esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
## 3 Topologies of BMS
### Modularization  
For applications in need of higher power and/or with greater energy demand, the battery pack has to consist of several cells. ICs are offered for these kinds of systems that provide monitoring for several cells at once and also provide means for balancing, which is not needed in one-cell-systems. In these kinds of systems, usually more advanced functions are implemented in one central module or Electronic Control Unit (ECU), sometimes known as \"BMS-Master\". Figure 3 shows the structure of a typical system of this class. As examples for tasks the master cares about, sophisticated SOC estimation, or power prediction algorithms, which need a certain amount of processing power, can be named. An overview can be found in [12].  
The modules carrying the front-end ICs are then often referred to as \"BMS-Slaves\". They are used for basic functions like signal acquisition, filtering, etc., which are carried out by the monitoring ICs named above. (see e.g., [24] or the examples in Section 3.3). As examples, Texas Instruments' bq76 PL536A [9] (used in _Tesla Model-S (Palo Alto, CA, USA)_ and possibly in _smart fortwo ed_, see Section 3.3), Linear Technology's (Milpitas, CA, USA) LT6802G-2 [25] (used in _Mitsubishi Motors i-MiEV (Tokyo, Japan)_), Maxim Integrated's (San Jose, CA, USA) MAX11068 [3] (used in _VW e-Up)_, or AMS' (Premstatten, Austria) AS8506C [26] can be named. The TI bq76 PL536A, MAX11068 and LT6802G-2 provide passive balancing, while the AMS device can be used for passive balancing topologies, but also provides active balancing capabilities using an external transformer. More on balancing can be found in Section 5. At the time of writing, there are already successors available for most of the named ICs, e.g., TI bq76 PL455 [27] or LTC6811 [28]--also see Section 4.2. As mentioned in the section on requirements, a certain level of redundancy regarding voltage monitoring is usually desired. Thus, so-called secondary protection ICs often can be used in combination with the described ICs (or are even contained in the same package) to provide an additional level of safety. Another possibility would be the usage of a completely redundant BMS, which, however, would largely increase the costs.
## 5 Parametric Analysis
### Effect of \(S_{\text{t1}}\)  
To determine the effect of \(S_{\text{t1}}\), parameter analysis was performed via a change (using 0 m, 1 m, 2 m, and 3 m widths) while keeping the other parameters constant. Figure 10 shows the lateral displacement and internal force diagram calculated under different \(S_{\text{t1}}\) values. Figure 10a reveals that the lateral displacement of a single pile at a given buried depth increases with the increase in \(S_{\text{t1}}\). When drawing insight from Figure 10b, it becomes clear that, within the burial depth range of 5 to 12.5 m, the variation in \(S_{\text{t1}}\) exerts the most pronounced effect on the pile's bending moment. The reason for these findings is that, as \(S_{\text{t1}}\) increases, the slopes, \(\beta_{3}\), on both sides of the scour hole are elevated, consequently prompting the point of contraflexture of the pile to shift downward.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bupial Depth (m)**} & \multicolumn{4}{c}{**Displacement (mm)**} & \multicolumn{4}{c}{**Bending Moment (kN\(\cdot\)m)**} \\ \cline{2-10}  & \(S_{\text{wb1}}=0.5\) m & \(S_{\text{wb1}}=1\) m & \(S_{\text{wb1}}=2.5\) m & \(S_{\text{wb1}}=4.5\) m & \(S_{\text{wb1}}=0.5\) m & \(S_{\text{wb1}}=1.1\) m & \(S_{\text{wb1}}=2.5\) m & \(S_{\text{wb1}}=4.5\) m \\ \hline
**0** & **23.17** & **23.22** & **23.33** & **23.37** & **0.00** & **0.00** & **0.00** & **0.00** \\
2 & 14.52 & 14.56 & 14.64 & 14.67 & 400.00 & 400.00 & 400.00 & 400.00 \\
4 & 7.23 & 7.26 & 7.32 & 7.33 & 752.19 & 752.37 & 752.53 & 752.59 \\
6 & 2.43 & 2.44 & 2.47 & 2.48 & 788.80 & 790.01 & 791.78 & 792.22 \\
8 & 0.23 & 0.23 & 0.24 & 0.25 & 504.97 & 506.53 & 509.76 & 510.67 \\
10 & \(-\)0.26 & \(-\)0.26 & \(-\)0.26 & \(-\)0.26 & \(159.89\) & 160.76 & 163.20 & 163.99 \\
12 & \(-\)0.15 & \(-\)0.15 & \(-\)0.15 & \(-\)0.15 & \(-\)0.00 & \(-\)7.83 & \(-\)7.16 & \(-\)6.92 \\
14 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)0.03 & \(-\)28.36 & \(-\)28.42 & \(-\)28.51 & \(-\)28.52 \\
16 & 0.01 & 0.01 & 0.01 & 0.01 & \(-\)10.18 & \(-\)10.23 & \(-\)10.36 & \(-\)10.40 \\
18 & 0.01 & 0.01 & 0.01 & 0.01 & \(-\)0.63 & \(-\)0.64 & \(-\)0.67 & \(-\)0.68 \\
20 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of \(S_{\text{wb1}}\) on displacement and internal force of single pile.  
Figure 10: Lateral bearing performance of pile at varying \(S_{\text{t1}}\) values: (**a**) lateral displacement curves and (**b**) bending moment curves.
## 2 Bounds Determination of Time-Invariant and Time-Variant Uncertainties under Insufficient Samples
### General Procedure of the Grey System Theory  
As stated in Section 2.1, the key to interval UQ analysis is to obtain accurate bounds information from insufficient samples, and thus to conduct the subsequent work of uncertainty propagation and reliability analysis. In this section, the Grey systematic approach in number theory, which regards uncertain static and dynamic parameters as Grey quantities constantly changing in certain scopes, is employed to deal with mechanical uncertainty issues. It does not treat the sample research in the view of finding statistical rules, but also adopts data processing operations to restructure the original rough data to regularly sequence and then gain the bounds information of uncertainties.  
Now, take the static case as an example. Suppose a group of measured experimental data corresponding to one specific uncertain variable \(x_{i}\) is listed as  
\[X_{i}=\{x_{i}(l),\ l=1,2,\ldots,m_{1}\} \tag{10}\]  
where \(X_{i}\) is the sample set of interval variable \(x_{i}\) including \(m_{1}\) data point. Arrange the sequence \(X_{i}\) in turn from small to big, and hence an updated sequence \(X_{i}^{(1)}\) is obtained by  
\[X_{i}^{(1)}=\left\{x_{i}^{(1)}(1),\ x_{i}^{(1)}(2),\ldots,\ x_{i}^{(1)}(m_{1})\right\} \tag{11}\]where \(x_{i}^{(1)}(l)\leq x_{i}^{(1)}(l+1)\), \(l=1,2,\ldots,m-2\). By accumulating the calculation of the sequence in Equation (11), one can obtain the new form of the data list as  
\[\begin{array}{l}X_{i}^{(2)}=\left\{x_{i}^{(2)}(l),\;l=1,2,\ldots,m_{1}\right\} =\left\{x_{i}^{(2)}(1),\;x_{i}^{(2)}(2),\ldots,\;x_{i}^{(2)}(m_{1})\right\}\\ =\left\{x_{i}^{(1)}(1),\;x_{i}^{(1)}(1)+x_{i}^{(1)}(2),\ldots,\;x_{i}^{(1)}(1)+ x_{i}^{(1)}(2)+\cdots+x_{i}^{(1)}(m_{1})\right\}\end{array} \tag{12}\]  
The definition  
\[\left\{\begin{array}{c}\Delta_{i}(l)=\frac{x_{i}^{(2)}(m_{1})}{m_{1}}\cdot l -x_{i}^{(1)}(l)\\ \Delta_{l}^{\max}=\max(\Delta_{i}(l))\\ s_{i}=c_{i}\cdot\frac{\Delta_{i}^{\max}}{m_{1}}\end{array}\right.,l=1,2, \ldots,m_{1} \tag{13}\]  
\(c_{i}\), named as the Grey constant coefficient, is generally given by empirical cognition based on statistical knowledge and \(s_{i}\) is a quantitative estimation of uncertainty of data \(X_{i}\), which embodies the deviation effect of \(x_{i}\). If the mean value is derived by \(\overline{x_{i}}=\frac{1}{m_{1}}\sum\limits_{l=1}^{m_{1}}x_{i}^{(1)}(l)\), the UQ result obtained by the Grey model can be eventually taken as  
\[x_{i}\in\left[x_{i}^{L},\;x_{i}^{U}\right]=\left[\overline{x_{i}}-3s,\; \overline{x_{i}}+3s\right] \tag{14}\]  
Obviously, in terms of dynamic uncertainty problems, the available sample curves for \(Y_{k}(t)=\left\{y_{k}(t,1),\;y_{k}(t,2),\ldots,\;y_{k}(t,n_{1})\right\}\) may be degenerated into sample points \(Y_{k}(t_{j})=\left\{y_{k}(t_{j},1),\;y_{k}(t_{j},2),\ldots,\;y_{k}(t,n_{1})\right\}\) by time discretization operations, and thus the aforementioned Grey system approach can be directly adopted to obtain the bounds information of the interval process \(y_{k}(t)\).
## 5 Gas Extraction Process Optimization and Field Test Validation  
### Analysis of Gas Extraction Processes  
#### 5.1.1 Comparative Analysis of the Effectiveness of Gas Extraction  
Based on the above analysis of the effects of the two preliminary extraction and depressurization gas extraction processes (high-level oblique boreholes and directional long boreholes), this study shows that the traditional high-level oblique borehole extraction and depressurization gas extraction process only has evident effects in the first 10 m of the advancement of the working face. It obtains an effective utilization rate of only 33.3%, an average gas extraction concentration of 46.1%, an average rate of pure gas extraction of 8.43 m\({}^{3}\)/min, and an average upper corner gas concentration of 0.46%. In the directional long borehole extraction and depressurization gas extraction process, the gas extraction concentration is 51.09%, the rate of pure gas extraction is 8.93 m\({}^{3}\)/min, and the average gas concentration in the upper corner gas is 0.39%. The effectiveness of gas extraction using the two preliminary extraction and depressurization gas extraction processes is shown in Figure 18.  
Figure 18: Gas extraction in the two primary extraction and pressure relief gas processes.  
As shown in Figure 18, compared with the high-level oblique borehole extraction and depressurization gas extraction process, the directional long borehole extraction and depressurization gas extraction process extracts a large quantity of pure gas and has a higher gas concentration, a lower upper corner gas concentration, and a better gas extraction effect because of the large depth of the directional long boreholes, which are always in the fracture zone and allow for a long effective extraction time. As a result, this greatly increases the utilization rate of the boreholes. Meanwhile, with the continuous advancement of the working face, the roof plate of the mining air zone gradually collapses and high-level oblique boreholes in the fracture zone are scrapped, resulting in a decline in the pure quantity of extracted gas in the high-level oblique boreholes, a decrease in the gas concentration, and an increase in the upper corner gas concentration.
## 4 Methodology and Experiment Design
### Data Collection
#### 4.4.2 Experiment Two  
The questionnaire was made and randomly distributed on the Questionnaire Star platform. Volunteers were randomly assigned to two groups. Questionnaire were also distributed via the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multirow{2}{*}{**Sample Distribution**} & **Experiment** & **1** & **Experiment** & **2** \\  & & **Counts** & **Proportion** & **Counts** & **Proportion** \\ \hline \multirow{2}{*}{**Gender**} & Male & 20 & 31.75\% & 23 & 37.70\% \\ \cline{2-6}  & Female & 43 & 68.25\% & 38 & 62.30\% \\ \hline \multirow{4}{*}{**Education**} & Junior college and & \multirow{2}{*}{\(13\)} & \multirow{2}{*}{\(20.63\%\)} & \multirow{2}{*}{\(7\)} & \multirow{2}{*}{\(11.48\%\)} \\  & below & & & & \\ \cline{2-6}  & BSc & 42 & 66.67\% & 42 & 68.85\% \\ \cline{2-6}  & MSc & 7 & 11.11\% & 11 & 18.03\% \\ \cline{2-6}  & PhD & 1 & 1.59\% & 1 & 1.64\% \\ \hline \multirow{4}{*}{**Disposable personal income**} & Below 1000 & 9 & 14.29\% & 10 & 16.39\% \\ \cline{2-6}  & 1001–1500 & 14 & 22.22\% & 20 & 32.79\% \\ \cline{2-6}  & 1501–2000 & 12 & 19.05\% & 12 & 19.67\% \\ \cline{2-6}  & 2001–2500 & 7 & 11.11\% & 6 & 9.84\% \\ \cline{2-6}  & Above 2500 & 21 & 33.33\% & 13 & 21.31\% \\ \hline \multirow{4}{*}{**Have online**} & cosmetic care & 50 & 79.37\% & 50 & 81.97\% \\ \cline{2-6}  & Sport and Outdoor & 45 & 71.43\% & 38 & 62.30\% \\ \cline{2-6}  & Underwear and & \multirow{2}{*}{\(36\)} & \multirow{2}{*}{\(57.14\%\)} & \multirow{2}{*}{\(34\)} & \multirow{2}{*}{\(55.74\%\)} \\ \cline{2-6}  & accessories & & & & \\ \cline{2-6}  & Jewelry & 22 & 34.92\% & 11 & 18.03\% \\ \cline{2-6}  & Home textile products & 29 & 46.03\% & 27 & 44.26\% \\ \cline{2-6}  & Books and Instrument & 44 & 69.84\% & 44 & 72.13\% \\ \cline{2-6}  & Flower and Pets & 19 & 30.16\% & 11 & 18.03\% \\ \cline{2-6}  & Digital and Home & \multirow{2}{*}{\(46\)} & \multirow{2}{*}{\(73.02\%\)} & \multirow{2}{*}{\(38\)} & \multirow{2}{*}{\(62.30\%\)} \\ \cline{2-6}  & Appliance & & & & \\ \cline{2-6}  & Clothes and Shoes & 53 & 84.13\% & 48 & 78.69\% \\ \cline{2-6}  & Fresh Food & 27 & 42.86\% & 31 & 50.82\% \\ \cline{2-6}  & Others & 23 & 36.51\% & 16 & 26.23\% \\ \hline \multirow{2}{*}{**Whether to watch short video when shopping online**} & Yes & 42 & 66.67\% & 42 & 68.9\% \\ \cline{2-6}  & No & 21 & 33.33\% & 19 & 31.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment samples’ demographic characteristics.  
QQ and WeChat social apps. Seventy-four volunteers were involved. The question \"do the videos display how the portable juice extractor is charged?\" was set to filter out effective experimental questionnaires. Sixty-one volunteers completed the experiment. This study adopted the method of controlling variables (with and without a tutorial information) to conduct the experiment, and the demographic characteristics of the volunteers who participated in this experiment are shown in Table 3.
# Fingerprint Based Codebook for RIS Passive Beamforming Training
## 3 Codebook-Based Solutions  
In codebook-based solutions, the passive beamforming schemes are performed in three necessary phases. Firstly, the system designs a codebook of RPs, which is a subset of the entire universal space, e.g., the random RPs codebook and the sum distance maximization codebook (SDM-CB). Second, the system examines and evaluates all RPs. In this stage, every RX sends a feedback signal containing information about its received signal to the TX, which estimates the end-to-end composite channel. Then, the system performs the ABF vector optimization process and evaluates the corresponding objective function (OF). Thirdly, a learning phase is performed to select the optimal RP based on a predefined target metric. For example, the rate learning method stores the observations corresponding to each RP, and the one that maximizes the target metric is utilized to configure the RIS in the data transmission period.  
Figure 1: RIS-aided wireless communication system.  
The codebook design phase is a fundamental step because an appropriate CB can guarantee a higher rate with lower system overhead. The original CB is the universal solution set, which contains all possible configuration patterns; however, this CB is very large, especially with a high number of RIS elements. Moreover, it is highly complicated and time-consuming to search within this CB. Hence, codebook generation methods are used to select \(Q\) codewords from the universal space, and then the PBF scheme searches these RPs to select the best one for RIS configuration. Hence, the system-required overhead when adapting CB-based schemes is \(\mathcal{O}(Q)\), while the computational complexity in the single-user case is \(O(QM)\). In the literature, different CB generation methods are suggested. Firstly, random CB generation was proposed in [6], where random values of the RIS elements' phase shifts are chosen without any further enhancement. Figure 2a shows an example of random CB with \(Q=6\) and \(N=1\). The random CB cannot efficiently cover the search space or use the limited allocated training period. Thus, a sum distance maximization CB (SDM-CB) was suggested in [17] to overcome these issues. The SDM-CB generation method is a heuristic mechanism based on maximizing the summation of the Euclidean distances between all RP pairs. However, it obtains a moderate gain comparable to random CB because it cannot examine all available RPs until convergence. Figure 2b shows an example of SDM-CB with \(Q=6\) and \(N=1\). It is still uncertain whether these CBs can cover the target area and achieve suboptimal performance after adapting probable learning schemes. Furthermore, searching through a large CB is a time- and resource-wasting process.
## 3 Results
### Comparison of the Simulation Results Obtained for the Two Models and the Experimental Test Results
Figure 9: Illustration of the diagnostic assessment of the relative damping \(\vartheta_{1}\) = Theta1 in the vehicle’s suspension system based on the minimum values of the phase shift angle \(\Phi_{\mathrm{min}}\) = Fimin for the contact force between the tyre and the vibration plate: (**a**) results of simulation and experimental testing of the rear suspension of the Opel Astra Van car; (**b**) results of simulation and experimental testing of the rear suspension of the Opel Agila car. Use of the Obtained Results of Simulation and Experimental Tests in the Assessment of Damping Characteristics of Suspensions of the Vehicles Under Test  
The results shown in Figures 7 and 8 were used for the assessment of damping characteristics of suspensions, chiefly shock absorbers, of the vehicles under test. For this objective to be attained, the test results (i.e., the set of values of the functions under analysis) in the form of the minimum values of the phase shift angle \(\Phi_{\min}\) (Fimin) defined the domain of the analysis and the values of the relative damping \(\vartheta_{1}\) ('Theta1') in the suspension system under test were to be found. This means an inversion of the dependencies that are graphically described by Figures 7a and 8a, i.e., only those that are related to the contact force between the tyre and the vibration plate or, in other words, those where the recommended correction taking into account the force of inertia of the vibration plate is applied. In consequence, Figures 9a and 9b have been plotted for the suspension systems of the Opel Astra Van and Opel Agila cars, respectively. Example results of the assessment of the relative damping coefficient \(\vartheta_{1}\) = Theta1 of the car suspension system for two example minimum values of the phase shift angle \(\Phi_{\min}\) = Fimin (\(\Phi_{\min}\) = 0.5236 rad = 30 deg and \(\Phi_{\min}\) = 1.0472 rad = 60 deg) have been marked in Figure 9 and juxtaposed in Table 6.  
The linear model lowers the values \(\vartheta_{1}\) because it merely represents the viscous damping of the shock absorber, and in a considerably simplified way at that (see [43]). The results of experiments and calculations carried out for the non-linear model (but for relatively high forces of the sliding friction in the suspension system, e.g., those for the Opel Astra Van car) are the most optimistic. This is due to the impact of the sliding friction as described herein. In the experimental and simulation tests carried out for the non-linear model, the assessment covers the overall damping characteristics of the suspension system instead of those of the shock absorber alone. It can be seen from the above that friction in the suspension system is an important disturbing element in the assessment of shock absorber conditions, as signalled previously in publications [3; 5; 25].
## 2 Materials and Methods
### ROS-Based Implementation
#### 2.2.1 Finite State Machine Architecture  
The two algorithms for admittance and direct force control are designed as a finite state machine (FSM), represented in Figure 4.  
Assuming the robot is not in touch with the environment, the algorithm starts executing the interaction control law and, in the attempt of reaching the desired force \(\mathbf{f}_{d}\), which could also be zero, moves according to the assigned dynamics.  
On a practical standpoint, before the robot starts operating, it is often desirable to bring the end-effector to the task's first configuration, with a separate command. The planning system can do this by providing a single setpoint, to be reached with absolute precision. To this purpose, pure motion control, in the form of independent joint control, is needed. In fact, if the starting point was tracked by the interaction control law, the reached point might differ from the target, due to the input force error. In addition, motion control allows commanding (and reaching) the end-effector orientation required by the task.  
The FSM consists of three states and five events. The states are:  
* _Static control law_: the algorithm simply executes the control law, with fixed force and position references;
* _Trajectory execution control law_: the algorithm still executes the control law while tracking the trajectory received as input (the tracking might not be accurate, along force-controlled directions, due to the control action);
* _Independent joint control_: the interaction control law is not executed, while the independent joint control allows reaching the desired position and orientation with an assigned tolerance.  
The transitions are:  
* _Init_: occurs when the controller is started by the _controller manager_[35];
* _Trajectory received_: a trajectory is received;
* _Setpoint received_: a setpoint is received;
* _Trajectory completed_: the execution of the trajectory is completed;
* _Setpoint reached_: the setpoint is reached.  
It is worth specifying that, once the target point is reached, the gravity bias is removed from the force/torque sensor measures, so that null measures are delivered until the sensor gets in touch with the environment. While a setpoint is reached or a trajectory executed, no other input is allowed.
# A Novel Bidirectional Wireless Power Transfer System for Mobile Power Application
## 5 Conclusions  
This paper proposed a novel wireless bidirectional power transfer system. The circuit operation principle is explained in detail and the equivalent circuits are also derived. The proposed wireless power transfer system can be seen as a bidirectional current-control current-source system. The power flow direction can be controlled using the transmitter side switch. The output voltage and/or current can be controlled using the switch duty ratio regardless whether the proposed system is operating in the forward or backward mode. Fortunately, only two switches are adopted to achieve the bidirectional power flow transmission. Therefore, the proposed converter is suitable for low power applications such as a mobile power source used in wearable smart devices or sensor devices. A prototype system  
Figure 16: Output voltage and current vs. distance between the windings in the forward mode operation.  
Figure 17: Output voltage and current vs. distance between the windings in the backward mode operation.  
Figure 18: One of the low-power Wireless power transfer (WPT) applications for mobile power.  
was constructed and tested. Both the simulation and experimental results verify the validity of the proposed bidirectional wireless power transfer system.  
The paper was a collaborative effort between the authors. The authors contributed collectively to the theoretical analysis, modeling, simulation, experiment, and manuscript preparation.  
This research is sponsored by the Ministry of Science and Technology of Taiwan under Grant MOST 108-3116-F-008-001.  
The authors declare no conflicts of interest.