esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Walking-Gait Parameters and Walking Strategy for Quadruped Robots
## 3 Results and Discussion
### The Gait Planning of the Bionic-Robot Dog
#### 3.3.1 Basic Concepts of Gait Planning
In the inertial coordinate system, if the coordinate of the center of gravity is \(P_{c}=[x_{c},y_{c},z_{c}]^{T}\), the mass of the machine is \(M\), the acceleration of gravity is \(g=[0,0,-g]^{T}\), the coordinate of point \(P\) is \(P=[P_{X},P_{Y},P_{Z}]^{T}\), and the combined force of the ground reaction force on the support point is \(F\), then the torque of \(F\) around the origin is as shown in Equation (22):  
\[\tau=P\times F+\tau_{p} \tag{22}\]  
Among them, \(\tau_{p}\) is the torque passing through \(P\). Figure 8: The relative relationships between limb phases in common gaits. (**a**) Walk. (**b**) Trot. (**c**) Pace. (**d**) Bound. (**e**) Gallop. (**f**) Pronk. Figure 9: ZMP definition diagram. Equation (23) can be obtained from the momentum theorem and angular momentum theorem:  
\[\left\{ \begin{array}{l}\dot{P}=Mg+F\\ \dot{L}=P_{\rm C}\times Mg+\tau\end{array} \right. \tag{23}\]  
Combining Equations (22) and (23), the expression for \(\tau_{p}\) can be obtained as Equation (24):  
\[\tau P=\dot{L}-P_{\rm C}\times Mg+(\dot{P}-Mg)\times P \tag{24}\]  
According to the definition of the ZMP, \(\tau_{px}=0\), and \(\tau_{py}=0\), the expression for the ZMP can be obtained as in Equation (25):  
\[\left\{ \begin{array}{l}x_{p}=\frac{Mg_{\rm C}+Z_{p}P_{X}-\dot{L}_{Y}}{Mg_{ \rm S}+P_{Z}}\\ y_{p}=\frac{Mg_{\rm C}+Z_{p}P_{Y}-\dot{L}_{X}}{Mg_{\rm S}+P_{Z}}\end{array} \right. \tag{25}\]  
If the quadruped robot is simplified to a mass point, the momentum and angular momentum are as in Equation (26):  
\[\left\{ \begin{array}{l}P=MP_{\rm C}=M\big{(}\dot{x}_{C},\dot{y}_{C},\dot{z} _{C}\big{)}^{T}\\ L=P_{\rm C}\times\dot{MP}_{\rm C}\end{array} \right. \tag{26}\]  
The coordinate expression of the ZMP is shown in Equation (27):  
\[\left\{ \begin{array}{l}x_{p}=x_{\rm C}-\frac{(z_{\rm C}-z_{\rm P})\dot{x}_{ \rm C}}{\dot{z}_{\rm C}+g}\\ y_{p}=y_{\rm C}-\frac{(z_{\rm C}-z_{\rm P})\dot{y}_{\rm C}}{\dot{z}_{\rm C}+g} \end{array} \right. \tag{27}\]  
The coordinates of the ZMP can be obtained based on the coordinates of the center of gravity and ground height. When the ZMP falls within the support area, the gait is stable.
## 3 Experiments and Results
### Satellite Network Delay Prediction Based on GMACC
The steady-state MSD in Table 4 also clearly shows the difference of steady-state performance between algorithms. Comparing the two simulation results in simulation 2 and simulation 3, one can observe that the asymmetric non-Gaussian noise is the real test of the algorithms, while the asymmetric Gaussian noise is not challenging. Simulation 4: The following simulation compared the MACC and MCC with the GMACC under different shape parameters. The kernel widths of the GMACC were the same as those of the MACC. The step sizes were set to unify the convergence rate of the algorithms. The noise was set to follow an asymmetric and non-Gaussian distribution of which the parameters were the same as those of simulation 3, and they are shown in Table 2. The simulation results are shown in Figure 8:0  
It can be observed from Figure 13 that the GMACC with a proper shape parameter can outperform the MACC in terms of the steady-state MSD under the same convergence rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Step Size \(\mu\)** & **Kernel Width \(\sigma\)** & \(\sigma_{-}\) & \(\sigma_{+}\) & **Steady-State MSD (dB)** \\ \hline LMS & 0.67 & - & - & - & - \\ MCC & 1.39 & 0.1 & - & - & \(-27.7406\) \\ MCC & 0.8 & 0.2 & - & - & \(-27.0129\) \\ MACC & 1.2 & - & 0.2 & 0.1 & \(-24.7491\) \\ MACC & 0.96 & - & 0.1 & 0.2 & \(-29.6021\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters and steady-state MSD of MCC and MACC in simulation 3. Figure 12: Steady-state performance comparison of different algorithms under asymmetric and non-Gaussian distribution disturbance.
# A Sustainable Location-Allocation Model for Solar-Powered
## 4 Discussion  
### Product Design and Product Installation  
At this stage, we should mention the product design. The design ideas were influenced by cost, because with UVUS, it is hoped that farmers in Indonesia can be helped economically by providing a means of reducing the costs of rice production through cheaper control of leafhoppers and sparrows. The product is environmentally friendly, because UVUS is expected to reduce the use of pesticides, which can reduce the level of soil fertility in rice fields and are unhealthy. The product has a social impact, because it is expected to stimulate the acquisition of new knowledge about technological developments and make farmers want to learn about renewable agricultural technologies.  
UVUS is considered safe because it uses a low voltage, so the current is also low and safer than PLN electricity. The ultrasonic frequencies can also be said to be friendly to humans because the ultrasonic frequencies released by UVUS cannot be heard by human ears; they are above the threshold of human hearing.  
The installation of UVUS products was carried out in the Delanggu rice fields using a social approach: we used a farming festival event and introduced UVUS slowly, taking the time needed to strengthen the narrative about the benefits of an environmentally friendly agricultural system. Many farmers are elderly and find it difficult to understand agricultural technology, so a social approach was important for the UVUS installation process. It is hoped farmers will operate UVUS independently with maintenance assistance by the UVUS manufacturer.  
We explained the possible negative impacts of UVUS, such as pests being forced to nearby places to look for food. To address that problem, there needs to be an allocation of food for these pests that are not driven completely away by the UVUS. The ultrasonic frequency issued by UVUS may also be ineffective, because the sparrows who initially felt uncomfortable were becoming used to the ultrasonic sound, but further research is needed on this.
## 3 Results
### Joint Angle and Joint Moment upon Forward Braking Motion  
The ankle joint angle in the longitudinal axis upon forward braking motion showed statistically significant differences between outside patterns at initial contact (A = \(-\)9.61 \(\pm\) 4.61\({}^{\circ}\), B = \(-\)10.64 \(\pm\) 4.27\({}^{\circ}\), C = \(-\)9.84 \(\pm\) 5.17\({}^{\circ}\), D = \(-\)8.08 \(\pm\) 5.02\({}^{\circ}\), F = 3.44, \(p\) = 0.03, \(\eta_{p}\)\({}^{2}\) = 0.256, statistical power = 0.714), take-off (A = \(-\)0.74 \(\pm\) 5.17\({}^{\circ}\), B = \(-\)1.15 \(\pm\) 4.25\({}^{\circ}\), C = \(-\)0.81 \(\pm\) 4.04\({}^{\circ}\), D = 1.20 \(\pm\) 3.69\({}^{\circ}\), F = 3.67, \(p\) = 0.02, \(\eta_{p}\)\({}^{2}\) = 0.268, statistical power = 0.745), and the ankle's maximum external rotation angle (A = \(-\)15.77 \(\pm\) 7.39\({}^{\circ}\), B = \(-\)15.71 \(\pm\) 5.77\({}^{\circ}\), C = \(-\)14.62 \(\pm\) 6.66\({}^{\circ}\), D = \(-\)12.96 \(\pm\) 6.87\({}^{\circ}\), F = 5.24, \(p\) = 0.01, \(\eta_{p}\)\({}^{2}\) = 0.344, statistical power = 0.891) (Figure 4, Appendix A Table A1). As a result of the post hoc test, D showed a smaller external rotation angle than that of A and B at initial contact and smaller than that of A, B and C at take-off from the ground. The ankle joint's maximum external rotation angle in D was smaller than that in A and B. Statistically significant differences between outside \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Biomechanical Friction**}} & \multicolumn{5}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{\multirow{2}{*}{_F (\(p\))_}} & \multicolumn{1}{c}{\multirow{2}{*}{_Effect_}} & \multicolumn{1}{c}{\multirow{2}{*}{_Pover_}} & \multicolumn{1}{c}{\multirow{2}{*}{_Power_}} & \multicolumn{1}{c}{\multirow{2}{*}{_Power_}} \\ \cline{2-3} \cline{5-5} \cline{7-10}  & & & & & & & & & \\ \hline \multirow{4}{*}{Forward Braking} & Max FoF (\(\mu\)) & 0.91 \(\pm\) 0.07 & 0.90 \(\pm\) 0.08 & 0.90 \(\pm\) 0.11 & 0.92 \(\pm\) 0.10 & 0.92 & \multirow{4}{*}{0.084} & \multirow{4}{*}{0.226} \\  & Max RF (Nm) & 3.66 \(\pm\) 2.49 & 2.81 \(\pm\) 1.68 & 3.57 \(\pm\) 1.98 & 4.21 \(\pm\) 3.65 & & 0.76 & \multirow{4}{*}{0.071} & \multirow{4}{*}{0.124} \\  & Min RF (Nm) & \(-\)28.49 \(\pm\) 6.74 & \(-\)28.12 \(\pm\) 7.34 & \(-\)27.27 \(\pm\) 6.40 & \(-\)29.46 \(\pm\) 8.15 & & 0.40 & \multirow{4}{*}{0.039} & \multirow{4}{*}{0.120} \\  & Max CoF (\(\mu\)) & 0.94 \(\pm\) 0.03 & 0.92 \(\pm\) 0.05 & 0.93 \(\pm\) 0.06 & 0.92 \(\pm\) 0.06 & & 0.94 & \multirow{4}{*}{0.086} & \multirow{4}{*}{0.232} \\  & Max RF (Nm) & 12.35 \(\pm\) 5.72 & 13.27 \(\pm\) 7.55 & 12.37 \(\pm\) 7.21 & 12.55 \(\pm\) 4.78 & & 0.10 & \multirow{4}{*}{0.010} & \multirow{4}{*}{0.066} \\  & Max RF (Nm) & \(-\)14.28 \(\pm\) 5.41 & \(-\)13.40 \(\pm\) 5.84 & \(-\)13.78 \(\pm\) 7.43 & \(-\)14.82 \(\pm\) 4.90 & & 0.32 & \multirow{4}{*}{0.031} & \multirow{4}{*}{0.089} \\  & RF: Rotational Friction. & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frictional force depending on outsole pattern upon motion in a biomechanical analysis.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Loss Function
#### 2.1.5 CLLR Loss  
The above improvements to the loss function are all designed to optimize the network parameters aiming at a working point on the Detection Error Trade-off (DET) curve. [PERSON] et al. [16] proposed a loss function based on the log-likelihood ratio cost, called CLLR loss, to optimize the whole curve. This loss function is designed to minimize the approximation of false alarm and missed alarm rates during network training. The specific formula is as follows:  
\[Ctar(\theta)=\sum_{y_{i}\in y_{tar}}\log\Bigl{(}1+e^{-s_{\theta}(x_{i},y_{i})} \Bigr{)} \tag{7}\]  
\[Cnon(\theta)=\sum_{y_{i}\in y_{non}}\log\Bigl{(}1+e^{s_{\theta}(x_{i},y_{i})} \Bigr{)} \tag{8}\]  
\[CLLR(\theta)=\frac{1}{2 log2}\biggl{(}\frac{Ctar(\theta)}{N_{non}}+\frac{Cnon( \theta)}{N_{tar}}\biggr{)} \tag{9}\]  
The use of CLLR loss allows the system to learn how to reduce the expected log costs of target and non-target examples. By designing metric functions that can be directly used as target functions to optimize network training, this kind of metric function with validation task can be more consistent with the SV task.  
The above are the popular loss functions currently used in the field of speaker recognition and verification. In this paper, the performance of each loss function is experimentally explored.
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 7 Conclusions  
The teak leaf mulch, leachate, and extracts of teak leaves, roots, banks and soil suppressed the germination and growth of several other plant species (Table 1). Phytotoxic substances, such as phenolics, benzofurans, quinones, terpen, apocarotonoids and phenyl-propanoids in the teak leaves, were isolated (Table 2). Those observations and findings suggest that teak is allelopathic and contains several phytotoxic substances with allelopathic activity. The evidence also shows that some of those phytoxic substances in teak are released into the soil under teak trees through the decomposition process of the litter. Those compounds possibly act as allelopathic substances. Allelopathic substances can inhibit the germination and growth of understory plants, both crops and weeds, in teak systems. Considering the importance of teak in agroforestry systems, the allelopathy of teak is potentially useful for weed management options in agroforestry and other agriculture settings to reduce commercial herbicide dependency for developing sustainable agriculture systems. The agroforestry system with teak may be able to integrate the balance of crop production, ecological aspects, and sustainability. However, the selection of crop plant species and development of their production systems with teak are necessary to avoid phytotoxic effects of teak and enhance crop productivity.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### Evaluation of Separate Camera Parameters Calibration Method  
#### 3.2.1 Intrinsic Parameter Details  
The intrinsic parameters of each of the six cameras (which constitute three stereo camera pairs) were determined by using [PERSON]'s famous method [38], respectively. In this study, the calibration target we used was a flat checkerboard with 10\(\times\)10 squares and 25 mm spacing, and 18 pairs of calibration images were captured by each camera. The results of intrinsic parameters of the six cameras are listed in Table 1. In the table, \(f_{\pi}\) and \(f_{y}\) denote the focal lengths, here expressed as the pixel value. \(c_{x}\) and \(c_{y}\) denote the coordinates of principal points of the camera. \(k_{1}\) and \(k_{2}\) denote the radial distortion coefficients of the lens.  
Figure 4: The test specimen and the scanning point cloud of the test specimen. (**a**) The test specimen at different viewpoints. (**b**) The scanning point cloud of the same viewpoints in (**a**).  
Figure 3: A 360-deg panoramic experimental setup.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 3 GNSS Jamming Experiment
### Experimental Setup  
The experiments were performed in the following way: The first 1.5 min were measured without the jammer being active to allow the receiver to obtain a fix, usually within 25 s, and establish a baseline for performance without jamming. After 1.5 min, the jammer was turned on, and after 3 min, measurement ceased. This process was repeated for distances between the jammer and receiver from 1000 m up to 10 km and for the different jammer bandwidths of 1, 2, and 4 MHz.  
The duration of the captured signals was limited by the hardware used to log the USRP-B210 output. Due to the hardware limitations, the maximum file size of the log files was 12 Gb, which led to a maximum duration of only 3 min.
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 4 Influence of Various Factors on Mud Cake Membrane Formation
### Comprehensive Analysis of the Influence of Various Factors on Foam Slurry Membrane Formation  
In summary, soil particle size, especially the maximum particle size (dmax), has a decisive impact on membrane formation. Smaller particle sizes facilitate the formation of the mud cake, while larger particle sizes increase permeability, making membrane formation more difficult or even impossible. Porosity and gradation parameters b and m mainly affect the membrane formation time. As porosity increases, the membrane formation time increases linearly. In terms of slurry composition, an appropriate swelling ratio (5%) and foam concentration (20%) are key to ensuring membrane formation effectiveness. However, excessively high foam concentrations are detrimental to clogging. The higher the pressure gradient, the more difficult it is to form a stable membrane. Overall, soil properties and slurry composition have a significant impact on the formation result, while the pressure gradient has a relatively limited effect on the final membrane formation.  
Figure 10: Effect of pressure gradient on membrane formation outcome.
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 3 The Proposed Dimming Control Scheme
### Construction of the Codeword Set
Then we encode the original binary codes like the last case which makes all the code weight of every codeword in set \(\mathcal{S}\) is \(i\). At last, we calculate \(m=n\times 2^{k}\times\gamma\), \(q=(m-2^{k}i)/2\), select \(q\) codewords in \(\mathcal{S}\) and replace two bit '0' with two bit '1' of the \(q\) codewords respectively. To make it easier to understand, we fix \(k=3\) provide two examples for the two cases. When \(k=3\), we can calculate \(n\geqslant 8\) from Equation (5), thus we fix \(n=8\). The examples are as follows:  
**Example 1**. : _When the dimming factor \(\gamma=1/8\), \(i=1\). Therefore, the mapping between the original binary sequence and the dimming coded binary sequence is shown in Table 1._  
**Example 2**. : _When the dimming factor \(\gamma=5/16\), we firstly find the range \(2/8<5/16<3/8\) and conclude that \(i=2\). Then we construct a codeword set \(\mathcal{S}\) with \(i=2\) like the last example and calculate that \(m=n\times 2^{k}\times\gamma=8\times 2^{3}\times 5/16=20\), \(q=(m-2^{k}i)/2=(20-2^{3}\times 2)/2=2\). Thus we select 2 codewords in \(\mathcal{S}\) and replace two bit '0' with two bit '1' of the 2 codewords respectively. Therefore, the mapping between the original binary sequence and the dimming coded binary sequence is shown in Table 2._  
The construction of the codeword set can be summarized in Algorithm 1. ```
Input:The dimming factor \(\gamma\), the length of original binary sequence \(k\) and the original binary signal \(\mathbf{s}\)  Fix proper the length of the coded binary sequence \(n\) by the constraint: \(n\geqslant 2^{k}\). If\(\gamma=\frac{i}{n}\), where \(i\) is a positive integer and \(0<i<n\)  Construct the codeword set \(\mathcal{S}\) in which the code weight of every codeword is \(i\). Else If\(\gamma\
eq\frac{i}{n}\) and \(\frac{1}{n}<\gamma<\frac{n-1}{n}\)  Find the range \(\frac{i}{n}<\gamma<\frac{i+1}{n}\)  Construct the codeword set \(\mathcal{S}\) in which the code weight of every codeword is \(i\). Calculate \(m=n\times 2^{k}\times\gamma\) and \(q=(m-2^{k}i)/2\)  Select \(q\) codewords in \(\mathcal{S}\) and replace two bit '0' with two bit '1' of the \(q\) codewords respectively. End End Output: The codeword set \(\mathcal{S}\)
```  
**Algorithm 1**Construction of the Codeword Set.
# Whole-Body Motion Planning for a Six-Legged Robot Walking on Rugged Terrain
## 2 Methods
### Stability Analysis of Six-Legged Robotic Walking  
Stability is the premise of effective motion of a robot. It is necessary to ensure sufficient stability margin in the whole process of six-legged robotic walking [19]. That is to say, the center of gravity (CoG) of the robot should be always in the horizontal projection of the supporting polygon formed by supporting legs at the beginning, during moving and at the end of each waking step. Figure 1 illustrates an example of tripod gait with a duty cycle of 0.5. In this example, \(a\), \(b\), and \(c\) are the supporting points of current supporting legs, \(\Delta\)abc is the formed supporting triangle; \(d\), \(e\), and \(f\) are the target supporting points for the following step of the robot, \(\Delta\)def is the supporting triangle for the following step; the shaded area is the overlap of \(\Delta\)abc and \(\Delta\)def; \(O_{1}\), \(O_{2}\), and \(O_{3}\) are the centers of the maximum inner circle of \(\Delta\)abc, \(\Delta\)def and the overlap, respectively. Without loss of generality, it is assumed that, at the beginning, the horizontal projection of the robot CoG is at the incenter \(O_{1}\) of \(\Delta\)abc. When the six-legged robot starts to move, the projection of the robot CoG on the horizontal plane gradually moves along the forward direction from \(O_{1}\) until the walking is completed. According to the definition of robot stability [19], in order to ensure the stability of the robot in the whole walking process, two conditions need to be satisfied: 1. Before the swing legs contact the ground, the projection of the robot CoG must be within \(\Delta\)abc; 2. After the swing legs contact the ground, the projection of the robot CoG must be within \(\Delta\)def. This requires that the projection of the robot CoG on the horizontal plane falls in the overlap of the two triangles at the moment of support-swing transition. Otherwise, when the supporting leg changes, the robot will become unstable, thereby leading to failure of the whole walking task or even damage to the robot hardware. Furthermore, in order to ensure that the robot can maintain a large stability margin at the moment of support-swing transition, it is better that the horizontal projection of the robot CoG falls at the incenter \(O_{2}\) of the overlap at the moment of transition.  
According to the above analysis, the motion of the robot torso in a walking step can be divided into two stages: as illustrated in Figure 1, the first stage is the movement from \(O_{1}\) to \(O_{2}\), and the second stage is from \(O_{2}\) to \(O_{3}\). This paper mainly discusses the planning problem of the first stage, which is the key point to ensure stable walking. The movement from \(O_{2}\) to \(O_{3}\) is implemented via control adjustment which is out of the scope of this paper.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.6 EMC Chambers  
EMC chambers are shielded environments where aviation equipment is tested for compliance with electromagnetic interference (EMI) standards. These chambers simulate controlled interference scenarios to determine how well systems perform in real-world conditions.  
To ensure that Aircraft systems meet acceptable electromagnetic performance thresholds, industry standards have been developed. The RTCA DO-160 standard is widely used in commercial aviation to define test procedures for environmental conditions, including electromagnetic emissions and susceptibility. In defense applications, MIL-STD-461 outlines detailed EMI/EMC requirements for airborne equipment. These standards guide design, testing, and certification processes to improve interference resilience across mission profiles.
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 4 Discussion
### Datasets  
For an overall comparison with other more advanced methods, we choose the SST-2 dataset [31], MR [32], CoLA [33], and R8 dataset [13], which are commonly used in other methods to evaluate our proposed method. The four datasets contain the content of reviews, grammar, and news. The SST-2 dataset is the Stanford Sentiment Treebank dataset, which is a binary single-sentence classification task consisting of sentences extracted from movie reviews and human annotations of their sentiments. We use the public version, which has 6920 training samples, 872 validation samples, and 1821 test samples. In total, there are 4963 positive reviews and 4650 negative reviews. The average length of reviews is 19.3 words. The MR dataset is a sentiment classification dataset for movie reviews. We used the public version in [34] with 3554 test samples, 6392 training samples, and 710 validation samples. The average length of reviews is 21.0 words. The CoLA dataset is a grammar dataset released by New York University. This task is mainly to judge whether the grammar of a given sentence is correct, which belongs to the single-sentence text binary classification task. We use the public version with 8551 training data, 1043 validation data, and 1062 test samples for a total of 6744 positives and 2850 negatives. The average length is 7.7 words. The R8 dataset represents a subset of the Reuters 21,578 dataset. The dataset includes eight categories, divided into 5485 training documents and 2189 test documents. We randomly select 20% of the training set of this dataset as the validation set. The average length is 65.72 words.