esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 3 Methodology
### Rol Segmentation  
Next, the fuzzy-based superpixel-clustering algorithm is proposed for RoI segmentation. This algorithm segments similar pixels in sMRI images, which is known as superpixels. In the proposed fuzzy-based superpixel-clustering algorithm, an objective function of fuzzy C means method adds histogram information, and this process reduces the number of colour levels in the superpixels. Here, every superpixel is denoted by the average value of colour pixels in the corresponding superpixel region. The computational speed of the superpixel-clustering algorithm is improved by incorporating the objective function of the fuzzy C means method [37; 38]. In this fuzzy-based superpixel-clustering algorithm, the acquired sMRI images are passed into the CIE-LAB, which comprises two vectors (colour value \(cv\) of the pixels and position of the pixels \(P\)). The colour value and the position of the pixels is mathematically expressed in Equations (1) and (2).  
\[cv=(g,a,b) \tag{1}\]  
\[P=(x,y) \tag{2}\]  
where \(g,a,\text{ and }b\) represent red green blue (RGB) colour values, and \(x\) and \(y\) denote sMRI image coordinates. Next, the similarity between the vectors are computed, and further, the superpixels are clustered utilizing the fuzzy C means method. In this clustering method,  
Figure 2: Acquired sample sMRI images: (**a**) ADNI dataset, (**b**) real-time dataset, and (**c**) AIBL dataset.  
the superpixel are clustered by categorizing the separated pixels \(Sp\) to the total pixels \(n\). Here, the size of the superpixel \(SSp\) is computed utilizing Equation (3).  
\[SSp=\sqrt{\frac{n}{Sp}} \tag{3}\]  
During the clustering mechanism, the seed points present in the sMRI images need to be moved towards the centred image region \(3\times 3\); otherwise, it results in interference. The similarity between the pixels are computed utilizing the own and adjacent seed points. The space distance \(Dis_{xy}\) and color difference \(Dif_{lab}\) between the pixels are computed utilizing Equations (4) and (5).  
\[Dis_{xy}=\sqrt{(x_{Sp}-x_{i})^{2}+(y_{Sp}-y_{i})^{2}} \tag{4}\]  
\[Dif_{lab}=\sqrt{(g_{Sp}-g_{i})^{2}+(a_{Sp}-a_{i})^{2}+(b_{Sp}-b_{i})^{2}} \tag{5}\]  
In this context, \(i\) is represented as the sMRI pixels, and the similarity with the pixels in \(D_{i}\) is computed utilizing Equation (6).  
\[D_{i}=Dif_{lab}+\frac{m}{S}Dis_{xy} \tag{6}\]  
where \(S=\frac{cv1-cv2}{255}\), and \(m=\sqrt{(Spt1-Spt2)2}\). The parameters \(S\) and \(m\) represent the colour similarity and distance between the seed points \(Spt1\) and \(Spt2\), respectively. When the computed value of \(D_{i}\) is maximum, the two pixels are very similar. As an outcome, a greater number of super pixels are segmented from the sMRI images, and the resultant image is presented in Figure 3. The hippocampus portions are marked in red colour in the respective sMRIs.
## 5 Conclusions  
The results obtained in this study revealed the feasibility of the two broadband multispectral (Sentinel-2 and RapidEye) sensors for retrieval of LAI in the saltmarsh of Schiermonnikoog via the inversion of PROSAIL. In general, LAI estimated from Sentinel-2 data had higher accuracy in comparison to those from RapidEye. Although Rapid Eye had a finer spatial resolution than Sentinel-2, the enhanced spectral range and resolution offered by Sentinel-2, e.g., three red edge bands, trumped the benefit of higher spatial resolution offered by Rapid Eye which in comparison had only one red edge band. The highest retrieval accuracies were obtained using all spectral bands of RapidEye and Sentinel-2 and the mean of the best ten solutions. When spectral subsetting of Sentinel-2 was considered, the spectral subset which contained four spectral bands of NIR and SWIR resulted in higher LAI retrieval accuracy in comparison to other spectral subsets studied and emphasised the importance of Sentinel-2 data for LAI studies. Our results highlight the suitability of PROSAIL when coupled with broadband satellite data for regional and global mapping of LAI in saltmarsh/grassland ecosystems.  
The LAI retrieval accuracy through model inversion is not only affected by uncertainties in satellite reflectance data and the RTM inadequacies [2] but also by, uncertainties related to LAI in situ measurements. In our study, LAI was measured using LAI-2200 instrument which accounts for the existence of both green and dead leaves. Although in this study we accounted for the percentage of non-photosynthetic components (dried leaves) of the measured LAI, no corrections for clumping effects or non-random distribution of leaves were considered. Further improvement of retrieval accuracy is expected when clumping effects or destructive sampling for LAI measurements are considered.  
[PERSON]. Conceptualized the paper and performed the experiment, analyzed the data, and wrote the manuscript. All the coauthors helped in editing the draft as well as providing critical comments. [PERSON] was also the project investigator. [PERSON] and [PERSON] participated in the field campaigns and assisted in field measurements.  
This study was partially funded by the European Space Agency's Innovators-III project \"Remote Sensing for Essential Biodiversity Variables\" (ESRIN Contract No. 4000113386).  
We acknowledge support from [PERSON] during the field data collection in 2015. We thank [PERSON] for his assistance with the image preprocessing.  
The authors have no conflict of interest associated with this publication.
# Characteristics of Ordinary Portland Cement Using the New Colloidal Nano-Silica Mixing Method
## 3 Results and Discussion
### Pore Structure
As a result, OPC samples decreased to 10.13%, 6.91%, and 6.44%, and OPC + Fly ash samples decreased to 12.56%, 9.30%, and 8.21%, respectively. The total porosity decreased with increasing amount of CNS, consistent with the results of this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**CNS Contents**} \\ \cline{2-7}  & \multicolumn{2}{c}{**0\%**} & \multicolumn{2}{c}{**30\%**} & \multicolumn{2}{c}{**50\%**} \\ \cline{2-7}  & **1-Day** & **28-Day** & **1-Day** & **28-Day** & **1-Day** & **28-Day** \\ \hline Large capillary pores & \multirow{2}{*}{61.51} & \multirow{2}{*}{48.33} & \multirow{2}{*}{67.25} & \multirow{2}{*}{42.72} & \multirow{2}{*}{32.65} & \multirow{2}{*}{26.2} \\ (10-0.05 \(\upmu\)m), (\%) & & & & & & \\ Medium capillary pores & \multirow{2}{*}{18.55} & \multirow{2}{*}{19.94} & \multirow{2}{*}{11.6} & \multirow{2}{*}{23.51} & \multirow{2}{*}{33.09} & \multirow{2}{*}{32.65} \\ (0.05-0.01 \(\upmu\)m), (\%) & & & & & & \\ Gel pores & \multirow{2}{*}{19.91} & \multirow{2}{*}{31.73} & \multirow{2}{*}{21.15} & \multirow{2}{*}{33.77} & \multirow{2}{*}{34.26} & \multirow{2}{*}{41.16} \\ (\(<\)0.01 \(\upmu\)m), (\%) & & & & & & \\ Total porosity, (\%) & 22.21 & 19.95 & 17.79 & 16.43 & 17.1 & 14.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pore size distribution according to the CNS replacement ratio.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 4 Experiments
### Comparisons with Prior Works  
We compared our proposed Inf-OSRGAN model with several state-of-the-art super-resolution models, including RCAN [40], SRGAN [35], RealSR [43], and Real-ESRGAN [44]. We utilized low-resolution (LR) images obtained by downscaling the images from the test set by a factor of four. The experiments were conducted on a Linux (Ubuntu 20.04 LTS) system with an NVIDIA RTX A6000 GPU (Nvidia, Santa Clara, CA, USA). Table 1 presents a comparison of our method with prior works in terms of runtime and number of parameters.  
While our approach, along with Real-ESRGAN (our baseline), exhibited a larger model size and slightly longer runtime, it compensated for this with superior visual quality. As illustrated in Figure 6, our method outperformed prior works in both overall aesthetic appeal and detail restoration, demonstrating a marked improvement in the fidelity of the images produced. Notably, Real-ESRGAN also exhibited better visual results compared to the other existing methods.  
The primary focus of our optimization efforts was to enhance the super-resolution performance and visual quality, rather than to prioritize processing speed. The decision to sacrifice some processing time in favor of significant improvements in visual outcomes is a worthwhile trade-off. This strategy aligns with our goal of delivering high-quality images that meet the demands of various applications where visual fidelity is of utmost importance. By prioritizing these aspects, we are confident that the benefits of enhanced visual performance substantially outweigh the costs associated with increased processing time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Parameters (M)** & **Processing Time Per Image (s)** \\ \hline RCAN & 1.67 & 0.2 \\ SRGAN & 1.47 & 0.2 \\ RealSR & 16.70 & 0.6 \\ Real-ESRGAN & 16.70 & 0.6 \\ Inf-OSRGAN & 16.70 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The various models along with their corresponding number of parameters and the time taken to process a single image. All models were tested on a Linux (Ubuntu 20.04 LTS) server equipped with an NVIDIA RTX A6000 GPU.  
Figure 5: Relationship between iterations and PSNR. (**a**) This panel depicts the relationship between the training iterations and PSNR for Inf-OSRNet. During this phase, as the number of iterations increased, the PSNR exhibited a trend of rapid initial growth followed by a slower increase. (**b**) This panel illustrates the relationship between the training iterations and PSNR for Inf-OSRGAN. In this phase, the PSNR demonstrated a decreasing trend as iterations increased.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation
### Experiments  
#### 4.2.1 Cluster Description  
We conducted experiments on a computer cluster with 14 nodes (one master and 13 slaves) at the Mobile Network and Big Data Laboratory of the College of Information and Communication Technology, Can Tho University. The configuration of each computer was with four Intel Core i5 3.2 GHz CPUs, 4 GB of RAM, 500 GB of HDD, and the Ubuntu operating system 14.04 LTS with 64 bits. The following versions of applications were used: Java 1.8, Hadoop 2.7.1, and Spark 2.0.
## 3 Results and Discussion
### Interaction between Ummi Dipeptides and Rattus Norvegicus mGluR1 Closed-Open Conformation
H bonds are shown as dashed lines, hydrophobic contacts are represented by green splines; the corresponding pocket residues are shown in the same color. Diagrams obtained from the ProteinsPlus PoseView tool, from which E amino acid was predicted to interact with Tyr74, Ser165, Thr188, Ser186, Asp318 and Lys409 by H-bonds; AH, with Tyr74, Ser186, Thr188, Aps318 and Lys409 by H-bonds, Trp110 by \(\pi\)-\(\pi\) stacking and a hydrophobic interaction; DA, with Tyr74, Ser165, Thr188 and Gly293 by H-bonds; DG, with Tyr74, Arg78, Ser165, Ser186, Thr188, Asp318, and Lys409 by H-bonds; EE, with Tyr74, Ser165, Ser186, Thr188, Met294, Gly319, Arg323, and Lys409 by H-bonds and Trp110 by hydrophobic interaction; ES, with Arg71, Tyr74, Arg78, Ser186, Met294, Arg323, and Lys409 by H-bonds and Trp110 by hydrophobic interaction; EV, with Arg71, Tyr74, Glu292, Gly293, Met294, Arg323, and Lys409 by H-bonds. _Rattus norgeticus_ mGluR1 key receptor residues from the closed-open conformation, Tyr74, Ser165, Ser186, Thr188, Asp318, Arg323, and Lys409 were predicted to interact with E (Ki = 15.58 \(\upmu\)M). With respect to the dipeptide AH (Ki = 8.26 \(\upmu\)M), the key residues Tyr74, Arg78, Ser186, Tyr 236, Glu293, Arg323 and Lys409 would be implicated. DA (Ki = 27.24 \(\upmu\)M) was calculated to interact with Tyr74, Ser165, Thr188, Tyr236, Glu292, Gly293, Asp318, Arg323, and Lys409, while DG (Ki = 6.0 \(\upmu\)M) would react with Tyr74, Arg78, Ser165, Ser186, Thr188, Tyr236, Asp318, and Lys409. On one hand, EE (Ki = 59.42 \(\upmu\)M) could bond with Tyr74, Arg78, Ser165, Ser186, Thr188, Glu292, Gly293 Arg323, and Lys409. On the other hand, ES (Ki = 9.17 \(\upmu\)M) would be able to form connections with Tyr74, Arg78, Gly293, Asp318, Arg323, and Lys409. EV (Ki = 7.87 \(\upmu\)M) was estimated to dock between Tyr74, Glu292, Gly293, Asp318, Arg323, and Lys409. Finally, VG (Ki = 811.24 nM) would bind to Tyr74, Arg78, Ser186, Thr188, and Lys409. Nevertheless, it is important to consider that non-key residues may participate in the stabilization, such as with Trp110, for the case of AH, EE, ES, and EV. Otherwise, residues, such as Arg323 and Lys409, can make interactions of different nature, as they can stablish H-bonds, hydrophobic forces, or even salt bridges. The majority of the interactions are of H-bond nature, even various with the same residue, such as with Tyr74, Ser165, or Arg323. The dipeptide EE, followed by ES, established the largest number of H-bonds, suggesting the presence of an E residue particularly promotes docking with the receptor in comparison with the other dipeptides. In addition, the nature of the C-terminal of these three dipeptides may provide insights on a polar side chain residue benefits the contact with the receptor. The number of hydrophobic interactions and salt bridges appear very similar between them for each dipeptide, except for glutamic acid, which could not form hydrophobic interactions by itself. Finally, the lowest Ki values were those from VG, then DG followed by EV and AH.
## 4 Current Studies Focused on PPO Activity  
### PPO Distribution  
PPO activity distribution is important, especially taking into account that in minimal processing some parts of the produce are valuable (such as pulp) at the expense of others (such as peel, stones, cores, etc.) which are usually removed. This way, there might be differences in PPO content depending on the state of the fruit or vegetable (whole or fresh-cut), affecting to the propensity to browning. As a consequence, this aspect was studied in some vegetables. In general, it was found that PPO activity was higher (two to four-fold) in photosynthetic tissues than in vascular ones. These findings were probably due to the location of PPO and POD in the thylakoid membrane of the chloroplasts [17; 56]. Concerning fruits, PPO activity distribution is variable depending on the tissue. For example, rambutan PPO activity was higher in spinterns than in the peel, probably due to the higher oxygen transmission into spinterns [57].
## 3 Results
### Effects of CWP on Concrete Durability Properties
#### 3.3.1 Water Absorption, Moisture Resistance and Permeability  
Water absorption is a key indicator of concrete durability. This reflects its ability to resist environmental degradation caused by moisture ingress. Studies generally show that CWP enhances the moisture resistance of concrete. [PERSON] et al. [68] and [PERSON] et al. [83] reported reductions in permeability with increasing CWP content. [PERSON] et al. observed that concrete with up to 30% CWP replacement exhibited reduced permeable pores at 90 days of curing, although permeability increased beyond 30% replacement due to dilution effects. Similarly, [PERSON] et al. found lower sorptivity in concrete containing up to 30% CWP replacement, enhancing the resistance to moisture ingress. [PERSON] et al. [82] demonstrated the variability in permeability reduction based on concrete strength. For concrete with a compressive strength of 50 MPa, the permeability decreased significantly at 20% CWP replacement but increased at higher levels. In contrast, concrete with strengths of 25 MPa and 75 MPa exhibited different trends, with 25 MPa concrete experiencing rapid permeability reduction beyond 20% CWP, while 75 MPa concrete showed a more gradual but consistent decrease. The reduction in water absorption and permeability is primarily attributed to CWP's pozzolanic activity. CWP reacts with CH during cement hydration to form additional C\(-\)S\(-\)H gel, which strengthens the microstructure by reducing the number of capillary pores. Moreover, the fine and dense CWP particles of CWP fill the voids in the concrete matrix, further limiting water ingress and enhancing durability. [PERSON] et al. [77] investigated the water absorption of concrete incorporating nano CWP at 7 and 28 days. The results showed a range of water absorption capacities decreasing from 7.2% at 7 days to 6.42% at 28 days, indicating improved density and reduced porosity over time. As the percentage of nano CWP in the concrete mix increased, the water absorption also increased. This study also highlights the delayed pozzolanic activity of CWP, as significant reactions do not occur during the early curing stages, as indicated by water absorption. This suggests that CWP may initially act primarily as an inert filler, with its pozzolanic properties developing over time.
###### Abstract
There was no significant difference in the occurrence of ACL avulsion fractures due to tibial rotation [6]. [PERSON] et al. used robotic cadaver simulation to determine ankle joint kinematics to evaluate the interaction between implants and bones in total ankle arthroplasty using a finite element model (FEM). The most critical condition for the interaction between implants and bones depended on the specimen and fixation design [7]. [PERSON] et al. used 3D motion tracking to capture anterior tibial translation and medial tibial rotation, analyzing the effects of ACL loss and ACL reconstruction on kinematics and ACL transplantation force during high tibial osteotomy. The combined correction of the varus and slope resulted in a corresponding decrease in tibial anterior translation in cadaveric knee joints due to ACL deficiency and reconstruction [8]. In summary, the current biomechanical methods for analyzing bones include using CT data to construct 3D models, simulating robot corpses, and 3D motion tracking. The construction of 3D models using CT data is common. However, this method may result in the loss of detailed bone information, potentially leading to an overall reduction in the accuracy of the resulting analysis [9]. Additionally, the commonly used material assignment methods are unsuitable for assigning values to bone materials due to their complex characteristics. Given this, the study is based on CT data of the lower limbs of the human body, using 3D visualization technology and a gradient grayscale assignment method to establish a FEM of the axial functional gradient of the human tibia. The FEA method is used to analyze the biomechanical characteristics of the tibia during flexion movements in different postures, as well as the tibial injury tolerance limit under different impact conditions and physiological characteristics. To accurately simulate the characteristics of the tibia, this method uses one-way linear interpolation to process CT images, increase the number of tomographic images, and reconstruct the surface of the 3D model to improve modeling quality. In addition, this study innovatively proposes a gradient grayscale assignment method, which divides the tibia into five regions, including the transition zone for material assignment, and obtains a FEM of the tibial axial functional gradient. The established tibial model was validated using statistical data on human tibial size and Yamada experimental data. This paper is divided into five chapters. Section 2 describes the method of constructing 3D models of human tibias via FEM to increase the models' quality. Section 3 provides an analysis of the biomechanical characteristics of lower limb flexion movement (LLFM) and the injury tolerance of the human tibia. Section 4 presents the experimental results, which includes the biomechanical properties and injury tolerance analysis of the lower limbs. Section 5 is the conclusion, which outlines the reasons for the in-depth exploration of the biomechanical properties of the lower limbs, discusses the factors that affect their injury tolerance, and summarizes the research in this article.
## 5 Challenges and Future Research Directions
### Energy Efficiency in ML  
ML models used in UDN management require substantial computing power, leading to high levels of energy consumption. The strong correlation between model complexity and energy use highlights the need for efficient AI models. Research should explore DL optimization techniques such as layer decomposition, pruning, knowledge distillation, and hardware acceleration using GPUs and FPGAs. These approaches can enhance computational efficiency while minimizing the power consumption in AI-driven network operations [4; 47].
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 1 Introduction
### Our Contributions  
In this paper, we first revisit the [PERSON] et al.'s CMIX-based scheme [14] and then point out the following security and privacy issues: (1) Linkability attacks during the communication outside the mix-zone, (2) unreliable Cuckoo Filters' (CFs) updates (in the low-density of the traffic), which breaks the privacy and safety of the whole system, and (3) mix-zones' encryption key leakage, which allows any compromised vehicle to break the safety of the system. We then propose an improved version that is resistant to these issues. To comply with [PERSON] et al.'s scheme, we utilise a modified version of [15] by replacing digital certificates with an Identity-Based Signature. In summary, our scheme provides the following features:  
* We use Adaptive Cuckoo Filter(ACF) instead of CF, as in [14], to mitigate the effect of false positives that may impact the performance of the filter; this enhances the system in sending Chaff messages to confuse eavesdropping adversaries that exploit traffic flow to breach unlinkability. We also use the pseudo-identity generation concept from [15], which has non-malleability to hide the certificates of actual vehicles.
* We provide unforgeability and non-repudiation by adding an _RSU_ signature and timestamp in each message, in particular, on the new fingerprinted Chaff messages before inserting them into the ACF. Hence, to preclude any possibility of malicious injections, we also remove the ACFs forwarding task from vehicles, like in [14]; in our scheme, _RSU_s are the sole authority to distribute them.
* We modify the mutual authentication between _RSU_s and vehicles based on certificates instead of IDs, as in [15], before generating the symmetric key of a mix-zone, as the generation of a shared key follows the Diffie-Hellman key exchange method to provide confidentiality and privacy by preventing any key leakage.  
These features allow our protocol to achieve unlinkability, unforgeability, and mutual authentication.