esa-sceva/satcom-chunk-collection huggingface dataset:
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
###### Abstract
Additionally, in order to alleviate the rigid collisions caused by position errors in the teleoperation inserting assembly, an integrated position/force control method which takes the demonstration position as the desired position was implemented. The output position command was a combination of the operator guide position and the compliant correction position, achieving the combination of the initiative of the operator and the compliance of impedance control. Finally, through experiments, we illustrate the system's advantages compared with the traditional position control and impedance method. The main contributions of this article are:  
* To build a wireless teleoperation assembly system based on wearable trackers to achieve flexible handling, allowing the user to operate the robot in a much more natural way. * To design a manual/automatic mixed control strategy which realizes the combination of efficient manual manipulation guidance and full-automatic precise and flexible assembly. * To propose an integrated position/force control method which possesses the capacity to weaken collision force caused by hand jitters or misoperation, and achieves the combination of the operator's initiative and the precision of automatic assembly. Apart from Section 1, the paper is organized as follows: Section 2 presents the composition of the teleoperation assembly system. Then, the human motion tracking method is presented in Section 3. Section 4 describes the control algorithms including master-remote position controller, a manual/automatic mixed controller based on Cartesian impedance control, and the novel integrated position/force controller. Next, in Section 5, the designed control methods are verified and analyzed experimentally. The proposed methodology is discussed in Section 6. Finally, Section 7 concludes the research of this paper and suggests future work.
## Monitoring and Analysis of the Roadway Forming Effect  
### Monitoring Scheme of Roadway Forming Effect  
In order to further verify the feasibility and safety of gob-side roadway forming, it is necessary to monitor the displacement of the roadway surface and the pressure on anchor cables in the gob-side roadway forming section.  
(1) Roadway displacement monitoring  
When observing the relative displacement and deformation across the roadway section, starting from the beginning of roadway forming, one measuring point is set for every 10 anchor cable tapes. The measuring points are arranged in a \"cross\", as shown in Figure 14. The measurement method is manual measurement. A total of 30 points are set. Each measuring point records the displacement of the roof and floor and the two sides as well as the advancement rate and the relative position to the working face once a day until the working face has been fully advanced.  
(2) Pressure monitoring of anchor cables  
Figure 12: Schematic diagram of gangue retaining.  
Figure 13: Field effect drawing of air blocking at the initial position of roadway forming.  
For the pressure of the anchor cable, we used the YAD400 mine bolt cable dynamometer to measure the tension of the anchor cable, as shown in Figure 15. The dynamometer is shown in the figure below: Starting from the completion of the roadway forming, the first group of mine cable dynamometers is installed on the fifth anchor cable tape (beam). After that, one group is installed for every 40 cable tapes. A total of four groups of dynamometers are installed. Observation is made once a day for the first month, and once a week afterwards. Observation is made for five consecutive months.  
The arrangement of monitoring points is shown in Figure 16.
## 4 Principle and Implementation
### Water Level Line Detection  
Once the original water gauge image is fed into the SegFormer-UNet model, the model outputs a mask image containing the segmentation results of the water body and the background. To obtain the position of the water level line in the water gauge image, only the mask image needs to be detected using the Canny edge detection algorithm [31]. The specific detection process is shown in Figure 7.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 3 Hardware Layer
### Energy-Aware Modulation Techniques  
Several aspects affect a radio module's power consumption, such as the modulation technique, data rate, and transmission power. Starting from the next paragraph, we focus on the modulation techniques used in energy-efficient WSN.  
The modulation is the process of coding information into the carrier frequency [24]. This process includes Amplitude Shift Keying, Frequency Shift Keying, or Phase Shift Keying. The choice of the modulation technique strongly impacts energy consumption. At the MAC layer, various multi-access methods have been evaluated regarding energy cost (we will see them later in this paper). In [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], Modulation techniques have been investigated in terms of energy efficiency from the physical layer perspective. In these investigations, the RF transceiver is often burst mode; it is shut off most of the time with a duty cycle of no more than 1%. Modeling the transceiver power consumption can help reduce the energy consumed, but an inaccurate model will lead to an incorrect result. For example, if a model assumes that the transceiver power consumption is proportional to the RF transmit power, which scales with the data rate, the transmit power must be increased to maintain the same transmission distance. Hence, to increase the battery life of the sensor, the data rate then must be reduced. However, the transmitter power consumption is not related to the data rate due to the fixed power cost of the transmitter.  
The electronic circuit components dominate the power consumption for RF transceivers that operate in the Gigahertz carrier frequency range. As a result, it is important first to model the circuit power consumption and then to model the communication protocols, modulation techniques, data rate, and channel conditions. This interplay between the communication protocols and the transceiver electronics is complicated and has rarely been investigated. One of the energy-aware modulation techniques is On/Off Key (OOK) modulation [38]. OOK is a special case of ASK modulation as no carrier is present when transmitting zero bits. Due to the simplicity and low cost of modulation, OOK puts the transmitter to idle mode when sending zero bits, which helps in reducing power consumption. Table 4 illustrates the overall system performance of the most known modulation techniques used in WSN.  
Based on the review of the different modulation techniques, the design of a wake-up radio module is a real challenge for the communication block unit. Hence, turning off the RF module in sensors is highly recommended because it is usually more power-consuming than other components. However, this technique will lead to missing events as the sensor can no longer receive data. The challenge is to develop a low-power communication channel to wake up neighboring nodes when needed, which is still an area of active research in chip design, modulation, and RF communication techniques.
# Fused Gromov-Wasserstein Distance for Structured Objects
## 4 \(Fgw\) in the Discrete Case
### Structured Optimal Transport Barycenter  
An interesting use of the \(FGW\) distance is to define a barycenter of a set of structured data as a Frechet mean. In that context, one seeks the structured object that minimizes the sum of the (weighted) \(FGW\) distances with a given set of objects. OT barycenters have many desirable properties and applications [26; 41], yet no formulation can leverage both structural and feature information in the barycenter computation. Here, we propose to use the \(FGW\) distance to compute the barycenter of a set of \(K\) structured objects \((\mu_{k})_{k}\in\mathbb{S}(\Omega)^{K}\) associated with structures \((C_{k})_{k}\), features \((B_{k})_{k}\) and base histograms \((h_{k})_{k}\).  
We suppose that the feature space is \(\Omega=(\mathbb{R}^{d},\ell_{2})\) and \(p=1\). For simplicity, we assume that the base histograms and the histogram \(h\) associated to the barycenter are known and fixed. Note that it could be also included in the optimization process, as suggested in [26].  
In this context, for a fixed \(N\in\mathbb{N}\) and \((\lambda_{k})_{k}\), such that \(\sum_{k}\lambda_{k}=1\), we aim to find:  
\[\min_{\mu}\sum_{k}\lambda_{k}d_{FGW,\alpha,1,q}(\mu,\mu_{k})=\min_{C,\ A\in \mathbb{R}^{N\times d},(\pi_{k})_{k}}\sum_{k}\lambda_{k}E_{1,q,\alpha}(M_{AB_ {k}},C,C_{k},\pi_{k}) \tag{27}\]  
Note that this problem is convex _w.r.t._\(C\) and \(A\), but not _w.r.t._\(\pi_{k}\). Intuitively, looking for a barycenter means finding feature values supported on a fixed size support, and the structure that relates them. Interestingly enough, there are several variants of this problem, where features or structure can be fixed for the barycenter. Solving the related simpler optimization problems extend straightforwardly.  
**Solving the barycenter problem with Block Coordinate Descent (BCD).** We propose minimizing Equation (27) using a BCD algorithm, i.e., iteratively minimizing with respect to the couplings \(\pi_{k}\), the structure metric \(C\) and the feature vector \(A\).The minimization of this problem _w.r.t._\((\pi_{k})_{k}\) is equivalent to computing \(K\) independent Fused Gromov-Wasserstein distances using the algorithm presented above. We suppose that the feature space is \(\Omega=(\mathbb{R}^{d},\ell_{2})\) and we consider \(q=2\). Minimization _w.r.t._\(C\) in this case has a closed form (see Prop. 4 in [26]):  
\[C\leftarrow\frac{1}{hh^{T}}\sum_{k}\lambda_{k}\pi_{k}^{T}C_{k}\pi_{k} \tag{28}\]  
where \(h\) is the histogram of the barycenter and the division is computed pointwise. Minimization _w.r.t._\(A\) can be computed with (Equation (8) in [42]):  
\[A\leftarrow\sum_{k}\lambda_{k}B_{k}\pi_{k}^{T}\text{diag}\left(\frac{1}{h} \right)\,. \tag{29}\]
# An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
## 5 Experiment
### Evaluation Index and Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**H**} & \multirow{2}{*}{**CD**} & \multirow{2}{*}{**CA**} & \multirow{2}{*}{**NWD**} & \multicolumn{6}{c}{**Val**} & \multicolumn{6}{c}{**Test**} & \multirow{2}{*}{**Parameters**} \\ \cline{5-16}  & & & & & & **P** & **R** & **mAP.5** & **mAP.95** & **P** & **R** & **mAP.5** & **mAP.95** \\ \hline YOLOv5s & & & & & 95 & 87.2 & 91.9 & 68.9 & 94.2 & 86.3 & 91 & 68.7 & 7,018,216 \\ A & \(\surd\) & & & & 96.8 & 93.3 & 96.4 & 75.4 & 96 & 93.2 & 96.2 & 75.7 & 7,185,888 \\ B & & \(\surd\) & & & 93.8 & 88.4 & 92.1 & 69.1 & 93.2 & 87.6 & 91.7 & 68.9 & 5,927,114 \\ C & & & \(\surd\) & & 92.2 & 89 & 92.5 & 69.3 & 91.7 & 88.3 & 91.8 & 69 & 7,152,272 \\ D & & & \(\surd\) & & & \(\surd\) & 94.7 & 89.4 & 93.2 & 70.7 & 93.5 & 89.4 & 92.6 & 70.7 & 7,018,216 \\ E & \(\surd\) & \(\surd\) & & & & 96.1 & 93.3 & 96.1 & 75.6 & 95.4 & 93.3 & 96.1 & 75.5 & 6,094,816 \\ F & \(\surd\) & \(\surd\) & \(\surd\) & & & 94.9 & 93.8 & 96.6 & 76.3 & 94 & 93.4 & 95.4 & 76.3 & 6,239,292 \\ Last & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 96 & 95 & 96.3 & 77.5 & 95.3 & 94.7 & 96.2 & 77.3 & 6,239,292 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments. Experiment D introduced the NWD loss function to replace the original CIOU loss function. After improvement, P slightly decreased, R increased by 2, mAP.5 increased by 1.6, and mAP.95 increased by 2, indicating that the improved model captures targets more comprehensively, although the possibility of misclassification slightly increased. Experiments E, F, and Last involved sequentially adding the CSPCM-DFC, CARAFE, and NWD components to Experiment A, respectively. The results showed a significant decrease in the number of model parameters after adding the CSPCM-DFC module, while the model's performance remained basically unchanged. The addition of CARAFE resulted in a decrease in mAP.5 and an increase in mAP.95, indicating improvements in target localization and high-confidence prediction. Finally, the addition of NWD resulted in an increase in mAP.5 and mAP.95, indicating an improvement in the detection performance of the model. In summary, the ablation experiments demonstrated that the performance of EV helmet recognition methods can be significantly improved by combining different components. The final experiments achieved significant improvements in precision, recall, mAP.5, and mAP.95 indicators. These findings provide strong support for our study and offer valuable insights for further development and applications of the EV helmet recognition method.
## 2 Theoretical Background
### Objective Functions
#### 2.1.2 Mode Shape  
Numerous studies have indicated that the location of damage can be detected using the changes in mode shape. MAC is used to compare the modal vectors quantitatively. Its value is limited between zero and one, with one representing fully-consistent mode shapes and zero representing inconsistent mode shapes.  
For two modes \(\mathbf{\phi}_{j}\) and \(\mathbf{\phi}_{j}^{*}\), the MAC value is given as:  
\[\text{MAC}=\frac{|\mathbf{\phi}_{j}^{\prime}\mathbf{\phi}_{j}^{*}|^{2}}{\left(\mathbf{ \phi}_{j}^{\prime}\mathbf{\phi}_{j}\right)\left(\mathbf{\phi}_{j}^{*\prime}\mathbf{\phi}_{ j}^{*}\right)} \tag{2}\]  
where \(\mathbf{\phi}_{j}\) and \(\mathbf{\phi}_{j}^{*}\) are the mode shapes of an undamaged and a damaged structure, respectively. For a given structure, if a MAC value obtained from a comparison of two mode shapes deviates from one, it can be taken as an indication of damage in the structure.  
The MAC-related objective function used in the current study is defined [43] as:  
\[F_{2}=\sum_{i=1}^{n}\frac{\left(1-\sqrt{\text{MAC}_{i}}\right)^{2}}{\text{MAC} _{i}} \tag{3}\]The correlation between two modes and the use of MAC values have been outlined by [PERSON] [2]. For a shell structure, [PERSON] and [PERSON] [3] found that if mode shape changes, it gives a more sensitive indication of damage as compared to a change in resonant frequencies.
## 4 Results
### Multiple Fire Points Location
As can be seen, flight Path2 has the fastest rate of optimal fitness decline, flight Path3 and Path4 have the middle rate, and flight Path1 and Path5 have a relatively slow rate, aligning with the progression of optimal path adjustments. Figure 8: Front view of UAV flight path on locating fire points. Figure 9: Top view of the UAV flight path for locating fire points. Based on the above result and analysis, the continuous multiple mountain forest fire points with different heights and locations are located by implementing the PSO algorithm. The UAV flight path is optimized using the cubic B-spline interpolation algorithm, ensuring a smooth trajectory that navigates around the adjacent mountain forests. The successful location of the fire points lays the foundation for subsequent fire identification and control.
## 4 Experiments and Discussion
### The Effect of Back-Surface Roughness on Notch Detection  
Figure 14 shows the experimental results of back-surface notch detection on rough back surface when the front surface was smooth. Similar to the observation of rough front surface, the normalized mean amplitude of notch signal decreased with an increase in \(R_{a}\). The mean amplitude of the notch signal was directly proportional to the notch depth. As can be seen in Figure 14b, the signal amplitude ratio factor (SARF) also decreased with increase in \(R_{a}\). The degree of attenuation caused by back surface roughness was slighter than that caused by front-surface roughness, observed from the relationship between variations in the mean amplitude of notch signal and \(R_{a}\). The SARF critical value corresponding to notches with different depths in this experiment was larger than that of the rough front surface. The SARF critical value of M-notch3 was 22.5 \(\upmu\)m in Figure 14b while the value was 15 \(\upmu\)m corresponding to a rough front surface. This was due to the fact that the attenuation of energy intensity of the pulse-echo of the notch caused by reflection on the rough back surface was less than that caused by transmission on the rough front surface.
# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 2 Literature Review
### Overview of Integrated Decision-Making Methodologies  
Effective supplier selection in complex industries like construction necessitates robust MCDM approaches. This study employs a novel integration of the SWARA, WASPAS, and VE methods. SWARA is a prominent subjective weighting method recognized for its simplicity and the ability to accurately capture expert opinions, particularly when assigning relative importance to criteria [37; 38]. Its stepwise nature facilitates the systematic determination of criteria weights, prioritizing the most influential factors based on collective expert consensus. Conversely, WASPAS is a powerful compensatory MCDM technique that combines the strengths of the Weighted Sum Model (WSM) and Weighted Product Model (WPM) to provide enhanced ranking accuracy and stability, making it suitable for evaluating alternatives across multiple attributes [39; 40]. Its utility lies in its capacity to generate precise outputs while remaining computationally straightforward, a key benefit for its practical application. On the other hand, the WASPAS method is subject to several important limitations that can affect the reliability of its outcomes. A primary and frequently cited weakness is the determination of the lambda (\(\lambda\)) coefficient. This parameter balances the influence of the WSM and WPM components in the final aggregated score. The selection of \(\lambda\) is often subjective. At the same time, a value of 0.5 is commonly adopted to give equal precedence to both models; this choice lacks a rigorous theoretical foundation and can be seen as arbitrary, meaning the final ranking can be heavily influenced by the decision-maker's preference rather than objective analysis [41]. Furthermore, because WASPAS incorporates WSM, it inherits WSM's susceptibility to the rank reversal phenomenon, where the ranking of alternatives can change with the addition or removal of an irrelevant alternative, thus questioning the stability of the solution [41].  
Integrating these methods is crucial for addressing the multifaceted challenges associated with selecting a precast concrete supplier. SWARA is utilized to derive the relative importance of the qualitative criteria, ensuring that expert knowledge directly informs the weighting structure. WASPAS then leverages these criteria weights to systematically evaluate and rank supplier alternatives, providing a comprehensive quality score. Crucially, the framework then incorporates VE, a systematic approach to maximizing value by optimizing the relationship between function, quality, and cost [42]. By dividing the WASPAS-derived quality score (representing \"value\" or \"utility\") by the supplier's normalized cost, VE provides a holistic performance metric that balances technical and quality considerations with economic feasibility. This layered integration ensures that the selection process is robust in its multicriteria evaluation and is strategically aligned with the practical project objectives of cost-effectiveness and overall value.
## 3 Automated Calibration of Chaboche Parameter
### Extraction of Stress-Plastic Strain Data  
The extracted stress-plastic strain curve provides the main result that is used to calibrate the ratcheting parameters. The elastic limit in Figures 6b and 7, must initially be defined and determined. Since ratcheting is a measure of accumulated strain, the stress at the elastic limit of the material becomes the starting point of analysis. The method of using offset strain to define elastic limit in monotonic test is done by finding the intersection on the stress-strain curve with a line parallel to the initial stress-strain slope running through the offset strain. Using the elastic limit, the stress-plastic strain curve for monotonic and upper stabilized hysteresis curves are calculated by Equations (14) and (15), respectively:  
\[\varepsilon_{P}=\varepsilon_{T}-\left(\left(\frac{\sigma}{E}\right)+ \varepsilon_{off}\right), \tag{14}\]  
\[\text{where }\varepsilon_{off}=\varepsilon_{TEI}-\frac{\sigma_{EI}}{E}\]  
\[\varepsilon_{P}=\varepsilon_{T}+\left(\left(\frac{\varepsilon_{0}+2*\sigma_{ EI}-\sigma}{E}\right)\right), \tag{15}\]  
where, \(\varepsilon_{P}\) is plastic strain, \(\varepsilon_{T}\) is total strain, \(\sigma\) is stress, \(E\) is [PERSON]'s modulus, \(\varepsilon_{off}\) is elastic strain offset, \(\varepsilon_{TEI}\) is total strain at elastic limit, \(\sigma_{EI}\) is elastic limit stress and \(\varepsilon_{0}\) is the start of half cycle strain.  
The elastic limit for stainless steel was assumed by [PERSON] et al. [22] as 0.55 times the yield stress which was determined using a plastic strain offset \(\varepsilon_{off}\) of 0.2%, as depicted in Figure 7. The basis of the factor of 0.55 comes from its use in the calibration of the Chaboche material models by AREVA GmbH Erlangen [22] as mentioned before. Multiplying the 0.2% proof stress of 305 MPa (in Figure 7) by the factor 0.55 gives the elastic limit of 167.2 MPa for post-stabilized monotonic and upper hysteresis curve shown in Figure 12.  
The factor of 0.55 is quite conservative, as discussed in Section 1. From Figure 9, using the proposed incremental uniaxial method on post-stabilized monotonic curve, the elastic limit for 316L stainless steel was measured as 244 MPa. This value of elastic limit is 0.80 times the yield stress as discussed in Section 2.4. Using the elastic limit of 244 MPa (from incremental method proposed by current study) and 167.2 MPa (from [PERSON]'s [22] method), the stress-plastic strain data is obtained and presented in Figures 12 and 13, respectively. Hence, the plastic strain range of the hysteresis curve has been enlarged to 2% and the slope of the upper hysteresis stress-strain curve extends the monotonic curve beyond 0.75% strain. This is because the plastic strain range of the hysteresis curve is supposed to be as large as all kinematic hardening rules curves saturated within the dedicated plastic strain range [25]. The backstress \(\alpha\) is calculated using Equation (8) and included in Figures 12 and 13.
## 2 Proposed Position Tracking System Based on Composite Sensors
### Positioning Tag and Anchor Based on UWB and MEMS IMU Sensor  
Herein, a position tracking system is proposed in which the UWB communication module and the MEMS IMU sensor are integrated, as shown Figure 1b; the positioning tag consists of the UWB module [8], MEMS IMU sensor, MCU, and battery module. The MEMS IMU sensor is removed while either Wi-Fi or Ethernet module is included for the anchor, because it is installed on a fixed position and does not need to measure the position data [9; 10]. In particular, the UWB modules [8] of the tag and anchor transmit signals between each other to measure the transmission time for estimating the distance between the tag and anchor. The MEMS IMU sensor provides the positioning calibration information on the basis of the accelerometer, angular velocity meter, geomagnetic meter, and altimeter. The tag, which is a moving node, is attached to a moving tool to measure the three-dimensional position of the tool and to deliver the coordinate information to the anchor, while notifying the operator as to whether the tool is performing the correct operation through alarming via an LED lamp. The anchor is installed in a fixed position and determines the distance between the tags and collects position data and alarm information from multiple tags. Figure 1c shows the UWB sensor board of the PCB assembly that we implemented and the design of the tag and anchor.