esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
###### Abstract  
Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn't consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (\(SNR\)) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (\(RSSI\)) and \(SNR\) will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of \(RSSI\) based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and \(SNR\) based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than \(SNR\) based ones in the transitional region. Nevertheless, compared with \(SNR\), the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.  
link quality estimation; wireless sensor networks; environmental impact; physical layer parameters; received signal strength indicator; signal-to-noise ratio; link quality indicator; communication distance +
Footnote †: journal: Sensors
# Enhanced Curvature-Based Fabric Defect Detection:
## 3 Curvature Algorithm  
Curvature is an invariant descriptor associated with a curve, representing the rate at which the curve deviates from its tangent line. It is thus natural to relate curvatures in an image to the level lines. This algorithm calculates curvatures and other parameters, such as radius, at sub-pixel resolution. Following the application of an independent anti-aliasing filter to the evaluated lines and curves, smooth trajectories along the boundary are obtained [14; 15; 16].  
In fabric defect detection, the integration of curvature analysis plays a critical role in enhancing the precision of the inspection process [17]. Initially, edge detection techniques, such as the Canny method, are applied to the raw fabric image [18]. This step is essential for highlighting boundaries and contour-like structures within the fabric. Next, a contouring algorithm is used to delineate and organize these accentuated edges, providing a foundation for further analysis [19]. The introduction of curvature analysis refines defect identification by examining the angles and radii of the extracted contours. Curvature analysis offers valuable insights into subtle variations in fabric patterns [20]. The combination of edge detection, contour algorithms, and curvature analysis creates a comprehensive approach, improving both the accuracy and reliability of fabric defect detection.
## 2 Analysis of the Impact of Cold Wave Events on Wind Power Output
### Influence of Cold Wave Events on Wind Power Output  
The relationship between wind generator power and wind speed is given by Equation (1), where wind power is proportional to the cube of wind speed. Therefore, the trends of wind power and the cube of wind speed should be similar. To further analyze this relationship, this study calculates the difference between predicted and actual power after regional normalization and compares the trends of wind speed, temperature, and power during the same period. The specific results are presented in Figure 4.  
\[P=\frac{1}{2}\rho Av^{3}C_{p} \tag{1}\]  
\(P\) is the output power of the wind turbine (in watts, W). \(\rho\) is the air density, \(A\) is the swept area of the wind turbine (in square meters, m\({}^{2}\)), \(A=\pi R^{2}\), and \(R\) is the blade radius of the wind turbine (in meters, m). \(Cp\) is the power coefficient, which indicates the efficiency of the wind turbine, with a theoretical maximum value of 0.59, typically lower in practice. \(v\) is the wind speed (meters per second, m/s).  
Figure 4a presents a comparison of normalized wind speed, temperature, and normalized power. The selected period, from 4 January to 9 January 2021, encompasses a complete cold wave event. The temporal evolution of meteorological parameters is characterized by hourly sampled data points. During this period, the temperature shows a rapid decline, accompanied by an increase in wind speed. However, wind power did not correlate with wind speed and instead followed the trend of temperature. This suggests that during cold  
Figure 4: Wind speed, temperature, and wind power output during cold wave periods.  
wave events, wind power decreases despite the increase in wind speed. Consequently, the correlation between wind power and temperature increases, while the correlation with wind speed decreases during cold wave events.  
Figure 4b illustrates the difference between the normalized forecasting and actual power, ranging from \(-1\) to \(1\). When the difference is greater than \(0\), it indicates that the wind speed is high, but the output power is low. This observation is consistent with the phenomenon of reduced wind power output during cold wave events, suggesting that cold wave events negatively affect wind power forecasting accuracy. Therefore, the difference between forecasting and actual power can serve as a useful reference index for detecting cold wave events and assessing its impact on wind power output.  
Figure 4c displays the first-order difference results for temperature. During cold wave events, the temperature demonstrates a persistent downward trend with consecutive negative differential values. The evolution of cold wave weather is marked by temperature drops and subsequent rises when temperatures return to normal levels, following typical diurnal pattern, indicating the restoration of conventional meteorological conditions.
## 2 Research Methodology
### Modeling Methods Improvements
The limitations include dependence on an empirically chosen number of sub-intervals, no treatment of the parameter correlations, and a lack of demonstrations of  
\[\varphi=h_{s}\Delta\omega_{K_{2}}\left[n_{3}+\frac{K_{2}(M_{c}-1)}{2}\right] -arg\left[y_{K_{2}}(n_{3})\right]+\frac{\pi}{2} \tag{43}\]1 Convergence analysis of the relative generalization error \(\varepsilon_{r}\) under varying degrees of uncertainty in support stiffness: (**a**) 5%; (**b**) 10%; (**c**) 15%. Adopted from [93]. complete rotor systems or signals containing time-varying defects. The work nonetheless marks a practical step toward unifying stochastic and interval model updating under a single, lightweight similarity measure. Table 9 summarizes the results of a comparative analysis for improving the modeling methods. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Domain * \\ \hline Fractional-order model & Allows for more accurate consideration of hydrodynamic and viscoelastic effects in rotor systems [35]. & Requires more complex computational methods than classical models [35]. & \\ \cline{2-3}  & Improves the assessment of the stability of rotor dynamic modes by using fractional derivatives [35]. & Difficult to interpret results [56]. & \\ \hline Incremental harmonic balance method (IHIB) and ANN [90] & Using a neural network to select initial values can significantly improve the convergence of the method. & High computational complexity with an increasing number of harmonics. & \\ \cline{2-3}  & Suitable for calculating nonlinear dynamic systems with many degrees of freedom. & Requires high-quality neural network training to determine the initial values accurately. & FD \\ \hline Genetic algorithm (GA) for differential equations [89] & Allows for the obtaining of reasonable approximate solutions for differential equations. & It does not guarantee the finding of an optimal global solution. & \\ \cline{2-3}  & It makes it possible to estimate challenging parameters by using analytical methods. & It may require significant computing resources, especially when the number of parameters is large. & \\ \hline Iterative system (equivalent reduction expansion process (SEREP) [88]) & Using an iterative approach improves accuracy in the high-frequency range. & High computational complexity in calculating the eigenvectors of the system. & \\ \cline{2-3}  & This allows for reducing the model dimension without significant loss of accuracy. & A reasonable choice of frequency range is required to reduce the model order effectively. & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative analysis of modeling method improvements. Figure 35: Comparison of calculation time between the Bhattacharyya distance (BD) and SIS with increasing data dimensions. Adopted from [94].
## 3 Results
### Machine Learning Classification Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Sampling Method** & \multicolumn{4}{c}{**Metrics**} & **Specificity** \\  & & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Specificity** \\ \hline \multirow{6}{*}{Logistic Regression} & SMOTE & 0.7067 & 0.3076 & 0.6606 & 0.4211 & 0.6667 \\  & ADASYN & 0.6933 & 0.29245 & 0.6483 & 0.4026 & 0.6042 \\  & ENN & 0.7867 & 0.3 & 0.25 & 0.2727 & 0.25 \\  & TomekLink & 0.8433 & 0.5556 & 0.10416 & 0.17543 & 0.1041 \\  & SMOTEENN & 0.6033 & 0.26174 & 0.8125 & 0.3959 & 0.8125 \\  & SMOTETomek & 0.72 & 0.3125 & 0.625 & 0.4167 & 0.625 \\ \hline \multirow{6}{*}{Support Vector Machine} & SMOTE & 0.7467 & 0.2749 & 0.3542 & 0.3091 & 0.3333 \\  & ADASYN & 0.7261 & 0.2424 & 0.3333 & 0.2807 & 0.375 \\  & ENN & 0.82 & 0.4066 & 0.3125 & 0.3571 & 0.3125 \\  & TomekLink & 0.84 & 0.5 & 0.0625 & 0.1111 & 0.0625 \\  & SMOTEENN & 0.6866 & 0.2788 & 0.6041 & 0.3815 & 0.6042 \\  & SMOTETomek & 0.7466 & 0.2813 & 0.375 & 0.3214 & 0.375 \\ \hline \multirow{6}{*}{RandomForest} & SMOTE & 0.5233 & 0.2254 & 0.8125 & 0.3529 & 0.8333 \\  & ADASYN & 0.4467 & 0.3777 & 0.3542 & 0.3656 & 0.9167 \\  & ENN & 0.8033 & 0.3777 & 0.3542 & 0.3656 & 0.3958 \\  & TomekLink & 0.8367 & 0.46153 & 0.125 & 0.1967 & 0.0833 \\  & SMOTEENN & 0.58 & 0.2468 & 0.7917 & 0.3762 & 0.8333 \\  & SMOTETomek & 0.5533 & 0.2378 & 0.8125 & 0.3679 & 0.75 \\ \hline \multirow{6}{*}{Gradient Boost} & SMOTE & 0.1933 & 0.1655 & 1.0 & 0.284 & 0.0104 \\  & ADASYN & 0.1767 & 0.1627 & 1.0 & 0.2799 & 0.0208 \\  & ENN & 0.7867 & 0.3333 & 0.3333 & 0.3333 & 0.3333 \\  & TomekLink & 0.8266 & 0.3571 & 0.1042 & 0.1613 & 0.1042 \\  & SMOTEENN & 0.53 & 0.2281 & 0.8125 & 0.3562 & 0.8333 \\  & SMOTETomek & 0.2567 & 0.1698 & 0.9375 & 0.2875 & 0.9375 \\ \hline \multirow{6}{*}{XGBoost} & SMOTE & 0.19 & 0.1649 & 1.0 & 0.2831 & 0.9896 \\  & ADASYN & 0.18 & 0.1633 & 1.0 & 0.2807 & 0.9896 \\ \cline{1-1}  & ENN & 0.7933 & 0.3542 & 0.3542 & 0.3542 & 0.3542 \\ \cline{1-1}  & TomekLink & 0.8133 & 0.1667 & 0.0417 & 0.0667 & 0.0412 \\ \cline{1-1}  & SMOTEENN & 0.45 & 0.2089 & 0.875 & 0.3373 & 0.875 \\ \cline{1-1}  & SMOTETomek & 0.2333 & 0.1727 & 1.0 & 0.2945 & 0.9896 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results with data augmentation.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 4 Experimental Results and Discussion
### Parameter Analysis  
To provide a comprehensive study of the proposed method, this section analyzes the effect of hyperparameter \(\lambda\) in the loss function on the classification results. We vary \(\lambda\) from 0 to 0.1 to learn different models, and the accuracy of these models in the ASGC, CCSN, and GCD datasets is shown in Figure 12.  
It can be seen that when \(\lambda\) is 0, the loss function contains only cross-entropy loss, which cannot adequately supervise feature learning at this point, resulting in poor performance.  
Figure 11: Confusion matrix for different datasets: (**a**) ASGC; (**b**) CCSN; (**c**) GCD.  
Figure 12: Analysis of the performing results of the \(\lambda\) parameters.  
When the center loss is supplemented, the joint supervision improves the discriminative power of the deep features, thus improving the classification accuracy. In addition, the model performance remains largely stable over a large range of \(\lambda\), where the proposed method obtains the best results when \(\lambda\) takes the value of 0.01. Therefore, \(\lambda\) is set to 0.01 to obtain the best classification performance for GCI classification when conducting experiments on each dataset.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 4 Experiments and Results
### Hyperparameter Tuning
#### 4.5.2 Window Size Segmentation Tuning  
As part of the hyperparameter tuning, we investigated the optimal window size for the baseline model. For the dataset, about 128 samples were captured every 3 s from both the inertial and context sensors on the smartphone [8; 68]. Some scholars suggested that sensing a complex activity from the smartphone sensors may require a longer window length compared to simple activities [15; 16]. How true, and if true, how long is suitable for the dataset? It was important that we experiment with different window lengths to ascertain the optimal window segmentation required to capture the complex activities in the dataset. Using the sliding window algorithm, we investigated several different window lengths ranging between 32 and 256. Concurrently, we examined the interaction between the window length and various batch sizes on a model's performance since the model learns from one batch of data at a time before its weight is updated [20]. The results show that increasing the window length does not guarantee superior performance (Figures S8 and S9), whereas increasing the batch size proves contrary. However, there is no significant improvement once the batch size passes the 64-mark. The drop in performance as window length increases may not have come as a surprise as some authors suggested that models containing LSTM layers suffer from poor parallelism across longer window lengths [67]. Although we did not investigate further, [PERSON] and [PERSON] [67] argue that increasing the LSTM layers can enhance the model's capacity to learn longer window lengths. Instead, we investigated the relationship between the two parameters. The results demonstrate that that a window length of 32 and any batch size between 64, 128, and 512 are good for the proposed model (Figures S8 and S9).  
We chose the smallest of the three, i.e., 64, for the subsequent experiments following similar work of [62]. Experiments show that smaller batch sizes help improve accuracy with stable convergence (Figure S10). Although batch size 64 slowed down the learning process significantly, it does, in the final stages, converge to a more stable model characterized by lower variance in generalization accuracy.
# The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
## 4 Discussion  
Comparing the simulations and assuming a similar utilisation date for all the robots, a convergence was observed in the number of heavy maintenance tasks. It resulted in the optimisation of the maintenance process. This finding denoted that only an increase in working hours (or maintenance man-hours), achievable primarily by increasing the team size, could decrease the cancelled tasks. This hypothesis was tested in scenario 4: as shown in Figure 3, the team size was increased until the point that no tasks were cancelled, and, as a result, higher robots' availability and OEE were obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Maintenance** & **Cancelled Tasks** & **Total Downtime (h)** & **Availability** & **OEE** \\
**Man-Hour (h)** & & & & \\ \hline
8153.8 & 19 & 10,281.8 & 88.23\% & 71.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario 3 results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Maintenance** & **Cancelled Tasks** & **Total Downtime (h)** & **Availability** & **OEE** \\
**Man-Hour (h)** & & & & \\ \hline
10,868 & 19 & 12,996 & 85.12\% & 68.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenario 2 results.  
The study was limited by the following assumptions:  
* No corrective maintenance was considered;
* Robots remaining useful life and degradation curve were not modelled; and
* Production demand, product quality, and robots' MTBF were considered constant.  
It meant that the performance of the supporting capability of a complex system at its end of life-cycle, when more corrective maintenance takes place, or scheduled maintenance is anticipated, could not be assessed. As mentioned in the conclusion, future work will expand the simulation up to the inclusion of such situations.  
Comparing the OEE and the availability obtained in each simulation, it was observed that the best results were obtained in the fourth scenario, as expected, since it featured not only assignments according to teams' expertise and increasing expertise over time, but also task rescheduling. The second scenario, characterized by randomized teams' assignment and constant expertise, presented the lowest OEE overall, confirming that not considering workforce skills in maintenance tasks' assignment is not a good strategy. Scenario 1, which considers workforce skills, presents improvements compared to scenario 1, while scenario 3, which in addition to teams' skills, considers skill improvements over time, as considered in the fourth scenario, further improves the results obtained in scenario 1. Figure 4 summarizes the comparisons of the results.  
Figure 4: OEE and availability comparison among simulations.  
Figure 3: Case Study 4—Cancelled tasks, availability, and OEE evolution in team size function.
## 3 Materials and Methods
### Dataset Construction and Preprocessing
#### 3.1.2 Data Preprocessing  
1. Text Preprocessing  
Noise Filtration: Social media-specific artifacts were systematically removed using regular expression patterns (e.g., rhttp\(\backslash\)S+1@\(\backslash\)w+1 #\(\backslash\)w+1 [\(\backslash\)w\(\backslash\)s]), targeting URL links, user mentions (e.g., @username), hashtag tags (e.g., #disaster), and non-alphanumeric characters. This approach aligns with established crisis text cleaning strategies, ensuring the extraction of semantically relevant content from noisy social media posts.  
Text Normalization: Text normalization was performed through sequential transformations: all characters were converted to lowercase to mitigate case-sensitive biases (Case Uniformization); abbreviations were resolved using the contractions library (v0.0.12) (e.g., \"can't\" \(\rightarrow\) \"cannot\", Contraction Expansion); temporal and ordinal expressions were converted to text via the inflect engine (v6.0.2) (e.g., \"2:00 PM\" \(\rightarrow\) \"two o'clock in the afternoon\", Numeric Transcription); and punctuation/non-ASCII characters were stripped to enforce uniform representation (Character Standardization).  
These steps transformed the original text \"RT @user: Flood in the city! #help [[http://example.com](http://example.com)]([http://example.com](http://example.com))\" into the processed text \"flood in the city help\".  
Tokenization: Tokenization employed the Byte Pair Encoding (BPE) tokenizer from CLIP, leveraging a 49,408-token vocabulary. Sequences exceeding 77 tokens were truncated at the [CLS] marker to comply with the model's input constraints, ensuring alignment with the preprocessing protocols of multimodal foundation models.  
1. Image Preprocessing  
Images were resized to 224 \(\times\) 224 pixels using bicubic interpolation (via torchvision.transforms.Resize), converted to RGB format, and normalized to the [0,1] range by dividing pixel values by 255.0, in accordance with the CLIP preprocessing protocol. This standardization ensures compatibility with the Vision Transformer architecture by aligning input dimensions and value scales with model expectations.  
For images with structural damage (e.g., missing pixel regions or severe blur), a nearest-neighbor replacement strategy was implemented: corrupted images were first identified via visual inspection and quantitative blur assessment (using Laplacian variance thresholds), then Euclidean distances were computed between RGB pixel vectors of corrupted samples and valid images within the same class, and finally each corrupted image was substituted with the closest valid sample (minimizing intra-class feature discrepancy) to maintain dataset integrity and prevent model bias from noisy inputs.
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
## 2 Mathematical Model  
Our mathematical model is based on a laboratory experiment, where gas bubbles are formed in a liquid and are transported after the formation process, see the plasma-experiment in [2]. The experiment is given as a thin capillary, where the gas bubbles are streamed in an homogeneous form and transported in a tube that is filled with liquid, see the Figure 1.  
We consider the profile of the tube and deal with the simplified approach of the experiment, which is given in Figure 2.  
Tube with water and periodical gas-inflows  
Figure 1: Sketch of our laboratory experiment (capillary with gaseous outflow into a tube filled with water).  
Figure 2: 2D model-reactor and inflow-conditions of the bubbles, in the upper figure, we present the 2D model of the reactor (**v** is the velocity field, \(c_{1,0},\ldots,c_{M,0}\) are the concentrations of the bubble inflow sources), in the lower figure, we present the concentration of the \(i\)-th gas bubble with periodically inflow (\(c_{i,0}\) is the \(i\)-th concentration of the bubble inflow source, \(t\) is the time, \(t_{0}\) is the initial time, \(\Delta t_{1}\) is the time-interval, where the source is switched on and \(\Delta t_{2}\) is the time-interval, where the source is switched off).  
Furthermore, experiments are carried out in conjunction with electro-magnetic fields to influence the bubble formation and transport, see [7; 10]. Here, the electric field is associated with the bubble oscillations, such as \([kHz]\)-frequency of the E-field is related to \([mm]\)-sized bubbles, see [10]. Based on the deforming and oscillation of the bubbles, while controlled with the frequency of the high voltage electrical field, we could achieve a shape, which is strategically more optimal (e.g., a small ellipsoid), see [21; 22], to enhance the E-field in the bubble. Then, the plasma ignition within the bubble gas can be done at reduced voltage, see [21] or [23]. These experiments are based on the following Figure 3, see also [7].  
Based on the decoupling of formation and transport, while we assume that the formation process is not influenced by the transport, see [24], we deal with two different decoupled models:  
* Near-field approach, which is based on a Young-Laplace equation, see [11], where we have a static shape after the formation of the bubbles.
* Far-field approach, which is based on a convection-diffusion equation, see [25], where we have a rewriting into a level-set equation, such that we could transport the static bubble shapes, see [26] and [13].  
The modelling approach is given in the following Figure 4.  
In the following, we discuss the different models.  
Figure 4: Modelling approach of the near–far-field coupling.  
Figure 3: Bubbling reactor: (**left**) we have non-influenced bubbles; (**right**) we have influenced bubbles based on an external high-voltage E-field.