esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 2 Related Works
### Summary  
In conclusion, the Fourier Transform and its variants, as widely used data preprocessing techniques, find extensive applications in various scenarios (Section 2.1.1). Numerous research studies have explored heterogeneous acceleration techniques for these transforms(Section 2.1.2). However, the predominant focus of these studies has been on GPUs and FPGAs, with comparatively less attention given to NPUs. This trend can be attributed to the fact that although the NPU primarily serves as an accelerator for deep neural networks, its architecture, particularly in data parallel processing, makes it suitable for computationally intensive tasks. This suitability arises from NPUs typically incorporating dedicated hardware units designed for matrix and vector calculations, which enhance the efficiency and parallelism of these calculations through specialized instructions. However, ongoing research and development efforts in NPUs mainly focus on accelerating neural networks, and support for typical engineering algorithm computation remains insufficient. Therefore, employing NPUs to accelerate typical engineering algorithms, especially in the study of the Fourier Transform, is significant.
# Scalable Dew Computing
## 3 Architecture Model  
A simplified modern architectural model, presented in Figure 1, consists of cloud servers on the top layer, smart devices in the middle layers presenting the edge and dew computing servers, and \"things\", representing the IoT devices at the bottom layer [30].  
Let us emphasize the difference of the possibility to exchange data between different layers and between different devices in the same layer. Smart devices are found at the edge of the network, which means they are always connected to the Internet, while the \"things\" work out of the edge and may also collaborate with smart devices and higher-level servers, fitting into the classical definition of dew computing.  
The main difference between the edge and dew computing is the operation mode [16]:  
* _Edge computing_ includes smart devices found at the edge, which can always collaborate with higher-level servers (always connected to the Internet).
* _Dew computing_ assumes that, in addition to the availability to communicate to the higher architecture levels, the smart devices and \"things\" (IoT devices) can work autonomously without communication to higher architecture levels.  
This distinction between Internet connection availability and autonomous feature impacts the implementation of the scalability feature, which is analyzed next.  
Figure 1: A three-layer architecture model [30], which consists of servers, smart devices, and things.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Heavy Metals
#### 5.1.1 Mercury  
Mercury is a naturally occurring metal resulting primarily from Earth's geothermal reactions. Mercury's toxicity can be tested through protein precipitation, enzyme inhibition, and corrosive action. The most poisonous form of mercury is methylmercury, which has a high affinity for sulfuryl ligands in amino acids, and thus, it may cause protein structure changes and loss of function. Among all environmental heavy metal pollutants, mercury contamination is one of the most studied due to its persistent accumulation in aquatic environments [45]; thus, IIPs were tested for selective detection and extraction Hg(II) in water samples.  
A novel Hg(II) electrochemical sensor, based on a reduced graphene oxide (RGO) surface imprinted modified glassy carbon electrode (GCE) using methacrylic acid (MAA) as the functional monomer, ethylene glycol dimethacrylate (EGDMA) as the cross-linker, 2,2\({}^{\prime}\)-((9E,10E)-1,4-dihydroxymethracene-9,10-diylidene)bis(hydrazine-1 carbothioade) (DDBHCT) as the chelating agent, and ammonium persulfate (APS) as initiator, is shown in Figure 3. The accumulation of Hg(II) ions at the surface of a modified glassy carbon electrode was used for tracing the Hg content electrochemically within a range of 0.07 to 80 \(\mu\)g L\({}^{-1}\). The limit of detection (LOD) was found to be 0.02 \(\mu\)g L\({}^{-1}\) (S/N = 3), which lies below the guideline value from the World Health Organization (WHO) [46].  
Another approach was developed based on sulfur containing carboxymethyl ion-imprinted polymers (S-IIPs). A bare glassy carbon electrode was modified by electropolymerizing a layer of pyrrole and then drop coating with S-IIPs. The developed ion-imprinted polymer electrochemical sensor was applied for the detection of Hg(II) in water. A detection limit of 0.1 mg L\({}^{-1}\) was calculated within a concentration range of 20-800 mg L\({}^{-1}\), which is lower than the WHO threshold (equal to 0.001 mgL\({}^{-1}\)). The results in real sample in comparison with inductively coupled plasma-optical emission spectrometry depict its potential applicability for Hg analysis [47].
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning  
Once the target is framed and the input data have been collected, the planners must develop a model. In most of the reviewed literature, CI problems usually involve the use of optimization algorithms to produce the AS, which is frequently complemented with a geospatial analysis. In this section, detailed discussions are presented on the methodologies through which the CI is allocated spatially and characterized in terms of electric attributes.  
Whether a model or data-driven approach is taken (see Section 3.2), most of the literature investigates transport driving patterns and charging demand, but does not explicitly produce an output in terms of allocation and/or sizing of CSs, as pointed out by [116]. Categories of interest include:  
* Public transit: in [123; 124; 125], the authors optimized charging-scheduling for public transit fleets. In [125], bus fleet charging operations are optimized in a way that includes the comprehensive effects of battery aging during night-time depot recharging.  
In this case, municipalities and public authorities could benefit from optimization algorithms in order to assess the extent to which the implementation of electric vehicles can complement already existing public transport patterns.  
* Smart charging: [110; 126; 127] explore how flexibility and V2G schemes can reduce peak load under grid constraints. The authors of [128] optimize scheduling through consideration of both V2G and G2V schemes, but without including a power flow analysis.  
* Controlled charging: in [129], the authors investigate controlled charging strategies based on price sensitivity, where the PHEV users can decide whether to charge the vehicle, offering some benefits in terms of social acceptability and charging control penetration. The research concludes that few economic benefits can be derived out of V2G for PHEV users.  
The quantification of flexibility and its effect on the grid is a highly investigated topic: the interest of these studies can support policies in favor of the active participation of EVs in the electric power system to increase renewable generation and decrease stress on the electrical infrastructure. Obviously, the quantification of the economic value of flexibility is of interest to investors, who aim to lower the total cost of ownership of recharging points through V2G energy market transactions [130].
## 6 Conclusions and Future Developments  
An incremental modal pushover analysis for bridges (IMPA\(\beta\)), which takes into account the effects of higher modes was proposed and applied to two case studies, a regular and an irregular bridge.  
The procedure required us to apply to the bridge two load patterns for pushover, multimodal (MPA) and uniform (UPA), to obtain the response according to an envelope of base shear and displacements. Being an incremental analysis, the performance points are evaluated at different seismic intensities.  
According to the presented results, the following conclusions can be drawn:  
* The procedure keeps the simplicity and low computational effort typical of a standard pushover analysis. In fact, IMPA\(\beta\) implies the execution of one nonlinear static analysis for each loading pattern, while performing IDA, several nonlinear response history analyses have to be performed, i.e., one for each of the seven ground motions, and it has to be repeated for every intensity level considered.
* If the capacity curve is obtained by using a multimodal load pattern only, it well matches the IDA curve, for both the regular and irregular bridge, up to the seismic design intensity only, while the present proposal allows us to analyze even higher intensities.
* IMPA\(\beta\) produces a capacity curve very similar to what derived with IDA also beyond the seismic design intensity (0.35 g), and, for very high seismic intensities (beyond 0.6 g), the capacity curve of IMPA\(\beta\) is conservative.  
In order to validate the generalization of IMPA\(\beta\) for bridges, statistical analyses of results based on responses obtained by using the single response spectra of the accelerograms used for IDA, the effects of the variation of the seismic input with intensity, the asynchronous seismic motion [42, 43], near fault seismic input and all the different bridge configurations (i.e., deck shape and sections, constraints, bridge pairs and bridge length) are in progress.  
Conceptualization, A.V.B. and C.N.; methodology, A.V.B., C.N. and D.L.; resources, A.V.B., C.N. and B.B., software analysis, A.V.B.; writing--original draft preparation, A.V.B.; writing--review and editing, A.V.B., G.F., C.N. and B.B. All authors have contributed substantially to the work reported. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the funding received by The Laboratories University Network of Seismic Engineering (ReLUS): research project ReLUS/DPC 2019-2021 Reinforced Concrete Existing Structures and the National Natural Science Foundation of China (grant no. 51778148).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Pre-Processing and Augmentation  
The data pre-processing and dataset creation is illustrated in Figure 2. The initial scintigraphic images contain artefacts embedded by the image acquisition technology. The reduction of focal regions is, therefore, mandatory. Moreover, the actual ROI is located at the centre of the image, surrounded by a black background, where no information of medical importance exists. Therefore, each image is cropped to 200 \(\times\) 200 (width, height).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Information** & **Value** \\ \hline \multicolumn{3}{c}{Training—validation dataset} \\ \hline Hospital & University General Hospital of Patras, Greece \\ Department & Department of Nuclear Medicine \\ Date & 2010–2020 \\ Participants & 648 \\ Subjects labelled by human experts & 648 \\ Negative result & 198 \\ Positive result & 450 \\ Abnormal PGs identified by human experts & 504 \\ \hline \multicolumn{3}{c}{External dataset} \\ \hline Hospital & University General Hospital of Patras, Greece \\ Department & Department of Nuclear Medicine \\ Date & 2021–2022 \\ Participants & 100 \\ Surgically verified subjects & 100 \\ Abnormal PGs & 118 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the study’s datasets.  
Inspection of the images revealed that this area is adequate to fit the complete thyroid without leaving out any area of clinical interest. Normalisation refers to transforming the pixel values, which range from 0 to 255, to the space [0, 1], which is preferable for machine learning (ML) and DL networks and speeds up the training process.  
During network training, data augmentation is performed to increase the training images. Data augmentation is essential and benefits the network because it provides multiple versions of the same findings [24]. In the augmented images, slight geometrical transformations take place. These transformations do not distort the actual finding in the image but introduce variation and help the network focus on the region of interest. Therefore, the network learns to ignore redundant features, such as the orientation of a PG or other irrelevant spatial characteristics. The study proposes slight data augmentations, including rotations by \(\pm 5^{\circ}\), width and height shifts by \(\pm 10\) pixels, and horizontal flips. The image's distortion and the generation of unrealistic PGs are avoided using these geometric transformations.
## 3 Key Technologies and Methodologies
### ANSI/ISA-95 (American National Standards Institute Instrument Society of America) Architecture  
Figure 3: ANSI/ISA-95 Standard Architecture.  
Figure 2: Java Agent Development (JADE).  
While the ADACOR describes the holons' functionalities, the holons' rules of interaction, and the holons' level of autonomy. It does not concern the distribution and the integration of these holons within the different levels of the industrial enterprise. ISA-95 is an international standard created by the International Society of Automation (ISA). It provides an integration model of the enterprise business planning, manufacturing management and control systems [25; 26; 27; 28]. Figure 3 illustrates the different layers of the ISA-95.According to the ISA-95 standard, level-2 holds the responsibility of inquiring the product recipe from level-3 and the process sensory information from level-1, to control the product execution based on the status of the shop floor. The ISA-95 recommends the separation of the shop floor model and the sequence of tasks for a specific product defined by a product recipe [29]. The main challenge of the ISA-95 level-2 is to increase the productivity by matching the changes in the product recipe to the shop floor operational resources overall status and number.
## 2 Background
### Triad Models  
Triad models [35,45] are graphic representations of the interactions between three elements: a passive object (S\({}_{\text{p}}\)\(\equiv\)S\({}_{\text{0}}\), the object of the useful effect produced by the two other triad elements) and two active elements, one of which is the carrier of the main useful action (Sa, active object) that provides the main system function, and the other, the so called 'enabling element' (Sen, enabling object), which ensures that the useful action is performed in an appropriate way at an appropriate time. According to [35], removal of any of these elements from the system without rearranging the functional relationships makes the system non-viable. In triad modelling, the Triad + Pruning algorithm is a problem-setting and problem-solving tool [35,45].  
An example of the triad model for the 'defibrillator' technical system is shown in Figure 5a.  
Figure 5: (a) A triad model for the ‘defibrillator’ technical system (adapted to SFA notation by authors after [35,36]); (b) Bi-triad model [44].  
Analysis of the proposed problem-oriented models and the effectiveness of their application suggests that the triad modelling method [35; 42; 45], which has been successfully applied for several years by the American firm BioFutures Incorporated, seems to be the most appropriate for setting and solving conflict problems of BMS design. As we have shown in [42], the triad model can be further improved by stepwise aggregation of two other problem-oriented models (the SF model and the TOP model) into the triad model, combined with the system transformation operator [42] and 9S operator [29; 34]. Thus, any of the substantial elements of the triad are regarded as representatives of a certain class of objects, which allows for simultaneous consideration of two passive functions--the main and the basic, and for significant extension of the options of system improvement and development by morphological search of alternative triad elements within the boundaries of a certain class of objects. The model obtained as a result of imposing the system operator on the aggregated TOP Triad model contains two triads 'nested' into one another, therefore we have proposed to call it a 'bi-triad model' [42]. A variant of such model for defibrillator TS is presented below (Figure 5b).  
The bi-triad representation of the TC allows for the expansion of the set of elements--candidates for removal in the 'Triad + Pruning' algorithm, and for the identification of new strategic directions for TC evolution.
## 3 Results and Discussion
### Influence of Other Parameters  
#### 3.5.1 Partial Pressure of Water Vapor in Humid Air  
The concentration difference of water vapor is determined directly by the partial pressure of water vapor around the cooling surface, which is one of the important factors that determines the frosting crystallization. The frost crystal morphologies under different partial pressures of water vapor (\(P_{\rm v}=350\) Pa, 935 Pa, 1400 Pa) at 10 min are shown in Figure 8. The frost crystal morphologies become more abundant with a higher partial pressure of water vapor, changing from needle to feather shape. The lateral size of frost crystals gradually becomes stronger with the lower working pressure around the cooling surface due to the augmenting of diffusion coefficient of water vapor. Furthermore, the effect of partial pressure of water vapor on the lateral growth of frost crystals is greater than the effect of overall working pressure.  
The frost thickness with working pressure around the cooling surface under different partial pressures of water vapor at 60 min is shown in Figure 9a. The frost thickness  
Figure 8: Frost crystal morphologies with working pressure around cooling surface under different partial pressures of water vapor at 10 min.  
Figure 7: Frost characteristics with different working pressures around cooling surface at 60 min.  
decreases with the higher working pressure around the cooling surface. In addition, the frost thickness increases with the higher partial pressures of water vapor, which can be attributed to the larger concentration difference of water vapor on the frost crystal surface. The frost accumulation and density at 60 min rise with the partial pressure of water vapor due to the enhanced driving force of mass transfer, as shown in Figure 9b,c. This rise is weakened with the higher working pressure around the cooling surface. Comparing the absolute differences of frost thickness, frost accumulation and density from low to high pressure (0.5 \(\times\) 10\({}^{5}\) Pa to 2.00 \(\times\) 10\({}^{5}\) Pa), they lessen from 1.43 mm to 0.89 mm, 0.247 g to 0.120 g and 50.48 kg/m\({}^{3}\) to 49.34 kg/m\({}^{3}\), respectively, when the partial pressure of water vapor increases from 350 Pa to 1400 Pa.
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 3 Results
### SRF Gasification Trials  
#### 3.1.1 Product Yields  
The mass balance closure was around 90% in both experiments (with and without steam), which is considered acceptable for the type of setup used in this work.  
In alignment with prior literature [43], tests with similar operating factors have shown that the solid yield is lowered with an increase in ER. The gas yield, referring to the volume of tar-free gas produced, is higher than 100% because the gasifying agent reacts with SRF and is part of the gasification gases. The results demonstrate an inverse correlation with the solid yield, with Test #1 having the highest solid yield and the lowest gas yield. The liquid yield, which refers to the whole fraction of liquid recovered in the condensers, exhibits a similar pattern to the yield of gas, with Test #1 displaying the lowest value and Test #2 the highest one. The addition of steam as a gasifying agent in Test #2, as well as the promotion of combustion reactions because of the increased ER, could explain the production of more liquid (water specifically) under these operational conditions. Finally, the tar yield, which refers to the organic compounds present in the liquid fraction, was reduced when both S/C  
Figure 5: Energy balance diagram for the combustion of gasification gas.  
and ER were increased. Reducing tar formation is a beneficial aspect of the subsequent application of the gasification gas for energy recovery.  
Table 3 also illustrates the gas production from the gasification process. The results show that the gas production rate increased from 2.3 m\({}^{3}\)STP gas/kg dry matter in SRF (Test #1) to 2.9 m\({}^{3}\)STP gas/kg dry matter in SRF (test #2), which means an increase of 26.08%. The main reason for this increase could be the higher ER used in test #2, which also leads to a higher flow of N\({}_{2}\) at the inlet and exit gas [36]. Introducing more steam as a gasifying agent also promotes gasification reactions (see reactions in Section 2.2.1 of this article), thus reducing the remaining solid in favour of the production of gas. The LHV of the gas varied from 2.9 to 4.4 MJ/m\({}^{3}\)STP. In contrast to the gas product flow rate, the results indicate that the best outcome for the LHV occurred at the lowest ER, corresponding to test #1, with an ER of 29.6%. As such, the LHV follows a decreasing trend concerning the ER, whose increase promotes complete oxidation reactions, thus generating more CO\({}_{2}\) to the detriment of the other gases.  
The cold gasification efficiency, which represents the ratio between the energy content of the produced gas (at room temperature) and the energy content of the solid fuel, was calculated. The cold gasification efficiency values obtained from the gasification tests ranged between 37.8% for test #2, which had the highest gas production but the lowest LHV, and 44.6% for test #1 (lower gas production but higher LHV of the gas).
# Thermoregulation and Soil Moisture Management in Strawberry Cultivation Munched with Sheep Wool
## 2 Materials and Methods  
### Preparation of Experimental Plots  
The effectiveness of wool as a mulching material was examined in experimental plots prepared in strawberry (_Fragaria x anansa_) plantations of the Ducat variety. The plots were situated in southern Poland in the Silesian region, at the foothills of the Beskids Mountains, at an altitude of 360 m above sea level (Coordinates: 49\({}^{\circ}\)44\({}^{\prime}\)6\({}^{\prime\prime}\) N; 18\({}^{\circ}\)40\({}^{\prime}\)24\({}^{\prime\prime}\)).  
The site was located in a temperate climate zone. According to data from the local meteorological station collected in the 30-year normal period 1991-2020 and calculated according to the WMO Guidelines on the Calculation of Climate Normals, the average annual temperature in this place equals 9 \({}^{\circ}\)C, while the rainfall reaches 998 mm. The summers are mild, sunny, and partly cloudy, with frequent showers and thundershowers. In the summer months, June and July, the average temperature is 16.8 \({}^{\circ}\)C and 18.5 \({}^{\circ}\)C, respectively. July has the highest number of rainy days. The average rainfall in this month is the highest in the year and equals 143.2 mm. The winters are freezing, snowy, windy,and mostly cloudy. In the winter months, January and February, the average temperature drops to \(-0.9\) and \(0.2\)\({}^{\circ}\)C, respectively [26].  
Four plots with an area of \(5\) m\({}^{2}\) in soil of average quality and a pH of \(5.5\) were prepared. In each plot, the strawberry seedlings were planted at an interval of 40 cm in two rows 60 cm apart.  
The first control plot was not multiched. The second plot was covered with mountain wool (_Ovis aries_), which was not subjected to any treatment. The third plot was covered with straw, while the fourth was covered with bark (Figure 1). The mulch was spread between seedlings on the field's surface, forming a compact layer of ca. 5 cm thickness.  
The plots were established in the summer of 2022. During the experiment, the plantation was maintained in natural conditions without additional irrigation and other extra protective covers.