esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Technology  
### Interactive Multiple Models Algorithm Based on Factor Graph Optimization  
The traditional IMM algorithm divides the motion states of vehicles into sub-systems and updates probability weights to obtain the final state estimation by calculating the weight values of each system, thus possessing adaptive characteristics [40]. We propose an improved interactive multiple model vehicle navigation algorithm based on factor graph optimization, which sets the measurements of sensors as sub-systems and adjusts weights in factor graph global estimation. The state equation of the sub-systems is the same as that of sensor nodes, and only position parameters are employed to adjust the weights through the IMM updating process. Then, the state function of the sub-model \(m_{n},n\in(1,2,3,\ldots)\) can be described as follows:  
\[\begin{split}\mathbf{Z}_{k}^{n}=\mathbf{H}^{IMM}\mathbf{X}_{k}+ \mathbf{V}^{n}\\ \mathbf{H}^{IMM}=\begin{bmatrix}\mathbf{0}_{3\times 6}&\mathbf{I}_{3\times 3}&\mathbf{0}_{3\times 6} \end{bmatrix}\end{split} \tag{16}\]  
where \(n\) represents the type of sub-models and \(\mathbf{Z}_{k}^{n}\) represents the measurement of sub-models at time \(k\). \(\mathbf{H}^{IMM}\) represents the measurement functions and only the positioning information is considered for IMM models. \(\mathbf{V}^{n}\) represents the error matrix and is the same as the error matrix of sensors. Then, the residual can be defined as follows:  
\[\mathbf{r}_{n}(\mathbf{Z}_{k}^{n},\mathbf{X}_{k})=\begin{bmatrix}\mathbf{p}_{ k}^{n}-\mathbf{p}_{k}^{W}\\ \mathbf{v}_{k}^{n}-\mathbf{v}_{k}^{W}\end{bmatrix} \tag{17}\]  
where \(\mathbf{p}_{k}^{n}\) and \(\mathbf{v}_{k}^{n}\) represent the position and velocity of the sub-model \(n\) at time \(k\), respectively. The information obtained from sub-models is transformed into the world frame. Then, the overall process of the IMMFGO algorithm can be constructed, as in Figure 3.  
Figure 3 shows that the IMMFGO algorithm consists of three parts: (1) Input interactions of models, (2) smoothing and fusion of models, and (3) probability update of models. Parts (1) and (2) can provide information for part (3), and the final updated values will optimize the global estimation in part (2) for the next moment. In this paper, only two sub-models are considered, and the function describes the formula of a single sub-model in the case of two sub-models. The number of the sub-models is two; sub-model \(m_{1}\) is the  
GNSS navigation system and sub-model \(m_{2}\) is the LiDAR navigation system. The formula is derived based on two sub-models, and the process of each part is shown as follows.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
###### Abstract  
Analysis of pile bearing capacity is an important task in the investigation of soil-structure interaction. The paper is dedicated to the prediction methods for the pile bearing capacity calculation based on the cone penetration test (CPTu) results, namely UniCone method, Laboratoire Central des Ponts et Chaussees method (LCPC), and the method involved in the Eurocode 7--2. A set of CFA piles was tested to obtain reference bearing capacity. The ability of the prediction methods to determine the bearing capacity of the pile was investigated. In each evaluation criteria using statistical tools, the methods were ranked based on their performance. The results of the study indicate that the UniCone method is most applicable for the given conditions. The EC 7--2 method showed the largest variability of results, and we do not recommend its application without a deeper analysis. The applicability of any presented method cannot be considered final or universal. It is advisable to use more modern and updated methods which have been developed from a larger database of pile tests. The development of these methods should continue by expanding the database of tested piles together with the application of more advanced rock environment testing procedures.  
CFA piles; cone penetration test; pile bearing capacity; static pile load test +
Footnote †: journal: Applied Sciences
## 3 Discussion
### Spin State Transition
\(\Delta\rho\) oscillates when \(\omega_{12}\) increases, but the tendency of \(\Delta\rho\) is increased. The amplitude of the oscillation decreases with the augment of the pulse width \(\tau\). \(\Delta\rho\) is approximately proportional to \(\omega_{12}\) when the pulse width increases to 300 fs. Because of the short pulse width, the oscillation of electrons suddenly stops before the oscillation reaches equilibrium, whereas the envelop of the electric field with a long pulse width changes gradually so that electron oscillation can follow the variation of electric field. Therefore, the determination of the final spin state resulting from the duration of laser is not important when the pulse width is greater than 100 fs. Figure 3: (**a**) The frequency spectrum of the femtosecond laser compared with the energy gap between 1\(>\) and 1\(>\) (\(\omega_{12}\)), 1\(>\) and 1\(>\) (\(\omega_{0}\)), 13\(>\) and 1\(>\) (\(\omega_{23}\)). The intensity of electronic filed \(\omega_{12}\) is twice that of \(\omega_{23}\). (**b**) Dependence of \(\rho_{3}-\rho_{1}\) on time. \(\rho_{3}-\rho_{1}\) represents the occupation probability difference between states 1\(>\) (spin-up) and 1\(>\) (spin-down). The spin determines the orientation of magnetization. The wavelength and intensity-related spin-flip supplies a channel to control the final state of the magnetization. It is obvious that the spin states keep balanced when E\({}_{12}\) = E\({}_{23}\) in Figure 4. When the sum of \(\omega_{12}\) and \(\omega_{23}\) is still 2.1 eV and \(\omega_{12}\) decreases from 1.05 to 0.5 eV, \(\Delta\rho\) will be odd symmetric to the flat of W12 = 1.05 in Figure 4. Therefore, by adjusting the distribution of the relative intensities that correspond to \(\omega_{12}\) and \(\omega_{23}\), the area where the magnetization is reversed can be smaller than that of the incident laser.
## 4 Results
### Optimization Parameters  
For optimization 24 h in advance, 2500 calculation runs proved to be sufficient, resulting in 2:30 h of computation time on a dedicated off-the-shelf personal computer (Core I7-7700, 4 cores, 3.6 GHz). Thereby, in the simulation timespan, it was necessary to include the previous and following 3 h in addition to reducing the settling time at start up and to include a possible rebound effect. A crossover probability of 0.5 and a mutation probability of 0.5 have proven to be most efficient. In addition, with very little computational demand (e.g., 4-600 simulation runs), good results might randomly occur. For a further implementation on mini PCs, it is planned to change the thermal demand prediction to an RC model or a self-learning algorithm. It is estimated that this would reduce the computational demand by a factor of 10.  
Figure 23: Composition of the equivalent annual cost of heat generation and specific heat price for different heating technologies in Germany compared to the costs in Wüstenrot (July 2019).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Investment Cost** & **Subsidy** \\ \hline Heat pump [€] & 10,000 & 5000 \\ Space heating buffer storage [€] & 1300 & 500 \\ DHW buffer storage [€] & 400 & \\ Battery storage [€] & 7000 & 7000 \\ PV system incl. inverter [€] & 17,000 & \\ DHC network connection costs [€] & 3240 & \\ Heat distribution and transfer station [€] & 9000 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Investment costs within an individual building. DHW: domestic hot water.
## 2 Technologies for Purifying the Tail Gases from N2O and NOx
The selective catalytic reduction of NOx with ammonia, depending on the temperature, oxygen content and NO\({}_{2}\)/NO\({}_{\rm x}\) molar ratio in the purified tail gases, can proceed according to the following reactions:  
\[4{\rm NO}+4{\rm NH}_{3}+{\rm O}_{2}\to 4{\rm N}_{2}+6{\rm H}_{2}{\rm O} \tag{8}\]  
\[{\rm NO}+{\rm NO}_{2}+2{\rm NH}_{3}\to 2{\rm N}_{2}+3{\rm H}_{2}{\rm O} \tag{9}\]  
\[2{\rm NO}_{2}+4{\rm NH}_{3}+{\rm O}_{2}\to 3{\rm N}_{2}+6{\rm H}_{2}{\rm O} \tag{10}\]  
Under non-optimal process conditions for a given type of catalyst, undesirable side reactions may occur in the reactor, leading to the formation of N\({}_{2}\)O and NO [28; 29; 30; 31]. The selective catalytic reduction of NOx with ammonia is usually carried out at a temperature higher than 200 \({}^{\circ}\)C, due to the risk of ammonium nitrate accumulation in the reactor. The upper limit of the process temperature is determined by the type of catalyst and the maximum operating temperature of the expansion turbine [32]. In the case of oxide catalysts based on transition metals (e.g., V\({}_{2}\)O\({}_{5}\)), it should be equal to or lower than 400 \({}^{\circ}\)C, because at higher temperatures the share of undesirable side reactions of ammonia oxidation to N\({}_{2}\)O and NO in the deNO\({}_{\rm x}\) process increases [33]. In the case of some zeolites modified with iron ions, it is possible to conduct the deNO\({}_{\rm x}\) process at temperatures up to 600 \({}^{\circ}\)C [34; 35]. Moreover, for this type of catalyst, using a temperature \(>\)400 \({}^{\circ}\)C is beneficial due to the increase in their activity in N\({}_{2}\)O reduction with ammonia (e.g., in the presence of Fe-ZSM-5 zeolite) [17; 36; 37]. Based on the scientific and patent literature, it can be concluded that the preferred catalysts for the deNO\({}_{\rm x}\) process are: oxides of transition metals [38; 39; 40; 41], rare earth metals [42] and zeolites modified with transition metals ions [31; 43; 44; 45; 46]. In the case of metal oxides, they are often deposited on ceramic supports, i.e., TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), ZrO\({}_{2}\) and CeO\({}_{2}\), which increase the mechanical strength of the catalyst and the stability of its operation. Commercial catalysts used in nitric acid plants in the deNO\({}_{\rm x}\) process are mainly V\({}_{2}\)O\({}_{5}\) supported on Al\({}_{2}\)O\({}_{3}\)[47] or TiO\({}_{2}\)[29; 30; 33; 48] and high-silicon zeolites (MFI, BEA, FER, MOR, FAU, MEL, SAPO-18/SAPO-34 and most often ZSM-5) [49; 50; 51] modified with transition metal ions, i.e., Cu (Cu-CHA [31; 52], Cu-ZSM-34 [44]) and Fe (e.g., Fe-BEA [34], Fe-ZSM-5 [35; 43]). Some of the literature and patent studies focus on methods of NOx removal from the tail gases stream in a two-stage process, combined with N\({}_{2}\)O removal.
## 7 Conclusions  
The article presented the serious game \"Exploring Ancient Greece\" that aims to contribute to the learning of history by Primary and Junior High School students by utilizing AR technology. The participants in the study were Primary and Junior High School students who had previously been taught the history of Ancient Greece in school.  
Based on the research results collected from the pre/post questionnaires completed by the students before and after engaging with the game, there was an 8.4% improvement in performance (RQ1). However, due to the small sample size, further research with a larger population sample is needed to confirm these results and ensure they are acceptable. From the students' responses in the game evaluation questionnaires, it is concluded that all students believe that the use of serious games and AR in education attracts their interest, making them enjoy the lessons more (RQ2). The students' experience playing the game was evaluated using the SUS questionnaire, asking questions on their game-playing experience and asking verbal questions during the game. It was found that the \"Learning through Traveling\" level, which included a tour of archaeological sites using AR, was their favorite. Additionally, the vast majority of students responded that they would play the game again; more than half the students found it easy to use; and just one student considered the game to be too time-consuming (RQ3).  
Finally, from the results of the above research and the studies examined, we can argue that the use of serious games with AR in education have the potential to improve both student performance and make the lesson more engaging. Moreover, this study has showcased ways of utilizing AR in serious games for history, creating a digital reality on top of the physical world that seems to give students the chance to explore places of historical interest at no cost. However, it is necessary to replicate the study with a larger number of participants in order to confirm the positive results both in terms of player experience and the learning effectiveness of the game.  
Conceptualization, A.B. and S.X.; methodology, A.B. and S.X.; software, A.B.; validation, A.B. and S.X.; investigation, A.B. and S.X.; data curation, A.B. and S.X.; writing--original draft preparation, A.B.; writing--review and editing, S.X.; visualization, A.B.; supervision, S.X. All authors have read and agreed to the published version of the manuscript.
# Learning Curve in Robotic-Assisted Total Knee Arthroplasty: A Systematic Review of the Literature
## 1 Introduction  
Total knee arthroplasty (TKA) is a safe and successful surgical procedure for end-stage knee osteoarthritis treatment [1, 2]. More than half a million surgeries are performed annually in the United States, and epidemiological studies predict a more than 650% increase by 2030 [3]. Despite high long-term survival of more than 90% after ten years, about 20% of patients are dissatisfied with their surgeries [1, 2, 3, 4].  
In recent years, several innovations have been introduced into clinical practice to improve TKA outcomes, such as upgraded materials, minimally invasive surgery, innovative TKA alignment, computer-assisted surgery (CAS), intraoperative navigation, augmented reality, and robotic-assisted (ra) surgery [5, 6, 7, 8, 9, 10, 11]. Ra surgery has fascinated many since its beginnings around 1980. Ra procedures allow surgeons to perform complex operationswith greater precision, flexibility, and control than conventional techniques and have become a staple in operating rooms worldwide. In recent decades, considerable technological advances have made robotics applications possible in multiple surgery fields, including orthopedics [12]. Robotic-assisted TKA (raTKA) has gained popularity, and its use is steadily growing [13; 14]. Despite its theoretical advantages, raTKA raises some concerns, such as increased cost and operative time, absence of long-term follow-up studies, and a significant learning curve to refine the technique [8; 14]. A not-yet well-defined learning curve is one of the major concerns of surgeons approaching raTKA [15]. The learning curve has a key role in training surgeons by allowing improved performance, accuracy, and reduced surgical time [16]. It is characterized by three phases: a rapid improvement through the first cases, a successive modest but steady improvement achieved with increased experience, and finally, a plateau phase where additional experience does not influence improvement. The \"inflection point\" is defined as the number (n) of procedures required to achieve proficiency in each stage of the surgical procedure [16; 17]. Identifying the learning curve of raTKA has several clinical applications for patient safety, surgeon training, and cost-effectiveness.  
The primary purpose of this systematic review is to analyze the surgical time learning curve of raTKA. The secondary aims are to investigate the learning curve associated with implant placement accuracy and complications related to raTKAs.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 3 Existing Frequency Contour-Line Method with Limitations in Portraying Damage
### Limitations in Frequency Contour-Line Method
#### 3.3.2. Incompetence to Identify Cracks in Noisy Conditions  
In terms of the contour-line method, when experimentally obtained eigen-frequencies, \(\omega_{i},\ \mathrm{i}=1,2,\cdots,n\), are exactly the points of the eigen-frequency panorama of the beam, the intersection point of projection curves, stemming from intersection contour lines between the eigen-frequency panorama and virtual horizontal planes incurred by \(\omega_{i},\ \mathrm{i}=1,2,\cdots,n\), can exactly point out the location and depth of the crack. Such a procedure is illustrated in Figures 2-5, where we assume that the experimentally obtained three eigen-frequencies,  
Figure 5. Frequency projection lines. (**a**) projection lines with common intersection point O. (**b**) Zoomed-in point O indicating crack parameter.  
\(\omega_{i},\;\mathrm{i}=1,2,\cdots,n\) are exactly the points of the eigen-frequency panorama, the intersection point O designates the genuine crack. Experimental acquisition of eigen-frequencies, \(\omega_{i},\;\mathrm{i}=1,2,\cdots,n\), is another key component for crack identification relying on the frequency contour-line method. During the course of actual testing, random noise is inevitably involved in the acquired eigen-frequencies, leading to \(\omega_{i}^{*},\;\mathrm{i}=1,2,\cdots,n\), which are deviated from the exact natural frequencies \(\omega_{i},\;\mathrm{i}=1,2,\cdots,n\). As performing the frequency contour-line method to identify a crack, such deviations will further pass down into the frequency contour lines and, further, the frequency projection lines in the \(x\)-\(y\) plane. Finally, the frequency projection lines do not intersect at a common point, as illustrated in Figure 6, where the noise-incurred deviation of \(\omega_{i}^{*},\;\mathrm{i}=1,2,\cdots,n\) from \(\omega_{i},\;\mathrm{i}=1,2,\cdots,n\) is \(0.1\%\) in the magnitude of the latter. In such a situation, [PERSON] and [PERSON] [45] posed a method to estimate the crack location and depth from a chosen intersectional triangle of projection curves. Nevertheless, different results of crack identification can be realized for a single crack when considering the noise randomness. Therefore, the frequency contour-line method cannot judicially reflect a crack under noisy conditions.
###### Abstract  
Real-time hybrid simulation (RTHS) is a versatile, effective, and promising experimental method used to evaluate the structural performance under dynamic loads. In RTHS, the emulated structure is divided into a numerically simulated substructure (NS) and a physically tested substructure (PS), and a transfer system is used to ensure the force equilibrium and deformation compatibility between the substructures. Owing to the inherent dynamics of the PS and transfer system (referred to as a control plant in this study), there is a time-delay between the displacement command and measurement. This causes de-synchronization between the boundary of the PS and NS, and affects the stability and accuracy of the RTHS. In this study, a Kalman filter-based adaptive delay compensation (KF-ADC) method is proposed to address this issue. In this novel method, the control plant is represented by a discrete-time model, whose coefficients are time-varying and are estimated online by the KF using the displacement commands and measurements. Based on this time-varying model, the delay compensator is constructed employing the desired displacements. The KF performance is investigated theoretically and numerically. To assess the performance of the proposed strategy, a series of virtual RTHSs are performed on the Benchmark problem in RTHS, which was based on an actual experimental system. Meanwhile, several promising delay-compensation strategies are employed for comparison. Results reveal that the proposed time-delay compensation method effectively enhances the accuracy, stability, and robustness of RTHS.  
Keywords:real-time hybrid simulation; adaptive delay compensation; Kalman filter; feedforward; Benchmark +
Footnote †: journal: Applied Mechanics and Physics Letters
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 3 Simulation
However, in Type-4 precoder, the \(MS\) needs processing resources to remove the IBI from adjacent cells. Note that, in the Type-4 case, the precoder at the \(BS\) removes only the interference from adjacent beams in the serving cell, while the combiner at the \(MS\) is designed to remove interference from adjacent cells. Table 2 also shows computational complexities required for the \(BS\) and \(MS\) (sensor node) when the proposed precoders are used. As can be seen in Table 2, all the computations required for the design of precoder (except Type-4 precoder) are performed at the \(BS\). Thus, Type-1, Type-2, and Type-3 precoders can be easily applied to sensor networks because no additional hardware and processing resources are not required for sensor nodes. However, it may be difficult to apply the Type-4 precoder to typical sensor networks because the Type-4 precoder requires the hybrid beamformer and processing resources in sensor nodes. Figure 7 compares the BER performances of different hybrid beamforming techniques when we used the channel estimated by the CBRS technique. As can be seen in this figure, the BER performances of the proposed hybrid beamforming techniques are slightly degraded owing to the channel estimation error, compared with the results in Figure 4. The channel estimation error comes from the noise part in the received signal because mobility is not considered in this simulation. However, the overall trend in the performance is similar. Note that the BER performances of \"Analog-only Beamforming\" and \"Single-cell, Single-user\" are the same as in Figure 4 because the channel information is not used in these cases. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Processing Time** & **Example** \\ \hline CBRS technique & \(T_{S}\) & 13.33 \(\upmu\)s \\ \hline \multirow{4}{*}{TDBS technique} & Type-1 precoder & \multirow{2}{*}{\(T_{S}\times B\times U\)} & \multirow{2}{*}{200 \(\upmu\)s} \\  & Type-2 precoder & \multirow{2}{*}{\(T_{S}\times B\times U\)} & \multirow{2}{*}{600 \(\upmu\)s} \\  & Type-4 precoder & & \\ \cline{1-1} \cline{2-3}  & Type-3 precoder & \multirow{2}{*}{\(T_{S}\times B^{2}\times U\)} & \multirow{2}{*}{600 \(\upmu\)s} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Processing time required for channel estimation in the proposed and conventional techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Type-1** & **Type-2** & **Type-3** & **Type-4** \\ \hline \multirow{2}{*}{\(BS\)} & Matrix dimension of \(\hat{\mathbf{H}}^{\mathbf{h}_{n}}\) & \(BU\times U\) & \(BU\times BU\) & \(BU\times BU\) & \(U\times U\) \\  & Computational complexity for \(\mathbf{F}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{ \mathbf{h}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{\mathbf{h}^{ \mathbf{h}^{\mathbf{h}}^{\mathbf{h}}^{\mathbf{h}}^{\mathbf{h}}^{\mathbf{h}} }}}}}}}}}}}\) calculation & \(O((BU)^{2}U)\) & \(O((BU)^{3})\) & \(O