esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimization
### Batch Normalization  
Although it is not an optimization algorithm, the Batch Normalization [20] method is one of the most significant innovations in deep model optimization in recent years. It stabilizes the learning process by reducing changes in hidden layers input data distribution caused by constant changes made to parameters from previous layers. The idea is to add new _normalizing_ layers that will transform data during the training in order to avoid unwanted changes in distribution.  
Input to the given unit is normalized for each mini-batch \(\mathcal{D}\). Let \(z_{i}\) be the input to the given unit that corresponds to the \(i\)th example of mini-batch \(\mathcal{D}\) of size \(m\). Normalization of \(z_{i}\) is done as follows  
\[z_{i}^{(norm)}=\frac{z_{i}-\mu_{\mathcal{D}}}{\sqrt{\sigma_{\mathcal{D}}^{2}+ \epsilon}},\quad i=1,\dots,m \tag{36}\]  
where  
\[\mu_{\mathcal{D}}=\frac{1}{m}\sum_{i=1}^{m}z_{i},\quad\sigma_{\mathcal{D}}^{2} =\frac{1}{m}\sum_{i=1}^{m}(z_{i}-\mu_{\mathcal{D}})^{2} \tag{37}\]are mean and variance estimates for mini-batch \(\mathcal{D}\) and \(\varepsilon\) positive constant added for numerical stability. An additional linear transformation  
\[\tilde{z}_{i}=\gamma z_{i}^{(norm)}+\beta,\quad i=1,\ldots,m, \tag{38}\]  
is applied to keep the expressive power of the hidden units. New parameters \(\gamma\) and \(\beta\), which are also learned during training (initialized with \(\gamma=1\), \(\beta=0\)), enable normalized data to have any mean and variance. During the test phase, an exponential moving average of mean and variance values calculated during training is used.
# A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond
## 1 Introduction
### Related Work
#### 1.1.2 Food Sharing Communities  
According to _Food Tank_ (https://food tank.com/, accessed on 13 January 2021) [60], there are several food sharing communities that are already in operation globally (for further details visit our arXiv paper [61]). Most of them use ICT in simple forms such as apps for volunteer organizations or social media for receivers to connect with the donors. With a more complex use of ICT, the communities can extend across borders, redistribute faster, communicate seamlessly, reach a wider audience, track food quality and movement, and exploit higher scalability, just to name a few.
## 5 Experimental Verification and Analysis
### Pulse Current Discharge Test  
In the pulse current discharge test, the initial SOC value is set to be 0.8 while the real SOC is 1.0. The process noise covariances are set as \(Q_{k}^{x}=1\times 10^{-6}I_{2}\) and \(Q_{k}^{\theta}=1\times 10^{-6}I_{3}\). The measurement noise variances used for DEKF are \(\Sigma_{k}^{x}=0.01\) and \(\Sigma_{k}^{\theta}=0.0005\). The initial parameter values of VB-ADEKF are set as \(\alpha_{\theta,0}=100\), \(\beta_{\theta,0}=0.0005\), \(\alpha_{x,0}=10\), \(\beta_{x,0}=0.001\).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Constant Current Test**} & \multicolumn{2}{c}{**Pulse Current Test**} & \multicolumn{2}{c}{**UDDS Test**} \\ \cline{2-7}  & **DEKF** & **VB-ADEKF** & **DEKF** & **VB-ADEKF** & **DEKF** & **VB-ADEKF** \\ \hline Maximum Absolute Error & 2.76\% & 1.28\% & 4.93\% & 5.00\% & 4.72\% & 4.10\% \\ Mean Absolute Error & 1.39\% & 0.64\% & 1.01\% & 0.68\% & 1.26\% & 0.89\% \\ Convergence Time & 335 s & 10 s & 698 s & 690 s & 675 s & 603 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of state of charge (SOC) estimation error (after 12 min) in three tests.  
Figure 5: Experimental terminal voltage results using VB-ADEKF in the constant current discharge test.  
Figure 7 presents the estimated values of the battery model parameters. Clearly, there is a stepped increase in the ohmic resistance from 34 m\(\Omega\) to 49 m\(\Omega\). The polarization resistance retains stable during the entire discharge process. The polarization capacitance first decreases rapidly then stays stable until the end of the discharge. Figure 8 shows the the measured terminal voltage and the estimated terminal voltage by VB-ADEKF to verify the effectiveness of the battery parameters identification. The maximum and mean absolute estimation errors are 0.086 V and 0.0027 V, respectively. It implies that the estimated terminal voltage has a good agreement with the measured voltage. It further shows the battery model parameters are well identified.  
Figure 6: SOC estimation results using VB-ADEKF and dual extended Kalman filter (DEKF) in the constant current discharge test.  
Figure 7: Results of parameters identification using VB-ADEKF in the pulse current discharge test.  
The SOC estimation results of VB-ADEKF and DEKF are plotted in Figure 9. It shows that VB-ADEKF and DEKF have comparable performance in convergence rate. However VB-ADEKF has more accurate SOC estimation than that of DEKF. This is also verified in Table 2, in which the mean absolute error of VB-ADEKF is 0.68%, while it is 1.01% for DEKF after discharge for 12 min.
## 4 Experimental Results
### Denoising Results  
The proposed models were evaluated and compared by peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and mean square ratio (MSE) using mammogram images (see an example in Figure 9), as shown in Table 2. The optimal mammogram is generated when the HH, LH, and HL bands are denoised using the threshold.  
Figure 8: Accuracy of the traditional FD dimension for both segmented and non-segmented images.  
Figure 9: An example of mammogram images: (**a**) ground truth; (**b**) noisy image.
## 2 Evolution, Virulence and Pathogenicity of _E. coli_ O157:H7
### Transmission of E. coli O157:H7 to Humans and Animals
#### 2.1.1 Contaminated Food as a Transmission Vector  
Contaminated food plays a crucial role in the transmission of _E. coli_ O157:H7 to humans [50]. Food products derived from animals (beef, lamb, chicken, pork, and ground beef) are considered to be the most common source of the pathogen and the transmission of the diseases amongst humans worldwide [51]. Studies have reported that _E. coli_ O157:H7 illnesses have also been linked to the ingestion of other food products originating from cattle, such as unpasteurized milk and other dairy products [9; 52]. Furthermore, the transmission of this pathogen from the skin, intestines, and excrement of diseased animals frequently leads to the contamination of the environment, including water sources [5]. As a result, consumption of water contaminated by _E. coli_ may lead to illness in humans.  
Fresh vegetables such as alfalfa, radish sprouts, lettuce, and spinach, in addition to unpateurized fruit juices and apple cider [53; 54; 55], have been connected to outbreaks of _E. coli_ O157-related disease in humans. It is thought that these fresh vegetables become contaminated when grown in soil that was exposed to infected animal dung or polluted water [9], while fruit juices and apple ciders become contaminated due to improper manufacturing processes [55]. In general, animal reservoirs are responsible for the greatest amount of _E. coli_ O157:H7 transmission to humans, and food-related transmission is common, making it necessary to manage the pathogens in animal-derived foods, especially beef. As a result, _E. coli_ O157:H7 poses a major risk to public health by endangering both human health and food safety.  
Figure 1: Transmission route of pathogenic _E. coli_ among animal, human, and environment (source: this study—diagram prepared by authors).
## 3 Results
### Performance Metrics
#### 3.6.2 Mean Absolute Error (MAE)  
The mean absolute error is a risk metric corresponding to the expected value of the absolute error loss. It measures the distinguished values between two continuous variables [47]. If \(\hat{y}_{i}\) is the predicted value of the \(i\)th sample and \(y_{i}\) is the corresponding true value for total \(n\) sample, then the mean absolute error (MAE) estimated over \(n\) samples is defined as follow.  
\[MAE(y,\hat{y})=\frac{1}{n}\sum_{i=0}^{n-1}|y_{i}-\hat{y}_{i}| \tag{2}\]
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
## 2 Background
### Fuzzy C-Mean  
Fuzzy C-mean (FCM) is a popular clustering algorithm that allows data points to belong to multiple clusters with varying degrees of membership. Unlike traditional hard clustering methods where a data point belongs solely to one cluster, FCM assigns fuzzy membership values, ranging from 0 to 1, reflecting the degree of belonging to each cluster. This flexibility is particularly valuable when dealing with complex data where boundaries between clusters are not well-defined. FCM utilizes an iterative process to optimize the cluster centers and membership values, minimizing an objective function that balancesthe distances between data points and cluster centers while considering the degree of membership. This approach leads to more robust and informative cluster assignments, particularly in noisy or overlapping data situations, making it a valuable tool for data analysis and pattern recognition. The main target of the FCM algorithm is to minimize an objective function. The objective function is the weighted sum of squared errors within groups and is defined as follows:  
\[J(U)=\sum_{i=1}^{n}\sum_{j=1}^{c}u_{ij}^{m}d_{ij}^{2} \tag{1}\]  
\[J(U,r)=\sum_{i=1}^{n}\sum_{j=1}^{c}u_{ij}^{m}\|x_{i}-r_{j}\|^{2} \tag{2}\]  
where \(n\) is the number of data points, \(m\) is the number of clusters, \(m\) is any real number greater than or equal to 1, \(x_{i}\) is the ith data point in a multi-dimensional space, \(r_{j}\) is the jth cluster center, \(d_{ij}=\left\|x_{i}-r_{j}\right\|\) is the distance or dissimilarity between data point \(x_{i}\) and cluster center \(r_{j}\), and \(u_{ij}\) is the membership degree of data object \(x_{i}\) in cluster \(c_{j}\) (\(0\leq u_{ij}\leq 1\)).
## 5 Algorithm  
As seen in Section 2, ON Q-learning-based algorithms usually do not maintain an explicit, graph-based model of the network. More specifically, the passage of time is usually captured by a simple parameter-decay mechanism, which depends on ad hoc configuration parameters that must be selected and tuned separately, usually through simulation.  
Our strategy uses an ONM as the representation of the network. The ONM is a graph, though not a connectivity one. Nevertheless, a path connecting nodes in ONM still represents a data trajectory connecting a source with a destination. A shortest-path algorithm with adequately chosen edge weights can be used to find an optimal delivery trajectory.  
There are two main classes of problems wherein RL is used to find the shortest path in a graph. The first class of problems is, naturally, network routing (see Figure 2b), where the graph is the connectivity graph of the network [21]. In this representation, edges are network links, and nodes are network routers. Each graph node is an agent that makes forwarding decisions that collectively solve the routing of messages from sources to destinations. As each node is an agent, each node trains only for the available actions; this is forwarding to neighboring nodes.  
The second class of problems is the robot navigation problem (see Figure 2a). In this case, the graph represents a map; for example, nodes are rooms, and edges are doors connecting rooms. The learning agent is a robot that must navigate through the map to reach a destination [15]. The agent has a policy to be trained, which dictates the next action (edge) to follow as it moves through the graph, like in a maze. Each graph node is a state in this representation, with a single Q-table _attached_ to the single agent. The Q-table is trained by repeatedly moving through the map.  
Neither of these two representations is well suited as a base for applying RL for opportunistic routing using ONM. The main problem is that the RL states coincide with the nodes from the underlying graph in both representations. As a result, though they differ in whom they consider an agent and thus how many Q-tables there are and where they are stored, the content of the Q-table is similar in both cases: it assigns a Q-value to a target graph node.  
In the ONM graph representation, nodes and edges are not fixed beforehand. They are defined during the network's lifetime and describe a single instance of the mobility scenario. Furthermore, each node is visited only once during this instance.  
Thus, because the graph nodes are associated with a particular real-world instance of the network, and they are poor candidates for Q-learning actions and states, which must be usable through multiple learning episodes.  
We propose _rl4 dtn_ an opportunistic routing algorithm based on applying Q-learning over an online-generated ONM graph (see Figure 3). The main characteristics are summed up in Table 1.
## Materials and Methods
### Developing the Classification Scheme  
Effective maps follow clear and transparent rationale for developing thematic classes, employing meaningful and well-described classes that are beneficial to the people that utilize them [49]. A consultation with regional coral reef experts was conducted to determine the benthic habitat classification scheme that would be most useful to marine resource managers throughout the Caribbean [50]. Potential classes were filtered and ultimately chosen based on a close examination of each classes' spectral separability using available field reference data. While PS imagery has higher spatial resolution, one of the constraints of using SmallSat technology is a low signal-to-noise ratio and the limited number of broad spectral bands [51], which can make it more challenging to successfully separate detailed classes, which appear very similar in spectral response, especially underwater features where the reflectance signal is greatly reduced. Consequently, a pre-selected list of detailed classes was collapsed into a smaller number of more general benthic classes, based on the likelihood that these classes would achieve greater mapping accuracy. Based on an initial round of testing, the final classification scheme was reduced to thirteen benthic classes (Table 2). The reef crest, including the back and fore reef features, as digitized in the geometric zones, were added as individual benthic classes, since they are recognized as important reef types for focused management and monitoring actions. A description for each of these classes with corresponding field photo examples can be found in the Supplemental Materials.
# Evaluation of Present Accelerated Temperature Testing and Modeling of Batteries
## 3 Case Studies
### Discussion  
The limitations of the accelerated temperature testing, inaccuracy of the Arrhenius model and falseness of the decade rule have been shown in this work. These issues will be further discussed and summarized in this section. Following the discussion, the proper methods in terms of accelerated testing and modeling are provided.  
The data in case study 2 suggest that the failure mechanisms can vary with the temperature range. For lithium-ion batteries, SEI layer growth and lithium plating are dominant at completely different temperature ranges as mentioned in Section 1. Not all failure mechanisms can be accelerated by temperature. Thus, in the accelerated temperature testing plan, the dominant failure mechanisms associated with the designed stress levels should be thoroughly understood.  
To keep the failure mechanisms unchanged, the amount of acceleration obtained through temperature elevation is often limited. For example, the organic electrolyte of lithium-ion batteries is not thermally stable at over 60 \({}^{\circ}\)C, the testing temperature of such batteries is thereby usually limited to 60 \({}^{\circ}\)C.  
The Arrhenius relationship was originally developed as an empirical model to evaluate the temperature dependence of certain chemical reactions. In the degradation of batteries, there are some major chemical reactions and hundreds of side-reactions taking place at the same time, leading  
Figure 4: Logarithm of \(Q_{\mathrm{loss}}\) versus reciprocal of temperature.  
to competing degradation processes at one temperature. Also, multiple dominant degradation processes associated with different activation energies can occur at different temperatures. It is thus not reasonable that people try to determine one activation energy for the various degradation processes in the battery. There does not have to be an activation energy for every degradation mechanism.  
To determine if the Arrhenius model works, multiple testing temperatures (\(\geq\)3) are suggested to conduct the analysis. Case study 1 presumed the effectiveness of this model with only two testing temperatures. However, the degradation characteristics of temperature dependence should be assumed to follow this relationship only when the activation energy keeps constant over cycles/time and a linear relationship is observed between the logarithm of \(K\) in Equation (2) and the reciprocal of kelvin temperature. In addition, the applicability is considered valid only within the testing temperature range.  
In addition, the Eyring model has a more generalized form with hyperparameters in terms of the temperature effect and provides a way to incorporate more stress factors. The Arrhenius model can be regarded as one typical Eyring model form, thus it can be expanded to consider the dependence of the aging with other stress factors than temperature and their interactions, as evidenced by [PERSON] et al. [35]. A verification process is still required to use the Eyring model.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 6 Discussion
### Security Analysis
Additionally, clients had to authenticate themselves to the CS as the legitimate owners of the data to access them. And, it could lead to compliance with forward and backward secrecy. However, the purpose of this method was to prove the legitimate owner rather than manage ownership. Hence, it differs from the dynamic ownership management considered in this paper. Ref. [6] proposed a server-side deduplication protocol that considered a dynamic ownership update and was compliant with all security requirements. However, the difference lies in the fact that it does not prioritize reducing the client's computation. Referencing Table 2, our protocol exhibits unique attributes in comparison to similar pro  
tocols, ensuring data privacy, verifying data integrity, and supporting both forward and backward secrecy concurrently. Prior existing protocols have occasionally lacked in fulfilling particular aspects or critical security requirements. In contrast, our protocol excels in delivering heightened security capabilities by comprehensively addressing these aspects.
# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
###### Abstract  
As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry 4.0 networks presents significant challenges, particularly in terms of security and the integration of diverse technologies into a functioning and efficient infrastructure. To address these challenges, emerging digital twin standards are enabling the connection of various systems by linking individual digital twins, creating a system of systems. The objective is to develop a \"universal translator\" that can interpret inputs from both the real and digital worlds, merging them into a seamless cyber-physical reality. It will be demonstrated how the myriad of technologies and systems in Industry 4.0 networks can be connected through the use of digital twins to create a seamless \"system of systems\". This will improve interoperability, resilience, and security in smart manufacturing systems. The paper will also outline the potential benefits and limitations of digital twins in addressing the challenges of Industry 4.0 networks.  
industrial internet of things; (IIoT); interoperability; digital twin; industry 4.0; smart manufacturing; artificial intelligence (AI); machine learning (ML); universal translator +
Footnote †: journal: _Journal of the Royal Society Interface_  
to their deployment. The paper will also analyse how digital twins can meet the challenge of providing an interoperable system across multiple sites if needed. As a key enabling technology, edge processing will be reviewed and the deployment of machine learning algorithms in smart manufacturing will also be assessed.  
Finally, this paper will discuss the deployment of a digital twin framework towards delivering a \"system of systems\". This discussion will include the benefits of integrating such an approach within a manufacturing facility and outline the impacts that such an approach would bring to the broader manufacturing sector. By the end of this paper, readers will have a thorough understanding of the challenges associated with enabling interoperability in manufacturing scenarios and how digital twin technology, edge processing, and machine learning algorithms can help to overcome these challenges. Additionally, readers will gain insight into the benefits of deploying a digital twin framework and its integration within manufacturing facilities.
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 2 VOO Flavor Perception
### Phenolic Compounds  
Many reviews that focused on the chemical, sensory, nutritional and health properties of VOO phenolic compounds have been published in recent years [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48].  
Bitterness and pungency are mainly ascribable to the quali-quantitative phenolic profile of the olive oils and are responsible of the different health functions [14]. Phenol concentrations lower than 220 mg kg\({}^{-1}\) correspond to non-bitter oils or with almost imperceptible bitterness; slight bitterness corresponds to 220-340 mg kg\({}^{-1}\); bitter oils have  
Figure 2: Results of functional Magnetic Resonance Imagining (fMRI) on the human brains of subjects exposed to rancid olive oil and extra virgin olive oil. Brodmann areas (BA) are marked with circles (Reproduced with permission from [PERSON]; [PERSON]; [PERSON] Agric. Food Chem.; published by ACS Publications, 2011).  
phenol contents ranging from 340 to 410 mg kg\({}^{-1}\); phenol contents higher than 410 mg kg\({}^{-1}\) corresponds to quite bitter or very bitter oils [49]. In particular, a positive correlation was observed with secoriridoids content, like oleuropein aglycon [50]. The derivatives of oleuropein such as the dialdehyde form of decarboxymethyl elenoic acid linked to hydroxytrosol (3,4-DHPEA-EDA or \"oleacein\",) and the aldehyde form of elenoic acid linked to hydroxytrosol (3,4-DHPEA-EA or \"oleuropein aglycon\") are the main responsible of VOO bitterness. In contrast, the dialdehyde form of decarboxymethyl elenoic acid linked to tyrosol (p-HPEA-EDA or \"oleacanthal\") and the deacetoxy-ligtsroside aglycon (p-HEPA-EA or \"ligtsroside aglycon\") seem to be the main compounds responsible for the pungency of VOO [12] (Table 1).  
It is interesting to note that the strong burning/pungent sensation produced by oleocanthal is perceived in the back of the throat. At this oropharyngeal area, [PERSON] and coworkers [51] have identified the receptor that oleocanthal selectively activates, i.e., Transient Receptor Potential cation channel, subfamily A, member 1 (TRPA1).