esa-sceva/satcom-chunk-collection huggingface dataset:
# Streaming-Based Anomaly Detection in ITS Messages
## 2 Materials and Methods
### Proposed Enhanced LSCP Algorithm (ELSCP)
Weights are computed for each detector by ranking the Pearson correlation scores such that the detector with the best score obtains the highest weight. 4. Using the correlation scores, the best detector is selected. The final score for the test instance is computed using a weighted average of the best detector's local region scores. ELSCP is adapted to the streaming context by implementing it through a pipeline. Given a dataset of CAMs, the stream simulator converts the data into a data stream. The stream is passed to ELSCP through a reference window model, which implements the windowing concept. Within the specified window size, a sliding window is implemented such that partial anomaly scores are generated for each model. The partial scores are then evaluated, and final scores are generated for each instance. A sliding window approach is employed in streaming anomaly detection, in which data samples inside a window are sorted by an outlier score, with highly ranked data samples being labelled anomalies. For ELSCP adapted to streaming context, a pipeline framework is adopted, where each incoming new instance \(x_{t}\) is passed through a pre-processor (unit norm scaler), which transforms \(x_{t}\) into a scaled feature vector without changing its dimensions. The scaled feature vector is then processed by the streaming anomaly detection model, which predicts the label \(y_{t}\) for the instance. This predicted label is then passed to the running average post processor, which converts the score to the average of all previous scores in the current window. Figure 4 depicts the proposed anomaly detection framework. Figure 3: ELSCP flow chart: The results for steps 1 and 2 are cached; steps 3–5 are re-computed for each test instance. The key advantage of using a sliding window is that with the arrival of a new data instance, a sliding window may be modified, resulting in an online and incremental updating process. The update mechanism necessitates the deletion of an old data instance and the storage of a new one, making it computationally efficient. The sliding window contains the latest subset of the dataset at any given time. As a result, a sliding window approach finds outliers based on the most recent subset and addresses the temporal property of data streams.
# CFD-Based Metamodeling of the Propagation Distribution of Styrene Spilled from a Ship
## 4 Conclusions  
The present study developed a new metamodel for predicting the styrene propagation distribution and boundary arrival time. Two important parameters were introduced and the extensive CFD simulations were carried out. A mathematical relationship between inputs and outputs was established using the kriging model. To feed the big data that were necessary to make the metamodel, the present study extensively conducted three-dimensional transient CFD simulations for 80 different cases from the taken scenarios. The following conclusions were drawn.  
The present study presented the CFD simulation results and analyzed the influence of current profiles on the propagation characteristics. Due to the floating behavior of styrene, the concentration fields were concentrated near the sea level and the interface of styrene moved much faster in the streamwise direction as the surface velocity increased. This was clearly because the advection effect became dominant and most of the styrene propagated near the sea's surface.  
1. A new metamodel was provided to estimate the propagation distribution and the boundary arrival time. Based on CFD results, the main parameters were calculated and implemented in the metamodel. A comparison was made between CFD results and the metamodel prediction, showing good agreement between them. This verified that the current model could predict the transient characteristics of styrene propagation well. Thus, the use of the metamodel would be a powerful tool for the quick estimation of HNS propagation that can be used to formulate a fast response in the early stages after accidents.
2. This metamodel was evaluated using the hidden point tests. By adding data from eight additional cases, the performance of the metamodel was improved. For instance, a 37.4% error was reduced to 11.8% due to the modification. Thus, the current model will be updated continuously to achieve better accuracy via modification with additional data from further CFD simulations.  
Conceptualization and supervision, S.H.L. and M.L.; investigation, C.H.J. and M.K.K.; methodology, C.H.J. and M.K.K.; supervision, S.H.L.; writing of the original draft, C.H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Development of Human Resources for Design Specialists in Applications of Functional Materials (Brain Korea (BK) 21) and the Chung-Ang University Graduate Research Scholarship (2016).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case No.** & **\(\delta_{1}\) (\%)** \\ \hline Case 1 & 5.0 \\ Case 2 & 4.0 \\ Case 5 & 9.7 \\ Case 6 & 11.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analysis for the modified metamodel.
## 3 Results and Discussion
### Classification of Milk Samples Through Dendrogram Analysis of Contaminants and Product Attributes  
The hierarchical clustering analysis elucidated distinct groups among CM samples based on similarities in AFM1 concentration, retail price, thermal processing, and organic labeling. As illustrated in Figure 3, the CM samples are systematically organized along the X-axis. At the same time, the vertical \(Y\)-axis denotes the Euclidean distance to the square, thereby reflecting the degree of similarity among them. The groups formed at shorter distances signify CM with more comparable profiles. The B sample exhibited the highest level of AFM1, and appeared to be isolated or to establish a separate branch, indicating a distinct contamination or production profile. This pattern may represent specific regional practices, supply chain conditions, or feed quality associated with that CM. To investigate contamination patterns in commercial CM, a hierarchical cluster analysis was performed using four variables: AFM1 concentration (ug/L), price per liter (MXN), milk classification (regular or organic), and thermal treatment (pasteurized or UHT).  
The generated dendrogram (Figure 3) identified three primary clusters illustrating variations in AFM1 levels and product characteristics. The first cluster included mills with low AFM1 levels (<0.5 ug/L), featuring several UHT-treated and/or organic CM samples, such as U and V. The second cluster consisted of CM with moderate AFM1 values (0.5-2.0 ug/L). In contrast, the third group contained the most contaminated CM, including sample B (7.62 ug/L), an outlier due to its concentration significantly exceeding international safety limits. The groupings above indicate that heat treatment and classification significantly influence pollution profiles. Prior research has suggested that UHT treatment may yield lower levels of AFM1 [73; 74].  
Nevertheless, AFM1 is widely acknowledged for its heat resistance [72; 75], implying that such variations are more likely to be related to CM sample processing rather than to the degradation of the toxin. This highlights the necessity for standardized analytical procedures when comparing CM subjected to diverse heat treatments. Furthermore, the analysis has demonstrated that elevated prices do not inherently correlate with superior microbiological quality concerning AFM1 contamination. This observation aligns with prior reports indicating that brand reputation and perceived value frequently influence pricing instead of objective quality assessment [76; 77]. Interestingly, organic CM tended to group within the low-risk category; nevertheless, regular CM in the same group indicate that organic labeling alone fails to serve as a sufficient predictor of reduced contamination [78; 79; 80]. The levels of AFM1 are predominantly influenced by factors such as feed quality, storage conditions, and climate [81; 82]. However, the observed trend aligns with the notion that organic dairy systems may implement more stringent feed sourcing practices. A more extensive sampling process is required to validate this association.
## 4 Evaluation
### Delay and Throughput  
In addition to energy efficiency, delay and throughput are critical factors impacted by FL in IoT networks. FL can introduce delay due to its iterative communication rounds, where each node \(n\) performs local training and sends model updates to a central base-station server. The total delay can be expressed as \(T\times R\), where \(T\) represents the transmission delay for each round, and \(R\) is the total number of communication rounds required for convergence. TFNN is an optimized single-shot approach which can significantly reduce this delay by achieving model consensus in one round.  
Throughput is defined as the effective amount of data processed per unit time and is often improved in FL. This is because FL reduces the data payload from raw data \(D_{\text{raw}}\) to smaller model updates \(D_{\text{model}}\), such that \(D_{\text{model}}\ll D_{\text{raw}}\). This reduction minimizes network congestion and enhances bandwidth utilization \(B\). The throughput can be approximated as follows:  
\[T_{p}=\frac{D_{\text{model}}}{\Delta t}, \tag{7}\]  
where \(\Delta t\) is the transmission time per update. Throughput and delay performance are key aspects, where FL and TFNN differ significantly. In FL, throughput is improved by reducing the data payload from raw data \(D_{\text{raw}}\) to smaller model updates \(D_{\text{model}}\), such that \(D_{\text{model}}\ll D_{\text{raw}}\). This leads to efficient utilization of bandwidth \(B\). However, FL typically requires multiple communication rounds (\(R\)) for model convergence, leading to cumulative delays proportional to \(T\times R\), where \(T\) is the transmission delay per round.  
In contrast, TFNN addresses this delay by leveraging single-round hierarchical data aggregation and decision making. Using clustering protocols like LEACH, TFNN combines local data within clusters and performs kNN classification in a single communication round.  
This minimizes the total delay to approximately \(T_{\text{ITFNN}}=T\), which is significantly lower than the multi-round delay in FL.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 3 Model Architecture
### Multi-Granularity Retrieval Algorithm  
For object recognition tasks involving few-shot and weak-feature data, traditional methods based on large-scale training samples are difficult to meet the application needs of scarce data scenarios. This study transforms recognition into a retrieval task, enabling accurate identification (even with one-shot matches).  
A multi-granularity retrieval algorithm, comprising coarse-grained and fine-grained stages, is proposed. During the coarse-grained retrieval phase, the similarity between the target image and the database images is calculated to identify high-similarity regions and compress the candidate search space; subsequently, in the fine-grained retrieval phase, multiple refined samples are generated within the reduced region to conduct high-precision feature matching; and, finally, by integrating the retrieval results of the sub-samples, the precise localization and recognition of the target are achieved. Figure 6 illustrates the workflow using a carrier-based aircraft tail hook recognition as an example.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 6 Proof of Concept
Hence, the proposed VP-based 2D OCDMA system was not only able to provide high capacity in terms of data, reach, and the number of users, but was also capable of providing relative support against feeder fiber failure, which is novel, as compared to the existing 2D OCDMA architecture. Figure 9: Eye diagrams for downlink traffic at (**a**) \(-6\) dBm, (**b**) \(-3\) dBm, and (**c**) \(0\) dBm. The 2D PV code performance, in terms of BER and number of supportable clients, is shown in Figure 11. As the number of users increased the quality of the received signal deteriorated due to the occurrence of MAI among them. Thus, system signal to noise ratio (SNR) and BER degraded. Moreover, BER values of 2D PV code (K1 = 62, K2 = 3; where K1 and K2 refers to number of subscribers in spectral and spatial domain) were compared with existing techniques: Diluted perfect difference code (M = 63, P = 3) and diagonal eigenvalue unity code (M = 63, P = 3; M and P represents number of subscribers in spectral and spatial domain). It was observed that supported clients were 20, 40, and 110 for 2D diluted perfect difference (DPD), 2D diagonal eigen value (DEU), and 2D PV code, respectively, at the minimum acceptable BER value of \(10^{-9}\). In particular, multi diagonal (MD) code (M = 63, P = 3) is also plotted and results indicate an overlap with our proposed code due to their similar properties. Figure 10: Eye diagrams for downlink traffic at (**a**) clockwise and (**b**) counterclockwise directions.
## 4 Related Work
The performance of the proposed method was compared with five recently reported methods on the histology dataset; results showed that the proposed model in this study achieved an accuracy of 96%, with an improvement of 2% compared to the others. To identify potential lesion areas from the images and to avoid processing the original image, the authors of [29] proposed a model based on the deep attention network to classify histological images extracted from the BreakHis dataset. The model showed a 98%accuracy rate in four different amplification types, as well as a 50% reduction in training time compared to the POMDP-based approach. In [30], a novel AM was developed to classify high-resolution histological images of breast tissue using a CNN and BLSTM. The model was trained on the ICIAR 2018 database. The experimental results revealed that the model achieved 85.50% accuracy for classification in four classes and 96.25% in two classes. The model was also compared with and without the AM. Moreover, it was compared with state-of-art methods, and it outperformed other methods when applied with and without the AM. The authors of [31] improved CNNs by integrating an innovative SE-Attention mechanism to classify 18,157 collected mammograms and built a new benchmarking dataset. The model outperformed other studies with an accuracy of 92.17%. To improve the performance on the BreakHis dataset, the authors of [32] suggested a tissue image classification framework for breast cancer based on RESNET with a convolutional block attention module (CBAM). The proposed model was used to classify 7909 images; it showed a significant improvement by achieving an accuracy of 92.6%, sensitivity of 94.7%, specificity of 88.9%, F1-score of 94.1%, and AUC of 91.8% for 200\(\times\) magnification factor. In addition, results of ResNet50 and ResNet-50 with a CBAM at patient-level and image-level were compared to the proposed method and others, showing a significant improvement in accuracy. In conclusion, Table 1 includes a brief review of related works, and we can infer that DL with deep AMs has not been applied on thermal images for breast cancer diagnosis. On the other hand, based on studies reviewed in the previous subsection, breast cancer detection in thermal images has shown promising accuracy results compared to other modalities. This encourages investigation of the effect of integrating DL methods with AMs in detecting breast cancer in thermal images. Thus, our contribution in this work is exploring and comparing the use of different types of deep attention mechanisms with CNNs in the detection of breast cancer in thermal images. Based on our review of the related works this approach has not been previously used.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 2 Individualized Feature Selection Using Mutual Information  
Firstly, we focus on selecting the most valuable spatial cues and their mapping relationship to the source position. Considering a complex environment, the additive noise and reverberations would distort the characteristics of both the phase and magnitude features, especially at higher frequencies (above 3 kHz) where the speech energy is comparatively low. This is exacerbated, as most elevation localization cues being generated by reflections and diffraction of the human body and pinna are above 3 kHz [24]. Therefore, a feature selection mechanism that maximizes the direction-dependent information and minimizes the effect of noise would be necessary, and a generic feature selection approach could lead to superior performance [25]. To investigate the dependency between the features and their corresponding source positions, the Mutual Information (MI) that exists between each spatial localization cue and the source location for particular feature spectra could be used as criteria to evaluate both the effectiveness and robustness of the feature vectors extracted for the localization process.  
We assume the left and right ear received signals are modeled in time-frequency (T-F) domain as:  
\[\begin{split} X_{l,k,f}&=H_{l,f}(\theta,\varphi) \cdot S_{k,f}+N_{l,k,f}\\ X_{r,k,f}&=H_{r,f}(\theta,\varphi)\cdot S_{k,f}+N _{r,k,f}\end{split} \tag{1}\]  
where \(k\) and \(f\) indicate the time frame index and frequency index, respectively. The \(S\) represents the source signal, and \(H_{l,f}\) and \(H_{r,f}\) denotes the relative transfer function with spatial parameters \(\Theta_{\text{IP}}=(\theta,\varphi)\) in the interaural-polar coordinate system [24]. \(N_{l,k,f}\) and \(N_{r,k,f}\) denotes the additive random background noise. The interaural cues IPD and ILD at frequency \(f\) are obtained by:  
\[\begin{split} v_{f}^{p}=&\angle\Big{|}\frac{X_{l,k, f}}{X_{r,k,f}}\Big{|}\\ v_{f}^{m}&=20\log_{10}\Big{|}\frac{X_{l,k,f}}{X_{r,k,f}}\Big{|}\end{split} \tag{2}\]  
Then, we organize the interaural phase and magnitude feature vector as:  
\[\begin{split}\mathbf{v}^{p}&\triangleq[v_{0}^{p},...,v_{L}^{p}]\\ \mathbf{v}^{m}&\triangleq[v_{0}^{m},...,v_{L}^{m}] \end{split} \tag{3}\]  
where capital letter \(L\) indicates the vector length when \(f_{\text{min}}\) is zero and \(f_{\text{max}}\) is a half of the sampling frequency. Here, the upper and lower limit of selected frequency for the phase feature and magnitude feature are identical, and are denoted as \(f_{\text{min}}\) and \(f_{\text{max}}\), respectively.  
We then define the original feature vector \(\mathbf{v}\in\mathbb{R}^{2L}\) by concatenating the phase vector \(\mathbf{v}^{p}\) and the magnitude vector \(\mathbf{v}^{m}\) as:  
\[\mathbf{v}\triangleq[\mathbf{v}^{p},\mathbf{v}^{m}]=[v_{0},...,v_{\mu}...,v_{2 L-1}] \tag{4}\]  
where \(v_{\mu}\) defines the \(\mu\) th element in the feature vector \(\mathbf{v}\) with element index \(\mu=0,1,...,2L-1\).
## 3 Results and Discussion
In general, for the contact between the rear Ag and Al electrodes, low Ag/Al contact resistance is favorable to achieve low series resistance (\(\rm R_{s}\)) and high cell efficiency. In this study, we found that a glass frit with lower \(\rm T_{g}\) in the rear Ag paste easily flowed and wrapped Al particles during the sintering process, causing a non-conductive barrier between the Ag electrode and Al electrode and resulting in a higher Ag/Al contact resistance. The influence of the glass frit on Ag/Al contact resistance was further investigated by using a field-emission scanning electron microscope, as shown in Figure 7. From the top SEM image, see Figure 7a, of the Ag electrode with P-1 paste, it was found that Ag precipitates with a diameter size of around 10 nm emerged and tended to be distributed in the grain boundaries. Some different results occurred in the case of using P-2 paste and no precipitates existed on the sintered Ag grain boundaries and surface of Ag grains, see Figure 7b. These Ag nanoparticles were formed from recrystallization of the Ag element, which was dissolved in the glass melt during the sintering of Ag paste. Accordingly, the location of Ag nanoparticles can mark the distribution of the glass phase. Basically, the lower the glass \(\rm T_{g}\), the earlier the glass starts to flow during the firing cycle [12]. Thus, in comparison with G-2 glass, the melt of G-1 glass frit had the larger distribution at the surface of the sintered electrode, causing a non-conductive glass layer on the Ag electrode. This non-conductive glass phase can hinder the current transmission between the Ag electrode and Al electrode and causes a high Ag/Al contact resistance. Figure 6: DSC curve of the as-prepared glass frit. Figure 7: Surface morphology of sintered Ag electrode. (**a**) P-1 paste; (**b**) P-2 paste.
## 3 Results
### Method Validation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{4}{*}{**Analyytes**} & \multirow{2}{*}{**Calibration Curves**} & \multirow{2}{*}{**Linear Range** (\(\upmu\) kg\({}^{-1}\))} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**LOD (\(\upmu\) kg\({}^{-1}\))**} & \multirow{2}{*}{**LOQ**} & \multirow{2}{*}{**LOD (\(\upmu\) kg\({}^{-1}\))**} \\  & & & & & & & & **(\(\upmu\) kg\({}^{-1}\))** \\ \hline \multirow{4}{*}{Milk} & Amantadine & y = 12.55\% + 24.64 & 10–1000 & 0.9955 & 2.8 & 10 & 10.3 & 10.4 \\  & Thiabendazole & y = 4.48\% + 16.74 & 28–1000 & 0.9965 & 9.3 & 28 & 3.2 & 10.4 \\  & Clenbuterol & y = 30.14\% + 56.87 & 10–1000 & 0.9957 & 3 & 10 & 4.3 & 8.5 \\  & Sulfamethazine & y = 116.7\% + 62.51 & 10–1000 & 0.9979 & 2.8 & 10 & 5.8 & 12.1 \\  & Ractopamine & y = 19.54\% + 8.53 & 10–1000 & 0.9951 & 2.8 & 10 & 7.5 & 11.8 \\  & Amantadine & y = 9.47\% + 13.45 & 12–1000 & 0.9954 & 3.6 & 12 & 8.7 & 13.4 \\  & Thiabendazole & y = 3.93\% + 31.51 & 35–1000 & 0.9966 & 11.7 & 35 & 8.2 & 9.2 \\  & Clenbuterol & y = 1.82\% + 16.19 & 13–1000 & 0.9976 & 4.3 & 13 & 7.4 & 9.1 \\  & Sulfamethazine & y = 92.19\% + 39.89 & 12–1000 & 0.9989 & 3.6 & 12 & 4.7 & 11.0 \\  & Ractopamine & y = 16.33\% + 0.78 & 12–1000 & 0.9981 & 3.5 & 12 & 7.3 & 9.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The standard calibration curves, linear range, limit of detection (LOD), limit of quantitation (LOQ), and repeatability of the SBA-15-SPME Arrow–PMS method for the determination of five veterinary drugs in milk and chicken. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{4}{*}{**Analyytes**} & \multirow{4}{*}{**Maximum Residue Limits**} & \multirow{4}{*}{**United States**} & \multicolumn{4}{c}{**Milk**} & \multicolumn{4}{c}{**Chicken**} \\  & & & & & \multicolumn{2}{c}{**Spiked Concentration**} & \multicolumn{2}{c}{**Spiked Concentration**} \\  & & & & & \multicolumn{2}{c}{**(\(\upmu\) kg\({}^{-1}\))**} & \multicolumn{2}{c}{**(\(\upmu\) kg\({}^{-1}\))**} \\  & & & & & & & & & **(\(\upmu\) kg\({}^{-1}\))** & \\ \hline \multirow{4}{*}{Amantadine} & 0 \(\upmu\) kg\({}^{-1}\) & 0 \(\upmu\) kg\({}^{-1}\) & 0 \(\upmu\) kg\({}^{-1}\) & 0 \(\upmu\) kg\({}^{-1}\) & Recovery (\%) & 87 & 102 & 102 & 99 & 88 & 102 \\  & & & & RSD (n = 3) & 13.4 & 5.4 & 6.3 & 8.3 & 13.9 & 4.6 \\ \multirow{4}{*}{Clenbuterol} & 0 \(\upmu\) kg\({}^{-1}\) & 0 \(\upmu\) kg\({}^{-1}\) & 0 \(\upmu\) kg\({}^{-1}\) & Recovery (\%) & 92 & 100 & 99 & 105 & 103 & 85 \\  & 100 \(\upmu\) kg\({}^{-1}\) in & & & RSD (n = 3) & 5.5 & 7.6 & 2.6 & 7.5 & 3.8 & 4.5 \\ \multirow{4}{*}{Sulfamethazine} & muscle, 25\% kg\({}^{-1}\) & 100 \(\upmu\) kg\({}^{-1}\) & 100 \(\upmu\) kg\({}^{-1}\) in & Recovery (\%) & 97 & 107 & 98 & 111 & 98 & 85 \\  & in milk, and & in muscle & muscle & RSD (n = 3) & 14.7 & 5.3 & 10.3 & 3.2 & 6.4 & 2.2 \\ \multirow{4}{*}{Chicken} & 10 \(\upmu\) kg\({}^{-1}\) in egg & & & & & & & & \\ \cline{1-1} \cline{6-10} \