esa-sceva/satcom-chunk-collection huggingface dataset:
## Effects of Backpack Loads on Leg Muscle Activation during Slope Walking  
[PERSON] 1\({}^{\copyright}\), [PERSON] 2, [PERSON] 1, [PERSON] 1 and [PERSON] 3,*  
1 Department of Mechanical and Engineering, Beijing Institute of Technology, Haidian, Beijing 100081, China; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (Q.S.); [EMAIL_ADDRESS] (M.Z.)
2 Robot Research Institute, Guizhou Aerospace Control Technology co., LTD, Economic and Technological Development District, Guiyang 550000, China; [EMAIL_ADDRESS]
3 Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Haidian, Beijing 100086, China
* Correspondence: [EMAIL_ADDRESS]  
Received: 15 June 2020; Accepted: 13 July 2020; Published: 16 July 2020  
Hikers and soldiers usually walk up and down slopes with a load carriage, causing injuries of the musculoskeletal system, especially during a prolonged load journey. The slope walking has been reported to lead to higher leg extensor muscle activities and joint moments. However, most of the studies investigated muscle activities or joint moments during slope walking without load carriage or only investigated the joint moment changes and muscle activities with load carriages during level walking. Whether the muscle activation such as the signal amplitude is influenced by the mixed factor of loads and grades and whether the influence of the degrees of loads and grades on different muscles are equal have not yet been investigated. To explore the effects of backpack loads on leg muscle activation during slope walking, ten young male participants walked at 1.11 m/s on a treadmill with different backpack loads (load masses: 0, 10, 20, and 30 kg) during slope walking (grade: 0, 3, 5, and 10\({}^{\circ}\)). Leg muscles, including the gluteus maximus (GM), rectus femoris (RF), hannstrings (HA), anterior tibialis (AT), and medial gastrocnemius (GA), were recorded during walking. The hip knee, and ankle extensor muscle activations increased during the slope walking, and the hip muscles increased most among hip, knee, and ankle muscles (GM and HA increased by 46% to 207% and 110% to 226%, respectively, during walking steeper than 10\({}^{\circ}\) across all load masses (GM: \(p=1.32\times 10^{-8}\) and HA: \(p=2.33\times 10^{-16}\))). Muscle activation increased pronouncedly with loads, and the knee extensor muscles increased greater than the hip and ankle muscles (RF increased by 104% to 172% with a load mass greater than 30 kg across all grades (RF: \(p=8.86\times 10^{-7}\))). The results in our study imply that the hip and knee muscles play an important role during slope walking with loads. The hip and knee extension movements during slope walking should be considerably assisted to lower the muscle activations, which will be useful for designing assistant devices, such as exoskeleton robots, to enhance hikers' and soldiers' walking abilities.  
muscle activations; electromyography; slope walking; backpack loads
## 1 Introduction  
There exists a wide range of constraint programming problems defined on Boolean functions of binary variables. One of the common approaches to solving CP problems is using specific appropriate solvers, e.g., SAT solvers. Note that regardless of the SAT-to-MILP relationships, the transformation of the Boolean expression to linear equations or inequalities is a challenge in computing science [1, 2, 3]. A rich lore of publications presents the variety of the systems of linear inequalities and linear equations equivalent to basic logical bi-variant functions; see Table 1.  
Returning to the SAT problems, one recalls that it targets checking the satisfiability of \"a long\" Boolean expression, presented in one of two special forms: either the conjunctive normal form (CNF) or the disjunctive normal form (DNF). If the Boolean function has the DNF or CNF representation, then the generation of the corresponding linear inequalities/equations can be done directly on the base of Table 1 because the DNF and CNF are compositions of the elementary bi-variant Boolean operations. This, however, requires a lot of auxiliary variables to be used for interim values of \(\wedge\) and \(\vee\) operands. This approach applies to all Boolean functions, because any of them can be converted into DNF or CNF. Yet, the main disadvantage of the \"SAT-based\" approach to getting the MILP representation is that it is based on the elementary DNF/CNF operands corresponding to a given Boolean function. The number of these operands can be much more than the number of the function's variables. Thus, the number of the auxiliary variables denoted by \(z\) in Table 1 (fortunately, all these \(z\)-s may be assumed continuous; they will take 0-1-values \"automatically\") and inequalities/equations may turn out to be very high, even for a function with a moderate number of variables. Moreover, the resulting system will have a lot of redundant secondary inequalities/equations following from the original ones.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study  
We conducted a comprehensive evaluation of our method on two widely used indoor datasets, ScanNet [19] and 3 DMatch [20]. Section 4.1 provides a detailed description of the dataset setup, the configuration of model training and testing parameters, evaluation metrics, and baselines. Section 4.2 presents the experimental results of our method on the two datasets and offers a thorough comparison with other approaches. Additionally, we analyze the performance variation of our method with respect to frame interval changes and compare it with PointMBF [24], further demonstrating the stability and practical applicability of our approach. Finally, Section 4.3 includes ablation studies that validate the simplicity and effectiveness of our proposed architecture.
## 2 Research on the Key Technology of Eye Control Methods
### Facial Expression Recognition
#### 2.1.3 Example of Face Image Data Set  
In order to improve the robustness and generalization ability of the facial expression recognition model, the Softmax loss function was adopted in the last fully connected layer of the VGG-19 network structure, which helped to accelerate the network convergence speed and effectively realize the seven classification tasks of facial expressions [5]. VGG-19 was chosen for this task because it performs well in image classification tasks, especially in capturing small spatial-scale features like facial expressions. Compared to other popular models like ResNet, VGG-19 strikes a balance between network depth and computational efficiency, making it effective at extracting local features from facial expressions while maintaining lower resource consumption. While ResNet excels in extracting deep features, its more complex structure results in additional computational overhead, which is not suitable for real-time applications. Additionally, although MobileNet offers advantages in lightweight architectures, its accuracy is insufficient for handling complex facial features. Therefore, VGG-19 provides a more balanced performance in terms of accuracy and resource usage, achieving higher classification accuracy in our experimental setup.  
Figure 2: Face image dataset example.  
The feature vector of the Softmax layer in the expression recognition model based on VGG-19 was extracted and stored as the feature output at the decision level of facial expression information. Since the dimension of the features at the decision level was equal to the number of categories of the classification task, the seven-dimensional feature vectors of the Softmax layer could be saved to represent the confidence scores of seven different expression categories, respectively. The saving format is shown in Formula (1) [8].  
\[\left[\begin{array}{ccccc}N_{0}&P_{0,0}&P_{0,1}&P_{0,2}&P_{0,3}&P_{0,4}&P_{0,5 }&P_{0,6}\\ N_{1}&P_{1,0}&P_{1,1}&P_{1,2}&P_{1,3}&P_{1,4}&P_{1,5}&P_{1,6}\\ \cdots\\ N_{(n-1)}&P_{(n-1),0}&P_{(n-1),1}&\cdots&P_{(n-1),5}&P_{(n-1),6}\\ N_{n}&P_{n,0}&P_{n,1}&P_{n,2}&P_{n,3}&P_{n,4}&P_{n,5}&P_{n,6}\\ \end{array}\right] \tag{1}\]  
Among them, N represents the total number of pictures in the face image dataset, n represents the number of pictures, i represents the category of expressions represented by numbers, and represents the seven-dimensional decision-level features saved, that is, the confidence scores of various expressions.
## 3 General Framework for the Multi-Time-Scale Coordinated Operation of Combined System
### Hour-Level Rolling Correction Schedule  
The starting point is that there is the power deviation between the day-ahead scheduled power outputs of battery-thermal-hydro-pumped storage and the actual wind-photovoltaic power load in real time. In the traditional day-ahead scheduling, the power regulation burden mainly depends on thermal power units, and system power balance cannot be easily maintained due to the lack of fast power adjustment capability, which greatly affects system operation security. On the other hand, when the time scale of forecasting decreases, the wind-photovoltaic power load prediction accuracy increases. Therefore, based on the 3 h-ahead updated wind-photovoltaic power load forecast data, exerting strengths of fast response generators can be used as a corrector to modify the day-ahead scheduling plan for gradually reducing the deviation between the day-ahead power generation plan and the system actual wind-photovoltaic power load, thus reducing the power regulation pressure on thermal power units.  
In the combined WPTHB system, the hydro-pumped storage can be effectively adjusted for operating at the start and stop states at a time scale of 15 min. Therefore, using the open-loop rolling optimization algorithm, the intra-day hourly rolling corrective schedule model could be established for power adjustment of thermal-hydro-pumped storage units. Specifically, with the premise of the fixed on-off status of thermal power units and the fixed power outputs of batteries in the day-ahead scheduling, the intra-day hourly power adjustment of thermal power generators and hydro-pumped storage units are solved based on the wind-photovoltaic power load updated 3 h ahead with an interval of 15 min.
## 4 Conclusions  
This paper introduces a numerical finite element (FE) model for ultrasonic vibration-assisted milling of Nomex honeycomb structures. Initially, an optimal mesh size was selected to balance CPU calculation time and result accuracy. Simulations were carried out after the validation of the numerical model to analyze the effect of the vibration amplitude on several parameters, notably the quality of the surface, the size of the chips, and the distribution of stresses and displacement in proximity to the cutting zone. Based on this study, the following conclusions can be made:  
* The study of the impact of the cutting width on the components of the cutting force is carried out, finding a significant increase in these components with increasing cutting width, both in simulations and in experiments. Our results suggest that the use of ultrasonic vibrations helps to mitigate the negative effects of the \(\mathrm{F_{x}}\) and \(\mathrm{F_{y}}\) components in both directions. Furthermore, a significant agreement between the results of the numerical model and the experimental data was observed.
* The amplitude of the ultrasonic vibration directly impacts the chip size, leading to a reduction in this one with increasing vibration amplitude.
* The amplitude of the vibration influences the surface quality, leading to an improvement of the latter when the amplitude of the vibrations is increased.
* Applying ultrasonic vibration to the cutting tool induces additional stress in the cutting area of the honeycomb cell wall, accelerating material deterioration while reducing cell wall deformation, facilitating a more efficient milling of the Nomex honeycomb core.
* By continuing this research, it is planned to develop the numerical model by taking into account other parameters in order to detect the burrs that form on the thin walls during the machining process.
* In the industrial context, the optimization of manufacturing processes often requires costly and time-consuming tests to evaluate different configurations. The 3D modeling presented thus offers a considerable advantage in terms of speed, efficiency, and profitability.  
[PERSON] performed analyzed and interpreted data and results. [PERSON] analyzed and interpreted data and was a major contributor to writing the manuscript. [PERSON] was a major contributor to writing the manuscript. [PERSON] analyzed and interpreted data and results. [PERSON] was a major contributor to writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Chip size for different vibration amplitudes.
## 2 Field Experiment
### Measurement Plan
#### 2.3.4 Accelerometer Transducer Setting  
Figure 4a also shows the set location of the MEMs accelerometer transducer, which is an INTEGRAL PLUS sensor with 15-min real-time deflection measurement capabilities. The sensor was adopted under a new technology information system within the Ministry of Land, infrastructure, transport, and Tourism Japan for inspection support. It is suitable for bridge spans less than 40 m, with a maximum running test of 40 km/h and an accuracy of 0.08 mm for steel bridges [19]. It was placed at the center span, as shown in Figure 4a, on the upstream girder side of the structure. It measured the dynamic behavior of the structure [19; 20; 21; 22]. The measure of the vertical displacement of the structure against time under different loading conditions was meant to determine the in-service behavioral response. Such conditions illustrate the ambient condition of the structure relating to its day-in response [19].  
Several parameters are employed in evaluating a structure's dynamic condition, such as the natural frequency, vibration modes, damping, and Dynamic Amplification Factor (DAF) [23; 24]. Among them, DAF is considered a critical parameter introduced in bridge design codes, for example, AASHTO 1996, 1997, and Eurocode 1-2. It establishes the structures' inherent uncertainties that correlate with other dynamic effects, such as pavement condition and extent of deterioration. Thus, it can contribute to bridges' structural condition. Therefore, a structural condition assessment of the Sukumo bridge was based on DAF. It was calculated using Equation (4) [25; 26].  
\[\text{DAF}=(1+\mu)=1+\left\{\frac{R_{dyn}-R_{stat}}{R_{stat}}\right\} \tag{4}\]  
where \(R_{dyn}\), \(R_{stat}\), and \(\mu\) denoting maximum dynamic, static response, and dynamic amplitude (deflection or deformation).
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 3 Performance Over AWGN Channel
Therefore, larger shaping gains of 128-GS are observed with respect to 128 QAM. In Figure 6, we depict the AIRs in Equation (2) for HD-FEC schemes with _M_QAM and _M_-GS. As can be seen, the GS constellations lead to a SNR gain in terms of SNR compared to uniform QAM in the case of multiple SEs. For a normalized AIR of 0.9 (diamond markers), the shaping gains of GS are 0.3 dB, 0.6 dB, and 0.35 dB for \(M=\{64,128,256\}\) with respect to _M_QAM, respectively. Note that the AIRs of 64 QAM and 256 QAM in Equation (1) for SD-FEC schemes are also shown as baselines in Figure 6. The gaps between \(I_{\text{SD}}\) and \(I_{\text{HD}}\) indicate a potential performance region for hybrid HD/SD FEC schemes. Figure 6 also shows (with markers) the SNR required for different CM schemes to achieve a post-FEC BER of \(10^{-6}\). We remark that such a SNR gain is due to a twofold advantage of GS-SA-SCC over conventional SCC with QAM: (i) the shaping gains, which comes from the optimized constellation matrices pair \(\{\mathbb{S}^{*},\mathbb{B}^{*}\}\), and (ii) the improved decoding capability provided by the SABM decoding. For GS and SCC with code rate of 0.83, we observe the shaping gains of GS are 0.3 dB, 0.6 dB, and 0.35 dB, which is in excellent agreement with the prediction of AIR curves. Thanks to the processing of SABM, it enables  
Figure 5: Additive white Gaussian noise (AWGN) performance: SNR vs. bit error rate (BER) for different modulation formats and SCC decoding algorithms. an additional gain on top of GS. The overall (shaping plus coding) gains for \(M=\{64,128,256\}\) with GS and SABM decoding are up to 0.55 dB, 0.83 dB and 0.63 dB with respect to \(M\)QAM with standard decoding, respectively. Note that GS-SA-SCC with 128-GS and code rate of 0.92 even achieved almost the same rate as \(I_{\text{HD}}\) with 128 QAM (see green square marker and blue dotted curve in Figure 6). Comparing to the results in Figure 1, GS-SA-SCC with code rate of 0.83 reduced 0.55 dB of the \(\sim\)2 dB gap to LDPC with very small added complexity. In general, the \(I_{\text{HD}}\) is the AIR of decoding system operating solely based on HD decoding, while the soft-aided decoders operate beyond this. In other words, the overall gains shown here are larger than \(I_{\text{HD}}\) prediction. An interesting research is to find the performance metric of the soft-aided decoders, which is left for future work.
## 3 Experimental Results and Discussion
### Crack Pattern and Failure Mode
#### 3.3.2 Late Stage of FP Series Specimens  
In contrast to the ribbed high tensile bar in specimen CS, the TMS plates without any holes in the FP series specimens have smooth and flat surfaces on both sides. This barely enabled the plates to form an adequate bonding with the concrete. This prevented the transfer of shear forces from the concrete to the TMS plate in the FP series specimens when loaded and resulted in the development of sudden diagonal tension cracks in the FP series specimens upon reaching failure load.
## 4 Results  
### Simplification Model Based on Azimuth Difference and Side Length (SM-ADSL)  
#### 4.1.1 Manually Simplifying the Envelope  
The selection of distribution envelope simplification samples followed two principles. First, to obtain a high simplification ratio, the sample vertices selected should be as few as possible. Second, to reduce the simplification error, the line segment between adjacent sample vertices should have a small deformation to the original envelope. These two principles are contradictory because a large simplification rate requires a long side length between sample vertices, which increases deformation and generates a large simplification error. Therefore, during sample selection, it is necessary to balance the above two principles and focus on envelope areas with large or small curvature
## 2 Materials and Methods
### Training of the Proposed SSAE Deep Neural Network  
We used the greedy layer-wise method for SSAE pre-training in order to train the proposed SSAE deep neural network for face recognition. This was achieved by introducing each layer individually. After pre-training, the trained SSAE was used to test the dataset set aside for extracting features for face classification. In order to learn basic features, an SAE first takes the inputs of a raw face \(x\) and a set of weights \(W^{(1)}\). The network trained to generate the specific activations of feature \(h^{(1)}(x)\) for each facial image feature \(x\) then receives its output. The sparse autoencoders use these fundamental features as \"raw input face\" to learn \(h^{(2)}(x)\). The next activation function for feature \(h^{(2)}(x)\) for each of the fundamental features \(h^{(1)}(x)\), the features are then sent into the second SAE (which corresponds to the vital features of the initial face image features of the input \(x\)). Once a soft-max classifier has been trained to associate secondary features with number labels, secondary features are input.  
The final step is to integrate all three layers to create SSAE, which has two hidden layers and a soft-max classifier that can accurately categorize the face traits from both the OLR and the Extended Yale-B database. Algorithm 2 presents the condensed training algorithm for the proposed SSAE deep neural network with a soft-max classifier.  
```
0: Extracted features by Algorithm 1
0: Authenticated image or not Initialization: bias \(b_{h}\), weight \(W\), input \(x\).
1: Training of facial image features // Training initial face image features using number of the pixels in each initial face feature
2: Compute hidden layer output: \(h=\epsilon_{x}(x)=s(Wx+b_{h})\) // where \(b_{h}\in R^{h_{h}}\) is a vector of a bias, and \(W\) is a \(d_{h}\times d_{x}\) matrix of weight
3: Calculate the next hidden layer output that will be used to predict the output value using Equation (8) as follows: \(h^{\prime}(k)=\left(h^{\prime}_{k}(k),h^{\prime}_{k}(k),\ldots,h^{\prime}_{k^ {\prime}_{k}}(k)\right)^{\prime}\) // where \(h\) is the input hidden layer // enter feature of the initial face and its exemplification at hidden layer! 4: Estimate the new feature-related output value / using \(f:R^{d_{h}}\to R^{d_{h}(2)}\) // which convert pixels of input raw of initial face image feature to \(h^{(2)}=f(x)\in R^{h_{h}(2)}\) // new feature exemplification specified
5: Estimate the soft-max optimization to predict the final output value using Equations (9)-(11). // all three layers are merged jointly to shape SSAE with two hidden layers // and an ultimate layer of soft-max classifier capable of classifying the face //attributes of both the OLR and the Expanded Yale-B race databases 6: Input unrecognized: // returning to Algorithm 1 if more face photos are required for training.
```  
**Algorithm 2**An algorithm for training stacked sparse autoencoder (SSAE) model with soft-max classifier