esa-sceva/satcom-chunk-collection huggingface dataset:
# Unveiling the Role of Minerals and Trace Elements of Thermal Waters in Skin Health
## 2 Minerals, Trace Elements, and Other Compounds of TSW in Skin Health
### Other Compounds
#### 2.4.1 Silica (SiO\({}_{2}\))  
Silica TSWs are known for their anti-irritant properties. A clinical study performed by [PERSON] et al. [52] demonstrated that Sao Pedro do Sul TSW, which is rich in silica, is able to reduce skin irritation and promote skin barrier recovery. Water-based creams formulated with silica-rich Monfortinho TSW improved skin hydration, and an in vitro study showed that it promoted skin barrier recovery [53]. Water-based creams formulated with silica-rich Monfortinho TSW reduced cell metabolism and the proliferation of keratinocytes and macrophages, which could explain the observed benefits of this TSW in hyperkeratotic conditions, such as psoriasis and atopic dermatitis [54].  
Blue Lagoon TSW is a highly mineralized water with an extremely high concentration of silica (137 mg/L). Studies have demonstrated that this TSW is able to reduce PASI scores in psoriatic patients [55; 56].
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 3 System Model
### Design of Zone-Chain
#### 3.2.2 Consensus Algorithm  
The consensus algorithm [26] is the most pivotal mechanism for multiple nodes in a blockchain network to reach an agreement, ensuring data consistency, security, and liveness of the entire system. In Zone-Chain, the consensus process is executed simultaneously by all ZSs. These ZSs compete for the qualification to write self-organized blocks into the blockchain via a consensus protocol. The winning ZS is designated as the leader and broadcasts its local block to other ZSs for validation. Once validated, the block is added to the permissioned blockchain to make it tamper-resistant. Traditional consensus protocols, such as PoW [27], entail high computational costs and communication overhead. Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) mechanisms [28], though addressing some issues, still rely on tokens and do not resolve key application challenges. In the multi-zone EC scenario considered in this paper, the primary concern is mitigating security risks posed by malicious ENs. This paper specifically focuses on such EC scenarios that involve servers acting as consensus nodes, where each ZS functions as an EC node covering multiple terminal devices within its service range. Depending on specific application requirements and infrastructure constraints, it is assumed that these scenarios typically involve 5 to 50 ZSs. Under the premise of mutual trust and honesty among all ZSs, Practical Byzantine Fault Tolerance (PBFT) [29] becomes a practical choice. PBFT ensures that the decentralizednetwork continues to function even if some nodes fail or act maliciously, and it requires significantly less computational power and time to generate blocks.  
Zone-Chain deploys a rotation policy to ensure that the leader selection process adheres to the order of ZS indexes. Specifically, if there are \(|M|\) ZS nodes and the current round is \(i\), the ZS selected as the leader will have the index \(L=i\) mod \(|M|\). This rotation policy ensures fairness by providing each ZS an equal opportunity to act as the leader, and take turns to execute node selection and global aggregation. Compared to a dynamic selection policy, the rotation mechanism is more efficient under the assumption that ZSs are fully trusted, as is assumed in this work. It reduces system cost by avoiding the computational and communication overhead associated with evaluating node performance or connectivity, ensuring a fair and low-cost leader selection process.
## 2 Materials and Methods
### Shapley Additive Explanations (SHAP)  
SHapley Additive exPlanations (SHAP) is a machine learning explainability technique recently developed by [PERSON] [71]. Intuitively, SHAP values decompose the ML model's predictions into a summation of terms corresponding to each individual feature by extending the so-called Shapley values [72], first proposed by [PERSON] in 1951 in the context of cooperative game theory. Formally, given a trained ML model \(f\), a local approximation \(g\) is defined as follows (Equation (2) [71]):  
\[g(\mathbf{w})=\phi_{0}+\sum_{i=1}^{m}\phi_{i}w_{i} \tag{2}\]  
where \(m\) is the number of features, \(\phi_{0}\) is the mean model prediction, \(\phi_{i}\) are the SHAP values, and \(\mathbf{w}\in\mathbb{R}^{m}\) is the so-called \"simplified input\" [71], i.e., a binary vector for which the \(i^{\text{th}}\) value shows whether or not the corresponding feature is _missing_. Thus, for each data point, the model's predictions are decomposed into terms pertaining to each individual term along with a constant term, i.e., the average prediction. Importantly, SHAP values satisfy a property called _missingness_[71], which states that features with missing values have no contribution to the model, i.e., (Equation (3) [71]):  
\[w_{i}=0\Rightarrow\phi_{i}=0. \tag{3}\]  
Following the notation of [PERSON] and [PERSON] [71], let \(M=1,2,\ldots,m\) be the complete feature set that was employed by the model and let \(S\subseteq M\) be a subset; then, the coefficients \(\phi_{i}\) are provided by the following Equation (4) [71]:  
\[\phi_{i}=\sum_{S\subseteq M\setminus\{i\}}\frac{|S|!(m-|S|-1)}{m!}(f(S\cup\{ i\})-f(S)). \tag{4}\]  
The intuitive interpretation of the above equation is that the SHAP values are a weighted average over all possible feature combinations of the gain in the model's prediction when feature \(i\) is included. From the above, it is apparent that if the underlying ML model has high enough accuracy, then the sum of the corresponding SHAP values will be close to the true target values.
# Comparison of Single Control Loop Performance Monitoring Methods
## 2 Materials and Methods  
Control performance monitoring is performed utilizing data from processes to estimate the state of control. Several methods have been adapted for these purposes, with different requirements and restrictions. Some methods utilize modeling of the process to obtain accurate estimation. Unlike these model-based methods, model-free methods require no process model.  
Model-free control performance measurement methods can be further divided into sub-categories such as statistical factors, integral time measures, correlation measures, and alternative indices. The CPM method classification according to [1] is shown in Figure 1.  
Among the model-free CPM methods, the statistical approaches have shown benefits for the cases of processes with non-linear properties. In [12], higher-order statistics-based methods are used to identify oscillatory behavior and diagnose the possible cause for the disturbance. Gaussianity and linearity are tested for in the process and possible identified oscillatory behavior can be characterized by visual analysis of the process output vs. the controller output plot. For example, valve stiction is generally identified by elliptical cycles and sharp corners in the plot [12]. Statistical approaches also include methods such as cross-correlation-based oscillation detection [13] and autocorrelation-based control performance monitoring implementation [14].  
Integral-based indices are a set of widely used performance indices such as Mean Square Error (MSE), Integral Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time-weighted Absolute Error (ITAE). Further adaptations of integral time measures have been developed, some of which are described in Section 2.1. Integral-based indices  
Figure 1: CPM method classification (adapted from [1]). The methods focused on in this work are highlighted in grey.  
have been widely utilized in the field of tuning control loops [15; 16; 17; 18]. This work considers application of these methods in dynamic control performance monitoring.  
Alternative indices utilize other methods such as wavelets and entropy. The applicability of dynamic response analysis is often limited in on-line control performance monitoring, as it might require repeated process experiments and excitation.
## 4 Results
### Air Temperature Distribution
#### 4.2.1 Air Temperature Field
The calculation of horizontal temperature difference between sensors corresponding to the same height but in different sectors showed that only the sensors located lower had greater deviations due mainly to the effect of solar irradiation from the window. This phenomenon was particularly evident between sectors 1 and 3 during the afternoon (13:00-14:00) of 15 February, with external solar radiation between 539 and 567 W/m\({}^{2}\). Indeed, in this case, the air temperature difference was \(-\)5.5 \({}^{\circ}\)C. For all other heights, this difference was much limited and lower compared with the operation period of fan coils. For instance, at 1.1 m, between S1 and S2 and between S1 and S3, this difference varied from \(-\)1.9 \({}^{\circ}\)C to 0.7 \({}^{\circ}\)C; at 2.0 m considering the first and fourth sectors, it varied between \(-\)0.4 \({}^{\circ}\)C and 0.3 \({}^{\circ}\)C; at 3.0 m for S2 and S4 it varied during the monitoring period between \(-\)1.8 \({}^{\circ}\)C and 1.1 \({}^{\circ}\)C.
## 4 Methodology
### General Architecture
#### 4.1.1 Cell Format  
We have made some improvements to the original Tor cell format to adapt to the current changes. To resist traffic analysis attacks, we maintain the fixed-size cell feature with 512 bytes [2]. As shown in Table 1, each data cell is composed of a cell header and cell payload, and the header contains two fields: circuit identifier (Circ_ID) and command identifier (command). The forwarding cell has its own protocol header in the cell's payload field and uses the first 11 bytes of the payload as its own forwarding cell command header (relay-header), and the remaining 498 bytes as the forwarding cell payload (relay-payload). The new cell format designed and implemented in this research, with the header information, is shown in Table 1. Divide_method, SliceNum, SliceLen, SliceID, and fragmentID are all new fields to identify data slices.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Header Field** & **Byte** & **Description** \\ \hline Circ\_ID & 2 & The circuit ID \\ \hline Command & 1 & The command identifier \\ \hline Relay Command & 1 & \begin{tabular}{c} Represents the command and use \\ of the packet \\ \end{tabular} \\ \hline Recognized & 2 & \begin{tabular}{c} For any non-encrypted packet, this \\ field is always 0. If the Tor client \\ finds that the field is not 0 after \\ decryption, it means that it cannot \\ be decrypted and will destroy the \\ circuit in time. \\ \end{tabular} \\ \hline Stream\_id & 2 & \begin{tabular}{c} Identifies the stream ID number \\ generated by the client \\ \end{tabular} \\ \hline Intergrity & 4 & \begin{tabular}{c} Hashes the entire cell field and \\ takes the first four bytes \\ \end{tabular} \\ \hline Split\_method & 2 & \
\begin{tabular}{c} The slice method \\ \end{tabular} \\ \hline SliceNum & 1 & The number of slices \\ \hline SliceLen & 1 & The length of the slice \\ \hline SliceID & 1 & Identifies a slice \\ \hline fragmentID & 1 &
\begin{tabular}{c} The first data slice in a \\ redundant slice \\ \end{tabular} \\ \hline DATA & 494 & The payload of the cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cell format.  
Figure 2: Multi-path defense architecture.
# Serial Decoders-Based Auto-Encoders for Image Reconstruction
## 2 Related Research
In data compression, these methods usually yield deep neural networks to substitute the prediction, transformation, or quantization units of classical methods. In signal-compressed sensing, these methods usually implement deep neural networks to substitute the measurement or recovery units of classical methods. For example, [PERSON] et al. utilized fully-connected deep neural networks to realize the intra prediction coding unit of video compression [25]. [PERSON] et al. adopted deep convolutional neural networks to replace the transformation coding unit of video compression [26]. [PERSON] et al. employed a deep convolutional neural network to accomplish the sampling and reconstruction units of image-compressed sensing [27]. This paper focuses on narrow auto-encoders, incorporates multi-level decoders into auto-encoders, and boosts the performance of data reconstruction. To the best of our knowledge, cascade decoders in auto-encoders have never been studied. Although serial auto-encoders have already been investigated, serial decoders in auto-encoders play a more important role in data reconstruction. In addition, [PERSON] et al. progressively trained generative adversarial networks by gradually increasing the layer numbers of generator and discriminator in order to improve the quality, stability, and variability in data reconstruction [28]. This method will be borrowed for progressively training the proposed cascade decoders-based auto-encoders. The proposed training method gradually increases the decoders of auto-encoders. It is difficult for us to train stable auto-encodersusing multiple decoders and large hypo-parameters. However, it is easier for us to train a stable unit of auto-encoders using a single decoder and small hypo-parameters. A decoder can merely learn low image variation, but serial decoders can learn high image variation. Hence, progressive training can efficiently strengthen the quality, stability, and variability of image reconstruction.
## 2 Related Work  
This section provides a review of geocast routing protocols. Link quality is considered one of the key factors in geocast routing that has a substantial impact on the performance of these routing approaches. We categorize the existing geocast routing approaches into two subsections based on link quality.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 3 Results
### Evaluation of the Color Uniformity  
The color-rendering results for red, green, and blue are presented in Table 4. Value pairs with color coordinate distances exceeding 0.01 are highlighted in red, while those within acceptable distances are marked in green.  
In the red test pattern, only the value pair between the center and bottom right demonstrated acceptable distances. Similarly, in the green test pattern, only the pair between the center and bottom left met the requirement. The blue test pattern showed three value pairs with acceptable distances. Overall, 25 out of 30 value pairs surpassed the tolerance threshold. Therefore, HoloLens 2 does not satisfy the criteria for primary display devices. There are no established quantitative standards for value pair spacing in secondary display devices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Color**} & \multirow{2}{*}{**Position**} & \multicolumn{2}{c}{**Color Coordinates**} & \multirow{2}{*}{**Pairing**} & \multirow{2}{*}{**Distance**} \\  & & \(\mathbf{u}^{\prime}\) & \(\mathbf{v}^{\prime}\) & & \(\mathbf{\Delta}\mathbf{u}^{\prime}\mathbf{v}^{\prime}\) \\ \hline \multirow{9}{*}{Red} & TL & 0.4434 & 0.5220 & TL/TR & 0.016 \\  & TR & 0.4589 & 0.5195 & TL/CE & 0.037 \\  & CE & 0.4802 & 0.5188 & TL/BL & 0.064 \\  & BL & 0.5070 & 0.5184 & TL/BR & 0.033 \\  & BR & 0.4764 & 0.5200 & TR/CE & 0.021 \\  & & & & & TR/BL & 0.048 \\  & & & & & TR/BR & 0.018 \\  & & & & & CE/BL & 0.027 \\  & & & & & CE/BR & 0.004 \\  & & & & & BL/BR & 0.031 \\  & TL & 0.1064 & 0.5568 & TL/TR & 0.011 \\  & TR & 0.1169 & 0.5556 & TL/CE & 0.031 \\  & CE & 0.1374 & 0.5555 & TL/BL & 0.025 \\  & BL & 0.1312 & 0.5578 & TL/BR & 0.065 \\  & BR & 0.1713 & 0.5545 & TR/CE & 0.021 \\  & & & & & TR/BL & 0.014 \\  & & & & & CE/BL & 0.007 \\  & & & & & DE/BR & 0.034 \\  & & & & & BL/BR & 0.040 \\  & TL & 0.1789 & 0.1314 & TL/TR & 0.005 \\  & TR & 0.1806 & 0.1270 & TL/CE & 0.017 \\  & CE & 0.1749 & 0.1475 & TL/BL & 0.032 \\  & BL & 0.1703 & 0.1625 & TL/BR & 0.022 \\  & BR & 0.1726 & 0.1525 & TR/CE & 0.021 \\   Blue & & & & TR/BL & 0.037 \\  & & & & & TR/BR & 0.027 \\  & & & & & CE/BL & 0.016 \\  & & & & & CE/BR & 0.006 \\  & & & & & BL/BR & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distance between the color coordinates of the five measurement points: top left (TL), top right (TR), center (CE), bottom left (BL), and bottom right (BR) on the HoloLens 2 display. Green = Requirement met; red = Not met.
###### Abstract  
The depth image based rendering (DIBR) is a popular technology for 3D video and free viewpoint video (FVV) synthesis, by which numerous virtual views can be generated from a single reference view and its depth image. However, some artifacts are produced in the DIBR process and reduce the visual quality of virtual view. Due to the diversity of artifacts, effectively handling them becomes a challenging task. In this paper, an artifact handling method based on depth image is proposed. The reference image and its depth image are extended to fill the holes that belong to the out-of-field regions. A depth image preprocessing method is applied to project the ghosts to their correct place. The 3D warping process is optimized by an adaptive one-to-four method to deal with the cracks and pixel overlapping. For disocclusions, we calculate depth and background terms of the filling priority based on depth information. The search for the best matching patch is performed simultaneously in the reference image and the virtual image. Moreover, adaptive patch size is used in all hole-filling processes. Experimental results demonstrate the effectiveness of the proposed method, which has better performance compared with previous methods in subjective and objective evaluation.  
3D video; view synthesis; depth image based rendering; artifact handling; adaptive inpainting +
Footnote †: journal: Applied Soft. 2019, 9, 1834; doi:10.3390/app9091834
# Pressure-Driven Nitrogen Flow in Divergent Microchannels with Isothermal Walls
###### Abstract  
Gas flow and heat transfer in confined geometries at micro-and nanoscales differ considerably from those at macro-scales, mainly due to nonequilibrium effects such as velocity slip and temperature jump. Nonequilibrium effects increase with a decrease in the characteristic length-scale of the fluid flow or the gas density, leading to the failure of the standard Navier-Stokes-Fourier (NSF) equations in predicting thermal and fluid flow fields. The direct simulation Monte Carlo (DSMC) method is employed in the present work to investigate pressure-driven nitrogen flow in divergent microchannels with various divergence angles and isothermal walls. The thermal fields obtained from numerical simulations are analysed for different inlet-to-outlet pressure ratios (\(1.5\leq\Pi\leq 2.5\)), tangential momentum accommodation coefficients, and Knudsen numbers (\(0.05\leq\text{Kn}\leq 12.5\)), covering slip to free-molecular rarefaction regimes. The thermal field in the microchannel is predicted, heat-lines are visualised, and the physics of heat transfer in the microchannel is discussed. Due to the rarefaction effects, the direction of heat flow is largely opposite to that of the mass flow. However, the interplay between thermal and pressure gradients, which are affected by geometrical configurations of the microchannel and the applied boundary conditions, determines the net heat flow direction. Additionally, the occurrence of thermal separation and cold-to-hot heat transfer (also known as anti-Fourier heat transfer) in divergent microchannels is explained.  
P oiseuille micro-flow; thermal field analysis; heat flow; divergent microchannel; direct simulation Monte Carlo (DSMC) +
Footnote †: journal: Applied Sci.  
0021
## 4 Antenna Simulation and Measurement Result
### Antenna Fabrication and Measurement Results  
The antenna fabrication is very simple; a thermal transfer method was used. The structure was printed on paper (thermal paper) with the help of CorelDraw or AutoCAD software. Then, the mask was transferred to the substrate (Roger-5880) using a heating machine, the HK320 SR.  
The S\({}_{11}\) (dB) of the antenna was measured using an AV3672 vector network analyzer (VNA) and the radiation patterns, gain, and efficiency were tested and calculated using an anechoic chamber. The radiation patterns of the antenna are shown in Figure 4. The antenna radiation patterns were tested in two different planes (E- and H-planes). There was some disagreement between the simulated and measured results due to fabrication tolerance. The measured S\({}_{11}\) (dB) results operated from 3.09 GHz to 27.55 GHz using an SMA connector (SMA-1 (D550B51H01-118)) and a SMA-2 (SMA2-D360B50H01-118) connector. The S\({}_{11}\) (dB) operated from 3.09 GHz to 40.2 GHz, as shown in Figure 5b. The figure shows a frequency shift by measuring using the SMA-1 connector. This shift occurs due to the fabrication tolerance and also to the limited frequency range (27 GHz) of the SMA connector. This connector is still not a suitable candidate for 70 \(\Omega\) antennas, but there was a small frequency shift and excellent agreement between the simulated and measured bandwidths. At present, we used this special connector (50 \(\Omega\)) due to the unavailability of a 70 \(\Omega\) SMA connector. A 13.009:1 bandwidth ratio of the operating frequencies band was shown in the measurement result. The gain and efficiency were calculated, as shown in Figure 5a. The measured gain of the antenna was from 1.6 dBi to 5.9 dBi throughout the operating bandwidth. A maximum measured efficiency of 92.5% was achieved; across the whole frequency range, the efficiency was from 84% to 92. 5%. Due to the wide bandwidth, we measured the proposed antenna gain and efficiency at lower frequencies (3-18 GHz) and at higher frequencies (30-38 GHz), as shown in Figure 5a. The measurement setup for both the E- and H-planes of the anechoic chamber is shown in Figure 6a,b, respectively. In the measurement setup, the transmitted antenna was fixed in front of the antenna under test (AUT).  
Figure 5: (**a**) Simulated and measured gain and efficiency results of the proposed antenna (**b**) Simulated and measured S\({}_{11}\) (dB) results with Proposed antenna prototype, front and back side view.
## 3 The Proposed Fault Diagnosis Methodology
### Fault Diagnosis of Unknown Alarm Sequences  
As described in Section 3.3.3 above, we can obtain the similarity score between two alarm sequences through the proposed S-ECNN model. When predicting the fault of one unknown alarm sequence, we compare it with every labeled alarm sequence and obtain the similarity score of each pair. The label with the maximum average similarity score will mark the unknown alarm sequence. Suppose the similarity score between one unknown alarm sequence \(X^{\prime}\) and one alarm sequence \(X_{p}^{q}\) with a fault label \(\mu^{q}\) is:  
\[S(X^{\prime},X_{p}^{q}),(p=1,2,\ldots,P^{q},q=1,2,\ldots,Q), \tag{10}\]  
where \(Q\) is the total number of fault categories, and \(P^{q}\) is the total number of alarm sequences with a label \(\mu^{q}\). The calculation method of the similarity score is the same as Equation (8). The average similarity score \(\overline{S}\) between \(X^{\prime}\) and the alarm sequence \(X^{q}\) with a label \(\mu^{q}\) is calculated as:  
\[\overline{S}(X^{\prime},X^{q})=\frac{1}{P^{q}}\sum_{p=1}^{P^{q}}S(X^{\prime}, X_{p}^{q}). \tag{11}\]  
The maximum average similarity score is expressed as:  
\[\overline{S}_{\max}(X^{\prime},X^{f})=\max\Bigl{\{}\overline{S}(X^{\prime},X^ {1}),\ldots,\overline{S}(X^{\prime},X^{Q})\Bigr{\}}. \tag{12}\]  
Accordingly, the label of the unknown alarm sequence is \(\mu^{f}\). In other words, the fault type of the unknown alarm sequence is diagnosed as fault \(f\).