esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pseudo-TCT and Verification Experiment
### Verification Experiment of Inter-Class Separability  
#### 3.2.1 K-Means Clustering and ISODATA Clustering  
Unsupervised classification is often used for remote sensing image classification because the acquisition of prior knowledge is tedious. The two commonly used unsupervised classification methods in remote sensing are K-Means clustering and ISODATA clustering, which can divide data with common features into meaningful clusters. This means that ground objects with strong intra-class consistency and high inter-class separability in the image are clustered into one class. Thus, the enhancement effects of the pseudo-TCT can be examined based on the clustering effect on buildings and roads.  
The main idea of K-Means is, that given K values and K initial cluster centroids, each point is assigned to the nearest cluster, and then the cluster class centers are recalculated, and then the cluster class centers are assigned and updated in iterations until the class cluster centers are unchanged or the specified number of iterations is reached. Generally, the distance to the cluster center was calculated using the Euclidean distance, which was calculated as follows:  
\[\text{D}(\text{x},\text{ y})=\sqrt{\sum_{\text{i=1}}^{\text{n}}(\text{x}_{ \text{i}}-\text{y}_{\text{i}})^{2}} \tag{4}\]  
where \(\text{D}(\text{x},\text{y})\) is the distance between points x and y; n is the data dimension of x and y; x\({}_{\text{i}}\) is the value of the i-th dimension of point x; y\({}_{\text{i}}\) is the value of the i-th dimension of point y.  
ISODATA clustering is similar to K-Means clustering, but ISODATA clustering has some improvements compared to K-Means clustering. The ISODATA clustering is based on the K-Means clustering and adds two operations of \"merging\" and \"splitting\" to the clustering results, that is, when the value of two cluster centers is less than a certain threshold, the two cluster centers are merged into one, when the standard deviation of a cluster is less than a certain threshold or the number of samples in a cluster exceeds a certain threshold, the cluster is split into two clusters, and even when the number of samples in a cluster is less than a certain threshold, the cluster is cancelled. In addition, ISODATA clustering does not need to set a specific number of categories.
## 3 Proposed Framework
### Offline Training Phase
#### 3.1.1 Detection and Tracking  
For the multi-target tracking module, a finely tuned detector model is employed with precise limits on the X, Y, and Z axes, ensuring accurate placement of the bounding box. Certain parameters, including minimum segmentation distances and minimum detections per cluster, are optimized to enhance object detection. Using the joint probabilistic data association (JPDA) tracker, the thresholds for track assignment, confirmation, and deletion have been carefully defined in this module [49]. Track positions and timings are stored in a data structure, enabling detailed analysis of each object's trajectory. Ground truth images were used to verify the tracker's performance using the JPDA algorithm applied to LiDAR data. There have been seven distinct tracks saved, each with a unique TrackID and x and y positions. The simulator displayed bounding boxes on these tracked objects during the movement of the ego vehicle. In total, seven tracks have been selected: TrackID 14, which represents a car in front of the ego vehicle; TrackID 20, which represents a building on the right side; TrackID 1416, a static pole; TrackID 1682, a tree; TrackID 1929, a small static pole; TrackID 3159 and TrackID 3549, both representing trees. Data from odometry were used to determine the location of the ego vehicle, allowing us to analyze the trajectory in absolute coordinates. In Figure 2, the ego vehicle's trajectory is illustrated in absolute coordinates, while Figure 3 shows the position of the seven tracks with their Track IDs relative to the ego vehicle.
## 3 Results and Discussion
### Schizochytrium Imacinum Growth on Synthetic VFAs and Resulting Fatty Acid Profile
#### 3.1.1 Biomass Production and VFA Consumption in Batch Fermentations
_Cryptecodinium colnii_ was also deeply investigated by [PERSON] et al., reaching similar results, with the acetic acid consumed relatively fast with good biomass productivity [15]. Despite the different published studies, all of them mainly focused on the use of acetic and butyric acid for the production of DHA or biodiesel [48; 49; 50; 51; 52]. The production of OCFA was studied by [PERSON] et al. [50] and [PERSON] et al. [14], but the effect of using short chain fatty acids (propionic and valeric acid) on biomass growth and fatty acid profile have not yet been studied for this species.
# Evaluating Cybersecurity Risks of Bulgaria's Energy Sector: Focus on PV and HVAC-R
## 2 Literature Review
### Cybersecurity of Heating, Ventilation, Air Conditioning, and Refrigeration Systems  
Heating, ventilation, air conditioning, and refrigeration (HVAC-R) systems are the other large energy consumers, amounting to 35-40% of total consumption in buildings [43; 44] and in public utilities, which are considered in this paper. These systems are also subjected to increased IoT penetration in recent years and are vulnerable to cyber-attacks, primarily DoS and FDI [45; 46; 47], but also to MITM, eavesdropping, spoofing,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cyber-Attack Events** & **Year** & **Reference** \\ \hline Cyber-attack on Ohio’s Davis–Besse nuclear power plant. & 2003 & [25] \\ Use of a Stuxnet worm on the Iranian nuclear program—the first ever malware targeting PLCs and SCADA systems. & 2010 & [25; 36; 37; 38] \\ Duqu and Flame malwares purposed to spy on an Iranian oil company. & 2011 and 2012 & [37] \\ HAVEX (Backdoor.Oldrea) malware, purposed for cyber espionage, targeting numerous industrial systems in energy, aviation, pharmaceutical, defense, and petrochemical companies, primarily in the USA and Europe. & 2013 & [8; 20; 39] \\ BlackEnergy malware—the BlackEnergy 1 attack on Georgia; the BlackEnergy 2 attack on critical infrastructures in the US; and the BlackEnergy 3 attack on three of Kyiv’s power distribution companies. & 2008 2014 2015 & [2; 17; 25; 27; 28; 39] \\ CRASHOVERIDE (Industroyer) malware—used in the second attack on Ukraine’s grid. & 2016 & [39; 40; 41] \\ TRITON (TRISIS) malware—an attack on the equipment of a petrochemical plant in Saudi Arabia. & 2017 & [40] \\ Cyber-attack on water treatment plant in Florida, USA. & 2021 & [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chronological summary of real-world cyber-attack examples.  
replay, etc. [48]. There are gaps in detection strategies that do not distinguish between cyber-attacks and physical faults, which further leads to inadequate defense and mitigation strategies being applied, as many of them are based on numerical models and their respective user-defined assumptions, which do not always accurately represent real-world situation, as noted by [PERSON] et al. [49], by [PERSON] et al. [50], and by [PERSON] et al. [44]. Based on the shortcomings of HVAC-R's detection and fault diagnosis methods, [PERSON] et al. [51] have proposed a customized transformer-based neural network design that ensures efficiency in table data analysis and data transfer. Additionally, the study of [PERSON] and [PERSON] [52] has proposed an approach based on a distributed two-level communication model predictive control of building automation systems that significantly reduces computational overhead. [PERSON] et al. [53] have proposed a framework-based methodology using a virtual machine where existing real datasets are converted to a BACnet protocol network traffic.  
The secure operation of HVAC-R systems should be prioritized [54; 55] as the equipment can be damaged severely [42; 56] and can even threaten injury to personnel [42].
## 3 Methodology
### Transforming the Sentiment Information into Social Criteria  
In the previous step the system has detected the aspects mentioned in a review and the polarity of the opinion about each of them. In order to integrate this information into a MCDM system, we consider each of the aspects as a social criterion. Thus, we generate a matrix in which each row represents an alternative, and each column is one of the aspects evaluated in the textual reviews. The value for a specific cell is the polarity index [9], which is a number from 0 to 100 that indicates the positiveness of the opinion of the users' reviews about a certain aspect for a given alternative. This index is calculated as follows (P and N are the amounts of positive and negative opinions, respectively):  
\[P_{index}(P,N)=\begin{cases}1-\frac{N}{P}&\text{if, }P>N\\ \frac{P}{N}-1&\text{if, }P<N\\ 0&otherwise\end{cases} \tag{5}\]  
The range of \(P_{index}\) is \([-1,1]\), so we transform it to \([0,100]\) by adding 1 and multiplying the result by 50. Thus, 0 is the lowest satisfaction and 100 represents the highest degree of satisfaction. Algorithm 2 illustrates the transformation steps.  
```
input : A review _rev_. output : A list of pairs \(L=\{(\alpha_{1},p_{1}),...,(\alpha_{l},p_{l})\}\), where \(\alpha_{k}\) is an aspect and \(p_{k}\) its corresponding polarity.
1\(L\longleftarrow Empty\);
2 Split _rev_ to a set of normalised sentences and store them in _Sents_;
3 for\(x\in\textit{Sents}\)do
4 Let \(\hat{A}\) be the set of aspects extracted from \(x\) using Equation 3;
5 for\(\alpha\in\hat{A}\)do
6 Let \(p\) be the polarity obtained with Equation (4);
7 Add the pair \((\alpha,p)\) to \(L\);
8 end for
9
10 end for
```  
**Algorithm 2**Aspect extraction and polarity detection algorithm.
## 2 Literature Review
### Congestions  
The congestion problem in cities is widely investigated so far, as it is a problem in the developed, developing, and undeveloped world. The work in [32] reports the principal traffic problems of cities in India that are common in undeveloped cities: inadequate traffic infrastructure, insufficient street network, the growth of ownership and use of motorized vehicles, outdated traffic control and traffic management, and traffic congestion (the most notorious and affecting all socioeconomic groups). The article in [33] presents a review of ways to identify and measure metrics to detect congestion; it is mentioned that in Korea, the Korea Highway Corporation (KHC) identifies congestion when the vehicles' speed is below 30 km/h, so a speed-based threshold is a suitable indicator of the congestion impact; the advantages of using speed for detecting congestions are highlighted: simplicity, ease of data collection, stability, repeatability, continuous value. The fact that transportation network companies (TNCs) are the biggest contributor to traffic congestion in San Francisco was revealed in [34], two years were compared through traffic metrics, 2010 and 2016, the last year prepared for two scenarios, with and without TNC. With a Bayesian network model in [35] were detected the variables (states) for a scenario to be likely congested: direction, day of the week, time of day, weather, incident on upstream links, incident on target link, and incident on downstream links.  
Different solutions for mitigating congestions and the problems associated with it have been explored. The implementation of transportation demand management policies (peak time shift and decentralization), through microsimulation to alleviate congestions and reduce travel times in cities of developing countries, in [36]. Improvements to the street network of the Mexico City Metropolitan Area (the construction of new arcs and the modification of existing ones) were proposed in [37], and the best scenarios in terms of flow circulation and the lowest tons of emissions were identified. Two policy measures for demand management, car-free day and odd-even plate were analyzed through simulations in [38], regarding the odd-even plate scheme in the first year it was noticeable the increment of average speed and the decrement of CO and NO emissions, nevertheless, as the years passed the average speed and the pollutants emissions become closer with the no scheme values since year after year more drivers adapt to the restrictions. The aspects that make it possible (or not) to introduce congestion pricing in four cities (Santiago de Chile, Mexico City, Sao Paulo, and Bogota) were investigated in [39]; the cities share some features: there are already in place traffic bans, the car ownership rate is high and, they have multiple levels of government authority.
# Antibiotics in Groundwater and River Water of Bialka--A Pristine Mountain River
###### Abstract  
Antibiotics are emerging pollutants of great concern, due to detrimental effects of their sublethal concentrations on microbial communities. Monitoring of antibiotics' presence and concentrations in the aquatic environment is of fundamental importance to the management of water resources. This study was aimed at filling the knowledge gap in terms of presence and concentration of antibiotics in surface water and groundwater in one of the mountain regions in southern Poland. The detailed aims included the assessment of whether there are spatial and/or temporal trends in antibiotic occurrence in water and the investigation of causes behind the changes in antibiotic concentrations. The study was conducted in seven sites (two groundwater and five river water) along the Bialka river valley. Antibiotics were subjected to solid-phase extraction, followed by UHPLC/MS detection. Clindamycin, erythromycinycin, ofloxacin and trimethoprim were the most frequently detected, while the highest concentrations were observed for oxytetracycline and clindamycin. No antibiotics were detected in only one of the groundwater sites. Sewage treatment plant effluent was the hotspot of antibiotic contamination of the river downstream. The detection rates of antibiotics in the examined region seem to be driven mainly by the stability of antibiotics in the environment.  
Keywords:antibiotics; aquatic environment; groundwater; mountain areas; sewage treatment plant; surface water +
Footnote †: journal: Applied
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.3 Meta-Heuristic-Based Approaches  
Meta-heuristic algorithms improve a candidate solution iteratively using high-level heuristics that guide the search for a good solution, rather than relying on a mathematical model [34]. Compared to heuristic algorithms, meta-heuristics algorithms can escape local optima and explore a larger portion of the search space, leading to better quality solutions. Some widely used meta-heuristic algorithms in fog computing task scheduling include Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization [3].
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### Frequency Domain-Based Filter  
Wavelet Transform Filter  
WTF is based on wavelet theory and performs in the wavelet domain. The original signal will be transformed to the wavelet domain by Wavelet Transform. For the reason that the sparsity of wavelet coefficient, i.e., a few large coefficients contains most of information of the signal, the filtering processing can perform according to a threshold. After the idea of soft-thresholding de-noising first presented by [PERSON] [10], many researchers [33; 34; 35] tend to apply the WTF filter to de-speckling of the medical ultrasound image. Generally, WTF can be expressed as follows  
\[I_{\underset{f}{J}}(t)=\frac{1}{C_{\varphi}}\iint W_{\varphi}(s,\tau)\frac{ \varphi_{s,\tau}(t)}{s^{2}}\mathrm{d}\tau\mathrm{d}s \tag{1}\]  
where \(C_{\varphi}=\int\left(|\psi(u)|^{2}/|(u)|\right)\mathrm{d}u\), \(\psi(u)\) is the Fourier Transform of \(\varphi(t)\), and \(W_{\varphi}(s,\tau)\) with modified wavelet coefficients is the Wavelet Transform of speckle image \(I_{g}(t)\), \(s\) is a positive value and defines the scale of WTF and \(\tau\) is a real number and defines the displacement of WTF.  
In this method, the decomposition of a signal in wavelet domain and its wavelet coefficients can be obtained. For the de-noising processing, wavelet coefficients lower than certain threshold values are modified to zero. Finally, the reconstruction of the estimated image can be derived by inverse wavelet transform with processed coefficients.
## 4 Thermal Stress Analysis and Optimization of Contraction Joint Distance  
### Description of the Calculation Model  
The 3D model for thermal stress analysis (TSA) has the geometry of one non-overflowing monolith of the concrete gravity dam (CGD) Platanovvrysssi (Greece) [42], 95.0 m high, and the surrounding rock mass of dimensions 154.0 \(\times\) 50.0 m (Figure 3). By modeling the rock mass, heat exchange with the dam's monolith is enabled.  
The lowest level in the model is 101.0 masl (level of the bottom of the rock mass) and the highest is 230.0 masl (level of the crest). The local coordinate system is oriented so that the positive X direction coincides with the direction of the river flow, the Z-axis towards the right riverbank, and the positive direction of the Y-axis points upwards (Figure 3).  
The dam's monolith, as opposed to the model presented in [21; 22], is divided into 78 blocks (Figure 4), arranged by combining long blocks and vertical joints (Figure 1C). The length of the blocks is equal to the length of the monolith (two separate analysis options with monolith lengths of 20.0 and 15.0 m are considered), the average width of the blocks  
Figure 3: (**Left**): Characteristic cross-section; (**right**): 3D model of the monolith with 78 blocks and the surrounding rock mass for thermal stress analysis.  
is 20.0 m, while the height varies depending on the position of the block (blocks 1-63 are 3.0 m high; blocks 64-77 are 3.5 m high, while block 78 is 4.0 m high).  
The blocks are shown in Figure 4, with the number of each block representing the sequence of pouring. Each block represents a new phase, which allows for a realistic simulation of the staged construction.  
For analysis purposes, a 3D FEM model is generated using Lussas Academic software [43]. The finite element mesh is created using 4068 hexahedron finite elements (HX8M) with eight nodes.
## 4 Experiment
### Homography Estimation Experiment
#### 4.3.3 Results of the Homography Estimation Experiment  
As shown in Table 1, The proposed matching algorithm, which fuses detector-based and detector-free approaches, demonstrates outstanding performance in homography estimation tasks. Under certain evaluation metrics, it achieves the highest accuracy. Specifically, the corner accuracy at 1, 3, and 5-pixel error thresholds reaches 0.55, 0.83, and 0.88, respectively, which is comparable to that of the detector-based SuperPoint+LightGlue and the detector-free LoFTR. Notably, it achieves the best performance at the 1-pixel threshold.  
In terms of illumination robustness, the method achieves accuracy rates of 0.76, 0.96, and 0.98 under the same thresholds, outperforming all other evaluated algorithms. This advantage stems from the effective complementarity between stable local features provided by the detector and the global matching strategy of the detector-free component. Moreover, the method exhibits strong robustness to viewpoint variations, further confirming its adaptability to scenarios involving significant geometric transformations.
## 5 Summary and Conclusions  
In line with the analyses carried out using machine learning models, the following can be concluded:  
* The collected data allow for the classification of the considered samples into groups determined by the degree of mold infestation;
* Although the data visualization shown by the SOM and PCA models and metric multidimensional scaling did not do a perfect job of showing the differences between the samples under consideration, the graphs determined by PCA and metric scaling showed these differences better, where first two PCA components explained almost 85% of the data variance;
* Classification by an unsupervised algorithm, such as the hierarchical clustering algorithm, yielded a low accuracy of 48.2% when classifying objects;
* Supervised learning methods performed much better on a given task, as both algorithms used classified objects into mold infestation groups 100% correctly, and this classification was correct for both the training set and the test set;
* The random forest turned out to be the better of the two models in terms of the fact that no tuning of the model's hyperparameters was needed, and even the model used with their default values classified all of the objects correctly.  
This paper presents the results of classification on one dataset, while the results of the experiments on completely new datasets, to which the learned models will be applied, are already in development. Given that the measurements themselves are calibrated, it is expected that the classification results on the new datasets will confirm the generalization abilities of the obtained models.  
**Author Contributions:** Conceptualization, G.L., M.P.-R. and D.M.; methodology, G.L., M.P.-R., D.M. and Z.S.; software, M.P.-R. and D.M.; validation, G.L., D.M. and Z.S.; formal analysis, G.L. and Z.S.; investigation, G.L., L.G. and Z.S.; resources, G.L., L.G. and Z.S.; data curation, L.G., M.P.-R. and D.M.; writing--original draft preparation, G.L., M.P.-R., L.G. and Z.S.; writing--review and editing, all of the authors; visualization, M.P.-R.; supervision, G.L., D.M., E.L., Z.S., V.K. and R.C.; project administration, G.L., D.M. and Z.S.; funding acquisition, G.L. and Z.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research was partially supported by the Czech Science Foundation within the project No. 22-00420S.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All necessary materials and data are included within the paper.  
**Acknowledgments:** This research was prepared within the activities of project Mobility FCE, Mobility CTU.  
**Conflicts of Interest:** The authors declare no conflict of interest.