esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### The Adsorption Kinetics of SSO-MIPs and NIPs  
It is clear from Figure 10 that the maximum adsorption capacity of the SSO-MIPs was found to be about 67.55 mg/g, and that of the NIPs was about 26.87 mg/g; therefore, the imprinting factor was 2.51. The adsorption capacity of the SSO-MIPs to solanesol increased rapidly in the first 50 min, and then increased slowly until the adsorption time was 180 min. At this time, the adsorption process reached equilibrium, and the adsorption capacity of the SSO-MIPs reached the maximum and remained stable. This is because there are many holes on the surfaces of SSO-MIPs, and the concentration of solanesol in the adsorption solution was high. The holes on the surfaces of SSO-MIPs could easily bind with solanesol in the solution, and they tended to be saturated with continuous adsorption. Solanesol can bind to the holes only when it diffuses into the deep layer, so the adsorption rate of the polymer decreased until adsorption equilibrium was reached.  
Figure 9: The influence of the temperature on the adsorption performance of SSO-MIPs and pore volume.  
Pseudo-first-order and pseudo-second-order kinetic models were used to analyze the adsorption kinetics, and the fitting results are presented in Figure 11, from which it is clear that the fitting effect of the pseudo-second-order kinetic model was better than that of the pseudo-first-order kinetic model. The pseudo-first-order kinetic equation is y = 0.021x + 0.24, and the linear correlation is 0.9785; the pseudo-second-order kinetic equation is y = 0.013x + 0.339, and the linear correlation is 0.99896. This demonstrates that while the pseudo-second-order kinetic model can better describe the adsorption process, the pseudo-first-order kinetic model can better describe the early adsorption process; this indicates that physical adsorption occurs during the early stage of the adsorption process.
# THz Rectennas and Their Design Rules
## 7 Fabrication Aspects  
Another important factor when deciding the optimum rectifier design is how the design will be realized. Realization of the rectifier should address: (a) a reliable production process and (b) comparable or lesser costs than other commercially available solutions. In fact, there are many production methods where each has its own advantages and drawbacks. Since the technology is highly dynamic, suffers from synonymy (meaning that the same production procedure can be named differently by different institutions) and can significantly change within a short time, our discussion will not focus on the detailed process of each fabrication method. Rather, we will focus on the general concepts of the methods used for experiments, as well as those expected to be deployed for commercial production.  
Producing nano-scale components with satisfactory accuracy has been a challenge. There are many examples where the produced prototypes fail to produce the required functionality due to fabrication deficiencies. [PERSON] [5] stressed the importance of MIM diodes' reproducibility as the main step to increasing the efficiency of the rectennas. It has been demonstrated in Section 6 with the novel fabrication method used by [PERSON] et al. [96] that some MIM diodes did not express a tunneling behavior as a consequence of fabrication flaws. Another interesting example is that of [PERSON] and [PERSON] ([37], p. 119) where extremely rough surface metal-insulator contacts can have significant impact on the diode response. The formation of the insulating layer in a tunneling diode is also not an exception where the crystallization form of the layer significantly affects its dielectric constant [42]. Therefore, reaching an accurate procedure to fabricate rectenna devices was the primary focus of many researchers.  
In fact, there are many methods used today to fabricate nano-scale features ranging from current lithographic techniques to mechanical nano-imprint and other advanced techniques that involve transferring the imprinted structure into a foreign or flexible substrate [104]. These methods can be differentiated based on different chemical agents, fabrication procedure, exposure time, etc. We, therefore, list some of the most frequently-used methods as they appear in the literature.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Development of the Model
#### 3.2.3 Multilayer Perceptron Model  
A Multilayer Perceptron (MLP) is an artificial neural network that can be applied to solve many problems, including classification, regression, and pattern recognition. A typical MLP model comprises three blocks of interconnected neurons: the input layer in which each neuron represents a data feature; the hidden layers that could have one or more layers depending on the complexity of the problem; and the output layer, representing the response output (Figure 3). Each neuron in the network is bonded to other neurons through connection weights. Each neuron in a neural network receives an input (\(X_{i}\)) that is multiplied by a weight (\(W_{i}\)) and these are summed with each other and added to the bias value (\(b\)). Then, the result is transferred to the activation function, which adjusts the final output (Figure 4). Each layer of neurons gathers input from the previous layers, and the outputs of neurons within one layer become the inputs to neurons in the following layer. Finally, the last layer produces the predictions of the model.
# Review of Advanced Medical Telerobots
## 2 Medical Telerobotic Topologies  
### Single-Leader Single-Follower Topology (SL/SF)  
The conventional topology used in telerobotic systems is composed of a single leader robot and a single follower robot. The single-leader-single-follower topology (SL/SF, see Figure 4) has been used, as mentioned before, to augment human sensorimotor competence [28; 86]. A successful example of an SL/SF telerobotic system is the da Vinci Surgical System [23], using which it is now possible to conduct minimally invasive laparoscopic surgeries inside a patient's body with an ultra-precision, reliability, and safety [65; 87; 88]. This topology transfers the pose commands of the surgeon collected by the leader console to the follower robot inserted through small incisions inside the patient body to conduct the surgery under the direct control of the surgeon while augmenting the sensory and motor skills of the surgeons [65; 87; 88; 89; 90], as explained in the next subsections.  
There are several other examples of applications for SL/SF topology in the medical domain (beyond laparoscopic surgery) when interactional forces can be much higher or much lower than laparoscopic surgery. One example is SL/SF telerobotic rehabilitation which allows for remote tele-physical sensorimotor interaction between an in-hospital  
Figure 4: Single-leader–single-follower topology where \(n\) is the number of network ports/terminals (Images ©2020 Intuitive Surgical, Inc., Sunnyvale, CA, USA).  
Figure 3: Time-domain passivity control (TDPC).  
clinician and an in-home patient for remote delivery of physical therapy as a new paradigm for delivering intensive active-assist therapy for patients with neurological damages or disorders addressing accessibility issues [64; 85; 91]. As another example of SL/SF topology telemicromanipulation can be mentioned, for instance, [92] introduces a haptics-enabled SL/SF magnetic micro-manipulation platform with promising potential for a range of micro-scale biomedical applications. The follower robot uses a controlled magnetic field for manipulating a ferromagnetic micro-device when the leader robot is a haptics-enabled device taking position commands of the user (to be scaled down and followed by the follower robot) and rendering the scaled-up force field. On the other side of the spectrum, high-force SL/SF teleoperation has been also used in medicine. Especially in oral surgery [93] and orthopaedics [94; 95; 96; 97; 98], drilling is a common high-force and sensitive task. In the rest of this section, we explain in more detail how telerobotic systems can augment the sensorimotor skills of clinicians and enhance the outcome of therapy. We will also discuss the existing challenges and future visions for this topology.
## 2 Principle of Heterodyne Phase Unwrapping
### Error Analysis  
Formulas (7) and (8) show that when \(\Delta 1\) and \(\Delta 2\) or \(\Delta 2\) and \(\Delta 3\) are close to each other, even if the values are extremely small, it will lead to a jump from 0 to 1 in \(\Delta 12\) and \(\Delta 23\) and the errors of the phase principal values obtained by grating 1, grating 2, and grating 3 are independent of each other. Therefore, for the three-frequency heterodyne phase unwrapping, each solution creates an error that eventually leads to an increase in the error of \(\Delta 123\). When the error is multiplied by the coefficient \(P_{12}/(P_{23}-P_{12})\), the error will be magnified significantly. Assume that  
\[H=\frac{P_{12}}{P_{23}-P_{12}} \tag{12}\]  
According to Formula (9), when \(\Delta 123=c/H\), \(c=1\), \(2\), \(3\), \(4\),..., \(N_{12}\) will jump, that is, the phase unwrapping will jump. According to Formulas (5) and (6), the theoretical value of the decimal part \(\Delta n_{124}\) of the fringe order derived using heterodyne should be equal to the actual value of the decimal part \(\Delta n_{124}\) calculated based on the principal value of the current phase. The theoretical value of the decimal part is  
\[\Delta n_{124}=n_{12}-N_{12}, \tag{13}\]  
In fact, due to the error of the phase principal value, the two values are not equal. According to the analysis above, if the number of cycles changes, the absolute value of the difference between the theoretical value and the actual value must be bigger or smaller than a certain value, and this value is related to the error of the phase principal value itself, set this value as threshold \(T\). Let the error of \(\Delta n_{1}\) refer to \(q_{1}\), the error of \(\Delta n_{2}\) refer to \(q_{2}\), and the error of \(\Delta n_{3}\) refer to \(q_{3}\), then the error of \(\Delta n_{123}\) is \((-q_{1}-q_{2}-q_{3}q_{1}+q_{2}+q_{3})\). Thus, the value of threshold T is  
\[T=H\cdot(q_{1}+q_{2}+q_{3}) \tag{14}\]  
The maximum error of the phase principal value extracted by the uncorrected four-step phase-shifting procedure is no more than 0.08 rad [15], and the value of \(H\) can be calculated from the grating pitch, so all quantities in Formula (14) are known.  
In summary, the method of adjusting the abnormal jump in the cycle is as follows:  
1. If \(\mid\Delta n_{12r}-\Delta n_{124}\mid<T\), the cycle will jump accurately and no adjustment is needed.
2. If \(\mid\Delta n_{12r}-\Delta n_{124}\mid\geq T\) and \(\Delta n_{12r}-\Delta n_{124}>0\), the cycle will jump upward and the number of cycles will be minus one.
3. If \(\mid\Delta n_{12r}-\Delta n_{124}\mid\geq T\) and \(\Delta n_{12r}-\Delta n_{124}\leq 0\), the cycle will jump downward and the number of cycles will be plus one.
# Behavior of Circular CFST Columns Subjected to Different Lateral Impact Energy
###### Abstract  
Forty-eight circular concrete-filled steel tube (CFST) columns subjected to lateral impact were tested to investigate the behavior of circular CFST columns under axial compressive load. Analyses of effects of concrete compressive strength, impact location and impact energy on residual ultimate axial capacity, ductility and initial stiffness are provided in this paper. It is found that lateral impact has negative effects on residual ultimate axial capacity of circular CFST columns from test results. Residual ultimate axial capacity decreases as impact energy increases and impact location comes close to the end of the specimen. It is also found that increasing concrete compressive strength can reduce the negative effects of impact location on residual ultimate axial capacity. Ductility and the initial stiffness of circular CFST columns decrease as impact energy increases. Ductility and the initial stiffness increase as impact location varies from middle-length to the end of specimens. When impact energy and impact location are constant, the ductility of the specimen with 30 MPa of concrete compressive strength is better than that of other specimens with different compressive strength. Besides, analyses of strain developments for 12 typical specimens to investigate failure modes under axial compressive load are provided in this paper. Strain developments have indicated that the steel at impact location becomes plastic faster than that at other locations. Based on the test results, a calculation formula is presented to predict the residual ultimate axial capacities of circular CFST columns subjected to lateral impact, and good agreement with experimental results has been achieved.  
lateral impact; impact energy; impact location; circular CFST; residual ultimate axial capacity
# Special Feature Vibration-Based Structural Health Monitoring
###### Abstract  
Structural health monitoring by vibration requires the understanding of multidisciplinary fields of engineering sciences. Since vibration behavior is influenced by structural properties, their analysis requires consideration of the excitation mechanism, vibration energy transfer and subsequent dissipation. For efficient monitoring, the relevant signal processing is crucial for cost-effective and reliable identification. Although the use of vibration for structural integrity monitoring is complex, its potential to be used for a wide variety of engineering fields is continuously increasing. The studies on the application of machine learning to the classification of vibration-based monitoring are being proposed by many research groups as summarized in the review [1]. To anticipate unexpected failure and estimate remaining life time with actual implementation, continuous vibration monitoring together with statistical feature extractions are important. The proposed Special Issue was intended to collect recent advances on the use of vibration for structural health monitoring. The studies show application fields with their potential to be expanded to a wide variety of machines and infrastructures.
# The Application of Mesenchymal Stem Cells on Wound Repair and Regeneration
## 8 Mesenchymal Stem Cells  
MSCs were initially characterized by [PERSON]'s group as being similar to fibroblasts and adherent to plastic surfaces. Also, these cells were first discovered in the bone marrow [47; 48]. In addition, they are defined by the International Society of Cellular Therapy as cells with plastic-adherence ability and that are capable of in vitro differentiation into three different cell lines (adipocytic, osteoblastic and chondrocyte). Moreover, they have the ability to express the surface markers CD73, CD90 and CD105 and do not exhibit hematopoietic markers such as CD14, CD45, CD34, CD19/HLA-DR and CD11b/CD79 [5; 7]. They can be isolated from different tissues such as bone marrow, adipose tissue, umbilical cord, umbilical cord blood, placenta, dermis and nerve tissue [5; 7]. These cells persist in the lesion site even after it has healed and are responsible for scars development, fibrosis, inflammation, angiogenesis and formation of granulation tissue [5; 18; 49].  
MSCs have immunosuppressive and immunomodulatory characteristics (releasing cytokines, growth factors and chemokines) which makes them a great option for allogeneic transplantation [9; 16; 18; 49]. Furthermore, the MSCs' conditioned medium has also been shown to stimulate the regeneration of damaged tissues [5; 50; 51; 52; 53; 54].
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 1 Introduction
Multi-objective optimization algorithms [14; 15] are usually used to solve multi-objective problems. These algorithms have advantages and disadvantages. For instance, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) [16; 17] converges quickly, but the effect for high-dimensional multi-objective problems is poor. Multi-objective particle swarm optimization (MOPSO) algorithms [18; 19; 20] cannot solve discrete optimization problems. The nondominated sorting genetic algorithm-II (NSGA-II) [21; 22; 23] has low computational complexity and high population diversity and is widely used to solve multi-objective optimization problems. However, there is no unique solution to a given multi-objective optimization problem; that is, the goal of multi-objective algorithms is to find a global optimal solution on the Pareto front [24; 25; 26; 27]. Therefore, for reconstruction problems that require a unique optimal solution, other solutions need to be considered. There are three common methods: (1) the generative method, i.e., generating the optimal solution set, which requires the decision maker to filter out the optimal solution according to the needs of the problem. (2) The interactive method finds the optimal solution through dialogue and interaction between the decision-maker and the analyst. (3) The transformation method requires the decision maker to judge the importance of the objective function, transforms the multi-objective problem into a single-objective problem for a solution and often uses an intelligent algorithm. This paper takes the self-assembled space telescope [28] as the mission setting and introduces optimization indicators such as the health state of the satellite swarm, the total energy consumption of the reconstruction, and the balance of fuel consumption. Based on this framework, a reconstruction optimization model for each stage is constructed, and a hybrid multi-objective genetic algorithm [29; 30] is used to perform the reconstruction. The core advantage of the method in this paper is that even if the satellite faults in the satellite swarm system are different, it still provides a multi-objective optimal reconstruction strategy.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 2 Methods and Materials
### Recruitment and Introduction to the Training Monitoring System  
The \"Training load monitoring in amateur Rugby Union: A survey of current practices\" survey included a question that gauged S&C coaches' interest in participating in a future study aiming to examine the relationship between training load and injury [7]. The rugby clubs that firstly expressed interest and secondly were using the IRIS injury surveillance system were the intended cohort to initially implement an online TMS. The S&C coaches were contacted electronically with an introduction to the study, the study aims, method of data collection, and data confidentiality. Players from seven men's senior teams were recruited to use the TMS for the 2019-2020 rugby season.  
The TMS was introduced to the players and coaches by means of a 20-min presentation, given in person by the primary author or via video recording. This presentation outlined the purpose and potential benefits of their participation, the definition and explanation of each of the measures included, and a demonstration of how to use the system.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis
### Qualitative Results Analysis  
Figure 5 depicts a visual comparison between the STARK-ST50 algorithm and our proposed algorithm on the LaSOT dataset. In the \"bird-2\" video sequence, the target in frame 255 is obstructed by similar objects, causing all algorithms to track incorrectly. However, our algorithm successfully repositioned the target in frame 275 and accurately captured it again in frame 345 after being partially occluded. In contrast, STARK-ST50 was unable to distinguish the interfering object. In the \"fox-20\" video sequence, both algorithms were able to track the target well in frames 2100 and 2102. However, STARK-ST50 displayed an abnormal position change in the prediction box from 2102 to 2104, resulting in an incorrect target position, which was gradually rectified in subsequent frames. In contrast, our algorithm tracked the target steadily throughout.  
Overall, our algorithm demonstrated a superior ability to discriminate interferers and suppress abnormal abrupt changes in predicted box positions, leading to improved tracking accuracy and success rate. The observed improvement in performance can be attributed to the optimization of the algorithm's template update, resulting from its enhanced ability to discriminate the reliability of tracking results, as well as its proficiency in capturing the motion of the target.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.2 Man-in-the-Middle (MITM) Attacks  
Both protocols include a key confirmation step using HMAC-SHA-256 to prevent MITM attacks, ensuring that both parties have derived the same shared secret and that an attacker has not successfully impersonated either party during the key exchange.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 5 Conclusions  
This paper proposes and evaluates three methods for identifying the impact frequency in a pneumatic DTH drill rig driven by an electric motor using MCSA. The estimated impact frequency obtained with the three proposed methods for an experimental reference setup was consistent with values obtained from an independent analysis of pressure and sound signal analysis during both the NP-stage (no percusive drilling) and the SP-stage (stable percusive drilling). The experimental study showed that the proposed side bands and the waterfall approaches present restrictions that may limit their application scope in practical scenarios. Overall, the proposed envelope approach represented a simple, more robust alternative for identifying the impact frequency.  
The application of the proposed methods for impact frequency identification tailored for drilling operations represents a novel application of MCSA techniques. Given the evidence about the feasibility of identifying impact frequency via MCSA, the future scope of this research will address integrating impact frequency analysis and related motor current amplitude into observed-based MWD systems to develop more robust rock mass characterization analytical tools for industrial applications.  
Conceptualization, G.R. and G.C.; methodology, G.R. and R.G.; software D.B.; validation, D.B., R.G. and G.R.; formal analysis, D.B., G.R., R.G. and G.C.; investigation, G.R. and D.B.; writing--original draft preparation, G.R., G.C., R.G. and L.R.-C.; writing--review and editing, G.R., G.C., R.G. and L.R.-C.; visualization, G.R., G.C., R.G. and L.R.-C.; supervision, G.R.; project administration, G.R.; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Agency for Research and Development (Agencia Nacional de Investigacion y Desarrollo ANID) through project FONDECYT 11180768.