esa-sceva/satcom-chunk-collection huggingface dataset:
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 4 Tuning the Values of the Hyperparameters  
Forgetful decision trees and random forests have six hyperparameters to set, which are (i) the evaluation function \(G(\cdot)\) of the decision tree, (ii) the maximum height of trees \(maxHeight\), (iii) the minimum size of retained data following initial cold start \(warmSize\), (iv) for forgetful random forests, the empirical probability that concept drift has occurred \(1\)-\(tThresh\), (v) the number of trees in the forgetful random forest \(nTree\), and (vi) the change in the retained data size \(iRate\). To find the best values for these hyperparameters, we generated 18 datasets with different intensity of concept drifts, number of concept drifts, and Gaussian noise, using a generator inspired by Harvard Dataverse [16].  
Our tests on the 18 new datasets indicated that some hyperparameters have optimal values (with respect to accuracy) that apply to all datasets. Others have optimal values that vary slightly depending on the dataset but can be learned. While the details are in Appendix D, here are the results of that study:  
* The evaluation function can be a Gini Impurity [17] score or an entropy reduction coefficient. Which one is chosen does not make a material difference, so we set \(G(\cdot)\) to entropy reduction coefficient for all datasets.
* The maximum tree height (\(maxHeight\)) is adaptively set based on the methods in Section 3.2 to log base 2 of \(rSize\). Applying other stopping criteria does not materially affect the accuracy. For that reason, we ignore other stopping criteria.
* To mitigate the inaccuracies of the initial cold start, the model will not discard any data in cold startup mode. To leave cold startup mode, the accuracy should be better than random guessing on the last 50% of data, when \(rSize\) is at least \(warmSize\). \(warmSize\) adapts if it is too small, so we will set its initial value to 64 data items.
* To avoid discarding trees too often, we will discard a tree only when we have \(1-tThresh\) confidence that the accuracy has changed. We observe that all datasets enjoy a good accuracy when \(tThresh=0.05\).  
There are _nTree_ forgetful decision trees in each forgetful random forest. We observe that the accuracy of all datasets stops growing after \(nTree>20\) both with bagging and without bagging, so we will set \(nTree=20\).
* The adaptation strategy in Section 3.2 needs an initial value for parameter \(iRate\), which influences the increase rate of the retained data \(rSize\). Too much data will be retained if the initial \(iRate\) is large, but the accuracy will be low for lack of training data if the initial value of \(iRate\) is small. We observe that the forgetful decision tree does well when \(iRate=0.3\) or \(iRate=0.4\) initially. We will use \(iRate=0.3\) as our initial setting and use it in the experiments of Section 5 for all our algorithms, because most simulated datasets have higher accuracy at \(iRate=0.3\) than at \(iRate=0.4\).
## 4 Results
### NLP Topic Modeling  
Table 4 summarizes the performance metrics of the topic modeling. The \"Best\" parameter indicates that LDA did better than NMF for all categories, based on the F1-score. The \"-T\" or \"-F\" after the model abbreviation indicates \"True\" or \"False\", respectively, if swapping the default topic assignments resulted in better model performance.  
The LDA model with the default topic-to-relevance mapping (\"-T\") yielded the highest F1-score for four of the five categories (SSB, EVC, CV, and eVTOL), while a flipped topic assignment (\"-F\") resulted in better performance for the LiDAR category. Key observations are that SSB achieved the highest precision (0.96), indicating very few false positives. Conversely, eVTOL had the lowest precision (0.15), reflecting significant misclassification of irrelevant documents as relevant. LiDAR achieved the highest recall (0.89), showing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **SSB** & **EVC** & **CV** & **eVTOL** & **LiDAR** & **Mean** \\ \hline Best & LDA-T & LDA-T & LDA-T & LDA-T & LDA-F & \\ TN & 27 & 412 & 205 & 542 & 402 & \\ TP & 179 & 218 & 144 & 125 & 170 & \\ FN & 68 & 172 & 76 & 33 & 21 & \\ FP & 7 & 382 & 175 & 713 & 143 & \\ Precision & 0.96 & 0.36 & 0.45 & 0.15 & 0.54 & 0.49 \\ Recall & 0.72 & 0.56 & 0.65 & 0.79 & 0.89 & 0.72 \\ F1 & 0.83 & 0.44 & 0.53 & 0.25 & 0.67 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Topic modeling performance for each technology domain.  
the model's ability to capture most of the relevant documents. SSB had a comparatively lower recall (0.72), indicating that the model missed some relevant documents. SSB had the highest F1-score (0.83), demonstrating strong overall performance. eVTOL had the lowest F1-score (0.25), primarily due to poor precision.  
The average precision across all categories was 0.49, indicating that topic modeling yielded moderate performance in identifying relevant documents. The average recall was higher at 0.72, suggesting the model was better at identifying relevant documents. The average F1-score was 0.55, reflecting a moderate trade-off between precision and recall. LDA consistently outperformed NMF in identifying relevant documents, with default topic-to-relevance mappings often providing the best results. The models provided the best overall performance on the SSB corpus, with high precision, recall, and F1-score, indicating well-separated topics by this method. The models performed the worst on the eVTOL corpus due to high false positives, indicating challenges in separating relevant from irrelevant topics in this technology domain. The model showed high recall on the LiDAR corpus, suggesting good coverage of relevant documents but at the cost of precision. These results highlight the strengths and weaknesses of topic modeling for relevance filtering across various technology domains and suggest that LDA is often better suited for this task than NMF, albeit with variability depending on the dataset characteristics.
## 2 Materials and Methods
### Analyzed Methods
#### 2.1.1 Robust Random Cut Forest (RRCF)  
The Robust Random Cut Forest (RRCF) [9] is a modification of the well-known Isolation Forest [29] methods, which extends the approach to data streams. Both methods work by isolating individual points from the rest of the data by recursively partitioning the dataset. This process can be represented by a binary tree structure, where each cut is represented by a pair of branches from the same node. The average path length can then be used as an anomaly score, as shorter paths indicate that a point is more likely to be anomalous [29]. One key difference between RRCF and Isolation Forest is that RRCF selects the next dimension to cut, with a probability proportional to the range of values in that dimension, rather than selecting it uniformly at random. This modification is meant to avoid cutting irrelevant dimensions and reduce the number of false positives, as well as to maintain a good recall [9]. Due to its anomaly scoring function _Collusive Displacement_ RRCF is also robust to the presence of duplicates or near-duplicates which could otherwise lead to outlier masking [30]. _Displacement_ refers to the classification of points as outliers, if they significantly decrease the model complexity when removed from the tree. _Collusive Displacement_ accounts for duplicates or near-duplicates by removing a subset of \"colluders\" \(C\) alongside the point of interest \(x\). It is defined as the expected change in the depth of points in a tree when removing a set \(C\cup\{x\}\). For an exact definition of the _Collusive Displacement_ scoring function, please refer to [9]. As RRCF works by isolating single points, one would expect its strength to lie in finding point anomalies. For the detection of anomalous subsequences, an additional preprocessing step for constructing window-based features could be considered and is analyzed in Section 3.4. We selected RRCF mainly due to its simplicity and comprehensibility.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 2 Study Area and Data  
### Study Area  
Feicheng county is located in central Shandong Province, China (35 deg53\"-36 deg19' N; 116 deg28\"-116 deg59\" E; Figure 1). The plain is the most important terrain of the county, accounting for 44.9% of the total area, followed by mountain, accounting for 33.6%, then hilly area, accounting for 19.9%, lowland regions only accounting for 1.6%. The main food crops are winter wheat and corn. Because the county's geographical and agricultural situation are representative of China, we selected it as our study area to perform the comparison experiments.
# A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments
## 3 Results
### App Risk Evaluator
#### 3.3.1 Dangerous Permission Risk Estimation  
The risk scores of the selected dangerous permissions are estimated as follows: let \(P=\{P_{1},P_{2},P_{3},\ldots P_{n}\}\) be a no n-empty finite set containing \(n\) dangerous permissions \(P_{i}\), where \(1\leq i\leq n\), and for any given set of apps \(X\), let \(\alpha_{i}\) be the ratio of the number of malicious apps in the set \(X\) using \(P_{i}\) to the number of all malicious apps in the set \(X\). That is,  
\[\alpha_{i}=\frac{NumberOfMaliciousApps\in theDatasetUsingP_{i}}{NumberOfMalicious Apps\in theDataset}\]  
Similarly, let \(\beta_{i}\) be the ratio of the number of benign apps in the set \(X\) using \(P_{i}\) to the number of all benign apps in the set \(X\). That is,  
\[\beta_{i}=\frac{NumberOfBenignApps\in theDatasetUsingP_{i}}{NumberOfBenignApps \in theDataset}\]  
For any \(i\) in the interval [\(1\), \(n\)], \(0\leq\alpha_{i}\), \(\beta_{i}\leq 1\).  
We calculate the risk score of a dangerous permission \(P_{i}\) that belongs to the set \(P\) as the value of the function \(r(i)\) defined by  
\[r(i)=\sqrt{\alpha_{i}-\alpha_{i}\beta_{i}} \tag{1}\]  
The risk score of a dangerous permission \(P_{i}\) estimates the risk posed by the use of this permission based on its usage patterns across the set of apps \(X\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **TP** & **FP** & **TN** & **FN** & **CA** & **ER** & **PR** & **RC** & **FM** & **FPR** & **FNR** & **FAR** \\ \hline C1 & 3435 & 153 & 1842 & 231 & 93.22 & 6.78 & 95.74 & 93.7 & 94.71 & 7.67 & 6.3 & 6.99 \\ C2 & 3504 & 144 & 1851 & 162 & 94.59 & 5.41 & 96.05 & 95.58 & 95.82 & 7.22 & 4.42 & 5.82 \\ C7 & 3472 & 171 & 1824 & 194 & 93.55 & 6.45 & 95.31 & 94.71 & 95.01 & 8.57 & 5.29 & 6.93 \\ EMLM * & 3583 & 76 & 1931 & 72 & 97.4 & 2.61 & 97.92 & 98.03 & 97.97 & 3.78 & 1.96 & 2.87 \\ \hline \hline \end{tabular}  
* EMLM—Ensemble ML model.  
\end{table}
Table 6: C1, C2, C7, and ensemble ML model performance (using the Hybrid dataset).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & **CA** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** \\ \hline \multirow{2}{*}{Permissions} & Max & **92.93** & **93.64** & 94.7 & 61.62 & 95.41 & 94.01 & **93.99** & 93.99 & 96.11 & 95.05 \\  & Min & **87.99** & **91.17** & 89.75 & 50.53 & 85.51 & 89.05 & **88.84** & 86.57 & 89.4 & 88.69 \\ \hline \multirow{2}{*}{Intents} & Max & **74.82** & **75.18** & 76.24 & 42.2 & 73.05 & 75.27 & **73.14** & 72.08 & 76.24 & 75.53 \\  & Min & **69.5** & **69.86** & 69.15 & 37.46 & 57.6 & 70.57 & **65.37** & 67.08 & 69.96 & 69.96 \\ \hline \multirow{2}{*}{Hybrid} & Max & **93.64** & **95.41** & 94.7 & 53 & 94.35 & 95.76 & **94.7** & 93.64 & 95.41 & 95.41 \\  & Min & **87.99** & **90.46** & 88.34 & 40.99 & 90.46 & 89.79 & **88.34** & 86.57 & 89.4 & 89.75 \\ \hline \hline \end{tabular}  
* EMLM—Ensemble ML model.  
\end{table}
Table 5: Tenfold cross-validation results.
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 4 Discussion
### Isofluavones Collection  
The resin open-circuit column test with Amberlite(r) FPX66 allowed the trapping of almost 80% of the remaining IFs in pre-filtered treatment water. However, considering the number of IFs that could be present in general in treatment waters, the fixation capacity of the resin might not be sufficient alone.  
Luckily, IFs and other flavonoids are known to adsorb to proteins in their native or glycosidic forms [46]. Therefore, it was not surprising to find them in the pellets after centrifugation. The percentages of IFs in the dried extract (13.02 \(\pm\) 2.27%) rank within currently commercialized IF extracts that can be found on the pharmaceutical market (2, 10, 20 or 40%). The recovery of dried IF extracts still needs to be tested at an industrial scale, as it can represent a significant quantity. This was not the case for the extract obtained by cartridge filtration, as it contained only 0.01% of IFs. The difference between the two separation techniques (centrifugation and cartridge filtration) comes from their respective cutting rates. With cutting at 1 um diameter, the filtration cartridge retained more matter than the centrifugation step. In addition, the affinity of the IFs for the PREcart PP 2 cartridge may be low, favouring the water solubility of the glycosylated IFs.  
Thus, to obtain the richest possible IF extract, it might be advantageous to combine the pellets produced by the centrifugation with the IFs recovered from the resin treatment of the supernatant. This alternative route should potentially lead to full recovery of IFs from treatment water.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 3: Long-Term Detection  
Apart from acute anomalies, machines can exhibit gradual drifts over days or weeks (e.g., air pressure). This phase periodically measures distribution shifts in embeddings or reconstruction errors over a batch window.  
1. Distribution Measures (KL/Wasserstein): Gather recent data \(\Big{\{}\mathbf{h}^{(m)}(t)\Big{|}t\in[T_{k},T_{k+1}]\Big{\}}\) to form \(P_{new}\). Compare with the baseline \(P_{base}\) via [16] \[D_{KL}(P_{new}|P_{base})\text{or }W_{1}(P_{new},P_{base}).\] (10) If this distance grows beyond threshold \(\gamma\) (statistically or via Bayesian update), a long-term drift is declared.
2. Multi-Scale Attention: A Transformer can incorporate specialized heads that capture short-range signals vs. long-range temporal trends [23]. This can automatically learn chronic vs. acute shifts. There is a long-term drift warning if \(\Delta>\gamma\). This mechanism catches subtle, slowly accumulating deviations.
# The Automatic Algorithm of Optimizing the Position of Structured Light Sensors
## 2 Optimization Method for the Number and Position of Multiple Sensors
### Specular Reflection Area Identification  
After calculating the coverage area of a sensor group, it is necessary to identify the specular reflection areas for each group of sensors. These areas are used as one of the parameter values optimized for the sensor system. The light propagation diagram of the sensor group is shown on the right in Figure 6b, where V is the pointing vector of the camera, \(I\) is the pointing vector of the projector, and \(R\) is the specular reflection vector of the projector's light on the surface. Specular reflection is the specular reflection of light on the surface of a smooth object where the angle between the reflected light and the direction of observation is small. It causes the brightness of the photographed object to exceed the  
Figure 7: Covered area judgment.  
camera's dynamic range, white areas to appear, and a loss of 3D measurement information. It should be noted that the specular reflection of each sensor group is closely related to the position and orientation of the sensors. Therefore, special attention is needed to solve the specular reflection area in the sensor system design. Identifying a specular reflection area involves calculating the angle between the reflection vector \(R\) and the negative vector \(-\)V of the observation vector. When this angle is within a specific range, the triangular surface is a specular reflection region. The reflection vector \(R\) is calculated by Equation (4).  
\[R=I-2(I\bullet N)\bullet N \tag{4}\]
## 3 Method for Lithology Classification  
Logging curves reflect the physical properties of rocks. For the same area, we assume the rocks with similar physical properties to belong to the same type. However, the physical properties of rocks are influenced by various factors, such as purity, borehole size, and fluids, making it very challenging to identify lithology based on logging curves manually. This process is subject to significant uncertainty and subjectivity. Observing core samples is the most direct and accurate method, but it is time-consuming and costly.  
The essence of machine learning (including traditional machine learning algorithms and deep learning) is to establish a nonlinear relationship between the observed data and the predicted outcomes from a statistical perspective, based on large amounts of data. Unlike physical models, once training is complete, machine learning can achieve rapid predictions within seconds, making it suitable for well log curve identification. Recently, more studies have attempted to compare the performance of machine learning methods for lithology identification [23; 24; 25]. The results showed that Random Forest had the best score among the algorithms considered. Additionally, CNN and RNN are classic sample models suited for small sample sets. Therefore, we tried to determine whether the neural network models, which are more adept at image recognition, outperform Random Forest models in well log curve identification.
## 5 Results and Discussion
### Low-Rank Decomposition Experiment  
We constructed the Conformer low-rank multi-head self-attention encoder and decoder using low-rank decomposition. Table 2 is the comparison between the compressed low-rank decomposition and other models.  
As shown in Table 2, Conformer has a larger number of parameters compared to the other models, which is 47.6 M. When the lightweight Conformer model is constructed using a low-rank decomposition, the number of parameters of the model decreases. When the rank is 128, the number of parameters of the model does not decrease. When the rank is 64, the number of model parameters decreases by 3.5 M. When the rank is 32, the number of model parameters decreases by 5.3 M. It is also observed that the word error rate of the model increases as the number of parameters is compressed and reduced.  
The following table shows the comparison between the Conformer model and the baseline model after decomposing the Conformer model with different rank sizes.  
As can be seen in Table 3, the size of the model decreases as the rank value decreases. When the rank is 128, although the storage size of the model does not change, the word error rate of the model decreases and is reduced by 0.03% compared to the baseline model. As the compression rank value decreases, the recognition rate of the model decreases. This also implies that the size of the rank value cannot be set too low, otherwise it will have a greater impact on the model recognition results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Parameters** & **WER** \\ \hline \multicolumn{3}{c}{Traditional hybrid structure} \\ \hline DNN-HMM [13] & - & 13.7\% \\ \hline \multicolumn{3}{c}{End-to-end architecture} \\ \hline BLSTM-CTC [13] & - & 28.8\% \\ Transformer-CTC & 31.7 M & 12.85\% \\ Baseline (Conformer-CTC) & 47.6 M & 10.36\% \\ Baseline + low rank(r = 128) & 47.6 M & 10.33\% \\ Baseline + low rank(r = 64) & 44.1 M & 10.85\% \\ Baseline + low rank(r = 32) & 42.3 M & 11.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of low rank compression and each model.  
Figure 4 shows the train loss for the Conformer baseline model and the low-rank decomposition Conformer model using different size ranks. It can be seen that the loss of the model after using the low-rank decomposition has increased to different degrees compared to the baseline model.
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 3 Theoretical Development
### Data Collection
#### 3.2.2 Subjective Conditional Probability  
The Noisy MAX model is used to generate conditional probabilities, and the survey is conducted to obtain the original parameters, involving human factors at four levels of HFACS [30]. Each node in the survey has two types of questions, corresponding to the dependencies between parent and child nodes and the countermeasures to be taken. Questions are divided into the following two types:  
* Type 1 is used for conditional probabilities assignment, which requires respondents to give conditional probabilities under the condition that only child and parent are considered.
* Type 2 is used for model validation, which requires respondents to select the countermeasures of different nodes that should be taken.  
We successfully collected valid questionnaire sheets from 26 air traffic controllers and experts familiar with ATC in China. The Likert scale was adopted to evaluate the survey, with 1 point representing a very low probability and 5 points representing a very high probability, which were used to indicate the influence degree of one node on another node. Taking the node ATC organizational climate in Figure 7 as an example, the Type 1 question amounted to: What is the probability that \"Safety Culture = good\" results in \"ATC Organizational Climate = good\"?  
Type 2 question: Which of the following measures do you think would reduce human errors and improve ATC performance? Choose several most influential factors or give your opinions. For instance, the corresponding measure lists include regular safety awareness training, improve management discipline, reasonable arrangement work time, strengthening ATC professional skills training, and so on. After the original probabilities were collected and normalized, the noisy MAX model above was used to generate CPT. Table 3 shows an example of CPTS. Out of those 42 sets of questionnaires, 26 sets were valid, reaching a valid response rate of 61.9%. The respondents' backgrounds are described in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **ATC Clearance Error** & **Distraction** \\ \hline State & Yes & No & Yes & No \\ Number & 48 & 94 & 29 & 113 \\ Probability & 0.3380 & 0.6620 & 0.2042 & 0.7958 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Marginal probabilities for nodes ATC clearance error and distraction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ATC Organizational** & **ATC/Crew Cooperation** & \multicolumn{3}{c}{**Communication**} & \multicolumn{3}{c}{**Safety Culture**} \\ \cline{2-9}
**Climate** & **Good** & **Normal** & **Poor** & **Good** & **Normal** & **Poor** & **Good** & **Normal** & **Poor** \\ \hline Good & 0.4776 & 0.2619 & 0 & 0.4468 & 0.3095 & 0 & 0.4762 & 0.3182 & 0 \\ Normal & 0.3406 & 0.4048 & 0 & 0.3404 & 0.4286 & 0 & 0.3333 & 0.4318 & 0 \\ Poor & 0.1818 & 0.3333 & 1 & 0.2128 & 0.2619 & 1 & 0.1905 & 0.25 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of conditional probability table (CPT) inputting.