esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Comparison of Quasi-Static Test Results of Columns before and after Cl\({}^{-}\) Corrosion
#### 3.3.2 Load-Displacement Hysteretic Curve and Skeleton Curve of Column  
The seismic performance of bridge columns can be comprehensively reflected by the load-displacement hysteresis curve under low cyclic loading, which is an important basis for evaluating the level of cumulative hysteretic energy dissipation under seismic action. The indicators of seismic performance such as deformation, stiffness, and ductility at different stages in quasi-static test can be reflected well by skeleton curve. As shown in Figures 23-25, the hysteresis loops of specimens LLE, MME, and MHE were far less saturated than the corresponding un-corroded specimens. The area of hysteretic loops shrunk by 56.35%, 88.01%, and 75.2%, respectively. The level of hysteretic energy consumption decreased significantly. As shown in Figures 26-28, in forward and reverse directions of loading displacement, the resistance of specimens LLE, MME, and MHE decreased more rapidly with the increase of loading displacement. The yield force, peak resistance, and ultimate lateral force of specimens LLE, MME, and MHE at damage were significantly smaller than those of the corresponding un-corroded specimens, which resulted in a considerable decline of the corresponding yield displacement, peak displacement, and limit displacement. For specimen LLE, values of displacement at three characteristic points dropped by 62.20%, 65.32%, and 59.99%, respectively. For specimen MME, values of displacement at three characteristic points decreased by 20.15%, 39.81%, and 53.90%, respectively. For specimen MHE, values of displacement at three characteristic points declined by 6.26%, 50.08%, and 49.06%, respectively. The above data show that the bearing capacity and ductility of bridge columns subjected to Cl\({}^{-}\) corrosion were on a downward trend.  
Figure 23: Comparison of load–displacement curves between specimens LL and LLE.  
Figure 24: Comparison of load–displacement curves between specimens MM and MME.  
Figure 27: Comparison of skeleton curves between specimens MM and MME.  
Figure 28: Comparison of skeleton curves between specimens MH and MHE.  
Figure 25: Comparison of load–displacement curves between specimens MH and MHE.  
Figure 26: Comparison of skeleton curves between specimens LL and LLE.
# Ischemic Preconditioning (IPC) Enhances the Accuracy and Stability of Proprioception
## 4 Discussion
### Practical Application of IPC for Influencing Proprioception  
Existing research has demonstrated that IPC intervention can enhance subsequent maximal strength levels and the number of repetitions performed at submaximal force intensities [25]. Furthermore, IPC has been shown to effectively improve anaerobic capacity [26] and aerobic capacity [24] during exercise. A limited number of studies also support an acute enhancement effect of IPC on power output and speed [9]. However, factors influencing athletic performance extend beyond strength, speed, power, and metabolic capacity; they also encompass fine motor control governed by proprioception. Indeed, the acuity and precision of such fine motor control constitute a prerequisite for the expression of other performance-related factors. The findings of the present study support the notion that IPC can improve the acuity and precision of proprioception, with this optimal effect occurring at 90 min post-IPC. Based on the findings of this study, coaching teams may consider implementing IPC approximately 90 min prior to important competitions to enhance proprioceptive function. The acute proprioceptive enhancements induced by IPC find application in specific sporting scenarios, including basketball free throws, soccer penalty kicks, and gymnastics apparatus-based routines. These proprioceptive improvements may mediate enhanced motor performance in such athletic disciplines, ultimately contributing to superior competitive outcomes. Nevertheless, whether acute improvements in sport-specific proprioception interfere with the consolidation of established motor adaptations warrants further systematic investigation. Future research should delve into these directions to refine the relevant theoretical framework.
## 2 System Description
### System Structure  
On the left side of Figure 1, the energy supply within the building is depicted, with different-colored blocks representing electricity, carbon, and gas from top to bottom. The electrical block includes equipment such as CHP, gas turbine (GT), photovoltaic power generation and wind power plant (PV&WPP), and energy storage (ES), while the carbon and gas blocks include CCS and the P2G device, respectively. The load side is shown on the right side, including electricity and heat loads, as well as the participation in the carbon market determined based on carbon emissions.  
In the proposed zero-carbon building system, there are four types of energy flows, including electricity flow, carbon flow, gas flow, and heat flow, which are represented by arrows of four different colors in Figure 1. Electricity is primarily supplied by PV&WPP, with additional contributions from CHP, GT, and ES. However, considering the uncertainty in the output of PV&WPP, the building is connected to the external electricity market to ensure a reliable power supply in the case of insufficient renewable energy generation. Except for a small portion used by P2G and the charge process of ES, the remaining electricity is supplied to the electrical load. The flow of carbon is closely related to CCS, which captures the CO\({}_{2}\) generated by CHP and GT and transfers a portion of the CO\({}_{2}\) to P2G as a raw material. Due to the efficiency limitations of CCS and P2G, some carbon is not successfully captured or utilized and is released into the atmosphere. By comparing the actual carbon emissions with the carbon quotas, it is possible to decide whether to purchase or sell carbon quotas on the carbon market, generating costs or revenue. Gas within the building is generated by P2G and supplied to CHP and GT for consumption. Similarly, for supply reliability, the building is connected to the external gas market. Finally, the flow of heat within the building is relatively simple, with heat generated by CHP and P2G to meet the heat load.  
Figure 1: System structure.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 4 Simulation and Results  
In this section, the proposed decentralized virtual linkage approach is applied to a multi-agents' formation control scenario using MATLAB. In the scenarios, nine agents are required to move around a circle while maintaining line formation shapes with a uniform distance of 0.1 m or performing formation reconfigurations by coordinating the desired virtual joint angles \(\Theta_{d}\) and the virtual linkage extract/expansion rates \(\lambda_{d}\).
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 3 The DAILC Algorithm of Voltage Control Subject to Sensor Saturation
### Dynamic Linearization Model of The DREGU
: _It is worth noting that the PPD-based dynamic linearization employed in this paper is an equivalent data-driven model, whose core advantage lies in enabling controller design without a precise physical model. However, we also acknowledge that this linearization is an approximation. The model is effective for typical voltage regulation tasks in microgrids, which involve relatively slow dynamics. Nevertheless, in scenarios with extremely fast dynamics (such as severe faults or large transient load impacts) or significant high-order harmonic distortion, the system's nonlinear characteristics become highly pronounced. Under these extreme conditions, the fluctuation of the PPD value may intensify, and the approximation accuracy of the linearized model could decrease, potentially affecting control performance. Therefore, the optimal application context for the proposed DAILC method is voltage regulation under normal operation and common disturbances. A deeper investigation into the model's validity boundaries under extreme conditions will be a direction for our future work._
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 2 Methods
### Improved POCS Method  
The process of POCS super-resolution reconstruction is mainly divided into three parts: constructing the reference frame, motion estimation, and iterative correction by the PSF. Among them, motion estimation is a key step to ensure whether the low-resolution range profile can be accurately mapped to the high-resolution reference range profile, whereas the traditional POCS using the block-matching motion alignment method is unable to provide pixel-level information alignment, which affects the quality of the details of the final reconstructed high-resolution range profile. To improve the accuracy of motion alignment, the Lucas-Kanade optical flow method based on the Gaussian pyramid is introduced for motion estimation of range profile sequences. The accuracy of the motion estimation can be further improved by modeling the motion vectors at different scales through a pyramid structure and performing optical flow calculations using the Lucas-Kanade method, which is iteratively calculated through the pyramid structure [25; 26].
# Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
## 2 Problem Formulation and Preliminaries
### Fuzzy Logic System Approximation  
Fuzzy logic systems (FLSs) are widely recognized as universal approximators for nonlinear functions. Typically, such an FLS consists of three key components: a fuzzification interface, multiple fuzzy inference rules, and a defuzzification module [13; 20]. In the present study, an FLS structure is employed to approximate an unknown continuous function \(f(\widehat{X})\) defined on a compact set \(\Omega_{\widehat{X}}\subset\Re^{l}\), which can be mathematically expressed as  
\[f(\widehat{X})=\kappa^{T}\tilde{W}(\widehat{X})+\xi(\widehat{X}),\;\forall \widehat{X}\in\Omega_{\widehat{X}}\subset\Re^{l} \tag{8}\]  
where \(\widehat{X}\in\Re^{l}\) denotes an \(l\)-dimensional input vector, \(\bar{\kappa}=[x_{1},x_{2},\ldots,x_{h}]^{T}\) represents the adjustable weight vector with dimension parameter \(h>1\), corresponding to the number of fuzzy rule nodes. The term \(\xi(\bar{X})\) indicates the approximation error, which is bounded by \(\widetilde{\xi}\in\Re^{+}\). The basis function vector \(\hat{W}(\bar{X})\) is defined as \(\left[W_{1}(\bar{X}),W_{2}(\bar{X}),\ldots,W_{h}(\bar{X})\right]^{T}\), whose components are characterized by Gaussian membership functions:  
\[W_{h}(\bar{X})=\exp\left(-\frac{\|\bar{X}-\bar{P}_{h}\|^{2}}{C_{h}^{2}}\right) \tag{9}\]  
where \(\bar{P}_{h}=\left[P_{h1},P_{h2},\ldots,P_{h|}\right]^{T}\) denotes the center position vector of the \(h\)-th receptive field, and \(C_{h}\) determines the width of the corresponding Gaussian distribution [6]. The longer the length of the center position vector, the higher the approximation accuracy of the fuzzy logic system. However, excessive length can also lead to overfitting issues. Therefore, the center position parameters should be reasonably selected based on the actual application background. The Euclidean norm operator \(\|\cdot\|\) is utilized to measure the distance between the input vector and the center vector.
## 3 Verification of the STAR-Derived Excess Phase and Bending Angle Profiles
### Bending Angle Uncertainties Caused by Inversion Algorithm Implementation  
To quantify the bending angle uncertainties caused by uncertainties in the excess phase, we compare the ROPP bending angle profiles while the excess phases are from (i) STAR and (ii) UCAR. We feed one day's UCAR atmPhs (excess phase files) into ROPP to generate the bending angle profiles compared to those using STAR excess phase as inputs. Figure 7 compares these two bending angle profile sets with those from UCAR operational bending angle profiles. Figure 7a,b shows that the fractional bending angle profiles generally agree well with UCAR. The striking differences occur above 35 km and below 10 km for the double-difference using UCAR operational bending angle as transfers (Figure 7c). At upper altitude, the bending angle becomes very small, and a significant standard deviation in relative bending difference (about 5% at 45 km) can be related to the excess phase differences, which are mainly from the clock bias residual, POD effects, and excess phase profile smooth as discussed. However, excess phase differences cannot explain the relative bending angle differences of the lower atmosphere.  
Below 10 km, the differences (to UCAR BA) are almost identical to the two ROPP results. There is a small zigzag structure below 4 km and less than 5% STD error (increasing with lowering height), which might be related to the smoothing procedure in ROPP and the cycle slip removal procedure differences in the excess phase. Therefore, the ROPP algorithm setup can explain the difference below 10 km between UCAR bending angle and STAR bending angle profiles. As mentioned in previous sections, ROPP uses the MSIS climatology model as background from various corrections for the excess phase data and bending angle filtering. ROPP generates the excess phase model using input satellite parameters while examining the excess phase profiles for NDM removal and cycle slip corrections. The excess phase model is then used to unwrap the wrapped excess phase. The excess phase model difference from UCAR provides the main difference for the excess Doppler feeding into the CT2 routine for the bending angle conversion.  
The ROPP algorithm caused bending angle difference from UCAR bending angle is similar to previous findings (e.g., ROM SAF with UCAR for Metop-A/B, [31]). To get a sense of how the ROPP bending angle inversion algorithm can cause the difference from the UCAR BA algorithm, we checked the comparison of ROM SAF reprocessed COSMIC-1 bending angle profile (more than 7000 profiles) in Jan 2016 by using the same excess phase from UCAR; thus, we can rule out the impacts of excess phase difference. The COSMIC-1 BA profiles comparison shows a similar pattern below 10 km, as shown in Figure 7b for COSMIC-2. Further examination shows that the excess phase model difference likely causes the main difference and different processing centers differentiate L2 processing. Discussion on the excess phase model will be another topic for further exploration.
## 5 Discussion and Conclusions
### Scalability of Intelligent Algorithms
However, the further application of intelligent algorithms is contingent upon the optimization of existing computing resources to enhance computational efficiency. In practical applications, numerous intelligent algorithms, including deep learning and large-scale eddy simulation (LES), necessitate substantial computing resources and time, impeding their efficacy in large-scale real-time applications. For instance, [PERSON] [107] employed a large-scale eddy simulation in conjunction with Lighthill acoustic analogues to obtain highly accurate noise predictions in an underwater noise study. This study's findings provide a foundation for accurate noise prediction in underwater noise research. This method has been demonstrated to accurately simulate acoustic behavior in complex flow fields; however, it is computationally intensive. Consequently, its practical application in large-scale and real-time noise prediction tasks is limited. Consequently, the optimization of computing resources and the enhancement of efficiency, while maintaining accuracy, represent pivotal challenges for the implementation of intelligent algorithms. The application of intelligent algorithms in the field of hydrodynamic noise demonstrates considerable potential. Nevertheless, the feasibility of implementing these algorithms is still constrained by the limitations of computing resources. However, as parallel computing technology and deep learning methods continue to evolve, future research endeavors can be pursued with the assurance of maintaining high accuracy. This enhancement will not only reduce the computational costs but also enhance the adaptability of algorithms in large-scale real-time applications. Consequently, this will furnish a more efficient solution for predicting fluid noise and for optimal control, thereby promoting technological progress in this field.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 5 Performance Evaluation
LEACH-DT itself outperforms the original LEACH, as shown in the figure. We could \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(L\)} & \multicolumn{3}{c}{**Energy Consumption (mJ)**} & \multirow{2}{*}{**Saving**} & \multirow{2}{*}{\((k_{opt},R_{opt})\)} \\ \cline{3-3} \cline{5-6}  & & **Original LEACH**[3] & & & \\ \hline \multirow{3}{*}{100} & 200 m & 10.5 & **9.2** & 12.4\% & (5.2, 286 m) \\  & 300 m & 31.9 & **24.1** & 24.5\% & (5.2, 428 m) \\  & 400 m & 86.4 & **62.8** & 27.3\% & (5.2, 571 m) \\ \hline \multirow{3}{*}{200} & 200 m & 12.2 & **10.7** & 12.3\% & (6.4, 263 m) \\  & 300 m & 31.2 & **20.3** & 34.9\% & (6.5, 393 m) \\ \cline{1-1}  & 400 m & 84.1 & **48.9** & 41.9\% & (6.5, 525 m) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy consumption per node in a round. Figure 4: Number of nodes alive (\(N=200,L=400\) m). further improve LEACH-DT by running it with our optimized parameters \(k_{opt}=6.5\) and \(R_{opt}=525\) m, i.e., we used uneven CH-selection probabilities while keeping the average of 6.5 CHs in each round and setting the transmission radius for control messages to be 525 m. This improved LEACH-DT significantly, as shown in the curve \"LEACH-DT with Optimization\". In Table 3, we compare performance in terms of time to percentage-node-death in rounds among the schemes compared in Figure 5. Our proposed optimization improved the network lifespan in terms of all criteria, 1-90%, in the table. We note that our optimization helps LEACH and LEACH-DT improve energy efficiency by increasing the lifespan dramatically with optimization. Consequently, our proposed optimization is expected to help existing cluster-based routing protocols to further improve in terms of network lifespan.
## 5 Stability Analysis of the Path Following Control System  
In this section, the stability analysis of the HGESO-ASMPFC scheme as presented in Figure 3 was carried out.  
Substituting \(\psi=\psi_{d}+\widetilde{\psi}\) into Equation (15), the cross-track error dynamics satisfies  
\[\dot{y}_{e}=-c\cdot y_{e}+c\cdot\Delta\cdot\widetilde{\alpha}+D_{y_{e}} \tag{56}\]  
where  
\[D_{y_{e}}(\widetilde{\psi})=-c(y_{e}-\Delta\widetilde{\alpha})\cdot(\cos \widetilde{\psi}-1)+c[\Delta+\alpha\cdot(y_{e}+\Delta\cdot\hat{\alpha})]\cdot \sin\widetilde{\psi}, \tag{57}\]  
which implies that \(D_{y_{e}}(0)=0\).  
The guidance system can be presented as  
\[\left\{\begin{array}{l}\dot{y}_{e}=-cy_{e}+c\cdot\Delta\cdot\widetilde{ \alpha}+D_{y_{e}},\\ \ddot{\widetilde{y}}_{e}=-\frac{\dot{\alpha}_{1}}{\dot{\epsilon}}\widetilde{y }_{e}+\widetilde{g},\\ \ddot{\widetilde{g}}=-\frac{\dot{\alpha}_{2}}{\dot{\epsilon}^{2}}\widetilde{y }_{e}-\dot{g}.\end{array}\right. \tag{58}\]  
According to Theorem 1, the nominal system  
\[\left\{\begin{array}{l}\dot{y}_{e}=-cy_{e}+c\cdot\Delta\cdot\widetilde{ \alpha}\\ \ddot{\widetilde{y}}_{e}=-\frac{\dot{\alpha}_{1}}{\dot{\epsilon}}\widetilde{y }_{e}+\widetilde{g}\\ \widetilde{g}=-\frac{\dot{\alpha}_{2}}{\dot{\epsilon}^{2}}\widetilde{y}_{e}- \dot{g}\end{array}\right. \tag{59}\]  
is ISS.  
To analyze the stability of the entire path following control system, the cross-track error dynamics and the tracking error dynamics are written as:  
\[\sum_{1}^{\prime}:\dot{y}_{e}=-cy_{e}+c\cdot\Delta\cdot\widetilde{\alpha}+D_ {y_{e}}, \tag{60}\]  
Figure 3: The block diagram of the HGESO-ASMPFC scheme.  
\[\sum\
olimits_{2}^{\prime}:\dot{\widetilde{\psi}}=f_{\psi}(\psi,\,\psi_{d}). \tag{61}\]  
where \(f_{\psi}(\psi,\,\psi_{d})\) can be obtained by letting \(s_{a}=0\) in Equation (47). Note that the cascade structure is completely modular in the sense that the subsystem \(\Sigma^{\prime}{}_{2}\) excites the subsystem \(\Sigma^{\prime}{}_{1}\). The major result of the entire closed-loop system is demonstrated in the following theorem.  
The guidance system, given by Equations (19) and (27) together with the control law (48), renders the origin of the entire closed-loop system GUAS.  
According to Theorem 2, the yaw tracking control system renders the equilibrium point \(\widetilde{\psi}=0\) of the subsystem \(\Sigma^{\prime}{}_{2}\) GUAS. According to Lemma 4, the cascade system of \(\Sigma^{\prime}{}_{1}-\Sigma^{\prime}{}_{2}\) is GUAS. Since the external input \(\dot{g}\) meets \(\lim\limits_{t\rightarrow\infty}\dot{g}=0\), it follows \(\lim\limits_{t\rightarrow\infty}y_{e}=0\).
## 1 Introduction
Until some years ago, the inferior sensitivity, temporal and spatial resolution of SPECT cameras compared to positron emission tomography (PET) cameras, together with the complex and contrived inorganic chemistry of this metal, were considered key problems in the development of new technetium-99m compounds for the imaging of more specific molecular targets [16]. Figure 1: Schematic representation of linkage of the carrier and the radioactive atom to form the radiopharmaceutical that interacts with a specific biological target. Advances in technetium chemistry over the last 20 years have facilitated the development of new technetium radiopharmaceuticals with great potential in clinical practice. In addition, recent advances in detector technologies and reconstruction algorithms clearly showed that the spatial resolution od SPECT is approaching that of PET without a concomitant decrease in sensitivity. In particular, novel multi-pinhole collimators, coupled with cadmium zinc telluride (CZT) solid-state photon detectors to further improve image quality and reduce scanner size, reduce the imaging time and radiation dose. The new SPECT scanners have demonstrated up to a 7-fold increase in photon sensitivity and an up to 2-fold improvement in image resolution. Furthermore, the adoption of CZT involves a superior energy resolution compared to a conventional gamma camera [17]. The above considerations lead us to believe that \({}^{99\text{m}}\)Tc-radiopharmaceuticals will continue to play an important role in nuclear medicine and MI. The versatile chemistry of technetium-99m is a major advantage in radiopharmaceutical development. The aim of this review is to describe and report the most efficient labelling methods to produce modern technetium-99m radiopharmaceuticals for nuclear medicine. Highly specific activities needed for targeting low-concentration targets are easily achievable through advanced chemical procedures involving the \({}^{99\text{m}}\)Tc-metal fragment approach. A section is dedicated to the development of new \({}^{99\text{m}}\)Tc-based radiopharmaceuticals for prostate cancer.