esa-sceva/satcom-chunk-collection huggingface dataset:
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## Materials and Methods
### Toxicity Assay of Urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs  
The toxicity of magnetic NPs was evaluated by Neutral Red Cell Proliferation and Cytotoxicityology Assay Kit (C0013, Beyotime, Shanghai, China). Before toxicity assay, 5000 human endothelial cells (C6246, Beyotime, Shanghai, China) were seeded into each well of 96-well plate with 200 \(\upmu\)L of cell culture solution for 2 h. Then, the urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs solutions with several different concentrations were respectively added into each well of the plate. After 72 h of cultivation, 200 \(\upmu\)L of cell culture solution and 20 \(\upmu\)L of neutral red dye were sequentially injected into the wells. Next, the plate was placed in a shaking incubator (HZQ-QB, Suzhou Weier Experiment Equipments Co., LTD., Suzhou, China) at room temperature for 2 h. The absorbance of each well was read using the microplate reader FlexStation3 at 690 nm. The culture solution without magnetic NPs was used as the control group. The determination of cell viability under the action of urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs can be measured by the absorbance ratio between the test solutions and the control solution. Each experiment was repeated five times. Figure 5 shows the measurement result indicating that, even if the concentration of the urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NP reaches 1.2 mg/mL, the cells still maintain more than 93% viability. This indicates that the prepared drug-loaded magnetic NPs have good biocompatibility, low cytotoxicity, and have good in vivo application prospects. In addition, concentration-dependent cell death was observed in the figure. This may be attributed to the mechanism that the oxidative stress related to the concentration of the iron oxide nanoparticles induces the cell injury and death [31]. However, in consideration of the shortage of the preliminary cell samples, more cell samples and technical evaluation means are needed to eliminate the possibility of misleading the event results.  
Figure 4: **(a)** Relationship between absorbance measured by FlexStation 3 and urokinase concentration in solution; **(b)** Relationship between the amount of urokinase bound to 1 mg PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs and the amount of urokinase added to the solution.
# Enhancing Recognition Memory in Virtual Memory Palaces Using Worlds-in-Miniature
## 6 Discussion
### Hypothesis 1: WIM Enhances Short-Term Memory  
The positive trend in short-term memory results (76.1% with WIM vs. 69.6% without WIM) and the reduced cognitive load observed in the NASA-TLX evaluation sup port hypothesis H1. Although the statistical significance threshold was not reached (\(p=0.0998\)), the proximity to this threshold suggests that WIM effectively aids memory recall by providing immediate spatial context. The reduced mental demand and effort reported in the subjective evaluations reinforce this finding. Future studies with larger sample sizes could help validate and strengthen this evidence.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Signal Filtering  
For smoothing the collected data, the Savitzky-Golay filer [21] is used. Savitzky-Golay is a moving average filter, which applies local regression to a subset of our entire dataset. More specifically, it smooths data by replacing each data point with the average of the neighboring data points within a defined span. This approach is equivalent to  
Figure 1: The overall architecture of our system [15].  
\[y_{s}(i)=\frac{1}{2N+1}*\left(y(i+N)+y(i+N-1)+...+y(i-N)\right)\]  
where \(y_{s}(i)\) is the smoothed value for the \(i\)th data point, \(N\) is the number of neighboring data points on either side of \(y_{s}(i)\), and \(2N+1\) is the span.
## 4 Conclusions  
In this study the development of ceramic microchannels using an Nd:YAG laser was presented. In contrast to the previous research based on expensive low pulse width systems, this study assessed the performance of the Nd:YAG laser in the microsecond range to develop good quality microchannels in structural ceramics. It is evident that both the laser fluence and pulse overlap are significant parameters in controlling the quality and dimensional accuracy of microchannels. It was found that the depth of material removed per laser scan is an important indicator of the overall quality of the microchannels. Afterwards, a methodology was presented for obtaining good quality microchannels with targeted depth. The following conclusions can be drawn from the study:  
1. The size and quality of microchannels are found to be strongly dependent on laser peak fluence and pulse overlap.
2. Irrespective of the peak fluence, high pulse overlaps give rise to high bottom width error (BWE) i.e., formation of V-shaped channels.
3. Moderate pulse overlaps are recommended over low pulse overlap as they can fabricate good quality microchannels.
4. Based on the analysis, a laser peak fluence of around 125-130 J/cm\({}^{2}\) corresponding to 3-5 \(\upmu\)m depth per scan is recommended for better accuracy.
5. Low depth per scan values with a greater number of laser scans are recommended to achieve the desired depth.  
Figure 11: Effect of laser scans on (**a**) TWE (**b**) BWE (**c**) MRR.  
Figure 12: SEM images of the microchannels (**a**) 200 \(\upmu\)m depth (**b**) 300 \(\upmu\)m depth.  
**Author Contributions:** Conceptualization, M.K.M. and U.U.; Methodology, M.K.M., and U.U.; Software, M.K.M. and U.U.; Validation, M.K.M., U.U., and H.A.; Formal Analysis, M.K.M. and H.A.; Investigation, M.K.M., U.U., and O.A.; Resources H.A.; Data Curation, M.K.M. and U.U.; Writing--Original Draft Preparation, M.K.M. and U.U.; Writing--Review & Editing, M.K.M. and H.A.; Visualization, M.K.M.; Supervision, M.K.M. and U.U.; Project Administration, U.U. and O.A.; Funding Acquisition, H.A.  
**Funding:** This research was funded by Deanship of Scientific Research at King Saud University, grant number RG-1440-026.  
**Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group number RG-1440-026.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
In the system proposed, this energy is converted into electrical energy by the network's nodes. The network is made up of secondary nodes and primary nodes. The former are kinetic-powered nodes which, by taking advantage of animal movement, are able to transmit minimal information, while the latter are battery-powered nodes which take care of secondary-node communication events, adding location and current time and transmitting it to a central monitoring system. We have not attempted to carry out a conclusive study of animals movement, but rather suggest a design method for mobile sensor networks which: _i)_ saves battery costs, _ii)_ is able to operate under sunlight restrictions and _iii)_ takes advantage of animals' kinetic energy. Therefore, we pursue the implementation of a wireless sensor network designed to provide quantitative data on animal behavior under natural conditions. The paper is organized as follows: Section 2 describes related work on harvesting systems and animal tracking. Section 3 sets out the network hardware, where the primary and secondary nodes are described. Experimental results are presented in Section 4. Finally, Section 5 concludes the paper and suggests future developments.
# Trends and Perspectives of mHealth in Obesity Control
## 3 Results  
### Trends in mHealth for Obesity Control  
The ChatGPT's responses to the items and questions assessing trends in mHealth for obesity control are presented in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Items** & **Questions** & **Responses** \\ \hline \multirow{8}{*}{Usage patterns and demographics:} & What are the current usage patterns of mHealth applications and devices for obesity control among different demographic groups (e.g., age, gender, and socioeconomic status)? & Usage patterns of mHealth apps and devices for obesity control vary across demographics like age, gender, and socioeconomic status, impacting adoption rates and preferences. \\ \cline{2-3}  & & The adoption of mHealth technologies for weight management varies across demographic groups, with younger generations leading in usage, growing interest among older adults, gender differences in preferences, socioeconomic disparities in access, and cultural considerations influencing adoption rates. \\ \hline \multirow{8}{*}{What new technologies and platforms are emerging in the field of mHealth for obesity control, such as artificial intelligence, virtual reality, or telemedicine?} & Emerging technologies in mHealth for obesity control include artificial intelligence, virtual reality, telemedicine, wearable devices, and mobile applications. These innovations offer personalized interventions, virtual support, remote monitoring, and enhanced engagement to promote sustainable weight management and healthier lifestyles. \\ \cline{2-3}  & & The integration of emerging technologies such as artificial intelligence, virtual reality, telemedicine, wearable devices, and mobile applications into existing mHealth solutions for obesity control enhances personalization, engagement, and outcomes. These innovations offer tailored interventions, immersive experiences, remote support, real-time monitoring, and comprehensive platforms to promote sustainable behavior change and improve long-term success in weight management. \\ \cline{2-3}  & & mHealth technologies are integrated into existing healthcare existing healthcare systems and workflows for obesity management through telehealth services, electronic health record synchronization, remote monitoring and tracking, patient engagement platforms, and population health management initiatives. \\ \cline{2-3}  & & Collaboration between healthcare providers, technology developers, and insurers to value-based care models, policy advocacy, and educational initiatives to promote the integration of mHealth in clinical practice. \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Items, questions, and responses assessing trends in mHealth for obesity control generated by ChatGPT.
## 1 Introduction
[14] analyzed the deformation and destruction of an existing station caused by the unilateral foundation pit of the station through a model experiment. [PERSON] et al. [14,15] studied the impact of foundation pit excavations on both sides of an existing station. The results indicated that foundation pit excavations on both sides of the station will cause the platform to rise, whereas an asymmetrical excavation will deform and damage the existing station. This research shows that in addition to the excavation and unloading, the stress generated by the deformation of the retaining structure will also harm adjacent structures [16]. Therefore, the effect of foundation pit excavation on adjacent structures can be reduced by improving the stability of retaining structures [17,18]. Moreover, the foundation pit produces a lateral deformation difference under an asymmetric load [19,20,21], which destroys the existing structure and degrades the stability of the retaining structure. According to existing research, the foundation pit and existing underground structures are mostly horizontally adjacent. However, a new construction was adopted at the transfer station of the Beijing Metro Pingguo Yuan Station. First, underground excavations were conducted on the second and third floors. Then, an open excavation construction above the existing station was carried out to build an additional underground floor, which saved underground space. However, the stronger cross-impact between the existing and new structures leads to a difficult construction process right above the station, because of the cross-impact between the existing and new structures in the open excavation and story-adding above the existing station. Based on the Pingguo Yuan Station project of Beijing metro line 6, this study analyzed the deformation mechanism for the existing station and foundation pit retaining structure in the open excavation and story-adding above the existing station by numerical simulation and field measurement. Moreover, we discuss the corresponding safety measures that can provide a reference for the story-adding and subway station extension procedures.
## 2 Model and Data
### Weibull Zero-Failure Data  
The Weibull shape parameter holds engineering significance because it reflects the dispersion of product lifespan and the consistency of production quality. A smaller shape parameter indicates greater dispersion in product life. This study focuses only on the case where the shape parameter \(\alpha_{i}\) is assumed to be known. As found by [PERSON] et al. [26], the shape parameter cannot be accurately estimated without available failure time data. In practice, a conservative value for shape parameter \(\alpha_{i}\) can be determined through various means, including (1) a statistical analysis of historical failure data from similar products and (2) referencing shape parameter values of widely used material structures. For example, a Weibull shape parameter value of 0.7 for a hard disc drive of a mature design is deemedappropriate in a typical in-house acceleration test [27]. In another example, [PERSON] et al. [28] combined expert experience, historical information, and test results of key components to determine the shape parameter of a satellite thruster to be 3.  
Let \(t_{ij}\), \(j=1,2,\cdots,n_{i}\), be a set of Weibull zero-failure data for the \(i\)-th component, including field test data and past service data, where \(n_{i}\) is the number of specimens. If the shape parameter \(\alpha_{i}\) is a known constant, the generalized total test time (GTT) of component \(i\), denoted by \(T_{i}\), is given by  
\[T_{i}=\sum_{j=1}^{n_{i}}t_{ij}^{\alpha_{i}},i=1,2,\cdots,m \tag{5}\]  
In the following, the Weibull zero-failure data of component \(i\) is expressed as \((\alpha_{i},T_{i})\). Based on the zero-failure data for all components, the LCLs of system reliability \(R(t)\) and reliable life \(t_{R}\) at the confidence level of \(\gamma\) will be given below.
## 5 Experimental Results  
### 5.1 System Description  
In this section, the proposed control scheme is performed and evaluated based on the hardware system in Figure 10a and the experimental setup in Figure 10b.  
The robot system with the three motors is driven by the three MD01 high-power motor driver boards. The UNO R3 ATmage328 provides control signals to the motor drivers. For the stability assurance of the Delta robot, the PD controller is programmed directly on the UNO R3 and combines the AFS control signal from a computer to generate the control signal. On the DC motor, the active joint angles are acquired by the mounted three Hall magnetic encoders. The resolution of encoders is 0.367 degrees per pulse based on the specifications. Then, the UNO R3 sends the feedback position signal to the computer for torque calculation in the next iteration. The desired positions of the Delta robot and  
Figure 8: Control signals generated by the controllers in the helix simulation and parabolic simulation: (**a**) control signal of joint 1; (**b**) control signal of joint 2; (**c**) control signal of joint 3 in the helix case; (**d**) error of joint 1; (**e**) error of joint 2; (**f**) error of joint 3 of the parabolic case.  
Figure 9: Disturbance torques estimation and weight update of joints from RBF NNs in the helix and in parabolic simulation: disturbance torque and disturbance torque estimation of (**a**) joint 1; (**b**) joint 2; (**c**) joint 3; weights update of (**d**) joint 1; (**e**) joint 2; (**f**) joint 3 in helix case; disturbance torque and disturbance torque estimation of (**g**) joint 1; (**h**) joint 2; (**i**) joint 3; weights update of (**j**) joint 1; (**k**) joint 2; (**m**) joint 3 in parabolic case.  
compensated torques for the tracking control are verified on the computer. After that, the data signals are transferred via the serial port to the microcontroller board. We programmed the AFS controller and GUI control with C# Visual Studio, and the algorithm runs on a Ryzen 7 4800 HS CPU, 16 GB Ram, and GeForce GTX 1660 Ti graphic card. The estimated sampling rate in the real-time system is 0.5 ms on the UNO program. The interval time changes from 14 ms to 20 ms on the computer.
# Lazy Aggregation for Heterogeneous Federated Learning
## 4 Evaluation
### Effects of the Proposed Algorithm
#### 4.2.2 WDR and Cross-Device Momentum  
In order to explore the effectiveness of WDR and cross-device momentum, we added fixed-interval aggregations and different proportions of momentum based on Mnist (class 1).  
As shown in Figure 4a,c, we sampled 2, 5, 10, and 20 devices for sequential training before aggregation. We found that sampling five devices is enough to obtain unbiased gradients in this experiment. Oversampling will not further improve the performance of the global model and reduces learning efficiency, or even has negative effects (knowledge forgetting, resource waste). According to inequality 3, WD mainly comes from the difference in the initial weights and sampling distribution. We prefer to sacrifice part of the former for the latter in non-IID. However, the number of sampling rounds is unknown. Fortunately, it could be observed that the growth rate of WD slowed down significantly after five rounds. Apart from the difference caused by initial weight, sampling distribution gradually tends to be consistent, and the second term of the inequality (6) approaches 0. This is why this paper proposes that WDR is used to find sufficient sampling in a timely manner. It can be used to adjust aggregated intervals dynamically, which makes the performance curve of the global model smoother and accelerates convergence.  
In Figure 4b,d, we explored the influence of proportions of historical gradients in FL. In early training, a higher momentum will hinder the convergence of the global model, but it can bring FL performance to a higher level in the later stage. The gradients of devices will gradually disappear with FL training; nonetheless, the main reason is that the device model falls into the local optimum. Whether increasing sampled devices or the proportions of momentum, their gradient qualities are enhanced remarkably, which is undoubtedly helpful to the promotion of the global model. Inspired by the experiment, the momentum mechanism with dynamic adjustment deserves further exploration in the future. To sum up, FedLA determines aggregation timing by monitoring WDR, flexibly handles complex FL environments, and CDM mechanism improves FL performance in both IID and non-IID.  
Figure 3: Mnist-MLP: Test top 1 accuracy and test loss under IID, class 1, dir 0.1. (**a–c**) test accuracy curves of FedLA(M) and baselines, (**d–f**) corresponding loss curves under three scenarios. To evaluate the convergence efficiency and stability.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 2 Evaluation Methods
### Data Quality Check  
The measurement quality assessment aimed to detect the GLONASS and GPS poor observations, which further affect the positioning performance of different combinations. TEQC [21] toolkit was employed to perform the quality assessment of GLONASS and GPSL1 and L2 signals, which are used for performance assessment. The quality check was conducted using the following indicators:  
1. Data Integrity (DI). Data integrity rate is the recorded valid observation data divided by the receivable observation data calculated by ephemeris and the station location.
2. Signal-to-Noise Ratio (SNR). SNR is the ratio of signal power to noise power within a given bandwidth. It is usually expressed in the unit of decibels.
3. Pseudorange Multipath (MP). Pseudorange multipath indicators are computed using the linear combination of pseudorange and carrier phase observations: \[\text{MP1}=\text{P}_{1}-\left(1+\frac{2}{\alpha-1}\right)\varnothing_{1}+ \left(1+\frac{2}{\alpha-1}\right)\varnothing_{1}\] (1) \[\text{MP2}=\text{P}_{2}-\left(\frac{2\alpha}{\alpha-1}\right) \varnothing_{1}+\left(\frac{2\alpha}{\alpha-1}-1\right)\varnothing_{2}\] (2) where MP1 denotes the multipath effect on L1 frequency and MP2 indicates the multipath effect on L2 frequency; P1 and P2 denote the pseudorange observations at L1 and L2 frequencies, respectively; \(\varnothing_{1}\) and \(\varnothing_{2}\) denote the carrier phase observations of L1 and L2 frequencies, respectively; \(\alpha=\left(\frac{\text{f}_{1}}{\text{f}_{2}}\right)^{2}\), where f1 denotes the L1 frequency, and f2 denotes the L2 frequency.
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
## 3 Phase Offset Estimation Algorithm
### The Phase Offset Estimation Method Combining Viterbi-Viterbi Algorithm with Kalman Filter  
To reduce the estimation error, a new algorithm combining VVPE with the Kalman filter (VVPE-KF) is presented in this paper. The Kalman Filter is considered as an optimal filter, by which the measurement values and prediction values are fused together. To estimate the phase offset, a one-order Kalman filter is employed.  
The input of the Kalman filter \(x_{i}=r_{k}^{4}=\left(A_{k}s_{k}+n_{k}\right)^{4}\) is the fourth power of real received symbol \(r_{k}\) including the channel gain and noise. The output of the Kalman filter \(x_{o}=\left[A_{k}^{4}s_{k}^{4}\right]^{\prime}\) is the estimation of \(\left[A_{k}s_{k}\right]^{4}\), which is the value of the signal symbol \(A(k)s_{k}\) after being raised to the fourth power. Then calculating the angle of \(\left[A_{k}^{4}s_{k}^{4}\right]^{\prime}\), the estimated phase offset \(\theta_{k}{}^{\prime}\) can be obtained.  
The measurement equation of this Kalman filter is:  
\[z_{k}=Hs_{k}^{4}+v_{k} \tag{15}\]  
\(z_{k}\) is the measurement value and \(H\) is the measurement matrix set to be 1 in this one-order Kalman filter. \(v_{k}\) is the measurement noise with its variance \(R\). According to the Winner process described in Equation (7), the phase offset in \(k-1\) time slot and \(k\) time slot can be considered to be equal approximately, thus the state prediction equation is expressed as:  
\[s_{k}^{4\prime}=\Phi_{k/k-1}{s_{k-1}^{4}}^{\prime}+w_{k-1} \tag{16}\]  
\(\Phi_{k/k-1}\) is the state transition matrix and it is set to be 1. \(w_{k-1}\) is the prediction noise whose variance is \(Q\). Based on the fusion principle of Kalman filter, the output of the filter is:  
\[r_{k}^{4\prime}={r_{k-1}^{4}}^{\prime}+K_{f}\Big{(}z_{k}-{r_{k-1}^{4}}^{\prime }\Big{)} \tag{17}\]The Kalman gain \(K_{f}\) in one-order Kalman filter can be obtained by:  
\[K_{f}=\frac{P(k/k-1)}{P(k/k-1)+R} \tag{18}\]  
where \(P(k/k-1)\) is the covariance of the prediction error and it can be calculated as:  
\[P(k/k-1)=P(k-1)+Q \tag{19}\]  
\(P(k-1)\) is the covariance of current error in \(k-1\) time plot. The covariance of the estimation error in the \(k\) time plot is renewed as:  
\[P(k)=\left(1-K_{f}\right)P(k/k-1) \tag{20}\]  
A total iteration is finished by (16)-(20). The schematic of the VVPE-KF used to estimate the phase offset for QPSK modulation is shown as Figure 2. By the delay device in the figure, the estimation of the previous time slot is stored temporarily. Then the prediction of the last time slot and the current measurement are forged together.