esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Although the success rate of our system in locating the specific sound source is significantly higher compared to the methods without using voiceprint and face features, the overall success rate still has room for improvement. By reviewing the process of the experiments, we identified two main reasons for the failure as follows.  
The voiceprint features extracted directly from the mixed signal sometimes cannot be matched with the features in the database. This happened particularly when the interfering signal in the environment was dominant. In other words, the ambient noise was much louder than the desired speech. At this point, it was difficult to extract the exact voiceprint of the target speaker and to obtain the correct face features of the target from the database, leading to the failure in localization and tracking of the target. We hold the view that differential microphone arrays can address this difficulty to some extent. In detail, by designing a specific beam pattern of the arrays, it is expected that the response from the non-target direction will be significantly reduced.  
In order to ensure the accuracy of face recognition, a high threshold was set for the face verification process. This resulted in the robot being unable to properly detect and recognize the target face when the face was in an extreme pose, thereby failing to locate and track the target speaker. To deal with this problem, an additional robot motion controller needs to be designed to equip the robot with the ability to actively move towards the front of a person to catch the target's face when there is no frontal human face in the FOVs of the camera, for example, when the target speakers have their backs to the robot.
## 1 Introduction  
### Concept  
Large diameter monopile is often applied to support offshore structures such as wind turbines (OWTs) [1; 2; 3; 4]. This is because of its sufficient theoretical basis for analysis design and mature manufacturing technology. However, the bearing capacities provided by monopiles are likely to not be suitable for the next generation of larger wind turbines in more severe environments [5; 6; 7; 8; 9]. For example, for OWTs with a full capacity of more than 10 MW, it would be costly and bring more challenges for the construction and installation procedure by merely increasing the diameter and embedment length of the monopile. Thus, a new type of single pile-friction wheel composite foundation has therefore been proposed, which combines the monopile and circular footing foundations. Compared to the traditional monopile foundation, this composite foundation provides higher bearing capacity and stiffness with less construction time and expense [10; 11; 12], as is shown in Figure 1. This composite foundation is initiated from the concept of pile caps and embedded retaining walls with stabilizing platforms, in which the addition of the circular wheel increases the shear stress and restoring moment against the lateral deflection [13; 14; 15]. The combined wheel can also be applied as an effective reinforcement for existing monopiles to provide additional bearing capacities and protections of the seabed from circum-pile scouring [16; 17; 15].  
Sand-overlying-clay soil deposit is a complex, but commonly encountered, soil profile in petroleum regions and offshore wind farms, for example, in the Yellow Sea of Korea, North Sea, Gulf of Mexico, South China Sea, offshore India and Thailand [18; 19].  
Existing understandings only cover the failure mechanisms of conventional types of foundations in such sand-overlying-clay soil profiles [18; 19; 20; 21; 22]. For example, [PERSON] et al. (2018) [23] found a 'punch-through' failure pattern for strip footings in the sand-over-clay layered deposits, where the upper sand block is pushed into the underlying clay due to the vertical compression under _V-H-M_ combined loadings. However, the bearing capacities and failure mechanisms of this composite foundation are still not well investigated in such soil conditions. It is also worth noticing that, in order to make full use of the bearing capacity provided by the friction wheel, the composite foundation is suggested to be installed in the regions where the strength of the seabed topsoil is great enough, such as pure sand, sand-over-clay and stiff clay-over-sand soil conditions [24; 25; 26]. Hence, a systematic study is badly needed for the application of composite foundations in sand-over-clay layered deposits.  
Figure 1: Schematic of single pile-friction wheel composite foundation.
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 6 Economy: Application of Biochar
### Application of Biochar in the Renewable Energy Sector
#### 6.1.4 Biochar as a Material for Batteries and Supercapacitors  
Biochar in lithium-ion batteries has the same function as graphite. Researchers have shown that biochar, due to its porous structure that enhances Li+ ion transfer, can be used as an anode material for lithium-ion batteries [184]. The addition of certain elements to biochar, i.e., nickel and tin, can increase its reusability and discharge capacity [185]. [PERSON] et al. [186] developed a composite of iron carbide and biochar as a cathode for a lithium-sulfur battery, which reflected a discharge capacity of 555 mAh/h after 250 cycles. [PERSON] et al. [187] produced biochar with potassium chloride, which was used as a cathode in a zinc-air battery and showed high specific capacitance and peak power density compared to a Pt/C-based zinc-air battery. [PERSON] et al. [188] developed the use of biochar as a pigment in solar absorber coatings, in which it reduces the reflectance of the material. [PERSON] et al. [189] successfully investigated the use of biochar in solar cells with dye instead of a platinum catalyst. [PERSON] et al. proved that the production of biochar via high-temperature pyrolysis yields biochar with a graphite-like structure with high electrical conductivity, so biochar can be used in electrochemical double-layer capacitors [190]. The diagram below (Figure 3) shows the process of obtaining and using biochar in supercondensers. Oxides of transition metals such as manganese dioxide, nickel oxide, and polyaniline have been used to modify biochar to produce electrochemical double-layer capacitors or composite supercapacitors [191].  
The key quantitative parameters for the application of biochar in energy storage devices are summarized in Table 3. The data highlight biochar's potential as an electrode material with good capacity, conductivity, and stability, especially after chemical or structural modifications.
## 4 The Application of Artificial Intelligence in the Design of Basic Materials for Sports
### Polymer Materials  
Polymer materials are widely used in the field of sports, mainly due to their excellent characteristics such as being lightweight, flexible, and durable. For example, high-performance polyurethanes and polyester fibers are used to create breathable, hygroscopic and sweat-welling sportswear to enhance athlete comfort. Carbon fiber reinforced polymers are used to make lightweight and high-strength tennis rackets, bicycle frames and other equipment to significantly improve athletic performance.  
In 2024, [PERSON] et al. [26] advanced ML-assisted design for polymer materials by analyzing structure-composition-performance data. This approach uncovers structure-performance relationships to screen polymers meeting target requirements. Challenges such as scarce polymer databases and complex multi-scale structure-performance correlations persist. To address these challenges, transfer learning and new methods like polymer fingerprint maps and cross-linking descriptors were introduced, leading to a multi-fidelity learning framework that integrates multi-source heterogeneous data.  
In 2024, [PERSON] et al. [71] proposed a machine learning-assisted multi-scale modeling strategy for carbon fiber reinforced polymers (CFRPs), which are known for their light weight and high strength, as shown in Figure 11. This strategy effectively predicts CFRP mechanical properties by combining ML models with molecular dynamics simulations. The predicted values align well with experimental data, offering a cost-effective solution for optimizing CFRP design.  
Figure 11: Flowchart of multi-scale modeling assisted by machine learning: (**a**) Establishing a database to calculate the properties of materials through molecular dynamics simulation. (**b**) Predicting the performance of materials by combining the database with machine learning algorithms [71].  
Furthermore, NNs can perform better than traditional machine learning models. In 2023, [PERSON] et al. (2023) by leveraging an ANN model optimized with a self-adjusting particle swarm algorithm. Prediction of nitrile rubber properties compared with traditional learning methods, achieved a 56.5% and 26.5% accuracy improvement, respectively.
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
## 3 Data Collection
### Flow Rate  
Data from the stream gauging station are used to determine the flow characteristics of the Zap River, particularly in the studied field. The data pertaining to the analyzed region were obtained from the General Directorate of State Hydraulic Works [35], covering the time span from 2016 to 2019. The station, named 2621-Stream Gauging Station, is located at 37 deg45'34'' northern latitudes and 44 deg03'21'' east longitudes, and is 3 km away from Bagisli Regulator, as shown in Figure 2. The design flow and mean flow of the Bagisli HPP and river, which are 31.24 m\({}^{3}\)/s and 15.16 m\({}^{3}\)/s, respectively, were also determined with data obtained from the mentioned gauging station. The gauged water is held in the regulator and transferred through the transmission tunnel to the penstock and then to the power house. It should be noted that the given gauge station has a crucial role in the operation of the power plant and the projection of energy generation.  
Figure 5 demonstrates the recorded daily flow rates via the stream gauge station located in the Zap River, for the range of 2016-2019. The mean flow rates for the given time interval showed that the minimum and maximum monthly total flow rates were 87 m\({}^{3}\)/s in September 2017 and 1972.8 m\({}^{3}\)/s in May 2019, respectively. Additionally, the monthly mean flow rate for four years is 381.2 m\({}^{3}\)/s. In terms of the annual mean flow rates, the lowest value was recorded in 2017 at 315 m\({}^{3}\)/s, while the highest value was recorded in 2019 at 510 m\({}^{3}\)/s. The long-term daily mean flow rate is obtained as 11.46 m\({}^{3}\)/s for the 60 years, while it is 12.53 m\({}^{3}\)/s for the studied time interval. The difference between the two flow rate values is not significant for the examination of the studied project field. It can be seen in Figure 6 that from October to the end of February, nearly a constant flow occurred. The flow rates peak in June and exhibit a steep upward trend starting from March. The daily average flows declined from the end of June, exhibiting a similar pattern to that seen between March and June, but in a decreasing form. It may be derived from the figure that the plots of 2018 and 2019 demonstrate a shifting trend to July according to the other years.  
Figure 4: Precipitation characteristics of studied field.  
The possible reason behind this shift may be the changing precipitation characteristics of the studied field.
## 4 Statistical Results
### Long-Period Region  
Figure 8 shows a comparison between the benchmark model and the scaled model for the EPP system with a strength reduction factor \(R\) of 4 and a period \(T\) of 2.0 s. The ground motions are scaled using \(\
abla S_{\text{a}}\) and \(\
abla PGD\), respectively. Similar to the system with \(T\) = 1.0 s (Figure 6), the platform section appears in the scaled model, indicating that the minimum \(\
abla IM\) in the scaled database is below the intensity threshold, and some aftershock records in the scale database would induce no additional damage for systems (i.e., \(\
abla DI\) = 1.0) in the long-period region. Using \(\
abla S_{\text{a}}\) as the scale indicator induces lower bias (i.e., 26%) than using \(\
abla PGD\) as the scale indicator (39%). Based on a thorough analysis of the data  
Figure 6: Comparison between the benchmark model and scaled model for the EPP system with \(R\) = 4 and \(T\) = 1.0 s: (**a**) \(\
abla S_{\text{a}}\), \(SF\) = 6.0; (**b**) \(\
abla SI\), \(SF\) = 6.0.  
presented in Figures 4, 6 and 8, it can be deduced that using \(\
abla S_{\text{a}}\) as the scaling parameter to scale up aftershock records (\(SF>1.0\)) tends to result in an overestimation of the additional damage. This can be observed in Figures 4b, 6a and 8b, where the predicted outcomes of the scaled model (blue straight line) are generally higher than those of the benchmark model (red dashed line). On the other hand, for \(\
abla PGA\) and \(\
abla SI\), scaling up the aftershock records tends to underestimate the additional damage. This can be observed in Figures 4d and 6b, respectively, where the predicted outcomes of the scaled model (blue straight line) are generally lower than those of the benchmark model (red dashed line).  
In the long-period range of 1.6-3.0 s, the average biases resulting from ground motion scaling are computed for EPP systems. The outcomes are presented in Figure 9 for eight distinct scale indicators. The scale indicators of \(\
abla SI\), \(\
abla PGV\), and \(\
abla I_{\text{C}}\) demonstrate superior performance compared to the remaining five scale indicators, including those associated with ground displacement (e.g., \(\
abla PGD\)). Furthermore, the biases associated with these  
Figure 7: Bias resulting from the scaling of ground motions assessed for the EPP systems in the medium-period region.  
three scale indicators typically fall within a range of 30%. The biases introduced by aftershock ground motion scaling with \(\
abla S_{\text{a}}\) are generally within 40%. The performance of \(\
abla PGD\) is worse than the above scale indicators, and the corresponding biases can exceed 60%. Similar to the results in the short-period region, the performance of different scale indicators for the systems in the long-period region is also inconsistent with the correlation analysis of natural ground motions, because the scale indicators related to the ground displacement show worse performance than those related to the ground velocity.
## 2 Materials and Methods
### Coverage Path Planning
#### 2.3.3 Design of Fitness Function  
The fitness function can judge the superiority and inferiority of individuals in a population, and has a great influence on the efficiency of the genetic algorithm in searching for optimal solutions. In traditional genetic algorithms, the path length is often used as the fitness function [36]. In this study, the total length of the path and the number of turns were considered from the point of view of minimizing energy loss. Aiming at the problems existing in the traditional fitness function, an improved dynamic fitness function is proposed to evaluate the length of the planned path and the turning angle of the connecting points in the path. The improved functions are as follows in Formula (3):  
\[fitness=\frac{1}{a_{1}S+a_{2}}\sum\limits_{i=1}^{N|M|-2}\theta_{i} \tag{3}\]  
where \(S\) is the path length, determined by the Formula (4), \(\theta_{i}\) is the steering angle from the \(i\) waypoint to the \(i+1\) waypoint turning to the \(i+2\) waypoint, as shown in Figure 2.  
\[S=\sum\limits_{i=1}^{N|M|-1}\|p_{i+1}-p_{i}\| \tag{4}\]  
\(p_{i}\in M\) is the waypoint on the flight path, \(\|p_{i+1}-p_{i}\|\) is the Euclidean distance between two adjacent waypoints on the flight path.  
Figure 2: Steering angle.  
\(a_{1}\) and \(a_{2}\) are weight coefficients, usually fixed constants. In this paper, they represent the influence of path length and steering angle, which determine the convergence direction of the algorithm. Throughout the flight sampling process, the flight distance consumes far more energy than the turning, and the shortest path is known in advance. In order to ensure that the iterative result satisfies the shortest path first, the algorithm preferentially converges to the shortest path during iteration by dynamically adjusting \(a_{1}\) and \(a_{2}\). When the number of individuals with the shortest path in a population reaches a certain critical value, the influence of steering angle increases. The specific formula is as follows (5).  
\[\begin{split} a_{1}&=\left\{\begin{array}{ll}a_{ \max}-\frac{a_{\max}-a_{\min}}{n_{\max}-n_{0}}(n-n_{0})&n>n_{0}\\ a_{\max}&n\leq n_{0}\end{array}\right.\\ a_{1}&+a_{2}=1\end{split} \tag{5}\]  
In the formula, \(a_{\max}\) and \(a_{\min}\) are the maximum and minimum values of \(a_{1}\), n is the number of individuals with the shortest path in the current population, \(n_{0}\) is the minimum critical value and \(n_{\max}\) is the maximum critical value. \(a_{1}\) is \(a_{\max}\) before n is smaller than \(n_{0}\), making the convergence direction more inclined towards the shortest path.
## 2 Related Work
### Deep Learning-Based or API-Call-Related Malware Classification  
There is a line of work focused on building malware classification systems based on extracted features. [PERSON] et al. [15] have proposed an innovative static analysis approach, integrating Natural Language Processing (NLP) with machine learning classifiers to discriminate between malicious and benign software. Their methodology entails the utilization of a PV-DBOW model for the extraction of features from diverse sources, including DLL imports, assembly code, and hex dumps, all derived from static analysis. Subsequently, these extracted features, or vectors, are input into Support Vector Machines (SVM) and k-nearest neighbor (KNN) classifiers for predictive inference. Another study proposed by [PERSON] et al. [14] used NLP techniques such as N-gram, Doc2 Vec (or paragraph vectors), and TF-IDF to convert API call sequences to numeric vectors before feeding them to the classifiers, including SVM, KNN, MLP, and RF. [PERSON] [16] also uses N-gram and TF-IDF to encode the API call sequences and employs a CNN to classify, which utilizes the ability of image representation. [PERSON] et al. [17] employ a third-order Markov chain to model the Windows API call sequences. [PERSON] et al. [18] classify malware into some clusters using characteristics of the behavior, which are derived from Windows API calls in parallel threads with N-gram and TF-IDF.  
Deep learning-based methodologies have exhibited remarkable potential for delivering more efficacious and adaptable features, yielding superior outcomes in malware classification. [PERSON] et al. [19] pioneered the application of convolutional and recurrent network layers for the extraction of features from comprehensive API sequences. Their pioneering work underscores the substantial accomplishments attained through the integration of deep learning techniques within API-sequence-based malware classification. In the same way, [PERSON]'s work [20] also demonstrates the RNN's ability to classify the API call sequences alone. In a subsequent development, [PERSON] et al. [21] have further refined the network architecture, introducing the extraction of inherent features from API sequences. Especially, their approach incorporates embedding layers to represent API phrases and semantic chains, along with the utilization of Bidirectional Long Short-Term Memory (Bi-LSTM) units to capture interrelations among APIs. The results of their endeavors demonstrate significant performance enhancements when compared to baseline methodologies, highlighting the efficacy of introducing additional intrinsic features associated with APIs. Some works consider the similarity among the features, especially API call sequences, and employ similarity to do the encoder, followed by some advanced models such as GNN [22], Random Forest, LSTM [23], and F-RCNN [24].
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 1 Introduction
The outputs of source test cases are recorded as O\({}_{1}\). Then, the follow-up test cases are given to the same original program. The outputs of follow-up test cases are also recorded as O\({}_{2}\). The outputs of both source and follow-up test cases are compared and if (O\({}_{1}\), O\({}_{2}\)) satisfy their related MR for all the test cases then it shows that the related MR is satisfiable. For comparing the outputs of two images, we have used structure similarity index measure (SSIM). SSIM has become a de-facto standard in the field of image processing [19]. It is a perception-based method that considers perceived change in the structural information. It emphasizes on the pixels that are spatially closed and carry important information [20]. It compares the images based on three similarities, i.e., luminance, contrast, and structure [21]. The 0 value indicates that the two images are not identical structurally, while the value 1 shows that both the images are exactly similar in structure. We have chosen this measure because in MRI images, luminance and contrast should be carefully observed for the accurate identification of edges and this measure checks the similarity based on these two attributes along with structure similarity. Then, we have checked the similarity of both the outputs through SSIM and checked the FDR of each MR by dividing the number of test cases violating the MR with the total number of test cases in the test suite. Results show that the FDR of MR\({}_{2}\) is lowest for all the three categories of images (T1, T2, and flair) having FDR of 12.12%, 27.58% and 3.03%, respectively. Similarly, the FDR of MR\({}_{4}\) is highest in T1, T2 and flair-type images with an FDR of 63.63%, 72.41%, and 27.27%, respectively.
## 3 Results and Discussion
### Thermal Conductivity Test
#### 3.2.3 Dry Density  
At a specific moisture content, the thermal conductivity of soil is strongly influenced by variations in dry density. Figure 12 illustrates that as dry density increases, thermal conductivity also rises. The thermal conductivity of the uncured loess increased continuously, with a growth rate of 7%, as the dry density was increased. For the carbonated solidified soil, when the dry density exceeded 1.6 g/cm\({}^{3}\), the growth rate of the thermal conductivity decreased from 6.5% to 2.1%. Similarly, a one-way ANOVA was conducted to analyze the thermal conductivity and determine whether significant differences existed across the various dry density levels. Statistical calculations were performed for each dosage, and the resulting \(p\)-values for all dosages were less than 0.05. This indicates statistically significant differences in the average thermal conductivity values among the different dry density levels. As the dry density of the sample increased, the soil became more compact, reducing the amount of air within the pores. Since air has an extremely low thermal conductivity (0.026 W/(m-K)) compared to soil particles, the reduction in pore air significantly enhances heat transfer within the soil matrix. Additionally, at higher dry densities, particle contact becomes tighter, facilitating more direct heat transfer through the solid phase and further increasing thermal conductivity. In carbonated solidified soil, the pores are filled with carbonation products. At high densities, the limited number of pores results in minimal changes in thermal conductivity, maintaining its stability. Therefore, soils with higher dry density exhibit greater thermal conductivity compared to lightly compacted soils, while carbonated solidified loess demonstrates the ability to maintain stable thermal conductivity.  
Figure 11: Thermal conductivity of different moisture contents.