esa-sceva/satcom-chunk-collection huggingface dataset:
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### System Process
#### 4.3.2 Parameter Setting and Initialization  
In order to hasten the process of training the network that was being used in this experiment, its parameters were initially set to match those of the ImageNet pretraining network. Because the categories that were utilized by ImageNet and our dataset were different from one another, the very last layer of the network required some fine tuning in order to function properly. The pretraining weights for this layer were unable to be utilized in any manner, shape, or form.  
For the initial setting, the hyper-parameter settings of each network in the experiment before optimization are as follows: Image size of 224 \(\times\) 224 (Xception 299 \(\times\) 299), Batch Size 64, Training Learning Rate set equal to 0.001 with a Momentum Factor equal to 0.9. During the phase of the experiment, the level of accuracy achieved by the training set was measured after each epoch, and the level of accuracy achieved by the test set was measured after the training was finished.  
In the beginning, the eight currently pre-trained versions (VGG16, Xception, Inception, ResNet-50, DenseNet121, MobileNet, EfficientNetB0, and InceptionResnet) were modified to the Game of Deep Learning Ship dataset in order to achieve the highest possible network performance in the suggested ship classification. The performance of a variety of different models was analyzed, and the one with the best overall performance was chosen for modification and used for the classification of the network. This helped to ensure that the results were as accurate as possible. The models were altered to ensure that they were consistent with the categories that were uncovered in the dataset that was made accessible to the public. The PyTorch framework was utilized to train these network models, and Adam was employed to maximize both the momentum and the learning rate. The cross-entropy loss function was used throughout the whole process to measure loss, and validation was completed after each epoch to see how well the network was learning while it was being trained.  
Figure 4 demonstrates that when compared to VGG16, Xception, Inception, ResNet-50, DenseNet121, MobileNet, and InceptionResnetV2, EfficientNetB0 achieved a greater level of accuracy in its predictions. The model with the lowest accuracy was VGG16, which differed by a margin of 8.62%. EfficientNetB0 had an accuracy of 94.25%, whereas InceptionResnetV2 accuracy was only 93.29%. The accuracy achieved by DenseNet121, 93.29%, was superior to that achieved by both VGG-16 and Xception. On the other hand, the Xception model achieved an accuracy of 92.65%, which is significantly better than the VGG-16 model. In addition, the accuracy of MobileNet was measured at 94.56%. Following the results of the experiment, it is hypothesized that enhancing the EfficientNetB model will result in improved accuracy and that the model will be able to be utilized for better performance results.  
Figure 3: Pre-processing process for input image.
# Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs
## 3 Old TB Drugs
### Isoniaid--Target: Mycolic Acids
These observations strongly supported the conclusion that INH and ETH target InhA. The Ser94 Ala mutation has been found in INH-resistant _M. tuberculosis_ clinical isolates carrying no mutations in _katG_[75; 79; 80; 81]. Mutations in many other genes have been identified in INH-resistant laboratory and clinical strains such as _kasA, mshA, ndl, nudC, alpC,_ and nat_ (the _M. tuberculosis N_-acetyltransferase) to cite a few, but very often these mutations were present in clinical strains already carrying a _katG_ or _inhA_ mutation or were also found in INH-susceptible strains questioning their roles in INH resistance [71]. _Area of investigation._ For the past 70 years, INH has had an essential role in TB treatment and control. One of INH downsides is its lack of activity against dormant/persistent _M. tuberculosis._ The reasons for this lack of activity in the dormant form of _M. tuberculosis_ are still up for debate; however, it is known that KatG has limited activity in the dormant state [44]. With KatG being a major factor in INH resistance and a potential player in INH shortcoming in dormant _M. tuberculosis_, new InhA inhibitors that would not require activation by KatG have been actively sought [82; 83; 84; 85; 86; 87; 88; 89]. Compounds such as GlaxoSmithKline's thiaadiazole GSK693 [90] or the diazaborine AN12855 [91; 92] are promising leads with good oral bioavailability, low toxicity, activity against _katG_-deficient _M. tuberculosis_ and in vivo efficacy similar to INH.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 4 Use Case and Implementation  
### Use Case: Solar PV Maximum Power Point Tracker (MPPT)  
The Maximum Power Point Tracking (MPPT) is a technique commonly used in solar photovoltaic (PV) systems to locate and maintain the highest operating power point of the solar PV. Solar PV cells have a complex relationship between solar intensity and operating resistance across the cells which produces a non-linear output efficiency that can be analyzed based on the _I-V_ curve. This non-linear property creates a phenomenon where only a specific operating voltage point can contribute to the maximum power point. Therefore, maintaining the solar PV system to operate at its maximum power point is vital to ensure the maximum solar energy extraction of the system [36].  
Most of the MPPT techniques control and adjust the operating voltage or current values of the PV modules to achieve maximum power point. The MPPT controller's algorithm first examines whether the solar PV module is operating at its Maximum Power Point (MPP). If the PV module is not operating at its MPP, the algorithm will calculate and locate the MPP of the solar PV. After that, the MPPT controller will send a command signal to the converter to make adjustments on its duty cycle (operating voltage). There are various MPPT algorithms available and two of the most widely applied algorithms are Perturb and Observe (P&O) [37] and Incremental Conductance (InCond) [38] algorithms. Both algorithms adopted the \"hill-climbing\" concept, which operates by initially measuring the voltage or current data to identify the current power point of the solar PV. The system then moves the operation point (voltage or current) in the direction where the power calculated increases. The increment continues until a decrease in power is detected, and the next perturbation is in in the opposite direction. The perturbation continues to oscillate around the MPP as long as there is no power drop.  
We adopted solar MPPT as our use-case for the following reasons: (1) Most solar MPPT devices are offline, mechanical devices that operate individually at their local area. The high complexity in MPPT technology and the diversity in MPPT techniques made MPPT devices difficult to be put online for centralized performance monitoring. Hence, it is a suitable use case to showcase the system interoperability limitation. (2) Being a highly time-sensitive system that requires great precision and instant response, the MPPT system is a suitable use case to test our proposed architecture on the suitability and viability to be applied in real-world scenarios and the realization of data-driven feedback mechanism.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
###### Abstract  
In order to study the deformation of a bored pile during concrete curing, it is necessary to monitor the strain of the pile. In this paper, Brillouin optical time domain reflectometer (BOTDR) technology is used to monitor the pile strain during concrete curing, and reliable monitoring data are obtained. These data provide a basis for the study of pile deformation and pile-soil interaction during curing of bored cast-in-place piles. Compared with the traditional point strain sensor, the distributed fiber optic sensor is simple in layout and highly accurate; it can fully reflect the strain changes of the pile; the experiment also shows the advantages of distributed fiber optic sensing technology over the traditional point monitoring method.  
bored pile; strain variation; concrete curing; BOTDR; optical fiber sensor; monitoring Article  
An Application of BOTDR to the Measurement of the Curing of a Bored Pile  
1148
## 1 Introduction
The authors employed resistance spot-welded (RSW) samples made of different low-carbon steel sheets and utilized artificial neural network (ANN) simulations to predict the static strength and fatigue life. The research successfully demonstrated the potential of using UT in combination with machine learning for the reliable and cost-effective assessment of spot-weld properties. [PERSON] et al. 's study [37] concentrated on the thermodynamic entropy approach for evaluating the fatigue of structures. The proposed approach by [PERSON] et al. [37] was applied to spot-welded joints and a high level of accuracy was reported through comparison with the traditional methods. There are three basic modes of spot-weld failure--interfacial, partial interfacial, and pullout--which appear during static tensile-shear testing of spot-welded specimens. However, dynamic stress initiates a crack usually nearby the nugget and propagates until a sudden fracture occurs. Investigating the fatigue characteristics of spot-welded structures is crucial for ensuring the reliability and durability of welded components. Understanding the failure mechanisms and failure modes associated with fatigue in spot-welded joints provides valuable insights for developing strategies to improve fatigue resistance and extend the service life of spot-welded assemblies. However, an accurate prediction of fatigue life is a challenge, especially in terms of the position of crack initiation, its propagation in the course of cycling loading, and the failure cycle with their dependency on the different design parameters. The majority of studies in the literature evaluate stress and displacement data using finite element models, which are then used to the assessment of the fatigue life of spot-welded components using high-cycle and low-cycle fatigue theories. Literature review reveals the lack of spot-weld fatigue analysis involving the crack initiation and its propagation, which is addressed in the current numerical study. In this paper, an extended finite element (XFEM) model for the crack propagation in the spot-welded structure is developed. The XFEM method allows for the simulation of crack growth in spot-welded structures without requiring the mesh to be modified, which makes it particularly effective in capturing intricate crack formations. Fatigue life values of spot-welded joints using the developed XFEM method have been correlated with the experimental data for three different load cases. The effect of various model parameters including the amount of the applied load, the load ratio, and the nugget diameter on the fatigue life assessment of spot-welded components were evaluated through advanced XFEM analyses.
## 5 BDS Data Description
### Time Series Data  
To satisfy the required data dimensionality of the CDAENet, we use a sliding window to segment the data. As shown in Figure 12, the time series length is chosen to be 128 s, and the sliding window step is taken to be 10 s. The window length is chosen in this way mainly because a compact set of tokens is sufficient to represent the changed semantic concepts of interest, while redundant tokens may hamper the performance of the model. When the window is too short, the model may lose some useful information related to the changed concept. In addition, the small step size may lead to a high degree of similarity between the two windows, which will reduce the recognition accuracy of the model.
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 2 Mathematical Tools
### GenesisXi Algorithm  
At this point, it becomes clear that simply knowing the functions within the model is insufficient. It is also crucial to determine which differential equation each function should correspond to. This shift from model identification to coefficient (parameter) identification is significant, particularly for real-world nonlinear problems.  
As a result, modifying the traditional SINDY algorithm involves integrating this additional information. The goal is to refine the results obtained so that they closely resemble known models, such as those from quadrotors, thus improving the applicability and accuracy of the algorithm in real-world scenarios.  
To achieve this objective, it is necessary to formulate an initial matrix \(\Xi_{0}\) wherein the positions of all candidate functions contained in \(\Theta(X,U)\) within the differential equations of the mathematical model are defined. To automate this process, namely, obtaining the aforementioned matrix \(\Xi_{0}\), the GenesisXi algorithm is proposed. The pseudocode for this algorithm is described in Appendix A.3, while the corresponding MATLAB function, with \(\mathit{Theta\_L}=\Theta_{L}\), is given in Appendix A.4.  
Thus, after applying GenesisXi algorithm, and in accordance with (5)-(17), the resulting matrix \(\Xi_{0}\) is:  
\[\Xi_{0}=\left[\begin{array}{cccccccccccc}0&0&0&0&0&0&0&1&0&0&0\\ 1&0&0&0&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0&0\\ 0&0&0&1&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&1\end{array}\right]. \tag{26}\]  
Notice that the columns of \(\Xi_{0}\) represent the differential equations in the nonlinear model, while the rows of \(\Xi_{0}\) represent the candidate functions included in \(\Theta(X,U)\). Thus, the matrix (26) indicates that \(f_{1}\) appears in the ninth differential equation, \(f_{2}\) appears only in the second differential equation, and so forth.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques
### LMMSE-Capon Hybrid Beamformer  
The _hybrid beamformer_ (HYB) circumvents the limitations of the LMMSE by combining the latter with the CAP exploiting the spatial reference of the LOSS. The beauty of the HYB is that it allows for an iterative implementation by making use of the recursive least squares (RLS) algorithm. It involves a computational load affordable for current microprocessors, thus making the technique suitable for handheld devices. The weights \(\mathbf{w}_{\text{HYB}}\) are computed as follows [7]:  
\[\mathbf{w}_{\text{HYB}}(\tau_{0},\varphi_{0})=a_{0}^{*}\mathbf{w}_{\text{ LMMSE}}(\tau_{0},\varphi_{0})+\beta(\tau_{0},\alpha_{0})\mathbf{w}_{\text{CAP}} \tag{9}\]  
where \(\beta(\tau_{0},\alpha_{0})\doteq 1-\alpha_{0}^{*}\mathbf{a}_{0}^{H}\mathbf{w}_{ \text{LMMSE}}(\tau_{0},\varphi_{0})\).
## 4 Parameters Affecting Nano-EOR  
### NP Size  
The size and charge density of NPs have a significant effect on the strength of the disjoining pressure. For a similar mass, smaller NPs will give a higher particle density and lower contact anglebetween the fluid and the rock surface. A higher particle density improves the structural disjoining pressure (see Section 5.3) significantly [148]. For less hydrophilic surfaces, smaller NPs will spread more readily than bigger particles. [PERSON] et al. [149] concluded that smaller particles would result in a higher charge density and stronger electrostatic repulsion, assuming the particle is stable.  
According to [PERSON] et al. [150], smaller particles have been proven to not only increase oil recovery considerably, but also improve the displacement efficiency. Several other experiments concluded that smaller particles would lead to higher ultimate oil recovery [89]. [PERSON] et al. [148] proved that decreasing the NP diameter from 30 nm to 18.5 nm increased the structural disjoining pressure about 4.3 times. The size of the particle should be small enough that it cannot be mechanically trapped, but big enough to avoid extra log-jamming [89]. Therefore, smaller particles are often preferable for higher oil recovery [109]. It should be noted that the high surface energy could result in severe agglomeration and surface adsorption of the ultrafine NPs, affecting the stability of the nanofluid. Furthermore, the NP size also affects the surface coating process. Therefore, we assume that there should be an optimal value with respect to the NP size.
## 4 Experiment
### Comparison to the State-of-the-Art  
To demonstrate the effectiveness of the proposed model, we compared it with 26 existing newer methods: FPN [45], PSPNet [46], Mask RCNN [47], HTC [48], and MSRCNN [49], DSC [50], BDRAR [51], UNet++ [52], and PraNet [53], PiCANet [54], BASNet [55], CPD [56], PFANet [57], EGNet [36], F3 Net [37], GCPANet [16], MINet-R [58], SINet [40], PFNet [59], UGTR [31], RankNet [19], BgNet [60], ERRNet [61], FEDER [17], MRRNet [62], and BCNet [63]. Table 1 shows the qualitative results of our model and some models in recent years.  
Table 1 shows the quantitative results of our proposed method compared to 26 other common methods on three benchmark datasets. Numerically, it can be seen that our method is quite competitive. Compared to the three methods from 2023, our \(M\) value is quite competitive. On the Chameleon dataset, our method improved by 0.002 over FEDER, by 0.005 over MRRNet, and by 0.006 over BCNet. On the Camo dataset, our model improved by 0.002 over FEDER, by 0.007 over MRRNet, and by 0.007 over BCNet. On the COD10k dataset, our model improved by 0.004 over MRRNet and by 0.009 over BCNet, obtaining good results.
## 3 Artificial Intelligence Methods for GWL Modelling
### Genetic Programming
This study uses observations from three wells to illustrate how uncertainty analysis can enhance the reliability and robustness of GWL models. A study by [68], an adaptive GP-based method for Bayesian experimental design, proved efficient in identifying groundwater contaminant sources. By integrating Markov Chain Monte Carlo (MCMC) methods, this study enhances the accuracy and efficiency of environmental monitoring efforts. Based on the findings of [72], GP outperforms the ANFIS in predicting monthly GWL, mainly when using precipitation and evaporation data. This research highlights GP's capability to capture the nonlinear relationships between climatic variables and GWL. Research by [69] demonstrates that GEP outperforms other AI-based methods and the ARMA in short-term GWL forecasting. By utilising daily GWL and rainfall data, GEP provides a robust and adaptable approach for dynamic and nonlinear groundwater systems. According to a study by [74], linear regression models are proficient in predicting summer GWL, whereas ANN is more effective in making winter predictions. This seasonal differentiation suggests that the choice of prediction models should consider seasonal variations to optimise accuracy. This study used local weather, lake stage, and stream flow data, demonstrating the importance of contextual inputs in GWL modelling for the Searsville basin. Table S5 (Supplementary Materials) summarises the evaluated articles that used GP methods and draws a general conclusion.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### Mechanistic Analysis of Geopolymer-Stabilized Soft Soil  
#### 3.5.1 Component Analysis  
XRD was used to analyze the physical phases of the geopolymer-stabilized soils to determine the hydration products formed when the geopolymer stabilized the soft soils. XRD analysis was carried out on the undisturbed soil and typical samples with curing ages of 28 days (Figure 10). Figure 10 shows that there are two main types of phases in Hangzhou soft soil: a clay phase and a crystalline phase. The clay phase was mainly composed of montmorillonite, and the second crystalline phase was mainly composed of quartz and albite. The XRD analysis of the geopolymer-stabilized soft soils revealed that they contained two types of phases. The crystalline phase was basically consistent with that of the soft soil, and no new minerals were produced during the process by which the geopolymer stabilized the soft soil.  
Based on the position of each diffraction peak, the amount of crystalline phase increased, and quartz and albite were clearly visible in the geopolymer soil compared with the undisturbed soil. In the 2\(\theta\) < 45\({}^{\circ}\) region of the XRD spectra, there are amorphous continuous diffuse peak packets, which indicate that the hydration and alkaline activator  
Figure 10: XRD patterns of undisturbed soil and geopolymer-stabilized soil.  
Figure 9: Variations in the compressive strengths of the samples with curing agent contents of 20% and 25% with an increasing number of wet–dry cycles.  
reactions of the FA, slag, and activator in the soft soils under the action of the water medium generated the amorphous phases. Calcium (alumino)silicate hydrate C-(A)-S-H and sodium aluminosilicate hydrate (N-A-S-H) were two of the main products of the geopolymerization reaction [12]. This is due to the potential active components within the silica-alumina in the raw geopolymer materials (slag and FA), which can be sufficiently excited by the alkaline activator, causing the Si-O and Al-O bonds to break and re-polymerize to generate these two main products [11].
# Laboratory Evaluation and Finite Element Modeling of SBS and Basalt Fiber Modified Mixtures
###### Abstract  
The incorporation of basalt fiber into asphalt mixtures offers potential improvements in their viscoelastic properties. This study explores the addition of basalt fiber to Styrene Butadiene Styrene (SBS)-modified asphalt mixtures with varying SBS contents. Specifically, 0.3% basalt fiber was added to an asphalt mixture containing 3% SBS, and its performance, measured in terms of dynamic stability and flexural strength, was compared with a mixture with 7% SBS content. Additionally, finite element analysis using the Modified Burger's Logit model was conducted to assess rutting and fatigue behavior. Given the high cost associated with increasing the SBS content, basalt fiber presents a cost-effective alternative without sacrificing performance. Laboratory tests, including the Marshall stability test, dynamic stability, flexural strength, and fatigue tests, were conducted to evaluate both mixtures. Results indicate that the mixture with 0.3% basalt fiber and 3% SBS outperforms the 7% SBS mixture, showing a 47% improvement in dynamic stability and rutting resistance and a 16% increase in flexural strength.  
Keywords:basalt fiber; finite element modeling; flexural testing; wheel rutting test +
Footnote †: journal: Applied Sciences