esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies could be compensated by Synthetic-aperture radar (SAR) observations given the ability of the microwaves radiation to penetrate through cloud cover independent of weather conditions. It is indeed expected that cyanobacterial blooms lead to significant attenuation of SAR signal since they act as a viscous lid covering the water surface. To assess whether and to what extent microwaves can detect scum events is the aim of the present study. A first step in this direction has been made by [PERSON] and [PERSON] [19], which integrated AVHRR and ERS-1 data to analyze and interpret a specific phytoplankton bloom phenomenon verified in the coastal area of California. In particular, the authors exploited the finer SAR spatial resolution to better identify the spatial distribution of the bloom quantified from the optical image. Thereafter, [PERSON] et al. [20] observed a significant decrease of the SAR signal for high values of chlorophyll-a cyanobacteria concentration for a wind speed between 2 and 6 m/s, by using data gathered during the Envisat mission from optical (MERIS) and microwave (ASAR) synchronous sensors. These findings support the use of microwave remote sensing as an additional tool to increase the temporal coverage of optical sensors. However, the use of radar alone to monitor algal blooms would be challenging. Indeed it is in general difficult to distinguish whether the decrease of the SAR signal is created by algal blooms or by other causes without additional information. On the other hand, optical/SAR integration might help differentiate natural slicks from those caused by oil or human activity. Unfortunately, with the end of the Envisat mission, synchronous acquisition of optical and microwaves images are not longer available making difficult to rigorously improve the methodology to detect cyanobacteria bloom from SAR. Therefore, this paper aims to propose a ready to use approach to assign the presence of cyanobacteria blooms on empirical basis by using the Level 2 (L2) product of the SAR satellite Sentinel 1. A good candidate as case study for the application of the proposed approach is the Curonian Lagoon, located in the South-Eastern Baltic Sea region. Indeed, while S1-L2 products are generally available only for open ocean, the closeness of the lagoon to the Baltic Sea makes that an exception. Moreover, cyanobacteria blooms occurring in the Curonian Lagoon have been widely investigated with dedicated field campaigns [21] and the optical water properties of the Curonian Lagoon in the presence of cyanobacteria have been ascertained [22; 23].
## 2 Related Works
### Throughput Optimization Strategy Based on Traditional Fog Architecture  
In traditional fog architectures, a multitude of F-APs were dispersed at the network edge, and their service coverage in the area can fluctuate over time. To efficiently allocate resources among different F-APs and improve the network throughput, a distributed online optimization method was required. [PERSON] et al. [12] proposed a fully distributed online learning method based on the non-uniform connection between AIoT devices and F-APs. This method constructed a graph for each time slot and business and used the stochastic gradient descent to decouple the optimal decision between each time slot, which can improve the network throughput.  
However, the deployment of dense F-APs at the edge of the network exacerbated the degree of interference between adjacent F-APs as the network throughput increases. To solve the above problems, [PERSON] et al. [13] analyzed the change in the throughput performance when utilizing distributed F-APs for joint transmission in an F-RAN with limited interference. Experiment results showed that the F-AP joint transmission scheme significantly improves the transmission reliability and reduces transmission delays. However, the algorithm was disabled when dealing with strong interference sources, without analyzing the root cause of the interference.  
To fully utilize the resources of each F-AP, some researchers proposed a multi-layer operation scheduling algorithm based on the fog architecture, which decomposed the AIoT service scheduling problem into two different operations: access control and resource scheduling [14]. Then, the Lyapunov optimization technique was used to analyze the trade-off relationship between the average network throughput and service delay in dynamic wireless networks based on fog architecture. Experimental results indicated that the multi-layer operation scheduling algorithm outperformed traditional algorithms in terms of the network throughput and service delay across various network scales and traffic load scenarios. The study [15] pointed out that the large quantity of data transmitted by the IoT devices resulted in the overhead of the bandwidth and an increased delay and large amounts of data transmission generated resource management issues and decreased the system's throughput; thus, an optimized task scheduling and preemption (OSCAR) model was designed to overcome the limitations and improve the QoS.
## 3 Results and Discussion
### Design of the Experiment  
A response of the photosensor prototype was experimentally studied using repetitively pulsed laser radiation with a tunable wavelength. An automated optical parametric oscillator LT-2214-PC pumped by a multi-harmonics Nd/YAG laser was applied as the radiation source (both units are the products of the \"Loti's TII\" company, Minsk, the Republic of Belarus). The converted laser beam passed through a concave quartz lens with the focal length equal to 50 mm. After the lens, the diverging laser beam fell onto the sensor surface placed at the distance of 150 mm from the lens. The diameter of light spot at the sensor surface was equal to 7 mm. The angle of laser beam incidence onto the sensor surface was approximately equal to 85\({}^{\circ}\) and the part of retro-reflected beam was used to evaluate the average power of laser irradiation with a Maestro power meter (the product of Gentec Electro-Optics, Inc., Quebec city, QC, Canada). Before experiments, the setup was calibrated in order to estimate the average power of laser light reaching the sensor surface. The experimental study was carried out with the following parameters of laser irradiation: the wavelength varied from 420 nm to 700 nm and from 800 nm to 1064 nm; the pulse duration was equal to 10 ns; the repletion rate was equal to 10 Hz; and the energy of laser pulses reaching the sensor surface varied between 2 mJ and 12 mJ. This corresponds to the optical power density of the laser beam in a pulse of 50-300 kW/cm\({}^{2}\).
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 2 Description of Periodic Defects
### Sample Amplification of Roll Marks  
The sample images in the experiment were captured from production lines of hot-rolled steel plates with the surface inspection systems developed by our team. As the number of sample images was limited, for a better model training, a sample generator was designed to augment the number of roll marks to expand the training set.  
The generator connected single images of roll marks and backgrounds, and arranged them in a real periodic situation. The generated samples included positive and negative samples.  
Positive samples mix the images of roll marks and the backgrounds, and 10 single images were connected to form a long rectangular sample. The occurrence pattern of roll marks on connected single images simulated the occurrence pattern of roll marks on the hot-rolled steel plate, which appears according to a certain periodicity. The generated defect samples were used as a training set. The generated positive samples are shown in Figure 4.  
Negative samples only mix the background images, excluding roll marks images. The generated negative samples are shown in Figure 5.  
It can be seen that the generated positive samples are arranged with a certain period. If 1 represents a roll mark and 0 represents background, then the periods of positive samples in Figure 4 are: 1100110011, 1000100010, 101101101, and 101010101, respectively. Defects in different samples have different periods, so the generated samples also have different periods, which make the network learn defects with different periods and enhance the algorithm's robustness.  
The generated negative samples are a mixed arrangement of ordinary backgrounds. Because the actual plate images have multiple kinds of interference, we chose background images with various kinds of interference (such as obvious scales) when generating negative samples, which ensured the adaptability of the network to the actual plate images.  
Figure 4: Positive samples of simulated periodic defects.
## 4 Discussion  
The presented thermal treatment modes were selected based on the analysis of the literature in terms of the effect of thermal treatment on the mechanical properties of metals manufactured by AM [22; 23; 24; 25; 26; 27; 28; 29]. The authors of [26] found that the creeping flow properties of IN718 samples alongside the 3D printing direction were better compared to those alongside the scanning direction. Such characteristics of anisotropic creeping flow are mainly explained by the difference in the Jung modulus directions and differing number of grain boundaries. The authors of [27] supposed that the location of the inter-dendrite phase (such as the \(\delta\) phase in the IN718 alloy) may reduce the creeping flow duration and plasticity of the samples. According to [25], thermal treatment at high temperatures may lead to re-crystallization and a high percent of dual boundaries in the C263 superalloy fabricated by selective laser melting, which hinders the growth of creeping flow cracks. As for the IN718 alloy, thermal treatment not only adjusted the grain size but also caused different precipitation states. After aging, the properties of IN718 treated at a temperature slightly above 1000 \({}^{\circ}\)C were better than those of the material treated at a lower temperature. This is explained by the further dissolution of the Laves phase or \(\delta\), and thus, more \(\gamma^{\prime}\) and \(\gamma^{\prime\prime}\) hardening precipitations form in the course of subsequent aging [28; 29].  
The 3D-printed sample not subjected to TT demonstrated the lowest tensile strength (952.7 MPa) and the highest plastic deformation (20%) among the investigated samples. Increased tensile strength (1055.2 MPa) and reduced elongation (9%) were caused by type 1 thermal treatment. After type 2 thermal treatment, tensile strength was 1069.8 MPa, and extension deformation was at 8%, which is in agreement with the data recorded by other researchers [30; 31; 32].  
Figure 6 and Table 2 show that at room temperature, the tensile strength, ultimate strength, and relative elongation of the tested material fabricated by SLM were higher than those of cast Inconel 718 alloy [33]. On the one hand, since the SLM process was characterized by a very high cooling rate, the printed part had a finer microstructure compared to the cast part. On the other hand, in the prominent Laves phase, casting shrinkage and dispersed shrinkage caused by the macro segregation of the components often occur in cast items, so the respective amount of hardening precipitations was reduced as well. Therefore, the investigated material fabricated by SLM was better when a fine Laves phase was precipitated in intercrystalline regions. However, as compared to forged Inconel 718, hardening particles of the second phase precipitate in lesser quantities in the researched selective material melted by laser, while the amount of a brittle Laves phase increased [33].
## 4 Real-World Use Cases with Sample Results
### Visual Odometry--x64-Based Mini-PC  
Accurate localisation of mobile robots (rovers) is one of the most challenging tasks--especially when the target environment is unknown to a large extent. In the case of space applications, it is even more challenging, as the appearance of exoplanetary environments is notably different to Earth environments, making appearance-based navigation implementation and testing significantly more difficult. Visual odometry (VO) enables self-localisation based on a series of images captured by the robot's onboard camera. It is a relatively low-cost solution compared to other localisation techniques that rely on expensive sensors or external infrastructure. It is lightweight and computationally efficient, making it suitable for real-time applications on mobile robots with limited processing power.  
To demonstrate the cooperation between our simulator and visual odometry, the Open-VSLAM framework was used [58]. This framework is compatible with ROS 2, utilises the fast ORB feature extraction, and supports mono, stereo, and depth cameras. In our experiments, a stereovision-based approach was selected (/lunarsim/left_camera/raw and /lunarsim/right_camera/raw topics). The trajectory of movement was generated using a visual odometry algorithm and was compared to the ground-truth trajectory (/lunar-sim/gt/pose topic) provided by the simulator (Figure 7). The calculated trajectory reached a mean error of 0.55 m and a maximal error of 1.26 m at the testing distance of 106.31 m. This outcome signified the ability of the VO algorithm to accurately reconstruct the trajectory, even in the face of challenging environmental conditions and a limited number of features, due to the significant self-similarity of the environment. Furthermore, the level of error implied that the simulation environment was just as demanding as real-world outdoor scenarios [58]. This simple proof of concept illustrates the simulator's capability for running vision-based navigation algorithms and using the generated ground truth information to evaluate their accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Train Dataset** & **Test Dataset** & **F1-Score** \\ \hline ALLD (synthetic) & ALLD (real) & 0.431 \\ \hline ALLD (synthetic) & ALLD (real) + our (real) & 0.428 \\ \hline Ours (synthetic) & ALLD (real) & 0.449 \\ \hline Ours (synthetic) & ALLD (real) + our (real) & 0.537 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model accuracy depending on the training and test data set used.  
Figure 7: _Cont._
## 3 Experimental Results
### Effect of Different Factors on Blockage Type  
Landsides blocking rivers are complex problems that involve debris flows and river flows [36]. The depositional process is highly dependent on the properties of individual landslides and rivers. These factors determine whether landslides can block the river before being flushed away. Every test was conducted under the same river conditions to explore the influence of other factors on the blockage type. Figure 8 shows that the deposition height increases notably with increasing landslide volume and varies for landslides with different particle sizes or slide angles. The effects of each parameter on the dam geometry and blockage type are analyzed in detail, as presented below.
## 2 Methodology
### Evolutionary Spectral Clustering  
Spectral clustering approaches are usually static algorithms and, hence, need to be adapted to be applied to dynamic evolving traffic. Indeed, the current clusters should depend on the current data traffic features without deviating too dramatically from previous histories [18]. For this reason, we propose two frameworks of the evolutionary spectral clustering for the partitioning of dynamic transportation networks, which are the preserving cluster quality (PCQ) and the preserving cluster membership (PCM). In both frameworks, the total cost function is defined as a linear combination of the snapshot cost (SC) and the temporal cost (TC):  
\[TotalCost=\alpha\;SC+(1-\alpha)\;TC, \tag{9}\]  
where \(0\leq\alpha\leq 1\) is a parameter assigned by the user, which reflects the user's emphasis on the snapshot cost and the temporal cost. In the upcoming experiments, we choose \(\alpha=0.6\) in order to place more emphasis on the snapshot cost while considering, at the same time, the temporal cost. The snapshot cost (SC), which is expressed in the same way for the two frameworks, measures the snapshot quality of the current clustering result with respect to the current data features, whereas the temporal cost (TC) is expressed differently.  
In the preserving cluster quality (PCQ) framework, the temporal cost (TC) measures the goodness of fit of the current clustering result with respect to historic data, while in the the preserving cluster membership (PCM) framework, the temporal cost (TC) is expressed as the difference between the current partition and the historic one.
# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 3 Experimental Results
### Preprocessing  
In preprocessing, the images were converted to grayscale, binarized, and then a morphological operation with a given filter size was applied. Two techniques were used for threshold selection: Otsu and Spline. The Otsu technique [29] uses variance to find the threshold value that minimizes variance between pixels within the same segment and maximizes variance between pixels in different segments. The Spline technique uses data obtained from histograms of grayscale images to select the intensity of the largest peak generated by dark pixels.  
The morphological filters used were opening and closing filters, which sequentially apply erosion and dilation (opening filter) or dilation and erosion (closing filter). The erosion filter consists of converting all pixels in the image that are surrounded by a pixel with a white value to white. The dilation filter consists of converting all pixels in the image that are surrounded in any of their eight neighboring pixels by a pixel with a white value to black, while the dilation filter consists of converting all pixels in the image that are surrounded in any of their eight neighboring pixels by a pixel with a black value to black. Additionally, different filter sizes were used: 2 \(\times\) 2, 3 \(\times\) 3, 4 \(\times\) 4, 5 \(\times\) 5.
## 2 Purpose of the Study  
There are certain categories of problems in the direction of authorship analysis of texts, namely:  
* authorship verification of texts--determination of whether a given text was written by a certain person.
* authorship attribution of texts, author recognition of texts, or authorship identification--determination of the author of a given text from predetermined, suspected candidate authors.
* plagiarism detection--detection of similarities between two given texts.
* author profiling--detection of author's characteristics or building author profile consisting of some characteristics--age, gender, education level, etc. based on the given text.
* detection of stylistic inconsistencies within a text--detection of text parts that do not correspond to the general writing style of the text that is written by more than one author.  
The purpose of the study is to solve the problem of determining the authors of texts. The problem arises in relation to the author of a given text among a priori well-known authors. It is possible that the author of the text is not among these candidates. In the considered research study, there is a set of texts written by candidate authors. The author of a text is determined using computer models that use assessments of the stylistic quantitative characteristics of the texts of this author and the stylistic quantitative characteristics of the texts of candidate authors.  
This evaluation can be performed with the help of machine learning methods. Each machine learning method can be used with different sets of text features. In the present study, frequencies of word lengths, frequencies of sentence lengths, frequencies of character n-grams, statistical characteristics of character n-grams frequencies in the text, and frequencies of selected words were used, where a word length, sentence length, and character n-gram refer to the number of letters in a word, the number of words in a sentence, and a given combination of \(n=1,2,\ldots\) given characters, respectively. Also, in addition to the frequencies of the selected words in a given text whose author is to be determined, the frequencies of the words in the candidate authors' texts in the training set were also used in some feature groups as text features. The generated feature sets were used with artificial neural network, support vector machine, and random forest methods, and machine learning models. In addition, an n-gram frequency character group was also used with a convolutional neural network in the form of a two-dimensional matrix.  
A comparative analysis of different feature sets with different machine learning methods and models was performed based on the results of computer experiments conducted on the example of works of literary fiction in the Azerbaijan language by eleven Azerbaijan authors.  
The results of the conducted research study on feature types, feature selection procedures, and machine learning techniques can be used for author recognition in many other languages.
## 3 Research Methods
### Tracking and Speed Measurement Central-ByteTrack Module
#### 3.2.2 Speed Estimation Based on Pixel Displacement  
First, the pixel displacement of the object's center point is computed based on the detection boxes across consecutive frames. Let the center coordinates of the target in frame \(t\) and frame \(t+1\) be \((x_{t},y_{t})\) and \((x_{t+1},y_{t+1})\), respectively. The center point's pixel displacement \(\Delta d_{p}\) can then be calculated using the following equation:  
\[\Delta d_{p}=\sqrt{  
suming a frame rate of \(f_{r}\) frames per second, the time interval \(\Delta t\) between two consecutive frames can be determined by  
\[\Delta t=\frac{1}{f_{r}} \tag{8}\]  
Based on the displacement-time relationship, the object's real-world velocity \(v_{r}\) can be computed using the following equation:  
\[v_{d}=\frac{\Delta d_{c}}{\Delta t} \tag{9}\]  
In the equation, \(v_{d}\) denotes the estimated real-world velocity of the object.  
By following the above procedure, the velocity estimation based on the displacement of the detection box center is accomplished. This method determines the target's motion speed by calculating its displacement across consecutive frames and translating it into real-world velocity according to the frame interval.