esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
VIs are linear, orthogonal or ratio combinations of reflectance calculated from different wavelengths of the visible (VIS) and near-infrared (NIR) part of the electromagnetic spectrum [12] and widely used proxies for temporal and spatial variation in vegetation structure and biophysical parameters of agricultural crops [13]. Numerous VIs have been developed in the last decades to improve the relationships between the spectral response and the respective characteristics of vegetation canopies such as net primary production, LAI, vegetation fraction, chlorophyll density or the fraction of absorbed photosynthetic active radiation (FAPAR) [14; 15; 16]. Most VIs suffer from strong non-linearity and sensitivity to external factors such as solar and viewing geometry, soil background and atmospheric effects [17]. Apart from limitations caused by insufficient spectral resolution of available sensors, most improvements refer to the reduction of these external factors. [PERSON] _et al._[18] give a brief review of different VIs, their limitations and proposed improvements. [PERSON] _et al._[7] implemented different VIs in generic relationships to successfully estimate LAI and nitrogen uptake of wheat varieties grown on micro-plots. [PERSON] _et al._[10; 11] demonstrated that narrow-band VIs derived from multi- and hyperspectral imagery captured during different UAV missions enable the detection of chlorophyll fluorescence emission variability as a stress status indicator of olive, peach and citrus trees. Although there is a growing demand on quantitative and spatially consistent data of major components of the carbon budget [19; 20], to our knowledge, UAV-based remote sensing have not been used for the estimation of the C export by harvest of crops to date. Apparently easy to achieve, considerable uncertainties arise from the spatio-temporal variation of environmental factors (soils, weather conditions) and harvesting techniques (crop residues) [19]. In this case study we examined the potential of narrow-band multispectral imagery to estimate the C export of lucerne (Medicago sativa) from plots in different terrain positions and soil types. The output of a workflow for data pre-processing, a calibrated orthorectified image mosaic, was used to examine the predictive accuracy of the normalized difference vegetation index (NDVI) [21], the transformed soil-adjusted vegetation index (TSAVI) [22], the two-band vegetation index (TBVI) [23] and the enhanced vegetation index (EVI) [24] for fresh phytomass of lucerne. The total C export was then calculated from relationships between ground-based measurements of phytomass and carbon content of lucerne. Finally, yield data collected at each harvest date were used to estimate the yearly C export.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load
#### 4.3.4 Analysis of the Slovak Market  
The Slovak electricity system is characterised by a low share of wind resources, therefore the available correlations are limited to the relationships between solar generation, residual load and prices. Despite this limited data structure, the research results provide important insights into the market sensitivity to solar energy and the structural factors influencing price dynamics.  
The overall Solar/Price correlation is +0.0738, which is a very weak positive correlation. This means that increased production from solar is not consistently associated with a decrease in prices--on the contrary, in some cases there is even a slight increase. The year-on-year development of the correlation shows large fluctuations, e.g., in 2017: +0.122, in 2024: \(-0.358\). These changes suggest that in some years solar generation occurred at times of higher demand, thus not reducing prices, or that its impact was overshadowed by other conventional sources that met a significant amount of demand, namely nuclear, coal, and hydro.  
The overall Solar/Residual Load correlation is \(-0.0034\), indicating no systematic link. This confirms the hypothesis that Slovak solar generation is low (little installed capacity), concentrated for a few hours a day (around noon) and not synchronised with changes in demand.  
In some years the correlation is positive (e.g., 2021: +0.061), in others negative (2025: \(-0.075\)), confirming the low integration of solar energy into the system in terms of demand balancing. On the other hand, the year 2025 is not yet complete.  
The Residual Load/Price correlation is +0.61 (overall), which is a moderate to strong positive relationship. The results suggest that prices in Slovakia are dominated by residual demand, i.e., the demand for conventional resources net of RES. This link is very strong in 2015-2020 (e.g., 2019: +0.708), weakens in 2021-2022 (e.g., 2022: only +0.0877), may indicate higher price volatility from external factors (e.g., imports, market uncertainties, fuel costs).  
In Slovakia, the impact of solar generation on prices is marginal and lacks the ability to systematically influence residual load. The electricity market is heavily dependent on traditional demand-based pricing for conventional generation, which is typical for systems with low RES penetration.
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
## 3 Methodology
### Sensors  
#### 3.1.1 Mq-135  
The MQ135 sensor is a gas detector, capable of detecting a large spectrum of gases, including ammonia, benzene, nitric oxide, smoke, and carbon dioxide. It can be operated in both indoor and outdoor environments. Moreover, MQ135 has good sensitivity to carbon dioxide. It is also cost-effective and has a long operational lifespan [16]. The sensor is shown in Figure 3.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Performance of the Proposed Method with Different Features  
State-of-the-art machine learning algorithms have used various features in the wavelet domain, frequency domain, and time domain to classify MI EEG data. To investigate the efficiency of the proposed model, we extracted different features from the EEG epochs. However, constructing a single overcomplete dictionary based on these features takes more computational time. Therefore, we compared additional features individually using the proposed method for accuracy and computational time. The performance of the proposed method using different features is presented in Table 3. We used 0.5 s epoch lengths to compare as they yield better classification accuracy. In addition, the combined wavelet features with energy and entropy produced better classification accuracy than other methods.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 3 Results
### F5 Splicing Modulation in HepG2 Cells  
In order to reduce the relative expression of FV-short, HepG2 cells were treated with increasing concentrations (0-20 uM) of MAOs targeting the FV-short-specific donor splice site (Donor-MAO) or acceptor splice site (Acceptor-MAO). After 48 h of treatment, total RNA was isolated and full-length FV and FV-short mRNA expression was analysed by RT-PCR and gel electrophoresis (Figure 4). Both MAOs caused a dose-dependent decrease of FV-short expression without affecting full-length FV expression. Similar results were obtained with a 1:1 mix of Donor-MAO and Acceptor-MAO (MAO-mix, i.e., 50% Donor-MAO and 50% of Acceptor-MAO to obtain an overall concentration of 5 uM, 10 uM and 20 uM), whereas treatment with a Control-MAO up to a concentration of 20 uM did not affect FV-short or full-length FV expression.  
The effects of the MAOs on the FV-short/full-length FV transcript ratio were quantified using real-time qPCR analysis (Figure 5). Untreated HepG2 cells typically expressed 2-3 orders of magnitude more full-length FV than FV-short mRNA. Treatment with the Donor-MAO dose-dependently decreased the FV-short/full-length FV transcript ratio to ~50% of its original value in untreated cells (Figure 5A, p-for-trend = 0.008). The Acceptor-MAO was somewhat less effective, reducing the FV-short/full-length FV transcript ratio by ~40% at the maximal dose of 20 uM (Figure 5B, p-for-trend = 0.063). The MAO-mix showed an intermediate effect (Figure 5C, p-for-trend = 0.010), while the Control-MAO did not decrease the FV-short/full-length FV transcript ratio (Figure 5D, p-for-trend = n.s.). Increasing the concentration of Donor-MAO or Acceptor-MAO to 50 uM did not result in any further decrease of the FV-short/full-length FV ratio (not shown).  
Figure 3: **Amplification of FV transcripts and identification of a new alternatively spliced transcript.****(A)** Schematic representation of the region spanning \(F5\) exons 12–14 in the full-length FV and FV-short transcripts. The positions of the primers used to amplify these transcripts (first primer set in red, second primer set in black) are shown and amplicon sizes are indicated. EX: exon. **(B)** Left: Typical PCR products obtained after amplification of HepG2 cDNA using the first (red) set of primers (note: since the unexpected 320-bp band was more prominent in qPCR than in normal PCR experiments, the figure shows a gel loaded with qPCR products). Right: Schematic representation and sequencing chromatogram of the new splicing variant lacking the first 123 nucleotides (nt.) of exon 13. The red forward slash in the protein sequence marks the position of the predicted 41-amino acid deletion. **(C)** Typical PCR products obtained after amplification of HepG2 cDNA using the second (black) set of primers.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 2 Related Work
### Large-Scale Localization with Deep Learning  
Various attempts at this taskhave been made based on the success of deep learning in various computer vision tasks. Earlier works in this field mostly looked at matching ground-level images with satelite images and were mostly formulated as retrieval tasks. [PERSON] and [PERSON] finetuned a CNN architecture trained on imagenet to match ground-level images and satellite images [24].  
Models utilizing Siamese networks such that the ground-level and top-down images share the same models, without weight sharing, have been researched [19; 25; 26; 27; 28; 29]. These models are typically learned using a triplet loss [30], which is a loss function that compares an input to a positive and negative example and minimizes the distance to the positive example, while the distance to the negative example is maximized.
# Hash Based DNA Computing Algorithm for Image Encryption
###### Abstract  
Deoxyribonucleic Acid (DNA) computing has demonstrated great potential in data encryption due to its capability of parallel computation, minimal storage requirement, and unbreakable cryptography. Focusing on high-dimensional image data for encryption with DNA computing, we propose a novel hash encoding-based DNA computing algorithm, which consists of a DNA hash encoding module and content-aware encrypting module. Inspired by the significant properties of the hash function, we build a quantity of hash mappings from image pixels to DNA computing bases, properly integrating the advantages of the hash function and DNA computing to boost performance. Considering the correlation relationship of pixels and patches for modeling, a content-aware encrypting module is proposed to reorganize the image data structure, resisting the crack with non-linear and high dimensional complexity originating from the correlation relationship. The experimental results suggest that the proposed method performs better than most comparative methods in key space, histogram analysis, pixel correlation, information entropy, and sensitivity measurements.  
DNA computing; image encryption; context-aware DNA permutation; hash DNA encoding +
Footnote †: journal: Applied Sciences
## 4 Wettability
### Wettability Model for Rough Surface
\[\cos\theta_{\rm c}=\frac{r_{1}\big{(}\gamma_{\rm S,G}-\gamma_{\rm S,L}\big{)}}{\gamma_{\rm LG}}+\frac{r_{2}\big{(}\gamma_{\rm S,G}-\gamma_{\rm S,L}\big{)}}{\gamma_{\rm LG}}=r_{1}\cdot\cos\theta_{1}+r_{2}\cdot\cos\theta_{2} \tag{7}\] \[\cos\theta_{\rm c}=r_{1}\cdot\cos\theta_{1}\ -r_{2} \tag{8}\]  
where \(r_{1}\) represents the fractional area of the surface with contact angle \(\theta_{1}\), \(r_{2}\) is the fractional area of the surface with contact angle \(\theta_{2}\), and \(\theta_{\rm c}\) denotes the Cassie contact angle. \(\gamma_{\rm S,G}\) and \(\gamma_{\rm S,G}\) are the wettability between the solid and gas, \(\gamma_{\rm S,L}\) and \(\gamma_{\rm S,L}\) are the wettability between the solid and liquid, and \(\gamma_{\rm L,G}\) is the wettability between the liquid and gas. The advantage of the Cassie-Baxter model is that it provides a more accurate description of real systems compared to the Wenzel model [108]. However, accurately determining the parameters \(r_{1}\) and \(r_{2}\) for randomly roughened surfaces is challenging. Additionally, the Cassie-Baxter model is not applicable in situations where contact angle hysteresis is a critical factor [104]. While [PERSON] and [PERSON] [109; 110] have criticized the accuracy of both models, studies by [PERSON] et al. [61] and [PERSON] et al. [60] have demonstrated that neither the Wenzel nor the Cassie model adequately describes the wetting behavior of coal surfaces under natural conditions. As a result, more complex and sophisticated models for rough and composite surfaces have been proposed [111; 112]. Despite their limitations, the [PERSON] model and the Cassie-Baxter model remain the primary models for assessing wettability on real (rough and heterogeneous) surfaces, even though pure Wenzel and Cassie-Baxter states are seldom observed [113].
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 3 RF-Access  
### Overview  
RF-Access uses the UHF RFID to perform user authentication. As shown in Figure 1, an RFID reader connects to one or more RFID antennas that locate on one side of the passageway. The user authentication is achieved by querying RFID tags attached to visitors who pass through the gate. If a tag is queried, the reader compares its EPC (tag's ID) with legal IDs stored in the backend server. Only the user with a legal RFID tag (or a tagged badge) will pass the system authentication. Since UHF RFID has a long communication range and a high reading rate, RF-Access does not demand users stop to swipe their identity cards as HF RFID. Instead, the visitors just go through the system without any stay, and meanwhile, RF-Access does the user authentication, which greatly improves time efficiency and quality of service. However, the long communication range makes the motion direction detection difficult. Additionally, since we remove the flap barriers, how we detect illegal intrusion by a user who does not carry any tags is another concern. In what follows, we detail how RF-Access addresses these two issues.
# Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
## 1 Introduction
So, this paper focuses on the study of the prognosis model, and it is a continuation of a previous study regarding the applicability of CVA for fault detection in processes operating under time-varying conditions, with the focus on the process analyzed in this research [5]. The remainder of the paper is structured as follows. Section 2 presents the related literature, Section 3 discusses the methodology, and Section 4 outlines the case study, with particular attention to the prognosis model, and the obtained results. The discussion of the obtained results, the conclusions, and the suggestions for future research are summarized in Section 5.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Liver**} & \multicolumn{2}{c}{**Right Kidney**} & \multicolumn{2}{c}{**Left Kidney**} & \multicolumn{2}{c}{**Spleen**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-11}  & **DSC** & **mIoU** & **DSC** & **mIoU** & **DSC** & **mIoU** & **DSC** & **mIoU** & **DSC** & **mIoU** \\ \hline Auto-DL [50] & 93.5 & 87.9 & 85.1 & 75.6 & 84.2 & 73.1 & 87.3 & 78.2 & 87.5 & 78.7 \\ NAS-UNet [50] & 93.7 & 88.3 & 87.5 & 77.6 & 85.4 & 74.0 & 89.3 & 80.2 & 89.0 & 80.0 \\ MS-NAS [50] & 94.1 & 88.9 & 88.4 & 79.3 & 88.5 & 79.4 & 90.0 & 82.9 & 90.3 & 82.6 \\ BIO-Net [51] & 91.7 & 85.8 & 87.2 & 78.2 & 85.1 & 75.7 & 82.8 & 73.2 & 86.7 & 78.2 \\ BiX-NAS [51] & 89.8 & 82.6 & 82.1 & 71.0 & 82.7 & 71.9 & 76.5 & 66.0 & 82.8 & 72.9 \\ Ours & 92.4 & 87.3 & 92.3 & 85.9 & 91.6 & 85.0 & 88.4 & 80.8 & 91.2 & 84.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative evaluation of different networks for the CHAOS(MRI) dataset in terms of DSC (%) and mIoU (%). Figure 2: Qualitative comparison of segmentation results of our framework on the CHAOS dataset: (**a**) ground truth, (**b,c**) results of Segnet and nn-Unet, and (**d,e**) results of our method implemented on Segnet and nnUNet. The red region represents the liver, the blue region represents the right kidney, the cyan represents the left kidney, and the yellow represents the spleen. The white line denotes the boundary of the ground truth. and HD for the hippocampus dataset. For the experiments based on Segnet, our framework could reach 88.0 \(\pm\) 0.3%, 78.8 \(\pm\) 0.5%, and 1.4 \(\pm\) 0.1 mm on metrics of DSC, mIoU, and HD, respectively. Concerning the experiments based on nn-Unet, our method could achieve 1.3% improvements from 86.9 \(\pm\) 0.5% to 88.2 \(\pm\) 0.4% on DSC and achieve 1.9% improvements from 77.1 \(\pm\) 0.7% to 79.1 \(\pm\) 0.6 % on mIoU, reaching 1.4 \(\pm\) 0.0 mm on HD. The qualitative results of our method on the hippocampus dataset are displayed in Figure 3.  
We compared our method with previous works on the hippocampus dataset. We compared our method with EMONAS-Net [52], C2 FNAS [53], VFN [54], residual DSM [55], and distill DSM [56]. Table 6 summarizes the comparison of previous methods on the hippocampus dataset in terms of DSC. The performance of our proposed framework ranks third among these methods. The EMONAS-Net and distill DSM could achieve 88.3% and 88.8% in DSC, which is slightly higher than ours. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Anterior**} & \multicolumn{3}{c}{**Posterior**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-10}  & **DSC** & **mIoU** & **HD** & **DSC** & **mIoU** & **HD** & **DSC(\%)** & **mIoU** & **HD** \\ \hline Segnet & 88.4 \(\pm\) 0.4 & 79.4 \(\pm\) 0.6 & 1.3 \(\pm\) 0.0 & 86.6 \(\pm\) 0.5 & 76.7 \(\pm\) 0.6 & 1.5 \(\pm\) 0.1 & 87.5 \(\pm\)
## 3 Materials and Methods
### Analysis Based on Real Datasets
#### 3.4.4 Probability of Getting the First Click  
We study the impact of \(FC\) on the probability of obtaining the first click emulating what we have done in Section 3.2.2 for a simulated scenario. We will use the two approximations presented before for the probability of a new impression and a click, respectively.  
To achieve our goal, we need to define two variables: \(X\) the total number of clicks by the user. We will also define two new probabilities: \(P_{NC}(n)\), where n is the number of previous non-clicked impressions as this is the metric used for the upper bound in the probability of click, and \(P_{I}(n)\), the probability of getting an impression after \(n\) previous impressions. Both probabilities are defined by the formulas computed in previous sections.  
Based on all the previous discussion, we propose  
\[P(X=0|FC=1) =P_{NC}(0)\] \[P(X=0|FC=2) =P_{NC}(0)(P_{I}(1)P_{NC}(1)+(1-P_{I}(1)))\] \[P(X=0|FC=3) =P_{NC}(0)(P_{I}(1)P_{NC}(1)(P_{I}(2)P_{NC}(2)+(1-P_{I}(2)))+(1-P_ {I}(1)))\] \[P(X=0|FC=m) =\prod_{n=0}^{m}P_{I}(n)P_{NC}(n)+\sum_{n=1}^{m}(1-P_{I}(n))\prod _{i=0}^{n-1}P_{I}(i)P_{NC}(i)\] \[P(X=0)=P(X=0|FC=\infty) =\prod_{n=0}^{\infty}P_{I}(n)P_{NC}(n)+\sum_{n=1}^{\infty}(1-P_{I }(n))\prod_{i=0}^{n-1}P_{I}(i)P_{NC}(i)\] Using this formula, we can compute the probability of getting the first click as \[P(X\geq 1)=1-P(X=0)\] In turn, using this formula, we can compute the maximum probability (remember that Section 3.4.2 gives us an upper bound) of getting the first click. Figure 7 shows the relation between the \(FC\) and the probability of the first click.  
We observe that \(FC\) has an impact on the probability of the first click for low \(FC\) values. We can also notice that the different click rate for each of the companies also affects these probabilities.  
Furthermore, we go beyond the probability of getting the first click from scratch. We analyze how this probability evolves as the number of non-clicked impressions increases. To illustrate the case, we will set \(FC=30\) and find the probability of getting the click after several non-clicked impressions. The results are shown in Figure 8.  
Figure 8: The figure shows the maximum probability of getting the first click of each client using the upper bound click rate approximation for each of them and under the assumption that the \(FC\) is 30.  
Figure 7: The figure shows for each \(FC\) the maximum probability of getting the first click of each client using the upper bound click rate approximation for each of them.  
The results show that the probability decreases initially because of the enormous risk of not getting any new impression. Nevertheless, after overcoming the high chances of not receiving more impressions, the probability increases quickly up to the fifth impression when it starts dropping again until it reaches 0.
## 4 Results and Discussion
### Prediction Model Application  
According to the results of the calculation speed and accuracy of the performance prediction model analyzed above, the ANN model showed the highest accuracy and the fastest calculation speed for the data format related to the performance prediction of the heat pump system. To verify the applicability of the developed ANN-based performance prediction model, the performance prediction model was applied to the BAS of the laboratory where the air-cooled heat pump system was installed. The BAS screen is configured to apply the performance prediction model of the heat pump system and monitor the results,  
Figure 8: Comparison of predicted and measured performance using KNN model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **ANN** & **SVM** & **RF** & **KNN** \\ \hline Computation time & 31 s & 1 min 20 s & 2 min 8 s & 2 min 22 s \\ \hline \hline \end{tabular}
\end{table}
Table 9: Computation time of prediction model.  
Figure 7: Comparison of predicted and measured performance using RF model.  
and Figure 9 shows the BAS application screen. In BAS, the performance of the two heat pumps and the performance of the heat pump system are confirmed.  
Figure 10 shows the results of analyzing the monitored performance as time series data after applying the artificial neural network-based performance prediction model to the BAS. The predictive model enables real-time performance monitoring during heat pump system operation and is saved in Excel file format. It can be used for diagnosis and efficient operation of heat pumps by checking and analyzing performance data during operation.  
Figure 11 shows the predicted performance monitored by the BAS during the cooling operation period, and the prediction result R\({}^{2}\) is 0.9954. Performance changes in the range of about 2.49 to 3.9 were observed during operation of the heat pump system, indicating even predictive performance in all ranges.  
It was confirmed that real-time performance prediction and monitoring during system operation was possible through the application of BAS. In the future, the performance degradation of the heat pump system can be checked, and in connection with this, it can be used for system failure diagnosis.  
Figure 10: BAS application and monitoring result of predictive model.  
Figure 9: BAS application of performance prediction model.