esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm  
In this section, the K3P algorithm will be described (see Algorithm 1), beginning with the description, the objectives of the algorithm, and finally, a global overview. Subsections A to H will discuss the details of every phase of the algorithm.  
The main objective of the K3P algorithm is to drive the CoM along an arbitrary trajectory at an arbitrary speed \(\mathbf{v}_{c}\), while the two subsets of limbs perform a cyclic gait pattern, swing-support, to follow the movement of the robot's CoM and to maintain the static stability criteria. The real-time operation of the K3P algorithm is obtained by an update rate \(\Delta_{t}\) at which the position of every limb is computed and updated. At the initial state, the two subsets of legs are landed and supporting the body of the robot. The movement begins with the odd subset starting the swing phase, while the even subset remains in the support phase of the gait cycle and propels the CoM \(B\) along the desired trajectory by updating the even tripod configuration according to the given velocity \(\mathbf{v}_{c}\). Concurrently, K3P also drives the tripod on the swing phase forward ahead in the direction encoded by \(\mathbf{v}_{c}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Kinematic Chain**} \\ \hline \multirow{2}{*}{\(n\)} & \multicolumn{3}{c}{**D-H Parameter**} & \multirow{2}{*}{**Description**} \\ \cline{2-2} \cline{4-5}  & \(\mathbf{d}\) & \(\boldsymbol{\theta}\) & \(\boldsymbol{a}\) & \(\boldsymbol{\alpha}\) & \\ \hline
0 & 0 & 0 & 0 & 0 & The mounting point for the leg \(M_{i}\). \\ \hline
1 & 0 & \(\theta_{s}\) & 0 & 0 & The swing DoF. \\ \hline
2 & \(d_{2}\) & 0 & \(l_{c}\) & \(\frac{\pi}{2}\) & The length of the coxa \\ \hline
3 & \(d_{4}\) & \(\theta_{l}-\frac{\pi}{2}\) & \(l_{f}\) & 0 & The lift DoF \\ \hline
4 & \(d_{5}\) & \(-\theta_{k}\) & \(l_{t}\) & 0 & The knee DoF, tip of leg (\(O_{Sp}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Denavit–Hartenberg parameters for every leg.  
to a triangle at ground level. All support points \(SP_{i}\) are described with respect to either \(E\) or \(O\) reference frames, depending on whether they belong to the even or odd subset (see Figure 2a). As will be shown later, the actual configuration of the robot can be computed from these frames at any time.  
In the following subsections, we will introduce some key aspects that give shape to K3P, starting by describing how the CoM is propelled forward along the desired trajectory, given an arbitrary speed command \(\mathbf{v}_{c}\). We will also cover how K3P seamlessly distinguishes the forward and turn maneuvers and how K3P performs the phase shift of the gait cycle using the three aforementioned criteria.
## 4 Implementation and User Scenario  
### Implementation  
CORES for VRShop was developed to offer users immersive shopping experiences across various stores. The CORES VRShop app (1.0) is designed to be compatible with VR headsets, but it does not require installation directly on the headset; instead, it can be run in conjunction with other devices, such as a PC, laptop, or smartphone, which handle the computational and data processing tasks. However, VR headsets provide a fully immersive 3D virtual environment and enable natural interactions through motion tracking, hand gestures, and head movement. By integrating a recommendation system, CORES enhances the experience by suggesting products based on users' preferences and behavior. Users can enter VRShop with a VR headset, navigating through a virtual malt that contains stores showcasing 3D replicas of physical products. Each store displays high-definition models of products, allowing users to interact with them by picking them up, rotating, and zooming in to examine intricate details. Users can access additional information such as price, color, size, material, product reviews, and application demonstrations.  
CORES analyzes users' behaviors, preferences, and past purchases to recommend relevant products. Personalized suggestions are provided based on browsing histories, search queries, and interaction patterns. For instance, if a user frequently searches for  
Figure 6: Three-dimensional scene reconstruction workflow.  
fitness equipment, the system will prioritize related items like yoga mats, dumbbells, and activewear.  
The CORES system is implemented consisting of two parts: the front end (VR users' interface) and the back end (CORES recommender). The front end processes users' requests by cleaning and formatting them (removing unnecessary characters), while the back end provides assistance and product recommendations. The front-end prototype utilizes 3D scene reconstruction techniques (as described in Section 4), with a Python library (https://streamlitio/, accessed on 11 February 2025) linking the user's interface to VR capabilities, enabling the placement of 3D objects within a virtual store. On the back end, the Langchain library facilitates the integration of GPT-4, providing tools to create accurate prompts for improved responses from GPT-4 and other large language models (LLMs).  
CORES is demonstrated through its ability to answer users' queries and recommend relevant products based on the user's preferences. The system adapts to context changes (e.g., size, style) to filter and refine item quality, ensuring better personalized recommendations. The user's first interaction with VR shopping is shown in the welcome interface (Figure 7), while digital stores are illustrated in Figure 8.
## 3 Inertial Aiding Method Based on a Nonlinear Tracking Differentiator
### Data Verification of Nonlinear Tracking Differentiator  
Firstly, let us take the Micro-Electromechanical System (MEMS) acceleration data collected under static conditions as an example (Figure 2(a)), and estimate the jerk with a time differentiator and nonlinear tracking differentiator, respectively; the results are shown in Figure 2(b) where the blue curve stands for the jerk obtained with the time differentiator and the red curve stands for the jerk obtained with the nonlinear tracking differentiator. Table 1 shows the offset and noise standard deviation (std) of estimated jerk under static conditions with the two methods.  
Secondly, let us take the simulated line-of-sight acceleration with 50 g to 80 g variation within one second as an example (Figure 3(a)). The offset of simulated acceleration is 0.1 g, and the noise standard deviation is 0.1 g. We compare the jerk obtained with the time differentiator and nonlinear tracking differentiator, respectively, and the results are shown in Figure 3(b). Table 2 shows the offset and noise standard deviation (std) of estimated jerk in the high dynamic condition with the two methods.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Estimated jerk by time**} & \multicolumn{3}{c}{**Estimated jerk by nonlinear**} \\  & \multicolumn{3}{c}{**differentiator (m/s\({}^{2}\))**} & \multicolumn{3}{c}{**tracking differentiator (m/s\({}^{2}\))**} \\ \cline{2-7}  & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline offset & 0.0001 & 0.0002 & 0.0001 & 0.00003 & 0.00004 & \(-\)0.00008 \\ std & 0.1911 & 0.1870 & 0.2026 & 0.0565 & 0.0567 & 0.0619 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Offset and noise standard deviation of estimated jerk in static condition with the two methods.  
Figure 2: Jerk estimated from the MEMS acceleration with the two methods (**a**) MEMS acceleration; (**b**) Jerk estimated with the two methods.  
As shown in Figures 2(b) and 3(b), the nonlinear tracking differentiator can overcome the time differentiator's disadvantage of enlarging noise and time delay. For Compass/INS integrated navigation system, if the following situation occurs:  
1. Because the inertial measurement unit with wide measuring range has large bias, the acceleration aiding information will have a constant offset;
2. When the integrated navigation system is starting, or the satellite signal is interfered or blocked, the integrated navigation output will be halted. The inertial error will fail to be corrected, and the inertial error will be enlarged gradually with time.  
Under general conditions, the third-order PLL or traditional inertially-aided PLL can track the satellite signal with any line-of-sight acceleration. If the above situation occurs and the receiver-satellite line-of-sight jerk is large, it is feasible to track the high dynamic signal with the proposed jerk aided PLL, _i.e._, estimate the line-of-sight jerk through inertial acceleration based on a nonlinear tracking differentiator, and adjust the process noise matrix coefficients of the Kalman-PLL.
## 2 Materials and Methods
### MRI Acquisition  
All the brain functional image data were collected in the MRI room of the Affiliated Hospital of Yangzhou University using a 3.0 t MRI scanner (GE Discovery MR750W 3.0T). To ensure the safety of the experiment and the effectiveness of the scan, the subjects had to wear a special scanning suit and take off all metal objects before entering the scan. Then, they entered the MRI scan room and laid down in a comfortable position. To reduce the impact of head movement on the data, the experimenters repeatedly instructed the head to remain as still as possible during the MRI scan and fixed the subjects' heads with a foam plate. The subjects wore earplugs to stop the noise generated by the subject during machine scanning. The rs-fMRI scan parameters (plane echo pulse train (EPI) scan): TR/TE = 2000/30 ms, layer number = 28, layer thickness = 3.0 mm, spacing = 1 mm, flip angle = 90\({}^{\circ}\), acquisition matrix = 64 \(\times\) 64, FOV = 224 \(\times\) 224 mm. Preprocessing of low frequency amplitude (ALFF) value: In this study, the DPABI3.1 software package was used to preprocess the experimental data. The software package needs to call the SPM8 software package and run in the MATLAB2013b environment. The whole pretreatment process was mainly divided into the following steps: (1) DICOM to NIFIT: The collected data were converted from DICOM format to NIFIT format which can be processed by software package; (2) Slice timing: The images in this study were interlayer scanning, with a total of 28 layers, and the reference layer is the 28 th layer; (3) Realign: Checking the head movement parameters and eliminating the subjects whose translation was greater than 1.5 mm and rotation was greater than 1.5 to prevent signal changes caused by excessive head movement; (4) Reorient: Redefine the origin of each brain map; (5) Normalize: Use the default template provided by the software, the sampling rate was 3 \(\times\) 3 \(\times\) 3 mm; (6) Smooth: 6 mm half-height full-width smoothing kernel was used to perform Gaussian smoothing on the image to improve the signal to noise; (7) Detrend; (8) Regress Out Covariates: Taking Friston24 head motion parameter as co-variable, the influence of head motion on image was regressive; (9) Calculation of low-frequency amplitude: The range of 0.01-0108 hz was used for filtering to reduce low frequency drift and high frequency physiological noise. The ALFF value of each voxel was calculated, and then the calculated ALFF value was divided by the mean value of the whole brain to obtain the ALFF value through standardization to eliminate differences in the overall level of whole brain ALFF between individuals. The specific calculation principle and method of ALFF was described by [PERSON] et al. [27].
## 5 Observation Model
### Case 1  
Figure 6 shows a slice cross section of a sample CT scan. The arrow indicates a nodule identified by a radiologist. The nodule is detected by our previous methods [8,27,28] as a nodule candidate. Figure 7 (a-c) shows a VOI of the nodule (Figure 7(b) shows the nodule). Figure 7(d-f) is template produced from the most likely nodule model that is depicted in Figure 8(a). They correspond to Figure 7(a-c), respectively. Figure 7(g-i) is produced from the most likely curved blood vessel model depicted in Figure 8(b), and Figure 7(j-l) is produced from the most likely bifurcated blood vessel model depicted in Figure 8(c). They correspond to Figure 7(a-c), respectively, in the same manner.  
The posteriori probabilities of the most likely object models \(o^{N}\), \(o^{B_{c}}\) and \(o^{B_{b}}\) are 0.124, 0.087 and 0.100, respectively (the common constant values are omitted), and the ratio of the posteriori probabilities \(\rho_{1}\) is 1.24. Because the ratio is larger than the threshold \(T_{\rho}\), the nodule candidate is correctly determined to be a nodule.  
The nodule (Figure 7(a-c)) is faithfully reconstructed by the templates (Figure 7(d-f)). Although the nodule is shifted downward against the VOI (the nodule is not observed in the upper slice section shown in Figure 7(a)), the center of the nodule model is moved adequately. The nodule size is also estimated exactly. The diameters of the nodule and the nodule model are approximately 5.0 mm and 5.3 mm, respectively.  
The effectiveness of Equation (3) can be seen in the templates of the curved and bifurcated blood vessel models shown in Figure 7(g-i) and (j-l), respectively. Because the nodule candidate is in the peripheral area of the lung region (see Figure 6 again), the mean radius of a blood vessel model \(\mu_{r1}^{B}(\mathbf{x}^{B_{c}})\) becomes rather small, 0.89 mm, at this position. The small mean radius produces thin regions on the templates and the thin regions make the correlation coefficients low. Therefore, the probabilities of the blood vessel models become smaller than that of the nodule model.  
Figure 6: A slice cross section of a sample thoracic CT scan. The arrow indicates a nodule identified by a radiologist.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### Route Benefit Description
#### 4.2.2 Benefit Function Calculation  
When the source node cannot directly communicate with the sink, the forwarding node needs to be used to transfer the data. The selection of the best forwarding node uses the benefit function (BF) as a basis. The benefit function is composed of the remaining node energy, the distance to sink, and the data reception probability. The calculation formula is as follows:  
\[\text{BF(i)}=(\text{E}_{\text{res}}(\text{i})/\text{D}(\text{i},\text{sink})) \times\text{P}. \tag{10}\]  
where BF(i) is the benefit function of node i, E\({}_{\text{res}}\)(i) is the residual energy at node i, D(I, Sink) is the distance between node i and the sink, and P is the probability of the node receiving data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Receiving** & **Normal Temperature Interval** & **Heating Interval** & **High-Temperature Interval** \\ \hline Emergency data P1 & 1 & 1 & 0 \\ Normal data P2 & 1 & \((\text{t}^{2}-2\text{ T}_{th}\text{t}+\text{T}_{th}^{2})/(\text{T}_{1}-\text{T}_ {th})^{2}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data receiving probability of nodes in different temperature intervals.  
Figure 2: Temperature state interval.  
Since the data are divided into two types in this paper, the benefit function of emergency data is calculated by the following formula:  
\[\text{BF(i)}=(\text{E}_{\text{res}}(\text{i})/\text{D}(\text{i},\text{Sink})) \times\text{P1}. \tag{11}\]  
The benefit function of normal data is calculated using the following formula:  
\[\text{BF(i)}=(\text{E}_{\text{res}}(\text{i})/\text{D}(\text{i},\text{Sink})) \times\text{P2}. \tag{12}\]  
After each node calculates the benefit function, the neighbor node with the maximum benefit function is selected as the best forwarding node of the path. The source node selects the corresponding forwarding node to transmit the data according to the data type, and the forwarding node receives, aggregates, and transmits the data to the sink. The BF table of sensor nodes is updated every 10 s once the system reaches stability.
## 2 Materials and Methods
### Experimental Modal Analysis  
Frequency response function (FRF) matrices computed from the ratio of the output displacements \(s\) and the input sound pressure \(p\) in the frequency domain were calculated for all the TM surface points with various middle-ear manipulations.  
\[\text{FRF}(x,y,\omega)=\ \frac{FFT(s(x,y,t))}{FFT(p(t))}, \tag{9}\]  
where \(x\), \(y\) are the coordinates of TM points, \(t\) is time, and \(\omega\) is the frequency.  
\(\text{FRF}(x,y,\omega)\) was then flattened into a 2D matrix \(H(n,\omega)\) where \(n\) is the number of the nodes. FFT denotes fast Fourier transform.  
Complex mode indicator functions (CMIFs) were calculated by taking singular value decomposition ([17], Equation (1)) of each FRF matrix,  
\[[H]=[U][\Sigma][V]^{H}, \tag{10}\]  
where \([H]\) is the flattened frequency response function matrix; \([U]\) is the left singular vector matrix (unitary); \([\Sigma]\) is the singular value matrix (diagonal); \([V]\) is the right singular vector matrix (unitary); and \([]^{H}\) marks the Hermitian (conjugate transpose) of matrix. The CMIF is defined as the main diagonal elements of the singular value matrix.  
\[\textit{CMIF}=\text{diag}[\Sigma] \tag{11}\]
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 2 Materials and Methods
### Synthesis
#### 2.1.2 General Procedure for the Synthesis of the \(\alpha\)-Lipoic Esters of Triterpenoids 6-9
3,28-Diacetyl-30-(5-(1,2-dithiolan-3-yl)pentanoyl)) betulin 8 Yield 68%; mp 93-95 \({}^{\circ}\)C; R\({}_{\text{f}}\) 0.43 (dichloromethane/ethanol, 60:1, \(v/v\)); \({}^{1}\)H NMR (600 MHz, CDCl\({}_{3}\)) 6: 0.86-0.87 (m, 9H,3\(\times\)CH\({}_{3}\)), 0.99 (s, 3H, CH\({}_{3}\)), 1.09 (s, 3H, CH\({}_{3}\)), 1.42-1.49 (m, 6H, CH\({}_{2}\)), 0.80-1.90 (m, 25 H, CH,CH\({}_{2}\)), 1.94 (m, 1H, CH\({}_{2}\)CHS), 2.06 (s, 3H, CH\({}_{3}\)C=0), 2.09 (s, 3H, CH\({}_{3}\)C=0), 2.38-2.51 (m, 3H, CH\({}_{2}\)S, CH\({}_{2}\)CHS, H-19), 3.13-3.23 (m, 2H, CH\({}_{2}\)C=0), 3.60 (m, 1H, CHS), 3.84 (d, 1H, \(J\) = 10.8 Hz, H-28), 4.26 (d, 1H, \(J\) = 10.8 Hz, H-28), 4.48 (m, 1H, H-3), 4.58 (m, 2H, H-30), 4.96 (s, 1H, H-29), 4.98 (s, 1H, H-29) (Figure S9, Supplementary Materials); \({}^{13}\)C NMR (150 MHz, CDCl\({}_{3}\)) \(\delta\): 14.8, 16.0, 16.2, 16.5, 18.2, 20.9, 21.1, 21.3, 23.7, 23.7, 24.7, 26.6, 27.0, 28.0, 28.8, 29.7, 31.1, 34.1, 34.4, 37.7, 37.1, 37.5, 37.8, 38.4, 38.5, 40.2, 40.9, 42.7, 43.8, 46.3, 49.5, 50.2, 55.4, 56.3, 62.5, 66.0, 80.9, 110.6, 148.9, 171.0, 171.6, 173.2 (Figure S10, Supplementary Materials); IR (v max cm\({}^{-1}\), KBr): 2943, 1734, 1458, 1244, 898 (Figure S11, Supplementary Materials); HRMS (APCI) \(m/z\) (neg): 729.4164; C\({}_{42}\)H\({}_{65}\)O\({}_{6}\)S\({}_{2}\) (Calculated 729.4222) (Figure S12, Supplementary Materials). 3-(5-(1,2-Dithiolan-3-yl)pentanoyll)-28-acetylbettulin **9** Yield 55%; mp 89-92 \({}^{\circ}\)C; R\({}_{\rm f}\) 0.65 (dichloromethane/ethanol, 60:1, \(v/v\)); \({}^{1}\)H NMR (600 MHz, CDCl\({}_{3}\)) \(\delta\): 0.86-0.87 (m, 9H, 3\(\times\)CH\({}_{3}\)), 0.99 (s, 3H, CH\({}_{3}\)), 1.05 (s, 3H, CH\({}_{3}\)), 1.40-1.51 (m, 6H, CH\({}_{2}\)), 1.70 (s, 3H, CH\({}_{3}\)), 0.80-1.90 (m, 24H, CH, CH\({}_{2}\)), 1.93 (m, 1H, CH\({}_{2}\)CHS), 2.09 (s, 3H, CH\({}_{3}\)C=0), 2.32 (t, 2H, \(J\) = 7.2 Hz, CH\({}_{2}\)S), 2.33-2.51 (m, 2H, CH\({}_{2}\)CHS, H-19), 3.13-3.23 (m, 2H, CH\({}_{2}\)C=0), 3.60 (m, 1H, CHS), 3.87 (d, 1H, \(J\) = 10.8 Hz, H-28), 4.27 (d, 1H, \(J\) = 10.8 Hz, H-28), 4.49 (m, 1H, H-3), 4.61 (m, 1H, H-29), 4.71 (m, 1H, H-29) (Figure S13, Supplementary Materials); \({}^{13}\)C NMR (150 MHz, CDCl\({}_{3}\)) \(\delta\): 14.7, 16.0, 16.2, 16.6, 18.2, 19.1, 20.8, 21.1, 23.7, 24.9, 25.2, 27.1, 28.0, 28.8, 29.6, 29.7, 34.1, 34.5, 34.6, 34.7, 37.1, 37.6, 37.9, 38.4, 38.5, 40.2, 40.9, 42.7, 46.3, 47.7, 48.8, 50.3, 55.4, 56.4, 62.8, 80.8, 109.9, 150.2, 171.7, 173.3 (Figure S14, Supplementary Materials); IR (v max cm\({}^{-1}\), KBr): 2943, 1735, 1456, 1234, 883 (Figure S15, Supplementary Materials); HRMS (APCI) \(m/z\) (neg): 67.4287; C\({}_{40}\)H\({}_{63}\)O\({}_{4}\)S\({}_{2}\) (Calculated 67.4168) (Figure S16, Supplementary Materials).
## 2 Related Work
### Back-Translation  
Back-translation [24] is an effective way to improve translation performance by translating target-side monolingual data to generate synthetic sentence pairs, widely used in research and industrial scenarios. Most studies on back-translation for zero-shot translation generate synthetic sentence pairs for all language pairs in an offline manner [16; 33], making it more computationally expensive and not very scalable for massively multilingual translation. For example, in the OPUS-100 settings, there are 9900 language directions, of which 9702 are zero-shot translation directions. [PERSON] et al. [21] addressed this problem by performing online back-translation paired with randomly sampled intermediate languages. They generated synthetic sentence pairs by back-translating target-side sentence \(y\) from language \(l_{y}\) into \(x^{\prime}\) from language \(l_{x}^{\prime}\) where \(l_{x}^{\prime}\) is randomly sampled, named random online back-translation (ROBT). They empirically demonstrated, for the first time, the feasibility of back-translation in massively multilingual settings to improve zero-shot translation. Other studies investigated the pros and cons of online back-translation for unsupervised machine translation, proving the effectiveness and scalability of online back-translation[34; 35]. The study by [PERSON] et al. [21] is closest to ours, having an emphasis on massively many-to-many machine translation. Our work mainly investigates ROBT's shortcomings and improves it with a more effective and complete algorithm.
###### Abstract  
The injection of CO2 to displace CH4 in coal seams is an effective method to exploit coalbed methane (CBM), for which the CO2 injection temperature and pressure are important influential factors. We performed simulations, using COMSOL Multiphysics to determine the effect of CO2 injection temperature and pressure on CO2-enhanced coalbed methane (CO2-ECBM) recovery, according to adsorption/desorption, seepage, and diffusion of binary gas (CO2 and CH4) in the coal seam, and derive a thermal-hydraulic-mechanical coupling equation of CO2-ECBM. The simulation results show that, as CO2 injection pressure in CO2-ECBM increases, the molar concentration and displacement time of CH4 in the coal seam significantly decrease. With increasing injection temperature, the binary gas adsorption capacity in the coal seam decreases, and CO2 reserves and CH4 production decrease. High temperatures are therefore not conducive for CH4 production.  
Keywords:coalbed methane; CO2-ECBM; thermal-hydraulic-mechanical; temperature; pressure +
Footnote †: journal: Applied Sciences