esa-sceva/satcom-chunk-collection huggingface dataset:
# HandFormer: A Dynamic Hand Gesture Recognition Method Based on Attention Mechanism
## 6 Conclusions  
This paper presents a new deep neural network model based on an attention mechanism, which is a variation of the Transformer model, for dynamic gesture recognition. The proposed model builds upon the MetaFormer model. The input video sequence is first processed by the PoolFormer block, which performs fast down-sampling feature extraction for each frame. The Spatial Attention Former Block (SAFB) is then applied to each frame's gesture image to extract spatial attention features based on the spatial attention mechanism. The spatial attention features from each frame are then fused in the channel direction, incorporating the temporal features of the dynamic gesture into the channel dimension. The Temporal Attention Former Block (TAFB) based on the channel attention mechanism is then applied to extract temporal attention features for gesture action classification. The proposed method was evaluated on the Jester and NVGesture public datasets, as well as a self-built dataset in a complex environment. The experimental results show that the proposed method outperformed the current state-of-the-art methods and exhibited good robustness for data with complex backgrounds, such as low-light conditions, strong light backgrounds, and tilted shooting angles.  
In future work, we will explore more robust models for dynamic gesture recognition tasks, especially in outdoor conditions and multi-hand interaction environments. We hope to propose a universally applicable method with high recognition accuracy and robustness in daily life. This work will be driven by a wild gesture dataset collected in various environments. We will create a huge dynamic gesture dataset in different environments in the future. Additionally, we will design an unsupervised network model to apply to the massive dataset which we will create.  
Conceptualization, F.W. and Y.Z.; methodology, F.W. and Y.Z.; software, F.W. and Y.Z.; validation, F.W.; data curation, F.W.; writing--original draft preparation, F.W.; writing--review and editing, F.W.; visualization, F.W.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China (61262043), The Science and Technology Program of Yunnan Province (2011 FZ029), and The open research fund from Yunnan Provincial Key Lab (2020106).  
Not applicable.  
The data are not publicly available due to restriction of privacy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Characteristics** \\ \hline MTUT [8] & Unimodal, multimodal training, unimodal data, 3D convolution \\ GRX3D [14] & Motion Fused Frames, data-level fusion, convolutional neural network \\ SlowFast [28] & SlowFast network, two streams, 3D convolution \\ PointLSTM [7] & Point cloud, LSTM, CNN, unimodal data \\ \hline Ours & Fusion of CNN with Transformer, multimodal data, lite, \\  & attention mechanism \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of our method with other methods.
## 3 Design of the Improved NMPC Controller  
In this section, an improved NMPC controller is designed for the trajectory tracking control of a free-floating dual-arm space robot serving in orbit. The principle of the improved NMPC controller designed in this paper is shown in Figure 4. As shown in Figure 4, the controller proposed in this paper is mainly composed of a predictive control unit for the rolling optimization of a nominal dynamic model and a sliding mode variable structure control unit for the purposes of weakening the influence of disturbance. The improved NMPC controller introduced the idea of SMC control and added the sliding mode variable structure control technology to suppress the disturbance, which improved the performance of the traditional NMPC controller.  
According to Figure 4, the control law of the proposed controller can be expressed as follows:  
\[\mathbf{u}=\mathbf{u}_{\rm m}+\mathbf{u}_{\rm c} \tag{20}\]  
where \(\mathbf{u}_{m}\) is the torque generated by the predictive control unit and \(\mathbf{u}_{c}\) is the torque generated by the sliding mode variable structure control unit.
## 3 Interview Results
### The Impact of Dialogue Types on Fatigue Driving Awakening  
In the context of fatigue driving, the type of dialogue between the driver and the voice assistant significantly affects the mitigation or exacerbation of the driver's fatigue. Analysis of the interview data revealed that different types of dialogue have varying impacts on drivers' fatigue states, which can be categorized into effective and ineffective dialogue types.  
Effective dialogue types are typically characterized by strong interactivity, emotional relaxation, and cognitive stimulation. For example, P03 (a long-haul truck driver) stated, \"_When I'm feeling fatigued, if the voice assistant can make me laugh through humorous and lighthearted dialogue, it really wakes me up and driving doesn't feel as exhausting_\". Similarly, P09 (another long-haul truck driver) shared, \"_I enjoy interactive Q&A. When the voice assistant asks light, fun questions or shares interesting trivia, it breaks the monotony_\". These highly interactive dialogues offer engaging content that captures the driver's attention, effectively reducing cognitive fatigue and helping to maintain focus. This effect is especially pronounced during night driving or prolonged driving periods, where humor and Q&A-style conversationshelp regulate the driver's mood and strengthen emotional connection during driving, thereby alleviating fatigue.  
In contrast, ineffective dialogue types may intensify the driver's sense of fatigue, particularly when the interaction is mechanical, repetitive, and emotionally neutral. P06 (a long-haul truck driver) noted, _\"If the voice assistant keeps repeating unnecessary messages or mechanically reminds me to drive safely, it feels like I'm being forced to listen, which actually makes me feel more tired\"_. [PERSON] (a taxi driver) echoed this sentiment: _\"Sometimes the voice assistant says things in such a dull or serious tone, it feels like a lifeless machine, and that makes me even sleaper\"_. These types of mechanical and repetitive dialogues fail to provide emotional relief and may lead to decreased attention and heightened fatigue. Especially during extended driving sessions, the lack of emotional connection and interactivity can cause mental exhaustion and distraction, increasing the risk of fatigue-related driving incidents.  
Overall, effective dialogue types help mitigate fatigue by offering emotional regulation and cognitive activation. Humorous and interactive conversations can positively influence drivers' emotional states and attentional focus. Conversely, ineffective dialogue types, particularly those lacking emotional engagement, tend to worsen fatigue symptoms, leading to distraction and emotional disengagement. Therefore, in the design of voice assistant systems, it is crucial to incorporate emotionally rich and interactive dialogue content tailored to drivers' fatigue levels to enhance their well-being and driving safety.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 2 The Principle of Temperature Independence
### Temperature Dependence of the Center Wavelength  
As described above, an AWG is used to multiplex and demultiplex signals of a set wavelength, but this wavelength (center wavelength) changes according to temperature. The refractive index of the waveguide material changes as the temperature changes, and  
Figure 1: Light waveguide circuit structure of AWG.  
the substrate and waveguide expand or contract, changing the length of the optical path. As a result, the focal position shifts in the input of the input slab waveguide, and the wavelength of the signal entering the input waveguide changes. In Equation (1), setting \(\phi\) to zero and differentiating the differential equation with respect to \(\lambda\) by temperature \(T\) expresses the center wavelength's magnitude of temperature dependence.  
\[n_{c}\Delta\mathrm{L}=m\lambda \tag{2}\]  
\[\frac{dn_{C}}{dT}\Delta\mathrm{L}+n_{c}\frac{d(\Delta\mathrm{L})}{dT}=m\frac{ d\lambda}{dT} \tag{3}\]  
\[\begin{array}{c}\frac{d\lambda}{dT}=\frac{1}{m}\left(\frac{dn_{C}}{dT} \Delta\mathrm{L}+n_{c}\frac{d(\Delta\mathrm{L})}{dT}\right)\\ =\lambda\left(\frac{1}{\kappa_{c}}\frac{dn_{C}}{dT}+\frac{1}{\Delta\mathrm{L}} \frac{d(\Delta\mathrm{L})}{dT}\right)\\ =\frac{\lambda}{n_{c}}\frac{dn_{C}}{dT}+\ \lambda\alpha_{s}\end{array} \tag{4}\]  
where \(\alpha_{s}=\frac{1}{\Delta\mathrm{L}}\ \frac{d(\Delta\mathrm{L})}{dT}\) is the substrate's coefficient of expansion for silicon.  
Since the coefficient of thermal expansion of the substrate is much greater than that of the waveguide, the coefficient of thermal expansion of the substrate was applied.  
The first term to the right of the equation represents the temperature dependence of the refractive index, and the second term is the change in refractive index caused by the stress applied to the waveguide due to changes in the waveguide path's length as the material shrinks or expands. The temperature dependence of the silica glass refractive index is 8 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C, and the substrate's coefficient of expansion is 3 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C (for silicon, \(\alpha_{s}\)), so the change in center wavelength is about 0.011 nm/\({}^{\circ}\)C.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results
### Path Loss Predictions for Both Models and Transmitter Heights  
Table 2 presents the MAE and R\({}^{2}\) values for both models and transmitter heights. Each model was constructed with 700 trees.  
It is clear that the prediction is more precise at the 35 m case. Moreover, XGBoost leads to better results than Random Forest for both transmitter heights.
# Experimental and Mathematical Study of Flexible-Rigid Rail Vehicle Riding Comfort and Safety
## 2 Rail Vehicle Mathematical Modelling
### Flexural Motion Equations  
Euler-Bernoulli beam theory of the free-free type is utilized for the bending and torsional vibration of the car body. Equation (1) is used to evaluate the vibration due to bending [18].  
\[EI\frac{\partial^{4}v(x,t)}{\partial x^{4}}+\rho_{b}A_{b}\frac{\partial^{2}v(x, t)}{\partial t^{2}}+C_{i}\frac{\partial v(x,t)}{\partial t}=-f_{sfr}\delta \left(x-x_{f}\right)-f_{sfl}\delta\left(x-x_{f}\right)-f_{srt}\delta(x-x_{r})- f_{srl}\delta(x-x_{r}) \tag{1}\]  
Figure 1: ICF coach mathematical model.  
where \(f_{sfr},f_{sfl},f_{srr}\) and \(f_{srl}\) are the forces induced by the secondary suspensions (represented by the first subindex \(s\)) on the car body. The second and the third subindices represent the front \((f)\) or the rear \((r)\) wheelset and the left \((l)\) or the right \((r)\) wheel, respectively.  
Modes due to bending for unsupported beams can be calculated as  
\[X_{n}(x)=\cos\ \beta_{n}x+\cos\ h\ \beta_{n}x-\frac{\cos\ h\ \beta_{n}L-\cos\ \beta_{n}L}{\sin\ h\ \beta_{n}L-\sin\ \beta_{n}L}(\sin\ h\ \beta_{n}x+\sin\ \beta_{n}x) \tag{2}\]  
where \(\beta_{n}\) are values of \(\beta\) that solve Equation (3) [18]:  
\[\cos\beta L\cos\ h\ \beta L=1 \tag{3}\]  
The equation of the torsional vibrations of the beam with a polar moment \(J\) and a modulus of rigidity \(G\) can be calculated as follows:  
\[\rho\frac{\partial^{2}\phi(x,t)}{\partial t^{2}}-GK\frac{\partial^{2}\phi(x, t)}{\partial x^{2}}=m_{t}(x,t) \tag{4}\]  
\(m_{t}(x,t)\) is the torque which is applied by the secondary suspensions on the car body. \(K\) is a torsional constant. The shape of the torsional modes of the free-free beam may be determined from Equation (5):  
\[X_{n\phi}(x)=\cos\frac{n\pi}{L}x \tag{5}\]
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Related Works
#### 2.1.5 Wi-SUN  
Wi-SUN (Wireless Smart Ubiquitous Networks) technology is maintained by the Wi-SUN Alliance and consists of wireless communication networks that are based on the IEEE 802.15.4g standard and are designed to be reliable and have low power consumption. Wi-SUN allows the establishment of networks that integrate smart devices from different manufacturers and is able to implement different topologies, including star, mesh, orhybrid,making the coverage area wider [18]. Wi-SUN adopts a Gaussian FSK (GFSK) modulation scheme, operates in the ISM bands, has low latency when compared to other LPWANs technologies, and has a transmission rate of around 300 kbps [18; 19].
## 3 Results
### Biomechanical Variables in the 4 \(\times\) 50 m and 100 m Tests
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & 4 \(\times\) 50 m sprints & \\ \hline \multirow{2}{*}{Variables} & Time Points of & \multirow{2}{*}{G-MS} & \multirow{2}{*}{G-ME} & \multirow{2}{*}{G-CON} \\  & measurement & & & \\ \hline \multirow{2}{*}{SR (cycles-min\({}^{-1}\))} & Pre & 43.06 \(\pm\) 3.98 & 42.81 \(\pm\) 5.61 & 41.10 \(\pm\) 3.17 \\  & Post & 42.55 \(\pm\) 2.23 & 44.75 \(\pm\) 5.57 & 41.00 \(\pm\) 4.86 \\  & \%\(\Delta\) & \(-\)0.61 \(\pm\) 8.68 & 4.69 \(\pm\) 5.48 & \(-\)0.49 \(\pm\) 5.77 \\ \hline \multirow{2}{*}{SL (m-cycle\({}^{-1}\))} & Pre & 2.11 \(\pm\) 0.18 & 2.06 \(\pm\) 0.20 & 2.11 \(\pm\) 0.18 \\  & Post & 2.20 \(\pm\) 0.15 & 2.05 \(\pm\) 0.18 & 2.15 \(\pm\) 0.20 \\  & \%\(\Delta\) & 4.52 \(\pm\) 6.94 & \(-\)0.17 \(\pm\) 5.62 & 1.98 \(\pm\) 4.02 \\ \hline \multirow{2}{*}{SI (m\({}^{2}\)-s\({}^{-1}\)-cycle\({}^{-1}\))} & Pre & 3.19 \(\pm\) 0.52 & 3.00 \(\pm\) 0.41 & 3.04 \(\pm\) 0.39 \\  & Post & 3.43 \(\pm\) 0.49 * & 3.11 \(\pm\) 0.38 * & 3.12 \(\pm\) 0.28 * \\  & \%\(\Delta\) & 8.03 \(\pm\) 8.31 & 4.03 \(\pm\) 7.56 & 3.41 \(\pm\) 7.02 \\ \hline \multicolumn{5}{c}{100 m test} \\ \hline \multirow{2}{*}{Variables} & Time points of & \multirow{2}{*}{G-MS} & \multirow{2}{*}{G-ME} & \multirow{2}{*}{G-CON} \\  & measurement & & & \\ \hline \multirow{2}{*}{SR (cycles-min\({}^{-1}\))} & Pre & 38.81 \(\pm\) 3.38 & 39.66 \(\pm\) 5.28 & 38.47 \(\pm\) 3.86 \\  & Post & 41.82 \(\pm\) 2.78 * & 41.72 \(\pm\) 5.29 * & 39.23 \(\pm\) 4.64 * \\  & \%\(\Delta\) & 8.67 \(\pm\) 14.80 & 5.32 \(\pm\) 3.58 & 1.90 \(\pm\) 5.19 \\ \hline \multirow{2}{*}{SL (m-cycle\({}^{-1}\))} & Pre & 2.15 \(\pm\) 0.33 & 2.07 \(\pm\) 0.23 & 2.09 \(\pm\) 0.19 \\  & Post & 2.09 \(\pm\) 0.16 & 2.06 \(\pm\) 0.20 & 2.10 \(\pm\) 0.24 \\  & \%\(\Delta\) & \(-\)1.71 \(\pm\) 11.70 & \(-\)0.56 \(\pm\) 4.12 & 0.58 \(\pm\) 6.03 \\ \hline \multirow{2}{*}{SI (m\({}^{2}\)-s\({}^{-1}\)-cycle\({}^{-1}\))} & Pre & 3.00 \(\pm\) 0.69 & 2.80 \(\pm\) 0.33 & 2.79 \(\pm\) 0.19 \\  & Post & 3.04 \(\pm\) 0.40 & 2.91 \(\pm\) 0.31 & 2.86 \(\pm\) 0.39 \\  & \%\(\Delta\) & 3.68 \(\pm\) 13.90 & 4.09 \(\pm\) 6.22 & 2.96 \(\pm\) 8.65 \\ \hline \end{tabular} \%:\(\Delta\) Post- vs. pre-measurements, SR: stroke rate, SL: stroke length, SI: stroke index; * \(p<0.05\), post vs. pre-measurements.  
\end{table} 
Table 4: The biomechanical variable changes in the 4 \(\times\) 50 sprints and 100 m front crawl test prior to and post the 6-week training mesocycle for the three groups of swimmers participating in the study. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group. Figure 4: The correlations of the %\(\Delta\) stroke rate (SR, panel **(a)**), stroke length (SL, panel **(b)**), stroke index (SI, panel **(c)**) with the %\(\Delta\) in performance time for groups of maximum strength (G-MS), muscular endurance (G-ME), and control group (G-CON).
## 4 Experimental Analysis
### Evaluation Metrics  
To evaluate our proposed method, we adopt the Intersection over Union (IoU) and F-Score@1% to follow similar evaluation methods of previous works [14]. They measure the accuracy of predicted voxel grids and reconstructed 3D surfaces, where higher values of the IoU and F-Score@1% indicate good reconstruction quality.  
Intersection over Union (IoU) is used to evaluate the ground truth and reconstruct voxel grid overlap. It is formulated as follows:  
\[IoU=\frac{\sum_{(i,\ j,\ k)}\ I\left(\overset{\wedge}{p}(i,\ j,\ k)>t\ \right)I(p(i,\ j,\ k))}{\sum_{(i,\ j,\ k)}\ I\left[I\left( \overset{\wedge}{p}(i,\ j,\ k)>t\ \right)+I(p(i,\ j,\ k))\right]} \tag{7}\]  
where \(\overset{\wedge}{p}(i,\ j,\ k)\) and \((p(i,\ j,\ k))\) represent the ground truth and predicted utilization probability at the voxel \((i,\ j,\ k)\) position, respectively. An indicator function \(I(.)\), and the voxelization threshold denotes \(t\). In the end, IoU quantifies how precisely the ground truth aligns with the reconstructed voxel grids.  
F-Score@1% is used to measure the precision and recall of ground truth and predicted point clouds to evaluate the reconstruction quality of 3D surfaces [33]. It is formulated as follows:  
\[F-Score(d)=\frac{2P(d)R(d)}{P(d)+R(d)} \tag{8}\]  
where \(P(d)\) and R(\(d\)) represent the precision and recall for a distance threshold \(d\) within the ground truth and the predicted points. These are calculated as follows:  
\[P(d)=\frac{1}{n_{\mathcal{R}}}\sum_{r\in\mathcal{R}}\left[min_{g\in\mathcal{G }}\ \parallel r-g\parallel<d\right] \tag{9}\]  
\[R(d)=\frac{1}{n_{\mathcal{G}}}\sum_{r\in\mathcal{G}}\left[min_{r\in\mathcal{R }}\ \parallel g-r\parallel<d\right] \tag{10}\]where \(\mathcal{G}\) and \(\mathcal{R}\) are the ground truth and reconstructed point clouds, \(n_{\mathcal{G}}\) and \(n_{\mathcal{R}}\) are the number of points in \(G\) and \(R\), respectively, and \([.]\) is the Iverson bracket. For the voxel representations, the object surface is generated using the Marching Cubes algorithm, and 8192 points are taken from the surface in order to measure the F-Score. The F-Score@1% refers value of the F-Score when the distance threshold \(d\) is set to 1%.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 1 Introduction
combined Q learning with the DWA algorithm and proposed two new evaluation functions. This demonstrated a high navigation efficiency and success rates in complex unknown environments, but incurred a higher time cost. [PERSON] [23] et al. implemented adaptive weight coefficients for the DWA algorithm for complex environments, making the path of the mobile robot smoother when avoiding obstacles. Many new and improved algorithms have been derived for obstacle avoidance in dynamic environments. [PERSON] [24] et al. combined the A* algorithm and the DWA algorithm to produce an algorithm that was closer to the global optimal path but with less smoothness. Nevertheless, the improved DWA algorithms are still unable to avoid the problem of local optimal solutions and may fail to complete the task due to the lack of global planning. An algorithm may get stuck at local minima during planning and consume more energy. A suitable path planning algorithm should be able to plan a complete collision-free path that satisfies the robot dynamics. Therefore, to resolve the path planning problem in a dynamic environment, this paper proposes an RRT*-FDWA algorithm. The contribution of the proposed algorithm to the path planning problem is as follows:  
* A fuzzy controller is added to the adaptive weight index of the DWA algorithm. This makes the weights adaptive, improves the safety of path planning, and enables timely avoidance of dynamic obstacles. * By combining the RRT* algorithm with the FDWA algorithm, local planning can avoid hitting local minima. The RRT*-based path re-planning is able to replan the global path after local path planning; this enhances the robot's maneuverability and improves target fit. The manuscript is divided into the following parts: Section 2 explains the original algorithm, the dynamics required to move the robot, and the related work. Section 3 discusses the FDWA algorithm and the fuzzy control principle. Section 4 discusses the RRT*-FDWA algorithm. Section 5 provides experimental results to demonstrate the effectiveness of the RRT*-FDWA. Section 6 discusses the results and future work.
## 4 Conclusions  
A 128-channel readout ASIC, SALT, was designed and fabricated in CMOS 130 nm process to readout silicon strips in the UT detector of the LHCb experiment. SALT is a SoC type ASIC, containing an analogue front-end and 40 MSps 6-bit ADC in each channel followed by a DSP, serial data transmission and many other blocks, including PLLs, DLLs, I\({}^{2}\)C, TFC, DACs, SLVS and monitoring circuitry. All together, they provide all required functionalities on-chip. The SALT development represents the first ASIC with ADC implemented in each channel achieving the readout of all channels with a high speed (40 MSps) and low-power, which allows for collecting all data at an LHC bunch crossing rate of 40 MHz. Combined with fast signal shaping, it allows us to cope with full channel occupancy.  
The prototypes of SALT were tested as bare chips and together with silicon sensors, confirming the expected functionality and fulfilling the specifications. Recently, following these positive results, assembly of the UT detector with the final readout system has begun.  
Conceptualization, M.F., T.F., M.I., J.M. and K.S.; methodology, M.F., T.F., M.I., J.M. and K.S.; software, J.W.; validation, M.F., T.F., M.I., J.M., K.S. and J.W.; formal analysis, M.F., T.F., M.I., J.M., K.S. and J.W.; investigation, C.A.B., D.A., M.A., B., S.B. ([PERSON]), R.B., S.B. ([PERSON]), A.C., I.C., B.C., N.C., F.D.B., S.D., S.E., M.F., T.F., P.G., D.G., N.G., M.I., X.J., W.K., L. (Timing Lj.), Z.L., X.L., S.L., Y.L. ([PERSON]), L.M., J.M., M.P., H.P., S.S., E.N., K.S., T.S., M.T., J.W., M.W., H.W., F.Z. and Q.Z.; resources, C.A.B., D.A., M.A., B.S. ([PERSON]), R.B., S.B. ([PERSON]), A.C., I.C., B.C., N.C., F.D.B., S.D., S.E., M.F., T.F., P.G., D.G., N.G., M.I., X.J., W.K., Y.L. (Timing Lj.), Z.L., X.L., S., Y.L. ([PERSON]), L.M., J.M., R.M., M.P., H.P., B.S., S.E.S.N., K.S., T.S., M.T., J.W., M.W., H.W., F.Z. and Q.Z.; data curation, A.C., N.C., F.D.B., P.G., M.P. and E.S.N.; writing--original draft preparation, M.F., T.F., M.I., J.M. and K.S.; writing--review and editing, N.C., F.D.B., T.F., P.G., M.I. and K.S.; visualization, A.C., N.C., F.D.B., M.F., T.F., P.G., J.M., M.P., E.S.N. and K.S.; supervision, M.A., M.I. and J.W.; project administration, M.A., M.I., B.S., T.S. and J.W.; funding acquisition, M.A., T.S. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Higher Education of Poland grant number MEiN DIR/WK/2017/2020/04-1. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] were supported from the ERC Consolidator Grant SELDOM G.A. 771642. The APC was funded by European Organization for Nuclear Research (CERN).  
Not applicable.  
Data Availability Statement: Data available on request. The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] for valuable help and support.  
The authors declare no conflict of interest. The founders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 4 Measuring the LT Precession via Pulsar Timing
### Lense-Thirring Precession in \(\dot{\omega}\)
More recent MeerKAT observations, especially those taken in the UHF band, showed an even better timing precision than that used in the simulations [79]. This analysis has been applied to the 16 yr data of PSR J0737\(-\)3039A collected from six telescopes without MeerKAT, and an upper limit \(I_{\rm A}<3.0\times 10^{45}\,{\rm g\,cm^{2}}\) (90% confidence) was found [58]. The ongoing combination of the 16 yr data set with MeerKAT data is expected to deliver a better constraint on \(I_{\rm A}\). As noted in Refs. [23; 101], with the mass and spin of the pulsar (which are precisely measured with pulsar timing), even an MoI measurement with 10% accuracy would enable sufficiently accurate estimation of the pulsar radius and provide significant constraints on the EoS of NSs.