esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Self-assembly of fluorescent \(\pi\)-conjugated small molecules into well-defined 1D, 2D and 3D nanostructures has proven to be an interesting strategy for fabricating functional devices with potential applications in chemosensors, biosensing, nanotechnology, biotechnology and optoelectronics [1; 2; 3; 4; 5; 6; 7; 8]. A disadvantage of conventional organic fluorophores is that they show aggregation-induced quenching (ACQ) effects due to the formation of excimers or exciplexes [9]. To overcome this drawback, in 2001 [PERSON] et al. investigated, for the first time, the Aggregation-Induced Emission (AIE) phenomenon [10]. AIE fluorophore emits upon aggregation, whereas it is non-emissive when dissolved well in solution [11]. Thus, AIE fluorophores are an ideal building block for fabricating nanostructures with luminescent properties [12]. Tetraphenylethylene (TPE) and its derivatives have shown interesting AIE properties in the solid state due to its unique structure [13]. These AIE fluorophores have been employed to construct self-assembled fibers, nanowires, microrods, microotubes, hollow microspheres, microparticles, microblocks, microparticles, bird nest-like and flower-like structures, and helical morphologies in tetrahydrofuran (THF)/water solvent systems [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. These molecular assembliesderive from weak non-covalent interactions, such as hydrogen bonding, electrostatic interactions, \(\pi\)-\(\pi\) stacking, van der Waals forces and solvophobic interactions. The favourable properties of TPE make it a candidate for applications in chemosensors, biosensors and optoelectronic materials such as field-effect transistors (FETs), organic light emitting diodes (OLEDs) and solar cells [26; 27; 28; 29; 30; 31; 32; 33]. The performance of an optoelectronic material is dependent upon the intermolecular interactions and emission efficiencies of the \(\pi\)-conjugated molecules that it is made from. Therefore, the design, synthesis and development of molecules with high solid-state efficiencies is central to the requirements of materials for optoelectronic applications.  
We recently reported the self-assembly of a dumbbell-shaped TPE derivative connecting to an alkyl chain via an amide bond [34]. Scanning Electron Microscopy (FE-SEM) images revealed that this compound had self-organised into a flower-like assembly through a combination of amide-hydrogen bonding and van der Waals interactions. With these results in hand, in order to study the influence that alkyl chain length has on the self-assembly and optical properties of dumbbell shaped TPE molecules, we prepared compounds **TPE-Se**, **TPE-Az**, **TPE-Su** and **TPE-Pi**, possessing n-decyl, n-nonyl, n-octyl and n-heptyl alkyl chains, respectively (Scheme 1), and studied their morphology after self-assembly and AIE behaviour.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
###### Abstract  
Frequent aviation safety accidents of civil aircraft misses and crashes lead to an urgent need for flight safety assurance. Due to long-time flights over different backgrounds, accompanied by the changes in flight altitude and speed, it is difficult for a conventional space-based infrared detection system to use a set of fixed parameters to meet the stable detection requirement. To enhance the awareness of civil aircraft surveillance, a real-time parameter optimization method based on Automatic Dependent Surveillance-Broadcast (ADS-B) data is proposed. According to the background spectral characteristics and the real-time flight data, the most reasonable spectral band is analyzed, using the joint signal-to-noise/clutter ratio (\(JSNR\)) as the evaluation criteria. Then, an automatic parameter adjustment is used to maximize the integration time and switch the integration capacitor gear. Numerical simulation results show that the \(JSNR\) increased by 1.16 to 1.31 times, and the corresponding noise equivalent target radiant intensity (\(NET\)) reduced from 2.4 W/Sr to 1.2 W/Sr compared with a conventional fixed-parameter detection system. This study lays a solid theoretical foundation for the spectral band analysis of space-based AVD system design. Meanwhile, the proposed method can be used as a standard procedure to improve on-board performance.  
civil aircraft surveillance; air vehicle detection; spectral band optimization; on-board parameter optimization  
Detection Based on ADS-B Data  
_Appl. Sci._**2023**, _13_, 6931. [[https://doi.org/10.3390/app13126931](https://doi.org/10.3390/app13126931)]([https://doi.org/10.3390/app13126931](https://doi.org/10.3390/app13126931))
## 3 Methodology
### FasterNet Backbone
In the figure, k denotes the convolution kernel size, and C and C\({}_{\text{p}}\) represent the number of channels. In our design, C\({}_{\text{p}}\) < C, indicating that the PConv module performs standard convolution operations on only a subset of the input channels. Figure 2: PConv module structure. sions, feature lower memory access density and can thus accommodate denser computation at higher FLOPs. Integrating FasterNet into PerMSCA-YOLO delivers two primary advantages: first, the PConv-PWConv synergy substantially reduces inference latency without compromising feature discriminability, providing a feasible hardware deployment path for real-time rail defect detection; second, its hierarchical multi-scale extraction seamlessly complements the subsequent Multi-Scale Convolutional Attention Module (MSCAM), reinforcing the model's capacity to capture elongated cracks and fine spalling defects. From a computational complexity theory perspective, FasterNet's refinements can be seen as a profound restructuring of the neural network's computational graph. By eliminating unnecessary memory accesses and compressing redundant operators, FasterNet realizes a more efficient tensor dataflow within the computation graph. This not only aligns with contemporary processor architectures' reliance on memory locality but also offers a theoretical blueprint for synergistic optimization between models and hardware in edge computing scenarios.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 4 Result Analysis
### Effect of SMOTE on Outcomes  
Table 8 compares accuracy across models with and without using SMOTE for the LC and LM datasets. The results indicate that most models had improved accuracy scores when SMOTE was applied to both datasets. This implies that SMOTE effectively addresses class imbalance issues and suggests that the technique is generally effective in enhancing model performance, regardless of the specific dataset. In this research, using the SMOTE approach is beneficial in enhancing performance. SMOTE enhances classifier sensitivity to minority classes (smelly code), improving recall and MCC. Gradient Boost and XGBoost benefited the most from SMOTE, especially in the more imbalanced LC dataset. The performance trend aligns with prior Java-based results, affirming SMOTE's cross-language applicability.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Model’s Name**} & \multirow{2}{*}{**LC**} & \multicolumn{2}{c}{**LM**} \\ \cline{2-5}  & & **With FST Accuracy** & **Without FST Accuracy** & **With FST Accuracy** & **Without FST Accuracy** \\ \hline Bagging & 0.93 & 0.95 & 0.9816 & 0.98 \\ \hline Gradient Boost & 0.94 & 0.92 & 0.9845 & 0.96 \\ \hline
**Max voting** & **0.96** & **0.94** & **0.97** & **0.97** \\ \hline AdaBoost & 0.94 & 0.93 & 0.96 & 0.94 \\ \hline XGBoost & 0.93 & 0.92 & 0.9823 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: LC and LM dataset accuracy with and without applied chi-square FST. Note: ‘Without FST Accuracy’ implies both FST and SMOTE were not applied. Bold values indicate the highest performance achieved among the ensemble methods.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Model’s Name**} & \multirow{2}{*}{**LC**} & \multicolumn{2}{c}{**LM**} \\ \cline{2-2}  & & **With SMOTE Accuracy** & **Without SMOTE Accuracy** & **With SMOTE Accuracy** & **Without SMOTE Accuracy** \\ \hline Bagging & 0.93 & 0.92 & 0.9816 & 0.95 \\ \hline Gradient Boost & 0.94 & 0.90 & **0.9845** & 0.96 \\ \hline
**Max voting** & **0.96** & **0.94** & 0.97 & 0.97 \\ \hline AdaBoost & 0.94 & 0.93 & 0.96 & 0.94 \\ \hline XGBoost & 0.93 & 0.94 & 0.9823 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 8: LC and LM Dataset Accuracy with and without Applied SMOTE. Note: ‘Without SMOTE Accuracy’ implies both SMOTE and FST were not applied. Bold values indicate the highest performance achieved among the ensemble methods.  
**RQ.3 Answer:** To answer RQ.3, it was observed that SMOTE improved classification performance across both datasets, especially enhancing recall and MCC. These results are consistent with findings in Java-based detection studies, demonstrating that SMOTE remains effective for handling imbalances in Python datasets as well.
# Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
## 4 Methodology
### Evaluation  
The selection of the most adequate evaluation metrics is based on the commonly adopted metrics in the revised literature, considering that the transactional dataset contains no explicit or implicit rating. Therefore, the proposed system is evaluated through a set of defined metrics: _Hit Rate_, _Portfolio Coverage_, _Average Price_, and _Standard Deviation of Price_. These metrics are the basis for the adjustment of hyper-parameters, i.e., the selection of inputs, matrix sizes considered in intermediate calculations, and the weighted sum in the hybridisation step.  
The _Hit Rate_, computed using expression (1), is defined as the percentage of predicted purchases. This measure aids in understanding the relevance of the RS's output through its predictive power, although it does not consider the direct effect of the recommendation, given that the user did not receive it.  
\[HitRate=\frac{\textit{Number of recommendations containing the next purchased item}}{\textit{Total number of recommendations}} \tag{1}\]  
The ability to cover a high percentage of the portfolio is an important measure of a RS, as it indicates the reach of the system. The portfolio coverage was thus calculated using expression (2). A high _Portfolio Coverage_ results in a broader range of recommended items, introducing novelty and variety from the user's perspective, thus avoiding a repetitive and general recommendation.  
\[\textit{Portfolio Coverage}=\frac{\textit{Number of unique products recommended}}{\textit{Total number of unique products}} \tag{2}\]  
The _Average Price_ and _Standard Deviation of Price_ measures serve to understand eventual trends of biases existent in the model when comparing with the metrics of the dataset. Additionally, the computational time is considered throughout the comparison of various compositions due to the necessity of providing recommendations on a regular basis--speed being a significant factor to consider.
# Effects of Cement-Mineral Filler on Asphalt Mixture Performance under Different Aging Procedures
## 1 Introduction
According to colloidal theory, when active ore powders with a large specific surface area, high degree of irregularity, and high content of alkaline components are mixed with asphalt, the rheological properties of the asphalt cement significantly change because of the chemical properties and particle packing of the ore powder [28]. The acidic component of the asphalt reacts with the ore fines so that the interface between the asphalt and the ore fines forms a strong bond [29]. Cement is a more active alkaline material, and not only has the characteristics of a mineral powder but also has a pH of 12 after silicates C\({}_{3}\)S\(\backslash\)C\({}_{2}\)S and water in the cement are digested; on the other hand, the pH of general limestone ore powder is only 9 [30]. When the alkaline component of the cement reacts with the hydroxy acid in the asphalt, the resulting product has an improved adsorption performance [31]. Moreover, when the asphalt reacts with the cement, the oil content (molecular weight MW \(\sim\)300 to 1000) and asphaltene (MW \(\sim\)2000 to 10,000) with a low molecular weight will enter the cement due to the irregularity of cement particles and presence of large open voids. The oil content refers to the saturated fraction and aromatic fraction. At this time, the consistency of the asphalt cement is also increased, thereby improving the temperature stability and water stability of the asphalt concrete. Colloidal theory can be applied to analyze the interaction mechanism between different fillers and asphalt. However, the asphalt binder is a mix of asphalt and filler, and asphalt concrete is a mixture of asphalt after the dry mixing of filler and aggregate. That is, in the asphalt mortar, the filler is directly dispersed uniformly in asphalt to form a colloidal dispersion. In the asphalt mixture, the filler (e.g., cement) is not directly and uniformly dispersed in the asphalt but is present in the contact surface of the asphalt and aggregate and is dispersed in the structural asphalt film. Considering the difference in the production mode and filler distribution between asphalt cement and asphalt concrete, the influence of the filler on the rheological properties of asphalt cement cannot directly reflect its influence on the performance of the asphalt mixture. Therefore, when studying the effect of the filler on the mixture properties, the mixture must be selected as the test sample. Based on the current research status, this study designed a performance test for styrene-butadiene-styrene- (SBS)-modified asphalt mixtures for three different aging procedures. The fillers were various cement-mineral powder combinations (five ratios). By analyzing the performance test results of the modified asphalt mixtures with the different cement-mineral powder composites under the three aging procedures, the effect of the cement-mineral composite filler with different blending ratios on the long-term performance of the asphalt mixture was determined.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 4 Experiment
### Result  
#### 4.3.1 Qualitative Results  
To rigorously assess enhancements in full-frame video reconstruction, along with mitigation in cropping, distortion, and reductions in visual artifacts, we executed a comprehensive visual analysis and engaged in an in-depth discussion of the findings. The empirical outcomes, as illustrated in Figure 6, exhibited a pronounced divergence from the L1 path methodology [4], which is notably susceptible to cropping anomalies. Conversely, our novel methodology adeptly ensured the generation of complete full-frame visuals, circumventing the loss of pivotal visual information. Furthermore, despite maintaining a consistent ratio between the ground truth and our rendered outcomes, we observed a subtle deviation in the field of view, attributable to the methodological integration of sequential data via the fusion of multiple frames, refined through warping with the RAFT optical flow [10].  
A detailed visual inspection within the \"Stairs\" category revealed that although PCA-flow-based video stabilization [8] significantly addressed the cropping issues inherent in conventional methodologies such as the L1 path [4], it inadvertently amplified the distortion manifested through texture seams and blurring (refer to Figure 7). Notably, our approach manifested superior visual quality by strategically employing both motion compensation and neural rendering techniques.  
Figure 6: Qualitative comparison results (category: Run).  
In our empirical analysis, we particularly concentrated on discrete segments within the test footage to critically evaluate the efficacy of our motion compensation technique. This intensive examination was aimed at ascertaining the capability of our technology in restoring complex details typically obliterated due to camera motion or instability. Implementing our proposed methodology resulted in a discernible enhancement in maintaining fine details, such as the distinct morphology characteristics of foliage, the precise grid patterns on masonry, and the intricate textures of terrestrial surfaces (as depicted in Figure 8). These insights were corroborated by robust experimental validation, signifying that our motion compensation methodology substantially augmented the visual lucidity of these aspects. The revitalization of these intricate details within the visual footage accentuated the proficiency of our methodology in preserving the fidelity of intricate scenes, ensuring that both natural and architectural elements were accurately rendered and readily identifiable, thereby delineating our endeavor to advance the frontiers of video stabilization technologies.
## 2 Theoretical Background
### K-Fold Cross-Validation and Verification Metrics  
K-fold cross-validation is an obvious method for generalization performance evaluation. Generally, the k-fold cross-validation is inserted in the model training process, the results of k-fold cross-validation can guide the hyper-parameters updating [62; 63]. After reviewing, 10-fold cross-validation was accepted by most researchers, and considered to be a suitable model verification method [64]. For carrying out that procedure, the training dataset will be divided into 10 parts, and 9 parts will be trained with current hyper-parameters. After this, the developed predictive model will be checked using the remaining part, and each dataset has the opportunity to be tested during the 10-fold cross-validation process [65]. After 10 runs, the average performance, which is normally evaluated by _RMSE_, will be used to represent the generalization performance of the trained model.  
After model training, the trained model should be evaluated to check the applicability of unknown data prediction by using testing datasets, so some verification metrics should be selected. In this study, _VAF_ (variance account for), \(R^{2}\) (determination coefficient) and _RMSE_ (root mean squared error) were employed to evaluate the relationship between predicted \(BRZT\) (\(BRZT^{PRE}\)) and actual \(BRZT\) (\(BRZT^{ACT}\)) [66; 67; 68; 69; 70; 71].  
\[VAF=\left[1-\frac{\text{var}\left(BRZT^{ACT}_{i}-BRZT^{PRE}_{i}\right)}{\text {var}\left(BRZT^{ACT}_{i}\right)}\right]\times 100 \tag{4}\]  
\[R^{2}=\frac{\left(n\sum\limits_{i=1}^{n}BRZT^{PRE}_{i}BRZT^{ACT}_{i}-\sum \limits_{i=1}^{n}BRZT^{PRE}_{i}\sum\limits_{i=1}^{n}BRZT^{ACT}_{i}\right)^{2} }{\left(n\sum\limits_{i=1}^{n}\left(BRZT^{PRE}_{i}\right)^{2}-\left(\sum \limits_{i=1}^{n}BRZT^{PRE}_{i}\right)^{2}\right)}\cdot\frac{1}{\left(n\sum \limits_{i=1}^{n}\left(BRZT^{ACT}_{i}\right)^{2}-\left(\sum\limits_{i=1}^{n} BRZT^{ACT}_{i}\right)^{2}\right)} \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(BRZT^{PRE}_{i}-BRZT^{ACT}_{ i}\right)^{2}} \tag{6}\]  
Here, \(n\) means the number of checked datasets, the best values of _VAF_, \(R^{2}\), and _RMSE_ are respectively 100, 1 and 0.
## Online Dynamic Load Identification Method
### Online Force Identification Method
2: Dynamic load identification
3:\(\widetilde{\mathbf{R}}_{k}=\mathbf{H}\mathbf{P}_{k|k-1}^{x}\mathbf{H}^{\top}+ \mathbf{R}_{k}\)
4:\(\mathbf{J}_{k}=\left(\mathbf{D}^{\top}\widetilde{\mathbf{R}}_{k}^{-1}\mathbf{D }\right)^{-1}\mathbf{D}^{\top}\widetilde{\mathbf{R}}_{k}^{-1}\)
5:\(\mathbf{\hat{u}}=\mathbf{J}_{k}\left(\mathbf{y}_{k}-\mathbf{H}\mathbf{X}_{k|k- 1}\right)\)
6:\(\mathbf{P}_{k}^{u}=\left(\mathbf{D}^{\top}\widetilde{\mathbf{R}}_{k}^{-1} \mathbf{D}\right)^{-1}\)
7: Measurement update step
8:\(\mathbf{K}_{k}=P_{k|k-1}^{x}\mathbf{H}^{\top}\widetilde{\mathbf{R}}_{k}^{-1}\)
9:\(\mathbf{x}_{k|k}=\mathbf{x}_{k|k-1}+\mathbf{K}_{k}\left(\mathbf{y}_{k}-\mathbf{ H}\mathbf{x}_{k|k-1}-\mathbf{D}\mathbf{\hat{u}}_{k}\right)\)
10:\(\mathbf{P}_{k|k}^{x}=\mathbf{P}_{k|k-1}^{x}-\mathbf{K}_{k}\left(\widetilde{ \mathbf{R}}_{k}^{-1}-\mathbf{D}\mathbf{P}_{k}^{u}\mathbf{D}^{\top}\right) \mathbf{K}_{k}^{\top}\)
11:\(\mathbf{P}_{k}^{xu}=\left(\mathbf{P}_{k}^{ux}\right)^{\top}=-\mathbf{K}_{k} \mathbf{D}\mathbf{P}_{k}^{u}\)
12: Time step update
13:\(\mathbf{X}_{k+1|k}=\mathbf{A}\mathbf{X}_{k|k}+\mathbf{B}\mathbf{\hat{u}}_{k}\)
14:\(\mathbf{P}_{k+1|k}^{x}=\left[ \begin{array}{cc}\mathbf{A}&\mathbf{B}\end{array}  \right]\)
```  
**Algorithm 1** Dynamic load online identification algorithm based on Kalman Filter  
We assume here that the structure parameters including the mass, damping and stiffness matrices are given. The schematic diagram in Figure 2 shows the input and the output of the proposed method. It should be stressed that the response data is updated at the time intervals \(\delta t\), hence, the dynamic load is identified at the same intervals. Figure 2: Schematic diagram of the input and the output of the dynamic load identification method based on Kalman filter.
## 1 Introduction
Whenever the system recognized a signal from a Bluetooth beacon, it provided the truck driver with information about the current location and route via the mobile device. [PERSON] and [PERSON] [23] developed a mine production management application that could support efficient underground mine loading-haulage system operations using Bluetooth beacons and tablet computers. The application included a navigation function that output truck location information, a proximity warning function that helped prevent collisions between vehicles, and a function that automatically created a production log. Although some studies have been conducted to use Bluetooth technology in the mining industry, little attention has been paid to diagnose the operational status of truck transport systems in mines by analyzing data acquired from Bluetooth beacons. The purpose of this study was to develop a system that can collect and analyze log data related to truck transport times in underground mines using Bluetooth beacons and tablet computers. First, a limestone underground mine in Korea was selected as the study area, and log data related to truck transport times were collected by applying the developed system. Next, using the log data uploaded to the cloud server over a certain period, the truck transport routes in the study area were diagnosed and analyzed; the results are presented herein.