esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 5 Electroflotation-Facilitated Dewatering for Microalgal Biomass Generation
### Factors Affecting the Performance of Electroflotation-Based Microalgal Dewatering Process
#### 5.2.2 Electrode Characteristics
A combination of plate- and mesh-type electrodes, placed horizontally at just 20 mm from the bottom of the electroflotation cell with interelectrode distance of 3 mm has also been reported [68].
# Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
## 1 Introduction
In this study, a smart sensor tag system was established that measures the status of pork using low-cost red, green, and blue (RGB) sensors, along with the development of a machine learning model that estimates the pork's state, and the system was completed. Where batteries are used, there is a problem with their disposal and exchange. Particularly, if replacement is not carried out when appropriate, mass disposal problems can arise. These problems can be addressed through the application of RF energy harvesting technology. Furthermore, using electromagnetic energy is a clean energy source, so no additional waste is produced [9; 12; 13; 14; 15; 16]. The primary contributions of this study can be summarized as follows:  
* The design of a compact sensor tag as a key component for monitoring the freshness of pork;
* The design of a system that can harvest RF energy, simplifying the process of designing and configuring the system;
* The design of a printed collinear antenna operating in the UHF band of 915 MHz, with the system's efficiency enhanced by using an antenna with high RF energy harvesting efficiency;
* The design of a machine learning algorithm for detecting meat quality, with the 1D convolutional neural network (CNN) model outperforming other machine learning models;
* The development of an early warning system that prevents meat poisoning due to improper storage and exposure to warm temperatures during summer. Section 2 presents an overview of smart sensor tags based on RF energy harvesting, and outlines the machine learning models used in the freshness monitoring system based on the presented sensor tag. In Section 3, the content of RF harvesting experiments using the proposed system is described, along with the antenna design approach and sensor module for RF energy harvesting. Section 4 explains the RF energy harvesting experiments. Section 5 presents the approach for food monitoring using machine learning and data preprocessing processes. Section 6 introduces dimensionality reduction for data visualization, and in Section 7, the machine learning framework is presented. Finally, Section 8 presents the conclusions of this study.
## 2 Materials and Methods
### Polar Fraction Characterization
#### 2.5.3 Antioxidant Activity Determination  
The ABTS assay is based on the scavenging of the 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulphonic acid) radical cation, ABTS\({}^{\star\bullet}\) converting it into a colorless product. In this methodology, ABTS\({}^{\star\bullet}\) cation was previously prepared to react 10 mL of ABTS 7 mM solution with potassium persulfate 2.45 mM, following a methodology described before with minor modifications [40]. This mixture was then incubated in the dark for 16 h, at room temperature. Before usage, the ABTS\({}^{\star\bullet}\) solution was diluted with methanol to obtain an absorbance of 0.700 \(\pm\) 0.02 at 734 nm. Briefly, 30 \(\upmu\)L of extracts (with concentrations ranging between 150 and 348 \(\upmu\)g mL\({}^{-1}\)) or Trolox, used as standard (50-400 \(\upmu\)g mL\({}^{-1}\)), were mixed with 3 mL of ABTS\({}^{\star\bullet}\) solution. The absorbance was measured after 6 min at 734 nm (Shimadzu UV-1800 spectrophotometer, Kyoto, Japan), and the results were expressed as mmol TE g\({}^{-1}\) dw. Triplicate measurements were performed.  
Antioxidant activity of elderberry stalks' polar fraction was assessed using the stable free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH\({}^{\bullet}\)) to measure their hydrogen-donating or radical scavenging ability, following an adaption of the methodology described previously [39] for 96-well plates. Briefly, 9.6 \(\upmu\)L of extract (with concentrations ranging between 120 and 190 \(\upmu\)g mL\({}^{-1}\)) was added to a 96-well plate containing 29 \(\upmu\)L of DPPH radical solution (0.8 mM in methanol) and 192 \(\upmu\)L of methanol. The mixture was shaken vigorously and left to stand for 20 min in the dark at room temperature. The absorbance was then measured at 517 nm in the microplate reader (Biotek Eon microplate spectrophotometer). Trolox calibration curve (50-800 \(\upmu\)g mL\({}^{-1}\)) was prepared in methanol. The radical scavenging results were expressed as mmol Trolox equivalents (TE) per g of dw. All determinations were performed in triplicate.
## 4 Experimental Validation
### Case 2: Laboratory Bearing Dataset of Shenzhen Technology University (SZTU)
#### 4.2.2 Experimental Setup of Case 2  
In an effort to demonstrate the generalization ability of the proposed ISCV-ViT method, repeated comparative experiments are conducted consistently with those on the public dataset PU in the laboratory test rig. According to the sampling frequency and speed setting value, 20,480 data points are taken as a sample. Other experimental settings are consistent with case 1. Two hundred samples are extracted from each category, and finally, the training set and validation set are divided according to a ratio of 4:1. Each group experiment is carried out for 200 epochs and repeated three times.
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 2 Methodology
### B.g. [PERSON] DCT Algorithm Optimization
#### 2.2.1 Addition Using Error-Tolerant Adders  
The error-tolerant adders used for image processing applications published in our work in [23] have been modified to perform signed operations for the arithmetic operations involved in the DCT/IDCT architecture. The additions and subtractions involved in the butterfly diagrams of the [PERSON] algorithm have been achieved using the error-tolerant adders. The signed Modified CSLA Selector-Based Error-Tolerant Adder (SBETA) proposed has been utilized in designing the addition and subtraction operations, as shown in Figure 3.  
The sum and carry equation for the SBETA is as given in Equations (5) and (6).  
\[\text{Sum }=\text{ A? B or C}_{in}:\text{ B and C}_{in} \tag{5}\]  
\[\text{Carry }=\sim C_{out} \tag{6}\]  
The sign and the arithmetic operations to be performed are shown in Algorithm 1 below. The utilization of the proposed error-tolerant adders reduced the delay and power involved in the overall DCT algorithm implementation.  
```
if(Sign(A) \(\&\) Sign(B) == 1)  Sign(Output = I  Output = A + B  else if((Sign(A)==1 \(\&\) Sign(B)) == 0) \(\&\) (\(A>=B\)))  Sign(Output = I  Output = A - B else if((Sign(A)==1 \(\&\) Sign(B)) == 0) \(\&\) (\(A<B\)))  Sign(Output = 0  Output = B  else if((Sign(A)==0 \(\&\) Sign(B)) == 1) \(\&\) (\(B>A\)))  Sign(Output = 1  Output = B - A else if((Sign(A)==0 \(\&\) Sign(B)) == 1) \(\&\) (\(B<=A\))))  Sign(Output = 0  Output = A - B else  Sign(Output = 0  Output = A + B  End
```  
**Algorithm 1**Determination of sign and operations of the butterfly diagram  
Figure 3: Signed Error-Tolerant Adder for DCT/IDCT arithmetic operations.
## 6 Proposal of a Block Tearing Failure Mechanism
### Model for Orthogonal Reinforcement  
In the case of orthogonal reinforcement, see Figure 16a, the only forces that are supposed to act on the jacketed block are:  
* F Vertical force applied on the half joint;
* T Hanging force in the stirrups;
* C Force in the compressed chord;
* V Shear friction resistance.  
Horizontal equilibrium needs \(V=C\) and vertical one needs \(F=T\).  
Figure 16: Block tearing failure mechanism for orthogonal (**a**) and inclined (**b**) reinforcement.  
Vertical stirrups should at least be able to carry the vertical force F, but if their section is bigger than the one needed to carry F, they can provide additional resistance in the shear friction model according to clause 6.2.5 of EN1992-1-1 [30] presented below:  
\[V=(c\cdot f_{ct}+\mu\cdot\rho\cdot\sigma_{av})A_{cj} \tag{2}\]  
where the cross-section of the joint \(A_{cj}\) is the length of the steel plate multiplied by the web thickness \(A_{cj}=500\times 200\) mm.  
Therefore Equation (2) can be rewritten as:  
\[V=c\cdot f_{ct}\cdot A_{cj}+\mu\cdot A_{sm}(f_{y}-F/A_{sm}) \tag{3}\]  
where:  
* \(c\) and \(\mu\) are friction factors that depend on the roughness of the interface. In the considered case, being the surface a free one left without further treatment after vibration, the values of \(c=0.35\) and \(\mu=0.60\) are considered;
* \(f_{ct}\) is the mean tensile strength of concrete, equal to 2.33 MPa;
* \(A_{sm}\) is the cross-section area of the stirrups crossing the joint (628 mm\({}^{2}\) for the sound specimen and 427 mm\({}^{2}\) for the damaged one);
* \(f_{y}\) is the mean yielding strength of rebars, equal to 530 MPa;  
Calling \(e\) the distance between the forces F and T and \(z\) the distance between the forces V and C, the equilibrium to rotation about a generic point of the torn block reads:  
\[V\cdot z=F\cdot e \tag{4}\]  
The maximum resisting force F can be found substituting the expression of \(V\) given by (3) into (4), obtaining with some simple passages:  
\[F=\frac{c\cdot f_{ct}\cdot A_{cj}\cdot z+\mu\cdot A_{sm}\cdot f_{y}\cdot z}{e+ \mu\cdot z} \tag{5}\]  
The results obtained following this verification for the sound side and the damaged side are presented in Table 3. The steel jacketing is designed in order to be able to carry 100% of the load outside of the half joint to a part of the beam able to resist (where corrosion damage is absent or small). The block tearing resistances presented in Table 3 are different for the 4 cases (orthogonal sound and damaged, inclined sound and damaged) because the amount of rebars crossing the interface between the jacketed area and non-jacketed one is different in the four cases. The plates are the same, but the reinforcing bars inside the concrete web at the end of the plates are not.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 11 Routing in Multi-hop Single Immobile Sink for Different Objectives under Distance Uncertainties  
Optimization models were considered in [6] for WSNs subject to distance uncertainty for three different objectives:  
* minimizing the energy consumed,
* maximizing the data extracted,
* maximizing the network lifetime.  
Robust optimization was used to take into account the uncertainty present. In a robust optimization model the uncertainty is represented by considering that the uncertain parameters belong to a bounded, convex uncertainty set. A robust solution is the one with best worst case objective over this set.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion
### Results of Ablation Experiment  
In the method proposed in this paper, we use a SAE and Unet combination for the reconstruction process. To prove the effectiveness and necessity of the algorithm above, we conducted an ablation experiment using SAE, Unet and EMNP model under the same parameters at MR = 0.01, 0.05, 0.08 and 0.1. The result is shown in Figure 10. It is evident that the combination of SAE and Unet significantly enhances the reconstruction quality compared to a single network.
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 8 Discussion  
The proposed MAAF integrates message contributions linearly into action logits. MAAF outperforms baseline MARL methods in experiments, offering two key advantages: (1) At the early training stage, learning observation-dependent actions provides stability, even if performance is initially constrained to a single RL setting. (2) The communication branch facilitates further training, helping to balance the two action branches.  
The performance gap observed for combinatorial message types highlights the need for adaptation. A common belief about neural networks is that they can filter out irrelevant features, making combinatorial search seemingly unnecessary. There are several reasons why message types show a considerable performance gap. One reason is aggregation methods, RNN, or self-attention (MHA), which includes more complex operations processing messages from \(n\)-agents. This complexity makes it challenging to train effective filtering in MARL. Therefore, choosing appropriate message types as an inductive bias may be more effective than using all types as input features. This work demonstrates the benefits of separating message contributions but does not explore the impact of advanced communication protocols like TarMAC or CommFormer. Since the proposed message adaptation is compatible with different communication protocols, we anticipate that these advanced protocols could yield similar performance improvements.  
Separating action branches can lead to slower or less stable training when adaptation is unstable, as observed in the SMAC 8M environment. To address this, future research should focus on developing appropriate optimization strategies. Additionally, regularizing the discrepancy between the base policy and the adapted policy could help stabilize the action distribution after communication, which may be beneficial for addressing safety concerns. Another notable limitation is the assumption that message features are inherently noisy and unstable, which states that message features are noisy and unstable. This may not be valid in advanced communication architectures, such as graph-based CommFormer and sparse attention-based IMGS-MAC. Further research is needed to explore the effects of MAAF on these advanced communication methods.  
Figure 12: Counterfactual interpretation of actions following the trajectory of a base policy. Each panel shows the trajectories of an episode in the Tag environment, featuring two predators and one prey. The trajectories are represented by stars, with darker colors indicating later time steps. Green arrows represent the prey’s actions, while red arrows show the actions of the predators. The gray arrows denote counterfactual actions generated through communication between predators. This setup allows us to interpret actions without communication and compare them to cooperative actions facilitated by communication.
# Optimization of Glass Edge Sealing Process Using Microwaves for Fabrication of Vacuum Glazing
## 2 Experimental Setup  
### Equipment Setup  
The microwave chamber for glass edge sealing was designed and constructed based on the electromagnetic wave distribution analysis using the HFSS program by ANSYS [6]. This experiment used the waveguide model WR-340 with a frequency range of 2.20-3.30 GHz, a voltage standing wave ratio of 1.25:1, and physical dimensions of 86.36 \(\times\) 43.18 mm. The magnetron for microwave emission comprised six power sources with an output of 1 kW. The dimensions of the heating chamber were 400 (w) \(\times\) 400 (d) \(\times\) 192 (h) mm. Figure 1 shows the layout of the microwave chamber.  
Figure 1: Schematic and real picture of microwave heating chamber.  
A thin sheet of glass with a thickness of 0.5 mm was placed in between the glass and glass to keep the distance between the two soda lime glasses, each with a thickness of 5 mm. Generally, microwaves permeate through glass instead of reflecting from or heating the glass. However, manufacturers add impurities or additives so that the glass interacts with the microwaves. Therefore, when glass is heated using microwaves, the thermal energy is often concentrated resulting in thermal shocks and damage. To prevent this and enable efficient heat distribution, graphite plates were placed above and beneath the glass.  
Figure 2a,b shows the schematics of the chamber for glass sealing and an image of the fabricated chamber, respectively.
## 4 Conclusions  
The measurement speed and accuracy are important characteristics of the PDSH circuit. Therefore, this paper investigates the measurement speed and accuracy of PDSH circuits using OTA-based PDSH circuits. This paper establishes a model for the measurement process of PDSH circuits while also examining the factors that impact measurement speed. Meanwhile, in order to correct errors caused by the voltage step, this paper also proposes an equivalent circuit model for the diode and derives the factors that affect the magnitude of the voltage step error during the hold phase in PDSH circuit.  
Figure 15: The error between the correction value of the final output voltage \(V_{f}\) and the peak voltage \(V_{m}\) for different types of diodes.  
The simplified model proposed in this study for the measurement speed of the PDSH circuit facilitates accurate predictions of the rise time of the PDSH circuit's output voltage. The application of the simplified model to forecast the rising time of the output voltage yields a maximum deviation of 1.98 ns and 2.03 ns, respectively, when compared to the results obtained from simulations and experiments. Moreover, after the calibration of the voltage step error, the maximum measurement error of PDSH circuit is 19.8 mV, significantly improving the measurement accuracy of the PDSH circuit. Thus, the modeling and prediction of the PDSH circuit in this study enable the precise measurement of peak values for nanosecond-width pulses to become achievable. However, there are also limitations regarding the application of the model [26; 27; 28]. Specifically, the voltage step model proposed in this study can only be used to correct the output of the PDSH circuit when the input pulse signal exceeds the threshold voltage. Due to the voltage step, when the amplitude of the input pulse is below the threshold voltage, the output of the PDSH circuit is 0, making using the PDSH circuit a significant challenge to measure and calibrate the extremely weak pulse voltage amplitudes. Therefore, accurately measuring tiny pulse voltage amplitudes is a crucial research focus for the future.  
Methodology, J.S.; Validation, Y.D.; Formal analysis, J.S.; Data curation, Y.D.; Writing--original draft, J.S.; Writing--review & editing, J.S.; Supervision, H.Z., K.L. and J.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fudan University and Changchun Institute of Optics Joint Foundation, grant number FC2017-002.  
Not applicable.  
The data presented in this study are available in [[https://github.com/[PERSON]/PDSH-circuit](https://github.com/Mrjshu/PDSH-circuit)]([https://github.com/[PERSON]/PDSH-circuit](https://github.com/Mrjshu/PDSH-circuit)).  
The authors declare no conflict of interest.
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 3 The Simulation Results and Discussion
Corresponding to these full divergence angles, the Transmitted beam waist diameter (\(2w_{0}\)) for each setting is calculated using Equation (16). In the analysis of how these varying transmitted full divergence angles affect the communication system, both the received aperture and the received FOV are held constant. They are fixed at 200 mm for the received aperture and \(\pi/2\) rad for the received FOV. This methodical approach allows for a focused investigation into the impact of the transmitted full divergence angle on UWOC system performance, ensuring that other variables do not confound the results. The specific settings for the received FOV and aperture are also detailed in Table 3. The received aperture sizes are set at 25 mm, 50 mm, 100 mm, 200 mm, and 400 mm. The values for the received FOV are varied and include \(\pi/90\) rad, \(\pi/30\) rad, \(\pi/22.5\) rad, \(\pi/15\) rad, \(\pi/7.5\) rad, \(\pi/2\) rad, \(\pi/1.5\) rad, and \(\pi\) rad. In the analysis that focuses on the impact of these variable received aperture and FOV on the communication system, the transmitted full divergence angle is consistently fixed at 0.5 mrad. This approach enables a targeted examination of how changes in the received aperture and FOV affect the performance of the UWOC system, isolating these variables to understand their specific contributions.
## 2 Design Procedure
### Current Distribution Analysis  
The filtering mechanism of the proposed dual-band BPF can be further investigated using the current density distributions at the passband frequencies (4.42 GHz and 7.2 GHz) and at the stopband frequencies (1.75 GHz and 6.3 GHz) as demonstrated in Figure 7. It can be seen that both the open stubs and the resonators along with the bended coupling system experience a strong current flow, resulting in a very small insertion loss in the operating frequencies of the filter (left-side snapshots in Figure 7). However, the bended coupling system does not allow flowing strong currents at out-of-band frequencies (right-side snapshots in Figure 7), leading to large insertion loss outside of the operating frequencies of the filter. As can be seen, the flag-shaped resonators at 4.42 GHz dominantly contribute to the first passband, while, in the case of the second passband, both stepped-impedance and flag-shaped resonators contribute to create the passband.  
Figure 6: Completed dual-band bandpass filter (BPF): (**a**) Layout, A\({}_{16}\) = 2.6, A\({}_{17}\) = 0.8, A\({}_{18}\) = 1.4, A\({}_{19}\) = 0.8, A\({}_{20}\) = 0.3, A\({}_{21}\) = 1.3 (all in mm), (**b**) EM simulation, (**c**) LC model of stepped-impedance resonator.
## 3 PPDT Classification Model
### Computation of Path Cost by Matrix \(\times\) Vector Operation  
In our method, we encrypt the path costs described in Section 2.1 and send them to the server to keep decision tree model secrets and allow the server to calculate the path costs. Specifically, we transform the path cost \(\mathsf{pc}_{k}=p_{k\oplus}+p_{k\downarrow}b_{1}+\cdots+p_{k,m}b_{m}\) for a leaf  
Figure 2: Our computation model.  
node \(L_{k}\). Therefore, we introduce two vectors, \(P_{k}\) and \(B\), that can be used to calculate the path cost:  
\[\mathsf{pc}_{k}=\left\langle[p_{k,0},p_{k,1},\cdots,p_{k,m}],[1,b_{1},\cdots,b_{ m}]\right\rangle=\langle P_{k},B\rangle. \tag{19}\]  
Here, \(B\) is a comparison result vector while \(P_{k}\) is a path vector. We define path matrix \(\mathbf{P}\) of decision tree \(\mathcal{T}\) as follows:  
\[\mathbf{P}=\begin{bmatrix}P_{1}\\ \vdots\\ P_{m+1}\end{bmatrix}. \tag{20}\]  
Therefore, it is possible to replace the calculation of the path cost of the decision tree by \(\mathbf{P}\times B\). To obtain the correct multiplication result for a \((k,\ell)\) matrix and a vector of length \(\ell\) using Equation (18), the constraint \(k\ell\leq n\) must be satisfied. Path matrix \(\mathbf{P}\) is an \((m+1,m+1)\) matrix; thus \((m+1)^{2}\leq n\) must be satisfied. Therefore, we divide the unconstrained path matrix \(\mathbf{P}\) into several rows and compute each of them. Specifically, \(\mathbf{P}\) is an \((m+1,m+1)\) matrix; thus, \(m^{\prime}=\lfloor n/(m+1)\rfloor\) rows can be computed. The path matrix is divided into \(S=\lceil(m+1)/m^{\prime}\rceil\) matrices. When \(S>(m+1)/m^{\prime}\), we add vector \(I=[1,0,\ldots,0]\) for several times so that each partitioned matrix has \(m^{\prime}\) rows, which hides the size of the decision tree. With the above operations, we obtain \(S\)\((m^{\prime},m+1)\) path matrices as follows:  
\[\mathbf{P}_{1}=\begin{bmatrix}P_{1}\\ \vdots\\ P_{m^{\prime}}\end{bmatrix},\mathbf{P}_{2}=\begin{bmatrix}P_{m^{\prime}+1}\\ \vdots\\ P_{2m^{\prime}}\end{bmatrix},\ldots,\mathbf{P}_{S}=\begin{bmatrix}P_{(S-1)m^{ \prime}+1}\\ \vdots\\ P_{m+1}\\ I\\ \vdots\\ I\end{bmatrix}. \tag{21}\]  
Therefore, the secure matrix \(\times\) vector operation in Section 2.3 allows us to securely compute the path cost with the encrypted path matrix.