esa-sceva/satcom-chunk-collection huggingface dataset:
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 4 Enzyme Inactivation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Enzyme** & **Processing** & **Inactivation** & **Reference** \\ \hline FRUIT AND VEGETABLE PRODUCTS & & & & \\ \hline \hline \multirow{2}{*}{Aracça-boi pulp} & Polyphenol oxidase (PPO) & 19 kHz, 400 W, 88.9 \({}^{\circ}\)C, 8.75 min & 1.19 U/min & [83] \\ \hline \multirow{2}{*}{Goldenberry puree} & \multirow{2}{*}{Peroxidase (POD)} & 20 kHz, 1000 W, 90\% & \multirow{2}{*}{92\%} & \multirow{2}{*}{[84]} \\  & & amplitude, 65 \({}^{\circ}\)C, 10 min & & \\ \hline \multirow{2}{*}{Mango juice} & Polyphenol oxidase (PPO), & 20 kHz, 600 W, pulsed & \multirow{2}{*}{100\% for all} & \multirow{2}{*}{[85]} \\  & Peroxidase (POD), & (5 s), 10 min, plus UV & & \\ \cline{1-1}  & Pectinmethylesterase (PME) & (20 W, 254 nm) & & \\ \hline \multirow{2}{*}{Pear juice} & Peroxidase (POD), Polyphenol oxidase (PPO), & 20 kHz, 750 W, 65 \({}^{\circ}\)C, 10 min & 95.70\%, 98.09\% and 96.75\%, 96.75\%, respectively & [45] \\ \cline{1-1}  & Pectinmethylesterase (PME) & 10 min & & respectively \\ \hline \multirow{2}{*}{Spinach juice} & Peroxidase (POD) and & 30 kHz, 600 W, 60 \({}^{\circ}\)C, 20 min & 0.66 and 0.016 & \multirow{2}{*}{[47]} \\  & Polyphenol oxidase (PPO) & 20 min & & \\ \hline PLANT-BASED MILK & & & & \\ \hline \multirow{2}{*}{Almond milk} & Peroxidase (POD), & 40 kHz, 600 W, 60 \({}^{\circ}\)C, 40 min & 93.65\% and 94.88\%, 94.88\%, 94.80\% & [49] \\  & Lipoxygenase (LOX) & 40 min & & respectively \\ \hline DAIRY & & & & \\ \hline \multirow{2}{*}{S skim milk} & Alkaline Phosphatase & 20 kHz, 5000 W, 10 min & 101 mu/L & [56] \\ \hline \multirow{2}{*}{Whole milk} & Alkaline Phosphatase and & 19 kHz, 475 W, 85 \({}^{\circ}\)C, 6 min 9 s & 100\% for both & [59] \\  & Lactoperoxidase & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Enzyme inactivation in selected food products by ultrasound.
## 5 Discussion
### Steady/Unsteady Boundary Layer  
The value of the unsteadiness parameter (\(A\)) indicates the type of motion such that for \(A=0\), the problem is reduced to the steady case, and \(A\
eq 0\) indicates the unsteady motion. Moreover, the positive sign of this unsteadiness parameter indicates accelerated motion and the negative sign indicates deceleration. Figure 3a,b indicate the effect of the unsteadiness parameter on the linear and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{K}\) & \(\mathbf{Sw}\) & \(\mathbf{\phi}\) & \(-\mathbf{f^{\prime\prime}(0)}\) & \(-\mathbf{g^{\prime}(0)}\) & \(-\mathbf{\theta^{\prime\prime}(0)}\) & \(C_{\mathcal{V}}/\overline{R_{c}}\) & \(-\mathbf{Nul}/\sqrt{R_{c}}\) & \(M_{x}\) \\ \hline \multirow{8}{*}{0} & \multirow{4}{*}{\(-0.5\)} & 0.0 & 4.190553 & 3.102011 & 1.339903 & \(-\)8.381106 & 1.339903 & \(-\)6.204022 \\  & & 0.1 & 3.717099 & 2.476667 & 1.365301 & \(-\)7.456761 & 1.818084 & \(-\)5.904910 \\  & & 0.2 & 3.245327 & 1.915456 & 1.393946 & \(-\)4.374844 & 2.433422 & \(-\)3.830912 \\ \cline{2-8}  & & 0.0 & 4.613003 & 4.263178 & 1.777243 & \(-\)13.83900 & 1.777243 & \(-\)10.65794 \\ \cline{2-8}  & & 0.1 & 4.043774 & 3.276003 & 1.810996 & \(-\)5.860422 & 2.411588 & \(-\)6.552006 \\  & & 0.2 & 3.490167 & 2.440392 & 2.177493 & \(-\)6.051520 & 3.801266 & \(-\)5.579264 \\ \cline{2-8}  & & 0.0 & 5.079028 & 5.919060 & 2.410378 & \(-\)10.15805 & 2.410378 & \(-\)11.83812 \\  & & 0.1 & 4.399817 & 4.367775 & 2.450397 & \(-\)8.826341 & 2.363038 & \(-\)10.413727 \\  & & 0.2 & 3.753898 & 3.128626 & 3.06 angular velocities. It is observed that increasing this parameter leads to a decrease in the linear and angular velocities when it is close to the surface. This effect is reversed when it is far away from the surface. Figure 3c indicates that the unsteadiness parameter has a finite effect on the boundary layer temperature. It is important to mention that only the steady motion, indicated by a black line, is plotted in Figure 3a-c. On the other hand, Tables 3 and 4 present a comparison of the physical characteristics of the steady and unsteady boundary layers through the values of the surface skin friction, couple stress, and Nusselt number. In the case of unsteady motion, the surface skin friction value is larger for Newtonian fluids (\(K=0\)) than it is for non-Newtonian fluids (\(K\
eq 0\)). This means that the skin friction is more affected by Newtonian fluids than by non-Newtonian fluids. Additionally, in the case of unsteady motion, we observe the same effect for the couple stress and the local Nusselt number. It is observed that increasing the surface heat flux, caused by the increase of the Nusselt number, will accelerate the rate of cooling.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results
#### 5.2.3 The Effect of the Number of Local Training Rounds  
In FL, clients can perform multiple local training stages before global aggregation to reduce communication costs. In Table 4, the results are provided for FedAvg, FedSage, and AD-FG with a total of 10 clients. Overall, as the number of training rounds increased, the accuracy improved significantly in the early stages, with FedSage and AD-FG showing a decline afterward, reaching the best performance at the third training round. FedAvg continued to improve even after the third round. In the early stages of training, the models had weak fitting abilities for the data, but through the local training stages, the clients could quickly learn the patterns in the local data, leading to rapid accuracy improvements. However, in the later stages of training, after several rounds of local training, the client models stabilized, and further optimization became limited. Moreover, excessive local training may cause the client models in the AD-FG algorithm to become overly reliant on local features, which affects the learning effectiveness of the global model under cross-domain guidance from the server.
## 8 Conclusions  
In the paper, a methodology has been developed to improve the fault diagnosis of a bearing by utilizing a hybrid observation. This methodology has been applied to a real case of an experimental CWRU dataset. Furthermore, within this method, the hybrid-based signal modeling for the normal resampled RMS signal using a smart autoregressive modeling approach with 98.5% accuracy was suggested. Moreover, to generate robust and reliable residual signals, the adaptive variable structure-Lyapunov proportional multi-integral observer was adopted. The support vector machine was recommended for the classification of residual signals. According to the experimental results, the average accuracy for crack size identification was 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads were 0 hp, 1 hp, 2 hp, and 3 hp. In addition, the average pattern identification for NORM, ROLF, INRF, and OURF was 100%, 99.34%, 95.67%, and 96.33%, respectively. The results suggest that the proposed approach was useful for the diagnosis of bearing failures. On the other hand, this approach could also be recommended in other systems such as condition monitoring. In that case, it would be important to consider some important factors such as the sampling rate frequency and motor rotational speeds to adapt the preprocessing stage since the accuracy of the fault diagnosis depends on them. In future work, we will focus on the improvement of the robustness, reliability, and precision of the proposed scheme. One of the possible directions for the improvement is to discover the robust function approximation by using a combination of the mathematical-based technique and data-driven-based signal modeling approach. Another direction is to improve the estimation algorithm by using the nonlinear architecture of the observation approach where we can combine the nonlinear robust observer with a deep learning approach to increase the performance of signal estimation and classification and reduce the complexity. Furthermore, the problem of different conditions/datasets should be analyzed, and the proposed algorithm needs to be validated by using the vibration and acoustic emission datasets with various motor speeds, torque loads, and crack sizes.  
Both authors contributed equally to conception of the idea, design of the experiments, analysis and interpretation of the results, and writing of the manuscript. Writing--original draft preparation: S.T. and I.K.; writing--review and editing: S.T. and I.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2021 Research Fund of the University of Ulsan.  
The data is publicly available.  
The authors declare they have no conflict of interest.
###### Abstract
In addition, we demonstrate that in a relatively short transmission system (100-200 km), the higher signal power profile symmetry, the higher the nonlinear threshold (up to 9 dB) when using OPC. We also show, in the numerical simulations, a Q\({}^{2}\) factor improvement of approximately 8 dB in the long-haul transmission systems (2000 km) using the optimized EDF-assisted Raman amplified spans in the link.
## 3 Results
### Comparison of Reddyproc and MLP Interpolation Effect
#### 3.3.3. Hourly Scale NEE Variation Characteristics  
The hourly variation in the NEE at the Longbao Station estimated with the MLP model is shown in Figure 8, and the monthly hourly absorption peaks at the Longbao Station were 14:00 in January, 14:00 in February, 13:00 in March, 11:00 in April, 12:00 in May, 13:00 in June, 11:00 in July, 12:00 in August, 12:00 in September, and 12:00 in October. The overall peak occurred between 11:00 and 14:00; the emissions peaked in January at 09:00, February at 09:00, March at 19:00, April at 20:00, May at 22:00, June at 21:00, July at 21:00, August at 22:00, September at 00:00, and October at 23:00, and the overall peak occurred between 21:00 and 00:00. The hourly variation in the NEE at the Longbao Station estimated with the Reddyproc model is shown in Figure 4. It was found that the peak of absorption estimated with REDDYPROC was 1-2 h later than that estimated with the MLP model in July and August. The peaks at the other time periods were similar, which was consistent with the above finding that MLP was estimated to be higher in the warm season (growing season) than in Reddyproc. As a result, the absorption rate of the NEE reached its peak faster in the MLP model.
###### Abstract  
Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework's effectiveness in detecting and localizing damage using unlabeled field data.  
os +
Footnote †: journal: Applied sciences  
article
# Temporal Encoding to Reject Background Signals
## 2 Results
### Satellite Systems Demonstration
**(b)** Schematic of the experimental setup for real time data transmission under simulated satellite power.
## 1 Introduction
In summary, the proposed system is an accessible, robust, and efficient solution that democratizes access to PV panel characterization, overcoming the cost and complexity limitations of commercial alternatives and the speed and automation deficiencies of manual methods, all without sacrificing the necessary accuracy for the proposed application. The system's open-source design, low component cost, and modular architecture make it suitable for deployment in educational labs, field diagnostics, and research in developing regions. Furthermore, the platform can be extended with wireless communication (e.g., ESP32) for future integration into smart PV monitoring and Internet-of-Things (IoT) applications.
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 4 Hiding Countermeasure for SCA Attacks
### Background  
In AES implementations, the power-consumption traces are correlated with data computed, and, at the same time, those computation data are correlated with the secret key _Key_. Mainly, the correlation peak emerges during the _SubByte_ step for the AES cipher. To address this security vulnerability, [PERSON] et al. [7] propose interfering with the power  
Figure 4: First-order CPA attack in the last round on unprotected AES.  
signal using an added correlated power signal. They use a parallel block to the classic AES core with the same cipher input data \(Plaintext\) but with an interfering random key \(Key_{intrf}\). The block includes the _AddRoundKey_ and _SubByte_ layers of the AES cipher, this architecture-level countermeasure is replicated in Figure 5.  
Therefore, on the first-round encryption, the input data is processed simultaneously by two encryption cores, the original and the CPNG block, so the global power consumption is due to both AES states, \(S\) and \(S_{intrf}\). The \(S_{intrf}\) signal is unconnected because it will not affect the encrypted output \(ciphertext\). In other words, the cipher power consumption of that round is correlated to input data and the keys: \(Key\) and \(Key_{intrf}\). Given that power noise exhibits a correlation with the input data, it becomes unfeasible to filter it out during a CPA attack. The unique condition of the countermeasure is that the interfering key value has to be different from the original key but the same size. Through experimentation, the authors demonstrate that their design is resilient to conventional first-order CPA attacks on the first encryption round [7].  
It is crucial to note that the countermeasure they propose primarily focuses on safeguarding the first round. It is reasonable to assume that the most critical point of vulnerability occurs in this initial round, as this is where the original key is combined with the plaintext. However, it is well-documented that the AES algorithm exhibits greater vulnerability in the final round. In this scenario, the attacker gains access to the key expanded and must engage in additional key recovery processing [13; 19], as explained in Section 3. Although the difficulty of finding the original key from the key extended depends on the key size, this process is practically manageable. Consequently, attacks on the intermediate and/or final rounds are of significant concern.  
Figure 5: AES diagram with Correlated-Power-Noise Generator exposed in [7].
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Network Scale  
A typical network communication architecture aims for scalability; no matter the size of the network, the same mechanisms should be used to encode the information and secure it. A great example showing the power of such methods are digital signatures; any node holding a public key (of a fixed bit size) can verify that a signed message originates from the owner of the corresponding private key.  
In certain situations, such as underwater mobile sensor swarms, the number of network nodes will never be high enough for scalability. Apart from physical congestion, the capacity of communication channel may prevent the creation of networks with a high number of nodes. Depending on the particular application, the network designer may be aware of the upper limit on the number of nodes, and as such may not seek out solutions that are asymptotically optimal, i.e., the solution has to solve the network problem up to a certain network size that is known in advance.  
It is known that combinatorial properties might be different for small and large numbers. A property that is asymptotically true for large numbers (that is, for \(n\) bigger than some threshold value \(n_{0}\)) need not be true for small numbers. This is both a challenge and an opportunity, as algorithms developed for arbitrary problem sizes are typically more elegant and 'clean'; however, given a limited problem size, there may be room for fine-tuning and optimization. We explore these fine-tuning opportunities in this paper.
## 1 Introduction
A sink that can detect a query loss at a sensor node recovers a query loss by resending the same query to that sensor node. In large networks, this recovery of query loss requires a much longer route than a local recovery of query losses near the point of failure. Our objective is to enable localized and distributed query loss discovery and recovery in query dissemination. The desired operation of localized discoveries and recoveries from query losses includes not only the capability of localizing the scope of discoveries and recoveries, but also the capability of automatically focusing on the query losses particularly at the nodes that are corresponding to the query. This target is essential for large size sensor networks and the sensor networks that have various user queries or support the diversity of sinks. The distributed operation here specially requires the desired approach to be spatially and temporally independent of sinks, since widely applied sensor networks should support diversity of sinks rather than only one sink node with fixed position. The reason for localized operation of query discovery and recovery is to enable the scalability of queries in large sensor networks. To achieve the above goals, we design a localized discovery and recovery approach that requires only local cooperation at sensor nodes to cope with query losses. The proposed approach is scalable in large sensor networks and enables a high success rate of loss discovery and recovery. In the proposed approach we particularly employ the following techniques to achieve the design goals: (1) distributed loss detector clustering for sensor nodes so as to enable localized discovery and recovery of query losses, where each sensor node can be a loss detector of its neighbor node; (2) distributed name resolution at each detector to enable localized and selective response to a query; (3) collective node operations in discovery and recovery to improve the success rate. Both mathematical analyses and computer simulations are conducted to verify the effectiveness of the proposed approach. We give mathematical analyses to examine the properties of the proposed approach including collective impact of loss discovery and recovery, as well as scalability. Furthermore, we examine the features of cooperation and competition at MAC and PHY layers in the processes of loss recoveries. We conduct computer simulations to verify the performance improvements with regard to the success of query delivery, scalability, and the impact of detector numbers. The rest of the paper is organized as follows. Section 2 surveys the related work. Section 3 describes the problems of query losses and recoveries in sensor networks. Sections 4 and 5 describe and analyze the proposed approach of the localized query loss discovery. Section 6 presents the simulation results of the proposed schemes and Section 7 concludes the paper.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### Phase Mode Excitation of Uniform Circular Array  
Now, we consider phase mode excitation of an \(N\) elements virtual uniform circular array (VUCA) in our case. The normalized beamforming weight vector that excites the array can be expressed as:  
\[w_{m}^{\mathrm{H}}=\frac{1}{N}\left[e^{jm\tau_{0}},\cdots,e^{jm\tau_{N-1}} \right]. \tag{11}\]  
Then, we can get the resulting array pattern \(f_{m}^{s}(\theta)\):  
\[f_{m}^{s}(\vec{\theta})=w_{m}^{\mathrm{H}}A(\vec{\theta})=\frac{1}{N}\sum_{n= 0}^{N-1}e^{jm\tau_{n}}e^{j\beta\cos(\phi-\gamma_{n})}, \tag{12}\]  
where the superscript \(s\) denotes the sampled aperture is discrete. As a result of \(|m|<N\),  
\[\begin{split} f_{m}^{s}(\vec{\theta})=& j^{m}J_{m}( \beta)e^{jm\phi}\\ &+\sum_{q=1}^{+\infty}[(j)^{g}J_{g}(\beta)e^{j\phi}+(j)^{h}J_{h} (\beta)e^{jh\phi}].\end{split} \tag{13}\]  
In the above equation, \(g=Nq-m\) and \(h=Nq+m\). The first term in this equation, the principal term, is the same as the far-field pattern of Equation (9) corresponding to the continuous aperture case. The remaining terms are caused by the sampling of the continuous aperture, and they are the residual terms to our case. After setting the perspective from Equation (13), we come to the conclusion that the residual terms can be ignored if \(N>2M\). Due to the attribute of \(J_{-m}(\beta)=(-1)^{m}J_{m}(\beta)\), the UCA array pattern for mode \(m\) is:  
\[f_{m}^{s}(\vec{\theta})\approx(j)^{|m|}J_{|m|}(\beta)e^{jm\phi}. \tag{14}\]
## 4 Results
### Pseudorange and Phase Time-Variant Mixed OSB Service System  
We established the multi-GNSS pseudorange and phase time-variant mixed OSB service system based on the above theoretical discussions. Figure 2 illustrates the flowchart of the mixed OSB service system. First, the data download module automatically downloads GNSS observations, precise orbits, and clock offsets from IGS MGEX and iGMAS. The IGS MGEX observation values and the precise orbit clock products of the WHU IGS and iGMAS analysis center are downloaded to generate the mixed OSB in this manuscript. If the precise orbit and clock offsets are missing, the ED GFIF methods are used as a backup algorithm for obtaining the phase-only time-variant IFCB. The phase-only time-variant IFCB can be transformed into OSB type. Typically, MFPPP requires both phase-only time-variant IFCB and DCB corrections [24; 41]. We synthesize the additional DCB products and phase-only time-variant OSB into the mixed OSB on the server side, which can simultaneously correct both pseudorange and phase observations. This way, it can reduce the complexity of client algorithms. If the precise orbit and clock offsets are not missing, the new undifferenced IFCB model coupled with satellite clock offsets methods is used to generate the mixed IFCB. Furthermore, this IFCB can be transformed into OSB. The client only needs to correct this bias to achieve MFPPP. Note that, unless otherwise specified, the mixed OSB in the following text refers to the pseudorange and phase time-variant mixed OSB.  
Figure 1: Distribution of the selected GNSS tracking stations for satellite mixed OSB estimation.  
Figure 2: Multi-GNSS pseudorange and phase time-variant mixed OSB service system.