esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Malicious Document Classification
### Model Construction
#### 3.4.3 Dnn  
Recently, thanks to the power of deep learning, various efforts on neural-network-based classification have been undertaken [20; 21; 40; 41]. DNN performs classification by training an artificial neural network with a multilayer structure. Each layer of DNN learns to turn the data it receives into a more abstract and more composite representation. After the neural network is trained, it can be used to predict the class of a given sample in testing. The time complexity of DNN is \(O(NTAB)\), where \(T\) represents the number of iterations, \(A\) and \(B\) represent the input and output dimensions of a hidden layer, respectively.  
In summary, we first preprocessed each electronic document into a Unicode text; second, we tokenized each Unicode text into a set of words; third, we built a vector representation of each document with BoW, TF-IDF, or Word2 Vec methods; then, we built a classification model with the vectorized set of documents (i.e., the training set) by using the decision tree, SVM, or DNN methods; lastly, we predicted whether each of the given set of unknown documents (i.e., the test set) is malicious by using the classification model.
## 3 Results
### Mechanism of the Nano-Nets Formation  
Nanolayers are one of the most significant kinds of nanomaterials. New physical effects and a wide variety of functionalities can be achieved by nanoscale engineering of surfaces and layers. Here the assumption is that the LDHs have a single-layer material, the dimensions of the nanosheet can be up to a micrometer, while the thickness is only about one nanometer. As a result, LDHs exhibit plate-like shapes and contain nanolayers. The separation process for LDHs is known as exfoliation or delamination. The delamination of the layered structures is spontaneous because of the weak interactions between the nanolayers of the LDHs. The individual crystallites are called nanosheets and have remarkable chemical and physical properties because of their small thickness with respect to a bulk material. Inorganic nanosheets are possibly suitable as building blocks for advanced inorganic materials. Therefore, these nanosheets might be a very promising material for fabrication of nano-nets.  
According to the structure of Co-Fe layered double hydroxides, they have a huge interlayered space and contain 15% interlayered anions (carbonate and cyanate) in addition to 10% interlamellar water. Formation of nano-nets could be explained through the catalytic effect of the cobalt ferrite nanoparticles that was supported over LDH structures. The treatment of LDHs with methanol for a long time caused interaction between the hydroxyl groups of methanol and the nanolayers of LDHs. At critical conditions of temperature and pressure, the nanoparticles of cobalt ferrite acted as catalysts for driving and accelerating the decomposition reactions of methanol molecules as shown in the equation  
\[\mathrm{CH_{3}OH}\rightarrow\mathrm{CO}+2\mathrm{H_{2}} \tag{2}\]  
Many authors confirmed this catalytic reaction [31; 32]. It means that each one mole of methanol produces three moles of hydrogen and carbon monoxide inside the solid material. By this way, large amount of gases was produced inside the LDH structures. Also, decomposition reactions of cyanate and carbonate anions increased the amount of gases among the layers. To release all these gases from the rigid nanolayers of LDHs, holes and vacancies were formed during the transformation of Co-Fe LDH to cobalt ferrite through the dehydroxylation process of the nanolayers. At the same time, the internal pressure of these gases inside the nanolayered structures directed the hexagonal plates of LDH to produce nano-net morphology.  
In order to confirm this speculation, the same technique was used to prepare magnetic nano-nets without LDH. It failed to produce the nano-nets. It showed nanoparticles of cobalt ferrite as shown in Figure 9. These results concluded that layered double hydroxides are suitable materials for fabricating the nano-nets of cobalt ferrite.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 3 Overview of Explainable Artificial Intelligence (XAI)
### Challenges in Implementing XAI  
Implementing XAI in the context of AD detection poses several challenges, as demonstrated in the articles reviewed. The availability of large, high-quality datasets is a major challenge, as the study in [50] pointed out the limited data available to validate ML models, especially in using DTI data and white matter characteristics as biomarkers. The article in [13] also pointed out the need to further verify the high accuracy achieved by the VGG16 model using external datasets, emphasizing that robust XAI requires large-scale data to ensure the reliability and generalizability of interpretations. Balancing the precision of complex models with the need for interpretability is a recurring theme. The article in [31] concluded that an XAI framework should provide clear and understandable explanations of the model's predictions to guide general physicians and non-specialists in making informed decisions. However, achieving this balance is difficult, as evidenced by the results in [57], where LIME highlighted non-cerebral regions as influential, raising concerns about the reliability of subsequent interpretations in very precise but complex models such as MobileNetV3. Translating XAI from research into clinical practice is another key challenge. While studies such as [55] propose cutting-edge methods for decoding neural networks, the practical application of these methods in real-world clinical settings remains untested. Likewise, the contribution of providing truly XAI tools, as discussed in [32], has not yet been implemented due to the lack of large datasets and real XAI data. This gap highlights the need for further research and development to make XAI tools practical and useful for clinical decision-making.
## 5 Conclusions  
In order to improve the environment perception ability of the ADAS, the new obstacle detection method based on the local spatial feature (OD-LSF) is proposed in this paper. Firstly, an obstacle detection method based on the plane normal vector (OD-PNV) is proposed. This method classifies the superpixels as obstacles or free spaces based on the superpixel local plane normal vector. It does not need to fit a road surface model, so it is less affected by the road condition. In addition, an obstacle detection method based on the superpixel point-cloud height (OD-SPH) is proposed. This method uses the superpixels classified as free spaces by the OD-PNV method to fit the road plane and classify them according to the superpixel point-cloud height. Finally, the OD-LSF method combines the detection probabilities provided by the above two methods, and the results show that the OD-LSF method combines the advantages of the two methods. The experiment on the KITTI dataset indicates that the OD-LSF method proposed in this paper combines the advantages of the above two methods. For the detection of the complete obstacle class and key obstacle subclass, the PTPR is 96.8% and 92.7%, respectively, and the PFPR is 3.5% for both. Even in the uneven road environment, our method achieves good detection results. However, the experimental results also indicate that the OD-LSF method has a limited detection capability for irregular vegetation covered areas. In our future work, we will study how to expand the detection range of the OD-LSF method. At the same time, optimizing the algorithm structure of OD-LSF and improving the operation efficiency are also our future work.  
Conceptualization, W.C., R.L. and S.L.; Funding acquisition, W.C., S.L. and X.W.; Methodology, W.C., R.L. and S.L.; Project administration, W.C.; Software, T.X. and J.X.; Validation, T.X. and J.X.; Writing--original draft, W.C. and T.X.; Writing--review and editing, W.C. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Scientific Research Fund of Zhejiang Provincial Education Department, China (grant No. Y202146002); by Zhejiang Provincial Public Welfare Technology Application Research Project, China (grant No. LGG22F030016); by Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems (grant No. 2022-17); by Innovation Team by Department of Education of Guangdong Province, China (grant No. 2020 KCXTD041); by Natural Science Foundation of Zhejiang, China (grant No. LQ21F030007).  
Not applicable.  
The authors would like to thank Editor-in-Chief, Editor, and anonymous Reviewers for their valuable reviews.  
The authors declare no conflict of interest.
# Femtosecond Pulse Laser Near-Field Ablation of Ag Nanorods
## 4 Conclusions  
In this study, we conducted femtosecond laser near-field ablation of Ag NRs with an average aspect ratio of 3.9. Dependence of the ablation morphology on the laser polarization direction and wavelength was observed. When the laser polarization was parallel to the long axis of the NR and a laser of 800 nm wavelength was used, the electronic field was enhanced at the ends of the NR, where fragmentation occurred, leading to a lobe-shaped morphology. When 400 nm wavelength excitation was employed, multipolar oscillation induced field enhancement in the middle of the NR, yielding a notch. By contrast, when the laser polarization was perpendicular to the long axis of the NR, material along the two sides was ablated, and small clusters formed because of the transverse excitation. By adjusting the electronic field distribution, useful nanostructures can be obtained. In addition, the near-field ablation evolution of Ag NRs was investigated, and a possible thermo-force coupling mechanism was proposed. During the transition from NRs to nanodots, small clusters were formed through thermal evaporation, and these clusters then melted and fused. The near-field force plays a critical role in ablation, and the notched NR was curved under the strong field gradient. Furthermore, the dependence of plasmonic welding of T-shaped NRs on polarization direction related to the spatial structure was determined. Only when the laser polarization was parallel to one of the two structures  
Figure 6: **(a)** TEM image of plasmonic welding at a Ag NR junction. The scale bar is 15 nm. The red dashed line is the nanojoint contour. Corresponding calculated electric field distributions of Ag NRs irradiated with 400 nm laser light and polarization angles (**b**) 0\({}^{\circ}\), (**c**) 45\({}^{\circ}\), (**d**) 90\({}^{\circ}\), and (**e**) 135\({}^{\circ}\). The angle between the two NRs was 90\({}^{\circ}\), and the gap was 4 nm. The arrows indicate the laser polarization direction. The color scales represent the electronic field intensity.  
was a strong nanajoint obtained because of the symmetric field distribution under the field coupling between the nanostructures. This study may promote the application of femtosecond laser ablation for the reshaping, cutting, and welding of nanostructures.  
Investigation, J.Y. and D.Z.; supervision, J.Y.; writing--original draft, J.Y. and D.Z.  
This research was supported by the National Key Research and Development Program of China (2017 YFB1104900, 2018 YFB1107200) and the National Natural Science Foundation of China (Grant No. 51775303).  
The authors would like to thank Professor [PERSON] at the Beijing Institute of Technology, China.  
The authors declare no conflict of interest.
## 5 Efficiency Analysis
### Qualitative Efficiency Comparison  
In Table 7 we compare the efficiency of our scheme with other similar schemes in the literature. Similarly to [46] let, \(T_{ge}\) denote the time required for an exponentiation in \(\mathbb{G}\), \(T_{gm}\) for a multiplication in \(\mathbb{G}\), \(T_{em}\), \(T_{ea}\) for scalar multiplication and point addition in the relevant elliptic curve, \(T_{bp}\) for a billinear pairing, \(T_{me}\), \(T_{mm}\), \(T_{ma}\) for modular exponentiation, multiplication and addition respectively, \(T_{bpc}\) for exponentiation in billinear pairing, \(T_{h}\) for computing a hash function and \(T_{bs}\) for performing binary search.  
Figure 5: RSU performance. (**a**) RSU Average Wait time (in sec). (**b**) RSU: Average Queue Length.  
Figure 6: Overall cost. (**a**) End-to-end cost for the secure communication protocol. (**b**) End-to-end average wait time 50–250 messages/s.  
From the vehicle side, our scheme requires 3 modular exponentiations, making it more efficient than [46, 63] but less efficient than [60, 61] which only require scalar multiplications. The scheme of [62] is the least efficient as it requires pairing functions for the vehicle. From the server side, our scheme requires two pairings and three exponentiations. Although lighter schemes without pairings exist like [60, 61], the extra computation cost allows our scheme to provide enhanced security against corrupted colluding authorities and at the same time strong privacy against honest but curious entities. Given that the extra computation burden is at the server and not at the vehicle side, and based on the performance analysis presented above, the proposed scheme can be efficiently implemented in realistic scenarios.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### System Details
#### 4.1.1 Driving Unit  
The driving unit comprises a deflection propulsion unit and a buoyancy control system, among other components. The actuator controller and pitch, yaw, and thruster actuators make up the propulsion system. Additionally, it has an actuator battery and a power supply to provide power. Propeller failures such as wing blockage, deformation, blade damage, jamming, slipping, and creeping can hinder the propeller from producing propulsion [74; 75]. Failure of the buoyancy pump as well as the steering motor and transmission system is another possibility [76; 77; 78]. Failure of the battery that powers the thruster could also prevent the thruster from working or the battery from being charged [79]. This system executes fault diagnosis procedures for the detection of water leaks and motor drive malfunctions.
## 1 Introduction  
Powerlifting is a weight-class strength sport that requires the competitor to lift as much weight as possible in three competition lifts (squat, bench press, and deadlift) [1]. The competitors have three attempts for each lift. The best attempt in terms of the amount of weight lifted according to the technical rules is recorded for each lift. Eventually, a total weight lifted is calculated for each lifter based on his/her best attempt in each lift. A system of points is utilized in powerlifting (GL-Points)to evaluate performance. The GL-Points are defined as score that accounts for body mass (BM), total weight lifted and an associated coefficient to calculate its relative strength. Considering this, body composition is a crucial component of powerlifting performance [1].  
In order to achieve high performance in powerlifting, it has been suggested that the body composition of a given lifter should be monitored throughout a competition season. In particular, fat-free mass (FFM) [2] and skeletal muscle mass (SMM) are the most determinant variables [3]. Researchers have previously reported that FFM is correlated with powerlifting performance (r = 0.86 to 0.95, \(p\leq\) 0.001) [2]; however, the latter has also been strongly correlated (mean r = 0.75 \(\pm\) 0.17) to longer professional powerlifting careers and body muscle mass variables [3]. While some authorshave reported that long-term changes in skeletal muscle tissue (hypertrophy) contribute to an increase in strength capacity [4], the contrary has also been reported [5]. Nonetheless, the reason why body composition improvements are a crucial component for powerlifting athletes is still up for debate.  
It has been shown that in order to elicit notable changes in body composition, a proper nutrition plan is required [6]. Therefore, to optimize the body composition of a powerlifter, an emerging approach is to apply nutritional periodization strategies [7]. Furthermore, a strength athlete should ensure: sufficient caloric intake to support energetic demands, adequate carbohydrate consumption to fuel the lifter during training, and optimal protein ingestion to support the recovery process [7]. Veganism is a dietary pattern that is adopted and publicized by some high-level athletes. In addition, vegan diets have been shown to confer some health benefits such as reduced risk of heart disease, lower low-density lipoprotein (LDL), blood pressure, type II diabetes, and cancer as compared to its omnivorous counterparts [8]. However, vegan diets tend to be lower in calories, protein, fat, vitamin B12, n-3 fats, calcium, and iodine [9] and, in turn, they tend to be higher in carbohydrates, fiber, micronutrients, phytochemicals, and antioxidants [9]. There are some reports of vegan endurance athletes [10], but this kind of diet remains unexplored in strength athletes.  
For this reason, this case study aimed to report body composition changes of a professional vegan powerlifter as well as to report advantages and disadvantages related to the nutritional strategies for six weeks.
## 1 Introduction
Channel estimation plays a critical role in the performance of wireless communication systems; this paper overviews RIS channel estimation schemes in various scenarios. In Section 2, we organised reviews about RIS channel estimation, which are based on the main directions and main contributions. In Section 3, channel estimation schemes in different frequency bands are introduced. In Section 4, different channel estimation signal processing algorithms are introduced, and this paper is concluded in Section 5. The notations used in the paper and their meanings are summarized in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Meaning** \\ \hline bold font lower case & Column vector \\ bold font upper case & Matrix \\ \((\cdot)^{\mathrm{H}}\) & Conjugate transposition operation \\ \((\cdot)^{\mathrm{T}}\) & Transposition operation \\ \(I_{M}\) & \(M\times M\) identity matrix \\ Diag\((\cdot)\) & Diagonal matrix \\ \(\otimes\) & Kronecker product \\ \(\|\cdot\|_{\mathrm{F}}\) & Frobenius norm \\ \(\mathcal{CN}(\mu,\sigma^{2})\) & Complex Gaussian distribution with mean \(\mu\) \\  & and variance \(\sigma^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation description.
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 2 Proposed Method
### MRI-Net Structure
#### 2.2.2 Feature Fusion Module  
Medical image segmentation is different from traditional scene segmentation and has higher requirements for fine-grained segmentation. The retinal blood vessel image is more complicated,  
Figure 5: Improved U-shaped neural network (MRU-Net) structure: the structure of the MRU-Net model is described in the lower right corner of the figure, including the methods used for encoder–decoder, feature fusion, and skip connection.  
Figure 6: Res-Block structure: each convolution process includes three processes: dropout, BN, and relu.  
and the difference is large in different parts, such as the main part of the blood vessel and microvessel part. We propose a multiscale dilated convolution [18] fusion method. The fusion method mainly relies on different dilation rates to provide multiple effective fields of view, thereby detecting segmented objects of different sizes. In mathematics, the dilated convolution calculation under two-dimensional signals is shown as follows:  
\[y[i]=\sum_{k}x[i+rk]w[k] \tag{3}\]  
Among them, \(x\) is the input feature map, \(w\) is the filter, and \(r\) is the expansion rate, which determines the stride of sampling the input signal. It is equivalent to convoluting input \(x\) with upsampled filters produced by inserting \(r-1\) zeros between two consecutive filter values along each spatial dimension. The schematic diagram of the dilated convolution is shown in Figure 7.  
Context semantic information is mainly determined by the size of the receiving domain. If the receiving domain can provide more abundant information, then more context information can be used. Pooling operations are usually used to increase the receptive field to extract image features at different scales, but the pooling operation also brings loss of image semantic information. In order to overcome this shortcoming and to effectively extract image features of different dimensions, we have adopted the dilated convolution operation mentioned above. Dilated convolution can expand the receptive field arbitrarily without the need to introduce additional parameters and can use the context information of the image, so it is very suitable for multi-scale image segmentation tasks.  
Our proposed multi-scale feature fusion module is shown in Figure 8. Generally, the convolution of a larger receptive domain can extract more abstract features of a large object, while the convolution of a small receptive domain is better for small objects. We use three branches to receive the semantic information in the encoder module. Firstly, the dilated rate in the dilated convolution is set to 1, 2, and 3 to expand the receptive field, thereby extracting feature information of different scales in the encoder module; then, the image semantic features extracted from different dilated rates are combined; and finally, in order to reduce the parameters and computational complexity, the Conv (1 \(\times\) 1) convolution operation is used to reduce the channel dimension of the feature map to 1/3 of the original dimension.