esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ML Algorithms  
For the purposes of the current study, four different classification models, each based on a different ML algorithm, were tested. Support Vector Machine (SVM), Categorical Boosting machine (CatBoost), Light Gradient-Boosting machine (LightGBM), and Adaptive Boosting machine (AdaBoost) were the well-documented ML algorithms that were employed in the scope of this work. This range of algorithms has been widely used by the research community [20; 21; 22; 23; 24; 25; 26; 27] with proven effectiveness in numerous medical scenarios.  
The SVM classifier [28] is a popular algorithm in machine learning used for classification and regression analysis. SVM is a supervised learning model that identifies the optimal hyperplane to separate two classes of data with the maximum margin. This approach makes SVM particularly useful for complex datasets. The algorithm has been used in various domains, including finance, healthcare, and natural language processing. Particularly, SVM has been used to diagnose breast cancer, to predict stock market trends, and to classify sentiment in social media data.  
CatBoost [29] is a gradient boosting algorithm that has become increasingly popular in recent years. The algorithm is designed to handle categorical data and can automatically preprocess this type of data, making it particularly useful for natural language processing tasks. CatBoost has been used in various domains, including healthcare, finance, and e-commerce. Various CatBoost applications include, among others, predicting breast cancer prognosis, identifying fraudulent credit transactions, and recommending products to online shoppers.  
On the other hand, LightGBM [30] is a gradient boosting framework that utilizes decision tree algorithms to improve the speed and accuracy of machine learning models. It was developed by Microsoft researchers in 2017, and has since gained popularity among data scientists and researchers due to its speed and accuracy. The algorithm uses a unique approach to build trees by splitting the data points that contribute the most to the loss function, which results in a more efficient and accurate model. LightGBM has been used in various applications, including image recognition, natural language processing, and financial forecasting.  
Adaptive Boosting [31], or as it is commonly known, AdaBoost, is a classification ML algorithm designed by [PERSON] and [PERSON] in 1995. It is a popular ensemble learning algorithm that combines multiple weak learners to create a stronger model. AdaBoost assigns weights to each training instance, and then trains weak learners to classify the data. The algorithm then combines the weak learners to create a final model. AdaBoost has been used in various domains, including computer vision, finance, and healthcare. For example, AdaBoost has been used to detect faces in images, to predict stock prices, and to diagnose heart disease.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 9 Conclusions and Future Works  
In conclusion, C-SHAP offers a significant advancement in feature interpretability for machine learning models by addressing the computational limitations associated with traditional SHAP. By leveraging K-means clustering, C-SHAP consistently reduces execution times without sacrificing the accuracy of feature selection. For instance, in the Heart Disease dataset, C-SHAP decreased the Random Forest execution time from over 400 s to just 0.29 s while maintaining consistent results with SHAP in identifying influential features. This improvement highlights C-SHAP's potential as an efficient interpretability tool across a variety of datasets and machine learning tasks, making it a viable solution for real-time applications.  
This study also underscores the need for scalable interpretability approaches in fields with large, complex datasets, aligning with the goals of emerging research at the intersection of machine learning and specialized domains, such as software product-line engineering. While our study focuses on SHAP and C-SHAP, incorporating widespread strategies like saliency maps into future work could provide comparative insights and extend interpretability to new domains.  
In future work, we aim to implement C-SHAP specifically in the software engineering domain, exploring its effectiveness in analyzing software engineering datasets and interpreting model decisions within this specialized field. This direction could further validate C-SHAP's adaptability and expand its practical applications.  
Future work will address the current limitations of C-SHAP by exploring dynamic clustering methods that adapt to dataset complexity and size. Techniques such as hierarchical clustering or density-based methods (e.g., DBSCAN) could offer greater flexibility and improve clustering fidelity in high-dimensional datasets. Moreover, metrics like clustering stability, feature attribution robustness, and fidelity in aggregated SHAP values will be incorporated to evaluate trade-offs between computational efficiency and interpretability. These improvements aim to enhance the adaptability and scalability of C-SHAP across diverse real-world applications.  
Conceptualization, G.R.; methodology, G.R. and D.R.R.; software, G.R.; validation, G.R. and D.R.R.; formal analysis, G.R. and D.R.R.; investigation, G.R.; resources, G.R.; data curation, G.R.; writing--original draft preparation, G.R.; writing--review and editing, D.R.R. and C.K.R.; visualization, G.R. and D.R.R.; supervision, D.R.R. and C.K.R.; project administration, C.K.R.; funding acquisition, C.K.R. and K.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 3 Setup of Numerical Models
### Field Scale Modelling--The Qamchuqa Reservoir  
The Qamchuqa reservoir is one of the producing reservoirs of the Jambur field, which is located in the north of Iraq. The reservoir consists of two thick-bedded limestone formations with an average thickness of approximately 600 m in total; further geological description of the Qamchuqa reservoir can be found in [PERSON] et al. [1]. The history matching project has been carried out on the reservoir for four decades of production history, where consistent matching results were achieved for most of the producers [2]. Even recently, in 2020, the production of the Qamchuqa reservoir has continued  
Figure 6: The simulated cases of the intermediate scale model, (**A**) fractures were saturated entirely with oil, and the model comprises a low salinity brine injector, and an oil producer (**B**) fractures were filled with water, and the fluid exchange occurred due to capillary, viscous, and gravity forces.  
using natural driving mechanisms, which resulted in a considerable pressure drop of (\(\approx\)1200 psi) in the reservoir to the end of 2015 [2]. Therefore, the low salinity technique represents a competitive, affordable recovery method to support the reservoir pressure and to improve the sweep efficiency from the matrix medium as well as to sustain the production from the Qamchuqa reservoir. As highlighted previously, the same model of the Qamchuqa reservoir, which has been discussed in details in our previous work of [PERSON] et al. [2], has been used to assess the performance differences utilising linear relationship and exponential approach of the weighting factor. The same low salinity water injection scenario of 14 years was performed by injecting 45,000 STB/D through six injection wells. Furthermore, the salinity of the injected brine is 3000 ppm, where both injected brine salinity and the reservoir salinity represented as total dissolved solids (TDS) in the simulation model using ECLIPSE black oil simulator.
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 4 Mission Profile Analysis of the Space Manipulator
### Task Network Simplification of the Manipulator  
Using the original tasks and operators defined above, a hierarchical task network was constructed according to the HTN theory. The core of HTN planning algorithm allows simplifying tasks and solving conflicts between tasks. Due to the serial execution of the manipulator, the planning problem is sequential, so an integrated task simplification and conflict handling strategy is proposed. The task network hierarchy of the planning problem is shown in Figure 3.  
The specific flow of task profile analysis of the space manipulator is presented below, as shown in Figure 4.  
1. Extracting tasks sequentially from the task network.
2. If the task is not the original task, find the corresponding one in the planning domain and replace the non-original task; The method of finding \(Me\) consists in comparing the effect mask of this task with that of each task in the \(Me\) set. If the set 1 item of the latter includes the former, \(Me\) can be used to simplify the task. Go to step 1.
3. Compare the current status with the completion status of the task. If the current status meets the task completion effect, it means that the task does not need to be executed, then delete the task from the task network.
4. If the current status meets the preconditions of the task, refresh the current status by using the operator of the task, go to step 1.
5. If the current status cannot meet the preconditions of the task, find a task that can meet the preconditions in the planning domain and add the task.
6. If the preconditions of the current task cannot be met, the result is planning failure.  
Figure 3: Task network hierarchy in task section analysis.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 3 RPL-Cga
### Requirements for RPL-CGA Routing Metrics  
To optimize the network performance, the employed routing protocol should abide by the properties of consistency, optimality, and no-loop. Consistency means that if packets from node \(v_{1}\) to \(v_{n}\) are delivered through the path \(P(v_{1},\)\(v_{n})\) = \((v_{1},\)\(v_{2},\cdots,\)\(v_{n})\), then other nodes on path \(P\) must take the same decisions. For example, packets form \(v_{2}\) to \(v_{n}\) should select path \(P(v_{2},\)\(v_{n})\) = \((v_{2},\)\(v_{3},\cdots,\)\(v_{n})\), and the same for \(v_{3},\)\(v_{4},\cdots,\)\(v_{n-1}\). Optimality means that the routing protocol should transmit packetsthrough the lightest path (minimal overhead, delay, or other metrics) or optimal routes among nodes. No-loop means that for every path from source to destination, any two nodes on the path are different.  
According to Ref. [18; 22] isotonicity and monotonicity of routing metrics are enough to guarantee that the routing protocol can produce consistency, optimality, and no-loop path during route computation period. So in the following paragraphs, isotonicity and monotonicity of routing metrics are given in detail.  
Monotonicity  
If the metric value of a path or link does not decrease when suffixed or prefixed by another link or path, the metric is monotonic. The mathematical expression is as follows:  
\[F(P(v_{a},v_{b}))\leq F(P(v_{a},v_{b})\oplus P(v_{b},v_{c})),F(P(v_{a},v_{b}) )\leq F(P(v_{c},v_{a})\oplus P(v_{a},v_{b})),\]  
\[\{\forall P(v_{a},v_{b}),P(v_{b},v_{c}),P(v_{c},v_{a})\in\Omega\}.\]  
Isotonicity  
If the order relation of any two links or paths is invariability when prefixed or suffixed by a third link or path, the metric is isotonic. The mathematical expression is as follows:  
\[F(P(v_{a},v_{b}))\leq F(P(v_{c},v_{d}))\Rightarrow\left\{ \begin{array}{l}F(P(v_{a},v_{b})\oplus P(v_{c},v_{f}))\leq F(P(v_{c},v_{d}) \oplus P(v_{c},v_{f}))\\ F(P(v_{g},v_{h})\oplus P(v_{a},v_{b}))\leq F(P(v_{g},v_{h})\oplus P(v_{c},v_{ d}))\end{array}\right.\]
## 1 Introduction  
Image distortion caused by electromagnetic interference (EMI) that interferes with the performance of electrical equipment is a very critical problem in various precision research applications [1]. Particularly in semiconductor manufacturing, to detect and classify various defects on wafers, many automatic defect classification (ADC) methods have been developed [2; 3]. In recent years, a variety of machine learning and deep learning techniques using SEM image data have been applied to defect detection and classification tasks in diverse areas, including semiconductor manufacturing [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. (2009) proposed a novel recognition technique for defect areas on semiconductor wafers using SEM images [18]. [PERSON] et al. (2020) investigated a deep convolutional neural network (CNN) for defect classification using SEM images and energy-dispersive X-ray (EDX) spectroscopy data [7]. [PERSON] et al. (2021) presented a review of the defect detection and classification in semiconductor processes using machine learning and deep learning approaches combined with SEM images [13]. [PERSON] et al. (2022) proposed an efficient method for processing the low-quality image data of integrated circuits to provide fundamental data for verification tasks [16]. [PERSON] et al. (2022) used the bag of visual words (BoVW) and Fisher vector (FV) coding methods for semiconductor wafer defect classification using SEM images [15]. [PERSON] et al. (2022) proposed a generative adversarial network (GAN) that enables precise pattern alignment by transforming SEM images into target-like computer-aided design (CAD) images [17]. However, most previous research focused on defect detection and classification in semiconductor wafers but not on the identification and quantification of the distortion degree of EMI-contaminated SEM images. Thus, in this research, we consider several denoising and edge detection algorithms and then extract five measures (i.e., image object area, image object contour, rectangular area of the image object, extend index, and solidity index) to quantify the image distortion caused by EMI in semiconductor SEM images. To select the best denoising and edge detection techniques, we considered two evaluation metrics, that is, the mean squared error (MSE) for denoising and classification accuracy for edge detection techniques. The rest of this article is organized as follows. The literature reviews of EMI analysis, denoising algorithms, and edge detection algorithms are presented in Section 2. The details of the proposed approach and both experimental framework and analysis results are given in Sections 3 and 4, respectively. Finally, the conclusions and some future research directions are discussed in Section 5.
## 1 Introduction
There have been studies proving that during the formulation of functional additives based on proteins or fibers next to phenolics, flavor compounds were also adsorbed on the applied carrier [19; 20; 21; 22]. Another important factor affecting the overall acceptance of the food products on the market is the characteristic flavor. The overall flavor profile of fruits is affected by the equilibrium between volatile compounds and their concentrations. Tart cherries are known for their specific and pleasant flavor. Different organic compounds such as carbonyls, alcohols, acids, esters and terpen make up the flavor of tart cherries [23]. During the formulation and preparation of food products including functional additives, changes in flavor composition are often a result of the impact of processing conditions which can cause different chemical changes in flavor compounds and other food constituents, and, additionally, processing conditions can lead to interactions between them. Such protein-based aggregates can be utilized for modulation of the phenolic profile, flavor profile, color and antioxidant potential of different food products to which they are added. The aim of this research was to formulate aggregates from tart cherry juice and dairy protein matrix to investigate whether different amounts (2% and 6%) of protein matrix have an impact on the adsorption of polyphenols adsorption as well as on the adsorption of flavor compounds. High-performance liquid chromatography (HPLC) was applied for the identification and quantification of polyphenols present in the samples, while the flavor profile was evaluated using gas chromatography with mass spectrometry (GC-MS) analysis. Color parameters were measured, and Fourier transform infrared spectroscopy (FTIR-ATR) screening of aggregates was performed. Additionally, antioxidant activity was determined spectrophotometrically using different assays.
# Water Quality Modelling, Monitoring, and Mitigation
## 1 Introduction  
Due to the rapid increase in anthropogenic activity in catchments, further adverse changes in access to water resources are expected in the future [1] Under these conditions, water quality (WQ) plays an important role that determines its economic utility, including in the potable or drinking water supply, recreation, and agriculture. In the modern era, the study of and commitment to monitoring, modeling, and mitigation have become important and meaningful aspects of the environmental impact assessment process [2]. Under various circumstances, the potentially adverse impacts on ecological flora and fauna can be mitigated through the strategic design and implementation of appropriate models, tools, or techniques to diminish the severity of the effects [3, 4]. Different types of nutrients, contaminants (heavy/trace metals), micropollutants, nanoparticles, microplastics, microbes, etc., disturb the ecological life in freshwater bodies [5, 6]. Therefore, evidence-based pollution control is urgently needed to focus on the elementary level of water governance, known as \"monitoring, modeling, and mitigation\". Monitoring sets the empirical basis by providing spatio-temporal information on substance (contaminants and WQ parameters such as dissolved oxygen, biochemical oxygen demand, chemical oxygen and demand, and nutrients) loads as well as the driving boundary conditions for evaluating WQ trends and statuses and for further providing useful information to mitigate contamination and to balance ecological life [7]. Thus, modeling helps to provide long-/medium- and long-term information for times and locations where monitoring is not at all possible [8, 9].  
The proposed Special Issue will explore cross-disciplinary approaches, modeling, and methods and will discuss water quality risks as well as solutions for the implications for environmental sustainability and for the further conservation of ecological life. Theinterconnectedness of this critical problem cannot be assessed with traditional approaches; instead, inter- and trans-disciplinary approaches are urgently required worldwide to deal with water resource problems and environmental sustainability challenges.
## 4 Selected Methods and Techniques in Landscape Assessment
### LIDAR in the Landscape Assessment  
One of the modern and fast methods of 3D data collecting is airborne laser scanning. Thanks to the use of LIDAR technology (light detection and ranging), it is possible to effectively scan the terrain surface with laser beams. The possibility of using data from laser scanning for the construction of a DTM is very large. This method is fast and enables the measurement of large surface areas and allows registration of both the height of the terrain relief. and the height parameters of the land cover elements, which makes possible the accurate three-dimensional modeling of buildings and infrastructure for spatial visualizations of the developed area. The weakness of the LIDAR measurements is the lower accuracy of determining the height of points (oscillating within 0.15-0.25 m) than in the case of other methods. However, it is sufficient, assuming the vertical resolution requirements of the DTM models, especially ones developed at smaller scales. The absorption of a laser pulse by some surfaces, such as water or asphalt, is also a problem because it can cause some difficulties during the creation of the DTM. Due to the accurate results, speed, and development possibilities of the application of this method, it displaces at a rapid pace the radar methods of measuring the Earth's surface [47].
## 5 Discussion
### Soil Variability and Broader Applicability  
However, the external validity of these findings hinges on the representativeness of the soil conditions in our study site. The experimental site in Changji City features sandy loam soils with specific physicochemical properties, including a bulk density of 1.37 Mg m\({}^{-3}\) and a water holding capacity of 0.35 cm\({}^{3}\) cm\({}^{-3}\) in the shallow layer (10-20 cm). Thesecharacteristics influence root growth, water retention, and nutrient availability, which are critical for cotton cultivation in arid environments. While our model calibration and optimization were tailored to these specific soil conditions, the framework's adaptability to other soil types remains an important consideration for broader applicability.  
Soil variability across different regions can significantly impact the performance of nitrogen management strategies. For instance, clay-heavy soils may exhibit different water and nitrogen dynamics compared to sandy loans, potentially altering the optimal fertilization schedules. The DSSAT model's strength lies in its ability to incorporate site-specific soil data, allowing for localized calibration. This feature enables the framework to account for soil heterogeneity by adjusting parameters related to nutrient diffusion, root uptake kinetics, and water-nitrogen interactions. Our sensitivity analysis confirms that the optimized strategy's robustness stems from its capacity to dynamically adapt to both biophysical processes and economic constraints, suggesting that the framework can be extended to other regions with appropriate soil data calibration.  
While these findings are rigorously validated for Changji City's semi-arid conditions, broader applicability requires caution. The confirmed outcomes--enhanced NUE, economic resilience, and fiber quality--are directly attributable to the site-specific calibration of cultivar parameters and soil profiles. However, extrapolation to divergent agroclimatic zones remains speculative without further validation. The study's theoretical contribution lies in demonstrating that ratio-driven optimization--balancing yield against fertilization intensity--structurally embeds risk mitigation into agricultural decision systems. Unlike machine learning models that extrapolate poorly beyond training distributions, DSSAT-GA's process-based constraints enforce biophysical plausibility, enabling reliable performance under novel perturbations. This hybrid paradigm offers a scalable template for sustainable intensification, particularly in volatile arid regions where traditional practices falter. Nevertheless, persistent challenges include real-time deployment barriers and climate uncertainty incorporation--frontiers demanding closer integration of IoT sensor networks and ensemble weather forecasting. By bridging these gaps, the framework could evolve into a dynamic decision-support tool, advancing precision agriculture while safeguarding soil health in global cotton systems.
## 3 Data Source and Analysis
### Using a Neural Network to Build an Early Warning Model  
#### 3.2.1 Questionnaire Design  
The \"Questionnaire Survey on Influencing Factors of Unsafe State of Construction Workers\" was developed through data inquiry and expert consultation. The questionnaire comprises fifty-seven questions in three parts. The first part includes basic personal information, while the second part is the measurement of factors, primarily consisting of 22 indicators. Each indicator is reflected through two questions. Each question has five options, corresponding to different points. The third part measures worker safety state levels through six questions, each with five options. The questionnaires were distributed to multiple construction workers. A total of 500 eligible questionnaires were eventually returned. The survey objects are shown in Table 9.