esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 2 Mechanisms of LSW Factors on Vegetation Cover
### Geometric Description of the Gully by LSW and Corresponding Ecological Response  
First, existing research indicates that slope width and gully extensibility are negatively correlated [24]. The term \"extensibility\" refers to the ratio of the length to width of the gully's outer rectangle perpendicular to the slope surface. As a result, the larger the slope width within the slope, the more elongated the internal gullies are. Since the cross-sectional morphology of fine gullies is insensitive to changes in slope length [34], and such structures do not contribute significantly to lateral erosion of the valley, the low rate of gully wall expansion can preserve the current basic morphology of the river valley [35]. In addition, elongated gullies have a positive impact on increasing surface area and raising ground temperature, and play a similar role to trenching in cultivated areas to achieve water conservation and retention [36], thereby improving soil water and salt distribution and soil quality status [37]. Furthermore, it has been observed that there is an inverse correlation between the slope width and the variability of the gully slope [24]. Gully slope variability is defined as the ratio of the elevation difference between the head and tail of the gully to its length. For regions with wider slopes, the gravitational potential energy in the descending direction of the slope does not change greatly. As a result, the erosion effect of surface runoff is not significant, and the relatively slow water flow rate provides a greater opportunity for organic deposition [38; 39], again creating possible conditions for vegetation growth.  
Based on the close dependence of the LS factor on soil loss and erosion, it is initially assumed that the LSW has a certain influence on the growth of greenery, and the LSW factor of the study area is calculated with reference to the method of \(G_{L}+G_{S}+G_{W}\) (where \(G_{L}\), \(G_{S}\) and \(G_{W}\) are the normalized values of L, S and W, respectively), with a hope to find new applications of the new factor on vegetation cover evaluation.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Visualization and Comparison of the Results of the Proposed Model  
In this study, a Grad-CAM (gradient-weighted class activation mapping) visualization approach was utilized to assess the interpretability and localization performance of the proposed skin cancer classification model. Grad-CAM generates heat maps that highlight the regions in the input image that contribute most significantly to the model's prediction, thus providing valuable insights into the decision-making process. The HAM10000 dataset, which consists of a large collection of annotated skin lesion images, was used to evaluate the model. A key advantage of using Grad-CAM is its ability to pinpoint the specific areas of the images that correspond to the skin cancer lesions, offering a transparent way to visualize the model's focus during classification. Figure 7 displays the original images from the HAM10000 dataset, alongside their corresponding heat maps and the final model predictions. The heat maps were overlaid on the original images to show which parts of the images the model emphasized in making its decisions. These visualizations are critical in confirming the model's ability to accurately localize lesions. Each class including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC) was represented with its corresponding heat map. The heat maps clearly demonstrated that the proposed model effectively focused on the relevant areas of the lesions, making it a reliable tool for skin cancer classification.  
The visual results from the Grad-CAM heat maps highlight the robustness of the proposed model as it consistently identifies and classifies skin lesions with high accuracy. The model's ability to both classify and localize lesions reinforces its practical potential for use in clinical applications, where interpretability and accurate lesion localization are crucial. Overall, the results confirm that the proposed model is both effective and reliable for skin cancer classification, showing strong performance in lesion detection and classification across different skin cancer types.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Comparative Analysis
#### 5.2.5 Residual Energy  
It is a major parameter in WBAN that measures the energy efficiency of the system. The initial distribution of energy is equal for all sensors. The main issue in perceiving WBAN communication is to manage the residual energy of body sensors. The residual energy can be varied in accordance with the time, packet size and number of rounds of processing. Furthermore, based on the connectivity to the neighbor sensors and coverage, residual energy values are different. The shorter residual energy consumption for each processing, such as sensing, transmission and reception, proves that the model is energy efficient. A mathematical formulation of residual energy is given below,  
\[R_{E}(i)=\frac{(E_{node}\times E_{init})}{100} \tag{18}\]  
where \(E_{node}\) is the residual energy consumption rate of the node, \(E_{init}\) represents the energy level at the initial stage. However, the transmission energy is directly proportional to the distance from the source to the destination.  
The residual energy defines the remaining energy of a sensor after transmitting a single packet. As per the increase in the number of rounds, residual energy gradually decreases. Residual energy is compared with the two parameters, simulation rounds and simulation time, for both emergency and nonemergency packets. Previous works are reduced to the lower level of residual energy at 50 rounds. Designing a lightweight framework increases the \(QoS\), which is achieved by three kinds of mechanisms: security by blockchain technology, fault data elimination by \(RDF\), and two sink deployment for emergency and nonemergency packet transmission in the proposed B-DEAH method, which increases the residual energy, thereby increasing the energy efficiency. Figures 14 and 15 represent the residual energy in terms of simulation rounds for emergency and periodic packets. Figures 16 and 17 represent the residual energy in terms of simulation times for emergency and periodic packets.  
Figure 14: Residual energy vs. simulation rounds (emergency packets).  
Figure 15: Residual energy vs. simulation rounds (periodic packets).  
The proposed B-DEAH is 40% better than CF-EHARP, PCA and E-HARP. For every communication, the residual energy of each sensor and relay sensor is considered for multihop-based routing. The higher residual energy gives the higher network lifetime, and the average behavior of the proposed work confirms that it does not take higher residual energy and gives the stochastic nature of higher residual energy availability.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 4 Experimental Results
### Results Analysis (Dataset 1)  
Table 2 synthetizes the achieved average results, in terms of C.R. The second and third columns of Table 2, report the AVQ and the AVQIS achieved average results, respectively. The results are reported for each one of the image sequences of Dataset 1 (first column).  
From such a table, it is noticeable that the compression performances of the AVQIS algorithm are better, with respect to the AVQ algorithm, in two cases: _Seq. 1_ and _Seq. 3_. In relation to the sequence referred to as _Seq. 2_, the AVQIS algorithm achieves slightly worse performance. Indeed, the AVQIS algorithm achieves an average C.R. of 2.41, while the AVQ algorithm achieves an average C.R. of 2.44.  
In Figure 10, we graphically report the trends of the achieved PSNR metric for each one of the image sequences of Dataset 1, in relation to the AVQ algorithm (green line) and the AVQIS algorithm (magenta line). From Table 3, which synthetizes the achieved average PSNR, it is noticeable that the AVQ algorithm achieves a better PSNR for the _Seq. 2_ and _Seq. 3_ sequences, while, in Seq. 1, the AVQIS algorithm obtains a higher PSNR. In Table A1,  
\begin{table}
\begin{tabular}{c c c} \hline
**Image Sequence/Average PSNR** & **AVQ** & **AVQIS** \\ \hline _Seq. 1_ & 6.72 & 9.79 \\ _Seq. 2_ & 2.41 & 2.44 \\ _Seq. 2_ & 2.09 & 2.13 \\ \hline \end{tabular}
\end{table}
Table 2: Average C.R–Dataset 1.  
Figure 10: Graphical comparison of the achieved PSNR between the AVQ algorithm (green line) and the AVQIS algorithm (magenta line), for (**a**) _Seq. 1;_ (**b**) _Seq. 2;_ and (**c**) _Seq. 3_.  
Figure 9: Graphical comparison of the achieved C.R. between the AVQIS algorithm (blue line) and the AVQ algorithm (red line) for (**a**) _Seq. 1;_ (**b**) _Seq. 2;_ and (**c**) _Seq. 3_.
## 5 Discussion
### Geotechnical Challenges and Remediation Strategies  
The prediction and assessment of the ultimate bearing capacity (UBC) for shallow foundations on cohesionless soils pose several geotechnical challenges that need to be addressed for reliable and safe foundation design. One of the primary challenges is the complex and heterogeneous nature of cohesionless soils, which can exhibit significant spatial variability in properties such as grain size distribution, void ratio, and shear strength. This variability can lead to unpredictable bearing capacity behavior, making it challenging to accurately estimate UBC using traditional methods. Moreover, the presence of soft spots, loose zones, or weak layers within the soil profile can further complicate the foundation response and load-carrying capacity. Dealing with these uncertainties and spatial variations is crucial to ensure the integrity and stability of shallow foundations.  
To overcome these geotechnical challenges, the implementation of advanced soft computing techniques, such as Genetic Programming (GP), Classification and Regression Trees (CART), and Genetic Algorithm-Emotional Neuron Network (GA-ENN), as demonstrated in this study, offers promising remediation strategies. These AI models possess the capability to learn and adapt to complex patterns and non-linear relationships within the data, enabling more accurate and robust predictions of UBC for shallow foundations on cohesionless soils. By utilizing these AI models, engineers can account for the spatial variability and uncertainties in soil properties, leading to more reliable and safer foundation designs.  
Another significant geotechnical challenge is the consideration of various influential parameters, such as the width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (\(\gamma_{\text{d}}\) or \(\gamma^{\prime}\)), and internal friction angle (f), when estimating UBC. The interplay of these parameters requires a comprehensive understanding of their individual and combined effects on the foundation's load-bearing capacity. Soft computing techniques, as used in this study, facilitate sensitivity analysis to assess the impact of each input parameter on the UBC prediction, aiding engineers in identifying critical factors and optimizing foundation design.  
In addition to AI-based approaches, thorough site investigations, including detailed soil exploration and testing, play a vital role in addressing geotechnical challenges. The collection of high-quality data enables engineers to characterize the soil properties accurately and capture the spatial variations, providing a solid foundation for the AI models' training and validation.
###### Abstract  
A unique aspect of this research lies in the combination of polyethylene terephthalalate (PET) nanofibers with Au@FPh2-PIILP to create a nanogold composite (NGC). This NGC has proven to be highly efficient in removing methylene blue (MB) from wastewater. The prepared nanogold composite NGC was characterized by Fourier-transform infrared spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FE-SEM), transmission electron microscopy (TEM), Energy Dispersive X-ray Spectroscopy (EDAX), and Elements Distribution Mapping (EDM). Several factors were examined in batch adsorption experiments to determine their impact on dye adsorption. These factors included the initial pH range of four to eight, the dosage of NGC adsorbent ranging from 0.001 to 0.008 g, the initial concentration of MB dye ranging from 10 to 50 mg L\({}^{-1}\), and the contact period ranging from 10 to 80 min. It has been observed that NGC is more efficient in removing MB from polluted water. The results of the pseudo-second-order model show good agreement between the calculated adsorption capacity (\(q_{e}\))\({}_{cal}\) (4.3840 mg g\({}^{-1}\)) and the experimental adsorption capacity (\(q_{e}\))\({}_{exp}\) (4.6838 mg g\({}^{-1}\)) values. Experimental findings suggest a monolayer capping of MB dye on the NGC surface with a maximum adsorption capacity \(Q_{m}\) of 18.622 mg g\({}^{-1}\) at 20 \({}^{\circ}\)C, indicating that it is well-fitted to the Langmuir isotherm.  
polyethylene terephthalalate; nanofiber; nanogold; methylene blue; nanocomposite
## 1 Introduction
### Hybrid FSO/RF System  
As the name suggests, the hybrid FSO/RF system consists of two systems, as shown in Figure 1. One is the FSO system and the other is the RF system. For hybrid systems such as this, we distinguish between primary and secondary systems. The primary system in this case is the FSO system, as it provides much higher transmission speeds than conventional RF systems. For this reason, the RF system is a secondary system that ensures redundancy of the FSO system in the event of adverse conditions for the primary system. Precisely because of redundancy, hybrid systems use systems with different methods of information transmission. This combination was chosen deliberately, as the biggest problem with the FSO system is fog, and this atmospheric phenomenon does not have a major impact on the operation of RF systems [9]. In this case, it is necessary to consider the transmission speed of individual systems. As the FSO system achieves speeds of a few Gbps and RF only a few hundred Mbps, it is also necessary to consider compensating for these losses in transmission speed. In our hybrid FSO/RF system, we do not monitor speed comparisons, but the availability and reliability itself (99.99999%) by a combination of a primary FSO line and a backup RF line.
## 4 Genomic Instability, Fe-S Clusters and Iron Chelators
### Ribonucleotide Reductases
It is also much more readily able to penetrate the cell membrane at physiological pH as it is very lipophilic [129]. PIH had three series created which depended on the number of ligands attached; they were known as the pyridoxal benzoyl hydrazone analogues (100 series), the salicaldehyde benzoyl hydrazone analogues (200 series) and 2-hydroxy-1-naphthylaldehyde benzoyl hydrazone analogues (300 series) [130]. PIH and all analogues are tridentate ligands that bid in a 2:1 ligand to iron complex [131]. The 300 series was shown to be the most lipophilic and thus could penetrate the most readily and had the greatest efficacy at inhibiting RnR [127,132]. An example of this is the iron chelator 2-hydroxy-1-naphthylaldehyde isonicotinoyl hydrazone; NIH, which has been shown to inhibit tumour growth in cell lines of breast, bladder and neck and also the CCRF-CEM cell line [133]. The main mechanism accredited for this was the inhibition of the tyrosyl radial in RnR, its effectiveness was shown in that it had an IC50 20-fold lower than desferoxamine. However, studies show redox cycling did not play a large role in NIH's antiproliferative activity [134]. This suggests that it inhibited RnR's function either directly or by removing iron from the labile iron pool. Triapine (3-AP) is an iron chelator synthesised as a potent RnR inhibitor. It is used clinically for CML and is in clinical trials for aggressive myeloproliferative neoplasms, cervical cancer and solid-state tumours [135,136]. It has shown to be more potent than Hydroxyurea but appears to use a different mechanism as it works on Hydroxyurea resistant cells [137]. As triapine is redox-active it produces ROS, which is thought to make the di-iron centre of RnR unstable and inactive [25,138]. RnR is required for DNA replication and repair and has thus been a target for therapeutic agents since it was first discovered. Understanding its biosynthesis and metallocofactor assembly lead to the use of more iron chelators that target the iron core of RnR [40,139]. These iron chelators advanced over the years by taking advantage of how the design could further affect its function, inhibiting RnR. Iron chelators that could redox cycle have been used most recently in cancer cell lines as they can work in a variety of ways, including removing iron from the labile iron pool and interfering with the tyrosyl radical in RnR via the production of ROS [140,141]. This makes chelators an attractive option for future RnR inhibition and cancer therapy.
## 1 Introduction
[22] examined the effect of the airfoil leading edge radius by 2D CFD simulations, and it turned out that the airfoil leading edge radius had an influence on the strength and diffusion rate of the shed vortices, leading to large differences in the torque characteristics of the blades in the upwind region and small differences in the downwind region. [PERSON] et al. [23] quantified the effect of tower tilting and found that the effect on the tangential forces was stronger than that on the normal forces. [PERSON] et al. [24; 25] focused on the movements of platforms of offshore floating VAWTs. They illustrated that both the pitch and surge motions of the platform aggravated variations in the aerodynamic forces and may have increased the fatigue loads. [PERSON] et al. [26] also evaluated the aerodynamics of an H-type floating VAWT with surge-heavy-pitch floating foundation motions by a double-multiple streamtube model and acquired similar findings. The normal pitch axes of a VAWT are coincident with the aerodynamic center lines of the blades. However, a previous study proposed an innovative VAWT with inclined pitch axes which could convert the drive mode of the pitch and achieve better structural performance [4]. The inclined pitch axis of a blade is still in the plane of the chord lines, but there is a nonzero intersection angle between the pitch axis and the aerodynamic center line. A pitch movement of the blade is achieved while folding around the inclined pitch axis, along with flapwise and edgewise movements at the same time. Therefore, it leads to quite different aerodynamics for the blade and performance of the wind turbine, which are illustrated at length in the present study. To summarize, the instantaneous aerodynamic loads and overall performance of a straight-bladed VAWT with inclined pitch axes are investigated using an actuator line model. Instantaneous AoAs and the tangential and normal forces of a blade versus azimuth are evaluated under the conditions of various fold angles and incline angles, as are the power output and average aerodynamic forces in the streamwise, lateral and vertical directions of the wind turbine. The main objective of the present study is to investigate the effect of the pitch parameters on the aerodynamic forces of a VAWT with inclined pitch axes.
###### Abstract  
The ability to automate machine translation has various applications in international commerce, medicine, travel, education, and text digitization. Due to the different grammar and lack of clear word boundaries in Chinese, it is challenging to conduct translation from word-based languages (e.g., English) to Chinese. This article has implemented a GPU-enabled deep learning machine translation system based on a domain-specific corpus. Our system takes an English text as input and uses an encoder-decoder model with an attention mechanism based on Google's Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on English-Chinese bilingual text sentences from the News area of the UM-Corpus. The parallel training process of our model can be performed on common laptops, desktops, and servers with one or more GPUs. At training time, we not only track loss over training epochs but also measure the quality of our model's translations with the BLEU score. We also provide an easy-to-use web interface for users so as to manage corpus, training projects, and trained models. The experimental results show that we can achieve a maximum BLEU score of 29.2. We can further improve this score by tuning other hyperparameters. The GPU-enabled model training runs over 15x faster than on a multi-core CPU, which facilitates us having a shorter turn-around time. As a case study, we compare the performance of our model to that of Baidu's, which shows that our model can compete with the industry-level translation system. We argue that our deep-learning-based translation system is particularly suitable for teaching purposes and small/medium-sized enterprises.  
Keywords:neural machine translation; transformer; GPUs; multi-domain corpus +
Footnote †: journal: Applied
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)  
In order to practically validate the architectural solutions and strategies proposed in the previous section, trials are planned in the Muuga/Tallinn-Vuosaari/Helsinki corridor in the context of the 5G-ROUTES project. The plan is to utilize Eckero Line's Finbo vessel as a test platform and to equip it with the necessary radio and communication devices. The plan also includes supplementing Finbo with a second vessel/boat, which can operate more freely, since Finbo operates between Vuosaari (Finland) and Muuga (Estonia) according to a tight daily schedule. This section provides a description of the planned trials, for which a set of use cases have been defined.  
As has been described in the previous sections, the maritime use cases will utilize 700 MHz, 3.5 GHz and satellite communication as the backbone and ferry coverage. These scenarios will be extended with the multi-hop connections. All these solutions will be tested and analyzed both separately and together in a real maritime operation environment. The 5G-ROUTES use cases were defined so that they will cover most of the potential customer and user groups in the ferry, i.e., passengers, freight companies and ferry operators/staff and, hence, the future opportunities and benefits for these user groups could be analyzed. In the maritime pilots, which will be prepared and pre-tested in lab trials that are starting in the first half of 2023, satellite communication will be used to provide an alternative for cellular-based backbone connectivity. It is clear that, especially on open-sea cases such as the one considered in this paper, the role of satellite communication becomes paramount, while closer to shore, other options might be more suitable. Accordingly, studying strategies for load balancing between cellular and satellite backbone connection will be part of the trials.