esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Personality Traits Using Handwriting and Deep Learning
## 5 Experimental Results
### CNN Model  
The CNN architecture shown in Figure 3 was implemented in MATLAB R2021b using the Deep Network Designer (see Figure 5). The neural network has one input layer, three hidden layers, and one output layer. Figure 5: Software implementation (MATLAB R2021b) of CNN (1 input layer, 3 hidden layers, and 1 output layer). Choosing the parameters of a convolutional neural network (CNN) involves several key decisions that depend on the specific task (e.g., image classification, object detection) and the dataset you are working with. These parameters can be grouped into three classes depending on the elements they refer to: convolutional layer parameters, dense layer parameters, and training algorithm parameters. Convolutional layer parameters are the number of layers, the number of convolutional filters of each layer, the size of filters, the activation function for each convolutional layer, and the size of the pooling filters. The problem we have to solve is of low complexity (each neural network must classify 3-5 types of handwriting), and the structure we have chosen has a small size. Thus, the network is made up of three convolutional layers with the following parameters: layer 1 uses 32 7 \(\times\) 7 filters, layer 2 uses 16 5 \(\times\) 5 filters, and layer 3 uses 16 3 \(\times\) 3 filters. The first layer detects simple features (edges, textures), while the last layer detects complex features. Each convolution is followed by a max pooling operation with a 2 \(\times\) 2 window and a stride of 2. We also use batch normalization, which is an important technique used in deep neural networks, including CNNs, to help improve training stability and speed. As activation functions, we used ReLU (rectified linear unit) functions, which are the standard choice for convolutional networks. After flattening the output from convolutional/pooling layers, we used three dense layers with 128 nodes. The final layer size matches the number of classes for classification (in our case, three or five) with softmax as the activation function. The training options are the following:  
- Optimization algorithm: stochastic gradient descent with momentum;  
- Initial learning rate: 0.01;  
- Maximum number of epochs: 10;  
- Objective metric: loss;  
- Minibatch size: 20. The parameters of the neural network are presented in Table 5.  
The program was run on a PC with the following specifications: GPU processor 7 th generation i7-7700 HQ, 2.80 GHz, NVIDIA GeForce GTX 1050 Ti, and DDR4 16 GB (NVIDIA, Santa Clara, CA, USA). The training progress of a CNN for handwriting baseline analysis is shown in Figure 6.  
In this example, we set a maximum of 10 epochs with seven iterations per epoch. It can be seen that the algorithm converges after only two epochs, and the training time is very short. The accuracy achieved is 95%.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 3 Protein Families
### Major Late Protein/Ripening-Related Protein  
Act d 11, also known as kirola, is a 17 kDa protein belonging to the major latex protein/ripening-related protein (MLP/RRP) family, which is also part of the Bet v 1-like family [22; 88]. Act d 11 is found in high quantities in ripened golden and green kiwifruit. This protein is ripening-related, and the concentration present is influenced by natural ripening and post-harvesting treatments [22; 58; 88]. Act d 11 has been structurally characterized into multiple crystal forms [22]. Although the sequence identity is low, the 3D structure of Act d 11 is highly similar to other Bet v 1-like allergens [22]. Due to this high structural similarity, specifically within the glycine-rich loop region, the immunological correlation and cross-reactivity of Act d 11 with other PR-10 family allergens, such as Bet v 1 from birch pollen, are observed [22; 70].  
Act d 11 is classified as a minor kiwifruit allergen. A study of 91 kiwifruit-allergic patients reported that 22/91 (24.2%) patients had IgE to the natural purified Act d 11 when sera were tested by an ISAC microarray system [88]. In the same study, 18/91 (22%) of patients reacted positively to SPTs when tested with natural purified Act d 11 [88]. Sensitization to Act d 11 is reported to primarily occur through Bet v 1-sensitized individuals, suggesting that Act d 11 is involved in cross-reactivity rather than primary sensitization [1; 22].  
Additionally, it is interesting to compare the structural similarity of Act d 8 with Act d 11 (Figure 6). Despite sharing less than 25% sequence identity with Act d 8, the structure of Act d 11 shows hallmarks of the Bet v 1 superfamily. This structural similarity suggests that Act d 8 and Act d 11 may present similar IgE-binding epitopes, which explains why IgE antibodies generated against one of these allergens may be able to recognize the other as well.
# Role of Nicotinamide in Genomic Stability and Skin Cancer Chemoprevention
## 6 Conclusions  
NAD+ levels are critical for genomic responses to genotoxic insults [2,4]. Maintaining adequate NAD+ level is therefore essential in skin cancer patients and individuals at risk of exposure to genotoxic agents. Intracellular NAD+ levels are regulated by many cellular processes, including glycolysis, OXPHOS, mitochondrial metabolism, transcription and signaling, and are significantly influenced by diet, exercise, and other health conditions. However, NAD+ depletion is associated with aging and age-related diseases, including cancer. Recently, beneficial effects of NAD+ precursors (e.g., NR, NMN, NAM) in delaying signs of age-associated diseases have been found [2,5,88].  
NAM prevents ATP depletion and glycolytic blockade induced by UV radiation boosting cellular energy and enhances DNA repair activity in cultured keratinocytes and animal models [3,4]. Thus, NAM is involved in the maintenance of genomic stability and may have beneficial effects against aging-related skin changes and tumor development [1,4]. NAM reduces skin cancer incidence and prevents the immune-suppressive effects of UV radiation in mice when given topically or orally [72,73]. Furthermore, clinical studies have shown that topical use of NAM reduced skin aging signs [77, 78, 79, 80]. Oral NAM administration reduces the level of immunosuppression induced by UV radiation without altering baseline immunity and it lowers the rate of NMSCs, including AKs.  
Therefore, a NAM replenishment strategy may be a promising approach for the chemoprevention of NMSC.  
Author Contributions:Conceptualization, L.F. and E.D.; writing--original draft preparation, E.D.; writing--review and editing, L.F., C.M., E.C. ([PERSON]), E.C. ([PERSON]), D.A. and E.D.; visualization, E.D. All authors read and approved the manuscript.  
This research received no external funding.  
This work was supported, in part, by the \"Progetto Ricerca Corrente 2019\" and \"RF-2016-02362541\" of the Italian Ministry of Health, Rome, Italy.  
The authors declare no conflict of interest.
# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 2 Materials and Methods
### Out-of-Fold Prediction  
Machine-learning algorithms often use k-fold cross-validation to evaluate model performance. During k-fold cross-validation, the model makes predictions on test samples that are not used for training, and such predictions are called out-of-fold predictions. The process of out-of-fold prediction for this experiment is shown in Figure 5. The dataset was first divided into a training set and a test set at a ratio of 4:1. Next, the training set was divided equally into K groups, and each subset of data was made a validation set once. The rest of the K - 1 group subset of data was used as the training set. These training sets were then trained on the base-model so that K models were obtained; these are referred to as M\({}_{1}\), M\({}_{2}\)...M\({}_{K}\), and the datasets used to train these K models were called K\({}_{1}\), K\({}_{2}\)...K\({}_{K}\). After that, the trained M models were used to predict the test set and its corresponding validation set, and the prediction probabilities P\({}_{\text{test}}\) and P\({}_{\text{valid}}\) were saved for each class, so that after K models were predicted, there were a total of K different P\({}_{\text{valid}}\) and P\({}_{\text{test}}\). These different P\({}_{\text{valid}}\) were stacked together as the feature input to the meta-model. At this point, the number of samples was exactly equal to the number of training sets. For different P\({}_{\text{test}}\), the predicted probabilities corresponding to each class were summed and averaged, and the result was used as the test set of the meta-model. In this experiment, K was set to 5. Random forest, logistic regression, and support vector machine were used as the meta-model; and AlexNet, VGG16, ResNet50, DensenNet121, and InceptionV3 were used as the base-model.
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 4 A Matheuristic for DVB Design
### ILP Improvement Heuristic  
Using the best solution found by the GA as basis, we attempt to find a better solution by running an ILP heuristic that corresponds to executing a very large neighborhood search _in an exact way_, i.e., modeling the search as an ILP problem solved through a state-of-the-art solver [11]. This is based on the observation that, in contrast to the complete hard-to-solve problem, suitable subproblems of the complete problem, derived through fixing the value of a subset of variables, may be efficiently solved by a state-of-the-art solver.  
In the ILP heuristic, we use a modified Relaxation Induced Neighborhood Search (RINS) (see [62] for a full description of the algorithm). Let \((\bar{x},\bar{z})\) be a feasible solution of the strengthened power-indexed problem DVB-PI+ and let \((x^{TLR},z^{TLR})\) be an optimal solution of the linear relaxation, strengthened by the cuts found by CPLEX in the root node of the branch-and-bound tree. Moreover, let \((\bar{x},\bar{z})_{j},(x^{TLR},z^{TLR})_{j}\) denote the \(j\)-th component of the vectors.  
The modified RINS that we use and that we denote by _mod-RINS_ corresponds to solving a subproblem where we a-priori fix the value of those decision variables whose value in \((\bar{x},\bar{z})\) and \((x^{TLR},z^{TLR})\) differs of at most \(\rho:0<\rho<1\), as specified by the rules that follow:  
\[(\bar{x},\bar{z})_{j}=0\ \wedge\ (x^{TLR},z^{TLR})\leq\rho\ \implies\ (x,z)_{j}=0\]  
\[(\bar{x},\bar{z})_{j}=1\ \wedge\ (x^{TLR},z^{TLR})\geq 1-\rho\ \implies\ (x,z)_{j}=1.\]  
The resulting problem is then passed to CPLEX, to which a solution time limit is imposed.
## 5 Simulation Results and Discussion
### Ablation Study: Component-Wise Performance Comparison  
In order to measure the relative importance of the ResVGG-SwinNet architecture's constituent parts, we conducted an ablation experiment. The following tests assessed how well certain parts worked when separated. Tabulated in Table 12 is a summary of the findings.  
The inability of ResNet to fully capture contextual dependencies leads to reduced detection accuracy and sensitivity, as seen in Table 12. Performance is enhanced with the inclusion of VGGNet because it enhances feature refinement. For the best classification accuracy and the fewest false alarms, the Swin-Transformer component is used, which improves long-term contextual awareness even more.
# Cutting Force and Cutting Quality during Tapered Milling of Glass Magnesium Board
## 3 Results and Discussion
### Optimization and Verification  
According to the range analysis and the ANOVA of the cutting force and surface roughness (Tables 4-7), the optimum cutting combination was \(\lambda_{1}\)n\({}_{3}\)h\({}_{1}\), and cutting speed had an insignificant contribution to both the cutting force and surface roughness. In this case, considering the economic benefit, the spindle speed should be lower but still within an appropriate range, because a lower spindle speed reduces the energy consumption of the machine and causes more severe tool wear. Therefore, the optimal cutting parameters are assumed to be \(\lambda_{1}\)n\({}_{2}\)h\({}_{1}\), in which the taper angle is 15\({}^{\circ}\), the spindle speed is 5000 rpm (36.63 m/s cutting speed), and the cutting depth is 0.5 mm. However, this combination of parameters was not previously performed in the orthogonal test. In order to verify the feasibility of this combination, a verification test was carried out and then compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & **Sum of squares** & **Degrees of freedom** & **F** & **Prominence** \\ \hline Taper angle (\(\lambda\)) & 10.378 & 2 & 9.941 & Insignificant \\ Spindle speed (\(\mathrm{n}\)) & 1.418 & 2 & 1.358 & Insignificant \\ Cutting depth (\(\mathrm{h}\)) & 40.098 & 2 & 38.408 & Significant \\ Error & 1.04 & 2 & & \\ Total & 52.934 & 8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis result of surface roughness (\(\mathrm{Ra}\)).  
Figure 5: Cutting parameters and surface roughness trend chart.  
with a control group, where the lowest cutting force and surface roughness were measured by orthogonal testing.  
As shown in Table 8, when the taper angle was 15\({}^{\circ}\), the spindle speed was 5000 rpm (36.63 m/s cutting speed), and the cutting depth was 0.5 mm, the cutting force and surface roughness were 36.67 N and 5.94 um, respectively, which are all slightly less than the results of 39.43 N and 6.00 um acquired by the combination of \(\lambda_{1}\)n \({}_{1}\)h\({}_{1}\). In other words, the optimal cutting parameters assumed are matched and valid. Therefore, during the tapered milling of glass GMO board, the optimum cutting condition is 15\({}^{\circ}\) taper angle, 5000 rpm spindle speed (36.63 m/s cutting speed), and 0.5 mm cutting depth, which is proposed for actual machining in order to achieve a smoother machined surface and greater economic benefit.
# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 4 Content-Based Methods
### Linguistic Features  
Linguistic features are characteristics that are used to classify phonemes or words, along with their inter-relationships. Of special importance in the context of fake news detection are morphological, syntactical, and grammatical features like Part-Of-Speech (POS) and noun phrases (NPs) [30; 87; 89; 117; 118]. Various works showcase the importance of uncertainty constructs like possibility adverbs, conditional particles, and certainty constructs like demonstrative adjectives/pronouns and declarative conjunctions. For example, the authors in [88] study the structural difference between fake and true news for the health domain. They claim that the size of the headline is a key element of a news article, and they analyze the presence of some common patterns like the use of demonstrative adjectives, numbers, modal words, questions, and superlative words. Similarly, the authors in [119] report that the content provided by trustworthy sources tends to be expressed in a passive voice. Finally, the use of morphological Part-Of-Speech (POS) n-grams was not found to improve fake news detection over a TF-IDF baseline [120].
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
## 3 Numerical Efficient Solution
Certainly, other time allocation proportions can also be assumed as long as the time constraints C3, C4, and C5 are satisfied. Once the time resource is allocated, the joint power and time allocation problem (5) becomes a convex power allocation problem, and then the power allocation can be achieved in the associated convex set. _Algorithm Complexity_. Now we evaluate the computational complexity of the iterative algorithm. The iterative procedure of Algorithm 1 shows that the computation complexity is mainly determined by the solving of problem (14). According to [25], the computational load for solving a convex optimization problem with \(M\) optimization variables is \(\mathrm{O}\big{(}M^{3.5}\big{)}\). For the considered problem (14), the set of optimization variables is given by \(\left\{\tau,\tau_{k},p_{D,k},p_{U,k},q_{U},q_{D,k}\right\}\) that consists of \(M=4K+2\) members. After considering the iterations \(N\), the total computational load of Algorithm 1 is given by \(\mathrm{O}\Big{(}N\big{(}4K+2\big{)}^{35}\Big{)}\). As will be exhibited in the simulations, the algorithm converged only after several round of iterations, and thus the polynomial complexity with practical \(K\) is very low and the proposed method is feasible.
## 3 System Architecture
### LED Detection  
RoI algorithms are already well known because of their high performance in object detection. Both object and feature-based detection approaches are used in most RoI algorithms [25].  
The information regarding the shapes and structures in the object-based method is used for detection. The feature-based method uses the optical features of the collected pixel to apply RoI algorithms. The main problem with this approach is that if all pixels do not have a sustainable feature, this method is inefficient for detection. Consequently, the object-based approach is better than the feature-based approach in terms of RoIs.  
In this paper, an RoI algorithm is used to detect the positing anchor at four corners of an LED array, which is clearly presented in Section 4. The four outermost corners of the LED array are always in the \"on\" status, which helps the receiver side easily detect via a RoI algorithm. From the coordinates of the four outermost corners in each image, the coordinates of another LED can be easily detected and decoded. In cases with multi-users and 360-degree rotation support, we applied artificial intelligence (AI)-assisted detection to increase system performance.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 2 System Models for RIS-Assisted Systems
### MIMO Systems  
Studies in [1; 2; 3; 17; 25; 26; 69; 70; 79; 80] focus on creating optimal channel estimation algorithms in RIS-assisted mMIMO and MIMO systems. In [3], the realistic characterization of the achievable downlink of the RIS-assisted mMIMO systems is presented, accounting for user mobility and I-CSI under correlated Rayleigh fading conditions, when regularized ZF (RZF) precoding is applied. In [25], a two-stage strategy is used for the estimation of cascaded uplink channels without using a standard user for the RIS-assisted MU mmWave system. In [26], an efficient three-stage channel estimation method with a low pilot overhead is presented in an RIS-assisted single-antenna MU mmWave communication system, and the BS, RIS and UE are equipped with UPA. In [17], an RIS angular-based hybrid beamforming (AB-HBF) system requires low CSI overhead for the mmWave mMIMO system with a mmWave channel model based on 3D geometry of three stages: (i) RF beamformers, (ii) BB precoder/combiner, and (iii) RIS phase shift design. In [1], the authors use a tensor algorithm for the joint estimation of involved channels and imperfections in an RIS-assisted MIMO system. In [69; 70], the authors create phase shift design schemes in uplink with linear ZF detection in the receiver side of an RIS-assisted MIMO system. Furthermore, in [81; 82], the authors develop simple iterative and closed-form channel estimation algorithms based on the PARAFAC modeling of the single-user MIMO scenario. Finally, in [79; 80], the researchers focus on a cell-free RIS-assisted system architecture where channel estimation is achieved sequentially. Next, we present a cell-free and a cell RIS-assisted mMIMO mmWave system.
# Image Inpainting Based on Multi-Patch Match with Adaptive Size
###### Abstract
[18] used dynamic patch size rather than the original fixed one to make the inpainting more flexible with different textures, but significantly increased the computational complexity. Moreover, all of the above methods (except [17]) still need to traverse the entire source region to find the best sample patch, and only to fill a single target patch in a single iteration, which requires lots of iterations and match time. In fact, low efficiency caused by iterative match process is known as a common problem for the patch-based methods. Although [PERSON] et al. [17] tried to narrow the match area by picking out those candidates whose sum of the pixel values is close to the target patch's, this strategy is not an effective way to filter out those bad sample patches. [PERSON] et al. [19] limited the match area to the neighborhood of the target patch--this method is likely to miss the optimal patch if it is not located nearby. How to speed up the inpainting process while maintaining the quality remains a challenge. Our work also uses a similar framework as [PERSON]'s [10], with several improvements that have successfully tackled the aforementioned issues. Our novel solution not only adopts the dynamic size of the patch to improve inpainting quality, but also narrows down the match area effectively and harmlessly, and reduces the total iterations by enabling multi-patch match strategy to achieve further inpainting efficiency. More specifically, our main novelties and contributions are:  
1. We first propose a metric to evaluate the nonuniformity in an image, and;
2. To achieve a more accurate and flexible inpainting, the patch size is adaptively determined according to its nonuniformity;
3. To save the match time, our subregion search strategy allows the match only between patches with similar content. This trick not only helps to narrow down the match area to a large extent while without missing the optimal sample patch, but also skips those bad sample patches to avoid mismatch;To reduce the total number of iterations, our multi-patch match strategy enables the patch match to proceed in multiple subregions with different nonuniformity levels, so that multiple target patches can be filled in a single iteration;  
The rest of the paper is organized as follows: Section 2 will briefly introduce the classical [PERSON]'s algorithm, the baseline of our work. Section 3 shows the details of our improvements, including the nonuniformity model, determination of adaptive patch size, strategy of subregion search, and multi-patch match. Experimental results and analysis will be given in Section 4, and we compare our results with related patch-based methods proposed in recent years. Section 5 draws the conclusion and presents future work.