esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation of STST Based on TVCM-VPF  
### Prototype Structure  
A three-story steel frame structure with a TLD was chosen as the prototype structure to validate the effectiveness of the proposed method. The structure has spans of 1840 mm in the X direction and 2040 mm in the Y direction, and an inter-story height of 1200 mm, as shown in Figure 3. This frame was composed of Q235-grade steel with a yield strength of 235 MPa, and an elastic modulus of 206 GPa. The columns were made up of square steel tubes, each \(40\times 40\times 3\) mm, and the beams were formed by U-shaped steel, each \(100\times 50\times 5\) mm. Two vertical herringbone braces consisting of steel angles, each of \(40\times 40\times 3\) mm, were arranged in the X direction of each story to suppress the torsional rotation around the vertical axis of the structure. A herringbone brace was arranged in the horizontal plane of each floor to improve the floor stiffness. Each brace was rigidly connected to the frames or beams. The structure was excited by earthquake excitations and tested in the Y direction.  
Figure 2: Shaking table substructure testing (STST) based on a three-variable control method with velocity positive feedback (TVCM-VPF).  
Concrete block masses of 250 kg were placed on the first and second floors of the frame to simulate the weight on each floor. One TLD was welded on the third floor of the frame. The total mass of the TLD and the fixed bottom plate was approximately 252 kg, used as the third-floor counterweight. The total mass of the whole frame was approximately 1120 kg. The fundamental circular frequency of this frame structure in the Y direction was \(\omega_{1}=17.9\) rad/s, obtained by the spectrum analysis of the structure under a white noise excitation test on the shaking table.  
The TLD was well designed according to the optimal control effect for the three-story frame structure; the configuration of the TLD is shown in Figure 4. To control the fundamental vibration mode of this frame, the fundamental sloshing frequency of the liquid in the TLD should be consistent with that of the structure. The single water tank was designed with \(a=90\) mm along the Y direction, \(b=600\) mm along the X direction of excitation, and a height of 200 mm. The water depth in the tank, \(h=40\) mm, was determined to ensure the fundamental circular frequency of the frame as \(\omega_{1}=17.9\) rad/s. In the frame structure, the TLD was composed of 24 water tanks to meet the expected mass ratio requirement of 4%.  
Figure 4: Configurations of tuned liquid damper (TLD)in shaking table test (STT) and in shaking table substructure testing (STST).  
Figure 3: Schematic diagram of the prototype steel frame.
## 3 Results and Discussion
### Temporal Dynamics of the Electrical Current Density
Once the coalesced bubble detaches from the surface, a rapid increase in the electrical current density occurs, resulting in a local maximum and providing maximal active area of the microelectrode surface exposed to the electrolyte, thus enabling the growth of the next hydrogen bubble. The total volume of the produced hydrogen can be calculated using [PERSON]'s law, which states the quantitative relationship between the substance deposited at electrodes and the quantity of electric charge or electricity passed. The mass of a substance deposited at any electrode is directly proportional to the amount of charge passed. The equation for the theoretically produced volume of hydrogen during electrolysis (\(V_{\text{calc}}\)) is calculated using the following equation:  
\[V_{\text{calc}}=\frac{E\ I\ \Delta t}{F\ M_{\text{H}_{2}}}\cdot\frac{r\ T}{p}, \tag{7}\]  
where \(E\) is the equivalent weight (chemical equivalent of the substance), \(I\) the measured electrical current, \(\Delta t\) the time period of interest, \(F\) the Faraday constant (96,485 C mol\({}^{-1}\)), \(M_{\text{H}_{2}}\) the molar mass of hydrogen, \(R\) the ideal gas constant, \(p\) the pressure, and \(T\) the temperature. Using Equation (7), the volume of hydrogen produced during the measurements is shown versus time in Figure 7 for potentials between 2.9 and 3.5 V. The total volume of produced hydrogen increases linearly as the electrical current density remains almost constant after the initial drop at the beginning of the measurement. The power used for hydrogen generation \(P_{el}\) can be simply calculated from the electrical current \(I\) and the applied potential \(\Delta U\) as  
\[P_{el}=U\ I. \tag{8}\]  
The chemical energy content of the where \(V_{H_{2}}\) is the calculated volume of hydrogen, \(\rho_{H_{2}}\) represents the density of hydrogen at NTP (0.08376 kg m\({}^{-3}\)), \(HHV_{H_{2}}\) denotes the higher heating value of hydrogen (141,860 kJ kg\({}^{-1}\)) and \(\Delta t\) is the elapsed time. In Figure 8, the efficiency of electrolysis is assessed by comparing the volume of hydrogen produced to the theoretical volume based on Equation (7), representing the equivalent of electrolysis efficiency. The volume of generated hydrogen, as determined from high-speed video recordings, is compared to the theoretical volume, which is estimated based on the applied electrical current. Individual frames from the high-speed video recordings were analyzed to measure the radii of several hydrogen bubbles at the moment of detachment from the electrode surface. Assuming a spherical bubble shape, their volumes were calculated accordingly. To determine the hydrogen production rate, the growth duration of each evaluated bubble was measured.
## 4 The Back-Analysis Approach in Geomechanical Problems
In the simplified case that there are only two geomechanical parameters to be \"calibrated\" through back-analysis (x and y, for example), the calculation procedure is simplified: it essentially aims to describe the results of the numerical analyses considering all the established combinations of the two geomechanical parameters (x\({}_{i}\), y\({}_{j}\)) within the variability range considered valid for each of them. If we call f\({}_{k}\) the k-th function that describes a certain result of the numerical calculation (for example, the displacement in a certain node of the model), depending on the uncertain parameters x and y, we can define the error function in the following way:  
\[\epsilon_{i,j}=\frac{1}{m}\cdot\sum_{k=1}^{m}\left[\frac{\eta_{k}-f_{k}\big{(} x_{i},y_{j}\big{)}}{\eta_{k}}\right]^{2}=\frac{1}{m}\cdot\sum_{k=1}^{m}\left[1- \frac{f_{k}\big{(}x_{i},y_{j}\big{)}}{\eta_{k}}\right]^{2} \tag{1}\]  
where  
\(\epsilon_{ij}\) is the mean specific quadratic error function (always positive) which depends on the value of the single uncertain parameter (x\({}_{i}\) and y\({}_{j}\)), the in situ measurements being known;  
\(\overline{\eta}_{k}\) is the k-th in situ measurement obtained during the underground work construction and refers to a specific result of the calculation f\({}_{k}\)(x\({}_{i}\),y\({}_{j}\));(x\({}_{i}\),y\({}_{i}\)) is the combination of two uncertain input parameters chosen inside the variability interval of each of them (x\({}_{\rm min}\leq\) x\({}_{\rm i}\leq\) x\({}_{\rm max}\)), (y\({}_{\rm min}\leq\) y\({}_{\rm i}\leq\) y\({}_{\rm max}\));  
m is the number of in situ measurements used to develop the back-analysis procedure. In this specific case, the elastic modulus E of the marble (left side of the bench) and the coefficient k\({}_{0}\) were considered uncertain. For the elastic modulus, the variability range of 30-40 GPa was adopted, while for the k\({}_{0}\) the range of 1.0-1.5 was adopted. Each interval was divided into five subintervals in order to obtain six possible values of the two parameters (including the interval extremes): E = 30, 32, 34, 36, 38, 40 GPa; k\({}_{0}\) = 1.0; k\({}_{0}\) = 1.1; k\({}_{0}\) = 1.2; k\({}_{0}\) = 1.3; k\({}_{0}\) = 1.4; k\({}_{0}\) = 1.5. The total number of combinations was, therefore, 36. The number of measurements considered reliable and, therefore, suitable for use in the back-analysis procedure was eight (m = 8): four displacement measurements and four stress measurements. Thanks, therefore, to the comparison between the carried out measurements and the corresponding calculation results, it was possible to calculate 36 different error values (from \(\varepsilon_{1,1}\) to \(\varepsilon_{6,6}\)) based on Equation (1). The minimum error (\(\varepsilon_{\rm min}\) = min(\(\varepsilon_{\rm ij}\))) allowed for the identification of the following parameter values, calibrated thanks to the back analysis:  
E = 36 GPa (marble left side); k\({}_{0}\) = 1.4.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 3 Gesture-Based Teleoperation System for Robot Motion
### Hand/Tool Motion Scaling and Control
#### 3.2.2 Single-Axis Motion  
In some circumstances, the robot is required to move along an axis. In order to increase the robustness, a single-axis motion mechanism is proposed. During the demonstration, in \(t\) and \(t+1\) time steps, suppose the hand positions are \({}^{\Sigma_{L}}P_{t}\) and \({}^{\Sigma_{L}}P_{t+1}\). The offset value is  
\[\Delta P_{t+1}=\left[\begin{array}{c}\Delta x_{t+1}\\ \Delta y_{t+1}\\ \Delta z_{t+1}\end{array}\right]={}^{\Sigma_{L}}P_{t+1}-{}^{\Sigma_{L}}P_{t}. \tag{5}\]  
The gradient, i.e., the axis with maximum offset, is treated as the desired movement direction. Ignoring other movement, one gets  
\[\Delta\bar{P}_{t+1}=\left\{\begin{array}{c}\left[\begin{array}{ccc}\Delta x _{t+1}&0&0\\ \end{array}\right]^{T}&\left|\Delta x_{t+1}\right|=\max\left(\left|\Delta P_{t +1}\right|\right)\\ \left[\begin{array}{ccc}0&\Delta y_{t+1}&0\\ \end{array}\right]^{T}&\left|\Delta y_{t+1}\right|=\max\left(\left|\Delta P_{t +1}\right|\right)\\ \left[\begin{array}{ccc}0&0&\Delta z_{t+1}\end{array}\right]^{T}&\left| \Delta z_{t+1}\right|=\max\left(\left|\Delta P_{t+1}\right|\right)\end{array}. \tag{6}\]  
It is noticeable that in single axis mode, the mapping Equation (4) cannot be used. The first reason is that the offset is evaluated between \(t\) and \(t+1\) instead of \(t_{0}\). The second reason is that the accumulation of ignored movements may cause serious negative effects when the principal axis changes. Therefore, Equation (4) was modified into  
\[{}^{\Sigma_{B}}\mathbf{r}_{t+1}=^{\Sigma_{B}}\mathbf{r}_{t}+\mathbf{s}\times ^{\Sigma_{B}}R_{\Sigma_{L}}\times^{\Sigma_{L}}\Delta\bar{P}_{t+1}, \tag{7}\]  
where \({}^{\Sigma_{B}}\mathbf{r}_{t}\) and \({}^{\Sigma_{L}}\Delta\bar{P}_{t+1}\) are updated in each step to remove the accumulation effect.
## 3 Results of Numerical Analysis and Discussion
### Failure of Concrete Lining Support  
The failure proximity [10; 11] is an evaluation index based on the failure criterion defined by [PERSON], which is used to evaluate the safety of the materials or structures used under specific working conditions. The Mohr-Coulomb criterion is applied in a numerical simulation to determine the failure probability and the length of the tunnel at which the failure proximity reaches one.  
In civil engineering, it is normally assumed that the failure proximity is a significant factor to consider when proceding with the jacking process. Therefore, the results of the numerical analysis represent the possible length for excavation of a tunnel using pipe-jacking technology supported by a concrete-lining support at different inclination angles, highwall slopes, and tunnel shapes. Figures 6 and 7 show the effects of and relationships between the aforementioned conditions.  
It is shown that a small angle of inclination for the tunnel allows for greater advances using the pipe-jacking technology. According to Figure 6, the lengths of circular and rectangular tunnels are approximately 48.6 m and 41.3 m, respectively, when the tunnel is inclined by 5\({}^{\circ}\), which is nearly horizontal. When increasing the inclination angle to 15\({}^{\circ}\), the tunnel length decreases by 16.9 m for the circular tunnel and by 12.2 m for the rectangular tunnel.  
Different tunnel inclinations and highwall slopes increase the in-situ stress and overburden the depth when extending the tunnel, significantly limiting the tunnel length that can be excavated by pipe-jacking. The numerical results for the changes in the highwall slopes are also similar. Figure 7 shows that the lengths of the circular and rectangular tunnels are approximately 48.1 m and 41.1 m, respectively, for a highwall slope of 7.5\({}^{\circ}\). However, when the highwall slope angle reaches 30\({}^{\circ}\), the length of the tunnel that can be  
Figure 5: Roof displacement measured by extensometer 01.  
supported by concrete lining is decreased by 26.8 m and 19.5 m for the circular tunnel and the rectangular tunnel, respectively.  
In Figures 6 and 7, it is clearly shown that a smaller inclination angle of highwall slope allows for a farther pipe-jacking distance. For instance, in the case of a circular tunnel, when the highwall slope angle increases up to 30\({}^{\circ}\), the tunnel length significantly decreases by around 55%, but when the tunnel inclination angle reaches 15\({}^{\circ}\), the tunnel length decreases by 35%. It is important to note that a small inclination angle and a gentle highwall slope can produce a longer and safer tunnel when supported by concrete lining.
## 3 Review of Global Appearance Descriptors
### Descriptor Based on Gist  
The descriptor based on gist was initially introduced in [57] and extended in [58]. This descriptor was further developed in studies such as [33] where it was tested in outdoor environments and as a result, the authors obtain a descriptor whose computational cost is relatively reduced. Some other applications can be found in [59], where a navigation system based on gist is tested; in [60], where gist is calculated in panoramic images and is used to solve localization in urban zones; in [61], where a descriptor based on gist is calculated and dimensionally reduced by using Principal Components Analysis and subsequently used to solve loop closure problems. Finally, [PERSON] et al. [11] use such methods to create clustering methods and to perform Visual Place Recognition.  
The version of the descriptor used in the present work is built from intensity information, obtained after applying some Gabor filters with different orientations to the image in several resolution levels. To reduce the volume of data each filtered image is divided in a set of cells, and the average intensity of each cell is calculated. As in HOG, classical methods divided the image using horizontal cells [11], whereas new alternatives also tried to use this descriptor using vertical ones [49]. Definitions used in this paper are presented in Table S2 and comprehensively described in [48; 49]. Figure 3 shows the process to build the gist descriptor using horizontal cells.  
Following that, once the image is filtered with the different masks and scales, the algorithm divides each resulting image into horizontal cells (position descriptor) or into overlapped vertical cells (orientation descriptor), as seen in Figure 1, and the average intensity inside each cell is calculated.  
Figure 2: Process to build the HOG descriptor using horizontal cells.  
In the horizontal-cells descriptor, \(m_{gp}\) indicates the number of orientations of Gabor filters, \(k_{gp}\) designates the number of cells in which the image was split and \(r_{gp}\) indicates the number of different resolution levels used. With these parameters the image can be reduced to a position descriptor whose size is \(\vec{d}_{p}\in\mathbb{R}^{r_{gp}\cdot m_{gp}\cdot k_{gp}\times 1}\). When the cells are vertical, \(dist_{go}\) indicates the distance between the beginning of consecutive cells. This parameter is related to the number of vertical cells \(k_{go}\) since \(k_{go}=N_{y}/dist_{go}\), where \(N_{y}\) is the number of columns of the panoramic image. \(m_{go}\) is the number of orientations of Gabor filters and \(r_{go}\) indicates the number of different resolution levels used in orientation descriptor. The orientation descriptor using gist is a vector whose size is \(\vec{d}_{o}\in\mathbb{R}^{r_{go}\cdot m_{go}\cdot k_{go}\times 1}\). The parameters of the descriptors are summarized in Tables S1 and S2, and the values used in the experiments are specified in Section 6.1.
## 2 Principle of TDOA Method and Beamforming Method
### Beamforming Method
The calculation speed is affected by the length of the calculation signal, the number of sensors, the localization accuracy (the higher the accuracy, the more points need to be scanned), and the size of the structure. Among these influencing factors, signal length, number of sensors, localization accuracy, and structural size are all rigid requirements, and it is difficult to change them for the speed of calculation. Figure 4: A typical energy output of beamforming method [22]. Figure 3: The operational principle of the beamforming method.
## 1 Introduction
The simulation results of this approach are compared with the results of simulations of the empirical Multi-Wall model used in the simulation tool I-Prop [11]. For the presented approach, we focus on verifying the functionality, repeatability, accuracy of the results and time consumption. Considering the simulation results, it can be concluded that this approach is applicable to simulate the UHF signal coverage from an RFID reader in an indoor environment. The authors of [11] compare the results of the Multi-Wall model with the real measurement results. Based on the \(p\)-value in the ANOVA table being less than 0.01, the authors conclude a statistically significant relationship between the results. Comparing the two original and two simulated heat maps, the maximum difference is 6.7% in the range of RSSI values \(-\)20 to \(-\)30 dBm for antenna 1. In terms of the highest and lowest differences for RSSI ranges, it is 6.5% in the range \(-\)1 to \(-\)20 dBm and 0.9% in the range \(-\)80 to \(-\)100 dBm for antenna 1. The total time to create the simulated environment is less than 10 min and the simulation time to create the heat map is less than 1 min. The rest of this paper is structured as follows: Section 2 describes the approach to the simulation with all the required settings. The results from the heat map comparison and the discussion of functionality, repeatability, accuracy of the results and time consumption of this approach are presented in Section 3. The conclusion of this paper is presented in Section 4.
## 5 Discussion
### Discussion of the Consistency between the OWCM-/GM-Derived SM and RFSM
#### 5.1.2 Temporal Consistency  
EDA was utilized to test the time-series correlation R between the OWCM-/GM-derived SM and the original RFSM in different time windows over the Naqu network. The results are shown in Figure 8. Overall, in Naqu, the OWCMs performed significantly better than GMs, and had a certain degree of optimization to the original method. Over the entire period and the local periods, the correlation between the GM-derived SM and  
Figure 6: Spatial distributions of the statistical metrics R and ubRMSE between the aggregated OWCM-/GM-derived SM and the original RFSM.  
Figure 7: Frequency distributions of the statistical metrics R (1 st column), ubRMSE (2 nd column), and bias (3 rd column) between the ATA (1 st row)/RF (2 nd row)/ER (3 rd row)/NN (4 th row)-based OWCM/GM SM and the original RFSM over the QTP.  
the original RFSM was improved to some extent by the OWCM when the correlation between GM-derived SM and the original RFSM was poor (or even negative). At the same time, it can be observed that the periods of poor correlation with the original RFSM for all OWCMs and GMs were generally confined to the central days (\(x\)-axis) from 90 to 160--corresponding to the period from June to August in the summer. According to Section 4.2, OWCM-/GM-derived SM was generally lower than the SM measured in situ during this period. However, it is worth noting that such underestimation is more significant for GMs than for OWCMs, indicating the improvement in the underestimation by replacing GMs with the corresponding OWCMs.  
Figure 8: EDA-based average correlation coefficient R plots between GM-/OWCM-derived SM and the original RFSM in Naqu.
## 6 Conclusions  
A DRL based resource allocation algorithm with a clustering method was proposed in this work for cellular V2X communication. The maximization problem of system energy efficiency was formulated using the Markov decision process. The RRH grouping and vehicle clustering method was designed to minimize signaling overhead and communication complexity. A DRL based resource allocation method was adopted to maximize system capacity. To demonstrate that the proposed algorithm outperformed the other algorithms, simulations were carried out with a SUMO simulator to compare system energy efficiency, achievable data rates, average SINR and outage probabilities.  
In real-world vehicular networks, it is difficult to collect channel state information from each vehicle in the network. The training data are also distributed among vehicles, causing bandwidth overhead from data upload. Federated learning has the advantage of distributed learning [27; 28]. Further studies should be performed that include federated learning to solve the aforementioned problems, to relieve the load of fronthaul links and RRHs, and to manage the resources efficiently in the presence of rapidly changing mobility.  
Conceptualization, H.P. and Y.L.; Methodology, H.P.; Software, H.P.; Writing--Review & Editing, H.P. and Y.L.; Supervision. Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No.2021R1F1A1047113).  
The authors declare no conflict of interest.  
Figure 10: Comparison of system energy efficiencies with various maximum cluster sizes: (**a**) With low scenario; (**b**) with high scenario.