esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology
#### 2.3.5 Model Evaluation Metrics  
Multiple complementary metrics were employed to comprehensively evaluate model performance across different dimensions [85, 86, 87]:  
The coefficient of determination (R\({}^{2}\)) measures the proportion of variance in UCS values that is predictable from the input variables. R\({}^{2}\) values range from 0 to 1, with higher values indicating better model fit. For geotechnical applications, R\({}^{2}\) values above 0.9 generally indicate good predictive capability [88]. While R\({}^{2}\) provides an intuitive measure of overall fit quality, it can be sensitive to dataset characteristics and may not fully capture prediction accuracy.  
Mean squared error (MSE) measures the average squared difference between predicted and actual UCS values. This metric was used as the primary optimization objective during hyperparameter tuning.  
Root mean square error (RMSE) quantifies the average magnitude of prediction errors in the same units as the target variable (kPa). By squaring errors before averaging, RMSE gives higher weight to larger errors, making it particularly relevant for engineering applications where larger deviations may have significant consequences.  
Mean absolute error (MAE) represents the average absolute difference between predicted and actual UCS values (kPa), providing a more intuitive measure of typical error magnitude that is less sensitive to outliers than RMSE.  
Mean absolute percentage error (MAPE) expresses errors as percentages relative to actual values, offering a dimensionless measure of prediction accuracy that facilitates interpretation across different scales.  
Explained variance score (EVS) measures how well the model accounts for the variation in the data. Differences between R\({}^{2}\) and EVS can indicate the presence of systematic bias in predictions.  
Maximum error captures the worst-case prediction scenario, which is particularly relevant for engineering applications where safety margins must account for potential prediction inaccuracies.  
Median absolute error (MedAE) measures the median of all absolute differences between predicted and actual values. As a robust statistic, it provides insight into typical prediction error magnitude while being less influenced by outliers than MAE.  
Coefficient of variation of RMSE (CV RMSE) normalizes the RMSE by the mean of observed values, expressed as a percentage. This relative error metric facilitates comparison across different scales and enhances interpretability in engineering contexts. Values below 10% generally indicate good performance for engineering applications.  
These diverse metrics enable comprehensive evaluation of model performance beyond single-dimensional assessment, facilitating the selection of models that balance different aspects of prediction quality relevant to geotechnical applications.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 4 End-Edge Computation Offloading Based on Lyapunov-Guided DRL  
In this section, we first explicitly formulate the network computation rate maximization problem, then decouple the problem by Lyapunov optimization and finally propose the intelligent computation offloading algorithm based on Lyapunov-guided DRL.
# Research on Hybrid Vibration Sensor for Measuring Downhole Drilling Tool Vibrational Frequencies
###### Abstract  
The vibration parameters during drilling play a critical role in enhancing drilling speed and ensuring safety. However, traditional downhole vibration sensors face limitations in their power supply methods, hindering widespread adoption. To address this challenge, our research introduces a novel solution: a hybrid downhole vibration sensor (HDV-TENG) utilizing triboelectric nanogenators. This sensor not only enables the measurement of low- to medium-high-frequency vibrations using self-power but also serves to energize other downhole devices. We utilized a self-constructed vibration simulator to replicate downhole drilling tool vibrations and conducted a comprehensive series of sensor tests. The test results indicate that the frequency measurement bandwidth of the HDV-TENG spans from 0 to 200 kHz. Especially, the measurement errors for vibrations within the low-frequency range of 0 to 10 Hz and the high-frequency range of 10 to 200 kHz are less than 5% and 8%, respectively. Additionally, the experimental findings regarding load matching demonstrate that the HDV-TENG achieves an output power level in the milliwatt range, representing a significant improvement over the output power of traditional triboelectric nanogenators. Unlike traditional downhole vibration measurement sensors, HDV-TENG operates without requiring any external power supply, thereby conserving downhole space and significantly enhancing drilling efficiency. Furthermore, HDV-TENG not only offers a broad measurement range but also amplifies output power through the synergy of a triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), and electromagnetic power generator (EMG). This capability enables its utilization as an emergency power source for other micropower equipment downhole. The introduction of HDV-TENG also holds considerable implications for the development of self-powered underground sensors with high-frequency measurement capabilities.  
triboelectric nanogenerator; piezoelectric nanogenerator; electromagnetic power generation; downhole drilling tools; vibration sensor +
Footnote †: journal: Applied Sciences  
1 Shaanxi Daanxi Coal Caojitan Mining Co., Ltd., Yulin 7719100, China; [EMAIL_ADDRESS]  
2 China Coal Technology & Engineering Group Coal Mining Research Institute, Beijing 100013, China;  
[EMAIL_ADDRESS]  
3 Mining Research Institute of China Coal Science Research Institute, Beijing 100013, China  
4 Faculty of Mechanical and Electronic Information, China University of Geosciences (Wuhan),  
Wuhan 430074, China; [EMAIL_ADDRESS]  
5 Tiandi Science & Technology Co., Ltd., Beijing 100013, China  
*Correspondence: [EMAIL_ADDRESS]
On this basis, the optical parameters of BDS-3 MEO satellites are adjusted, and an a priori model of the satellite's cuboid body is established. We compared it with the a priori model derived from ground station data. The correlations between the SRP parameters of the ABW model are discussed. The systematic errors in the orbits and clock offsets obtained in the modeling are compared and evaluated. Finally, the a priori model is used to compensate for the ECOM model, and its performance is evaluated in terms of the obtained orbits and clock offsets.
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
## 5 Degradation Analysis of Building Interstory Backbone Curve Parameters
### Simulation Procedure  
In this work, the nonlinear Euler beam element is used to simulate the corroded RC frame structures. The curvature-bending moment hysteretic model of the nonlinear Euler beam element adopts a trilinear backbone curve model and a single-parameter hysteretic model as shown in Figure 2. The detailed simulation procedure of corroded RC frame structures is shown in Figure 7.  
The simulation procedure of corroded RC frame structures can be divided into five steps as follows:  
1. Collect design information of the RC frame structure, including dimension of components, reinforcement details, and degree of corrosion of each component.
2. Simulate the curvature-bending moment hysteretic curves of all beam and column components in uncorroded state. There are a lot of methods available for simulating the performance of slender RC components in uncorroded states. Among them, the fiber beam model exhibits good computational efficiency and can well capture the nonlinear properties of steel and concrete  
Figure 7: Simulation procedure of corroded RC frame structures.  
materials [61]. Therefore, a fiber beam model is used in this step to calculate the hysteretic curves of uncorroded components.
3. Calibrate the trilinear backbone curve and the single-parameter hysteretic models (Figure 2) according to the simulated curvature-bending moment hysteretic curve.
4. Adjust the backbone curve parameters of corroded components according to the degree of corrosion of each component and the reduction equations obtained in Section 4.
5. Construct the nonlinear Euler beam model of corroded RC frame structures. Perform pushover analyses to obtain the interstory backbone curves of corroded RC frame structures.
## 1 Introduction
In our previous study, we assumed that all sensor nodes are uniformly distributed. However, this is not realistic and it might raise unexpected problems when we deploy the protocol in reality. 5. No GPS (_Global Position System_) or like is required to provide node's location information. In SCODE, we have assumed that sensor nodes need to be aware of their locations. However, attaching GPS or like increases high cost for sensors, and thus reducing the feasibility of the deployment. By using deployment knowledge, the proposed scheme can establish a key infrastructure and end-to-end communication for the sensor network. The major contributions of this study are then four fold:  
1. We propose a first, novel secure routing protocol and key management scheme supported sink mobility in WSNs. 2. Compared with existing approaches, the proposed routing protocol is more energy-efficient, lower communication overheads, high packet delivery ratio (\(>\)97%), and secure against the common attacks in sensor networks. 3. Our key management scheme takes advantage of the hexagonal topology and expected location information not only to reduce the memory cost but also get better resilience against node compromise attacks. Moreover, the scheme can eliminate the impacts of node compromise attacks on links between non-compromised nodes. 4. Intensive theoretical as well as simulation-based analysis and evaluation studies show that the proposed scheme achieves better security and efficiency than our previous work as well as other popular approaches like TTDD and DD. The rest of the paper is organized as follows: Section 2 discusses common attacks in sensor networks routings. Section 3 contains brief description on the GAF protocol, as it is used as an underline protocol in our proposal. Section 4 briefly describes our previous study SCODE. Section 5 presents SCODE_plus_ scheme in details including key management scheme and routing protocol. We analyze security and cost of the scheme in Section 6. Section 7 shows performance evaluation results. Section 8 discusses some existing routing protocols as well as their vulnerabilities and shortcomings. Finally, we conclude the paper in Section 9.
## 1 Introduction
Recently, some research has shown that some carbon materials such as black carbon or activated carbon improved the kinetics of leaching of sulfide minerals such as chalcopyrite [24; 25; 26; 27]. A substantial increase in the copper extraction rate after carbon material addition was observed, probably due to the low redox potential as well as galvanic interaction between sulfide mineral and carbon material [26; 27]. However, to our knowledge there are no previous studies on the effect of carbon-based materials on the Zn leaching from sphalerite or oxidized minerals that are traditionally present in some mine tailings. The use of carbon-based materials as catalysts in the leaching of low-grade ore could be advantageous. Carbon-based materials could be obtained by carbonization/activation of biomass, a renewable resource [25] and the great stability of carbon-based materials may facilitate their recovery after leaching process and re-utilization, in a similar way to the use of activated carbon in gold metallurgy [28]. The main objective of the present work is to study the use of two carbon-based materials in the leaching of zinc and copper from low-grade tailing waste. Furthermore, the effect of different H\({}_{2}\)SO\({}_{4}\) concentrations was studied in order to optimize the leaching conditions. Zinc and copper extracted from mine tailings was determined at 2, 4, and 6 h. Parameters including pH and redox potential of the leaching solutions were monitored during the process at 1, 2, 4, and 6 h.
## 1 Introduction
Notations: Matrices and vectors are denoted by boldface upper case and boldface lower case, respectively. \((\cdot)^{T}\), \((\cdot)^{H}\), \((\cdot)^{-1}\), and \((\cdot)^{*}\), respectively, denote the transpose, Hermitian transpose, inverse, and conjugate. \([\cdot]_{ij}\) and \([\cdot]_{i}\) denote the \((i,j)\)-th element of a matrix and the \(i\)-th element of a vector. \(\mathbb{C}^{M\times N}\) denotes the space of \(M\times N\) complex-valued matrices. The trace, \(l_{1}\) norm, and Frobenius norm are denoted by \(\text{Tr}(\cdot)\), \(|\cdot|_{1}\), and \(\|\cdot\|_{F}\), respectively. \(\mathcal{R}\{\cdot\}\) and \(\mathbb{E}(\cdot)\), respectively, denote the real-part and expectation operation. \(\circ\) denotes Hadamard products. Furthermore, \(\mathbf{I}_{N}\) denotes the \(N\times N\) identity matrix. \(\text{diag}(\mathbf{a})\) is a diagonal matrix with the elements of \(\mathbf{a}\) on its main diagonal and \(\text{diag}(\mathbf{A})\) represents the extraction of the diagonal of \(\mathbf{A}\). \(\mathcal{CN}(\mathbf{0},\mathbf{K})\) denotes the circularly symmetric complex Gaussian distribution with zero mean and covariance matrix \(\mathbf{K}\).
## 3 Results and Discussion
### Comparison of the Key Odorants in RZT Screened by OAV and GC-O
Therefore, the above 17 odorants, including linalool, gerani, (Z)-jasmone, \(\alpha\)-ionone, (E)-\(\beta\)-ionone, indole, linalool, gerani indole, hexanoic acid methyl ester, 2-pentylfuran, 3,5-ocatadien-2-one, \(\beta\)-cyclodextrin, methyl salicylate, heptanal, hexanal, octanal, nonanal, 1-ocen-3-ol, and 2-heptanone, verified by OAV and GC-O methods, were considered the key odorants of RZT. The distinctive aroma quality of RZT arises from the complex interactions among these key odorants, which have thus been characterized as the key contributors to RZT's aroma in this study. It is significant to note that prior research on Rizhao green tea's aroma also identified linalool, hexanal, heptanal, octanal, and nonanal as the predominant compounds responsible for its chestnut-like aroma [54], aligning with our findings. The overall quantity of key odorants and volatiles of RZT found in the aforementioned study, however, slightly diverges from the findings of this investigation, this discrepancy being potentially attributable to differences in the methodologies employed for aroma extraction and detection. In addition to the characteristic chestnut-like aroma of RZT, the volatile compounds responsible for its seaweed-like aroma are equally appealing. It has been proven that hexanal contributes to the formation of seafood flavors by providing its seaweed-like aroma [55] and is also identified as one of the principal compounds contributing to the aroma of Japanese matcha [56; 57]. Additionally, 2-heptanone, found abundantly in various  
Figure 5: Key odorants based on OAV calculations and GC-O analysis in RZT. **(a)**: a venom diagram of key odorants identified by OAV calculations and GC-O analysis. **(b)**: chemical structure of 17 key odorants with dual verification of OAV and GC-O (the unknowns in GC-O cannot be categorized as key odorants because their names and chemical structures were not clear). seafoods such as squid, seaweed, and crabs, is also a key aroma component of many seafood products [58; 59; 60]. Nevertheless, it exhibits a cream-like and cheese-like odor, which implies that 2-heptanone may contribute to the seaweed-like aroma of RZT through interactions with other components.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 4 APT Analysis Model
### Risk Assessment Model  
Risk assessment models are used to quantify threats in IIoT systems, in order to determine which threats pose a greater risk to system security and prioritize their mitigation. For example, the risk matrix model [93] is a method of classifying and evaluating risks based on probability and impact, helping decision-makers identify and manage risks. The risk index model [94] calculates a risk index by combining factors such as the likelihood of a risk occurring, its impact, and sensitivity, to reflect the size and severity of the risk. The risk-scoring model [95] is a method used to predict the probability of an event or disease occurring, by calculating a risk score based on relevant risk factors. The STRIDE model [96] categorizes threats into six categories, including spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege, helping analysts to understand and identify system security threats from the attacker's perspective and formulate corresponding mitigation measures.  
Regarding the node-level epidemic model [97; 98], nodes represent people or things, and infectious diseases represent malware or attacks. In IIoT, nodes can be equipment or sensors connected to the system, while malware can be malicious code or attackers. By simulating the spread of APTs in IIoT, organizations can understand how APT attacks spread and determine how to reduce the impact of attacks. [PERSON] et al. [99] put forward an evaluation method, which describes the situation of APT attacks with a problem domain ontology and could evaluate the risk of an APT attack on an organization. The work [100] used an algorithm to predict the security risk of each node in the network, effectively evaluated the security situation of the network, helped the defender to take measures to prevent APT attacks, and improved the security of the network.  
In conclusion, risk assessment models can help identify and evaluate the likelihood, impact, and losses of APT attacks and formulate corresponding defense strategies and response measures. Risk assessment models can enhance network security awareness and capabilities and strengthen prevention and resistance to APT attacks. Risk assessment models can provide a scientific basis and guidance for network security management and optimize resource allocation and input-output ratios.
## 3 Results  
### Overview  
Pre-operative radiological evaluation in double-leg standing position and clinical scoring confirmed the diagnosis of medial knee osteoarthritis, with a mean varus deformity of 9.3 \(\pm\) 3.2\({}^{\circ}\) on average for the 25 patients, associated with a mean total KOOS score of 56.2 \(\pm\) 14.0 and a mean VAS pain on activity score of 6.3 \(\pm\) 1.7. Six months after surgery, the varus deformity significantly dropped down to 2.5 \(\pm\) 2.8\({}^{\circ}\) (R = \(-\)0.76, \(p\)\(<\) 0.0001), i.e., a more physiological hip-knee-ankle alignment, which was associated with an average total KOOS score of 82.0 \(\pm\) 8.3 and an average VAS pain on activity score of 1.5 \(\pm\) 1.7. The pre-versus post-operative changes in KOOS (R = 0.75) and VAS scores (R = \(-\)0.81) were both highly significant (\(p\)\(<\) 0.001). In terms of [PERSON]'s line with respect to the medial ridge of the tibial plateau, the corresponding pre-operative ML distance changed significantly from a mean of 9.5 \(\pm\) 13.2% pre-operatively to a mean of 39.6 \(\pm\) 11.0% post-operatively (R = 0.87, \(p\)\(<\) 0.0001), i.e., much closer to the centre of the tibial plateau, as desired.  
In gait analysis, the raw (cm/s) and normalized (% body height/s) values for the self-selected walking speed measured pre-operatively were 105.0 \(\pm\) 25.4 and 61.0 \(\pm\) 14.8. Post-operatively, these values became 108.7 \(\pm\) 18.2 and 62.9 \(\pm\) 11.2, respectively. Although not statistically significant, post-operative speed was slightly higher and more consistent among the patients.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 2 Related Studies
### Shadow Detection Methods in Drone Images
#### 2.2.3 Machine Learning
The approach to aggregate the feature representation was similar to the pooling operation used in PSPNet [24]. Instead of aiming to detect the most noticeable shadows only, [9] proposed a shadow detection network based on a multiscale spatial attention mechanism (MSASDNet) to solve false detection in weak illumination regions and small shadow regions. Another architecture that is widely used in various types of segmentation tasks is U-Net. It is an architecture that performs well with a small training dataset and was proven to improve image localization [25]. For shadow detection in aerial images, [26] considered non-local spatial contextual information, and proposed GSCA-UNet, a model composed of a U-shaped encoder and decoder, and a GSCA module for flexibility. Much other research in shadow detection also chose to build network architecture inspired by U-Net, such as [27; 28; 29], which indicates that U-Net architecture is one of the popular architectures in this domain. For this reason, given a small number of images, U-Net architecture is applied in this project to produce acceptable shadow detection results. In the study, some limitations are identified to be further discussed, which are an insufficient training dataset, along with the need to spend too much time on dataset preparation due to complex shadow regions [10], unexpected noises in the resulting shadow masks [26], and inefficiency of trial-and-error post-processing steps [4]. Instead of using existing annotated datasets, such as AISD, which exclude the labeling of small shadowed regions, a new dataset is prepared for this study to focus on low altitude aerial images. Through this process, this study contributes a new annotated dataset with more of a variety of images and allows for more method comparisons in the future.
## 4 Experimental Results and Analysis
### Verification for Automatic Language Identification Using R-Vector
#### 4.3.1 Evaluation and Analysis of Automatic LID
For read speech recorded in clean environments, the R-vector showed superior performance comparable to CNN when it employs SVM, whereas for dialogic speech data recorded in real environments, the R-vector-based approaches provided better or similar accuracy compared to the conventional LID approaches. The results explain that the R-vector conveys reliable and language-discriminative information as a useful LID feature. In particular, the R-vector-based approach requires much less computation complexity than the conventional techniques.
## 2 Materials and Methods
### System Architecture  
The proposed multi-sensory MC detection system follows a centralized architecture where the diagnosis process takes place in the gateway. This allows us to implement lightweight nodes for the distributed independent signal processing units. The simplification in the nodes could reduce their costs and energy consumption drastically, which are the main constraints for WASN deployment in the field.  
Figure 3: The prototype of designed wireless sensor nodes: (**a**) architecture of the wireless AE node; (**b**) architecture of the soil moisture sensor.  
The whole system structure diagram is depicted in Figure 4. It contains three main elements: (1) the wireless nodes, which are in charge of sensing the AE signals and environmental parameters, along with transmitting the featured data to the sink through wireless communication; (2) the gateway, which has a strong computational ability and is able to coordinate the whole wireless network and gather all the sensor nodes' data; and (3) the diagnostic program, which provides the intelligent learning functionality through the fusion of the AE signal and environment parameters' individual scores.