esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Samples for Research  
Three biological species of seaweed were collected, namely (_E. crinita_, _U. intestinalis_, _C. limum_). Freshwater algae (_A. platensis_ and _Chlorella_ spp.), cultivated in a bioreactor in Bulgaria (near Varvara), were also studied.  
The seaweed were extracted from a depth between 1 and 3 m in June 2021. Taxonomic analysis of the samples was performed at the Institute of Oceanology--[BAS]--Varna (Table 4).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & _U. intestinalis_ & _C. limum_ & _E. crinita_ \\  & & & **(Formperly Cystoseira crinita)** \\ \hline
**Phylum** & Chlorophyta & Chlorophyta & Phaeophyta \\
**Class** & Ulvophyceae & Cladophorophyceaeae & Phaeophyceaeae \\
**Order** & Ulvales & Cladophorales & Fucales \\
**Family** & Ulvaceae & Cladophoraceae & Cystoseiaceae \\
**Genus** & _Ulva_ & _Chaenorhabditis_ & _E. crinita_ \\
**Locality** & Asparuhovo, Varna & Asparuhovo, Varna & Pomorie \\
**Geographical** & 43.173645, 27.916596 & 43.189680, 27.884336 & 42.560546, 27.633244 \\
**Coordinates** & 3 & 3 & 3 \\
**Number of samples** & 3 & 3 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Taxonomy of investigated marine macroalgae.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disease, Associated** & **Model** & **Treatment** & **Results** & **References** \\ \hline Obesity & Rats & 1000 mg/kg/day for 30 days & Improvement of the measured & [52] \\ \cline{2-5}  & Human & 2.8 g of _Arthrospin_/_Spirulina_ & Statistically significant reduction & \\  & Human & thrice a day over a period of & of body weight in obese & [53] \\  & & 4 weeks & outpatients & \\ \hline High cholesterol & Human & \begin{tabular}{c} _Arthrospin_/_Spirulina_ \\ 2 g daily for 2 months \\ \end{tabular} & \begin{tabular}{c} Reduces total cholesterol and \\ triglycerides, free fatty acid levels \\ \end{tabular} & [54] \\ \hline Cerebral ischemia & Rats & \begin{tabular}{c} _Arthrospin_/_Spirulina_ \\ at a dose of 180 mg/kg once a day, for 7 days. \\ \end{tabular} & \begin{tabular}{c} Improvement of \\ neurological deficit \\ score, reduction of \\ oxidative stress \\ biomarkers \\ \end{tabular} & [55] \\ \hline Diabetes & Human & \begin{tabular}{c} 2 g water soluble fraction of \\ _Arthrospin_/_Spirulina_ for 21 days \\ \end{tabular} & Reduce blood glucose & [56] \\ \hline Oral leukoplakia & Human & \
\begin{tabular}{c} 1 g/day for 1 year \\ \end{tabular} &
\begin{tabular}{c} Complete regression of lesions in \\ 45% of the intervention group \\ \end{tabular} & [57] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effects of the use of _Arthrospin_/_Spirulina_ against various diseases.
## 2 Experiments and Methods
### Hyperparameter Tuning and Reproducibility  
Daily meteorological variables were split in training (2011-2015) and testing (2016-2017) sets. The test sets were used to estimate the ETo after successful (convergence) training of the models. Extensive tests were performed to determine the hyperparameters of LSTMs. Several hyperparameters including neurons, learning rates, optimizers, batch sizes, and dropout effects were tested for higher accuracy. The highest performing learning rates, neuron and batch sizes were used in models training and testing. Due to large data points used in this study, several data normalization techniques were tested to reduce the noise effects in data. The max-min normalization performed better with our data in comparison with standard scalar, absolute scalar, power transformer, and normalizers. After training of models, the data were back transformed to the original scale.  
The TensorFlow framework was selected because of its wide applications in industrial deployment. TensorFlow is an open source library developed by the Google Brain team in November 2015. TensorFlow (Version 2.0) was used in the study along with Keras. Keras is another open source library developed by a Google engineer, [PERSON]. The purpose of Keras was to simplify the neural net modeling for user community. The other libraries used in this study included Numpy version 1.18.1 (community project), Matplotlib version 3.1.3 (developed by [PERSON]), Pandas version 1.0.1 (community project), and Scikitlearn version 0.22.1 (developed by [PERSON]) used with Python version 3.6 (developed by [PERSON]) programming language. All models were trained using Dell Latitude 5580 workstation, with Intel Core I7 7600U CPU, 8 GB ram, Nvidia GeForce 930 MX, and Ubuntu 16.04 x 64 operating system (developed by [PERSON]). Furthermore, for reproduceable results, all random seeds were set to avoid randomness. For examples, Python-hash seeds were set to 0, Numpy random seeds were set to 111, Python random seeds were set to 10, and TensorFlow random seeds were set to 89. All the results displayed in this study were retrieved using the above mentioned random seed configuration.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Theory: Landslide Detection with SAR
#### 2.1.3 ARIA Method  
It has been suggested that differencing a pre-event coherence map (calculated from two pre-event SAR images) and a co-event coherence map (calculated from two SAR images spanning the event) can differentiate between areas where coherence is always low and areas where it has decreased, e.g., due to building collapse or landsl [21; 35; 39]. The method developed by NASA's ARIA project for rapid generation of urban damage proxy maps is based on this approach [21; 39].  
Figure 4 shows the steps which go into producing ARIA damage proxy maps, described by [PERSON] et al. [21; 39]. Two pre-event SAR images and one post-event image are taken and used to calculate pre-event and co-event coherence maps. The histogram of the coherence values in the co-event coherence map is then'matched' to the histogram of the pre-event coherence map in order to mitigate any bulk changes in coherence between the two images, for example due to variability in weather conditions. This histogram matching process is shown for the simple case of 16 pixels in Figure 4b, in which the co-event cumulative histogram is mapped onto the pre-event histogram. For this step, pre-event and co-event pixels are sorted by value. In order to obtain a strict ordering, co-event pixels are sorted first by their value and then by the values of the eight pixels around them as in [PERSON] et al. [40]. The ordered co-event pixels are set equal to the values of the ordered pre-event pixels, and then placed in their original spatial positions, resulting in a co-event coherence map whose coherence frequency distribution is identical to that of the pre-event map. The histogram-matched co-event map and the pre-event map are then differenced to produce a classification surface. For their purposes, the ARIA team then classify pixels whose co-event coherence is lower than their pre-event coherence as 'damaged', with increasing confidence in more negative pixels (Figure 4c).  
The ARIA method was originally developed for urban damage mapping. However, when applying the method following the 2015 Gorkha earthquake, [PERSON] et al. [21] noted that it showed some promise in landslide detection. [PERSON] et al. [21] identified an area of decreased coherence spatially correlated with the Langtang Valley landslide, an exceptionally large and destructive landslide triggered by the earthquake [41]. However, [PERSON] et al. [21] judged that overall, coherence was not sufficiently stable through time in vegetated regions, resulting in many false positives: pixels incorrectly identified as damaged due to coherence changes unrelated to the earthquake.
## 5 HUVEC Model Applications
### Cardiovascular and Metabolic Diseases
#### 5.1.1 Hypertension  
Hypertension is a chronic and multifactorial disease [1]. In recent years, its incidence has increased, and onset occurs at earlier ages. This disease induces endothelial dysfunction by decreasing the secretion of NO and endothelin in EC, which causes arterial vasoconstriction and favors apoptosis [74; 112]. Although there has recently been great progress on the knowledge of hypertension, the molecular mechanisms underlying endothelial injury are not fully understood. Some relevant findings achieved in HUVEC models are described below.  
MicroRNAs are short non-coding RNAs that regulate gene expression at the post-transcriptional level by binding to sequences in the 3'UTR region of their target mRNA [112; 113; 114]. In a recent study, patients with hypertension showed increased microRNA-34a expression in peripheral blood as compared to controls [115]. MicroRNA-34a inhibition in HUVEC promoted proliferation, migration, and G1-S transition of EC. Bioinformatic analyses predicted that mRNA of the transforming growth factor \(\beta\)-induced factor homeobox 2 (Tgif2) is a microRNA34-a target, suggesting the potential use of microRNA-34a as a diagnostic biomarker [112].  
Zofenoprital is known to inhibit the action of the angiotensin-converting enzyme, a therapeutic target in CVD. Using a HUVEC model, [PERSON] et al. demonstrated that this drug also induces H\({}_{2}\)S production and decreases the expression of proinflammatory genes involved in the recruitment of inflammatory cells and platelets in the vascular endothelium. The authors conclude that zofenoprital exerts a protective effect for the endothelium by multiple complementary mechanisms, thus, proposing this drug as one of the first choices for treatment of CVD [74].  
Finally, HUVEC models have been very useful to evaluate the protective effect of several drugs on the endothelium, such as sodium nitroprusside [116], safflor yellow B [117], resveratrol, piceatannol, and astringin [118], and have been useful to create new therapeutic and prevention strategies [116; 117; 118].
## 3 Long-Term Implications: The Question of Urban Development and Sustainability  
### Urbanism and Urban Sprawl  
A question that is not usually addressed in the AV-based research for transport systems is what automation in urban mobility will mean for urban development. Urban sprawl and the relocation of residents to cheaper neighbourhoods is a direct result of car-dependence, a behaviour that may witness further increases if AV private vehicles are readily available in the consumer market [65]. A preliminary study on the potential impact of AVs by [PERSON] et al. [66] was conducted in the city of Rome. The authors found that more than 70% of survey respondents showed interest in relocation to suburban areas based upon the market penetration rate of private AVs. The authors also projected through choice modelling and traffic simulation that by the year 2050, around a 50% penetration rate of private AVs is expected to result in excessive urban sprawl, which in turn will result in heavy traffic congestions in the suburban road network. On the other hand, PT usage may see a rapid decline after AV introduction in the private vehicle market [67] if adequate long-term planning measures are not implemented.  
Transit-oriented development (TOD) can be adopted to reduce the overburden on personalised vehicle transit, and encourage PT uptake if mixed-use, high-density, residential developments are clustered around efficient, fast and reliable PT [68, 69]. [PERSON] et al. [70] performed a choice modelling study for the residents of Atlanta, GA, USA. They found that the integration of SAVs with TOD can result in extending the PT service radius of TOD to 3.22 km.  
This study by [PERSON] et al. [70] assumed that SAVs are expected to serve the first and last-mile need of local residents, and the survey results showed around 39.3% of the residents supported AV integration with TOD. However, [PERSON] et al. [70] also recommended that future research should use real-word traffic flow data to develop traffic simulation models and investigate the application of AV-based PT system towards solving mobility issues. The AV-based intelligent transport study, currently being performed by the authors of this paper, is aimed towards addressing this recommendation.
## 3 Results
### Metric Results
#### 3.1.2 ROC, PR and \(F_{1}\)-Score-Threshold Curves  
Figure 7 shows the ROC curve of the model. Its layout is scrupulously close to the upper left corner. Therefore, an increase in sensitivity does not necessarily imply the appearance of more false positives (FPs) and, consequently, does not affect the excellent performance of the model, summarized in the area under the curve (ROC AUC = 0.97), very close to 1.  
With unbalanced data, which is not the case in the present work, if there are few positive samples, the value of the ROC curve, as well as the area under the curve (ROC AUC), could present high values; the FP rate (number of false positives/number of negative samples) tends to remain low due to the large number of negative observations, which makes its informative function less relevant by not reflecting the true performance of the classifier. In this regard, the PR curve, analyzed below, becomes a complementary indicator. However, for balanced data, as is the case at hand, the ROC curve and the area under the curve (ROC AUC) are appropriate indicators. For the rest, the ROC curve clearly illustrates the relationship that is established with the false positives. As already advanced with the confusion matrix, they are counted more significantly than the false negatives: 50 of 780 for FP, compared to 3 of 780 for FN.  
In Figure 8, the Precision-Recall curve of the proposed model is represented. This curve shows the relationship between the metrics _precision_ and _recall_. The more the curve trace extends towards the upper right corner, reflected in the trend to 1 of the area under the curve PR AUC (\(\sim\)0.94), the better the model performance. The PR curve makes it possible to determine from what value of precision recall degrades and vice versa. The PR curve suggests the most significant number of positive samples (stressed state) the model can predict in scenarios with balanced data. In the presence of unbalanced data, with few positive samples, the PR curve would deviate from its optimal regime, making it a representative precision indicator, given the low probability of the positive class.  
Figure 7: (Color online) ROC curve and ROC AUC for the proposed stress-detection model.  
Finally, the \(F_{1}\)-score-Threshold curve, shown in Figure 9, completes the information provided by the ROC curve and the area under the curve ROC AUC since it integrates with \(F_{1}\)-score the precision and sensitivity. In Figure 9, the stability and performance of the model can be clearly appreciated. For a wide range of threshold values, the value of \(F_{1}\)-score remains constant at a height of practically 1, with a maximum value of \(\sim\)0.97 for a threshold of 0.4565 of the positive classes.
## 3 Results and Discussion
### Aldehydes
Various mechanisms have been proposed to explain this decrease during storage, including hydration followed by condensation [36], oxidation to the corresponding acids, or enzymatic reduction [34]. In contrast, other aldehydes are considered to contribute to a lesser extent [26,30], but they enrich the aroma of WDF samples with pleasant fragrances. Hexanal, 2-hexenal, 4-heptenal, 2,4-hexaional, 2-heptenal, 2,4-heptadiene, and 2,4-decadienal can be generated by the oxidation of polyunsaturated fatty acids and, to a lesser extent, by autoxidation [1,3,6,37]. These molecules, already detected in some watermelon cultivars, are particularly abundant in foods rich in fatty acids such as avocado, fish oil, and olive oil [5,37]. The C6 aldehydes hexanal and 2-hexenal play very important roles in the food and perfume industries, since they impart \"green\" and fresh fragrances, which are particularly appreciated and sought after [1]. Some authors [25] have suggested the possible formation of hexanal in small quantities due to lycopene degradation, a carotenoid abundant in watermelon fruit. This aldehyde reached an amount 2.5 times higher after 24 months (WDF_t2). Similarly, 2-methyl-propanal, pentanal, 2-methyl-butanol, and 3-methyl-butanal levels increased over time. The origin of these last two compounds has been associated with the catabolism of the amino acid leucine [38] in a complex degradation process that involves some sugars, which are certainly present in the analyzed samples. The oxidation of 2-methyl-butanol and 3-methyl-butanol gave rise to volatile compounds present only in aged samples, such as 2-methyl butanoic and 3-methyl butanoic acids, species absent in WDF_t0, and present in increasing quantities after 30 days (WDF_t1) and 24 months (WDF_t2) of storage (Table 1). 2-methyl-propanal was also assumed to have the same origin [38], which could explain the significant increase in its concentration over time. These analytes impart a \"green\", \"fermented\", and \"fresh\" aroma. The results of HS-SPME-GC-MS analysis showed a strong loss of the typical aroma of the fresh fruit, watermelon, during storage. The abundance of C9 aldehydes decreased significantly over time and was practically absent after 24 months. After 30 days, the \"green\" and \"herbaceous\" notes prevail with some \"fruity\" nuances, which disappear completely in WDF_t2, where the pleasant fragrance perceived is given only by the first two aromas.
# Dynamic SDN Controller Load Balancing
## 2 Related Works
Towards that target, the DCF architecture has been updated to enable the application of existing algorithms [18; 20; 21; 23] in the \"_Reassignment_\" process in each cluster. Furthermore, a clustering algorithm has been developed that takes into account the controller-to-controller distances in the load balancing operation. The problem has been formulated as an optimization problem, aiming at minimizing the difference between cluster loads with constraints on the controller-to-controller distance. This is a challenging problem due to these opposite objectives. We assume that each controller has the same limit capacity in terms of requests per second that it can manage. The controllers are dynamically mapped to clusters when traffic changes. The challenge is to develop an efficient algorithm for the above-mentioned problem i.e., re-clustering in response to variations of network conditions, even in large-scale SDN networks. We propose a novel Dynamic Controllers Clustering (further denoted as DCC) algorithm by defining our problem as a K-Center problem at the first phase and developing, in a second phase, a replacement rule to swap controllers between clusters. The idea for the second phase is inspired by Game Theory. The replacements shrink the gap between cluster loads while not exceeding the constraint of controller-to-controller distances within the cluster. We assume that \(M\) controllers are sufficient for handling the maximal request rate in the network (as mentioned earlier, there are already many works that found the optimal number of controllers). Our architecture and model are different from aforementioned existing works since we enable not only dynamicity inside the cluster but also between clusters which did not exist before.
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
###### Abstract  
With the growing number of patients living with chronic conditions, there is an increasing need for efficient systems that can automatically capture and convert medical device readings into digital data, particularly in home-based care settings. However, most home-based medical devices are closed systems that do not support straightforward automatic data export and often require complex connections to access or transmit patient information. Since most of these devices display clinical information on a screen, this research explores how a standard smartphone camera, combined with artificial intelligence, can be used to automatically extract the displayed data in a simple and non-intrusive way. In particular, this study provides a comparative analysis of several You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD) models to evaluate their effectiveness in detecting and recognizing the readings on medical device displays. In addition to these comparisons, we also explore a hybrid approach that combines the YOLOv8l model for object detection with a Convolutional Neural Network (CNN) for classification. Several iterations of the aforementioned models were tested, using image resolutions of \(320\times 320\) and \(640\times 640\). The performance was assessed using metrics such as precision, recall, mean average precision at 0.5 Intersection over Union (mAP@50), and frames per second (FPS). The results show that YOLOv8l (640) achieved the highest mAP@50 of 0.979, but at a lower inference speed (13.20 FPS), while YOLOv8n (320) offered the fastest inference (129.79 FPS) with a reduction in mean average precision (0.786). Combining YOLOv8l with a CNN classifier resulted in a slight reduction in overall accuracy (0.96) when compared to the standalone model (0.98). While the results are promising, the study acknowledges certain limitations, including dataset-specific biases, controlled acquisition settings, and challenges in adapting to real-world scenarios. Nevertheless, the comparative analysis offers valuable insights into the trade-off between inference time and accuracy, helping guide the selection of the most suitable model based on the specific demands of the intended scanning application.  
artificial intelligence; display digitization; object detection; single shot multibox detector (SSD); you only look once (YOLO) +
Footnote †: journal: Applied sciences  
Article