esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Discussion, Contributions, and Future Directions
### Key Contributions  
* Integrated Data Acquisition Across Heterogeneous Protocols: By enabling direct communication over OPC UA, ModbusTCP, Profibus, and native connections, the architecture unifies both legacy and modern infrastructures, creating a more agile data flow and paving the way for streamlined knowledge modeling.
* Scalable Edge-Cloud Architecture: The combination of on-site edge gateways (for low-latency analytics) and cloud or database-based repositories (for long-term storage) ensures near-real-time anomaly detection while retaining large-scale historical data for in-depth process optimization.
* Continuous Knowledge Modeling (KAVA): By linking industrial signals to a flexible knowledge model, our system merges automated data processing with domain expertise. This supports advanced reasoning--e.g., trend detection, anomaly classification, and rule-based decision-making--in real time.
* Practical Validation in an Industrial Setting: Field trials under varied load conditions confirm the ability to handle frequent signal updates (sampling rates up to 100 Hz) and data volumes exceeding 10-15 MB/s, underscoring the approach's practical feasibility.
## 3 Analysis of Encrypted Traffic Features
### Analysis of Traffic Base Features and Combined Features
In contrast, the careful design of feature cross-combination strategies significantly enhances feature expressiveness, allowing for the extraction of richer, more nuanced semantic information from encrypted traffic. This discovery not only underscores the central role of feature fusion in encrypted traffic analysis, but also provides valuable insights and guidance for the development of efficient and accurate models for identifying encrypted traffic. Although combined features hold substantial promise for encrypted traffic analysis, the current methods for constructing feature sets--such as those employed by CICFlowmeter--rely heavily on manual definitions, which present clear limitations and gaps. Manually defined features often fail to encompass the full spectrum of possible communication scenarios and modes. Additionally, as network environments and encryption technologies evolve, these feature sets can quickly become obsolete. Consequently, a pivotal challenge in advancing encrypted traffic analysis is to achieve a profound, automated integration of base features to maintain and enhance analytical capabilities. Figure 1: Analysis of feature importance in encrypted malicious communications. (**a**) Top 20 important features at the session level. (**b**) Top 20 important features at the IP level. The features highlighted in red are the base features.
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
###### Abstract  
The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.  
remaining useful life prediction; three-stage feature selection; degradation indicator; LSTM-AdaBoost prediction model +
Footnote †: journal: Applied Sciences
# Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS
## 3 Results and Discussions
### Residual Variations in Matched ZTDs between occZTD and gnsZTD
In the northern and southern high-latitude areas (latitude greater than 60\({}^{\circ}\)), due to the low temperature and water vapor, the \(\delta\)ZTD\({}_{3}\) biases are extremely small, and both are less than 5 mm; the standard deviations are less than 15 mm. As the latitude decreases, the temperature and water vapor increase, as well as \(\delta\)ZTD\({}_{3}\). In particular, the bias near the equator is more than 10 mm, and the standard deviation is more than 30 mm. In addition, the bias and standard deviation of the ZTD near the mid-low latitudes in the Northern Hemisphere are slightly smaller than its bias and standard deviation in the Southern Hemisphere, which shows that the gnsZTD in the Northern Hemisphere performs better than that in the Southern Hemisphere. Figure 10: HSL variations in (**a**) frequency; and (**b**) residual ZTDs of matched atmPrf and gnsZTD in 2008.
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Method for Spectral Information Analysis
#### 4.1.1 Principal Component Regression  
Principal Component Regression is a foundational regression technique employed to establish a relationship between independent (predictor) and dependent (response) variables in a linear regression framework [71]. PCR leverages Principal Component Analysis and uses the primary components, notably those with significant variances, as the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Work** & **Category** & **Signal Type** & **Cost** & \begin{tabular}{c} **Covering** \\ **Range** \\ \end{tabular} & **Central Frequency** & \
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **Capacity** \\ **of Node** \\ \end{tabular} & **Error** \\ \hline
[12] & Moisture & Wi-Fi & Less than 100 & \
\begin{tabular}{c} Up to 30 cm \\ depths \\ \end{tabular} & 2.4 GHz & High & Low &
\begin{tabular}{c} Less than \\ 3\% \\ \end{tabular} \\ \hline
[13] & Moisture & Wi-Fi & Less than 100 &
\begin{tabular}{c} Up to 38 cm \\ depths \\ \end{tabular} & 2.5 GHz & High & Low & 1.1\% \\ \hline
[14] & Moisture & RFID & Low & 2 m & 902.75–927.25 MHz & Extremly Low & High & 5\% \\ \hline
[15] & Moisture & LTE & 55 & 2.4 km & 700–800 MHz & Medium & Low & 3.15\% \\ \hline
[16] & Moisture & LoRa &
\begin{tabular}{c} 7.5 for LoRa \\ Switch \\ \end{tabular} & 100 m & 915 MHz & Low & High & 3.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of IoT-based soil sensing systems.  
predictors in place of the original data points for regression [71, 77]. In the study by [33], a comparison was made between the PCR model performance and the PLSR method. Typically, the PLSR approach requires fewer latent variables than PCR for comparable model outcomes.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.2 CYGNSS Data
Indices are same with Table 2.  
(iv) \(\Gamma_{ratio}\) was derived by using the ratio of the reflected and direct SNRs (\(ddm\_snr\) and \(direct\_snr\), respectively), which are first calibrated by the range terms, as previously practiced [23]. Separate and combined effects of these reflectivity calculations were investigated in the SM retrieval, and the results reported in Section 4 demonstrate that \(\Gamma_{brcs}\) alone has given the highest learning performance. This can be attributed to diverse levels of errors coming from changing calibration parameters in different reflectivity calculations. Therefore, the term \"reflectivity\" will, hereafter, be used for \(\Gamma_{brcs}\), unless otherwise stated. SP incidence angle (in degrees) is used as given in the CYGNSS data. Incidence angle should be taken into account in the CYGNSS-based SM retrieval methods because of two reasons: (i) CYGNSS observes the Earth surface over a wide range of incidence angles spanning from 0\({}^{\circ}\) to 70\({}^{\circ}\) with a mean of approximately 30\({}^{\circ}\) and a standard deviation of roughly 17\({}^{\circ}\), (ii) Observed reflectivity values are dependent on the SP incidence angle [24]. Calibrating the reflectivity values for changing incidence angles can be done by two techniques: (i) Normalize the reflectivity values at any angles to their corresponding level at 0\({}^{\circ}\) by using a curve fit function, (ii) Input the incidence angle as a feature to the learning model and let the model capture the angle dependent curve fit for the reflectivity values. We examined both of these approaches, implementing the former by applying as [PERSON] et al. considered [24]. Statistical results of the investigation are demonstrated in Section 4; however, using the SP incidence angle as an input feature to the ANN model (latter approach) worked slightly better. We attribute the weaker performance of the former to the fact that the curve fit function is based on empirical observations for typical loam soil parameters, which may not be the case for all the ISMN sites in our analysis. TES is computed as the slope of the trailing edge of the reflectivity delay waveform, as defined by [PERSON] et al. [35]. TES calculation is dependent on the shape of the CYGNSS DDMs and is, therefore, directly related to the coherency/incoherency of the GNSS-R signals. More incoherent mixing through the scattering surface makes TES smaller [35]. Even though this study assumes the dominance of the coherent reflections, we consider the inclusion of TES in the SM retrieval method to be useful for feeding the regression with a coherency/incoherency metric.
## 3 Underwater Routing Protocols
### Flmpc  
The authors in [46] modified the layered multipath power control (LMPC) algorithm into the forward layered multipath power control-one (FLMPC-One) and forward layered multipath power control-two (FLMPC-Two) algorithms. The network is segmented into layers, and the source nodes are placed at the bottom layer. All the sensor nodes are randomly deployed in the network and initially are in sleeping mode. Upon receiving data packets, the nodes leave sleeping mode and become active. Unlike LMPC, a source node in FLMPC-One ensures that a forwarder node has neighbor nodes till two hops before selecting it as a forwarder node. FLMPC-Two ensures that a neighbor node has three hops. These strategies control the absence of forwarder nodes and, therefore, control the loss of data packets. They also avoid unnecessary forwarding of data packets, which leads to energy efficiency. However, checking the number of hops of the neighbor nodes increases the end-to-end delay.
# Sleep Posture Recognition Method Based on Sparse Body Pressure Features
## 4 Methodology
### Ensemble Learning  
Ensemble learning is a strong machine learning strategy that tries to improve the performance of a model by combining numerous learnings to solve a problem. The core notion of ensemble learning is to build a final prediction by constructing many base learners and having them make independent predictions on the input data, and then somehow merging the predictions of each base learner to produce a final prediction [36]. There are four main ways to ensemble learning: Bagging, Boosting, Stacking, and Voting. Among them, the representative algorithm for Bagging is Random Forest (RF) [37], and the representative methods for Boosting include Extreme Gradient Boosting (XGBoost) [38].  
Voting is utilized to make the ultimate decision by integrating the prediction results of numerous base classifiers. Specifically, for each test sample, different base classifiers will give their respective prediction findings, and then the voting technique will pick the final category based on these predictions. One of the most representative methods is soft voting [39], which calculates the final probability of the ensemble learning model for the sample belonging to each category by weighting the average of the predicted probabilities of the samples belonging to each category output by each base classifier.  
\[y_{j}=\operatorname*{arg\,max}_{c_{k}}\left(\sum_{i=1}^{n}w_{i}P_{ijk}\right) \tag{7}\]  
where \(c_{k}\) represents the category, \(w_{i}\) represents the weight of each base classifier in soft voting, and \(p_{ijk}\) represents the prediction probability of each base classifier.  
Stacking, on the other hand, builds a more powerful ensemble learning model by combining several different base learners [40], the basic idea of which is to first use several different base learners to learn and predict the training data, and then pass the outputs of these base learners to the meta-learner as new features to allow it to learn and train on these new features, and ultimately obtain the prediction results of the Stacking ensemble learning model's prediction results.  
In the design and execution of this experiment, we prioritized the optimization of model performance and the effective utilization of resources. We developed a model fusion technique that employs a soft voting procedure and layered meta-learning layers. A soft voting technique was initially employed on the complementary models to enhance the performance of the basic learner. Thereafter, these results were conveyed to the meta-learning layer for consolidation. The meta-learning layer derived deep feature correlations from the data and later generated the final outcomes. This method substantially diminishes the intricacy of the model architecture and prevents the development of excessively huge and complicated model frameworks. Simultaneously, it facilitates a substantial decrease in the utilization of computational resources.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Segmentation Task in Histological Image Analysis
#### 5.5.3 Tissue Segmentation  
The use of GANs for segmenting and processing histological tissue images has encompassed diverse approaches, each addressing unique challenges in digital pathology. [PERSON] al. [82] introduce a novel end-to-end framework named automatic consecutive context perceived transformer GAN (ACCP-GAN) for fully automatic serial sectioning image blind inpainting. The first stage network (auto-detection module) is designed to detect and repair broken areas roughly. It then guides the second stage network (refined inpainting module) to generate expected patches precisely; therefore, the segmentation part integrates with the restoring part. Based on a self-attention mechanism, the transformer module (SPTransformer) is introduced to enable the refined inpainting module to focus on features from neighboring images, assisting in correcting inpainting results.  
[PERSON] et al. [93] propose a Residual CycleGAN for domain adaptation across different histology stains. This work is particularly valuable for segmentation tasks as it tackles the common issue of stain variability, ensuring that segmentation models trained on one stain generalize more effectively across datasets. By transforming tissue images between domains while preserving underlying structures, their method enables robust segmentation across diverse staining protocols.  
[PERSON] et al. [60] introduced a novel stain-adaptive self-supervised (SASSL) learning method framework for histopathological image analysis. The method focuses on harmonizing stain variations, which, like [PERSON] et al. [93], indirectly strengthens segmentation pipelines by improving data consistency without requiring manual labels.  
[PERSON] et al. [85] take a creative angle with a perceptual embedding consistency-driven GAN for generating seamless virtual whole-slide images (WSIs). This approach enables the synthesis of large-scale, realistic tissue images while maintaining fine structural details. Although their focus is image synthesis, the ability to generate high-fidelity tissue representations provides valuable data augmentation for segmentation tasks, particularly in rare or complex tissue structures.  
Supervised learning is key for segmentation in histology, and several studies use GANs to enhance data quality. [PERSON] et al. [61] present a UNet+/seg-cGAN model for translating label-free images into histology-like images, facilitating visual reviews by pathologists. Meanwhile, [PERSON] et al. [95] explore the generation of deepfake histological images using a Pix2 Pix-GAN model. The work highlights the potential of synthetic data to augment and diversify training datasets for segmentation models in prostate cancer histological images using simple semantic labeling.
## 3 Results and Discussion
### Lubrication Performance under Different Discharge Pressure
#### 3.2.1 Total Torque \(T_{\rm t}\) in a Whole Period  
The included angle between two adjacent teeth is \(\gamma\). Calculating the lubrication properties, the calculation period is not \(2\pi\) but \(\gamma\), since the gate-rotor coincides to itself after a rotation angle of \(\gamma\). In this calculation, \(\delta\) is set 0 throughout. The calculation result is shown in Figure 12.  
Total torque \(T_{\rm t}\) is negative in the whole period, which shows that water films at trailing sides are more powerful than those at leading sides without deflecting (\(\delta=0\)). That \(T_{\rm t}\) is negative also implies  
Figure 12: The total torque of a whole period under different discharge pressures.  
that the water film at leading side will bear the load of \(|T_{\rm t}|\). The smaller value of \(T_{\rm t}\) means \(|T_{\rm t}|\) is bigger and more difficult to bear by the leading side films.  
It is easily found that \(T_{\rm t}\) decreases with the discharge pressure raising for any definite MPP. This proves that \(T_{\rm b}\) grows faster than \(T_{\rm a}\) and the load \(|T_{\rm t}|\) for the leading side films raises with the discharge pressure increment. Taking MCT as an example, the peak of \(|T_{\rm t}|\) increases by 494.52% from 0.8 MPa to 1.6 MPa.  
It can also be found that under any discharge pressure, the SCT has the minimum \(T_{\rm t}\). The SSLT has the maximum of \(T_{\rm t}\) under 1.6 MPa and 1.2 MPa and the MCT has the maximum of \(T_{\rm t}\) under 0.8 MPa. Under the discharge pressure of 1.6 MPa, \(T_{\rm t}\) of SSLT varies from \(-\)2.905 N-m to \(-\)0.477 N-m, \(T_{\rm t}\) of SCT varies from \(-\)8.526 N-m to \(-\)2.258 N-m, \(T_{\rm t}\) of MCT varies from \(-\)5.506 N-m to \(-\)0.531 N-m. The SSLT has the minimum load \(|T_{\rm t}|\) and the SCT has the maximum load \(|T_{\rm t}|\). However, SSLT cannot be regarded as the best profile since it is still related to the water film stiffness.  
In addition, all the \(T_{\rm t}\) curves in Figure 12 are negative. This can be explained by the fact that although there are three teeth meshing with the screw, the contributions of different tooth to \(T_{\rm t}\) is not the same. The first tooth (See Figure 6) is the dominant one due to the high gas pressure applied on it. In the first tooth, the meshing line at trailing side is longer than that at leading side. Therefore, the water film torque generated at trailing side of the first tooth is the maximum one as \(\delta=0\). This is also the basic reason why all the \(T_{\rm t}\) curves are negative.
## 3 Results and Discussion  
During this study, the composting process was carried out satisfactorily. Continuous monitoring of the mixture temperature and the temperature of the heating devices was conducted. It should be noted that during the composting of the sludge with the addition of zeolite, the self-heating process (following initial forced heating) was more intensive. Furthermore, the thermophilic phase lasted longer. This may be attributed to zeolite enhancing the conditions for microbial growth and reproduction. Zeolite adsorbs ammonium nitrogen and other nutrients, making them more accessible to microorganisms. This accelerates the decomposition of organic matter, leading to increased temperatures within the composting mass. Additionally, the porous structure of zeolite enhances the overall porosity of the compost, improving aeration. This provides better access to the oxygen required by aerobic microorganisms. Moreover, zeolite's ability to retain moisture helps maintain optimal moisture levels, which also promotes increased microbial activity and, consequently, higher temperatures. These temperatures were correlated within an error margin of \(\pm\)2 \({}^{\circ}\)C and are reflected in the temperature profile of the composting process (Figure 4). The incorporation of zeolite into the composting process modifies the temperature dynamics, leading to a more efficient and stable composting system. Future studies should further investigate the optimal zeolite dosages and assess the long-term impacts on the compost quality and soil health. The composting process commenced with a phase of forced heating (1) of the reactors in water baths, which increased the temperature from +20 \({}^{\circ}\)C to +40 \({}^{\circ}\)C over a period of 40 h. Once the target temperature was reached, the thermal regime of the process was maintained autonomously due to the decomposition of the sludge and the activity of the microorganisms. The thermophilic phase (2) lasted approximately 10 days, followed by a mesophilic phase (3) that continued for 18 days. After the active decomposition phase, the cooling and maturation phase of the compost (4) began. During this stage, the temperature of the mass was monitored daily. Ultimately, the entire composting process took 1240 h. The overall results of the analyses for all samples are summarized in Table 1.  
Figure 4: Temperature profile of composting: (1) heating phase; (2) thermophilic stage; (3) mesophilic stage; (4) cooling of the compost. Figure 5 shows that the accelerated freeze-thaw-drying cycles resulted in the least decomposition of organic matter in the sludge. The rapid temperature fluctuations inhibit the microbial growth and enzymatic activities necessary for degradation. The freezing temperatures cause cellular damage to microorganisms and slow down metabolic processes, while rapid drying reduces the moisture content below levels suitable for microbial activity. Biological degradation of the material did not commence within the short timeframe, particularly under freezing conditions, as microbes require stable conditions to adapt and proliferate.