esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Antenna Fabrication & Measurements  
The printed isotropic antenna was then fabricated using FR4 substrate (\(\varepsilon\)r = 4.4, \(h\) = 1.6 mm) The measurements of the printed antenna were performed in the anechoic chamber and the results were compared for the centre frequency. Figure 21 shows the fabricated antenna, where antenna can be seen mounted on the measurement platform within the chamber. Antenna reflection coefficient was measured using E5071C, VNA while radiation pattern measurement was done using MI Technology (MI-3000) system. A comparison of measured and simulated results is presented in Figures 22 and 23. It can be seen from Figure 22 that the simulated and measured return loss results are in good agreement, except for slight frequency shift. Various measurements of radiation pattern in different elevation planes were taken except at angles, where it was not possible because of the feed cable and the mounting arrangement and the one with maximum variation was selected for comparison. The measured radiation pattern is compared in Figure 23. The pattern is measured in azimuth plane. It can be seen that the overall difference in total gain (for measured results) came out to be 2 dB. Further difference in the simulated and measured, as shown in Figure 23, can be attributed to the field scattering that occurs due to nearby connector, cables, and the antenna platform within the chamber. Although the ease of fabrication and integration of this antenna makes it very attractive for portable equipment and devices, its proximity would affect the pattern and gain values.  
Figure 21: Fabricated antenna in anechoic chamber.  
Figure 20: 3D Gain of microstrip fed printed antenna.  
Table 2 shows the comparison of measured variation in total gain of the proposed antenna with those of the designs that were reported in literature, and it can be observed that the proposed design matches the performance of previously reported designs.  
Table 2 shows that the total measured variation in proposed antenna gain is almost the same as that of the antenna design proposed in [10]. However, amongst all of the isotropic designs reported in the literature, our proposed antenna has the lowest design (simulated)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Design** & **Gain Variation** & **Bandwidth (\%)** \\ \hline Printed Arc antenna & \(\leq\)2 dB & 4 \\ Patch antenna [10] & 1.95 dB & 0.61 \\ L-shaped monopole [15] & 5.75 dB & 20.82 \\ Dielectric antenna [18] & 3 dB & 6.9 \\ Crossed dipoles [19] & 3.7 dB & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of maximum gain variation of fabricated proposed antenna with existing antennas. The bold text refers to design of this research.  
Figure 23: Comparison of the simulated and measured gain pattern of the printed isotropic arc antenna (**a**) xy plane and (**b**) xz plane.  
Figure 22: Comparison of simulated and measured return loss of the printed isotropic arc antenna.  
total gain_ variation. Furthermore, the impedance bandwidth of [10] is only 15 MHz (0.61%), while the impedance bandwidth of the proposed antenna is 40 MHz (4%).
## 4 Results and Discussion  
### Core Description  
The sediment core was sub-divided into three sedimentological units, each one with specific textural and geochemical characteristics. Since the XRF core scanner provides a semi-quantitative geochemical profile, elemental concentrations are presented in log\({}_{10}\) (base 10) ratios that can be interpreted in response to the environmental status of the marsh field.  
The upper unit is characterized by the lowest HU values, therefore the lower relative density (Figure 3). The soil horizon is visible from 0.00 to 0.50 m, with unconsolidated organic-rich sediment. A significant differentiation of HU values before and after 0.50 m can be attributed to the water content of the sediment. From 0.00 to 0.50 m, the unconsolidated soil horizon is primarily composed of organic residues and shell fragments, visible in the CT scan section. Water content is significantly lower, compared with the water clogged section from 0.50 to 1.00 m. Increased organic content in this unit is also marked by the increased values of the Br/Cl ratio [47].  
The middle unit is characterized by a distinct transition to coarser material. HU presents higher values due to the increased relative density and the decreased water content compared with the upper section. Organic matter and root remains are visible on the CT scan sections. The Br/Cl ratio declines, and this trend is in accordance with the lower values of organic content (TOC). An increase in Si/Ti is associated with the sand fraction [48] in this unit and presents a monotonous signal. Mn/Fe also increases in this section, indicating more toxic conditions [49; 50].  
Figure 3: Log profile of core AG presenting a line scan image of the core, 2D HU profile, grain size analysis, HU absolute values, Total Organic Carbon (TOC), elemental ratios in log\({}_{10}\) scale, and mean conductivity as measured by the EMP-400 profiler.  
The lower unit presents a monotonous distribution of all studied parameters with no distinct variations. HU presents similar values to the prior unit due to the sediment's similar characteristics. However, no root remnants or organic residues were identified throughout the lower unit.  
Grain size values (sand, silt, and clay) and the TOC content measurements are also presented in Table S1 (Supplementary Materials).
## 3 Functional Properties of Potato Proteins  
The functional properties of proteins are determined by factors such as hydrophobicity, the presence of cross-links, spatial structure (secondary, tertiary and quaternary) and molecular flexibility/rigidity [4; 9; 51]. According to the definition presented by [PERSON] [11], functional properties of proteins are \"those physical and chemical properties that influence the behaviour of proteins in food systems during processing, storage, as well as preparation and consumption of food products\". Due to the favorable functional properties, proteins are often used as ingredients to impart rheological and structural properties to products with their participation, to influence the water binding or solubility of products. As it is believed that potato protein allergies are much less common, they may be a replacement for the traditionally used wheat, soybean, egg, fish or milk proteins, with proven high allergenicity [4].  
Preparations of potato proteins in non-denatured form, obtained under laboratory conditions by many authors [10; 12; 13; 52], starting from the 1970s, were characterized by favorable functional properties, especially solubility, emulsifying and foaming properties [27; 34; 38; 53]. Stable emulsions with the participation of all potato proteins were obtained by [PERSON] and [PERSON] [12], who showed that, in terms of emulsion properties, native potato proteins are comparable to commercial soy protein preparations. [PERSON] et al. [52] showed that potato protein hydrolysates are active in inhibiting lipid oxidation in soybean-oil-in-water emulsions. Separated fractions of patatin proteins and inhibitory proteins have been shown in studies by various authors [4; 9; 19; 51; 53] to possess more favorable functional properties than preparations containing all fractions of potato proteins.  
Due to the low denaturation temperature of patatin proteins (in the range of 50-55 \({}^{\circ}\)C), the method of their isolation from the raw material influences the development of these properties [46]. Thus, the preservation of the beneficial functional properties of patatin is associated with the use of mild temperature conditions in the method used to obtain them. [PERSON] et al. [20], analyzing the functional properties of potato proteins, showed that the activity of protease inhibitor proteins in the formation and stabilization of foams and emulsions is relatively low compared to patatin proteins, which is related to the higher denaturation temperature of inhibitory proteins, i.e., in the range of 55-70 \({}^{\circ}\)C.
## 1 Introduction  
### Summary of the Available Models for DLSCs  
There are multiple models in scientific literature to represent SCs [1]. The most widely used is the RC model, with constant capacitance C connected in series to its equivalent resistance (ESR) [2; 3; 4; 5; 6]. This simple model has several advantages; on the one hand, both capacitance and ESR are provided by the manufacturers in the cell data specifications. In addition, both variables are obtained by standardized tests. On the other hand, this model allows relatively simple analytical expressions to be obtained when using SCs in practically all their operating modes, even when charging or discharging at constant power, something that can be much more difficult when working with other more complex models [7]. This electrical model is also used when studying the thermal behavior of the SC [8; 9] or when sizing the capacity of an SC bank for a particular application [10; 11].  
Despite all the above advantages, this is not the most suitable model for the analysis of DLSCs, hence the existence of studies that develop more sophisticated models, based on different mathematical approaches. Depending on the application in which the DLSC is used, it may be necessary to include additional parameters. For example, the model developed by [PERSON] et al. [12] allows for a complete electrical study when the DLSC works with currents containing different frequency components. Furthermore, in order to improve the dynamic response of the final model, it is possible to emulate DLSCs with several RC branches connected in parallel, each one with a different time constant [13], and it is even possible to represent it as a transmission line, which fits excellently to the real physical structure of this type of cell, with good dynamic behavior and accurate results [14]. These very complex models provide good results, although they have some drawbacks which, on many occasions, force researchers to use simpler approaches. On the one hand, due to their complexity, it is difficult to obtain analytical expressions from them, so in most cases it is necessary to use numerical computation or simulation. In addition, the value of the variables needed to correctly build them are not provided by the manufacturers, so the only way to know them is by means of laboratory tests combined with complex estimation algorithms. Some of them are not even circuit models, which makes it difficult to use them with simulation tools, having to resort in many cases to numerical calculation methods.  
The model of DLSC with variable capacitance, dependent on its internal voltage, has been used with excellent results [15; 16; 17; 18]. This model is more accurate than the constant capacitance RC series model and does not present the complexity of other, much more sophisticated models. Due to their ease of use, one of these two models (constant capacitance or variable capacitance) is often chosen in numerous industrial applications. Using one or the other will depend on the precision needed in each particular case.
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### MISO/MIMO Techniques in NOMA-Based VLC Systems
#### 3.1.2 PD-NOMA for Underwater Applications  
Because of the rising usage of underwater sensor networks (USNs) for a variety of tasks, including environmental assessment, port security, oil exploration, tactical surveillance, and collecting data, scientists have begun to examine underwater wireless networking options [86; 87]. Acoustic communications have been a popular approach for USNs since they can sustain transmission lengths of several kilometers with modest data rates on the order of kbps. UVLC has been offered as a supplementary connection option, with data speeds in the tens of megabits per second (Mbps). Light is significantly attenuated when it travels through water, especially at ultraviolet and infrared wavelengths. The optimal wavelength for underwater transmission is in the blue-green region of the VL spectrum. Whereas the green spectrum attenuates less in seawater, the blue spectrum attenuates more in the wide ocean [87].  
There is an expanding body of material on UVLC in which green or blue LEDs or LDs are employed as wireless transmitters [88]. Nevertheless, the majority of these evaluations are restricted to single users and point-to-point linkages. However, the actual application of USNs necessitates the creation of numerous access systems to accommodate multiple sensor nodes. As a result of this, various multiple access strategies for UVLC systems have been developed [89]. The experimental demonstration of NOMA-UVLC with a blue laser transmitter was presented in [90], with a cumulative rate of 4.686 Gbps attained for two users. Some studies investigated the numerical performance of multiuser PD-NOMA over a lognormal fading channel, which is generally applicable in low turbulence situations as experimentally validated in [91]. Ref. [92] examined the error rate performance and feasible capacity of a PD-NOMA-enabled UVLC system over the exponential-generalized gamma (EGG) distribution, which is appropriate for turbulence with air bubbles. To the best of our knowledge, only [82; 83] have investigated the PD-NOMA for UVLC over lognormal turbulence channels. The influence of turbulence severity on channel capacity as well as the impact of the number of nodes and targeted rate on coverage probability were explored in [82]. The influence of the PA coefficient on coverage probability and attainable capacity is investigated in [83]. An overview of NOMA-UVLC is illustrated in Figure 5.
## 4 Discussion
For construction site scenarios, this is often the case for hazardous situations since intendedly endangering workers should be avoided. Furthermore, weather conditions may appear on which the detector was not explicitly trained. On the basis of the similar evaluation results on synthetic and real world data, we demonstrate the capabilities accompanied by a virtual environment as developed in this paper in an exemplary way. In this example we use a computer generated video to show the evaluation of a tracking system on a risky situation without exposing any worker to a hazard. Furthermore, it illustrates that various environmental conditions can be simulated without the need for tedious repetitive recordings on multiple days. In order to highlight these benefits, we apply our tracking system to a modified version of synthetic scene 1. We change the weather conditions from a sunny to a heavy rainy day and include a crane lifting and pivoting its load above the heads of pedestrian workers. As depicted in Figure 7, again all four workers are tracked by our system despite the rainy weather conditions, which were not explicitly trained. Table 4 shows that only four tracks were assigned, each corresponding to one of the workers. Though, the trackers accuracy slightly decreases. This indicates that the performance of our tracker on a similar real world scenario should basically be still sufficient but its precision might decrease slightly. However, additionally training the detector on data samples of rainy days might counteract this. Furthermore, Table 4 unveils that the tracking is not interrupted even though the workers are temporarily occluded by the crane's load. This demonstrates that the tracking system proposed in this paper is even capable of dealing with certain hazardous situations. By identifying the hazardous area around the crane's load, the crane operator could be warned against approximating pedestrian workers so that risky situations could be prevented. Further tests have to verify whether the system can also cope with larger loads and more difficult weather conditions.
# Ising-Based Kernel Clustering +
## 5 Related Work  
In addition to Ising-based kernel clustering proposed in this paper, another way to perform clustering of irregular data is Ising-based binary clustering using the kernel  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ising-Based Kernel Clustering** & **SGMK** \\ \hline YALE & 28.11\% & 62.04\% \\ \hline JAFFE & 10.35\% & 99.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of NMI between the Ising-based kernel clustering and SGMK.  
Figure 17: The NMI of Ising-based kernel clustering for YALE and JAFFE datasets.  
method [40]. This method involves utilizing only one single decision boundary to linearly separate the data mapped onto a high-dimensional feature space. However, conventional Ising-based binary clustering utilizing the kernel method is limited to cases with only two clusters, rendering it impractical. The reason why binary clustering using the kernel method can only group data into two clusters is the use of decision variables representing a binary value. In contrast, the proposed method presented in this paper can be applied more generally across a wide range of applications to categorize irregular data into two or more clusters.  
Combinatorial clustering based on the Ising model has also been utilized for semi-supervised clustering. [PERSON] et al. proposed constrained clustering, which employs various types of supervisory information [41], while [PERSON] et al. proposed balanced clustering, ensuring that all clusters have the same number of data points [13; 14]. The QUBO formulation employed in these semi-supervised clustering techniques can be converted into Equation (14). Consequently, the approach proposed in this paper that employs the kernel method can also be extended to semi-supervised clustering. When irregular data are given that are challenging to categorize, the inclusion of supervised information can be beneficial. Therefore, integrating the kernel method into semi-supervised clustering can enhance the practicality of combinatorial clustering. A promising area for future research would be to explore kernel clustering using supervisory information based on the Ising model.
## 3 Results
### Classification
DFU_FNet employs a simpler architecture to extract features for training classifiers like SVM and KNN, while DFU_TFNet utilizes a deeper architecture enhanced with transfer learning, improving performance on medical imaging tasks. Both models were implemented on field-programmable gate arrays (FPGAs) and graphics processing units (GPUs). DFU_TFNet achieved remarkable results, outperforming models such as AlexNet, VGG16, and GoogleNet. The FPGA platform, while slightly slower in processing than GPUs, demonstrated significantly lower power consumption, making it an ideal candidate for portable, real-time diagnostic applications. [PERSON] et al. [40] presented a machine learning (ML) approach for DFU classification, utilizing reinforcement learning (RL). It integrates compositional pattern-producing networks (CPPNs) for recognizing structured and unstructured images, SVM for classification, hierarchical clustering for grouping data, and ELM with a single hidden layer for fast classification. Additionally, they evaluated deep learning models, including AlexNet, VGG16, GoogLeNet, and ResNet50, on the DFU2020 dataset [32], with ResNet50 achieving the highest classification accuracy of 99.49% for ischemia detection. Data augmentation techniques such as affine transforms were applied. The results demonstrate significant improvements in classification efficiency, making this approach a promising advancement in DFU diagnosis. According to the clustering scenario analysis of DFU, the classification efficiency varies based on ulcer severity levels, with the model effectively distinguishing between four clusters: Cluster One: Mild to Moderate Localized Cellulitis; Cluster Two: Moderate to Severe Cellulitis; Cluster Three: Moderate to Severe Cellulitis with Ischemia; and Cluster Four: Life- or Limb-Threatening Infections. Table 1 summarizes recent studies, highlighting AI models, data types, and performance metrics used for DFU classification.
## 2 Data and Methods
### Data Collection
#### 2.1.2 Eddy Covariance GPP and Environmental Variables at DM Site  
Data were measured at different flux tower heights with a 10 HZ sampling rate, including net ecosystem exchange (NEE) of CO\({}_{2}\), PAR, air temperature (Ta), sensible heat flux (H), latent heat flux (LE), etc. The raw data were converted to 10 min averages using Eddypro post-processing software ([[https://data.tpdc.ac.cn/en/](https://data.tpdc.ac.cn/en/)]([https://data.tpdc.ac.cn/en/](https://data.tpdc.ac.cn/en/)), accessed on 22 May 2022) [36; 37]. To match the 30 min averaged step of SIF, we aggregated 10 min averaged flux data into 30 min averaged flux data. Before partitioning NEE, we preprocessed the 30 min averaged data to reduce the uncertainties of the measurements under unfavorable weather conditions, including marking the data one hour before and after precipitation and negative NEE values at night as data gaps. Then, we used the online Eddy covariance data processing tool Reddyproc ([[https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html](https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html)]([https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html](https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html))), developed by Max Planck Institute for Biogeochemistry, to identify conditions with insufficient turbulence, marked those conditions as data gaps based on the Moving Point Test (MPT) method, and then filled these data gaps [38; 39; 40]. Finally, the NEE was partitioned into GPP and ecosystem respiration (\(R_{cco}\)) based on the night-time flux partitioning method through the ReddyProc tool [40; 41]. In this study, we chose parameters including GPP, Ta, H, LE, and PAR for analysis. Meanwhile, we removed negative GPP values and converted the half-hourly data into a daily scale.
###### Abstract  
This paper investigates the selectivity of GMA-based-non-woven fabrics adsorbent towards copper ion (Cu) functionalized with several aliphatic amines. The aliphatic amines used in this study were ethylenediamine (EDA), diethylenediamine (DETA), triethylenetetramine (TETA), and tetraethylenepentamine (TPEA). The non-woven polyethylene/polpropylene fabrics (NWF) were grafted with glycolidyl methacrylate (GMA) via pre-radiation grafting technique, followed by chemical functionalization with the aliphatic amine. To prepare the ion recognition polymer (IRP), the functionalized amine GMA-grafted-NWF sample was subjected to radiation crosslinking process along with the crosslinking agent, divinylbenzene (DVB), in the presence of Cu ion as a template in the matrix of the adsorbent. Functionalization with different aliphatic amine was carried out at different amine concentrations, grafting yield, reaction temperature, and reaction time to study the effect of different aliphatic amine onto amine density yield. At a concentration of 50% of amine and 50% of isopropanol, EDA, DETA, TETA, and TPEA had attained amine density around 5.12, 4.06, 3.04, and 2.56 mmol/g-ad, respectively. The amine density yield decreases further as the aliphatic amine chain grows longer. The experimental condition for amine functionalization process was fixed at 70% amine, 30% isopropanol, 60 \({}^{\circ}\)C for grafting temperature, and 2 h of grafting time for attaining 100% of grafting yield (Dg). The prepared adsorbents were characterized comprehensively in terms of structural and morphology with multiple analytical tools. An adsorptive removal and selectivity of Cu ion by the prepared adsorbent was investigated in a binary metal ion system. The IRP samples with a functional precursor of EDA, the smallest aliphatic amine had given the higher adsorption capacity and selectivity towards Cu ion. The selectivity of IRP samples reduces as the aliphatic amine chain grows longer, EDA to TPEA. However, IRP samples still exhibited remarkably higher selectivity in comparison to the amine immobilized GMA-\(g\)-NWF at similar adsorption experimental conditions. This observation indicates that IRP samples possess higher selectivity after incorporation of the ion recognition imprint technique via the radiation crosslinking process.  
pre-radiation grafting; chemical functionalization; aliphatic amine; radiation crosslinking