esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 3 Attitude Target Pointing Generator  
The _ATPG_ phase produces the reference attitude profile required to acquire the ground target, i.e., to maintain the camera view axis \(Z_{PRF}\) aligned with the position unit vector of the ground target \(\widehat{O_{3}T}\). This phase is performed at a user-defined operative frequency so that, at regular time intervals, it provides the attitude guidance to track the ground target. The analytical _ATPG_ solution is carried out by using two consecutive Euler rotations (\(\theta_{1,2}\)) around two different principal axes (\(\hat{\mathbf{e}}_{1,2}\)):  
1. The first rotation occurs around the unit vector \(\widehat{O_{2}O_{3}}\) and it has the purpose of bringing the \(Z_{PRF}\) axis to lay on the plane identified by \(\widehat{O_{2}O_{3}}-\widehat{O_{2}T}\), without modifying either the direction or the magnitude of \(\widehat{O_{3}T}\) (as shown in Figure 3): \[\begin{cases}\hat{\mathbf{e}}_{1}=\widehat{O_{2}O_{3}}\\ \theta_{1}=\text{atan2}(\sin\theta_{1},\;\cos\theta_{1})\end{cases}\] (11) where \(\sin\theta_{1}=||\hat{\mathbf{n}}_{1}\times\hat{\mathbf{n}}_{2}||\), \(\cos\theta_{1}=\hat{\mathbf{n}}_{1}\cdot\hat{\mathbf{n}}_{2}\) and  
Figure 2: Illustration of the proposed solution. The maneuver is split into two phases, first _APPG_ and then _ATPG_, but the solution is evaluated starting from _ATPG_.  
The second rotation brings the view axis of the camera \(Z_{PRF}\) to coincide with the \(\widehat{O_{3}T}\) direction by means of an Euler's rotation around a principal axis, such that  
\[\begin{cases}\hat{\mathbf{e}}_{2}=\hat{\mathbf{n}}_{1}\\ \theta_{2}=\beta_{f}-\beta_{0}\end{cases} \tag{13}\]  
where, with respect to Figure 4, \(\beta_{0}=\cos^{-1}(\widehat{O_{2}O_{3}}\cdot\widehat{O_{2}T})\), \(\beta_{f}=\pi-\alpha-\gamma_{f}\), \(\gamma_{f}=\sin^{-1}(\sin\alpha||\widehat{O_{2}O_{3}}||/||\widehat{O_{2}T}||)\) and \(\alpha\) is equal to the angle between \(\widehat{O_{2}O_{3}}\) and \(Z_{PRF}\).  
Therefore, the discrete quaternion time-history can be obtained by repeating, at each time instant, the aforementioned procedure and the equation \(\mathbf{q}=[\cos(\theta/2);\hat{\mathbf{e}}\sin(\theta/2)]\). It is noteworthy that, when \(O_{2}\equiv O_{3}\), the first step becomes meaningless, whereas it can be easily demonstrated that the second step reduces to a rotation of an angle \(\theta_{2}=\cos^{-1}(\widehat{O_{2}T}\cdot Z_{PRF})=\pi-\alpha-\beta_{0}\) (as \(\gamma_{f}=0\)) after substituting the direction \(\widehat{O_{2}O_{3}}\) with any line in the plane given by \(Z_{PRF}\) and \(\widehat{O_{2}T}\) and the normal to this plane with \(\hat{\mathbf{n}}_{1}\).
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
## 4 Experiment and Analysis
### Sensor Network Performance by Communication Control  
To measure the performance of the sensor network, it was tested, separate from the overall system. NS-3 network simulator was used to test the sensor network. For comparison, the LEACH-C module, a sensor network hierarchical management method, was used. The number of sensors was increased to 50, 100, 150 and 200. Transmission speed was set to 1 Mbps on the sensor field (100 m \(\times\) 100 m area). Sensors were fixed for an effective test. Figure 14 provides the life time by the number of devices. Life time was increased by 9.43%, 12.27%, 21.17% and 24.41%, respectively. This result indicates that the proposed algorithm shows better performance when there are more devices.  
Figure 13: Average Latency and Network Usage Comparison.
## 5 Related Work  
The work associated with joint entity and relationship extraction can be divided into three categories.  
The first category is tagging-based methods. This type of approach [17; 18; 19; 24; 29; 36; 37; 38] can be used for relational triple-extraction tasks when applied to multiple interrelated sequential labeling problems. It first marks the start and the end positions of the entity and then identifies the relation between them. For instance, NovelTagging [29] is a typical approach, designing a complex labeling scheme that contains information about the start position of the entity, the end position of the entity, and relations. Several studies employ a sequence-tagging network to identify the positions of the entity, then classify the relationship using multiple classification networks. The following endeavors have recently gained much attention and achieved a competitive performance. [PERSON] [17] treats the relational triple-extraction task as a subject-to-object mapping relation, first identifying all possible entity heads and then applying a relation-based sequence tagging network to each entity head to identify the corresponding entity tails. PRGC [19] proposes an extraction method based on relation prediction and global correspondence, which constrains the relation classification to a subset of predicted relations, greatly reducing the complexity of determining all relations.  
The second category is table-filling based methods. This type of approach [25; 32; 39; 40; 41] forms a table of input sentences and identifies the relations between entities by filling the table. Some works keep a table for each relation, with items representing the start and end positions of the two entities with this relation. Graphrel [32] uses words as nodes and identifies the relations between word pairs by discriminating between nodes. TPLinker [25] converts the relational triple-extraction task into a linking problem between entity pairs and introduces a handshake-marking scheme to align the boundaries of the entity pairs.  
The third category is seq2 seq-based methods. This type of approach [2; 30; 33; 35; 42] treats a triple as a sequence of tokens. It transforms the triple-extraction task into a generation task, which uses an encoder-decoder framework to generate the elements of the triple. For instance, CopyRE [30] uses a copy method to extract the hidden relations behind entities and can resolve the triple overlapping issue. R-BPtrNet [35] proposes a unified framework with a binary pointer network, which can extract explicit relational triples and implicit relational triples through a binary pointer network.
# Evaluation of the Effect of Biostimulation on the Yielding of Golden Delicious Apple Trees
## 4 Summary  
Research conducted in the years 2008-2017 showed that the fertilization technology of Golden Delicious apple trees and the year of research clearly influenced the weight and quality of the crop. Plants treated with sea algae extracts had a significantly higher number of apples per tree, yield, percentage of fruit with a diameter above 7.5 cm and average weight of apples in relation to the control combination. In most of the analyzed years of research, total yields exceeded the threshold of 90 t \(\times\) ha, regardless of the fertilization program. In 8 out of the 10 years of research, the total yield of trees treated with biostimulation technology was higher than in the control. In 7 out of the 10 years of research, commercial yield exceeded 70 t \(\times\) ha\({}^{-1}\) and was significantly higher than in the remaining years. The technology based on biostimulating preparations had a positive effect on the commercial yield throughout the research cycle, and different relationships were demonstrated only in 2015. Fruit extract and fruit firmness for the entire cycle did not differ significantly between the evaluated fertilization technologies, regardless of the year of study. It was shown that the biostimulation program in the years with unfavorable weather conditions (2009, 2010 and 2017) significantly affected the percentage of fruit above 7.5 cm, and thus the marketable yield. Biostimulating preparations based on brown macroalgae extracts of species \(A\). _nodosum_ improve the ability of plants to cope with adverse weather conditions during apple cultivation. The use of biostimulation technology is highly recommended for practitioners. The above studies show a significant increase in qualitative and quantitative parameters from apple cultivation.  
Conceptualization, M.K. and K.K.; methodology, M.K.; software, K.K.; validation, K.B., A.S. and T.K.; formal analysis, K.B., A.S., T.K. and. A.B.; resources, M.K.; data storage, K.K.; writing, original study, M.K., K.K. and K.B.; writing, review and editing, M.K. and K.K.; visualization, K.B.; supervision, M.K. and K.K.; obtaining financing, M.K. and K.K.; Project administration, A.B. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Fruit firmness (kg \(\times\) cm\({}^{-1}\)) of apple trees of the Golden Delicious variety depending on the technology used.  
**Funding:**: Statutory activity of the University of Life Sciences in Lublin.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
_Article_  
**Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 3, [PERSON] 4 and [PERSON] 1,***  
1 Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea;[PERSON]IL_ADDRESS]du.bd (A.R.); [EMAIL_ADDRESS] (L.M.K.); [EMAIL_ADDRESS] (S.L.)
2 Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
3 Environmental Microbiology and Food Safety Laboratory, BARC-East, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, USA; [EMAIL_ADDRESS] (M.S.K.); [EMAIL_ADDRESS] (H.L.)
4 National Academy of Agricultural Science, Rural Development Administration, 310 Nongsaenmyeong-ro, Wansan-gu, Jeonju, Jeollabuk-do 54875, Korea; [EMAIL_ADDRESS]
5 Correspondence: [EMAIL_ADDRESS]; Tel.: +82-42-821-6715; Fax: +82-42-823-6246 Academic Editor: [PERSON] Received: 3 December 2016; Accepted: 17 January 2017; Published: 21 January 2017  
**Abstract:** The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000-1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (\(r_{pnd}\)) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.  
**Keywords:** hyperspectral imaging; intact tomato; diffuser lighting; moisture content; pH; soluble solid content; partial least squares regression
## 4 Results and Discussions
### Bimodular Effects of FGMs on Deformation
This phenomenon indicates that the dominant factor influencing the stiffness is still the modulus of elasticity, but the introduction of the bimodular effect has brought some new features. In case (c), the tensile modulus and the compressive one, \(E^{+}(z)\) and \(E^{-}(z)\), are uniformly greater than the neutral surface modulus \(E_{0}\), hence there is no doubt that it is the strongest in terms of the capacity resisting deformation. On the contrary, for case (d), \(E^{+}(z)\) and \(E^{-}(z)\), are uniformly less than \(E_{0}\), it is easily concluded that this case is the weakest on the capacity resisting deformation. The question worthy of discussion is case (a) \(E^{+}(z)>E_{0}>E^{-}(z)\), and case (b) \(E^{+}(z)<E_{0}<E^{-}(z)\). From the relative magnitude among \(E^{+}(z)\), \(E^{-}(z)\) and \(E_{0}\), it is hard to say which case, in case (a) and case (b), is the stronger on the capacity resisting deformation. It seems that from Figure 4, case (a) is slightly stronger than case (b). This phenomenon may be rationally explained by analyzing two different effects which simultaneously exist in thin plates with large deformation, the bending effect and the tensile effect. Under large deflection, two effects will resist the external load together, and if the tensile modulus tends to be larger, or in other words, \(E^{+}(z)>E_{0}>E^{-}(z)\) but not \(E^{+}(z)<E_{0}<E^{-}(z)\), the tensile effect becomes more obvious, thus, the total capacity resisting deformation becomes stronger.
## 5 Discussions  
The deformation evolution prediction of large-scale landslides is vital for pre-warning and prevention of landslide hazards. The complexity and randomness of landslides and their influencing factors complicate the accurate description of landslide development. Although many methods for the prediction of landslide displacement series have been presented [2; 4; 7; 8; 9; 10; 12; 42; 43], most of them belong to the category of single-factor models. As is well known, landslides can be influenced by a variety of factors during their evolution such as rainfall and changes in the water level. Machine learning methods such as an ANN and an SVM can consider the influence of multiple complex factors on landslides. Specifically, an SVM is a novel machine-learning method and achieves the best generalization ability [31]. The method is most suitable for resolving small-sample, nonlinear, and high-dimensional problems, with a good predictive ability [31]. It overcomes certain  
Figure 10: Monitored and predicted displacement rate of three-factor SVRM model over time.  
Figure 9: A 3D contour map for parameter determination for the three-factor SVRM model using the grid-search method.  
disadvantages of other machine learning methods, such as local minimum or overlearning problem when applying an ANN. To date, the approach has been widely used in many different fields [44; 45; 46; 47].  
The prediction ability and effect of the models can be determined by calculating the root mean square error (RMSE) (see Table 2). In general, the smaller the RMSE is, the higher the model accuracy.  
Table 2 shows that the SVRM prediction models achieve a good predictive performance, with all of the RMSE values between the predicted and monitored data being less than 0.02. Among the different models, the accuracies of the two- and three-factor models are slightly higher than that of the single-factor model. Both two-factor models (displacement rate and rainfall) have the highest accuracy, with the smallest RMSE being 0.00614. The three-factor model has the second highest accuracy, and the single-factor model has the lowest accuracy, with the largest RMSE of 0.01644.  
The landslide used in this study is a typical, slow, and large-scale landslide. Its deformation and evolution are generally affected by rainfall, changes in the reservoir water level, and other predisposing factors. According to a monitoring data analysis conducted since 1998 and a field survey, the deformation of the landslide has obvious regional differences, elevation differences, depth differences, and seasonal change characteristics. The deformation rate is mainly related to rainfall and rainfall intensity, followed by a continuous and significant change in the reservoir water level. The reservoir water level has an obvious influence on the landslide deformation during the early impounding stage. Thus, the prediction models considering the rainfall factor in the study have higher accuracies. This is helpful for understanding why the accuracy of a two-factor (displacement rate and rainfall) model is the highest.
## 2 Study Area and Data Collection
### Data Collection  
This study integrates comprehensive air quality and meteorological data from two primary sources to analyze the dynamic relationships between air pollutants and weather parameters. Air quality data were collected from the Sakarya Central air quality monitoring station, managed by the Provincial Directorate of the Ministry of Environment, Urbanization, and Climate Change. Monitored pollutants included particulate matter (PM\({}_{10}\)), nitrogen monoxide (NO), nitrogen dioxide (NO\({}_{2}\)), and carbon monoxide (CO), which are key urban air quality indicators. Meteorological data, sourced from the Sakarya  
Figure 1: (**a**) Geographic distribution of air quality monitoring stations across Turkriye; (**b**) Close-up view of Sakarya Province, highlighting the Sakarya Central urban air quality monitoring station (AQMS).  
Meteorological Office, covered WS, temperature (Temp), RH, and rainfall (RainF), as these factors affect pollutant behavior in the atmosphere.  
The datasets span from 1 January 2021 to 31 December 2023, providing a robust temporal framework to examine seasonal and annual trends. The air quality and meteorological data were carefully preprocessed to ensure reliability and consistency. Preprocessing steps included addressing missing values, standardizing formats, and synchronizing measurement intervals, ensuring the datasets were suitable for advanced statistical and machine learning analyses.  
This dataset offers a unique opportunity to explore the intersection of meteorological variability and urban-industrial emissions, addressing a critical gap in regional air quality modeling. By integrating multiple data sources, this study provides a comprehensive understanding of how weather conditions shape pollutant behavior in a rapidly urbanizing and industrializing region like Sakarya.
## 2 Background
### IoT Devices
#### 2.2.2 Smart Traffic Lights  
These IoT-enabled traffic lights can dynamically adapt based on real-time traffic data. They can communicate with vehicles to optimize traffic flow and reduce congestion, while also sending signals to vehicles about upcoming red lights or emergency situations.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 1 Introduction
In particular, the proposed deep learning-based channel estimation framework exploits a neural network to learn the features of the actual channels by utilizing the channel estimates obtained from the LS estimation as the input;
* We provide three examples of exploiting DNN structures: a fully connected DNN, CNN, and bi-LSTM. With these typical examples, we evaluate the degree to which the system performance is improved by the assistance of a DNN in comparison to the LS estimation;
* We evaluate the performance of the DNN-based channel estimation framework by extensive numerical results and show its effectiveness by comparing it with the traditional LS estimation and LMMSE estimation, in terms of both the mean square error (MSE) and bit error rate (BER). We further analyze whether the proposed estimation is robust to Doppler effects. This paper is organized as follows: Section 2 presents in detail the considered MIMO-OFDM system for the 5G-and-beyond channel profile. The deep learning framework that enhances the channel estimation quality is presented in Section 3 with the three popular neural network structures. The computational complexity of the proposed framework is also analyzed in this section. The extensive simulations used to verify the machine learning-based channel estimation are shown in Section 4 with different setups. Finally, Section 5 presents the conclusions of the paper. _Notation_: The upper and lower-case bold letters are used to denote the matrices and vectors, respectively. The notation \(\mathcal{CN}(\cdot,\cdot)\) denotes the circularly symmetric Gaussian distribution and \(\mathbb{C}\) is the complex field. The notation \(\mathbb{E}\{\cdot\}\) is the expectation of a random variable. The notation \(\otimes\) is the convolutional operator, while \(\odot\) is the Hadamard product. \(\mathcal{O}(\cdot)\) is the big-\(\mathcal{O}\) notation that expresses the order of computational complexity. Finally, \(\|\cdot\|_{2}\) and \(\|\cdot\|_{F}\) denote the Euclidean of a vector and the Frobenius norm of a matrix, respectively.
## 4 Process and Result
### Identifying Unknown Artifact Types Based on Multiple Linear Regression Model
#### 4.3.2 Testing for Multicollinearity  
If the data matrix X does not satisfy the column rank, i.e., a variable can be linearly expressed by other explanatory variables, then there is \"strict multicollinearity\", Stata software was used to calculate the VIF of each variable, and the test results were as follows Table 8:  
It is generally believed that when VIF \(>\) 10, the regression equation has severe multicollinearity; SiO\({}_{2}\) and PbO both exceed 10, but the \(p\)-value of SiO\({}_{2}\) is higher than 0.05, which is not significant, so its coefficient is not considered in the equation model. PbO, although VIF exceeds 10, the p-value is lower than 0.05 because the coefficient is still significant with variance inflation; if there is no multicollinearity, the regression coefficients would be more significant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **SiO\({}_{2}\)** & **K\({}_{2}\)O** & **CaO** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** \\ \hline Coef. & \(-\)0.014 & 0.048 & \(-\)0.001 & 0.004 & \(-\)0.299 & 0.035 \\ P \(>\) |t| & 0.749 & 0.000 & 0.965 & 0.919 & 0.001 & 0.105 \\ \hline
**Type** & **PbO** & **BaO** & **P\({}_{2}\)O\({}_{5}\)** & **SUW** & **y\({}_{1}\)** & **-cons** \\ \hline Coef. & \(-\)0.166 & \(-\)0.161 & \(-\)0.018 & 0.352 & 0.000 & 0.746 \\ P \(>\) |t| & 0.001 & 0.021 & 0.078 & 0.000 & 0.000 & 0.082 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression coefficient \(\beta\) and significance \(p\)-value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**P** test & Prob \(>\) chi2 & 0.1725 \\ \hline White test & Prob \(>\) chi2 & 0.4095 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the BP test and White test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **VIF** \\ \hline SiO\({}_{2}\) & 27.28 \\ PbO & 21.06 \\ BaO & 6.36 \\ K\({}_{2}\)O & 5.11 \\ P\({}_{2}\)O\({}_{5}\) & 2.75 \\ CaO & 2.68 \\ MgO & 1.83 \\ Al\({}_{2}\)O\({}_{3}\) & 1.82 \\ Fe\({}_{2}\)O\({}_{3}\) & 1.63 \\ Mean Value & 7.84 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the variance inflation factor analysis.  
3.3 Testing the Fit of the Experimental and Actual Values of the Model and Identifying the Unknown Artifact Types  
Due to the fact that the dependent variable is the category of glass artifacts, there are only two categories: high-potassium and lead-barium, so it can be treated as a 1-0 variable. If the experimental value is close to 1, then it is considered the high potassium category; if the experimental value is close to 0, then it is considered the lead-barium category. From Figure 5 and Table 9, it can be seen that the 66 samples fit very well, almost no chance data occur, and the identification results of 8 unknown cultural relic types are completely consistent with reality, so it can be considered that the predicted value of this multiple linear regression equation is quite accurate.