esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation
### The Recovery Probability for Server Errors  
In the \(t\)-out-of-\(n\) aggregation method for federated learning that we propose, the selected \(t\) servers cannot be chosen at random and need to comply with the rules specified in Section 4.2.2 for reconstructing the original gradient. Therefore, even if several servers are damaged, federated learning can still operate normally within a certain probability. However, if the server is damaged beyond this threshold, gradient reconstruction may fail, causing federated learning to be halted. Here, we consider the situation of selecting \(t\) servers from \(n\) servers, where \(n=t^{2}\). Each server is numbered in base \(t\) and identified by a two-digit number, which means that each digit can only be a number from 0 to \(t-1\). Regardless of whether the first or second digit is used to group servers with the same number, there will be \(t\) groups, with \(t\) servers for each. For example, using the first digit to group, the group with the number 0 will contain the servers \(00,01,\ldots,0t-1\).  
We select \(t\) servers. As long as one server is selected from each group, the mechanism can operate normally regardless of whether it is based on the first or second digit. On the other hand, the entire mechanism will fail if all servers in at least one group are damaged based on both the first and second numbers. Under the condition of \(n=t^{2}\), let \(F\) be the system failure event and \(b\) the number of damaged servers. If \(b<2t-1\), it will definitely not affect the operation of federated learning, \(P(F)=0\). If \(b\geq t\times(t-1)+1\), the mechanism will definitely fail, \(P(F)=1\). If \(2t-1\leq b<t\times(t-1)+1\), there is a possibility that the mechanism cannot operate.  
In the experiment, we listed all the combinations from \(b=1\) to \(n\) and assessed each individually according to whether, when including both a first and second digit, they will fail to find \(t\) servers with different numbers. Figure 11 shows the probability that the mechanism will fail due to different numbers of damaged servers in the 4-out-of-16 and 5-out-of-25 situation. It can be seen that, when \(b<2t-1\), the mechanism is not affected. When \(2t-1\leq b<t\times(t-1)+1\), the more servers that are damaged, the higher the probability of failure. After \(b\geq t\times(t-1)+1\), the mechanism will definitely fail.  
Through probability analysis, it can be confirmed that our proposed \(t\)-out-of-\(n\) additive secret sharing method exhibits a certain level of fault tolerance compared to traditional additive secret sharing, thereby rendering the overall mechanism more stable.  
Figure 10: Comparison of the average execution time using the Non-IID MNIST [29] and Non-IID FashionMNIST [30] datasets for different numbers of clients and servers with FedAvg [2], FedShare [8], and our method.
# Nutritional Analysis of Plant-Based Meat: Current Advances and Future Potential
## 3 Nutritional Challenges in Plant-Based Meat Products
### Protein Quality  
The nutritional adequacy of any food product is governed by its protein quality. Protein quality can be defined as the minimum intake of high-quality protein containing all the essential amino acids that supports the maintenance of an appropriate body composition and promotes growth at a normal rate for age, assuming normal physical activity [25]. According to the Food and Drug Administration (FDA), adults must consume 50 g of protein per day as part of a 2000-calorie diet. Nevertheless, an individual's specific requirements for protein will depend on their age, sex, activity levels, and other factors. The recommended dietary allowance to prevent protein deficiency in an average sedentary adult is 0.8 g/kg of body weight [26; 27]. Generally, protein quality is ascertained by measuring its digestibility and quantity of essential amino acids. FAO has recommended the DIAAS (digestible indispensable amino acid score) as a metric to measure protein quality, which is determined using the following equation [28]:  
\[\text{DIAAS}(\%)=100\times\frac{\text{mg of digestible dietary indispensable amino acidin 1 g of the dietary protein}}{\text{mg of the same dietary indispensable amino acid in 1 g of the reference protein}} \tag{1}\]  
Though plant-based meat products are made from protein-rich sources such as soy, wheat gluten, and pea, they often lack a balanced composition of essential amino acids. Factually, plant-based meat products have a higher content of glutamic acid and cysteine and a lower amount of alanine, glycine, and methionine [29]. This could be because of the deficiency of these amino acids in their respective sources of plant protein. Cereals lack lysine, and legumes are lacking in sulfur-containing essential amino acids such as methionine and cysteine. Hence, plant proteins are nutritionally incomplete due to their deficiency in one or more essential amino acids. Table 2 compares the essential amino acid content between proteins derived from different plant and animal sources. The deficiency of plant proteins in essential amino acids can lead to lower muscle protein synthesis associated with the consumption of plant-based foods [30]. This is a major concern with respect to the elderly population, who are vulnerable to muscle depletion [31]. The recommendation is to consume a broad variety of plant proteins to warrant the intake of an adequate amount of all the amino acids [32].
## 4 Discussion
reported that the surface roughness of K3 XF R-phase NiTi endodontic rotary instruments increased after autoclaving, but this did not affect the cyclic fatigue resistance after 30 sterilization cycles [32]. [PERSON] et al. found that repeated sterilization cycles did not impact resistance of NiTi endodontic rotary instruments to fatigue except for Twisted Files R-phase NiTi endodontic rotary instruments, which showed a significant decrease in flexural resistance after three sterilization cycles (\(p<0.05\)) [33]. [PERSON] et al. showed that multiple sterilization cycles significantly increased the cyclic fatigue resistance of Reciproc M-Wire NiTi endodontic reciprocating instruments, but decreased the cyclic fatigue resistance of Wave One M Wire NiTi endodontic reciprocating instruments in an artificial root canal (not anatomically modeled) with 60\({}^{\circ}\) of curvature in a static cyclic fatigue device [34]. [PERSON] et al. also reported that the sterilization cycles significantly increased the cyclic fatigue resistance of Protaper Next M-Wire NiTi endodontic rotary instruments (\(p<0.05\)) and Protaper Gold alloy NiTi endodontic rotary instruments (\(p<0.05\)) [35]. [PERSON] et al. showed that the cyclic fatigue resistance of pre-sterilized HyFlex CM CM Wire NiTi endodontic rotary instruments and K3 XF R-phase NiTi endodontic rotary instruments increased significantly (\(p<0.05\)) after 10 sterilization cycles in an artificial root canal (not anatomically modeled) with a curvature of 60\({}^{\circ}\) and a radius of 3 mm in a static cyclic fatigue device [36]. Most of the martensitic endodontic rotary instruments (M-Wire alloys) have demonstrated an increase in cyclic fatigue resistance after heat sterilization procedures. However, in the present study, the cyclic fatigue resistance of austenite NiTi endodontic reciprocating files was not affected after 10 autoclave sterilization cycles (\(p>0.05\)). The thermal treating of M-Wire endodontic rotary instruments makes the martensitic phase of NiTi alloy less subject to breakage during clinical use and stabilizes the NiTi alloy. The thermal heating induced by heat sterilization procedures make it even more stable, increasing cyclic fatigue resistance [31].
# Compact Four-Port Circularly Polarized MIMO X-Band DRA
## 5 MIMO Performance Analysis  
The diversity characteristics and performance of the MIMO system could be examined by using the envelop correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), multiplexing efficiency (\(\eta_{max}\)), mean effective gain (MEG), total active reflection coefficient (TARC), and channel capacity. ECC, DG, and CCL were calculated using the simulated and measured port parameters, and the simulated far-field, and the results are  
Figure 16: (a) Peak gain and (b) efficiency of proposed four-port DRA.  
Figure 17: Radiation patterns of four-port DRA (a) at 9 (b) at 11 GHz.
# A Review of Image Inpainting Methods Based on Deep Learning
## 7 Ethical Considerations of Image Inpainting Techniques  
As the realm of image inpainting techniques continues to expand, so does its range of applications. However, the widespread adoption of this technology has ushered in a series of ethical dilemmas.  
First and foremost, the misuse of inpainting techniques can lead to image forgery. For instance, historical photographs or news images can be manipulated using this technology, thereby altering the authenticity of the original events they depict. Such alterations not only have the potential to mislead the public but can also inflict undue harm on individuals or organizations by misrepresenting them.  
Furthermore, inpainting techniques can be weaponized to infringe upon personal privacy. A case in point is the potential use of this technology to extract an individual from a publicly available photograph and subsequently place them within an entirely different context, thereby fabricating misleading evidence or scenarios.  
To ensure the ethical deployment of inpainting techniques, the following recommendations are proposed:  
1. Transparency: Whenever an image is modified using inpainting techniques, it is imperative to clearly annotate the altered sections. Additionally, providing the original image as a point of reference can maintain the integrity of the content.
2. Education and training: Offering training sessions for image processing experts and journalists can equip them with a comprehensive understanding of the potential risks and ethical implications associated with these techniques.
3. Technological constraints: The development of innovative algorithms and tools dedicated to detecting and preventing image forgery is crucial. These tools can act as a deterrent, ensuring that the technology is used responsibly.
4. Legal and policy frameworks: The formulation and enforcement of pertinent laws and policies can serve as a robust mechanism to penalize those who misuse inpainting techniques. Such legal frameworks can act as a deterrent, ensuring that individuals and organizations think twice before manipulating images unethically.  
In conclusion, while image inpainting techniques offer a plethora of benefits, it is paramount to navigate their use with a keen sense of responsibility. By adhering to the aforementioned recommendations, we can harness the power of this technology while upholding the highest ethical standards.
## 2 System Structure
### Selected Structure  
The claims above are generally agreeable, but we prioritized complexity, which is closely linked to the weight, volume, and cost of the HPS. Since the developed HPS will be commercialized with a security robot, higher priority should be given to cost with consideration for mass production. As described above, the introduction of fuel cell technology into a HPS is a strategic choice to achieve the 24 h continuous operation of security robots. However, a hydrogen fuel cell is expensive to construct and operate. To minimize cost increases, the one-stage series structure was adopted, which is the least complicated configuration among the active hybrid structures proposed thus far. In addition, the one-stage series structure is of equal or greater quality than other configurations in four out of seven performance metrics, including efficiency, complexity, controllability, and responsiveness to load changes. Based on their advantages and disadvantages, details on this comparison can be found in [18].  
Figure 2: Comparison of active hybrid structures. (**a**) One-stage series (semi-active), (**b**) two-stage series (fully active), and (**c**) two-stage parallel.  
In the selected structure, the converter steps the fuel cell voltage down to the DC bus voltage, allowing the fuel cell to run at a lower voltage level. According to the output voltage of the converter, a voltage difference develops between the converter and battery, which determines the power flow. Although the power flows of the fuel cell (outgoing) and load (incoming) are fixed by nature, that of the battery is variable. If the converter voltage is set higher than the battery voltage, then the battery is charged (incoming) until the voltage difference is eliminated (see Figure 3a). Otherwise, the battery is discharged (outgoing) until the voltage difference is eliminated if the load is applied to the HPS (see Figure 3b). Under load, a voltage difference is induced by the difference in dynamic characteristics between the two power sources in that the battery supplies power to the load before the fuel cell. In doing so, the power flow from/to the battery flips back and forth, such that the battery can maintain the state-of-charge (SOC), and the HPS can maintain the DC bus voltage higher than the rated voltage of the load.
## 1 Introduction
* Simulation findings demonstrate that the proposed model not only exhibits a fast and effective convergence performance but also significantly decreases the system costs compared with a benchmark approach. The rest of this paper is structured as follows. In Section 2, the work pertaining to task offloading models will be highlighted, whereas the system model and the problem formulation are introduced in Section 3. Following that, in Section 4, we develop a novel and efficient task offloading algorithm based-deep reinforcement learning to derive the offloading solution. Afterward, experimental results are presented and discussed in Section 5. Finally, the conclusion and the future recommendations are presented in Section 6.
# Containerised Application Profiling and Classification Using Benchmarks
## 2 Related Work and Research
### ML-Based Application Classification and Categorisation  
Different attempts and approaches to effectively characterise the behaviour of an application from different points of view are already examined. For example, an effort for profiling MapReduce applications has been made, by which application profiles are created after scraping CPU, memory, and disk metrics from a running application. The collected metrics are matched to profiles that are deduced by a modified k-means clustering algorithm [9]. A similar proposal [10] tried to extract the same kind of information by using LTng for tracing and the SWAT prototype. Furthermore, more complex machine learning approaches have been proposed. For example, [11] examines a self-directed workload-prediction model based on the black hole algorithm, which captures the latest forecasting error in order to enhance the accuracy of further predictions. In [12], we see an approach that uses transfer learning in order to predict application performance on the cloud. Application profiles are based on their execution on different cloud instances in combination with knowledge gathered from previous executions on a noncloud environment, whereas a strategy for selecting just a subset of available cloud configurations is proposed. Our methodology is also machine learning-based and utilises generic metrics in pursuit of the most generic profiles possible.  
The closest work to our method is [13], which presents an offline machine learning-based methodology for predicting performance of HPC applications, that means computationally intensive applications deployed in the cloud. Hardware-independent metrics are collected with the use of an LLVM plugin to create profiles of applications which means that the source code must be available to the user, because LLVM makes use of instrumented code which is produced at compile time. We also complete the whole benchmarking process in an offline environment by applying machine learning algorithms, and show that it tends to be hardware agnostic.
# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
###### Abstract
With sentiment analysis, subjective information in the source material is recognized, extracted, and characterized through contextual manipulation of the text. This helps many businesses to understand the social sensitivity of their products or services. Thus, sentiment analysis enables early detection of negative emotional expressions, managing crises, and enabling customer relationship management to take practical actions in negative customer experiences. It determines an entity's attitude towards the text's overall contextual polarity and detects emotional state or emotional communication [3; 4]. One of the most widely used strategies in sentiment analysis is machine learning. Machine learning is an approach that largely overlaps with statistics, and many evaluations are empirical [5]. With machine learning, a model is built with data using a good and helpful approach and is used to solve real-world problems. It has evolved from efforts to discover whether computers can learn to mimic the human brain to a discipline that generates fundamental computational theories in multidisciplinary fields such as statistics, information theory, philosophy, artificial intelligence, psychology, and neurobiology [6]. In this study, sentiment analysis was used to evaluate customer reviews for 24 different cosmetic brands on Trendyol, one of the largest e-commerce sites based in Turkey. Manual tagging created a domain-specific normalized dictionary model consisting of 65,378 words, and the model's performance was evaluated with various machine learning approaches. In the study flow, the sentiment analysis studies carried out in the cosmetics sector in the international literature were summarized. The materials and methods used were summarized step by step. The classification performances of the machine learning algorithms were compared, then the findings were presented, and a discussion was carried out.
## 2 Methods  
### Patch Antenna without Size Reduction  
Patch antennas consist of a patch and a ground plane made of a conductor material, in between the patch and the ground plane a dielectric material is placed. The patch plane is typically rectangular, and the resonant mode of the patch antenna depends on the wavelength. The width and length of the patch plane and the height of the dielectric material for a specific frequency are calculated using the formula for patch antenna found in [23]. For the case of a patch antenna operating at a frequency of 300 MHz, as used in a 7T MRI system, and if the dielectric material has a permittivity \(\varepsilon_{\mathrm{r}}=6.45\) and height of 30 mm, the computed values for the width would be 258.9 mm and length 209.9 mm; such an antenna is show in Figure 1a. The resonant modes after matching to 50 Ohms of the patch antenna are shown in Figure 1b. The first resonant frequency is at 300 MHz, and the second is located at 430 MHz. The magnetic \(\ket{\mathrm{B}_{1}}\) -field pattern of this antenna at 300 MHz in the \(y\)-z plane is show in Figure 1c, and the corresponding electric field at 429 MHz is shown in Figure 1d.  
This dual resonance frequency can be exploited such that the first resonant frequency can be used for transmitting and receiving in the MRI procedure and the second frequency can be employed for hyperthermia applications.
## 2 Materials and Methods
### Multiple Regression Analysis  
Regression models are widely used to analyze the relationship between dependent and independent variables. In order to compare the performance of a DNN model with a traditional statistical model, multiple regression analysis was performed as a control group. This analysis involved using a number of features extracted from soil surface images as independent variables to estimate the WC and BD.  
However, due to the high degree of correlation between features, multicollinearity can become a problem. To address this, a correlation matrix between variables was initially reviewed to identify factors with high correlation. The Pandas library in Python was used to calculate the pairwise correlation coefficient of features. Additionally, the correlation matrix was visualized via the Seaborn library in Python using the result of pairwise correlation coefficient of features. Furthermore, the variance inflation factor (VIF) of the independent variables was computed to check the multicollinearity problem [29].  
\[\text{VIF}_{i}=1/\left(1-{R_{i}}^{2}\right) \tag{3}\]  
where \(i\) is the \(i\)-th independent variable on the remaining ones. The VIF is a measure of the amount of multicollinearity in a set of predictors, and values greater than 10 indicate significant multicollinearity [30]. The independent variable with the highest VIF was sequentially eliminated from the analysis until the VIF of all variables was below 10. This process helped to ensure that the final model was free from multicollinearity and that each predictor made a unique contribution to the analysis.  
Following the selection of the features, multiple regression analysis was conducted using them as independent variables to estimate the WC and BD. For multiple regression analysis and the calculation of VIF, Python's Statmodels library was used. The resulting model was compared with the DNN model using evaluation metrics.
## 3 Proposed System
### Iterative and Non-Iterative Feature Extraction Methods
Thus, feature extraction can be performed via a simple production using Equation (17). The pseudo-inverse can be implemented via the singular value decomposition [39]. If we assume that the matrix \(\mathbf{W}\) is decomposed as  
\[\mathbf{W}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathrm{H}}, \tag{18}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) denote \(K\times K\) and \(R\times R\) matrices, respectively, and \(\mathbf{\Sigma}\) denotes the \(K\times R\) diagonal matrix whose elements are singular values of \(\mathbf{W}\), then the pseudo-inverse \(\mathbf{W}^{\dagger}\) can be calculated as  
\[\mathbf{W}^{\dagger}=\mathbf{V}\mathbf{\Sigma}^{\dagger}\mathbf{U}^{H}, \tag{19}\]  
where \(\Sigma^{\dagger}\) is a matrix formed from \(\Sigma\) by taking the element-wise inverse of the non-zero elements and shaping the matrix as \(R\times K\). In practice, the small singular values can be ignored as  
\[\left[\mathbf{\Sigma}^{\dagger}\right]_{r,k}=\left\{ \begin{array}{ll}1/ \left[\mathbf{\Sigma}\right]_{k,r}&\text{if }\left[\mathbf{\Sigma}\right]_{k,r}>\delta,\\ 0&\text{otherwise},\end{array} \right. \tag{20}\]  
where \(\left[\mathbf{\Sigma}\right]_{k,r}\) denotes the \((k,r)\)th element of the matrix \(\mathbf{\Sigma}\) and \(\delta\) is an arbitrary threshold.