esa-sceva/satcom-chunk-collection huggingface dataset:
# _Oncom_: A Nutritive Functional Fermented Food Made from Food Process Solid Residue
## 6 A Potential Functional Food? Health-Promoting Properties of _Oncom_
### Oncom and Cholesterol Reduction
This is particularly relevant in the context of dietary interventions aimed at reducing oxidative damage and promoting overall health. The antioxidant mechanisms of O-miso, therefore, not only enhance its nutritional profile but also position it as a valuable component in dietary strategies aimed at improving health outcomes. Lactic acid bacteria (LAB) are considered beneficial probiotics or a type of \"good\" bacteria that can provide a variety of health benefits [158]. LAB are commonly found in fermented foods and are intentionally added or obtained by a process. LAB are also present in _oncom_[93]. LAB can synthesize different polysaccharides, e.g., exopolysaccharides (EPSs), which may play a role as prebiotics. LAB-based EPSs can affect the gastrointestinal microbiome and reduce cholesterol [93]. Regarding the hypocholesterolemogenic effect of probiotic bacteria, the potential mechanisms responsible include deconjugation of bile salts, modulation of lipid metabolism, and decreased absorption of intestinal cholesterol through co-precipitation of intestinal cholesterol with the deconjugated bile salts, incorporation and assimilation of cholesterol in the cell membrane of the probiotics, intestinal conversion of cholesterol in coprostanol, and inhibition of the expression of the intestinal cholesterol transporter Niemann-Pick C1-like 1 (NPC1L1) in the entrocytes [159]. LAB can incorporate cholesterol into their cell membranes, effectively lowering the amount of free cholesterol available for absorption [82; 125]. This process is complemented by the conversion of cholesterol into coprostanol, a sterol that is less readily absorbed by the body [126]. Additionally, LAB have been shown to inhibit the expression of the intestinal cholesterol transporter Niemann-Pick C1-like 1 (NPC1L1), further reducing cholesterol uptake in enterocytes [127]. Research has demonstrated that specific strains of LAB, such as _Lactobacillus fermentum_ and _L. plantarum_, possess significant cholesterol-lowering properties [122; 128]. For instance, _L. fermentum_ has been characterized for its ability to lower cholesterol levels in animal models, suggesting its potential as a probiotic for managing hypercholesterolemiaemia [129]. The mechanisms by which these LAB strains exert their effects are multifaceted and warrant further investigation to fully elucidate their roles in lipid metabolism. Moreover, the consumption of LAB-rich foods like _oncom_ can lead to beneficial changes in the gut microbiome, promoting a diverse and balanced microbial community. This balance is crucial for maintaining metabolic health and preventing dyslipidemia, which is often associated with chronic diseases such as cardiovascular disease [160]. The interplay between LAB, their metabolic byproducts, and the host's microbiome highlights the importance of dietary interventions that include fermented foods.
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 3 Discussion
### Effect of External Thermal Condition  
Spacecrafts endure drastic thermal cycles in orbit, resulting in a large temperature range in the outer space. To study whether the \(\lambda\) criterion is still applicable to the real thermal condition in orbit, another two temperature, which is 173 K and 393 K, using 1 mm leak hole model is calculated. Together with 300 K using 1 mm leak model calculated in last section, three examples are analyzed in this section, basically covering the whole temperature range.
## 4 Experimental Results
### Additional Experiments
#### 4.4.2 Self-Supervised Training on Whole ImageNet Dataset  
The experimental results presented until now were based on a self-supervised pretext task training on 10% ImageNet data (about 128 K images). Such a pretext task training took about 14 h by using two advanced GPUs and, in most cases, it allowed us to reach decent CG forensics performances. It would be possible and beneficial to use more data for the pretext task training, which, however, would increase the training time with higher requirements of computing resources. A pretext task training with the whole ImageNet dataset took nearly 6 days on the two GPUs and would last about two weeks if only one ordinary GPU was available. Considering the repetitive procedure of the design and test of pretext task (several iterations were necessary), it would be quite expensive and time-consuming to use all ImageNet for pre-training, especially for researchers and practitioners having rather limited computing resources. Luckily, we were able to carry out one training of our 9-class classification pretext task on the whole ImageNet dataset and evaluated the pre-trained network for the main task of CG forensics. Table 6 presents the obtained experimental results, with comparisons to existing methods, under the fine-tuning evaluation protocol for the multi-class CG forensic problem. Here, for comparisons, we downloaded and used the pre-trained (on whole ImageNet) models of RotNet, Jigsaw, BarlowTwins, SimCLR, MoCo-V2 and ImageNetClassi from the VISSL repository [51]. From Table 6 we can see that our method still performed better than existing self-supervised schemes. It can also be observed that the results in Table 6 were better than those in Table 5 and that the pretext task training with more data (though being more time-consuming, more expensive and less ecological) was beneficial in terms of the performance on the main task of CG forensics. For example, the performance gap between our self-supervised method and the supervised ENet was very small under the fine-tuning on whole training set, and, as shown in the last column of Table 6, several self-supervised methods (including ours) achieved better performance than the supervised ENet under the fine-tuning on 20% data. In practice, for the design and implementation of a self-supervised learning method, it is recommended to find a good trade-off between different factors, like main task performance, computational time, affordability of computing hardware and energy consumption.
## 4 Simulation
### Theoretical Basis of Sulfate Ion Diffusion Model
#### 4.1.1 Diffusion Coefficient of Sulfate Ions in Cement Mortar  
The volume fraction of the cement mortar (\(V_{s}\)) is closely related to the diffusion coefficient of sulfate ions in the mortar, the degree of hydration (\(\alpha\)), the maintenance time (\(T\)), and the porosity (\(\varphi\)). In this paper, we adopted the method of [PERSON] et al. [36] to calculate the following:  
\[\varphi_{1}=\frac{0.19\alpha}{w/c+0.32} \tag{9}\]  
\[\varphi_{2}=\frac{w/c-0.36\alpha}{w/c+0.32} \tag{10}\]  
\[\varphi_{s}=\varphi_{1}+\varphi_{2}=\frac{w/c-0.17\alpha}{w/c+0.32} \tag{11}\]  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Specimen** & \begin{tabular}{l} **Porosity** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{l} **Average Pore** \\ **Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{l} **\textless{}10 nm** \\ \end{tabular} & \begin{tabular}{l} **100–1000** \\ **nm** \\ \end{tabular} & \begin{tabular}{l} **1000–10,000** \\ **nm** \\ \end{tabular} & \
\begin{tabular}{l} **10,000–1,000,000** \\ **nm** \\ \end{tabular} &
\begin{tabular}{l} **\textless{}1,000,000** \\ **nm** \\ \end{tabular} \\ \hline M3-0 & 13.58 & 58.87 & 1.48\% & 68.41\% & 13.4\% & 4.76\% & 5.33\% & 6.62\% \\ M3-60 & 12.57 & 55.26 & 3.78\% & 58.75\% & 23.73\% & 6.87\% & 3.2\% & 3.67\% \\ M3-120 & 14.17 & 36.09 & 4.52\% & 63.77\% & 15.97\% & 7.57\% & 3.3\% & 4.87\% \\ C3 & 20.06 & 177.56 & 0.19\% & 23.27\% & 8.53\% & 5.62\% & 23.96\% & 38.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pore characteristic parameters.  
In the above equations, \(\varphi_{1}\) is the capillary space, \(\varphi_{2}\) is the gel pore, and \(\varphi_{s}\) is the initial porosity of cement mortar.  
The degree of hydration (\(\alpha\)) was obtained by [PERSON] et al. [37] as follows:  
\[\alpha=0.716T^{0.0901}\exp\left[-\frac{0.103T^{0.0719}}{(w/c)}\right] \tag{12}\]  
According to the theories of [PERSON] et al. [38] and [PERSON] et al. [39], the diffusion coefficient of sulfate ions in cement mortar (\(D_{s}\)) can be deduced, and the calculation model can be obtained, as shown in Equations (13) and (14):  
\[D_{s}=\frac{\varphi_{s}D_{e}}{\tau} \tag{13}\]  
\[\tau=\frac{wh_{a}\beta_{s}\beta_{sa}\big{(}1+0.35\sqrt{V_{s}}\big{)}\sqrt{ \big{(}1-\sqrt{1-\varphi_{s}}\big{)}^{2}+0.25}}{1-\sqrt{1-\varphi_{s}}} \tag{14}\]  
In the above equations, \(D_{e}\) is the coefficient of diffusion of the sulfate ions in the pore solution, and the value is 1.7 \(\times\) 10\({}^{-11}\) m\({}^{2}\)/s [40]. \(w\) is the adjustment coefficient, for which the value is 1.28; \(\beta_{s}\) is a parameter with a value of 1.77; \(\beta_{sa}\) is the sand shape coefficient, for which the value is 1.08; and \(V_{s}\) is the sand volume fraction [39].
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 3 Proposed Methodology
### Segmentation Network  
This section begins by introducing the segmentation component of the DEU-Net, which combines MBConv and a multi-scale fusion. It then provides a detailed description of the structure of the MBConv module and the multi-scale fusion mechanism.  
The segmentation structure used in this paper is illustrated in Figure 2, incorporating MBConv and a multi-scale fusion mechanism to amplify segmentation accuracy while maintaining efficiency. The pivotal component module of the segmentation network in this study is the MBConv block, where the 5 \(\times\) 5 convolution kernel is substituted with a 3 \(\times\) 3 convolution kernel to curtail computational costs and parameters. In the proposed methodology, MBConv1, 3 \(\times\) 3 signifies an MBConv block layer with an expansion factor of 1 and a convolution kernel size of 3 \(\times\) 3 (as delineated in Section 3.1.1). The expansion factor denotes the number of output channels compared to the input channels in the expansion convolution. Likewise, for MBConv6, k3 \(\times\) 3 designates an MBConv block layer with an expansion factor of 6 and a convolution kernel size of 3 \(\times\) 3. For comprehensive network efficiency, MBConv1 is employed in the encoding and decoding layers of the initial two layers. It is essential to maintain consistency in the MBConv block types at corresponding positions in the encoder and decoder within the MBConv encoder-decoder architecture. This ensures improved compatibility between the encoder and decoder.  
Figure 1: The overall structure of the proposed defect detection.  
Figure 2: The architecture of the segmentation network (DEU-Net).  
The U-Net structure's skip connection method is also adopted, introducing a direct connection between max pooling and deconvolution. This method allows certain features to be directly propagated from the encoder to the corresponding decoder, retaining contextual information from the encoding part and introducing multiple paths for gradient backpropagation, alleviating the issue of gradient disappearance.  
The outputs En1, En2, En3, and En4 of all layers in the decoding part are fused to obtain the final feature. The deeper features are relatively coarse, providing strong responses for larger targets and the partial edges of targets. Meanwhile, the shallow features can complement the deep features with sufficient detailed information. Additionally, each unit's receptive field is different, contributing well-integrated features to the final feature map.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### End-to-End Speech Synthesis  
A TTS system typically consists of a text analysis front-end, an acoustic model and a speech synthesizer. Since these components are trained independently and rely on extensive domain expertise which are laborious, errors from each component may compound. To address these problems, end-to-end speech synthesis methods which combine those components into a unified framework have become mainstream in the speech synthesis field. There are many advantages of an end-to-end TTS system: (1) it can be trained based on a large scale of \(<\)text, speech\(>\) pairs with minimum human annotation; (2) it doesn't require phoneme-level alignment; and (3) errors cannot compound since it is a single model. In the following, we will give a brief introduction to the end-to-end speech synthesis methods.
# Securing SDN-Based IoT Group Communication
## 3 Overview  
### System Entities and Security Assumptions  
Our solution considers an SDN network that interconnects IoT _endpoints_ acting as CoAP servers with IoT service clients. Each IoT endpoint owns an Ed22519 public key [11], denoted by \(Endpoint_{ID}\). Network operators maintain a list of \(Endpoint_{ID}\) identifiers belonging to authorized IoT devices. IoT devices are connected to the SDN network through an access device; the type of this device depends on the available communication technology (e.g., WiFi, ZigBee, LoRa, etc.); nevertheless, our solution is oblivious to used technology. Access devices are connected to edge switches acting as the network attachment point (NAP), and it is assumed that there are mechanisms that allow NAPs to access this list of authorized IoT devices. Therefore, it should be not possible for an attacker to join a network by impersonating an authorized IoT device inside the SDN network. Similarly, the network operator owns a well-known Ed22519 public key, denoted by \(Network_{ID}\), which acts as the root of trust in our system; all endpoints are preconfigured with this key, and all NAPs can generate signatures using the private key that corresponds to a \(Network_{ID}\). Whenever a key is included in a text-based file, we are using its Base64 url encoding [12].  
IoT devices provide access to resources. We focus on resources that can be accessed using CoAP and CoAP group communication.  
Additionally, we consider that the SDN controller knows the full network topology, and it is capable of constructing forwarding paths from an \(Endpoint_{ID}\) toward one or more \(Endpoint_{ID}\) identifiers (see also Section 2.1). These paths are identified by a Bloom filter-based identifier denoted as \(Fwd_{ID}\). Finally, we consider that there is a well-known \(Fwd_{ID}\) that can be used by endpoints to broadcast packets in the network.  
Our solution is focused on IoT endpoints acting as CoAP servers; for this reason, we neither consider clients as part of our threat model nor are we concerned with client-facing security operations such as client authentication and authorization.
## 3 System Model  
As shown in Figure 1, we consider a UAV-assisted MEC system consisting of \(M\) users and a UAV. Each user has an MD, and the set of MDs is described by \(\mathcal{M}\triangleq\{1,2,\ldots,M\}\). The UAV is configured with an edge server with powerful computing power and flies to an area of size \(L\times W\), providing services for the MDs. Tasks that need to be offloaded from the MDs can be unloaded to the UAV over the wireless link. The system runs during the specified task period, the slot length of each task period is \(\tau\), indexed by \(n\in\mathcal{T}\triangleq\{1,2,\ldots,N\}\). During each slot, the UAV is assumed to serve its associated MDs. Each user \(i\in\mathcal{M}\) is scattered randomly in a dense area, and its horizontal coordinate is given by \(u_{i}(n)=(x_{i}(n),y_{i}(n))\). Suppose the UAV is flying at a constant altitude \(H\) above the ground, the horizontal coordinate of the UAV can be expressed as \(q(n)=(x(n),y(n))\). The UAV flies for \(t_{fly}\) seconds in each slot and hovers the rest of the time. the distance between the user \(i\) and the UAV in the time slot \(n\) can be expressed as the Formula (1).  
\[d_{i}(n)=\sqrt{H^{2}+\parallel q(n)-u_{i}(n)\parallel^{2}} \tag{1}\]For UAV-MD wireless communications, line-of-sight (LoS) channels typically have advantages over other channels. A wireless channel-free space path loss model for UAVs is similar to [20], and can be expressed as Formula (2).  
\[h_{i}(n)=\beta_{0}(d_{i}(n))^{-2} \tag{2}\]  
where \(d_{i}\) is the distance between the i-th MD and the UAV, and \(\beta_{0}\) is the channel power gain at the reference distance \(d_{0}=1\;m\). Furthermore, we consider that the maximum horizontal coverage of the UAV is \(R^{max}\). This UAV's energy usage can be described using a Formula (3).  
\[E_{fly}(n)=\phi\parallel v(n)\parallel^{2} \tag{3}\]  
where \(\phi=0.5M_{\text{UAV}}t_{fly}\)[8], \(M_{\text{UAV}}\) is related to the payload of the UAV and \(v(n)\) indicates velocity at time slot \(n\).  
Each MD can assign missions to a UAV or conduct them locally. To distinguish between various states, a binary variable \(s_{i}(n)\in\{0,\,1\}\) is introduced. If \(s_{i}(n)=1\), then user \(i\) is connected to and being serviced by UAV at slot n; if not, \(s_{i}(n)=0\).
## 4 Differential Evolution  
The synthesis problem presented in Section 3 is highly nonlinear and subject to several constraints, and the possible combinations of dimensional parameters are incommensurable. Because of this, the metaheuristic techniques from evolutionary computation and swarm intelligence [36] are alternatives used to obtain adequate solutions to this type of problem at an affordable computational cost [27]. As previously discussed, the metaheuristic technique of Differential Evolution (DE) [30] has been very successful in finding the solutions of dimensional synthesis problems of mechanisms [31; 32; 33].  
DE is a simple and effective stochastic algorithm for solving challenging optimization problems. This algorithm is inspired by the theory of the natural evolution of species according to Neo-Darwinism. Algorithm 1 summarizes the overall DE operation. At the beginning of the DE (lines 1 and 2), an initial population \(P\) is generated with \(NP\) random individuals (candidate solutions to an optimization problem randomly generated between bounds \(\vec{p}_{l}\) and \(\vec{p}_{u}\)). Subsequently, an evolutionary cycle is executed (lines 3 to 9), and different operations are performed on each individual \(\vec{p}_{i}\) in the population \(P\) (lines 4 to 8) to try to improve the original solutions and achieve convergence. Thus, for each \(\vec{p}_{i}\), \(b\) pairs of parent individuals are selected (line 5). These generate a \(\vec{v}_{i}\) mutant individual (line 6). Next, the mutant individual \(\vec{v}_{i}\) is recombined with the original \(\vec{p}_{i}\) to obtain an offspring \(\vec{u}_{i}\) (line 7). Finally, the offspring individual \(\vec{u}_{i}\) competes with the original \(\vec{p}_{i}\), and the fittest one persists in population \(P\), while the other is discarded. At the end of the evolutionary cycle, the fittest individuals are expected to be in the population \(P\), from which the best alternative is chosen as the solution to the optimization problem (lines 10 and 11).  
```
Input: Number of generations (\(G_{max}\)), Population size, Other hyperparameters (Crossover Rate (\(CR\)), Scaling factors (\(F\) and \(K\))). Output: Best individual (\(\vec{p}_{best}\)).
1\(G\gets 0\)
2 Generate an initial population \(P\) with \(NP\) random individuals.
3 while\(G<G_{max}\)do
4 foreach\(\vec{p}_{i}\in P\)do
5 Select \(b\) pairs of parent individuals to generate a mutant and/or offspring individual.
6 Apply mutation operations on the parent individuals to generate a mutant \(\vec{v}_{i}\).
7 Apply crossover operations on the original individual \(\vec{p}_{i}\) and the mutant \(\vec{v}_{i}\) to generate an offspring \(\vec{u}_{i}\).
8 Choose between \(\vec{p}_{i}\) and \(\vec{u}_{i}\) the fittest individual that will persist in population \(P\); the other is discarded.
9\(G\gets G+1\)
10 Select the best individual \(\vec{p}_{best}\) from the population \(P\).
11 return\(\vec{p}_{best}\)
```  
**Algorithm 1**Differential Evolution.
# FlickPose: A Hand Tracking-Based Text Input System for Mobile Users Wearing Smart Glasses
###### Abstract  
With the growing use of head-mounted displays (HMDs) such as smart glasses, text input remains a challenge, especially in mobile environments. Conventional methods like physical keyboards, voice recognition, and virtual keyboards each have limitations--physical keyboards lack portability, voice input has privacy concerns, and virtual keyboards struggle with accuracy due to a lack of tactile feedback. FlickPose is a novel text input system designed for smart glasses and mobile HMD users, integrating flick-based input and hand pose recognition. It features two key selection methods: the touch-panel method, where users tap a floating UI panel to select characters, and the raycast method, where users point a virtual ray from their wrist and confirm input via a pinch motion. FlickPose uses five left-hand poses to select characters. A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. FlickPose was tested against the standard virtual keyboard of Meta Quest 3 in three tasks (hiragana, alphanumeric, and kanji input). Results showed that raycast had the lowest error rate, reducing unintended key presses; touch-panel had more deletions, likely due to misjudgments in key selection; and frequent HMD users preferred raycast, as it maintained input accuracy while allowing users to monitor their text. A key feature of FlickPose is adaptive tracking, which ensures the keyboard follows user movement. While further refinements in hand pose recognition are needed, the system provides an efficient, mobile-friendly alternative for HMD text input. Future research will explore real-world application compatibility and improve usability in dynamic environments.  
mixed reality; text input; hand tracking; hand pose recognition; deep learning Article