esa-sceva/satcom-chunk-collection huggingface dataset:
holographic portraits, demonstrating the potential of artificial intelligence in refining holographic methods [33].
* Multiplanar Object Reconstruction: A novel inverse cross-correlation approach was proposed to enhance depth of field in holographic imaging, addressing challenges in the visualization of complex, layered structures [34].
* Serial Maximum a Posteriori Detection for Holographic Data Storage: A new detection algorithm based on maximum a posteriori (MAP) detection was developed to address two-dimensional interference in holographic data storage systems. This innovation improves data accuracy and retrieval efficiency, demonstrating the utility of holography in high-density data storage applications [35].
## 3 Results
### Toxicity of BPA in Activated Sludge
#### 3.1.5 BPA Interferes with the Microbial Population Structure of Activated Sludge
_Acinetobacter_ and _Comamonas_ play key roles in the removal of ammonia nitrogen and phosphorus under aniline stress [54]. _Sphingobium_ is the dominant bacterial genus for the treatment of pharmaceutical wastewater in membrane bioreactors [64]. _Sphingopyxis_ can secrete EPSs, which play an important role in enriching phosphorus and stabilizing the granular structure of activated sludge [65]. In a study by [PERSON] et al., it was confirmed that _Methylotenera_ used nitrate and nitrogen as nitrogen sources to take up carbon through the ribulose monophosphate pathway [66]. _Methylobacillus_ is a species of methylotrophic aerobic bacteria that can utilize organic compounds such as formaldehyde, methanol, and methylamine as carbon sources, thereby contributing to the digestion of COD in sludge systems [67]. The results of this study showed that the bacterial phyla and genera in domesticated sludge were rich in diversity, and the sludge contained a large number of functional bacteria genera, including genera involved in the high-efficiency degradation of organic matter, flocculation, and nitrogen and phosphorus removal. However, under the stress of different concentrations of BPA, the abundances of these functional bacterial genera decreased, indicating that the related functions of activated sludge were inhibited, and that the organic matter degradation ability, and the ability to remove nitrogen and phosphorus, decreased. Although the addition of BPA altered the bacterial niches of activated sludge functional microorganisms, thereby leading to a decrease in the sludge water purification ability, the dominant bacteria under BPA stress still had functions including organic matter removal, EPS secretion, and nitrogen and phosphorus removal. This may have been the reason why the activated sludge reactor could still maintain operation. The nitrogen and phosphorus removal performance of the reactor activated sludge was closely related to the functional bacteria genera mentioned in the literature, and the microbial community under BPA stress changed significantly. This was also the main reason why the removal efficiency of nitrogen and phosphorus from activated sludge after exposure to BPA decreased and did not recover significantly.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 3 Problem Description  
### Market Information and Assumption  
The market structure studied is the day-ahead (DA) electricity market. As shown in Figure 2, the dynamic pricings of the grid, load, PV and WT generation are forecasted on an hourly basis. The power scheduling is proposed by the participants for each hour of the coming day before 12 p.m. The spot price is settled and commitments are made for the participants.  
Figure 1: Schematic diagram of the virtual power plant (VPP).  
The operation of the VPP in the DA market scenario is based on some assumptions. Based on the dynamic pricing of the grid, the VPP dispatches the power to the grid for profit maximization. The second assumption is that the DERs in the VPP are centrally controlled.  
In this paper, energy management for the VPP is developed by optimally scheduling the generation and minimizing the generation cost. Moreover, the multi-step offering method can be used by separately calculating the profit for each generation based on resource availability and generation cost. The risk of the profit variation is considered in the problem formulation by the incorporation of uncertainty modeling of PV and WT generation using the beta distribution and Weibull distribution methods. The power imbalance due to the uncertainties of renewable generation can be taken care of by purchasing the power from the grid [53].
# A Multithreaded Algorithm for the Computation of Sample Entropy
## 6 Discussion
The purpose of this comparison was: (a) to show the efficiency of bucket-assisted algorithm to efficiently compute the value of sample entropy and (b) to give the opportunity to researchers for future comparisons, by providing execution times and pseudo-code for this implementation. For randomly generated signals, the straightforward implementation required on average 9.153 s to complete the computation, while the bucket-assisted algorithm only 0.546 s, i.e., 16.76 times faster. For the set of 54 Holter recordings, the corresponding times were 517.58 s for the straightforward implementation and 68.56 s for the bucket-assisted algorithm, yielding a ratio of 7.55. Parallel implementations further reduced the execution times. For 12 threads, (6 plus 6 virtual) the average computation time for a random signal was reduced to 0.081 s, while for the 54 Holter recordings, it was reduced to 12.42 s.  
Three different parallelization policies were tested. Execution times are depicted in detail in Tables 1 and 2, while speedups are shown in Figure 2. The two data sets present different behaviors. In the first one, points are uniformly distributed, leading to uniform distribution of points in the buckets. In the second data set, a similar load balancing is more difficult to reach, since the number of points in the buckets may differ significantly. This in turn affected the parallelization rates, as it can be easily noticed in Figure 2, where speedups for the random time series are significantly higher than those for the Holter recordings. Comparing the three examined parallelization policies, one can easily notice that the static policy behaves better in the random time series. This is due to the uniform distribution of points in the buckets as well as the low management and synchronization overhead. For the Holter recordings, where the distribution of the points in the buckets is not uniform, the dynamic load balancing policy becomes better. The _in-bucket_ load balancing is always worse than the two other policies, but the difference between the _in-bucket_ and the other two policies is significantly smaller for the random time series. This is, again, due to the uniform distribution of points in the buckets. This policy theoretically gives the optimal distribution, since each thread is assigned almost exactly the same number of points. However, the additional management overhead and the reduction of the size of the loops lead to additional overhead. However, for large and relatively uniform time series, this policy might produce better parallelization rates than the other two. In conclusion, the dynamic load balancing policy seems to be the most efficient one in the general case, even though there are cases in which the two other policies may result in lower execution times.
# Mobility-Multihoming Duality
## 7 Discussion
### Incremental Deployment  
As ILNP is an end-to-end protocol, deployment will be via updates to hosts. As we have discussed in Section 2, this model for deployment is, arguably, the least disruptive and most scalable in today's environment of over-the-air updates. ILNP is designed to fall back to IPv6 operation when ILNP operation is not possible--specific procedures for this are defined [43; 49]. Additionally, the use and operation of ILNP for individual installations can be controlled via sysctl calls. This means that users, from individual users at home to system administrators for managed network installations, can control the installation and use of ILNP.
## 2 Materials and Methods
### Experimental Methods  
The modified ferrite treatment experiments were conducted at room temperature. PVA wastewater was initially prepared at various concentrations (3-7 g/L) by mixing commercial PVA (Mw 89,000-98,000, 99+% hydrolyzed, Sigma Aldrich, St. Louis, MO, USA) with cleaning solution at specific ratios. In a 15 mL reaction system, PVA wastewater addition ranged from 1.20 mL to 2.30 mL, with corresponding adjustments in cleaning solution volume (13.8-12.2 mL). Pre-prepared ferrite solution was then introduced to the reaction system under continuous stirring [20]. The pH was monitored throughout the reaction process, showing a decrease from 9.9 to 7.1 with increasing PVA concentration. After reaction completion, the mixture was settled for 10 h followed by filtration to separate the precipitates and filtrate. The collected precipitates were characterized by XRD analysis, while the filtrate was preserved for subsequent analysis. The morphology of PVA-Fe\({}_{3}\)O\({}_{4}\) was characterized using a transmission electron microscope (TEM, Thermo Fisher Scientific, Waltham, MA, USA, Talos F200X G2) operated at 200 kV. Samples for TEM were prepared by drop-casting 10 \(\upmu\)L of the suspension onto carbon-coated copper grids and allowing them to dry under ambient conditions for 2 h. XRD patterns were recorded on a Bruker D8 Discover Plus TXS diffractometer (Bruker, Billerica, MA, USA) using Cu K\(\upalpha\) radiation (\(\uplambda=1.5406\) A) in the 2\(\uptheta\) range of 20-80\({}^{\circ}\) with a step size of 0.02\({}^{\circ}\) and a scan rate of 1\({}^{\circ}\)/min. The Fourier-transform infrared (FTIR) spectroscopy analysis was performed using a Shimadzu IRTracer-100 FTIR spectrometer (Shimadzu Corporation, Kyoto, Japan) equipped with an HATR (horizontal attenuated total reflectance) accessory with a ZnSe crystal. Liquid samples were directly applied onto the ATR crystal surface without any pretreatment. For each measurement, approximately 50-100 \(\upmu\)L of the solution was placed on the crystal and analyzed. The spectra were recorded in the mid-infrared range of 4000-700 cm\({}^{-1}\) with a spectral resolution of 4 cm\({}^{-1}\), averaging 64 scans per sample to improve the signal-to-noise ratio. A background spectrum of deionized water was collected before each sample measurement to eliminate solvent interference. The ATR crystal was thoroughly cleaned with ethanol and deionized water between measurements and dried with nitrogen gas to prevent cross-contamination. All measurements were performed at room temperature. Magnetic properties were measured using a Quantum Design MPMS-XL SQUID magnetometer (San Diego, CA, USA) at room temperature. The hysteresis loops were recorded with applied fields ranging from \(-6000\) to 6000 Oe. Samples were prepared by placing approximately 50 mg of dried powder in a gelatin capsule and fixing it in a plastic straw. Chemical oxygen demand (COD) was measured using a HACH DR3900 spectrophotometer (Loveland, CO, USA) with digestion vials (HACH method 8000) at 150 \({}^{\circ}\)C for 2 h, followed by colorimetric measurement at 620 nm wavelength.
## 3 Ultrasonic and RIS-Assisted mmWave Joint Beamforming Problem for Massive MIMO V2X  
In this section, beamforming design is performed for ultrasonic and RIS-assisted mmWave massive MIMO V2X systems. Considering the maximum transmit power of the BS and the unit-mode constraint of the RIS passive reflection elements, a joint optimization of the phase shift matrix of the RIS \(\mathbf{\Phi}\), the beamforming matrix of the BS \(\mathbf{F}_{all}\) and the combined matrix of the vehicle \(\mathbf{W}_{all}\) are performed to maximize the spectral efficiency.  
The joint beamforming problem modeled in this paper is represented as  
\[\begin{array}{ll}\max_{\mathbf{w}_{all},\mathbf{F}_{all},\mathbf{\Phi}}&\log_{2}\Big{(} \text{det}\Big{|}I_{N_{t}}+\frac{P_{s}}{\sigma^{2}N_{s}}W_{all}^{H}H_{all}F_{all }F_{all}^{H}H_{all}^{H}W_{all}\Big{|}\Big{)}\\ &\text{s. t. }\mathbf{H}_{all}=\mathbf{H}_{RL}\mathbf{\Phi}\mathbf{H}_{\text{BR}}\\ &\text{tr}\big{(}\mathbf{F}_{all}\mathbf{F}_{all}^{H}\big{)}\leqslant P_{\max}\\ &\mathbf{\Phi}=\text{diag}(\mathbf{e})\\ &|\mathbf{e}(i)|=1,\;i=1,2,\cdots,N_{RIS}\end{array} \tag{37}\]  
where \(P_{\max}\) is the maximum transmit power of BS and \(\mathbf{e}(i)\) is the \(i\)-th element in vector \(\mathbf{e}\).  
Since \(\mathbf{W}_{all}\), \(\mathbf{F}_{all}\) and \(\mathbf{\Phi}\) are mutually coupled in the objective function and the joint beamforming problem of the above equation is nonconvex, a direct calculation will be difficult to solve. In order to simplify the above problem, we can solve the problem by alternating optimization, i.e., fixing several other matrices when solving one matrix, followed by alternating updates of the matrices to be solved in several iterations. Therefore, we will design the phase shift matrix of RIS \(\mathbf{\Phi}\), the beamforming matrix of BS \(\mathbf{F}_{all}\), and the combination matrix of the vehicle \(\mathbf{W}_{all}\) separately. For the RIS-assisted MIMO V2X system, a non-convex optimization function is constructed with the objective of maximum spectral efficiency, and there is no optimal algorithm. Therefore, this paper then proposes a beamforming design based on alternating iterations using the characteristic that the qualifying conditions of the optimization function are not coupled with each other. Specifically, the scheme is based on alternating iterations to achieve suboptimal design of the RIS phase shift matrix, BS beamforming matrix, and vehicle combination matrix in order to solve the optimization problem. In the design of the RIS phase shift matrix, this paper combines the subspace self-organizing iterative algorithm to transform the nonconvex optimization problem into a series of easily solvable subproblems. Numerical simulation results show that the scheme in this paper achieves a better compromise in terms of spectral efficiency improvement compared with existing schemes.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Model**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**YOLOv3**} & \multicolumn{2}{c}{**YOLOv3 with ONNX**} & \multicolumn{2}{c}{**YOLOv3-Tiny**} & \multicolumn{2}{c}{
\begin{tabular}{c} **YOLOv3-Tiny with** \\ **ONNX** \\ \end{tabular} } \\ \hline
**Data** & **Resolution** & **mAP** & **FPS** & **mAP** & **FPS** & **mAP** & **FPS** & **mAP** & **FPS** \\ \hline Video & 1920 \(\times\) 1080 & 93.5\% & 3 & 96.3\% & 6 & 89.5\% & 5 & 90.7\% & 8 \\ \hline Live video & 640 \(\times\) 480 & 90.4\% & 4 & 94\% & 8 & 86.1\% & 7 & 88.3\% & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model speed and accuracy with and without ONNX representation. Figure 7: In-lab tools used to perform real-time strawberry plant detection. It is also noticeable that the mAP is affected by the type of data used, possibly because of variations in acquisition resolution. As illustrated in Table 6, testing the model on higher-resolution videos yields superior results across all scenarios. This improvement could also be attributed to the stability of the acquisition process; for instance, the tested video exhibits less vibration compared with live video feeds. Furthermore, the quality of the training data may contribute to these outcomes. However, greater flexibility and diversity in factors such as angle, lighting conditions, focal length, sensitivity, and exposure duration during image capture result in a larger volume of collected data. Moreover, employing a variety of data augmentation techniques enhances the effectiveness of model training, leading to higher detection accuracy. In summary, the results of this study indicate that YOLOv3 with ONNX representation notably enhances detection accuracy. Additionally, it significantly boosts the image processing speed, which makes it suitable for implementation in electronic boards, such as the one used in this study.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### Wavelet Packet Analysis Denoising  
The idea of wavelet packet analysis and wavelet soft threshold denoising [27] is basically the same, but the analysis method of wavelet packet is more complex and flexible. When analyzing, it decomposes the low-frequency part and high-frequency part of the previous level at the same time, making the local analysis capability more accurate. The wavelet packet decomposition also needs to choose the wavelet packet base and level of decomposition. For the selection of the wavelet packet base, it is necessary to choose the wavelet packet base with better symmetry and regularity. The wavelet with better symmetry does not produce phase distortion, and it is easy to obtain a smooth reconstructed signal with the wavelet with better regularity. In the wavelet base family, the Symlets family is similar to the Daubechies family, which also has orthogonality, biorthogonality, compact support and approximate symmetry, and can perform discrete wavelet transform. Therefore, in the part of wavelet packet analysis and denoising, the wavelet packet base of this family is used for denoising.
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.3 Flavonoids  
Flavonoids, which are also accountable for the color and taste of tea soup, particularly the astringency and bitterness, may have health advantages [21]. In addition, flavonoids also intensify the bitterness of other substances, such as coffee [23]. We screened 73 flavonoids that were significantly altered after stuck fermentation, of which 53 showed a decreasing trend in FHYH (Figure 3C), including the strongly astingent catechin gallate (CAS:130405-40-2), epicatechin gallate (CAS:1257-08-5) and epigallocatechin-3-gallate (CAS:989-51-5). Consistent with our findings, [PERSON] et al. reported that 113 flavonoids were down-regulated and only 20 flavonoids were up-regulated after microbial fermentation of tea [23]. Furthermore, flavonoid glycosides including thataromadendrin-7-O-glucose (CAS:28189-90-4) and phloem-2\({}^{\prime}\)-O-glucose (phlorizin) (CAS:60-81-1) decreased after microbial fermentation, which was accompanied by a concomitant increase in their flavones, such as aromadendrin (dihydrokaempfer) (CAS:480-20-6) and phloemtis (CAS:60-82-2). We hypothesize that the thermal and enzymatic hydrolysis of glycosides likely reduced the bitter taste of flavonoids [8], which is consistent with prior research [23]. Interestingly, while isosalipurposide (CAS:4547-85-7), quercetin-3-O-sophoroside-7-O-rhammoside and naringenin-4\({}^{\prime}\)-O-glucosidasehow also decreased during fermentation, there was no increase in the corresponding flavones, most likely since the latter may immediately combine with other compounds. Furthermore, all myricetin glycosidic compounds, including myricetin-3-O-galactoside-3\({}^{\prime}\)-O-hammoside and myricetin-3-O-(6\({}^{\prime\prime}\)-malony) glucoside, decreased after stuck fermentation. In contrast, all myricetin-related other compounds such as myricetin-3-O-sulfonate and dihydromyricetin (ampelopsin) (CAS:480-20-6) showed an increase. After microbial stuck fermentation, the color of the black tea soup may become more intense due to the conversion of flavonoids to other substances with a dark hue. [PERSON] et al. also showed that microbial fermentation deepened the color of the tea soup, which was attributed to the increased levels of theabrowins, the oxidized polymers of catechins [8]. Together, these results indicate that the reduction of flavonoids caused by microbial stuck fermentation alleviates the bitterness and astringency of summer black tea, and intensifies the color of tea soup.
# Long-Term Effects of External Sulfate Attack on Low-Carbon Cementitious Materials at Early Age
## 3 Results
### Mass Changes and Visual Inspection
Furthermore, after 95 weeks of exposure, mix P1 experienced complete degradation, while the rest of the mixes only displayed some cracking on the edges of the specimens. Additionally, even after 120 weeks, the blended mixes did not exhibit more than insignificant cracking, as illustrated with the mix Q1. Figure 3: Visual inspection of P1 (reference mix) and Q1 after 4, 60, 95, and 120 weeks of exposure (the red circles highlight areas where cracks are observed in mix Q1). In general, the expansion rates and the steps of increase aligned with the mass gain rates and visual observations. During the initial weeks, as the specimens underwent the curing process in a sulfate-enriched solution (initiated 24 h after casting), there was ongoing hydration, water absorption, and the formation of ettrinigite and gypsum due to sulfate (contributing to mass gain, internal stresses leading to expansion, and observable visual changes) [42]. Following this initial period (4 weeks for the reference mix P1 and approximately 12 weeks for the blended pastes), subsequent changes are primarily attributed to the reaction between sulfate and the hydrated products, leading the consequences of the ESA [46; 47]. Based on those results, including expansion, mass variation, and visual observations, analyses such as TGA, FTIR, and water porosity were conducted at three key time points: 24 h after casting (Ti0), 8 weeks into the initiation of ESA (Ti1), and at 80 weeks (Ti2).
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Datasets  
To evaluate the effectiveness of the proposed strategy, we utilized two datasets: the CWRU Rolling Bearing Dataset and the Gearbox Dataset.  
* CWRU Rolling Bearing Dataset [36]. This dataset contains vibration acceleration data from the motor drive end, encompassing four distinct bearing health statuses: normal, inner race, ball, and outer race. For each issue location, we considered three separate fault severity levels. The fault diameters in different domains were measured at 7 mm, 14 mm, and 21 mm, respectively, while the corresponding loading speeds ranged from 1797 rpm to 1730 rpm, covering load conditions of 0 HP, 1 HP, 2 HP, and 3 HP.
* Gearbox Dataset [37].This dataset consists of gear acceleration data gathered from the gearbox under various conditions. It encompasses three types of gear faults: normal condition, tooth chipping, and tooth missing. Each gear fault category was meticulously examined under different loads and speeds, specifically at frequencies of 30 Hz, 35 Hz, and 40 Hz, and further classified as either low or high. The samples' length in this dataset is 3300, and the sampling frequency is 66.67 KHz, with each instance comprising 500 samples.