esa-sceva/satcom-chunk-collection huggingface dataset:
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 1 Introduction
The triangle algorithm usually selects at least three true stars to generate some triangles and uses the angular distance or intersection angle of the graph sides as match elements. However, redundant or error matches of feature units in these methods often appear especially with noise, and their database size is also large. Several optimization strategies are presented to refine triangle algorithms in three terms: (1) the unit construction scheme, such as DUDE and the Delaunay principle introduced in [26] and [27]; (2) searching efficiency, for example, K-L transform [28] and hash map [29] methods proposed by [PERSON] and [PERSON] to reduce the search dimension; and (3) verification reliability, such as the reference map verification method by [PERSON] presented in [21] to eliminate the error identification result. However, substantive defects of triangle algorithms are still not corrected due to the low dimension of the feature unit. By adding an extra star in the feature unit of a triangle, the pyramid algorithm utilized four stars to build a geometric polygon, named the pyramid feature unit, with six angular distances. Moreover, the k-vector approach is adopted to accelerate the matching efficiency of the high-dimensional feature unit, and it is proven to be robust to false stars. However, the pyramid algorithm becomes time-consuming when false stars are used to generate a feature unit. [PERSON] et al. [25] presented a geometric voting technique by counting angular distance votes to determine the match relationship between image stars and guide stars. The real-time performance of this algorithm is acceptable when a few false stars exist but severely degrades with more spikes or false stars. To improve the performance of the star-identification algorithm in LIS mode, a novel star-identification method utilizing a global multi-triangle voting (GMTV) scheme is proposed in this paper. Several triangle units are built by sensor stars as feature match units to determine the initial match relationship by matching these vote units and counting the number of votes of catalog stars, and then the initial results are filtered by a verification step based on the largest cluster method to remove errors and retain correct identification results as the final output. In addition, the principal component analysis (PCA) method is implemented to reduce feature dimension, and a 2D lookup table is also designed to accelerate searching efficiency in the initial match. The algorithm performs much better than the geometric voting algorithm and the grid algorithm in the presence of star position noise, magnitude noise, and false stars. The rest of this paper is divided into the following sections: in Section 2, details of algorithm steps, including the initial match part and verification part, are presented. In Section 3, simulations are carried out to test the proposed algorithm's performance in terms of the identification rate and efficiency and show the comparison results with two typical reference algorithms. In Section 4, key conclusions are given.
# The Spotting Distribution of Wildfires
## Appendix. Ember Release, Burning, Flying and Fuel Ignition
### Models for Firebrand Temperature  
A model for firebrand temperature based on energy conservation is given in [22]. Let us define the density \(\rho\), volume \(V\), specific heat capacity at constant pressure \(c_{p}\), and surface area \(A\) for a given firebrand. Then the temperature \(T(t)\) appears in the energy conservation equation,  
\[(\rho Vc)\,\frac{DT}{Dt}=-A(q_{\text{conv}}+q_{\text{rad}}) \tag{105}\]  
where the terms \(q_{\text{conv}}\) and \(q_{\text{rad}}\), appearing on the right hand side in the latter equation, represent the heat loss due to convection and radiation; the notation \(\frac{DT}{Dt}\) represents the material derivative of \(T\) along the firebrand's trajectory. The convective heat flux can be modelled by [PERSON]'s law of cooling [22],  
\[q_{\text{conv}}=h(T-T_{a}), \tag{106}\]  
where \(h\) is an average heat-transfer coefficient, and \(T_{a}\) is the ambient temperature. To determine the heat-transfer coefficient, we first introduce the average _Nusselt number_\(Nu\),  
\[Nu=2+0.6 Re^{1/2}Pr^{1/3}, \tag{107}\]  
where \(Pr\) the _Prandtl number_ for air, and \(Re\) is the Reynolds number as described following Equation (102) [22]. The Prandtl number is a dimensionless constant which measures the ratioof viscosity to thermal diffusivity. For example, when \(P_{r}<<1\), heat diffuses quickly compared to momentum. For air, we have \(Pr\approx 0.70\)[22]. Then the average heat transfer coefficient is given by:  
\[h=\frac{k_{\text{air}}Nu}{2r}, \tag{100}\]  
where \(k_{\text{air}}\approx 2.7\times 10^{-2}\ m^{2}/s\) is the thermal conductivity of air, and \(r\) is the firebrand's radius.  
The radiative heat flux can be approximated by the Stefan-Boltzmann law, which states:  
\[q_{\text{rad}}=\sigma\epsilon(T^{4}-T_{a}^{4}), \tag{101}\]  
where \(\sigma\) is the Stefan-Boltzmann constant, and \(\epsilon\) is the emissivity. From experiment, \(\epsilon=0.9\).  
It follows that the energy balance equation for the temperature evolution can be written:  
\[\frac{dT}{dt}=-\frac{6}{\rho Vc2\ r(t)}\left[h(T-T_{a})+\sigma\epsilon(T^{4}-T _{a}^{4})\right]. \tag{102}\]  
This can be solved assuming the firebrand is initially at flaming temperature, e.g., \(T(0)=993\) K, to determine the temperature evolution once flaming combustion has halted when \(m=m_{c}\).
# Optical Sensing Technologies for Cryo-Tank Composite Structural Element Analysis and Maintenance
## 4 Structural Element Analysis
### Methodology
#### 4.1.1 First-Order Derivative Sign of FBG Quasi-Static Strain Data  
Structural damage is expected to give rise to local strain distribution discontinuity as a consequence of the generated material or geometric irregularity. In this case, strain distribution is therefore expected to exhibit significant gradients in the region of interest. Based on these considerations, a simple post-processing methodology is proposed, based on the first-order derivative, recognizing local discontinuities in the strain pattern. In the experiments carried out, the bending load is reached by progressive increments, with plafond steps at 0.3%, 0.5%, and 0.6%, as shown in Figure 10. At these levels, the structure  
Figure 8: Fiber optic strain signal: (**a**) the FBGs’ strain measurements before and after hydrogen and cryogenic exposure under tension loading and unloading; (**b**) the distributed strain before and after hydrogen and cryogenic exposure during 4 cycles of tension loading and unloading.  
Figure 9: Methodology of integrated monitoring approach by fiber optic sensors and data processing.  
behaves regularly. After 0.7%, some events occur. In detail, the FBGs are used to log data via the MOI-sm130 instrument (Micron Optics, Inc., Atlanta, GA, USA) at a max data rate of 250 Hz. Two signal drops are logged, with relevant strain losses as a result of a flaw onset. Post-processing analysis using the first-order derivative (red curve) and the vector product (blue curve) is applied to highlight the events. The sharp local edge onset (red spike) and the negative sign product (blue arrow) indicate that that load increased while strain loss occurred.  
The proposed methodology is a non-model-based technique that does not require a preliminary characterization of the investigated item. It indeed uses the sign of the load as a reference value. Such an approach is able to detect and partially quantify the damage occurrence in the time domain, but without the capability of producing information about the extent of the damage. To resolve this issue, FBG dense arrays or distributed sensing approaches appear more suitable and are introduced in the next paragraphs.
## 5 Experimental Analysis
### Experiment Set-Up and Dataset Description
Following that, we partitioned the data into two sections: the training section and the testing section. Both of these sections were distinct with attack and not attack portions. We set aside 80% of the data for the training test and 20% for the actual test. Additionally, in this experiment, we utilized the concept of cross-validation using the GridSearchCV API [27]. Therefore, we did not need to allocate dedicated data for the validation, while the training data were automatically split through the API based on cross-validation generator parameters. For instance, we set the cross-validation parameter to be 10 for KNN, which means the training set would be split into 10 partitions, and we would alternatively consider each partition as a validation set. Figure 3: Illustration of six degrees of freedom by yaw, pitch, and roll for UAM attitude control. Figure 4: Correlation matrix between attacks (spoofing or jamming) and UAM attitude control parameters. We have summarized our key parameters in Table 1 so that interested readers can reproduce the results. We considered a k-range from 1 to 100 for checking the neighbors to achieve the best classification. During the KNN training, we chose 10-fold cross-validation for mitigating the bias for determining spoofing or jamming attacks. We also considered the number of neighbors to be 79, while the weights were considered in a uniform distribution. We set 0.8 as an inverse of the regularization strength for the LR, since a smaller value could deal with strong regularization. In the case of random forest classification, we selected the random state to be 42, the number of estimators to be 50, and the maximum depth to be 8. We chose a maximum depth of 5 and minimum sample leaf \((3,6,10)\) for grid search-based decision tree training. Furthermore, we performed 10-fold cross-validation during this model training. In the case of LSTM model training, we considered 50 LSTM units, 100 epochs, and a batch size of 155 due to the time dependency among each training data point. We evaluated the mean absolute error (MEA) and executed an ADAM optimizer for LSTM model training.
## 2 Adler's Equation and Phase Locking and Synchronization of Gyrotrons
A method providing megawatt-level output power in continuous wave (CW) or long-pulse second-harmonic (SH) gyrotrons using phase-locking by a weak external monochromatic signal has been proposed in [48]. Using a self-consistent multimode model, it was demonstrated that injection of a seeding signal at the operating second-harmonic mode (priming) can suppress the spurious generation at fundamental-harmonic modes during the startup process. The results of numerical simulation demonstrate the applicability of this approach to provide about 1 MW radiation power in a 230 GHz second-harmonicgyrotron with the high-order \(TE_{34,14}\) operating mode, which is generally unattainable for harmonic operation without priming. In all these studies on phase locking, the legacy of [PERSON]'s equation is evident. Even though more sophisticated models are available today, the [PERSON]'s relation still provides a great deal of insight and is easier to comprehend than its successors. Moreover, its predictions (albeit not precise) can serve as benchmarks when applying more complicated approaches.
# Line Beam Scanning-Based Ultra-Fast THz Imaging Platform
## 3 Experiments
as shown in Figure 4. However, besides the distinguished contraband, a few spots of high intensity still occurred in the THz image, some of them were generated by the THz signals reflected from the metallic parts such as the metallic zipper, and some of them were generated from the THz signals reflected from bones. However, with improved image processing methods [33; 34], the contraband can be accurately distinguished according to their special configuration and reflectivity. Moreover, the line beam scanning-based ultra-fast THz imaging platform can realize complete human body imaging within 0.2 s, indicating high-speed scanning and imaging capability. It is worth noting that the high relative humidity decreases the contrast in THz imaging due to the THz signal loss in the water vapor, therefore, high relative humidity affects the performance of the device. In our experiments, the THz imaging was implemented in an open environment with room temperature, and the image was captured with the relative humidity of ~60%, and the contrast is still satisfied since the metallic gun can be clearly distinguished, indicating that the proposed THz imaging platform can be adopted in practical applications. Moreover, compared to the devices as PNNL [35; 36], JPL [37; 38], and TeraScreen [28; 29], the proposed line beam scanning-based ultra-fast THz imaging platform still has high-quality performance in THz imaging. The scanning range of the proposed platform reaches 2 m \(\times\) 0.8 m, which is comparable with the TeraScreen, but much larger than the 0.5 m \(\times\) 0.5 m of JPL. The scanning speed of the proposed platform is 0.17 s/frame, a little lower than the 0.13 s/frame of TeraScreen, but still much faster than the 1 s/frame of JPL. According to its high-quality performance and practical application in safe screening of the human body, as well as the comparison with the existing systems, it is believed the proposed line beam scanning-based ultra-fast THz imaging platform can be adopted for THz imaging and detecting in various applications.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 2 Dataset and Method
### Evaluation Indicators  
#### 2.3.1 Precision, Recall, F1-Score, and IoU  
Precision and recall are often used as model evaluation indicators in target detection. Precision, also known as accuracy, reflects the proportion of correct classifications among the number of positive samples classified by the model. Recall, known as the completeness rate, reflects the proportion of correctly predicted positive samples out of the number of actual positive samples. The area under the precision-recall (P-R) curve represents the correctness of the model (AP), and the AP values of all categories are averaged to obtain the average correctness (mAP), which is calculated with Equation (8). The F1-score is the weighted harmonic average of the precision and recall. The IoU, the overlap rate of the candidate bound and ground truth bound generated in target detection; that is, the ratio of their intersection to their union, is calculated as Equation (9).  
\[\begin{split}\text{P}=\text{TP}/(\text{TP}+\text{FP})\\ \text{R}=\text{TP}/(\text{TP}+\text{FN})\end{split} \tag{8}\]  
\[\begin{split}\text{F1-score}=(1+\text{a}^{2})\times\text{P} \times\text{R}/(\text{a}^{2}(\text{P}+\text{R})),\text{ a}>1\\ \text{F1-score}=2\times\text{P}\times\text{R}/(\text{P}+\text{R}), \text{ a}>1\\ \text{IoU}=(\text{A}\cap\text{B})/(\text{A}\cup\text{B})\end{split} \tag{9}\]  
where TP is the number of correctly predicted positive samples, FP is the number of negative samples incorrectly predicted as a positive sample, and FN is the number of positive samples incorrectly predicted as a negative sample. In Equation (9), a is the number of target categories, A is the area of the detected frame, and B is the area of the real frame.
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 2 Methods and Data
### Background and Related Works
In our previous study, we collected the influencing factors of team communications in NPPs' emergencies and organized them into five elements; these elements and the relationships between them formed our preliminary team-communication model [29]. While several studies proposed teamwork-related models in NPP, we found limited works proposing a team-communication model in particular. An analytic framework for communication called the H-H-S framework was proposed [30]. However, its main focus was to support the operators in adapting to the human-machine interfaces in the advanced NPP control room. One of the early works on operator performance models classified the performance-influencing factors into four groups--human, system, task, and environment--in which team communication was included in the environment group [31]. A team-interaction model was proposed by [32], in which \"the team-interaction relationships are constituted by the communication between team members\", and a team-process model to evaluate performance was adopted [33], consisting of input, team process (cooperation, information exchanges, communication, etc. ), and output. Recent studies proposed conceptual models for team situation awareness [34] and team decision-making [35]; the former was a causal model, whereas the latter divided the model elements into input, process, and output of team decision-making. These NPP team- and performance-related modeling studies showed that the efforts had become more elaborated, from simply listing the influencing factors and classifying them in exclusive groups to considering the relationships between factors as causal or the input-process-output of performance. Various approaches to team communication in the NPP were studied [29].
## 3 The Proposed Semantic Internet of Things Middleware
### Design Decisions: Incorporating AI into the Middleware  
The introduction of AI into the middleware represents a significant advancement in the domain of IoT and Health Informatics, and our approach required a great deal of thought and deliberation. We considered three options:  
* Option 1: Introducing a dedicated AI module within the middleware.
* Option 2: Embedding AI functionalities within each component of the middleware.
* Option 3: Implementing a standalone centralised AI entity outside the middleware.  
Each of these options had its unique merits and drawbacks. Option 1, introducing an AI module within the middleware, offered a fine balance between enhancing the system with AI capabilities and preserving the decentralised nature of the middleware. This approach ensured centralised control over the AI functionality while maintaining the autonomy of other middleware modules. It aligned with the principle of separation of concerns, allowing each component to concentrate on its core functionalities while the dedicated AI module focuses on enhancing system-wide performance and learning. It further streamlined the management, up-gradation, and scalability of the AI capabilities. However, the integration of a new module required significant architectural modifications.  
Option 2, integrating AI within each component, promised a high level of tailored intelligence and autonomy for each component. This could result in more context-aware and adaptive functionality. Nevertheless, the downside was the potential for redundant AI development across components and the necessity for stringent oversight to maintain consistency and reliability of AI decisionmaking.  
Figure 2: Service Integration and Composition workflow of SIM.  
Option 3, a centralised AI entity, provided a singular point of control and consistency for AI functions. However, this approach contradicted the inherently decentralised nature of IoT middleware and could limit the autonomy of individual components and devices.  
Upon weighing the pros and cons, we concluded that Option 1--introducing a dedicated AI module within the middleware--was the most suitable approach for our requirements. This decision hinged upon the need for centralised AI capability while still respecting the decentralised architecture of the middleware. The new AI module allows for continuous learning and improvement, thereby enhancing the overall middleware performance and usability.
## 3 Classification of the Reviewed Literature
### Information Filtering Approaches
Even though contextual pre-filtering is the most adopted filtering approach, article [37] compares these different information filtering approaches in terms of accuracy and diversity and concludes that none of the three approaches uniformly prevail the others across all factors.
# Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector
###### Abstract  
The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.  
Keywords:extreme gradient boosting (Xgboost); regression; sales forecasting; data preprocessing; augmented data +
Footnote †: journal: Applied sciences
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADC Analog-to-digital converter AWGN Additive white Gaussian noise BER Bit error rate BPF Band pass filter BPSK Binary phase shift keying C-ELM Extreme learning machine in the complex plane CO Coherent optical COS Cosine CP Cyclic prefix CPE Common phase error DAC Digital-to-analog converter ELMs Extreme learning machines EVM Error vector magnitude FEC Forward error correction FFT Fast Fourier transform HL Hard limit HT Hyperbolic tangent ICI Inter-carrier interference OFDM Orthogonal frequency division multiplexing PAE Pilot-assisted equalization QAM Quadrature amplitude modulation QPSK Quadrature phase-shift keying RC Real-complex RF Radio frequency SIG Sigmoid SLFN Single-hidden layer feedforward networks SNR Signal to noise ratio RB Radial basis R-ELM Fully-real extreme learning machine TB Triangular basis