esa-sceva/satcom-chunk-collection huggingface dataset:
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparative Experiment with Other Convolutions
As a result, AdaptiveConv2d better adapts to diverse input data, reduces information loss, and enhances both the training effect and the convergence speed of the network [42]. Compared with the conventional channel self-attention mechanism, the AdaptiveConv2d module has several key characteristics:  
* Different Operational Placement: AdaptiveConv2d applies channel weighting after each convolutional operation, whereas conventional channel self-attention typically applies weighting before or after all convolutional layers. This approach allows the network to make better use of post-convolution feature information, thereby enhancing context awareness [43];
* Enhanced Residual Connection: AdaptiveConv2d introduces residual connections after channel weighting to retain original features, minimizing information loss and improving model stability and gradient propagation;
* Adaptivity and Dynamic Adjustment: AdaptiveConv2d adaptively adjusts channel weights after each convolutional layer by leveraging global context information and residual connections. Unlike conventional static channel weighting, this module dynamically processes features at different levels, enhancing the model's adaptability and its ability to capture complex feature relationships;
* Energy Optimization and Regularization: The AdaptiveConv2d module includes an energy loss and a regularization term to optimize channel weight generation. This optimization ensures that the generated weights are more effective, improving the model's generalization ability while maintaining the consistency of feature maps. Conventional self-attention mechanisms, by contrast, often rely on a static weighting strategy, which limits their ability to achieve global optimization;
* Reduced Computational Complexity: AdaptiveConv2d employs global average pooling and \(1\times 1\) convolution to reduce computational complexity while maintaining efficient feature extraction. This makes it suitable for use in resource-constrained environments without sacrificing performance. With these improvements, AdaptiveConv2d offers a more versatile and robust approach compared to conventional channel self-attention mechanisms, making it a powerful tool for enhancing deep learning models.
## 4 Performance Analysis  
Eight benchmark functions (including both conventional and highly complex ones, as shown in Table 1) were selected from the CEC2013 competition dataset--which is commonly used for two-dimensional multimodal optimization research--in order to test the performance of the TCDE algorithm. For further comparison, 15 of the latest high-performance multimodal optimization algorithms were used as baseline methods. Finally, we explored how to apply TCDE to the next generation of wireless communication networksin engineering applications to address the multimodal optimization problems that exist within them.
## 3 Development of a Two-Dimensional Finite Element Model of a Unit Load Segment
### Model Discretization  
Meshing was assigned individually to each object in the model. The pallet analog was discretized using the Abaqus CPE4I element type, which includes incompatible deformation modes, to include additional degrees of freedom in order to capture the bending kinematics more accurately. The quadrilateral elements for the pallet segment were sized at 2.54 mm, with a total of 3200 elements.  
In order to arrive at the optimal number of elements for the pallet analog  
Boxes were generated as 2D solid elements, formulated as a plane/strain linear elastic material with reduced integration, specifically the Abaqus CPE4R element. The elements were globally sized at 12.7 mm for every box, regardless of its size. To select this element size, a mesh convergence study was conducted. Figure 5 and Table 5 show the results conducted specifically for the scenario where three columns of boxes of three layers were loaded on the board. The midspan pallet analog deflection results were considered converged after the number of elements reached 6075 for the nine boxes, or when the seed size was 12.7 mm. Decreasing the box element size further, to a 5.08 mm seed, changed the results by 1.09% but increased the number of elements 6.3 times, making it unnecessarily demanding.  
The tracking beam supports were discretized as 2-node, linear, discrete, rigid elements with a length of 2.54 mm and rigid link formulation, represented in Abaqus as rigid 2-node 2D (R2D2) element. A summary of the mesh properties for each object is shown in Table 6.
## 2 Materials and Methods
### Signal Preprocessing
#### 2.3.2 Outlier and NaN Handling  
Under normal conditions, data collection from sensors in real-life applications can be problematic. Throughout experimentation, hardware failures and malfunctions can occur, resulting in gaps or irregular values in the dataset. During experimental sessions, sensors could randomly stop collecting data, thus creating gaps in the dataset. These gaps were identified in the early stages, and the entire experiment was discarded completely. On the other hand, in some cases, the sensors would record all data properly. However, during the pre-processing phase, irregular values, i.e., outliers, could appear in the dataset. These outliers usually differ from most values by orders of magnitude, and they do not represent the physical behavior of the subject. In the present study, there was a limited number of outliers, which were all removed manually from the dataset.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 1 Introduction
Compared with the flow field of a typical transverse jet, it was found that the shock wave induced by the cavity reduced the velocity of the main flow, which affected the bow shock wave generated by the downstream jet, resulting in increased penetration depth and mixing efficiency. [PERSON] et al. [22] researched on transverse jets with a cavity structure by numerical simulation, found that oblique shock waves help transport fuel into the cavity to improve mixing efficiency, and enhance fuel diffusion. [PERSON] et al. [23] found that the double shock wave generator could increase the mixing efficiency more than the single shock wave generator by changing the number and position of the shock wave generator in the supersonic transverse jet injection. The supersonic transverse jet with cavity structures has been widely studied, but most of the research has been conducted under the premise of uniform inflow. In the actual operation of scramjets, in fact, the reflected shock wave and complex vortex will affect the flow field structures, and the inflow and outlet conditions of the inlet are obviously non-uniform [24; 25]. The influence of non-uniform inlet conditions on fuel mixing in a combustion chamber with a cavity structure is currently unclear. Therefore, it is necessary to conduct research on the supersonic transverse jet with cavity structures under non-uniform inflow conditions. Due to the flight attack angle and the compression of the aircraft body surface, the airflow entering the combustion chamber is non-uniform. This study used the Reynolds-averaged Navier-Stokes (RANS) equations to perform numerical simulations on an X-51-like vehicle. By adjusting the isolator length and injection scheme, it analyzed the mixing characteristics of fuel in the combustion chamber from the perspectives of the interaction between the incident shock wave and the bow shock wave, as well as the non-uniform pressure distribution. The influence of the injection position on mixing efficiency was also investigated.
# Role of 4-Hexylresorcinol in the Field of Tissue Engineering
## 3 4-HR Application in the Tissue Engineering  
### Application of 4-HR in Bone Regeneration  
#### 3.1.1 4-HR with Bone Graft Substitutes  
4-HR has various beneficial properties for use with bone graft substitutes. It has antimicrobial and antiseptic properties, and thus can prevent bacterial contamination and infection of the graft material [15]. It suppresses the NF-xB pathway and TNF-\(\alpha\) expression, decreases osteoclast activity, and contributes to new bone formation. It has been used with bone substitutes and shows favorable results in bone formation. 4-HR incorporated into porcine bone promotes new bone formation in rat calvarial defects. 4-HR suppresses the TNF-\(\alpha\)-induced NF-xB pathway, and increases bone formation and osteogenic marker expressions, including osteoclast, osteoproteger, and alkaline phosphatase [11].  
The biodegradation of biomaterial is an important process for the success of grafted materials. 4-HR can accelerate the biodegradation of silk through MMP activation, and induces rapid degradation of bovine bone grafted in rat calvarial defects [13; 14; 15]. Degradation of the graft material can be disrupted by the foreign-body reaction and giant-cell formation around the material. 4-HR inhibits giant-cell formation and decreases the phagocytic activity via suppression of the DAGK expression to allow it to contribute to degradation of the graft material [14].
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 3 Limitations and Advances of Road Infrastructure
### Lane Support Systems (LSS)  
#### 3.2.1 Limitations  
ADAS that depend on lane detection, such as lane departure warning (LDW), lane-keeping assistance (LKA) or automatic emergency steering (AES), show limitations under adverse weather and lighting conditions similarly to other MV systems. In general, studies have shown that more problems are experienced in the detection of road markings by ADAS during daytime, due to visual clutter [10]. In daytime conditions, it has been highlighted that the optimal contrast ratio between markings and pavement is 3-to-1 [9; 10]. On the other hand, contrary to humans, MV has fewer problems during the night-time. Namely, the contrast between markings and pavement is much more pronounced during the night-time, and even lower retroreflection levels of markings can produce adequate contrast ratio needed for MV. Overall, most studies suggest that, for proper functioning of MV during night-time, the retroreflectivity of road markings should be at least 100 mcd/lx/m\({}^{2}\)[9; 10; 63; 64; 65]. With the exception of the aforementioned, other conditions such as wet pavement, rain, fog, dirt, residual markings or other maintenance issues related to markings, or glances from oncoming traffic, may reduce the confidence rate of lane detection significantly [66]. Along with environmental and maintenance issues that affect not just ADS but also human drivers, the rethinking of some design patterns can lead to improved lane detection performance. Design issues are primarily related to the continuity of markings, configuration of the dashed lines as well as colour and width of the markings. Some of these issues, such as standardisation of the configuration of dashed lines, may be difficult to achieve, keeping in mind that countries (especially in Europe) have their own established practices and standards, and changing these would be a long a costly procedure. On the other hand, continuity, colour and the width of the markings could be addressed, and some countries are already proposing such changes (US with their proposal for new MUTCD, for example). It is evident that the continuity of markings is one of the crucial factors for MV detection. Furthermore, several studies investigated how an increase in lane marking width from 100 to 150 mm influences the detection rate, and it was concluded that there is a positive correlation between the confidence of detection and wider lane marking [67]. Other studies regarding colour imply that recognition shows better performance for white marking than yellow [9; 65; 66].
## 3 Selected Methods and Experimental Approaches for Analysis of HS Structure
### Liquid State Investigations
#### 3.3.1 Scattering Techniques
For the authors, those values represented an aggregation of numerous smaller molecular fragments joined together by weak forces such as H-bonds, ion-dipole interactions, and the attraction and aggregation of non-polar (i.e., aromatic and aliphatic) components of the AHA structure. [PERSON] et al. [176], by carrying out in-situ X-ray imaging experiments, found globular (200-450 nm) and ring-like (0.3-1.2 \(\upmu\)m) microstructures of SRFA in dilute acidic and high ionic strength solutions. As the concentration of SRFA in solution increased, the inclusion of the globular and ring-like aggregates into large sheet-like microstructures (2-8 \(\upmu\)m) was also observed. From the data treatment of SAXS and SANS curves performed on several types of HS in D\({}_{2}\)O, [PERSON] et al. [177] reported that HS solutions could be described as dynamic systems with concentration-dependent and reversible aggregation processes. In particular, spherical particles with maximal diameters of about 3.5 nm, single \"huge\" agglomerates with sizes up to 2 \(\upmu\)m and (for some HS) regular chainlike structures at higher HS concentrations (up to 10 g/L) were found. In the presence of copper ions large disordered agglomerates and network structures were formed, depending on the Cu\({}^{2+}\) concentration. Moreover, below a threshold Cu\({}^{2+}\) concentration large disordered HS-Cu\({}^{2+}\) agglomerates were formed proportionally to the Cu\({}^{2+}\) addition. Evidences of fractal structures of Suwannee River fulvic acid (SRFA) in D\({}_{2}\)O characterized by a lower limit for their radii of gyration (R\({}_{\text{g}}>\) 242 nm) were reported by [PERSON] et al. [178] at different values of solution pH, concentration and background electrolyte (NaCl). The authors used the fractal model developed by [PERSON] et al. [161] to fit the SANS curves. However, owing to the absence of both well-defined Guiner (low \(Q\)) and Porod (high \(Q\)) regions in the experimental SANS curves, the authors were unable to extract meaningful values of d\({}_{f}\), particle-particle correlation length \(\xi\), and particle radius R\({}_{\text{p}}\) for SRFA aggregates.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 5 Discussion and Future Research Direction
* Exploiting ML and DL algorithms: studies have shown that more severe attacks are possible using these methods. For instance, convolutional neural networks (CNN) have been used to break many cryptographic algorithms. These security issues must be taken into account in the design of IIoT networks. * Privacy concerns: ML and DL algorithms may cause potential leakage of private data. For example, DL classifications can be broken easily, and it potentially causes privacy breaches. In addition, there are some adversarial attacks possible against the DL process, which exploit the detection methods used in the DL. Therefore, more research in this space is essential. * Implementation of ML/DL at the edge: this is another challenging area that needs further research. In edge computing, these methods (i.e., ML and DL) can help minimize cloud dependency and delay processing. Further research in this area is required.
## 2 Type of Ball Mills
### Magnetic Field Induced Ball Milling  
Ball milling is a simple and convenient method for the preparation of powders, alloys, and composite materials. This technique results in products having very fine particle size and grain size. Anisotropic magnetic particles with nanosized grain structures can be fabricated by ball-milling under a magnetic field [47]. The structural and magnetic characterization results of sub-micron sized particles obtained by ball-milling in a magnetic field environment unveil strong magnetic anisotropy. Sm\({}_{2}\)Co\({}_{17}\) is considered as a building block for future high performance permanent magnets. Anisotropic Sm\({}_{2}\)Co\({}_{17}\) nano-flakes with high aspect ratio and a thickness of tens of nanometres were synthesized in the presence of heptane and oleic acid by magnetic field induced high energy ball milling [48].  
The application of a magnetic field on the morphology, structure, and magnetic properties of ball milled TbFe\({}_{2}\) alloy was investigated [49]. Researchers found that the rate of particle size reduction in presence of magnetic field was superior compared to simple ball milling. Further, the coercivity of field assisted (magnetic) particles are as high as 6500 Oe whereas it is 3850 Oe only in the absence of magnetic field.  
Powder based alloys of NdFeB and SmCo are produced in a ball mill kept in a uniform magnetic field [50]. The duration of milling was 100 h and samplings were performed every 25 h of milling. The particle size of the powder alloy was \(\sim\)100 nm (after 100 h milling) and their grain size was \(<\)20 nm. The remanence ratio of milled alloy under magnetic field was higher than those prepared in the absence of magnetic field. Furthermore, it was evident from XRD peaks that the material prepared by ball milling in presence of magnetic field had better alignment than those produced by conventional ball milling.
###### Abstract
Unlike others, this review employed a systematic protocol to retrieve data from databases, ensuring objectivity. The contributions of this review paper are summarised as follows:  
* Outlining the background of the palm oil breeding programmes. * Introducing the factor that affects the palm oil growth and the fruit quality. * Comprehensive critical assessment of ML-based palm oil prediction algorithms, critical evaluation of feature sets used, and comparison of relevant research. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Objectives of the Review** & **Lack of the Review** \\ \hline
[38] & Applications of remote sensing technology for palm plantation monitoring. A list of the existing knowledge gaps is compiled. The findings are followed up with recommendations for further investigation. & This review omits any mention related to the algorithm prediction of palm oil yield prediction. \\ \hline
[39] & An overview of the biotechnology used in palm oil breeding. This paper only reviews the application of molecular methods. & It does not employ ML techniques. These methods are insufficient to forecast specific breeding. \\ \hline
[41] & An overview of the techniques used for detecting palm oil nutrition deficiencies using proximal images and application examples. & This review does not mention algorithms for predicting palm oil yields and breeding. \\ \hline
[44] & An overview of existing ML models for predicting crop yields. Various performance metrics are employed to assess the effectiveness of various strategies. & Excludes other factors such as breeding. It only uses ML models to predict crop yield and few predictions on palm oil. \\ \hline
[45] & An overview of several existing ML models in forecasting palm oil yields. Multiple performance metrics were used to determine the effectiveness of various strategies. & Excludes other factors such as breeding and only uses ML models to predict palm oil yield. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An analysis of recent literature reviews on algorithms in palm oil yield prediction. * A thorough examination of the advantages and drawbacks of ML algorithms in predicting palm oil yield. This review outlined current types of research in predicting palm oil production using ML by extensively collecting various forecasting models for the palm oil ML framework. The rest of the paper is organised as follows: Section 2 presents a detailed review of related studies on the background of palm oil and factors affecting its growth and quality. Section 3 introduces the existing research on predicting palm oil yield using ML. Meanwhile, Section 4 discusses issues of palm oil predictions, future directions, and the selection of the optimum prediction algorithm. Section 5 summarises the framework prediction of palm oil, finalising with the conclusion in Section 6.
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
###### Abstract  
Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.  
cooperative spectrum sensing; convolutional neural networks; LeNet; AlexNet; VGG-16 202111440. [[https://doi.org/10.3390/app110440](https://doi.org/10.3390/app110440)]([https://doi.org/10.3390/app110440](https://doi.org/10.3390/app110440))  
[PERSON]; [PERSON].  
Cooperative Spectrum Sensing Based on Convolutional Neural Networks.  
_Appl. Sci._**2021**, _11_, 4440. [[https://doi.org/10.3390/app11104440](https://doi.org/10.3390/app11104440)]([https://doi.org/10.3390/app11104440](https://doi.org/10.3390/app11104440))
## 3 Sampling Strategy, Analytical Methods, and Results
### Trace Element Composition: LA-ICP-MS Result
#### 3.5.1 Amphibole  
The Rare Earth Element (REE) concentrations in the amphiboles are enriched by 5 to 80 times relative to the chondrite. The chondrite-normalized REE patterns are convex-upward (Figure 7a,c,e,g and Table S3 of the Supplementary Materials) except at the edges of Type 2 (Figure 7a) and Type 4P (Figure 7e) amphiboles. The MREEs are especially enriched compared to the light REEs (LREEs) and heavy REEs (HREEs). This trend weakens for more enriched La and Ce concentrations in Type 2 (Figure 7a) and Type 4C (Figure 7g). Slightly negative Eu anomalies (Eu/Eu* = 2 EuN/ [SmN + GdN] = 0.75-1.22, where the subscript N denotes normalization to the chondrite composition of [46]) may or may not be observed. Notably, the edges of the samples usually show slightly negative Eu anomalies, with the Type 2 amphibole (D17-B1-01) showing the largest anomaly (0.75) (Figure 7a).  
The Type 4P amphibole (D17-B1-05) exhibits a more complex core-mantle-rim texture (Figure 7e). The core and mantle share similar chondrite-normalized REE patterns, but the core has higher total contents. Compared to the core and mantle, the edge is enriched in LREEs and shows intermediate concentrations of MREEs and HREEs (Figure 7, Type 4P, Figure 7e).  
The samples show primitive mantle-normalized multi-element patterns that are enriched in Ba, Pb, and Sr but depleted in Th, U, Zr, and Hf (Figure 7b,d,f,h). Th and U contents are higher at the edge than the core for most samples. The Type 2 amphibole (D17-B1-01, Figure 7b) exhibits a distinctive negative Sr anomaly (Sr/Sr\({}^{*}\) = 2 Sr\({}_{\rm PM}\)/[Ce\({}_{\rm PM}\) + Nd\({}_{\rm PM}\)], where the subscript PM denotes primitive mantle normalization [46]), unlike other grains. Unlike samples with high Th and U concentrations at the edge, amphibole Type 3C (D17-B2-01, Figure 7d) shows consistency between the rim and core.