esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Lisfranc joint connects the forefoot to the midfoot. Tarsometatarsal (TMT) arthrodesis is commonly employed for Lisfranc joint injuries; however, there is active discussion regarding the optimal method of fixation for TMT arthrodesis. The purpose of this systematic review is twofold: to assess the stability of various constructs used in TMT arthrodesis and to evaluate joint motion and force distribution in the foot following arthrodesis. The PubMed, CINAHL, MEDLINE, and Web of Science databases were searched for articles evaluating biomechanical and kinetic properties of TMT arthrodesis constructs in accordance with PRISMA guidelines. The preliminary search yielded 367 articles and the final review included 14 articles with 195 cadaveric and 70 synthetic bone constructs. Plantar plates and intramedullary screw fixation at the first TMT joint were consistently found to bear significantly greater loads and resist diastasis more effectively than crossed screws. Furthermore, whole foot and lateral column arthrodesis significantly elevate calcaneocuboid and lateral column pressures. This increase was not observed with isolated fourth or fifth TMT arthrodesis. TMT arthrodesis should aim to avoid the lateral column and fuse as few joints as possible. Overall, plantar plates are an effective construct for first TMT arthrodesis due to their ability to withstand both compressive and tensile forces while maintaining stable alignment of the foot via reinforcement of the transverse arch. Intramedullary fixation devices are an alternative option that provide a high degree of active compression across the joint space while avoiding irritation of surrounding soft tissue structures.  
TMT arthrodesis; Lisfranc joint; biomechanics; hallux valgus; plantar plate; midfoot +
Footnote †: journal: Applied Sciences
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 6 Discussion
### Safety Insurance  
The safety of the system is guaranteed in three aspects. First, the tracking method is based on UWB, which ensures functional safety. UWB could keep tracking the target person with strong anti-interference capability, even when there is obstruction. Although the precision is not perfect, with filtering, it is suitable for the target application scenario. Second, the control algorithm requires the stroller always to maintain a proximate distance from their parents (about one meter), which guarantees that the stroller is easy for the patient to reach the baby. Third, the safety mechanism, the rope and pull switch, not only prevents other people from interfering between the stroller and the user but also gives the user authority to stop the stroller whenever danger is predicted. After the experiments, some participants reported that the safety insurance mechanism is very important for their mental relief.
## 3 Results and Discussion
### Ignition Probability of Marine Oil on Heated Surface
During the first stage, the typical heated-surface temperature does not reach a high value, generally below 450 \({}^{\circ}\)C. In this heated-surface temperature range, the ignition probability of marine oil is accordingly low. Taking this experiment as an example, in the 30 igniting tests usually completed, about one to three ignitions occur, and the rest of the time the oil is not ignited. When the heated-surface temperature rises to more than 460 \({}^{\circ}\)C, the ignition probability of marine oil will fluctuate greatly. When the heated-surface temperature goes up to nearly 475 \({}^{\circ}\)C, the ignition probability of marine oil is 50%. This indicates that when marine oil leakage occurs in a fishing vessel's engine room, if the temperature near the equipment surface is close to 475 \({}^{\circ}\)C, there is a 50% probability that a fire accident will occur. If the heated-surface temperature continues to rise to about 500 \({}^{\circ}\)C or above, the leaked marine oil can be almost completely ignited on the heated surface. This indicates that once the marine diesel oil leakage accident occurs in the fishing vessel's engine room, and the heated-surface temperature of equipment exceeds 500 \({}^{\circ}\)C, ignition will inevitably occur. Whether it can further induce a fire accident in the engine room needs to be determined according to factors such as the amount of marine oil and surrounding items. Based on the fitting results of the experimental data, this research proposes a prediction model for ignition probability that is suitable for evaluating the ignition of leaked marine oil on a heated surface, as shown in Equation (18). The correlation coefficient R2 of this prediction model reaches 0.973, which indicates that this model can assist in the investigation and judgment of fire accidents caused by marine oil leakage in a fishing vessel's engine room. \[P_{\text{i}}=\frac{0.994}{1+\text{exp}[-0.194(T_{\text{s}}-T_{500\%})]} \tag{18}\]  
where \(T_{\text{s}}\) is the heated-surface temperature of fishing vessel's engine room, K; \(T_{50\%}\) is the hot surface temperature at 50% ignition probability, K.
## 3 Results and Discussion
### Impedance Analysis, 3D SHM Damage Localization
Extensive damage was also monitored by the IS measurements in areas with coordinates (1,1,3), (1,3,3), (2,1,3), (2,3,3) and (3,2,3) indicating that the back group of laminas was the most significantly affected part of the laminate. Figure 12: Impedance results for SHM mapping of the back group of laminas in the smart composite with 3D mapping capabilities (Bode plots). Figure 13: Impedance results for SHM mapping of the rear group of laminas in the smart composite with 3D mapping capabilities. (**a**) Phase delay Bode plots and (**b**) Nyquist plots. The phase delay Bode plots revealed minor information about damage, as their changes due to the impact test were negligible (Figure 13a). On the contrary, Nyquist plots exhibited significant shifts in the impedance values in the affected areas after the introduction of the damage. In terms of sensitivity for damage identification, changes of the electrical properties in Nyquist plots were more distinct compared to Bode plots for the magnitude of the impedance (Figure 13b).
## 2 Materials and Methods
### Data Relationship Modeling of the Wind Turbine Operating State
#### 2.1.3 Data Bin Processing  
Due to the turbulent character of the wind speed [22] and the large scale of wind turbine SCADA data, the relationships between the data parameters are complex and contain many nonlinear effects [23]. Thus, it is difficult to identify relationships from scatter plots, and bin processing must be carried out. Before bin processing, the data should be preprocessed as the following steps.  
Step 1: Extract the data of wind speed, rotor speed, and output power. The characters of \(v\), \(\omega\), and \(P\) are employed to denote these parameters, respectively.  
Figure 1: Sliding window model.  
Step 2: Eliminate the shutdown data and zero value data by judging the values of \(\omega\) and \(P\). If \(\omega\leq 0\) or \(P\leq 0\) hold, the corresponding state parameters should be eliminated. Furthermore, null data should also be eliminated.  
In this paper, wind speed \(v\) and output power \(P\) are used as examples, and a scatter plot of the relationship between wind speed and wind turbine output power is shown in Figure 2.  
According to the IEC 61400-12-1 standard [24], the bin method was used to process data in this study. The data in each window are divided into \(N\) bins using wind speed as the reference, where:  
\[N=\frac{v_{\text{max}}-v_{\text{min}}}{0.5} \tag{4}\]  
where \(v_{\text{max}}\) is the maximum wind speed in the window and \(v_{\text{min}}\) is the minimum wind speed in the window.  
The average wind speed and average power in the \(i\)th bin are then calculated as follows:  
\[\overline{v}_{i}=\frac{1}{L_{i}}\sum_{j=1}^{L_{i}}v_{i,j} \tag{5}\]  
\[\overline{P}_{i}=\frac{1}{L_{i}}\sum_{j=1}^{L_{i}}P_{i,j} \tag{6}\]  
where \(L_{i}\) is the number of data groups in the \(i\)th bin; \(v_{i,j}\) is the wind speed value of the \(j\)th group in the \(i\)th bin; and \(P_{i,j}\) is the power value of the \(j\)th group in the \(i\)th bin.  
After data bin processing, the relation curve between wind speed and power in the window can be obtained, as shown in Figure 3. Using the input parameter data as a reference, the output parameter data can be processed in the same way to obtain the relationship between various measures.  
Figure 3: Relationship between wind speed and output power after bin processing.  
Figure 2: Scattered relationship between wind speed and output power.
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 2 Theoretical Basis of Acoustic Information Monitoring for Cabin Structures
### Analysis of the Vibration and Sound Characteristics of Cabin Structures under Hybrid Excitation
The blue box is the amplitude of the equal potency source within each one third octave, and the red box is the amplitude of the equal potency source within the entire frequency band. As can be seen from Figure 3, the intensity of the external radiated noise caused by sound sources in the whole frequency band is similar to that of about 0.8 N of force source excitation. The amplitude of the equivalent point force source in the middle- and low-frequency bands is larger than that in the higher-frequency band, which shows that the influence of the sound source excitation on the radiation noise outside the cabin is more obvious in the middle- and low-frequency bands. Furthermore, to analyze the coupling components in the vibration and acoustic sampling information, the sound monitoring information \(P(\omega)\) and vibration monitoring information \(V(\omega)\) can be decomposed into the following forms:  
\[ \begin{split} P&=P_{P}+P_{V}\\ V&=V_{P}+V_{V}\end{split}  \tag{6}\]  
where \(P_{P}\) is the acoustic monitoring information attributed to the sound source excitation, \(P_{V}\) is the acoustic monitoring information attributed to the force source excitation, \(V_{P}\) is the vibration monitoring information attributed to the sound source excitation, and \(V_{V}\) is the vibration monitoring information attributed to the force source excitation. The sound monitoring information signal-to-interference ratio (VSIR) and vibration monitoring information signal-to-interference ratio (VSIR) are defined by the following expressions:  
\[ \begin{split}\text{PSIR}&=20\text{lg}\frac{P_{p}}{ V_{V}}\\ \text{VSIR}&=20\text{lg}\frac{V_{V}}{V_{P}}\end{split}  \tag{7}\]  
Figure 3: Equivalent point force source amplitude. Figure 2: Structural acoustic and vibration characteristics under force and sound excitation: (**a**) Mean square sound pressure. (**b**) Mean square vibration velocity. (**c**) Radiated sound power. Based on the above conclusions, the 1.25 Pa sound source excitation and 1 N force source excitation were selected to work together in order to ensure the same radiation noise ability of the two excitation forms in the studied frequency band, and the two sets of monitoring information are shown in Figure 4. The PSIR is greater than 20 dB at most frequencies, indicating that the main component of acoustic monitoring information is caused by sound source excitation, while the coupling component caused by force source excitation accounts for a relatively small proportion. The VSIR decreases with the increase in frequency and is generally lower than that of acoustic monitoring information, and it is lower than 20 dB in some frequency bands below 400 Hz and above. Therefore, vibration monitoring information should be decoupled before radiation noise prediction.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
## 3 Three Kinds of Tunable OAM Modes
### The Relationship Among the Three Kinds of Tunable OAM Modes  
According to the representations of three kinds of tunable OAM modes, we find that there is a progressive relationship from orbital PS to higher-order PS, then to hybrid PS. Figure 5 shows the intuitive sketch describing the phase and polarization distributions of north and south poles on the three PSs. The white arrows represent polarization states and black-and-white images represent phase distributions. From the orbital PS to higher-order PS, the SOPs on the two poles vary from the same polarization to orthogonal circularly-polarized polarization. From the higher-order PS to hybrid-order PS, the SOPs on the two poles are kept while the orbital states vary from same order to different orders.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 2 Converter Model and System Description
### Conventional FCS-MPC Scheme for Voltage Regulation  
Figure 2 shows a block diagram of a three-phase VSC based on a FCS-MPC algorithm. In its basic form, the algorithm is executed sequentially, and at the beginning of each sample, it applies the switching configuration, which is calculated in the previous calculation step. Then, it receives the measurements and selects the new switching configurations accordingly and apply them at the next instant. The following CF is implemented in a stationary reference frame (\(\alpha-\beta\)):  
\[g=(V_{ref_{a}}^{*}-V_{cf_{a}}^{p}(k+1))^{2}+(V_{ref_{g}}^{*}-V_{cf_{g}}^{p}(k +1))^{2} \tag{15}\]  
where \(g\) is the CF, \(V_{ref_{a}}^{*}\) and \(V_{ref_{g}}^{*}\) are the real and imaginary components of voltage reference signals in the stationary reference frame, while \(V_{cf_{a}}^{p}(k+1)\) and \(V_{cf_{g}}^{p}(k+1)\) are the predicted components for each possible switch configuration. The conventional CF, as in Equation (15), uses a regulation strategy, which results in a minimal magnitude error at the next sampling instance. In first order plants, the variable of interest is directly regulated by the control input, allowing an instantaneous change of its derivative at a particular sampling instance. Hence, looking further than one prediction step ahead is optimal only for the first order plants [25]. For second order plants, capacitor voltage in the LC filter configuration is regulated indirectly through the inductor current. As the respective current cannot change its value instantaneously, the capacitor cannot change its derivative instantaneously. Therefore, no respect is given to the capacitor voltage derivative, which determines the heading of its trajectory. There are two possibilities to improve the power quality of the discussed configuration. Firstly, by applying a two-step or three-step prediction strategy, but using this approach makes the computations too intensive for practical applications. The other possibility evaluates for one prediction step by assuming that the same configuration will be kept for the next two periods \((k+2)\) in advance. This approach requires a highly accurate model of the system and it does not consider the capacitor voltage trajectory during inter-sampling periods [18; 19].  
Figure 3: Voltage vectors generated by the inverter \((V_{i})\).
## 3 Results and Discussion
### Settling Velocity  
Corresponding to the three types of settling paths in Figure 13, the snapshot of the settling velocities of a single particle and two side-by-side particles are shown in Figure 14. For path type (i), after the discharge of the particles, the settling velocities \(u\) increased and the transverse velocities \(v_{x}\) in \(x\)-\(z\) plane remain around 0 m/s. As the light particle was dragged toward the CRV, at around 0.2 s, the \(v_{x}\) of light particle (\(v_{xL}\)) started to increase and reached a maximal positive transverse-velocity of 0.031 m/s at \(t=0.422\) s. Meanwhile, the \(u\) of light particle (\(u_{L}\)) became higher than the terminal velocity of a single particle (0.77 m/s for Si\({}_{3}\)N\({}_{4}\)), and continued to increase to the maximum value 0.866 m/s at \(t=0.641\) s. Then, the light particle rebounding from the CRV resulted in an obvious increase in the negative traverse velocity, while \(u_{L}\) would fall back.  
The variation of the particles settling velocities can be explained by the energy transfer. The sedimentation of a particle in a stagnant fluid can be regard as the transport between potential energy and kinetic energy accompanied by energy dissipation. For two particles settling side by side in PAAm solution, the energy dissipation due to the viscosity decreases when the light particle settles into the CRV, and more potential energy is transferred into the kinetic energy of the light particle and the fluid around it. Thus, the settling velocity of a light particle during above process will be larger than the terminal settling velocity when it settles as an isolated particle in the same fluid, as discussed above. Then the kinetic energy is transformed from the vertical direction into the horizontal direction once the lateral migration occurs, which is possibly due to the shear-thinning property of PAAm solution or the appearance of negative wake (Figure 6).  
In the cases of type (ii), the \(u_{L}\) can always exceed the terminal settling velocity for a single settling particle of the same density. This is possibly due to the combined effect of the lower amount of energy dissipated and the dragging of the fluid in the CRV. Moreover, as shown in Figure 14b, for mediate \(L^{\star}\), the particle-particle interaction tends to cause a slight fluctuation in the \(v_{xL}\), which corresponds to the lateral migration of the light particle in path type (ii). With the separation distance increasing, the effects caused by the shear-thinning property of PAAm solution and the negative wake act on the light particle became continuously weaker.  
As for path type (iii), no significant difference in the settling velocities between the single particle and the two side-by-side particles shown in Figure 14c was found.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 2 Model Description
### Attitude Dynamic Model
Substituting Equations (8), (13) and (14) into Equations (11) and (12), the attitude dynamics can be expressed as follows:  
\[\mathbf{B}_{\dot{\mathbf{\omega}}_{a}}^{0}\dot{\mathbf{\omega}}_{a}+\mathbf{B}_{\dot{\theta}_{ 1}}^{0}\ddot{\theta}_{1}+\mathbf{B}_{\dot{\theta}_{2}}^{0}\ddot{\theta}_{2}+\mathbf{B }_{other}^{0}=\mathbf{T}_{c}+\mathbf{B}_{o}^{0} \tag{16}\]  
\[\mathbf{B}_{\dot{\mathbf{\omega}}_{a}}^{j}\dot{\mathbf{\omega}}_{a}+\mathbf{B}_{\dot{\theta}_{ j}}^{j}\ddot{\theta}_{j}+\mathbf{B}_{other}^{j}=\tau_{scj}+\mathbf{B}_{o}^{j} \tag{17}\]  
where  
\[\mathbf{B}_{o}^{0}=\mathbf{T}_{ao}+\sum_{k=1}^{n}\left(r_{ck}^{\times}\mathbf{A }_{o}^{k}+\mathbf{C}_{sk}^{T}\mathbf{T}_{sok}\right)\quad\mathbf{B}_{o}^{j}=\mathbf{D}_{j}^{T }\mathbf{T}_{soj}\] \[\mathbf{B}_{\dot{\mathbf{\omega}}_{a}}^{0}=\mathbf{C}_{sj}^{T}\mathbf{I}_{sj}\mathbf{ C}_{sk}-r_{ck}^{\times}\mathbf{A}_{\dot{\mathbf{\omega}}_{a}}^{k}\mathbf{B}_{\dot{\mathbf{\omega}}_{ a}}^{j}=\mathbf{D}_{j}^{T}\mathbf{I}_{sj}\mathbf{C}_{sj}\] \[\mathbf{B}_{\dot{\theta}_{j}}^{0}=\mathbf{C}_{sj}^{T}\mathbf{I}_{sj}\mathbf{D}_{j} \quad\mathbf{B}_{\dot{\theta}_{j}}^{j}=\mathbf{D}_{j}^{T}\mathbf{C}_{sj}\mathbf{D}_{j}\] \[\mathbf{B}_{other}^{0}=\mathbf{\omega}_{a}^{\times}\mathbf{I}_{a}\mathbf{\omega}_ {a}+\sum_{k=1}^{n}\mathbf{C}_{sk}^{T}\Big{(}-\mathbf{I}_{sj}\mathbf{\omega}_{sk}^{\times} \mathbf{C}_{sk}\mathbf{\omega}_{a}+\mathbf{(C}_{sk}\mathbf{\omega}_{a}+\mathbf{\omega}_{sk})^{ \times}\mathbf{I}_{sk}(\mathbf{C}_{sk}\mathbf{\omega}_{a}+\mathbf{\omega}_{sk})-\mathbf{r}_{ck}^{ \times}\mathbf{A}_{other}^{k}\Big{)}\] \[\mathbf{B}_{other}^{j}=\mathbf{D}_{j}^{T}\mathbf{C}_{sj}\Big{(}-\mathbf{I}_{sj}\bm {\omega}_{sj}^{\times}\mathbf{C}_{sj}\mathbf{\omega}_{a}+\mathbf{(C}_{sj}\mathbf{\omega}_{a}+ \mathbf{\omega}_{sj})^{\times}\mathbf{I}_{sj}\big{(}\mathbf{C}_{sj}\mathbf{\omega}_{a}+\mathbf{ \omega}_{sj})\Big{)}\]  
The external torques acting on the MR-SPS include gravity gradient torque, solar pressure torque, and microwave radiation torque caused by the deviation of centroid and pressure center. Due to the symmetrical structure, the deviation is small, so the magnitude of solar pressure torque and microwave radiation torque is far less than that of gravity gradient torque and cannot be known in advance, so they can be ignored. The external torques can be expressed as follows:  
\[\mathbf{T}_{ao}=\frac{3\mu}{|\mathbf{r}_{a}|^{5}}(\mathbf{C}_{a}\mathbf{r}_{a})^{\times}\mathbf{I }_{a}(\mathbf{C}_{a}\mathbf{r}_{a}) \tag{18}\]  
\[\mathbf{T}_{soj}=\frac{3\mu}{\left|\mathbf{C}_{a}\mathbf{r}_{a}+\mathbf{r}_{cj}\right|^{5}}( \mathbf{C}_{sj}\mathbf{C}_{a}\mathbf{r}_{a}+\mathbf{C}_{sj}\mathbf{r}_{cj})^{\times}\mathbf{I}_{sj}( \mathbf{C}_{sj}\mathbf{C}_{a}\mathbf{r}_{a}+\mathbf{C}_{sj}\mathbf{r}_{cj}) \tag{19}\]
# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
## 5 Results  
The results obtained using standard parameters proved to be effective within the scope of this study. Table 4 presents the results for different types of tests. The best result was obtained using data windows of 50 s with a scatter plot representation. Unlike the temporal spectrum version, this method also gave very good results for validation. This data representation method also works for continuous data flows, so this method can also be used to recognize real-time activities. Also, from this table, it can be seen that the recognition algorithm works very well on the dataset with a single user, the recognition rate being 99%. To validate the dataset, a \(T\)-test was implemented in the Google Colab platform. Based on the recognition rate and the standard deviation coefficient, the \(T\)-test values and the probabilistic value were calculated. Table 5 shows the results for the sets tested. For the type of graphic representation of scatter plots, the introduction of the temporal spectrum did not bring any positive benefit. Instead, the differences between the representation with density map and scatter plot can be observed both from the recognition rate and from the \(t\)-test value. The scatter plot representation type is clearly superior to the density map type. To improve the method, several parameter values were tested. In Table 6, the recognition results value rate is highlighted, as well as the parameter values. The convolutional layer pooling value was modified because it has a direct effect on the features that are extracted in the learning process. In addition, additional convolutional layers were added. The best result was obtained with four additional convolutional layers. The optimization algorithm was modified, and AdamW and Adadelta had the best results. Unlike the initial version, the recognition rate improved by 4%. The proposed implementation trains an image classification model using a batch size of 32; these images are processed before the model weights are updated. Input images are resized to 227 \(\times\) 227 pixels before being fed into the model. A validation split of 0.3 is used, which means that 30% of the dataset is reserved for evaluating the performance of the model during training and prevent overfitting. The model is trained for 20 epochs, giving it multiple passes through the entire dataset to learn the patterns in the data. The Adam optimizer, known for its efficiency, is  
Figure 6: Sequential model architecture block diagram. employed to update the model's weights and guide the learning process. This optimizer helps in achieving faster convergence and potentially better accuracy.