esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 3 Approach
### Switching Algorithm
For clarity, we dropped the subindex identifying the station ID from \(x(t)\) with all future references of \(x(t)\) referring to the collocation time of a particular charging station. As mentioned in the previous section, the node recognises a location by demodulating and extracting a station ID embedded into a charging signal, a functionality supported by energy harvesting development kits such as PowerCast [6]. The goal of the Kalman filter is to estimate a state \(x(t)\) from noisy observations \(z(t)\). The Kalman filter operates recursively in two steps. The first step predicts the future value of collocation time \(\hat{x}(t)^{-}\) based on past collocation history:  
\[\hat{x}(t)^{-}=a\hat{x}(t-1)+w(t) \tag{7}\] \[p(t)^{-}=a^{2}p(t-1)+Q \tag{8}\]  
After observing the actual measurement \(z(t)\), the filter corrects the prediction and updates the model in an update step:  
\[k(t)=\frac{hp(t)^{-}}{\hbar^{2}p(t)^{-}+R} \tag{9}\] \[\hat{x}(t)=a\hat{x}(t)^{-}+k(t)(z(t)-h\hat{x}(t)^{-})\] (10) \[p(t)=p(t)^{-}(1-hk(t)) \tag{11}\]  
where \(p(t)\), \(Q\) and \(R\) are a posteriori error covariance, process and measurement noise covariance matrices, respectively; \(k(t)\) is a Kalman gain, which is the relative weight given to recent measurement and the current state estimate. For the scalar Kalman filter, \(Q\) and \(R\) correspond to a variance of the process and the variance of the measurement noise, respectively. We refer the reader to [14; 15] for a detailed description of Kalman filter operation. The detailed steps of the algorithm are shown in Algorithm 1. Initially, the node boots on a small capacitor which enables it to operate in all environments and switches to a larger capacitor only if sufficient energy is available. The predicted amount of available energy is based on the expected collocation time \(x(t)\), and if it is sufficient to charge a larger capacitor, i.e., if \(P_{thresh}\times x_{i}(t)\geq CV^{2}/2\), it initiates a switch. The sensor node keeps operating on a large capacitor after it moves away from the charging station until the voltage drops below threshold \(V_{low}\), when it switches back to a small capacitor. As the capacitor voltage drops below \(V_{min}\), the sensor node shuts down, and the residual energy in the capacitor is gradually dissipated. ```
Functionswitch_cap() ifcurrentcap==SMALL_CAPthen if\(P_{RF}>P_{thresh}\)and\(P_{thresh}*x_{i}(t)\geq C_{large}\)then switch_large_cap0;  end if else if(Vc<Vswitch)then switch_small_cap();  end if end FunctionKF_predict() xapriori=a*xaposteriori papriori=a*xaposteriori + Q return(self.xapriori) FunctionKF_update(z) residual=z-h*xapriori k=h*papriori/(h*h*papriori+R) paposteriori=papriori*(1-h*k) xaposteriori=xapriori+k*residual whiletruedo if\(P_{RF}>P_{thresh}\)then \(x_{i}(t)\) =KF_predict(); tracking =TRUE; else if(tracking==TRUE)then KF_update(\(coloc\_time\));  end if end switch_cap(); sleep(interval);  end
```  
**Algorithm 1**Switching algorithm.
## 3 Results and Discussion
### Abrasion Resistance
#### 3.4.2 Internal Layer  
The abrasion loss per unit area on the internal layer of different IRPC under sulfate attack is shown in Figure 8. The abrasion resistance of the internal layer was different from that of the surface layer. When the replacement rate is greater than 25%, the abrasion loss per unit area of IRPC increases with the increase of the replacement rate and the number of wetting-drying cycles. Therefore, it can be seen that the influence of aggregate grades and replacement rate on the abrasion resistance of the internal layer of IRPC was more significant than that of the surface layer. This is because the surface layer was mainly the compact mortar layer on the specimen surface, while the inner layer contained weak links, such as RCA, attached mortar and ITZ. Sulfate erosion products mainly accumulated and expanded from the ITZ inside the specimen, which could easily affect the abrasion resistance of the internal layer.  
Figure 8: Abrasion loss per unit area on the internal layer of IRPC with variable replacement rates after different sulfate wetting-drying cycles.  
Figure 7: Abrasion loss per unit area on the surface layer of IRPC with variable replacement rates after different sulfate wetting-drying cycles.
# Circulating Tumor Cells in Metastatic Breast Cancer: Clinical Applications and Future Possibilities
## 3 Therapy Monitoring Using CTC Counts  
Large trials have already proven the prognostic relevance of CTCs in metastatic breast cancer. Furthermore, there is evidence that monitoring CTC dynamics during the course of therapy can predict treatment response [7]. In most breast cancer patients, CTC numbers declined rapidly under systemic therapies compared to those before therapy started [8]. In the study by [PERSON] et al., CTCs were measured in patients receiving palliative chemotherapy. In the multivariate analysis, response to therapy and survival was associated with CTC counts after the first cycle of therapy [9]. Respectively, monitoring of CTC numbers might be able to predict sooner than conventional radiological imaging whether patients will benefit from therapy. Nevertheless, which therapeutic strategy for non-responders should be offered remains unclear.  
In the randomized phase III SWOG S0500 trial (NCT00382018), CTC levels were determined in 595 metastasized patients before and during first-line chemotherapy [10]. Out of this study cohort, 123 patients had increased CTC numbers prior to chemotherapy as well as after the first cycle. Patients were randomized either to maintain therapy or switch to another chemotherapy regime. Treatment change based on CTC persistence did not improve PFS or OS in these patients (PFS 4.6 vs. 3.5 months, HR = 0.92; 95%CI = 0.64-1.32; OS 1.5 vs. 10.7 months, HR = 1.00, 95%CI = 0.69-1.47). It is possible that these patients might represent a chemo-resistant population independent of the cytotoxic drug used that requires targeted or alternative treatment approaches.  
Moreover, the currently ongoing multicenter randomized Phase III CirCe01 study (NCT01349842) monitors CTC dynamics for therapy guidance. Besides clinical tests and radiologic imaging, CTC counts are assessed for therapy response in CTC-positive patients after two lines of therapy. Patients without a significant decrease in CTC counts after the first cycle of new chemotherapy are switched to an alternative regime, which is also be monitored by CTCs. To date, there are no results published. Both large trials attempt to demonstrate that patients with persistently elevated CTCs counts under chemotherapies should be early switched to targeted or alternative therapies to avoid inefficient and toxic therapies.
# Transmission Angle Analysis of a Type of Parallel Manipulators
## 1 Introduction
The transmission/pressure angle seems to be a way to solve this problem, and in this way, the motion transmission performance of a mechanism can be evaluated during the course of design [20]. Though angle analysis has been extensively applied to planar mechanisms, little work has been conducted focusing on parallel mechanisms. A method for dynamic dimensional synthesis in [21] was proposed for the Delta robot, in which the kinematic performance could be partly ensured via introducing the constraint condition of the transmission angle. Notably, the number of limbs may exceed the needed numbers for actuating the moving platform since it has only three-DOF translations at most in space, which can lead the manipulator to actuation redundancy. Therefore, the approaches are also applicable to implement the kinematic design of the redundant Delta-like robot mentioned in [22]. The purpose of this paper is to define a transmission/pressure angle for a type of parallel manipulators to achieve the translations in space with the same topological structures applied in Delta and Diamond robots. As illustrated in [5], each limb is formed from an active link as well as a passive link, where the active link may be driven by a revolute or prismatic joint. One end of the passive link is connected with the active link, and the other end is connected with the movable platform through a joint, which can be a rotary joint, a universal joint or a spherical joint. The remainder of the paper is organized as follows: Section 2 presents the kinematic models and Jacobian matrix analytically. Subsequently, two types of transmission angles are developed in Section 3, and direct and indirect singularities can be identified in a direct way. In Section 4, taking Delta and Diamond as examples, the relationship between the Jacobian angle and the condition number is studied to demonstrate the validity of the angle in ensuring the motion performance of the parallel manipulator. In Section 5, conclusions are given.
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 6 Results
### Empirical Model Simulation  
The parametric identification of the empirical model was optimized using a PSO algorithm as discussed in Section 4. The input for the model simulation was based on the actual input-output data obtained from vertical load system tests. The input data consisted of random wave of input pressure excitation for both quasi-static and quasi-rate dependency tests. All geometrical values of the McKibben muscle was carefully identified and measured. These values include initial length, diameter, braid angle, etc. The output data was the contracting length of the McKibben muscle with different dependency tests. This actual data was compared with model data obtained from the simulation. The comparison of these two data provides a fitness data (i.e., objective function or error data), which were used for parametric identification and optimization.  
Figure 5: Force dynamics characteristics at various static load and input pressure.  
Figure 6a shows existing empirical model simulation. The model simulation was carried out while considering quasi-static dependency at 0.5 Hz without parameter optimization. As suggested from the literature, constant coulomb and viscous friction models were implemented to compensate the hysteresis behavior of McKibben muscle. It was observed from the result that the hysteresis phenomena cannot be completely captured. This because, the friction at the contacting surface between the rubber tube and braided mesh sleeve increased with static load. Therefore, the friction model became dependent to change in static load. This behavior was similarly observed when excitation frequency of the input pressure changed (rate dependency). Figure 6b shows the output of the proposed empirical model simulation. The empirical model was revised based on dynamic friction modification while considering quasi-static dependency at 0.5 Hz. The model parameters were optimized using PSO method. It was observed from the simulation that the hysteresis phenomena behave accordingly while retaining the level of hysteresis throughout quasi-static dependency. The model verification with actual hysteresis data in Figure 2, while considering all dependencies, is shown in Figure 7.
## 3 Results and Discussion
### Land Use Impacts of USA Ethanol Mandate
In conclusion, these analyses show that moving towards regional land transformation elasticities reduces the magnitude of land conversion and using the two-nest land cover nesting structure decreases the share of forest in land conversion. The combination of these two changes will contribute to lower estimations for induced land use emissions due to ethanol production. Consider now Figure 9 which compares the regional expansions in cropland areas obtained from experiments A and D. This figure shows that the geographical distribution of cropland expansion obtained from these two experiments are very different. Comparing Figures 7 and 9, it is clear that experiment D does a much better job of representing the actual land use changes seen over the past six years. There are still differences, but the new model which uses a two-nest land cover and implements regional land transformation elasticities represents a significant improvement. And while the ethanol shock is just one of many actual shocks, the land use changes likely play out in a similar way to the idealized case with all shocks present. Finally, compare Figures 10 and 11 which compare induced land use changes by land types for experiments A and D. Figure 10 shows that in experiment A the ethanol expansion causes  
Figure 9: Cropland expansion due to USA ethanol mandate based on experiments A and D.  
deforestation (by 0.36 MH) and conversion of pasture land (by 0.68 MH) in the US. But Figure 11, which represents the results of experiment D, results in much lower USA land cover change due to ethanol production (about 0.09 MH deforestation, 0.06 MH pasture conversions, and 0.15 MH expansions in cropland). In addition, these figures show that the model with the new modifications projects larger land conversions in Central and South America and Sub Saharan Africa.
# A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear
## 5 Supervised Learning of Neural Network Controller  
In order to solve these limitations of the hybrid controller, an intelligent controller based on a neural network is developed. The structure of the neural network is shown in Figure 10. The neural network consists of an input layer, a hidden layer, and an output layer. There are four input signals: stroke, stroke velocity, aircraft displacement, and aircraft velocity. These feed into the input layer. It should be noted that, after training, the controller determines the command for the MR damper with respect to the inputs, i.e., real-time state variables, and does not require any knowledge of system properties or models. The inputs are normalized to [01] before being applied into the hidden layer for efficient learning. The hidden layer has only a single layer with \(n\) neurons. The output of the neural network is the desired damping force command. The relationship between the input signal (\(x\)) and the output signal (\(y\)) can be given by:  
\[y=f_{2}(f_{1}(W_{1}\times x+b_{1})W_{2}+b_{2}) \tag{8}\]  
where:  
\[f_{1}(x)=\frac{2}{(1+e^{-2x})-1} \tag{9}\]  
\[f_{2}(x)=\begin{cases}0,&x<0\\ x,&1\geq x\geq 0\\ 1,&x>1\end{cases} \tag{10}\]  
The target data for the supervised learning are generated by the drop test experiments using the hybrid controller with exact mass and force information, the latter of which is measured by the load cell. The experimental data of the hybrid controller are divided into the training set and testing set. The training set contains the experimental results from 27 landing cases of the combinations of \(m_{1}=150\) kg, 190 kg, 230 kg, and \(v=1-3\) m/s with a step of 0.25 m/s. The testing set is composed of the experimental results from 18 landing cases of intermediate mass values \(m_{1}=170\) kg and 210 kg and the same sink speeds.  
Figure 9: Control scheme in practical operation environment.  
In this research, the neural network design toolbox in MATLAB [25; 26] is used to determine the optimal parameter in the neural network. To evaluate the performance of the neural network, the coefficient of determination \(R^{2}\) is defined by:  
\[R^{2}=1-\frac{\sum(y-\hat{y})^{2}}{\sum(y-\overline{y})^{2}} \tag{11}\]  
where \(\hat{y}\) represents the calculated values of \(y\) and \(\overline{y}\) is the mean of \(y\)[27]. Figure 11 shows the coefficient of determination for varying numbers of neurons with the testing set and training set. As the number of neurons increases, the coefficient of determination with training data also increases, but at a diminishing rate. The coefficient of determination with testing data is maximized when five neurons are used in the hidden layer, which seems to be optimal, and reduces after the point due to overfitting. All the parameters in the neural network after training are given in Table 2.  
Figure 11: The coefficient of determination over the number of neurons with the testing data and training data.  
Figure 10: The structure of the neural network.
# Sage-Husa Algorithm Based on Adaptive Double Forgetting Factors
## 1 Introduction
Second, double forgetting factors are introduced, where a sliding window method is employed to obtain the prior and posterior residuals. The forgetting factors are adaptively updated using an exponential form, ensuring stability within the range of 0.95 to 1. Finally, the decision to update the statistical characteristics of the measurement noise is made based on the comparison between the prior and theoretical residuals. Simulation results demonstrate the effectiveness and feasibility of the proposed improved algorithm. The remainder of this paper is arranged as follows: Section 2 describes the adaptive Sage-Husa filtering algorithm in detail. Section 3 provides the simulation results and discussions. The conclusions are described in Section 4.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 1 Introduction
In addition, in accordance with the fitness function value, we divided the population into three groups, namely, optimal, suboptimal, and poor populations. Then, we constructed a Gauss mutation to identify the optimal population in particle evolution. The IPSO algorithm can avoid the defects of the SPSO algorithm and exhibits superior performance. We experimentally analyzed the efficiency and correct rate of the IPSO-AR algorithm by using several ambiguity float resolutions with various dimensions and precision under different parameter settings. We identified the factors that affect the performance of the IPSO-AR algorithm, and recommended the optimal parameter settings for the IPSO-AR algorithm on the basis of corresponding characteristics. Then, we used one known baseline to further validate the feasibility of the IPSO-AR algorithm. Finally, we presented some related conclusions.
# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Data Mining Technologies for Smart Cities  
In a smart city application, the production of analytics can lead to advanced insights, a better understanding of city phenomena, and supports the design of evidence-based urban strategies and innovation [53; 58]. Searching for interesting patterns and correlations [3] in the public-service facilities of developed cities using a DM approach has gradually become a significant area of research. The extracted patterns can be used to plan layouts or arrange new facilities in cities [59]. Advancements of big DM technologies can support, explore and discover environmental and societal changes, including how people go about their life, behavior, and preferences; social trends, and public opinion [45; 60]. DM and ML are vital technologies for data-centric applications for smart cities.  
DM is a broad field that includes many algorithms and techniques from statistics to ML and information theory to extract information from data [57]. DM aims to build computer programs that extract hidden, previously unknown, and potentially valuable information from data [61]. The process must be automated or, more usually, semi-automated, and the regularities or patterns discovered must be meaningful in a practical sense [62]. Big DM needs to extend the entire process to the front and back end, under the characteristics of big data. This involves processing and analysis of massive and heterogeneous data, automatically discovering and extracting implicit, hidden patterns, rules, and knowledge, and visualizing them in an easily understandable form [45].  
ML is the study of how to build computer programs that improve their performance at some tasks through experience [63] to address problems in which human expertise does not exist or when it is difficult to express it [64]. With this technology, the algorithm is training computers to learn from a past experience \(E\) regarding task \(T\) and some performance measure \(P\), if its performance improves on task \(T\), with experience \(E\), computed by \(P\)[62].  
Essentially, ML is an application of AI that provides computers with the ability to learn from data and provide relevant insights that increase operational performance from experience from data without being programmed [65]. According to [PERSON] et al. [66], ML is classified into four categories: supervised learning, unsupervised learning, semi-supervised learning, and active learning [62]. [PERSON] et al. [67] propose a typical ML pipeline (Figure 4) that starts with the raw data as input, clustering to further preprocess the data before the actual classification is performed. Finally, the computed results are displayed.
## 3 Results  
### Components of Eastern Guangdong MSW  
The statistics of the mass fractions of MSW components in the dry basis were illustrated in Figure 2 as box plot. The result showed that the MSW was majorly constituted by soil, glass, metal, paper, plastic, textile, grass, food waste, and white plastic. The order of mass fraction of each component in the dry basis was food waste \(>\) plastic \(>\) soil \(>\) paper \(>\) glass \(>\) grass \(>\) textile \(>\) white plastic \(>\) metal. Among all of the components, food waste accounts for 13.71-35.09%, with an average of 24.64%; plastic accounts for 10.53-28.90%, with an average of 17.95%; soil accounts for 8.86-32.32%, with an average of 16.98%; paper accounts for 4.83-26.78%, with an average of 13.99%; glass accounts for 0-20.87%, with an average of 8.06%; grass accounts for 0.90%-14.77%, with an average of 6.60%; textile accounts for 0-14.04%, with an average of 5.58%; white plastic accounts for 0.33-9.95%, with an average of 3.93%; metal accounts for 0-8.22%, with an average of 2.26%.
## 4 Discussion
### Efficient and Reliable Information-Sharing Strategy  
Most of the existing cooperative perception information-sharing strategies are implemented in the form of broadcasting [86]. As opposed to unicast and multicast, broadcast messages do not have an acknowledgement mechanism, which not only increases the burden on wireless channels but also makes it difficult to ensure the reliability of perception information transmission when the channel is congested. Perceptual information-sharing is carried out in the form of broadcasting, and the information about the same object may be shared with the autonomous vehicle multiple times, as shown in Figure 12. Vehicle C receives two pieces of information about vehicle X from vehicle A and vehicle B. At this time, vehicle C will perform target matching on the received objects to determine whether they are the same object, which will not only occupy more communication resources but also increase computational tasks for autonomous vehicles. In addition, vehicle C may not need to pay attention to the information of vehicle X so that the path planning and decision-making can be carried out normally. Therefore, proper redundant data sharing helps improve the accuracy of perception, but too much redundant information is obviously not beneficial.  
The communication resources of the vehicle network are very precious, so it is necessary to carry out reasonable task scheduling and resource allocation to the edge nodes. Through the reasonable optimization of the CPM-sharing strategy, the information in the area of interest of the vehicle is selectively shared, and the redundancy of data is minimized under the requirements of timeliness and accuracy. At the same time, the deployment of reasonable network technology will effectively reduce the waste of communication resources and realize the sharing of perception information between autonomous vehicles under the  
Figure 12: Broadcast-style sharing of cooperative perception data.  
constraints of limited communication resources according to different traffic flow, environmental complexity, and vehicle dynamics. Therefore, a reasonable information-sharing strategy will be the focus of future research on cooperative perception.