esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Rough Set Classification (RSC)  
Classification is another vital method for discovering knowledge in bioinformatics applications [7]. In the proposed framework, the peptide dataset is classified using a Rough RSC algorithm based on the following objectives [46; 47]: (a) RST is simple to implement, and it can be extended with any algorithms; (b) it does not require any input parameter except dataset; (c) it delivers better analysis result for the knowledge-making process; (d) it withstands uncertainties and imperfections present in the dataset. It works on a 10-fold cross-validation method to produce the decision rules and the lower and upper approximation of the RST. The equivalence relation of conditional and decision attributes classifies the peptides into two disjoint classes: anti-HIV-1 activity and low/nil anti-HIV-1 activity. The peptides are classified to forecast the anti-HIV-1 activity based on the constructed rules. The functioning of the RSC algorithm is comparatively assessed with five benchmark classifiers [48] in terms of Precision, Recall, F-Measure, Kulczynski Index, and Fowlkes-Mallows Index [49]. The proposed RSC algorithm is represented in Algorithm 3 in the following table.  
```
Algorithm: RSC (C,D) Input: Conditional attributes 1, 2,..., n - 1 and the Decision attribute \(D\). Output: Generated Decision Rules Step 1: Generate training data set and test data in 10:1 ratio, respectively. Step 2: The equivalence relation for the conditional attributes is to be constructed in the training set. Step 3: The equivalence relation for the decision attribute is constructed in the training set. Step 4: The rough set lower approximation for indiscernibility relation for conditional attributes and decision attribute to be built. \(\underline{RX}=\{x\in U:[x]_{R}\subseteq X\}\) Step 5: The rough set upper approximation for indiscernibility relation for conditional attributes and the decision attribute to be constructed. \(\overline{RX}=\{x\in U:[x]_{R}\cap\ X\
eq\theta\}\) Step 6: Generate the specific rules from the lower approximation of the rough set. Step 7: Generate the possible rules from rough set upper approximation. Step 8: Remove the redundant rules from the rough approximation space.
```  
**Algorithm 3**_Rough Set Classification Algorithm_
## 3 Test Methods
### Testing Time and Environmental Control  
The testing was conducted on the morning of 28 April 2023 at 9:00 a.m., as this time was characterized by stable weather conditions and low wind speeds, which minimized the potential interference of external environmental factors on the vibration measurements. The tests were carried out during peak train operation hours to ensure that the data collected accurately reflected typical conditions, with frequent train passages providing a robust dataset. The observation site was selected to be in a flat and open area, minimizing interference from other transportation or industrial activities that could introduce external noise.  
Prior to the measurements, vegetation around the measurement points was cleared to ensure proper contact between the sensors and the ground, avoiding potential data biases caused by vegetation obstruction. The sensors were securely installed at each measurement point to prevent any loosening or displacement during vibration events. Grounding measures were implemented for the data acquisition equipment to reduce electromagnetic interference and enhance the stability and accuracy of the data. Multiple repeated measurements were conducted at the same measurement points to ensure the reliability and consistency of the data.
## 4 Conclusions  
It has been established that the application of **MA** extract in the bio-hydrothermal synthesis of ZnO particles induced a series of beneficial physicochemical effects: decrease in the crystallite size, increased share of mesopores, formation of a two-phase (hydrozincite and wurtzite) crystalline structure, smaller band gap value, and formation of lattice defects (Zn\({}_{\text{i}}\) and V\({}_{0}\)). All of these effects determined the improved photocatalytic efficiency for discoloration of Malachite Green dye under UV light.  
The best _Mentha arensis_-modified ZnO sample exhibited a 74% discoloration degree of the Malachite Green model contaminant dye for 120 min under UV light. The reusability, monitored in three consecutive tests, proved the stability of the best **MA**-mediated ZnO. The applied simple and non-toxic approach for the preparation of stable and effective ZnO powders enables possibilities for a wide scope of applications.  
In addition, the green-synthesized particles with modified structure and morphological features could be used for the preparation of a zinc precursor solution for obtaining ZnO nanoparticles or ZnO-based nanocontainers with an inhibitor serving as an effective anticorrosion layer on steel. The dependence between the plant extract content and the physicochemical features of the green synthesized particles can be used as a means for the fine tuning of their final properties. This would allow us to widen the practical application of ZnO not only as a photocatalyst but also in other areas related to the protection of the environment, human health, and enhanced corrosion resistance of different industrial and civil structures.  
Conceptualization, D.S., I.S. and V.B.; methodology, D.S., I.S., P.G., K.Z. and V.B.; formal analysis M.S., R.M., O.D., P.M., V.D., Y.K., G.T., investigation M.S., R.M., O.D., P.M., V.D., Y.K., G.T., K.Z.; writing--original draft preparation; writing--review and editing: D.S., I.S., N.B. ([PERSON]), N.B. ([PERSON]) and V.B.; visualization.; R.M., O.D., P.M., G.T., K.Z., D.S. and I.S. All authors have read and agreed to the published version of the manuscript.  
National Science Fund, Bulgaria, KP-06-N37/16 (KII-06-H37/16) \"New environmentally friendly one- and multi-layer coatings for corrosion protection of structural materials with wide application\".  
Figure 11: Kinetic curves of UV-discoloration of Malachite Green dye using G0, G1, and G2 photocatalysts (**a**); Kinetic curves of UV-discoloration using the G2 photocatalyst after three consecutive photocatalytic cycles (**b**); Probable mechanism of the UV-induced discoloration of the MG dye using _Mentha arensis_-mediated ZnO (**c**).  
**Acknowledgments:** The authors express their gratitude for the Project with the National Science Fund, Bulgaria, KP-06-N37/16 (KTI-06-H37/16) \"New environmentally friendly one- and multi-layer coatings for corrosion protection of structural materials with wide application\" for the financial support to realize these investigations.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 5 Conclusions  
In this paper, a Recurrent Neural Network (RNN) model was developed to predict the injury severity of traffic accidents on the NSE, Malaysia. An optimized network architecture was determined through a systematic grid search for the suboptimal network hyper-parameters. Several hyper-parameters of the RNN model were critical to achieve the highest validation accuracy. The best optimization algorithm was determined to be the SGD with learning rate, momentum, and weight decay of 0.01, 0.80, and 0.9, respectively. In addition, the dropout technique helped us to reduce the complexity of the model and also the chance of over-fitting the training dataset. The RNN model achieved the best validation accuracy of 71.77% when compared to the MLP and BLR models. This indicates that additional information about temporal and contextual correlations among accident records could help the RNN model to perform better than the other models. In the sensitivity analysis of RNN sequence length, the best accuracy was achieved with a sequence length of 2, whereas the accuracy was decreased when a sequence length of 10 or 5 was used. This means that the accident data has temporal and contextual structures that could not be used by the MLP and BLR models. The impact of each factor in the RNN model was calculated using the profile method. The results showed that the most influential factors in predicting injury severity of traffic accidents are accident time and road bound. In the study area, the model predicted that the southbound section is more dangerous than the northbound, with respct to injury severity. The model also predicted drivers had a greater risk of injury on a dry surface and with lighting conditions such as dark with and without street lights.  
While the proposed RNN model outperformed the MLP and BLR models and could estimate the factors' impacts, further studies should focus on developing more flexible and optimized network structures to predict accident frequency and injury severity. Future theoretical studies are encouraged to focus on improving the ability of the RNN model to represent variables and data structures, and to store data over long timescales. In addition, more applied research is recommended to develop new techniques to make use of additional information in accident datasets, such as contextual structures,spatial and temporal interactions and underlying relationships between accident factors and injury severity outcomes.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
###### Abstract  
SNUGLITE (Seoul National University Global navigation satellite system Laboratory satellite) is a two-unit cube satellite (CubeSat) with dimensions \(10\times 10\times 23\) cm that requires an attitude system for missions and ground station telecommunication. A linear-quadratic-Gaussian-based optimal attitude system for the CubeSat platform has been developed using low-cost sensors, with the in-orbit verification of the attitude system being is one of main study objectives. Since launch, the SNUGLITE CubeSat has continuously broadcast in-orbit status information. In this study, a methodology for the analysis of in-orbit attitude estimation results using received data is presented, and this was achieved by comparing two sun-pointing vectors, i.e., the sun-pointing vector calculated using estimated attitude with the positions of the sun and the satellite and the reference vector generated by the power levels of the solar panels. Because the satellite position was required for the attitude analysis, the verification of the performance of the own-developed on-board Global Positioning System (GPS) receiver is also briefly described. Analyses indicate that the attitude estimation of the SNUGLITE CubeSat has achieved an in-orbit real-time pointing accuracy with a root mean square of \(6.1^{\circ}\).  
cube-satellite; SNUGLITE; on-board GPS receiver; satellite attitude; LQG Article
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
###### Abstract  
Passive radio frequency identification (RFID) tags have been widely used in logistics, supply chain, warehousing, and other fields. However, for RFID-based automatic inventory management in warehouses, the deployment of current methods is more complex, and the localization range still has some limitations. This paper proposes DNCL, which is a hybrid passive RFID localization scheme based on direction-of-arrival (DOA) estimation and nonmetric multidimensional scaling (NMDS) to achieve spatial coordinate localization of tagged objects on shelves or racks using a single antenna for simple 1D scanning. DNCL uses antenna dynamic scanning to generate a virtual antenna array for the dynamic information capture of tags in the scene, which helps eliminate the phase shift produced by ambient noise. We apply the angle profile linear model to identify the characteristics of each tag and introduce the NMDS algorithm to improve the robustness of the scheme through the fixed layout of the reference tags, which can reliably estimate the coordinates of the tagged objects in the space. This paper realizes a prototype system and validated its practical performance in real complex situationse by COTS RFID devices. The results indicate that DNCL can achieve high accuracy for the localization of passive tags in free space.  
cooperative localization; direction-of-arrival (DOA) estimation; radio frequency identification (RFID); synthetic aperture radar (SAR) +
Footnote †: journal: Electronics Electronics  
1
## Data and Methods
### LAI and FPAR  
TOPS requires estimates of Leaf Area Index (LAI) and fPAR to define the amount of vegetation and its photosynthetic capacity. For this study, LAI and fPAR were derived from the GIMMS 3g dataset using a neural network algorithm [25] and MODIS land cover [26].
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 3 Results and Discussion  
The proposed Time Series Analysis (TSA) has been compared with the existing FSO Communication System (FCS), Mode Division Multiplexing (MDM), Network Management Automation Algorithm (NMAA), and Strategy-Based Resource Allocation (SBRA). Here, the Network Simulator (NS-2) has the simulation tool used to execute the results. Time series analysis uses data collected from a system over time to reveal patterns and trends. Predictive capabilities are an essential part of this analysis as they allow for more informed decisions to be made about future events. By leveraging predictive capabilities, the network can make more proactive decisions about system management, resource allocation, and strategic direction.
## 2 Materials and Methods
### About Dataset and Its Preprocessing  
The dataset used in this study is originally in digital imaging and communications in medicine (DICOM) format, and was obtained from an open source repository [25]. Figure 1 presents a bar plot of the data, collected from 12 independent sites ranging from (\(S1\) to \(S12\)),which are represented by the X-axis, along with the mean and standard deviation (SD) of the ages of the patients for each site, whereas the Y-axis represents the number of patients. The first bar in each site indicates the total number of patients that underwent the CT scan, in which the number of male and female patients are represented by sky-blue and teal colors, respectively. The adjacent red bar highlights the number of patients with fractures. The present study utilizes a dataset comprising 2019 patients, yielding 15,123 CT images in the axial plane. The patients are classified into two categories, fracture and non-fracture. The number of images in the fracture and non-fracture classes is 7217 and 7906, respectively. The dataset, provided by the Radiological Society of North America (RSNA), contains images from multiple sites across nine countries and six continents, labeled by medical experts. The patient's ages in each site are represented on the X-axis in Figure 1, which shows that the minimum age of the patients is 23.63 years (58 location), and the maximum age 82.35 years (51 location). It indicates the diversity of data in terms of age, demographics, and class distribution, which may help to improve the generalizability of the model.  
The DICOM images can be enhanced by meticulously choosing the appropriate window width (\(w_{i}\)) and window center (\(c_{i}\)) [26]. The choice of appropriate \(w_{i}\) and \(c_{i}\) highlights the most relevant details, such as bone fractures, locations, and their extent. In the dataset used for this study, the Hounsfield unit (HU) range of CT images varies from \(-100\) HU to 800 HU, where \(-100\) and 800 represent the deepest black and the brightest pixels, respectively. Following some experiments with different values of \(w_{i}\) and \(c_{i}\), the values of \(w_{i}\) and \(c_{i}\) were selected to be 670 and 290, respectively, to highlight the vertebral bone structure and the soft tissues in the DICOM images. The DICOM format also contains patient metadata, which increase the file size and are irrelevant for base model training. So, after emphasizing the most significant details by choosing the appropriate \(w_{i}\) and \(c_{i}\), all the CT images are converted to JPG to train the models.  
Pixel intensity versus frequency graphs plotted for a sample image from both the non-fracture and fracture classes are shown in Figure 2. Figure 2a is skewed toward higher intensity, indicating that the presence of any fracture in bone is less likely. In Figure 2b, the middle spike indicates that there is a gap in the bone since the pixel intensity range 70-105 corresponds to gray shed, and that indicates that possibly, a fracture in the bone is present.  
Figure 1: Distribution of number of patients with various sites.
## 4 Discussion
### Outlook and Implications  
Benefiting from the enhanced spectral resolution for FLs depth [31], the spectral sensitivity of the GOSAT satellite to Fs filling is heightened. However, the overfitting of singular vectors to the SIF spectrum has reduced the sensitivity of the data-driven approach to Fs. Addressing these issues through improvements in the retrieval window, combined with the Doppler shift correction of satellite spectra, has yielded retrieval results with higher intensity levels, further exploring the potential of high spectral resolution satellites in SIF retrieval.  
SIF products can be utilized for monitoring vegetation drought and temperature stress [2; 3; 4]. This study significantly enhances the sensitivity of the data-driven approach to Fs, coupled with GOSAT's shorter revisit period, enabling more timely and accurate alerts for vegetation environmental stress.  
For other hyperspectral satellites, the key to the applicability of the retrieval window proposed in this study lies in two aspects. Firstly, the spectral resolution of the satellite payload should be sufficiently high to ensure the capture of spectral slope variations through narrow weak O\({}_{2}\) absorption bands. Secondly, the satellite spectrum should contain an adequate number of usable FLs to minimize the proportion of atmospheric absorption bands in the retrieval window, reducing the impact of O\({}_{2}\) absorption on the SIF spectrum.
## 2 Background and Related Work  
### Requirements Elicitation  
The requirements-elicitation process is considered one of the main activities in requirements engineering [4]. The primary goal of the activity is to elicit new requirements. Traditionally, various techniques, including conducting surveys, observations, or interviews and workshops, are used by a requirements engineer to gather new requirements [5]. This activity often utilizes structured requirements templates that follow pre-defined notations. The requirements-elicitation process generally includes various steps that, according to [PERSON] and [PERSON] [6], can be divided into five fundamental activities: understanding the application domain--where the environment of the requirements is analyzed; identifying the sources of requirements--where the origin and reasons of the requirements are determined; analyzing the stakeholders--where all stakeholders relevant to the requirement are identified; selecting the techniques, approaches, and tools to use--where the best possible strategy for requirements gathering, e.g., interviews or workshops, is determined; and eliciting the requirements from stakeholders and other sources--where the actual requirements-elicitation activity is conducted.  
The role of the requirements engineer has a central meaning within the process since this person acts as a facilitator, mediator, and manager of the actual requirements, including analysis and documentation [6].  
Requirements can generally be classified differently. The main types of requirements are functional requirements--requirements and tasks to be accomplished by a system,and quality requirements--also referred to as 'constraints' in literature--that describe the system's behavior [7].
## 6 Improving Projection Stability
### Validation of the Improvement in Projection Stability  
The aforementioned optimal simplification rates were applied to the bridge of our case study. As a result, 231 meshes were required for rectilinear elements (reduced by 143 compared to the original model), and 1455 meshes were required (reduced by 366 compared to the original model) to represent the curvilinear components. In total, 509 triangle meshes were reduced from the original AR model, resulting in a reduced model of 1686 meshes.  
Figure 8 shows the results of projecting the modified bridge model in AROS_\(\beta\). Compared to the images in Figure 4a, which showed unstable projections in AROS_\(\alpha\), the model overlapped correctly and was stable without any perceivable lags. Furthermore, the stable projection lasted more than an hour on the AROS_\(\beta\) screen without disappearing.  
Nevertheless, variances between the real structure and the projected model were still visually present, indicating that securing the projection stability of the model is possible through simplification, but the placement accuracy was not improved to the degree required to achieve precise overlap.
## 4 Analysis of Failure Mode Based on Core Discs Morphology and Surface Fractal Dimension
### Fractal Dimension of Core Disc Surface  
Fractal dimension measures the irregularity of complex shapes, and it can quantitatively describe the complexity of the ups and downs of cross-sections or broken lines. [PERSON] and [PERSON] [29] first proposed that the morphology of the rough surface could be characterized by the fractal dimension of the profile line. [PERSON] [30] applied fractal geometry to study rock section morphology and proposed that the rock fracture mechanism was closely related to the fractal characteristics. Researchers [31] summarized the dimensional fractal characteristics of shear failure and tensile failure.  
This study used the digital model of the core disc's surface to extract the elevation information to calculate the fractal dimension of the section. The center point of each core disc's surface was taken as the center, an area was cut every 22.5\({}^{\circ}\), and the upper and lower surface profile lines were recorded separately. Eight sections were selected for one core disc. The digital elevation information of the upper surface of no. 3-1 is shown in Figure 12.  
Figure 11: Digital model of core disc surface. (**a**) Test 3; (**b**) Test 4; (**c**) Test 5.  
Figure 12: Digital elevation information map of the upper surface of core disc no. 3-1.  
Imagej software was used to calculate the fractal dimension of a total of 16 distinct profile lines on the upper and lower surfaces of each core disc. The upper surface profile line data was abbreviated as U, and the lower surface profile line data was abbreviated as L. The number after the profile line number represents the variance of the 16 profile lines on these core discs' upper and lower surfaces, as shown in Figure 13. The fractal dimension of the profile line at each angle of the core disc surfaces are all between 1.11 and 1.17, and the variances are between 0.007 and 0.017. This shows that the surface undulations of the same core disc at different angles are generally similar. The fractal dimension values of the core discs' cross-sections show no directionality. The fractal dimensions of the core discs' surfaces do not accord with the shear failure characteristics. The fractal dimension of the tensile failure surface is generally concentrated and does not have significant directionality [32]. The surface fractal dimension of the core discs is consistent with the characteristics of the tensile failure surface. This further verifies that the failure mode of the core discs under low confining pressure is tensile failure.