process of training, testing and validating is carried out properly, virtually all datasets used in research will have certain limitations, and these always need to be properly considered when analyzing the results. Unfortunately, with some exceptions [33; 63; 94; 123], this is seldom the case. As a result, unrealistic claims are often found in scientific texts, hampering the progress on the subject.
## 3 MSP Algorithms and Master Data in Production Planning System
### MSP Algorithm
#### 3.2.2 Decision Tree  
When a decision tree is constructed, several branches may introduce variances in the training dataset owing to noise or outliers. This problem has been addressed as an overfitting in tree pruning, which uses statistical procedures to eliminate less accurate branches. It generally includes pre- and post-pruning [18]. Decision trees are among the most popular supervised learning algorithms used in machine learning. It learns decision rules based on the values of the attributes in the data and represents these in a tree structure [38]. The M5P algorithm is a type of decision-tree algorithm. It is a part of an algorithm called model trees, which works in a manner that is moderately different from that of decision trees. Unlike decision trees, the M5P algorithm trains and uses linear models at the leaf nodes. The hyperparameters of the M5P algorithm tree used by Weka are as follows:  
1. Unpruned: This is a Boolean value that prevents pruning. The default value is false, indicating that pruning would be performed.
2. UseUnsmoothed: This is a Boolean value that prevents smoothing. The default value is false, which implies that smoothing is disabled.
3. MinimumNumberInstances: An integer value that specifies the minimum number of instances required on each node. The default value is 6. Setting it to a smaller value can increase the complexity of the model. In the M5P algorithm, \"Minimum Number of Instances\" is important hyperparameters used to control the complexity of the model [39]. These can be adjusted to improve the performance of the model.
4. BuildRegressionTree: This parameter is used when generating the regression tree and is set to a boolean value. The default value is true.
5. Max saveInstanceData: This parameter is a boolean value that determines whether to save the training data. The default value is false, and if set to true, training data are saved.  
Decision Tree is used as a classifier for the M5P algorithm used in this study. The decision tree provides a structure for pruning unnecessary data and resolving overfitting based on the characteristics of metadata, such as ITEM ID and RESOURCE ID.