esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexural Behavior of Self-Prestressed RC Slabs with Fe-Based Shape Memory Alloy Rebar
## 3 Experiment Results and Discussion
### Load-Deflection Relationship  
#### 3.4.1 Effect of the Fe-SMA Rebar Activation  
Figure 10 shows a comparison of the load-displacement relationship between the non-activated specimens and activated specimens. As shown in Figure 10a, the initial crack of SL-2N, in which the Fe-SMA rebar was not activated, occurred at a load of 25.59 kN, whereas the initial crack of SL-2A, in which the Fe-SMA rebar was activated, occurred at a load of 35.88 kN, which was 40.2% higher than that of SL-2N. In addition, the initial crack loads of SL-3A, SL-4A, and SL-5A, where the Fe-SMA rebar was activated, were 69.2%, 81.7%, and 101.6% larger than those of SL-3N, SL-4N, and SL-5N, where the Fe-SMA rebar was non-activated, respectively. As shown in Figure 10, the stiffness of the specimen in which the Fe-SMA rebar was activated was greater than that of the specimen in which the Fe-SMA rebar was not activated. For example, the deflection of SL-2N and SL-2A at 50 kN was 18.82 mm and 15.54 mm, respectively, and the deflection of SL-2A was about 17% smaller than that of SL-2N. In addition, the deflection of SL-3N and SL-3A at 50 kN was 8.68 mm and 3.52 mm, respectively. The deflection of SL-3A was about 59% smaller than that of SL-3N. The larger initial cracking loads and stiffness of the activated specimens than those of the non-activated specimens are thought to be attributable to the recovery stress generated by rebar activation acting as a compressive force on the specimen. On the other hand, the ultimate load of the specimens with the same amount of Fe-SMA bar was only slightly different, changing in a range of 0.12-3.15% regardless of activation of the Fe-SMA rebar. Therefore, similar to conventional prestressed concrete, it can be confirmed that the effect of the prestressing force due to Fe-SMA activation on the ultimate load of the reinforced concrete member was negligible. It can also be confirmed through the experimental results that activation of the Fe-SMA rebar was more effective in terms of improving usability, such as improvement of initial crack load and initial stiffness, rather than improvement of the strength of the member reinforced with Fe-SMA rebar.
## 4 Experimentation and Results
### Experimental Procedure and Participants  
Our experimental procedure and data analysis techniques are based on the methods followed by [PERSON] et al. [61], [PERSON] et al. [30], [PERSON] et al. [62], [PERSON] et al. [63], [PERSON] et al. [55], and [PERSON] et al. [64]. We use a quantitative research method based on Quasi-Experimental (QE) design. In QE designs, groups of the participants are arranged on nonrandom criteria [65]. In our study, participants are divided into three different groups as Group-A, Group-B, and Group-C. Participants in each group have an equal level of learning skills and grades. The activity is performed in local schools in Peshawar, Pakistan, and the participants of the activity are selected from the primary sections of two different schools. Fifty students are assigned to each group having participants with the same level of academic achievements. This decision was made based on their previous academic grades provided by their affiliated schools.  
Figure 8: Different AR camera scenes of the AR Globe application where the 3D globe is augmented on the camera preview.  
Each group is assigned to a teacher and performed a learning activity with a different method of teaching. In Group-A, the students perform the learning with the help of our designed AR applications. A tablet is provided to each student of the group along with image targets and is guided by the teacher about the application. Similarly, the participants of Group-B performed the learning session of the same syllabus by watching videos and presentations. In Group-C, the learning activity is performed with the help of the traditional book-based method. The experimental procedure of the study is shown in Figure 9.  
In this activity, a total of one fifty students participated from two different schools. All these students belong to the primary section, with an average age of seven years. These students are classified, considering their previous academic marks as pre-test marks. The Mean (M) value of Group-A grades is 73.5, with a Standard Deviation (SD) of 8.5. Similarly, Group-B participants have average grades with M = 75.1 and SD of 9.2. In Group-C, students have average marks with M = 74.3 and SD = 7.8. Table 1 shows the participant sampling in different groups.
## 2 Materials and Methods
### Identification Methods
#### 2.3.3 Feature Classification
### Parameter Selection

All four tree-ensemble learning methods (RF, SAMME, GBDT, and XGBoost) have multiple tunable hyperparameters that can severely influence the PI performance. It is important to select optimal values for these hyperparameters based on the available data. Moreover, as two benchmark methods that have been used for EN-based PI, NN and SVM were compared with the tree ensemble learning methods. The tunable parameters of NN and SVM were also selected based on the same data. The determination of hyperparameter values was modeled as a function minimization problem, which uses the additive inverse of identification accuracy as the objective function. To select the optimal value combination for the hyperparameters, the global minimum of the constructed objective function was searched using the simulated annealing algorithm [22]. Table 2 lists the empirically determined range of the main tunable hyperparameters of the tested methods. Apart from the parameters listed in Table 3, other parameters were found to have a minor influence on the identification accuracies, and, thus, were fixed as

Figure 3: The raw and preprocessed measurements in a typical sampling cycle. (**a**) The raw sensing voltage measurements; (**b**) The preprocessed sensing voltage measurements. their default values. For example, the kernel parameter of SVM was always converged at 'polynomial' when its optimal value was searched for within the set of all valid values. Thus, the kernel parameter of SVM was not listed in Table 2 and was fixed as 'polynomial' in our experiments. As mentioned in Section 2.2, the feature set output by PCA was divided into ten equal parts according to the ten-fold cross-validation method. Each part was, in turn, used as the test set, while the others were used as the training set. For each pair of training and test sets, the optimal value combination was searched for the hyperparameters. Then, the optimal parameter value combination was used by the classification method for predicting the perfume type with respect to the test sets. Finally, the fraction of correctly classified
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 3 Materials and Methods
### The Application Generating Label  
After training the proposed method with a little training data, a user can perform organ segmentation on a CT image. Figure 3 shows our tool in use. It contains the input CT image, anchor pixels entered by a user, threshold value, and segmented target organ image. A user can create labels using our tool as following order:  
1. Open a CT image to perform segmentation;
2. Select the target organ in category;
3. Pick anchor pixels on the target organ. Once anchor pixels are picked, proposed model computes kernel density for all pixels;
4. Extract the segmentation label by filtering the density that is higher than a threshold value.  
To infer segmentation from a kernel density, a threshold value determines the lower density bound of the segmentation label. The suitable threshold value can be different for each organ since each organ has various sizes and shapes in a CT image. In order to enable adjusting the threshold value, we added a slider in the bottom of the interface, as illustrated Figure 3. Through the slider, a user can manipulate the threshold to generate a proper segmentation label on input CT image.
## 3 Results and Discussion
### Experiment Verification
#### 3.2.3 Linearization Verification of Dynamic Weighing Results  
This section mainly investigates the characteristics of the weighing system under dynamic conditions comprehensively. The results are shown in Table 4.  
It can be seen that the difference of experimental conditions has little effect on the linear relationship of the measured results. So the linear fitting results between the measured value and the true value of the measured mass are adaptive. The repeatability, hysteresis, and linearity values of weighing measurement data under different vacuum degrees, steam velocities, and liquid velocities are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement System** & **Equation** & \(\mathbf{R^{2}}\) \\ \hline Vacuum/kPa & & \\
76 & \(m_{1}=1.0161m-1.6190\) & 0.99997 \\
81 & \(m_{1}=1.0186m-1.6719\) & 0.99997 \\
85 & \(m_{1}=1.0175m-1.5234\) & 0.99995 \\
91 & \(m_{1}=1.0187m-1.6188\) & 0.99996 \\
93 & \(m_{1}=1.0188m-1.7030\) & 0.99997 \\ Steam flow rate/L\(\cdot\)h\({}^{-1}\) & & \\
0 & \(m_{1}=0.9680m-0.8627\) & 0.99997 \\
0.3 & \(m_{1}=0.9684m-0.7229\) & 0.99997 \\ Droplet velocity/g\(\cdot\)min\({}^{-1}\) & & \\
10 & \(m_{1}=0.9948m-0.3563\) & 0.99997 \\
20 & \(m_{1}=0.9769m-0.3164\) & 0.99997 \\
30 & \(m_{1}=0.9772m-0.5127\) & 0.99996 \\
40 & \(m_{1}=1.0009m-1.1095\) & 0.99995 \\
50 & \(m_{1}=0.9751m-0.6632\) & 0.99997 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equation and coefficient of determination (R\({}^{2}\)) under different experimental conditions.  
Figure 8: FFT transform results of transient mass signals at different liquid flow rates: (**a**) \(10/\)g\(\cdot\)min\({}^{-1}\); (**b**) \(20/\)g\(\cdot\)min\({}^{-1}\); (**c**) \(30/\)g\(\cdot\)min\({}^{-1}\); (**d**) \(40/\)g\(\cdot\)min\({}^{-1}\); (**e**) \(50/\)g\(\cdot\)min\({}^{-1}\).  
In the above table, the maximum value of repeatability, hysteresis, and linearity is 0.498%, 0.401%, and 0.311%, respectively, which are all reflecting the high accuracy of the system. Since repeatability is generally greater than linearity and hysteresis, it indicates that random error plays a greater role in system measurement than systematic error, which may be generated during the calibration of weights. Meanwhile, the linearity of the measured data is good with one-way change, and the value of the hysteresis error is larger than the linearity. It reflects that there is some deviation between the bidirectional and unidirectional characteristics of the system. However, for unidirectional mass change from small to large, the deviation has no significant impact on the accuracy of the system. Besides, it can be noticed that steam flow conditions have little difference in the experimental results, which indicates that the gas flow rate has little influence on the measurement results of the system. In conclusion, accurate weighing data can be obtained within the linearization range predicted by the model.
## 5 Analysis of Simulation Results
### Spectral Efficiency, Transmission Rate and Complexity  
In addition to bit-error performance, complexity and spectral efficiency are important indicators for measuring system performance. In this study, an exhaustive number of ML  
Figure 8: Actual experimental platform.  
detection algorithms were adopted as the computational complexity, and the detection complexity of OGSMP-MIMO is  
\[OGSM=N_{c}L^{N_{a}} \tag{31}\]  
where \(N_{c}\), \(L\) and \(N_{a}\) denote the actual transmit antenna combination, modulation order, and number of actually activated transmit antennas, respectively. According to Equation (3), the actual number of antenna combinations used is as follows:  
\[m=\left\lfloor\log_{2}C_{N_{t}}^{N_{a}}\right\rfloor \tag{32}\]  
When \(L\)-PAM modulation is used, the number of modulation bits is  
\[m_{s}=N_{a}\times\log_{2}(L) \tag{33}\]  
Therefore, the complexity of OGSMP-MIMO is  
\[OGSP_{ML}=2^{\left\lfloor\log_{2}C_{N_{t}}^{N_{a}}\right\rfloor}\times 2^{N_{a} \times\log_{2}\left(L\right)}\times N_{c}L^{N_{a}} \tag{34}\]  
Similarly, Table 4 can be obtained. It can be observed from Table 4 that the number of active antennas and the modulation order at the transmitter are the main factors that affect the system transmission rate, spectral efficiency, and complexity of the detection algorithm. When the system is determined, with an increase in the number of active antennas at the transmitter and in the modulation order, the transmission rate, spectral efficiency, and complexity significantly improve.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Occlusion-Aware Mask Propagation Network
#### 3.2.1. Occlusion State Perception with Mask IoU
\begin{cases}0,&\text{if }\max_{i\in O_{t}}(\alpha_{t}^{Ai})< \alpha_{gt}\text{ and }|W_{k}|\geq\theta_{k}\\ 1,&\text{if }\max_{i\in O_{t}}(\alpha_{t}^{Ai})\geq\alpha_{gt}\text{ and }|W_{k}|< \theta_{k}\\ 2,&\text{if }\max_{i\in O_{t}}(\alpha_{t}^{Ai})\geq\alpha_{gt}\text{ and }|W_{k}|\geq \theta_{k}\\ 3,&\text{if }\max_{i\in O_{t}}(\alpha_{t}^{Ai})<\alpha_{gt}\text{ and }|W_{k}|< \theta_{k}\end{cases}  \tag{20}\]  
where \(\alpha_{gt}\) is the minimum value of the IoU. This determines if the result is the same as labeling. \(\theta_{k}\) is the threshold used to determine if \(W_{k}\) decays too fast, and \(\max_{i\in O_{t}}(\alpha_{t}^{Ai})\) represents the maximum value of IoU between target \(A\) and other targets in frame \(t\):  
* If there exists a target \(i\) such that \(\alpha_{t}^{Ai}\geq\alpha_{gt}\), this indicates that target \(A\) overlaps with another target. To determine whether \(A\) is the occluder or the occluded, \(W_{k}\) is used:
* If \(A\) is smaller than \(W_{k}\), this indicates a rapid decrease in the mask area \(\beta_{t-T:t}^{A}\), which means that \(A\) is an occluder (\(o_{t}^{A}=1\));
* If \(A\) is larger than \(W_{k}\), this indicates no decreasing trend in the mask area, which means that \(A\) is the occluder (\(o_{t}^{A}=2\)). * If there is no target \(i\) that satisfies \(\alpha_{t}^{Ai}\geq\alpha_{gt}\), the occlusion state is also determined by \(W_{k}\):
* If \(A\) is larger than \(W_{k}\), this indicates no occlusion (\(o_{t}^{A}=0\));
* If \(A\) is smaller than \(W_{k}\), this indicates occlusion by non-target objects (\(o_{t}^{A}=3\)).
# Experimental Approaches in Delineating mTOR Signaling
## 2 Overview of mTOR Signaling
FK506 and rapamycin bind to the same site of FKBP12 but with different downstream targets [18]. Taking advantage of this knowledge, FKBP12-rapamycin complexes were used as the bait to fish for binding partners, and mTOR was found as the hit. Specifically, [PERSON] et al. relied on \({}^{32}\)P-labeled FKBP12 proteins with or without rapamycin to pulldown PC12 pheochromocytoma cell lysates, followed by DSS (disuccinidyl] suberate)-mediated cross-linking. This led to identification of a 260 KD protein named \"mTOR\" by autoradiography [19]. At the same time, [PERSON] et al. used a fusion protein GFK (GST fused with FKBP12) prebound with two structural variants of rapamycin to pull down MG-63, Jurkat, or rat basophilic leukemia (RBL) cell lysates. Silver staining of GFK-bound mTOR was identified in each cell line and further verified in bovine brain tissues [20]. Independently, [PERSON] et al. used a GST-FKBP12-rapamycin affinity matrix to isolate putative mammalian targets of rapamycin (mTOR) from rat brain and murine T-lymphoma cell extracts [21]. Meanwhile, using a yeast-two-hybrid system composed of human FKBP12 and a mouse embryonic cDNA PCR library, [PERSON] et al. also identified mTOR as a target for FKBP12/rapamycin complex [22]. In early 2000s, presence of mTOR was also observed broadly in other organisms including fission yeast ([23], based on sequence homology search as homologs to budding yeast _TOR1_ and _TOR2_), _C. elegans_ ([24], also based on BLAST homologous search for PIK proteins), and Drosophila ([25], through a tissue-specific genetic screen for recessive mutations regulating compound eye development or [26] through a Drosophila cDNA library screening coupled RACE (rapid amplification of cDNA ends) analysis).
## 5 Conclusions  
The mathematical model for a hub-driven electric vehicle considering the coupling effect in IWM was established based on electric magnetic field theory. The influences of the coupling effect on the vehicle performances were analyzed. Then, a reliable robust H\(\infty\) controller considering model uncertainties, actuator failure and electromagnetic force interference was designed. The results are given as follows:  
(1). The vertical UEF in the motor is strongly coupled with rotor rotation position, phase current, and rotor eccentricity. High phase current and large rotor eccentricity promoted by the coupling effect provoke the UEF of the motor. The mutual promotion phenomenon (i.e., the rotor eccentricity provokes the UEF; synchronously, the UEF intensifies the rotor eccentricity) aggravates the electromagnetic coupling effect, thus worsening the motor operation performance. Furthermore, this phenomenon aggravates the car body acceleration, deteriorating the ride comfort of vehicle.  
(2). Based on Lyapunov stability theory, a reliable robust H\(\infty\) controller for active suspension of a hub-driven EV associated with model uncertainties, actuator failure, and electromagnetic force interference was investigated. The control objective is to enhance the ride comfort and restrict the coupling effect in IWM while satisfying the hard constraints such as road holding capability, suspension deflection limitation, actuator failure, and parameter variation. Simulation results in both the frequency-domain and time-domain show the robustness and advantageous performances of the reliable robust H\(\infty\) controller method. This work can provide a practical model reference for the study of vibration performance of hub-driven EVs with PMSM and the optimization, control and improvement of its vibration performance. In the future, the influence of bearing nonlinear force and bearing clearance on the ride comfort and the motor operation performance will be studied. Moreover, the experimental validation will be investigated.  
Conceptualization, methodology, H.W. and L.Z.; software, formal analysis, H.W., Z.Z. and Y.Y.; writing and editing, H.W.; supervision and funding acquisition, L.Z. Y.Y. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 51875061), Technology innovation and application development special project of Chongqing (Grant No. cstc2019)sco-zdztzx0032), Chongqing Postgraduate Research and Innovation Project [grant number: CYB19063].  
The authors would like to extend special thanks to [PERSON] of Chongqing University for language polishing from the research supported by State Education Ministry and Fundamental Research Funds for the Central Universities (2018 CDJSK04 XK09).  
We declare that there is no conflict of interests in connection with the paper submitted.
## 4 Evaluation Models
### Black Box ML Models  
We utilize three black box ML models in our study comprising MLP, XGB, and RF models.  
1. MLP (multi-layer perception)  
We replicate a modified version of the ANN training applied by [27] to the PL prediction by means of the same training data set in our study. While in the aforementioned study, the features longitude and altitude are dropped by a feature selection step before training the model, we abstain from dropping any of the input explanatory variables through our study as longitude and latitude are containing site-specific information of the training, which leads to accounting for shadowing effects by modeling PL behavior. Hence, the ML models' input features through our paper are longitude, latitude, elevation, altitude, clutter height, and distance of separation between the corresponding transmitter and the receiver points. The model output feature is path loss throughout.  
The fully connected neural network applied in our study consists of two hidden layers, each comprising 32 neurons and the rectified linear unit-based activation function.
2. XGB (extreme gradient boosting)  
We apply the XGB model, which is one of the most prominent ensemble of tree- based models. Its high performance is based on parallelization, tree pruning, and hardware optimization, while requiring minimal data pre-processing [28].  
XGB is applied in multiple XAI researches of PL predictions [6; 7; 19]. The model trained in this paper is hyper-parametrized by using the randomized search function of the Python Sklearn 1.3.2 package through the parameter space within the search grid: \(50<``n\_estimator\"\)\((integer)<200\), \(3<``max\_depth\"\)\((integer)<10\), \(and\)\(0.01<``learning\_rate\"\)\((uniform)<0.1\). This results in utilizing 42 decision trees, incorporating the maximum depth attribute of the trees to 4, and setting the learning rate equal to 0.023.  
1. RF (random forest)  
We apply the RF model in our study, as it represents one of the most prominent ensembles of tree-based models [29]. It is applied frequently for XAI research of PL predictions [14; 15; 16; 17; 18; 19; 20; 21]. The model trained in this paper is hyper-parametrized by using the randomized search function of the Python Sklearn package through the parameter space within the search grid: \(200<``n\_estimator\"\)\((integer\)\(with\)\(100\)\(increments)<2000\), \(10<``max\_depth\"\)\((integer\)\(with\)\(10\)\(increments)<100,1<``mean\_sample\_leaf\"\)\((integer)<4\), and \(1<``min\_samples\_split\"<10\). It results in utilizing 1000 decision trees, incorporating the maximum depth attribute of the trees to 60, and setting the minimum sample leaf as well as the sample split equal to 2.