esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 3 Research Methodology
### Image Segmentation
#### 3.2.1 HSV Color Space  
The acquired images are in RGB format, where the color of any object in these images is represented with the combined values of the R, G, and B channels. The main problem with this color representation is that the objects' colors are affected by variations in the illumination conditions [2]. With the HSV color segmentation technique, as the name suggests, HSV color space is used, which describes the objects' colors independent of the illumination effect [16]. The difference between various color spaces is usually based on color representation. For instance, the object's color in the HSV color space is represented by three different parameters, namely the hue (\(H\)), saturation (\(S\)), and value (\(V\)). H represents the color of the object, whereas the S and V values represent the illuminance state of the object's color [16]. This type of description provides the ability to discriminate the color from the illuminance while avoiding the effect of the illumination changes on the object's  
Figure 3: Data augmentation was performed on different images.  
color. Therefore, the first stage of segmentation is to convert the image's color space from RGB into HSV. Generally, the transformation process from RGB into HSV can be performed using the following Equations [24]:  
\[R^{\prime}=\frac{R}{255}\,\ G^{\prime}=\frac{G}{255}\,\ B^{\prime}=\frac{B}{255} \tag{1}\]  
\[M=\max\bigl{(}R^{\prime},\ G^{\prime},\ B^{\prime}\bigr{)},\ m=\min\bigl{(}R^{ \prime},\ G^{\prime},\ B^{\prime}\bigr{)}, \tag{2}\]  
\[C=M-m \tag{3}\]  
\[H=\left\{\begin{array}{l}0^{\circ}\ if\ C=0\\ 60^{\circ}\times\bigl{(}\frac{G^{\prime}-B^{\prime}}{\mathbb{C}}mod\ 6\bigr{)}\ if\ M=R^{\prime}\\ 60^{\circ}\times\bigl{(}\frac{G^{\prime}-B^{\prime}}{\mathbb{C}}+2\bigr{)}\ if\ M=G^{\prime}\\ 60^{\circ}\times\bigl{(}\frac{G^{\prime}-B^{\prime}}{\mathbb{C}}+4\bigr{)}\ if\ M=B^{\prime}\end{array}\right. \tag{4}\]  
\[S=\left\{\begin{array}{l}0\ if\ M=0\\ \frac{G}{\mathbb{C}}\ if\ M\
eq 0\end{array}\right. \tag{5}\]  
\[V=M \tag{6}\]  
After the image transformation, a color bar is created, which provides intensity values for the (\(H\)), (\(S\)), and (\(V\)) channels. These values are used in the next stage for segmenting the image. Figure 4 shows an example of the original image, its HSV channels, and the color bar format.  
Figure 4: Illustration of image, its HSV channels, and color bar format.
## 3 Comparison of ASL and Genio Languages
### Actors and Use Cases  
ASL supports the specification of actors and use cases. Actors represent the entities that interact with the system, namely user roles and external systems [31; 34]. Use cases represent a sequence of actions performed by the system's actors, such as CRUD operations, as well as other specific actions (e.g., approve, export) [31; 34]. Figure 9 shows a fragment of ASL's metamodel with a focus on actors and use cases. For instance, it shows that: (1) UseCase aggregates a set of UCActions, operates around a given DataEntity, and has a primary Actor; (2) the primary Actor of the UseCase can either be a user of the system or an external system; (3) a UseCase can be classified as a certain UseCaseType and each UCAction as an ActionType.  
Spec. 3 shows a partial ASL specification of actors (e.g., aU_Operator and aU_Customer), and use cases (e.g., uc_CreateInvoice and uc_PrintInvoice). For instance, the use case uc_CreateInvoice defines the actor aU_Operator, who initiates it, and the involved data entity e_Invoice.  
As defined in ASL, the concept of actor partially exists in Genio, but only the equivalent of ASL's actors of type \"User\" can be defined. The concept of use case is supported in ASL but not in Genio. Instead, Genio supports the definition of access levels and access rights. Access levels can be assigned to the end-users (e.g., \"Manager\", \"Customer\"), while access rights define types of CRUD operations on data entities. For example, users with the access level \"Customer\" may read or consult invoices, and only users with the access level \"Manager\" may update invoices. Use cases are not explicitly supported in Genio but are an implicit aspect resulting from the combination of access levels with access rights, as shown in Figure 7.  
Figure 8: Partial Genio metamodel with list-related constructs (UML notation).
# Antibacterial Bio-Based Polymers for Cranio-Maxillofacial Regeneration Applications
## 1 Introduction
### Bio-Based Polymers  
Before discussing the numerous applications that these polymers could play in cranio-maxillofacial regeneration, it is essential to precisely define the term \"bio-based polymer\". In the literature, the terms bio-based polymers and biodegradable polymers have been extensively used. Both biodegradable and bio-based polymers are considered eco-friendly materials, and this has led to the frequent misconception of their correct interchangeability. However, there is a key difference between the two types of polymers.  
On the one hand, biodegradable polymers are those susceptible to degradation by biological activity, with the degradation accompanied by a lowering of its molar mass [10]. In contrast, the \"bio-based\" adjective does not refer to the properties of the polymers, but to the raw material from which they are obtained, being bio-based polymers derived from renewable resources, including plants, bacteria or algae, among others [11].  
In this sense, bio-based polymers can also be classified according to their degradability, with both biodegradable (e.g., polylactic acid, PLA) and non-biodegradable (e.g., bio-polyethylene) bio-based polymers. Similarly, not all biodegradable polymers are bio-based polymers (e.g., polycaprolactone, PCL) [12]. Thus, every polymer can be classified according to these two characteristics into four groups as summarized in Scheme 1[13].  
Bio-based polymers are considered eco-friendly materials based on the \"carbon neutrality\" concept. After being used, the non-biodegradable, bio-based polymers are burnt, generating carbon dioxide, which is then reconverted to biomass by photosynthetic organisms, and, therefore, they are being recycled. This fact may have enormous consequences for the economy. Due to the constant fluctuation in petroleum prices, it is imperative to find alternative resources in order to leverage the uncertainty of the oil market. However, nowadays, the bio-plastics market represents only about 1% of the 335 million tons of plastic that the world produces annually, and this production is continuously growing [12; 14].  
For this reason, increasing efforts have been made in recent years to design polymeric materials from renewable resources in order to replace, to a higher extent, the current non-renewable fossil fuel-based products [9; 15]. In this sense, the global bio-based-polymers market is expected to reach USD 9.6 billion by 2025, affecting this expansion to the biomedical sector too, an area which is not currently among the top five in the bio-based-polymers market [16]. The possibility to control the properties of the polymers by modulating different parameters such as their composition, thermal and mechanical stabilities or hydrophobicity/hydrophilicity ratio, allows us to produce a wide variety of polymer-based materials (e.g., hydrogels, scaffolds, membranes), which could be used with different applications for cranio-maxillofacial regeneration such as controlled drug-release, gene therapies and coatings, among others [9].
# An Analysis of Rhythmic Patterns with Unsupervised Learning
## 2 The Compositional Hierarchical Model for Rhythm Modeling
### Model Description  
The input of the RhythmCHM model consists of onset times and magnitudes of music events. Pitches and note durations are ignored. Onsets may be extracted either from audio recordings (using an onset detector) or from symbolic representations. Formally, the input can be described as a set of onset time and magnitude tuples:  
\[\mathcal{I}:\{\mathbf{X}:\mathbf{X}=[N_{o},N_{m}]\}. \tag{1}\]  
The first layer of the model, denoted \(\mathcal{L}_{0}\), consists of a single atomic part \(P_{1}^{0}\), where the superscript is defined by the layer and the subscript marks the part number. Since any _rhythm_ is composed of at least two events (i.e., a single event cannot solely represent a rhythmic pattern), the part \(P_{1}^{0}\) represents any occurrence of two events (onsets) in the input signal. The relation between the two events is encoded in the part's _activation_, which is calculated when an input sequence is presented to the model.  
The activation \(A\) represents any occurrence of a part \(P\) in the input and is composed of three elements: the onset time \(A_{T}\) is the onset time of the first event, the magnitude \(A_{M}\) is the activation magnitude, while the activation scale \(A_{S}\) represents the _scale_ of activation on the time axis. On the first layer, \(A_{S}\) is defined as the difference of onset times of both events. Namely, as each part, corresponding to a rhythmic pattern in our model is relatively encoded, the activation scale represents the timing (tempo/duration) with which the pattern has been located in the model's input. The scale distinguishes between two pattern occurrences found at the same onset, one faster (small scale), and one slower (large scale). The activation magnitude \(A_{M}\) represents the sum of event magnitudes. On the first layer, the magnitude represents the onsets' magnitudes \(N_{m}\), as described in Equation (1). The magnitude is therefore always non-negative; however, it can be diminished or inhibited (i.e., zero value) due to the hallucination and inhibition mechanisms described later on in this Section.  
More formally, the part \(P_{1}^{0}\) activates for all pairs of input events \(i_{1}=[N_{o}^{1},N_{m}^{1}]\) and \(i_{2}=[N_{o}^{2},N_{m}^{2}]\), where \(i_{1}\) occurs before \(i_{2}\), as:  
\[A=\langle A_{T},A_{S},A_{M}\rangle\leftarrow\langle N_{o}^{1},N_{o}^{2}-N_{o} ^{1},(N_{m}^{2}+N_{m}^{1})/2.\rangle \tag{2}\]
## 5 Conclusions  
Hydrogels are valuable concerning their ability to serve as an appropriate environment for cells due to their ease of preparation and similarity to the extracellular matrix of many human tissues. The extrusion-based bioprinting method is used widely to create hydrogel scaffolds for different tissue engineering applications. In this regard, it is recommended to mix hydrogels to achieve synergistic properties. Here, we examined the swelling, as well as degradation, rate, and mechanical properties (elastic moduli) of hydrogels with various compositions of alginate, gelatin, and MC. The results showed that composite hydrogels had better water absorption ability compared to a pure alginate hydrogel. Additionally, all combinations of hydrogels showed a decreasing pattern of elastic modulus with time, while alginate-MC combination gels showed the highest elastic moduli. After evaluating scaffolds from the mechanical perspective, more experiments were conducted to investigate the hydrogel printability. The results showed that biomaterial-related elements, e.g., viscosity, and fabrication-related ones, e.g., air pressure, nozzle speed, offset, and selected angular pattern, could influence the printing quality. Modulating these parameters, we improved the printability of different groups of hydrogels, including  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Applied Pressure (bar)** &
\begin{tabular}{c} **Linear Regression Models** \\ **(Nozzle Speed: \(N_{s}\), Pore Printability: \(P\), Strand Printability: \(S\))** \\ \end{tabular} \\ \hline
**1** & 0.2 & \(S=51.5-3.39\,N_{s}-50.1\,P+3.38\,N_{s}\times P\) \\ \hline
**2** & 0.2 & \(S=0.08+0.108\,N_{s}+0.53\,P-0.075\,N_{s}\times P\) \\ \hline
**3** & 0.1 & \(S=-2.06+0.0528\,N_{s}+2.96\,P-0.0525\,N_{s}\times P\) \\ \hline
**4** & 0.2 & \(S=-0.6298+0.06394\,N_{s}+1.534\,P-0.06222\,N_{s}\times P\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear regression models created based on the experimental results reported in Table 2.  
alginate, gelatin, and MC. Conducting research studies on printability can open the door for further improvements in the fabrication of hydrogel scaffolds using the extrusion-based technique. To conclude, taking biomaterial- and fabrication-related elements, printability can be improved and accordingly, scaffolds can be specialized depending on which tissue requires regenerative tissue therapy.  
Conceptualization, S.N. and X.C.; Methodology, S.N., M.S., Z.B.; Software, S.N.; Validation, S.N., N.K.S. and Z.B.; Formal Analysis, S.N., N.K.S., Z.B.; Investigation, S.N., Z.B.; Resources, X.C.; Data Curation, S.N.; Writing-Original Draft Preparation, S.N.; Writing-Review & Editing, S.N., X.C.; Visualization, S.N., M.S., and N.K.S.; Supervision, X.C.; Project Administration, S.N.; Funding Acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC, grant number RGPIN-2014-05648). Furthermore, we thank [PERSON] for assistance with the degradation test.  
The authors declare no conflict of interest.
The main aglycones of these compounds are kaempferol, quercetin, myriectin, apigenin, luteolin, naringenin, and isorhamneetin [5; 9]. A total of 41 tannin compounds have been identified, the majority being gallic (hydrolysable) tannins, along with five ellagitannins and four condensed tannins [5; 6; 12; 13]. The known phenylpropanol derivatives total 74, classified based on their structure into simple phenylpropanoloids, lignans, or coumarins [5; 6; 14]. Fatty acids have been identified in the seed oil of _Acer_ spp., such as _A. nonspessulanum_[15], _A. platanoides_, _A. pseudoplatanus_[16], _A. truncatum_[17; 18; 19], with \(\gamma\)-linolenic acid playing a significant role among omega-6 fatty acids. The pharmacological actions of _Acer_ spp. have been increasingly studied over the past 20 years, utilizing various types of extracts, preparations, or natural compounds [5]. Reported activities include hepatoprotective [5; 10; 12; 20], antioxidant [5; 11; 21; 22; 23], antiinflammatory [5; 12; 24; 25; 26], neuroprotective [5; 27; 28], antiproliferative [5; 12; 29; 30; 31],estrogen-like [5, 32, 33], bone-protective [5, 34], antimicrobial [5, 21, 35], hypoglycemic [5, 36], anti-obesity [5, 12, 20, 28, 37, 38, 39], anti-tyrosine and anti-melanogenic [5, 12, 40], and skin protective and anti-photoaging [5, 12, 41, 42, 43]. Additionally, studies have been conducted to explore the correlation between chemical structure and pharmacological activity [5]. Current data have highlighted aspects of the chemical composition and pharmacological actions of the five _Acer_ spp. that spontaneously grow in Romania. However, the majority of studies in recent years have focused on _A. truncatum_, a species native to northern China traditionally used for treating cardiovascular diseases. Due to its beneficial properties, it has been introduced to East Asia, Europe, and North America. Studies on _A. truncatum_ have confirmed its antioxidant, antimicrobial, antitumor, anti-inflammatory, and neuroprotective properties [8, 14, 17, 18, 19, 36, 37, 44, 45, 46, 47, 48]. Additionally, two subspecies of _A. tataricum_ (subsp. _gimala_ and subsp. _theiferum_) have been studied for their antioxidant activity, attributed to their rich polyphenol content. The leaves and branches of these subspecies are used to prepare a tea consumed in China as a green tea substitute [49]. Due to their content in toxic dipeptides, such as hypoglyin A (HGA), hypoglyin B (HGB), methylenecyclopropylglycine (MCPrG), and \(\gamma\)-glutamyl-MCPrG, _Acer_ spp. (e.g., _A. pseudoplatanus_) are responsible for some cases of herbivore poisoning [50, 51]. Our paper aimed to investigate, for the first time, the content of phenolic acids in leaves and fruits from _Acer_ spp., which grow spontaneously in Romania, collected from the southwestern region, along with their antioxidant and acetylcholinesterase (AChE) inhibitory activities. Also, the research provides entirely new data in the context of understanding _Acer_ spp. from a therapeutic perspective.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Heterogeneous Networking System Power Performance Evaluation  
The average number of handovers can be calculated by  
\[A_{HO}=\frac{\sum_{r=1}^{N_{r}}N_{HO}(r)}{N_{r}} \tag{9}\]  
where \(A_{HO}\) presents the average number of handovers and \(N_{r}\) represents the number of executions. \(N_{HO}(r)\) represents the number of \(r\)-th execution.  
Analysis of the Average Number of Handovers for Different Programs  
As Figure 9 shows the average number of handovers under the comparison of various schemes, a total of 1000 experiments were performed, where CBDHHO represents the horizontal handover of the CBDHVHO scheme, and CBDVHO represents the vertical handover of the CBDHVHO scheme. From the figure, it can be seen that the I-VHO scheme has the highest average number of handovers, which is 1.875. The reason is that the number of handovers in this scheme increases with the change in the VLC network. The D-VHO\({}_{t=0.5}\) scheme sets the residence time under a VLC network to 0.5 s, which means that when the residence time of a user receiving equipment under a VLC network is lower than 0.5 s, no handover is performed, so the average number of handovers is lower, 1.424. Similarly, the average number of handovers for the D-VHO\({}_{t=1}\) scheme is 1.04. The average number of handovers for the CBDHHO scheme is 1.006, but considering that the channel response interruption time in the communication blind area is very short, the CBDVHO scheme is used to assist in order to maintain continuous and stable communication with the user receiving equipment, so the total number of handovers will increase a little compared to the D-VHO\({}_{t=1}\) scheme.  
Figure 8: BER comparison of proposed ACO-OFDM and DCO-OFDM.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.2 Automation and Task Scheduling  
IoT and Distributed Computing Systems facilitate automation and task scheduling in business operations, driving efficiency, reducing manual effort, and enhancing productivity. IoT devices are equipped with instruments and connectors that allow businesses to automate routine and repetitive tasks traditionally performed manually [47]. This capability is particularly impactful in sectors like manufacturing, logistics, and healthcare, where precision and timely execution of tasks are crucial. For instance, manufacturing plants use IoT-enabled instruments to check operations performance and ensuing flaws, while automatically adjusting settings to optimize production processes [48; 49]. These sensors can trigger automated maintenance schedules based on real-time data, preventing equipment failures and reducing downtime. [PERSON] et al. [50] explain that the capacity to gather and respond on time ensures that machines operate at peak efficiency, minimizing waste and maximizing output. Distributed computing systems are key in this automation by processing the huge number of data created by IoT machines, running complex algorithms to predict maintenance needs, and scheduling tasks accordingly.  
IoT and distributed systems allow businesses to implement advanced task scheduling algorithms considering multiple variables, such as resource availability, task priority, and real-time data inputs. According to [PERSON] and [PERSON] [51], these algorithms can dynamically allocate tasks to the most appropriate resources, ensuring optimal use of personnel, equipment, and time. IoT devices, for instance in a plant, can check the status of different production lines and automatically assign tasks to the lines that are best equipped to handle them [52]. It helps balance workloads and prevents bottlenecks. Furthermore, automating administrative and back-office tasks through IoT and distributed systems frees manpower to focus on more strategic work [53; 54]. Routine tasks like data entry and report creation can be automated, reducing the risk of human setbacks and increasing overall productivity. Distributed computing ensures that these automated systems are robust, scalable, and capable of handling the demands of a growing business.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**T-A Group (n = 25)**} & \multicolumn{4}{c}{**B-A Group (n = 25)**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**\(p\)**} & \multirow{2}{*}{**Significance**} \\ \cline{2-2} \cline{4-9}  & **Mean** & **SD** & & **95\% CI** & & & & & & & \\ \hline T3–T1 & & & & & & & & & & & \\ SNA (\({}^{\circ}\)) & 2.65 & 2.31 & 1.70 & 3.61 & 3.29 & 1.55 & 2.65 & 3.93 & 0.63 & 0.26 & NS \\ SNB (\({}^{\circ}\)) & 1.92 & 2.61 & 0.84 & 2.99 & 0.77 & 1.67 & 0.08 & 1.46 & 1.14 & 0.07 & NS \\ ANB (\({}^{\circ}\)) & 0.73 & 1.55 & 0.09 & 1.38 & 2.51 & 1.34 & 1.95 & 3.06 & 1.77 & \textless{}0.001 & ** \\ SN-ANS (\({}^{\circ}\)) & 1.92 & 2.90 & 0.72 & 3.12 & 3.38 & 2.18 & 2.48 & 4.28 & 1.45 & 0.05 & NS \\ MP–SN (\({}^{\circ}\)) & \(-\)0.24 & 3.06 & \(-\)3.50 & \(-\)0.97 & \(-\)1.56 & 1.17 & \(-\)2.04 & \(-\)1.08 & 0.67 & 0.311 & NS \\ U1–SN (\({}^{\circ}\)) & 6.91 & 4.29 & 5.14 & 8.68 & 3.79 & 5.87 & 1.37 & 6.22 & 3.11 & 0.03 & * \\ N–Me, mm & 10.24 & 4.36 & 8.43 & 12.04 & 7.90 & 3.52 & 6.44 & 9.35 & 2.33 & 0.04 & * \\ U6/NF, mm & 4.56 & 1.57 & 3.91 & 5.21 & 2.60 & 1.35 & 2.04 & 3.16 & 1.95 & \textless{}0.001 & ** \\ L6/MP, mm & 2.60 & 1.58 & 1.95 & 3.26 & 2.87 & 1.33 & 2.32 & 3.42 & 0.26 & 0.52 & NS \\ Facial A & 2.11 & 2.21 & 1.19 & 3.02 & 1.33 & 1.92 & 0.53 & 2.12 & 0.77 & 0.19 & NS \\ Y axis & \(-\)0.15 & 2.68 & \(-\)1.26 & 0.95 & 0.26 & 1.81 & \(-\)0.48 & 1.0 & 0.41 & 0.52 & NS \\ Overjet & 2.88 & 2.71 & 1.76 & 4.00 & 3.58 & 2.24 & 2.65 & 4.51 & 0.69 & 0.32 & NS \\ IMPA & \(-\)0.31 & 3.83 & \(-\)1.89 & 1.26 & 0.53 & 3.83 & \(-\)1.05 & 2.11 & 0.85 & 0.43 & NS \\ \hline \hline \end{tabular} Data are results of Student \(t\)-tests. NS, not significant. Significantly different at \({}^{\star}\)\(p\) < 0.05 and \({}^{\star}\)\(p\) < 0.001.  
\end{table} 
Table 3: _Cont._before and after treatment (T2) and follow-up (T3) revealed significant changes in SNA, SNB, ANB, N-Me, U6/NF, and MP-SN between these time points in the T-A group. The ANB value significantly increased after treatment and significantly decreased during follow-up, but did not significantly differ between before treatment or after follow-up (ANB: \(-0.13^{\circ}\) vs. \(0.60^{\circ}\), \(p>0.05\); Table 4). All other SNA, SNB, MP-SN, N-Me, and U6/NF items significantly differed among T1, T2, and T3 (\(p<0.05\); Table 4). Changes in SNA, ANB, MP-SN, N-Me, and U6/NF were significant at the three time points in the M-A group. Unlike the T-A group, the SNB did not significantly change between before treatment and after follow-up. Unlike the T-A group, the ANB value also significantly increased immediately after treatment and during follow-up compared with that before treatment (ANB at T1 vs. T2, vs. T3: \(-1.30^{\circ}\) vs. \(1.58^{\circ}\) vs. \(1.20^{\circ}\), \(p>0.001\); Table 5).
## 2 Materials and Methods
### Matchup  
In this study, three methods were tested for matchup analysis: 1 \(\times\) 1, 3 \(\times\) 3 and 5 \(\times\) 5-pixel windows. In the 1 \(\times\) 1 pixel method, the station coordinate was directly matched to the associated pixel. To improve the interpretability and quality of the remote sensing images, radiometric correction was performed by comparing the coordinates on Landsat 8 products with the images on Google Maps. The comparison showed that the coordinates exactly matched the Google Map coordinates. The surface reflectance values of the 3 \(\times\) 3 and 5 \(\times\) 5-pixel methods were averaged to the pixel grid, surrounding the sampling point to diminish per-pixel noise.