esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion
### Ablation Study and Lightweight Optimization Directions  
To analyze the internal contributions of core architectural components in the MVT model, we conducted a preliminary ablation study involving the following interventions:  
* Removal of sinusoidal positional encoding.
* Reduction in the depth of the attention block (number of transformer layers).
* Replacement of the attention mechanism with fixed convolutional layers.  
Preliminary results indicate that positional encoding has a direct impact on model accuracy, with an average drop of 3.5 percentage points, highlighting its importance in preserving the sequential structure of the byteplot input. Similarly, reducing the attention depth impaired the model's ability to capture complex patterns, leading to a mean F1-Score decrease of 2.8%. Replacing attention layers with convolutional operations resulted in a significant performance degradation (\(-\)5.1%), suggesting that global attention mechanisms are critical for effective malware detection Figure 16.  
Figure 16: Impact of component ablation on MVT model performance.  
Given the computational intensity of transformer based architectures, future work should focus on lightweight model optimization for deployment in resource-constrained or real-time environments. The following strategies are proposed:  
* Quantization: reducing weight precision (e.g., INT8) to lower memory consumption and speed up inference without significant performance loss.
* Structured pruning: removing neurons, channels, or layers with low contribution while preserving overall accuracy through retraining.
* Knowledge distillation: transferring knowledge from a full MVT model to a smaller, optimized student model.  
These techniques are expected to enable the deployment of efficient and explainable models on platforms with computational constraints, while maintaining competitive accuracy levels.
## 2 Review of Related Works  
Studies on IoT adoption have been conducted globally across various geographic regions and technological domains. Geographically, these studies cover regions such as Europe, the United States, and Asia, as well as Eastern, Western, and Southern Africa. Technologically, the focus of these studies spans sectors like education, healthcare, agriculture, governance, and construction. Table 1 provides a summarized review of the related literature on IoT adoption, highlighting the diverse application areas and demonstrating the broad versatility of IoT technologies. Each sector encounters distinct challenges and opportunities in adopting the IoT, shaped by its specific technological, economic, and regulatory contexts. The following section synthesizes the findings by categorizing the studies based on their application areas: smart cities, education, healthcare, e-government, smart agriculture, and construction. IoT adoption studies focusing on smart cities emphasize the role of the IoT in improving urban infrastructure management. In India, [PERSON] et al. [26] identified barriers such as privacy concerns, regulatory gaps, and high operational costs in smart city waste management. Similarly, in Europe, [PERSON] et al. [27] examined IoT adoption across multiple smart city applications, including autonomous driving and smart agriculture, identifying trust, privacy, and cost as key barriers. These findings highlight that while the IoT can significantly enhance urban services, concerns over data security and financial sustainability continue to hinder its widespread adoption. In the educational sector, IoT adoption has been explored in both African and international contexts. The studies of [PERSON] et al. [28], conducted in South Africa and [PERSON] et al. [29], conducted in Nigeria, found that performance expectancy, effort expectancy, facilitating conditions, and social influence positively impact behavioral intentions to adopt the IoT in higher education institutions (HEIs). Another study in Nigeria [15] points to infrastructure issues, such as power supply shortages and the fear of cyber attacks, as major challenges. These studies reveal that while the IoT holds the potential to transform education, especially in resource-constrained environments, the success of its adoption largely depends on improving infrastructure and addressing security concerns. The healthcare sector presents a unique opportunity for IoT adoption, particularly in improving patient care and health management. [PERSON] et al. [30] conducted a systematic review of factors influencing medical professionals to adopt the IoT in healthcare, highlighting perceived usefulness, privacy risks, and financial costs as key determinants. The findings suggest that the IoT can bring significant benefits to healthcare, but issues related to privacy and financial feasibility must be addressed to encourage wider adoption by medical professionals.
# Anisotropic Yield Criterion of Rolled AZ31 Magnesium Alloy via Nanoindentation
## 4 Conclusions  
The Hill48 yield function is calibrated using data obtained from nanoindentation tests because the proposed plasticity model is applied to the rolling process, during which materials mainly experience compression. The mechanical behavior of the AZ31 alloys is investigated via nanoindentation tests with the CSM technique. The indentation hardness exhibits anisotropic behavior and is relatively large under high angles along the rolling direction, that is, the hardness values increase as the angle increases from 0\({}^{\circ}\) to 90\({}^{\circ}\). The hardness in the ND direction is smallest among the values obtained. The anisotropic mechanical behavior of the alloys is analyzed, and the hardness independent of the effect of indentation depth is calculated. Tabor's factor can be used to convert hardness values into yield stress values. The Hill48 yield function of rolled AZ31 magnesium alloys is calibrated using the experimental results.  
Conceptualization, [PERSON] and T.J.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and T.J.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and T.J.; data curation, [PERSON]; writing--originaldraft preparation, [PERSON]; writing--review and editing, T.J., Z.W. and X.H.; supervision, [PERSON]; project administration, X.L. and X.H.; funding acquisition, T.J. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China [11772215, 11772217, and 11802199]. [PERSON] is grateful to the Open Fund of the State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'An Jiaotong University [SV2019-KF-15] for its support. All financial contributions are gratefully acknowledged. And The APC was funded by [11772215].  
Authors have no conflict of interest to declare.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(F_{y}\) & \(G_{y}\) & \(H_{y}\) & \(L_{y}\) & \(M_{y}\) & \(N_{y}\) \\ \hline
0.406 & 1.184 & 0.468 & 0.723 & 0.723 & 0.723 \\ \hline \end{tabular}
\end{table}
Table 4: Anisotropic parameters in the Hill yield function for rolled AZ31 alloys.
## 4 Numerical Analysis and Discussion
### Numerical Analysis of Natural Frequencies  
This section provides a simple numerical analysis for the computation of natural frequencies of a cantilever subject to free oscillations. An approximated value of the circular natural frequencies, in case of a fixed cross section, is given by [34]:  
\[\omega_{n}=\alpha_{n}^{2}\sqrt{\frac{EI}{mL^{4}}} \tag{8}\]  
where:  
\(\omega_{n}=\) n-th circular natural frequency  
\(\alpha_{n}=\) 1.875, 4.694, 7.885  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Speed [m/s]** & **Thrust [N]** & **E [N/m\({}^{2}\)]** & **J [m\({}^{4}\)]** & **L [m]** & **Displacement d [m]** \\ \hline
2 & 18,321 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.0247 \\
2.5 & 28,627 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.0387 \\
3 & 41,223 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.0557 \\
3.5 & 56,109 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.0758 \\
4 & 73,285 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.0990 \\
4.5 & 92,751 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.1253 \\
5 & 114,508 & \(2.1\times 10^{11}\) & 0.323 & 65 & 0.1547 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Theoretical displacements.  
E = [PERSON]'s modulus J = moment of inertia m = mass per length unit L = cantilever length The natural frequency f\({}_{n}\) can be obtained from the circular frequency using the equation:  
\[f_{n}=\frac{\omega_{n}}{2\pi} \tag{9}\]  
To bring back the problem to the case of a massless cantilever with a discrete effective mass applied to the free end, we use the effective mass for the n-th frequency \(m_{\text{eff}}\)(n), given by:  
\[m_{eff}^{(n)}=\frac{3 EJ}{L^{3}\omega_{n}^{2}} \tag{10}\]  
This way we can add a \(m_{end}\) mass actually positioned at the free end of the cantilever and consider a total mass M\({}_{n}\) at the free end:  
\[\text{M}_{n}=m_{eff}^{(n)}+m_{end} \tag{11}\]  
The n-th natural frequency will be:  
\[\omega_{n}=\alpha_{n}^{2}\sqrt{\frac{3 EJ}{M_{n}L^{4}}} \tag{12}\]  
In our case, given the characteristics of the tower, its mean section and the wind turbine data [33] we can assume E = \(2.1\times 10^{11}\) N/m\({}^{2}\), J = 0.322 m\({}^{4}\), m = 2800 kg/m, L = 65 m. By using Equations (9) and (10), we obtain a discrete effective mass of 40,740 kg for the first frequency and of 1120 kg for the second one. We must add the weight of rotor and nacelle, equal to about \(8\times 10^{4}\) kg. By using Equations (11) and (12), we get, for the first three natural frequencies, the values f\({}_{1}\) = 0.394 Hz, f\({}_{2}\) = 3.10 Hz, f\({}_{3}\) = 8.596 Hz, and the relevant periods T\({}_{1}\) = 2.54 sec, T\({}_{2}\) = 0.32 sec, and T\({}_{3}\) = 0.11 sec.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 3 Method and Analysis
### Results of Processing Historical Inverter Data  
The ability of four algorithms to detect faults in PV plants was evaluated, and the results were achieved, as shown in Table 7.  
Precision is defined as the ratio of the TP to all the positives, as stated in Section 3.1. That would be the measures of defective panels that, out of all the panels with a fault, our model accurately recognizes as faulting according to our problem statement. For example, in July 2021, the Neural Network achieved a precision score of 0.944. This means that when predicting a panel failure, it is accurate approximately 94%. Similarly, the recall rate is obtained as 0.951. Recall rate also gives a measure of how accurately our model can identify the relevant data. A faulty panel that is not intervened in is an undesirable situation for us. Deciding accuracy of the model requires a tradeoff between precision and recall. Both metrics are important for our classification problem, and the results showed that our model has balanced precision and recall rates giving a good F1-score.  
Similar to the artificial neural network, the F1-score, precision, and recall values for the other three machine learning models were above 0.93, and there was a slight increase in these values compared to the artificial neural network. Looking at the averages of the performance values obtained from the data analyzed for three different years and a total of 18 different months, the highest numerical values were obtained for the kNN model.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyper Parameters** \\ \hline Neural Network & Neuron in hidden layers = 100, maximum number of iterations = 1000 \\ Random Forest & Number of trees = 10, do not split subsets smaller than 5 \\ kNN & Number of neighbors = 5 \\ Gradient Boosting & Number of trees = 100, learning rate = 0.100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyperparameters for selected algorithms.  
[PERSON] [43] presented the performance metric results of the classification of PV faults and compared the results of different studies given in the literature using the same dataset [43]. The results reported in the study show that the average precision was in the range of 88.55-98.24%, and average F1-score was in the range of 84.45-97.51%. Based on the comparison between the results of our study and results published in the literature, our approach is successful for anomaly detection in PV plants and consistent with the currently ongoing studies.
# Enhanced Label Noise Filtering with Multiple Voting
## 5 Experimental Work
### Extended Experimental Investigation  
As pointed out above, our approach was verified to work well if the noise ratio and additional noise-free dataset were available. To further confirm the usability of our approach, we evaluated it in an environment where the two kinds of information were not available, that is the noisy training dataset E was the only available information. The noise ratio was estimated by the CF\({}_{MF}\) algorithm. As an MVFilter, CF\({}_{MF}\) consists of t consensus filters. The decision point here equals t/2. In other words, if at least t/2 CFs identify data as mislabeled, then CF\({}_{MF}\) will regard that these data are mislabeled. For a noisy training dataset E, if n data are identified by CF\({}_{MF}\), then the estimated noise ratio is \(n/\left|E\right|\). The parameter configurations of CF\({}_{MF}\) were consistent with before (referring to the beginning of Section 4). The noise-free dataset was obtained by applying CF\({}_{1}\) algorithm on E. CF\({}_{1}\) consists of t consensus filters. If at least one of CF identify data as mislabeled, then CF\({}_{1}\) will regard these data as mislabeled. Conceptually, the noise detection is loose, which aims to remove all the potential mislabeled data. Suppose the noises recognized by CF\({}_{1}\) are A. Then, the noise-free dataset is the subset of E, which excludes A. The configurations of CF\({}_{1}\) were in accordance with above experiments. Tables 9-14 show the experimental results on the benchmark datasets. Under all five datasets and all the noise ratios, CF\({}_{ODP}\) and MF\({}_{ODP}\) still showed outstanding performance, which defeated other filters in most cases. When compared to the experiment results in Tables 3-8, we found that the performances of CF\({}_{ODP}\) and MF\({}_{ODP}\) had a certain extent of degradation in a few cases. However, in general, the performance change was moderate. This indicates that even without a noise ratio and an additional noise-free dataset, our proposed approach still worked well. One of the reasons was that in our approach, the mislabeled data distribution was estimated multiple times. Although the estimated mislabeled distribution for each time might be distorted, their fusion approached the real distribution. Then, the estimated ODP was also close to the real optimal decision point. The two independent experimental evaluations in Sections 5.1 and 5.2 proved that our proposed approach was effective and able to improve the performance of any MVFilter by selecting the optimal decision point. In particular, in the high noise ratio and high cost values, our approach showed significant improvements compared to other filters. _Appl.
# Deep Instance Segmentation of Laboratory Animals in Thermal Images
## 1 Introduction
For these reasons a significant number of systems analyze only one individual at a time [2, 13]. That is why the correct segmentation between objects is necessary for further analysis. Many segmentation techniques have been proposed in literature. However, the methods based on deep-learning have been recently found to provide the best segmentation results in many computer vision tasks. Segmentation techniques based on deep learning may be divided into two approaches: (i) semantic segmentation--pixel classification-based approach and (ii) instance segmentation-object detection and classification based approach. Segmentation using (i) does not discriminate between instances. In paper [16] we have used popular methods of semantic segmentation--U-Net [17] and V-Net [18] to separate two objects. Algorithms correctly segmented animals in close contact, but they were not able to draw the boundary between the objects at the time of body overlapping. Figure 1d shows the result of U-Net architecture trained on 200 images on five epochs, 2000 steps each. The animals are not separated and are recognized together as one object. In (ii) each object is detected, represented as a separate segment and labeled with the class name. Instance segmentation is one of the most challenging computer vision tasks. It was introduced by [PERSON] [19] and then popularized by COCO [20]. Instance segmentation algorithms classify and  
Figure 1: Results of the most popular algorithms used for segmentation of (**a**) original image by: (**b**) Otsu’s thresholding, (**c**) watershed with gradient regions markers, (**d**) U-Net—semantic segmentation. localize each object's bounding box while also precisely segment each instance. Both those subtasks can be performed as a single- or two-stage detection process.
## 5 Maintenance
### Datasets for Smart Maintenance  
There are a couple of public datasets that can help to train and validate machine learning algorithms in smart maintenance tasks. Reviewing these datasets can provide a deeper understanding of the machine learning algorithms, revealing the possible features and outcomes of each algorithm. Given the lack of real, industrial data, most of these datasets are synthetic. As fault detection is involved both in quality control and maintenance, ref. [119] is also a useful source for smart maintenance applications. Ref. [186] uses MetroPT, a benchmark dataset for predictive maintenance collected in 2022 about an urban metro public transportation service in Porto. The data contain samples from analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) for anomaly detection and failure-prediction purposes. Ref. [187] provides a dataset that consists of a sequence of alarms logged by packaging equipment in an industrial environment for classification, forecasting and anomaly detection purposes. The collection includes data logged by 20 machines, and deployed in different plants around the world, from 21 February 2019 to 17 June 2020. There are 154 distinct alarm codes, for which the distribution is highly unbalanced.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.4 Tropical Cyclones Data  
Data on tropical cyclones is sourced from the China Meteorological Administration (CMA) Tropical Cyclone Data center ([[https://tcdata.typhoon.org.cn/en/](https://tcdata.typhoon.org.cn/en/)]([https://tcdata.typhoon.org.cn/en/](https://tcdata.typhoon.org.cn/en/)), accessed on 1 June 2022) and the Hainan Province Meteorological Bureau, which provides the \"CMA Tropical Cyclone Best Track (TCBT) Dataset\" [70] and typhoon disaster data, respectively. TCBT includes 6-hourly track (longitude and latitude of the center) and intensity (minimum air pressure at the center, maximum wind speed near the center, etc.) analyses. The tropical cyclones selected in this study are those whose tracks pass through Hainan Island and its surrounding areas within 5 km from 1949 to 2021. According to the national standard of the \"Tropical Cyclone Scale\" (GB/T 19201-2006), tropical cyclones are divided into seven categories: 0--weaker than tropical depression, 1--tropical depression (TD, 38.88-61.74 km/h), 2--tropical storm (TS, 61.75-88.02 km/h), 3--strong tropical storm (STS, 88.03-117.54 km/h), 4--typhoon (TY, 117.55-149.22 km/h), 5--strong typhoon (STY, 149.23-183.42 km/h), and 6--super typhoon (SuperTY, \(\geq\)183.43 km/h).
## Materials and Methods
### Specim Preparation for Shear Bond Strength (SBS) Measurements  
Table 1 summarizes the created groups for shear bond strength measurements and surface treatment steps for each group. The classification was performed based on the applied adhesive (thermoplastic, 10-MDP containing bond material) and glass surface modification (bare, frosted, protonated). The SBS were measured immediately after the specimen preparations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Groups** & **Surface Treatment Description** \\ \hline Group 1: TPA & Thermoplastic adhesive applied on the bare glass surface \\ Group 2: MDP & MDP containing bond material + resin matrix on a frosted glass surface \\ Group 3: PRO-MDP & MDP containing bond material + resin matrix on the protonated, frosted glass surface \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental groups of shear bond strength measurements. TPA: thermoplastic adhesive, MDP: 10-methacryloyloxydechylahydrogen phosphate (10-MDP) containing bond material, PRO-MDP: protonated glass surface combined with 10-MDP containing bond material.  
The detailed description of specimen preparations are the following:  
GROUP 1: The samples (bone (n = 15), epoxy (n = 15), titanium (n = 15)) were stuck to a glass slide (n = 45) with a small amount of thermoplastic adhesive at 120 \({}^{\circ}\)C and then the specimens were placed into a clamping device to ensure the uniform thickness of the adhesive layer between the sample and glass slide until the thermoplastic adhesive cooled down. (Figure 1 orange color-coded boxes).  
GROUP 2: In this adhesive technique, the surface of the frosted glass (n = 45) and epoxy (n = 15), bone (n = 15), and titanium samples (n = 15) were moistened with MDP containing bond material by brushing of adhesive. The adhesive was gently dried with air blowing. Thereafter, resin matrix was applied on glass and sample surfaces to form a layered structure (Figure 2A). The specimens were placed into a clamping device and were photopolymerized by a dental hand lamp (Bluephase 20i, Ivoclar Vivadent, Liechtenstein, Austria) at high intensity for 20 s onto the glass surface. Then the specimens were placed into a light chamber unit (Scheu LC-6 Light Oven, Iserlohn, Germany) for 5 min (Figure 1 blue color-coded boxes).  
GROUP 3: The frosted glass (n = 45) was previously chemically etched by nitric acid for 3 h (determined in the preliminary experiment) after briefly distilled water rinsing and gently air drying, the epoxy, bone, and titanium samples were attached to protonated, frosted glass surface as it is described in previously presented at Group 2 specimen preparation steps (Figure 1 grey color-coded boxes).  
Figure 1 shows the chart flow of the experiments with different surface treatments and applied adhesive on the glass surface. Figure 2 shows a schematic picture from the specimen preparation to shear bond strength measurements and cohesive and adhesive failure modes detection.