esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Path-Planning Approach
### Conception  
The path-planning algorithm in this paper was performed using the KASS model mentioned in Section 2.3. The port selected was the Busan port, whose geometry is shown in Figure 5. The objective of this result is to use the TD3 (the pseudocode as shown in Algorithm 1) for training the model that can generate the path for the berthing process. First, the TD3 algorithm trains the model with the combination of maneuvering simulations suggested by the MMG model. This approach allows for the integration of realistic ship motion dynamics into the training process. Then, this model is used to generate the desired path for the berthing task by inputting the state of the ship and predicting the control signal \(n\) (propeller speed) and \(\delta\) (rudder angle), based on the input ship state \(s(x,y,\psi,u,v,\text{ and }r)\). The concept of path planning for automatic berthing tasks is shown in Figure 6.  
```
1 Initialize the critic network \(Q_{\phi\eta}\), \(Q_{\phi\rho}\) and actor-network \(\mu_{\theta}\) with random parameter \(\phi_{1},\phi_{2},\theta\)
2 Initialize the target parameter to the main parameter \(\theta_{\text{targ}}\leftarrow\theta,\phi_{\text{targ1}}\leftarrow\phi_{1}, \phi_{\text{targ2}}\leftarrow\phi_{2}\)
3 For \(t=0\) to \(T-1\) do:
4 Observe the state \(s\) of the environment and choose the action \(a=clip\Big{(}\mu_{\theta}(s)+\varepsilon,a_{low},a_{high}\Big{)}\) where \(\varepsilon\sim N\)  Execute action \(a\) in the TD3 environment to observe the new state \(s^{\prime}\), reward \(r\), and done signal that gives the signal to stop training for this step.
6 Store training set \((s,a,r,s^{\prime},d)\) in replay buffer D
7 If \(s^{\prime}\) taking to the goal point, reset the environment state
8 If it is time for an update:
9 For \(j\) in range (custom decided) do:
10 Randomly sample a batch of transitions \(B=\{(s,a,r,s^{\prime},d)\}\) from \(D\)  Compute Target actions: \(a^{\prime}(s^{\prime})=clip\Big{(}\mu_{\theta\text{targ}}(s^{\prime})+clip( \varepsilon,-c,c),a_{low},a_{high}\Big{)}\)  where \(\varepsilon\sim(N,\sigma)\)  Compute targets: \(y(r,s^{\prime},d)=r+\gamma(1-d)\underset{i=1,2}{\min}Q_{\phi\text{targ1}}(s^{ \prime},a^{\prime}(s^{\prime}))\)  Update Q-functions using gradient descent: \(\
abla_{\delta i}\frac{1}{|B|}\underset{(s,a,r,s^{\prime},d)\in B}{\sum} \Big{(}Q_{\phi i}(s,a)-y(r,st,d)\Big{)}^{2}\)  where \(\mathrm{i}=1,2\)  If \(j\) mod policy delay \(==0\) then:
12 Update policy by the one-step deterministic policy gradient ascent using \(\
abla_{\theta}\frac{1}{|B|}\underset{s\in B}{\sum}Q_{\phi 1}(s,\mu_{\theta}(s))\)  Update target networks: \(\phi_{\text{targ},i}\leftarrow\rho\phi_{\text{targ},i}+(1-\rho)\phi_{i}\) where \(\mathrm{i}=1,2\) \(\theta_{\text{targ}}\leftarrow\rho\theta_{\text{targ}}+(1-\rho)\theta\) End if
13 End for End if End until convergence
```  
**Algorithm 1**Pseudocode TD3 Algorithm  
The concept of path planning for automatic berthing tasks is shown in Figure 6.
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Process Debt--Questions 40 to 42  
40. Question 40: Have you correctly described your data handling procedures? Stakeholder: Organizers Score: 4 Justification: Data handling procedures are critical as they directly impact the quality of training data and subsequently affect the performance of the RL agent. Understanding how data is collected, preprocessed, and fed into the RL model is essential for ensuring the agent's effectiveness. Example: Ensuring that the data collection process is unbiased and that the replay buffer is managed effectively to prevent overfitting or underutilization of data. 41. Question 41: Have you correctly described your model development processes? Stakeholder: Organizers Score: 4 Justification: Model development processes outline how the RL algorithm is designed and trained. This includes aspects such as the choice of algorithm, network architecture, hyperparameters, and training methodology. Understanding these processes is fundamental for reproducibility and ensuring the model's reliability and performance.  
Example: Documenting the transition from a model-free to a model-based approach, detailing the algorithms used, and the rationale behind choosing specific hyperparameters.
* Question 42: Have you correctly described the deployment processes of your model? Stakeholder: Organizers Score: 4 Justification: Deployment processes are crucial as they determine how the trained RL model is integrated into the game environment for real-world interaction. Understanding deployment procedures ensures smooth transition from development to production, minimizing potential errors and ensuring the model functions as intended in the game environment.  
Example: Describing the process of integrating the trained RL model into the live game environment, including any safety checks and performance monitoring systems.
# Novel Polymer Sorbents with Imprinted Task-Specific Ionic Liquids for Metal Removal
## 3 Results and Discussion
### Reusability Test  
The sorbent application potential is influenced not only by the sorption capacity or the kinetics of the process but also by the desorption of the adsorbed component with the participation of relatively mild reagents. Therefore, experiments were also carried out in order to develop the most optimal desorbing agent. This agent not only should easily desorb the metal ions but also should enable sorption immediately after regeneration in subsequent cycles of the process. After sorption (100 mg/L Cu(II), Cd(II), or Zn(II) at a pH of 5 and at a temperature of 45 \({}^{\circ}\)C), the 1 g of sorbents (VBC-K3.10 and VBBr-K3.10) were contacted with 10 mL of 0.1 and 0.01 M HCl and with a 0.5 M solution of NaCl and Na\({}_{2}\)SO\({}_{4}\). Immediately after desorption, the separated sorbent was contacted with the fresh aqueous feed solution. The sorption-desorption cycle was repeated five times.  
The obtained results indicated that, regardless of the sorbent used and the metals ions removed, the desorption of the metals with the salts solutions was not very efficient (desorption with the NaCl solution ranged from 41 to 59%, while using the Na\({}_{2}\)SO\({}_{4}\) solution, it ranged from 52 to 64%). Slightly better results were obtained using 0.01 M HCl (58-72%), but the 0.1 M HCl solution was found to be the most efficient desorbing agent. The results also showed that five-fold repetition of the sorption-desorption procedure using the 0.1M HCl solution as the desorbing agent did not adversely affect the adsorption properties of VBC-K3.10 and VBBr-K3.10 (the differences in the removal efficiency were 5-7% and 4-8%, respectively).  
Figure 8: Effect of temperature on Cu(II), Cd(II), and Zn(II) sorption with VBC and VBBr series sorbents (M(II) = 100 mg/L, pH = 5, time: 180 min, sorbent dosage—0.1 g, and V—100 mL).
## 4 Conclusions  
This paper mainly investigates the changes in pore structure and mechanical properties of sandstone reservoirs after acidification-supercritical CO\({}_{2}\) composite treatment. The main conclusions are:  
1. The continuity of the T\({}_{2}\) spectral curve of the sandstone samples is not obvious. The acidification-supercritical CO\({}_{2}\) composite treatment results in the dissolution of a large number of minerals after the composite treatment, which makes the pore size distribution more uniform, and the T\({}_{2}\) spectral curve is trapezoidal, which is more favorable for the development of pores into macropores (T\({}_{2}\) 335 ms) and mesopores (T\({}_{2}\) 6.85 ms).
2. The acidification-supercritical CO\({}_{2}\) composite treatment has a significant effect on the pore size distribution and permeability of the sandstone. Compared with the sandstone under untreated or single treatment conditions, the chlorite, feldspar, and carbonate contents of the sandstone after the composite treatment are significantly reduced; the morphology of the sandstone is dramatically changed, and the number of initial microcracks is also dramatically increased after the acidification-supercritical CO\({}_{2}\) composite action.
3. After the acidization-supercritical CO\({}_{2}\) composite treatment, the peak strength, initial stress, and damage stress of the sandstone decreases by 49.7%, 20%, and 49.5%, respectively. The composite treatment accelerates the growth rate of crack volume strain in the sandstone; enhances the ductility of the samples; causes overall deterioration of the samples, leading to frequent intercrossing of internal cracks; and exacerbates structural damage.
4. Acid pretreatment greatly increases the pore volume of the sandstone and then greatly increases the volume of supercritical CO\({}_{2}\) and sandstone, while the residual acidic reactants and reaction products change the PH environment of supercritical CO\({}_{2}\) and sandstone mineral solubility, so there is a physicochemical effect of acid treatment on the reaction between supercritical CO\({}_{2}\) and sandstone.  
Conceptualization, Y.Z. and G.H.; methodology, Q.L.; validation, Q.C. and Y.Z.; formal analysis, Q.C.; writing--original draft preparation, Y.Z. and G.H.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by grants from the National Science Foundation of China (Grant No. 51674049).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 3 Statistical Characteristic of Zero-Bias
### Equivalent Reference Waveform Analysis
If the number of sampling points of the composite waveform is even, the accumulated value is zero. Otherwise, the accumulated value is non-zero if the number of sampling points is odd.
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 5 Conclusions  
This paper proposes a compact circularly polarized non-resonant slotted waveguide array antenna with a wide-beam scanning angle ability. We modified and improved the existing compact slotted waveguide antenna through a thorough analysis of its working principles and the optimization of design parameters, adding isolation gaps, and adapting it for use in non-resonant arrays and phased arrays. The array's performance was optimized through phase compensation and element power matching techniques. As a result, we achieved a compact slotted waveguide antenna that can work in non-resonant arrays with the capability for wide beam scanning angles. The prototype antenna is proposed, and the experimental results prove the effectiveness of the antenna design methodology. The designed antenna exhibits a phase scan capability of \(\pm 45^{\circ}\) in the azimuth plane and a frequency scan range of \(+11^{\circ}\) to \(+13^{\circ}\) in the elevation plane, without the grating lobe, and it maintains a low SLL. The axial ratio remains below 3 dB within this scanning range and bandwidth. Overall, the proposed antenna effectively combines circular polarization with wide-angle beam scanning capabilities in slotted waveguide array antennas.  
Conceptualization, G.H.; methodology, G.H. and W.L.; investigation, W.L.; resources, G.H.; visualization, W.L.; validation, W.L.; writing--original draft preparation, G.H. and W.L.; writing--review and editing, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Advanced Scanning Technology for Volume Change Measurement of Residual Soil
## 8 Conclusions  
The relevant methodologies, technology, and software for measuring soil volume change utilizing 3D scanning technology have been proposed in this work. For accurate 3D modeling with Meshlab and Meshmixer software, the geometry and texture settings should be adjusted at around 60%. For measuring soil volume, 3D scanning should be accurate to within 0.1 mm. Within soil suction of 100 kPa, the volume of the sample remained almost constant with no changes in the degree of saturation. The case study was presented to highlight the significance of 3D scanning technology in determining SWCC factors.  
The characterization of MH soil, with its high plasticity index and specific grain size distribution, highlights the intricate relationship between soil properties and engineering performance. The significant shrinkage observed during moisture fluctuations underscores the importance of considering volumetric changes in soil when assessing its behavior. Incorporating a shrinkage curve into the SWCC modeling is essential for accurately predicting water retention characteristics and understanding the transitions between macro and micro pore behaviors. This integration not only enhances the model's predictive capabilities but also reflects more realistic conditions that can inform construction and land management practices.  
Conceptualization, methodology, investigation: S.O., A.S., Y.K., H.R. and Z.Q.; data curation, visualization: S.O., Y.K. and Z.Q.; writing--original draft preparation: S.O., A.S. and H.R.; writing--review and editing: S.-W.M., J.K. and Y.K.; supervision: S.-W.M., J.K. and H.R.; project administration, funding acquisition: A.S., S.-W.M. and J.K. All authors have read and agreed to the published version of the manuscript.  
The Nazarbayev University Research Fund financed this study through Collaborative Research Program (CRP) Grant No. 11022021 CRP1512. The writers are appreciative for this help. Any opinions, findings, conclusions, or recommendations stated in this material are solely those of the author(s) and do not necessarily reflect the views of Nazarbayev University.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 4 Discussion
The Supplementary Material presents the results achieved in Tables S1-S4, and their mean values are summarised in Figure S1. The addition of one additional central GCP in the middle does not yield a significant improvement in the photogrammetric mode, as it can be checked with the RMSE on the GCPs, and it slightly enlarges the RMSE on the ChPs. Thus, including a single GCP in the centre of each block corroborates the error budget for planimetry accuracy [49]. The authors of [50] suggest the addition of convergent images to avoid the doming effect in the topographic models derived from UAV; alternatively, they also suggest adding control measurements. In this sense, we can confirm that the addition of transversal strips is also highly recommended, as suggested by the study in [47]; we confirmed this option for lower flying heights. In fact, the positive effect of including convergent images can also be obtained including lower flying cross strips, to appropriately determine the geometric camera calibration of the UAV-DAP survey (Scenario C). In regard to the a priori accuracy calculations obtained in Table 6, it can be seen that Scenario C has the lowest results for the multiplying factor in the GCPs and ChPs, and these are therefore the reference values selected as the best procedure for this type of flight. Better results are obtained when verifying the accuracy of the simple model compared with that used in [49], so with these new parameters, the accuracy can be estimated using Equation (3), described in the methodology, before conducting the flight, provided that cross strips, or otherwise, dense GCP perpendicular chains, are used at the edges of the block. To verify the accuracy, it is recommended to use the value of \(\mu_{\text{xyz}}\) ChPs, as these points show the accuracy of the photogrammetric adjustment not in the GCPs but in the ChPs. This study also demonstrates the influence of the type of camera used, as specified in Table 3. The Zennuse XSS camera achieves accuracies below 3 cm in the case of Block 3, and the DJI FC300X camera has an accuracy of 6 cm in the case of Blocks 1, 2, and 4 in Scenarios A, B, and D.  
Another important aspect to consider when measuring vast areas is that the flights can be divided into blocks, sharing the same GCPs at the edges and the lower cross strips, thereby reducing the topographic control required and ensuring a lower consumption of batteries, a limiting factor in multirotor UAVs.
## 6 Discussion
### Parametric Control of Valve Dynamics
#### 6.2.2 Spring Parameter Sensitivity  
Parametric studies reveal critical design constraints. Cylindrical spring stiffness increases reduce discharge valve maximum lift by 7.2% (\(\Delta L\) = 1.06 mm) and velocity amplitude by 2.74%. Spring preload elevation improves positional stability (0.02% lift reduction per 10 N increment). Structural parameters exert 2.2\(\times\) greater influence on closing dynamics than opening characteristics. These findings establish spring parameter optimization as a primary lever for controlling valve timing precision.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Optimization of Skeletons of Three Common Shapes
#### 4.1.2 Circular Cross-Section  
The magnetic flux through the circular cross-section is  
\[\Phi_{\text{m}}=\frac{\mu_{0}i\big{(}\sqrt{R_{1}+d}-\sqrt{R_{1}}\big{)}^{2}}{ 2}, \tag{18}\]  
where \(d\) is the diameter of the circular cross-section.  
If \(A_{0}=\frac{2\pi\Phi_{\text{m}}}{\mu_{0}i}\) is fixed, the perimeter of the circular section is given by  
\[l=\pi d=\pi\Big{(}A_{0}+2\sqrt{A_{0}R_{1}}\Big{)}, \tag{19}\]  
The minimum perimeter \(l\) does not exist in this case. Although shortening the circumference makes the stray capacitance smaller, it also reduces the magnetic flux and thus the mutual inductance \(M\). Therefore, a circular cross-section skeleton should be avoided from the perspective of keeping the mutual inductance unchanged while only minimizing the stray capacitance.  
Figure 11: Skeleton with rectangular cross-section.
## 1 Introduction
This causes the ICA and CCA, which are located vertically in the same hemisphere, to overlap on the same vertical line in the transaxial image, leading to signal interference between the vessels. This interference can distort the original blood flow signal waveforms, potentially hindering accurate PWV calculations. Although an image rotation technique has been proposed to minimize the signal interference between vessels, it only solves the problem in the case of two specific vessels aligned vertically, and there is still the possibility of signal interference with other vessels located in different positions. The limitations of vertical projection caused by parallel encoding can be resolved by radial projection using data acquired through radial encoding. Radial encoding acquires images by encoding in various directions, not just vertically [28], resulting in more frequent sampling of the center of k-space point than with vertical encoding. This preserves low-frequency components, leading to a higher signal-to-noise ratio (SNR) [29; 30]. Additionally,when a structural image is used to identify the positional information of various vessels, including the ICA and CCA, which are located in the same hemisphere, each vessel can be projected at an optimal angle to avoid interference with other vessels. Therefore, this technique can prevent signal interference between vessels, allowing for accurate PWV calculation using undisturbed blood flow signal waveforms from the ICA and CCA. In this study, we propose a new vascular stiffness measurement technique that can significantly reduce signal interference between blood vessels, which is a limitation of the vertical projection method. Additionally, we explored the validity of this technique using imaging parameters similar to those of an actual MRI environment and analyzed its potential through simulation. We considered that a projection method that can produce results similar to the actual PWV and obtain the full sampling effect despite undersampling in the k-space can contribute to a more accurate early diagnosis of vascular aging and diseases.
## 2 Methods
### Inference and Performance Evaluation  
The inference and performance evaluation of the system were performed using the random PID parameters considered initially. Then, the two neural networks recommended the new PID parameters from the response, response characteristics, and randomly input PID parameters. These recommended PID parameters were given as inputs to the simulator and the response, and its characteristics were checked again to confirm whether an acceptable tuning was achieved. The criteria of an acceptable response were when the settling time with a 5% error band was less than 1.5 s and the overshoot was less than 10%. If the response with the recommended PID parameters was not acceptable, the neural networks recommended new PID parameters based on the current response and the past values of the parameters. This was repeated until the tuning was complete. If tuning was not successful within 20 attempts, it was treated as a failure. To evaluate the performance of the system, the number of failures and trials to succeed were considered (Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Type of Network**} & **Number of Sampling** & **Response Characteristic** \\  & & **Data (N)** & **(RC)** \\ \hline A-21-RC & ANN & 21 & Included \\ A-11-RC & ANN & 11 & Included \\ A-21 & ANN & 21 & Excluded \\ A-11 & ANN & 11 & Excluded \\ L-21-RC & LSTM & 21 & Included \\ L-11-RC & LSTM & 11 & Included \\ L-21 & LSTM & 21 & Excluded \\ L-11 & LSTM & 11 & Excluded \\ \hline \hline \end{tabular}
\end{table}
Table 1: Learning method classified by the type of network, number of sampling data, and whether response characteristics were included as inputs. ANN and LSTM are abbreviated as “A” and “L”, respectively. The numbers “21” and “11” indicate the number of sampling data. “RC” indicates if the input included or excluded the response characteristics. If the name of the method does not end with “RC,” then the input of that method did not include response characteristics.  
In addition, the performance of the system with noise was examined. Random noise, which has a normal distribution with a standard deviation of 20% of the settling condition, was added to the position every 0.01 s (Equation (1)). The performance of the system when the target position changed during the operation was also examined.  
\[x_{t}=x_{controlled}+N_{t} \tag{1}\]  
when \(x_{t}\): position at time \(t\), \(x_{controlled}\): position from PID controller, \(N_{t}\left\{\begin{array}{ll}\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(x- \mu)^{2}}{2\sigma^{2}}}&(\textit{when mod}(t)=0.01)\\ 0&(\textit{when mod}(t)\
eq 0.01)\end{array}\right.\)\(\mu\): mean of noise = 0, \(\sigma\): standard deviation = 20% of settling condition = 0.2 \(\times\) \(\alpha\), \(\alpha\): settling condition = 5% of target position.