esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Process Safety Assessment  
Table 4 summarizes the score for safety parameters related to process operation. The temperature and pressure scores are the same for the stages as averaged operating conditions were set with the inlet and outlet streams of each equipment (T = 36.48 \({}^{\circ}\)C and P = 1 bar). The demineralization and deacetylation stages reached the highest values for heat reaction score suggesting that the design of large-scale chitosan production must consider safeguards in reactors at which such endothermic reactions take place. The PSTS score corresponded to 101.09, 101.19, and 9.29 for deacetylation, demineralization, and deproteinization stages, respectively. The maximum PSTS was quantified at 209.20 by considering the sum of HR scores per stage. This result was similar to those reported for methyl methacrylate (MMA) production based on the routes of acetone cyanohydrin and ethylene via propionaldehyde (PSTSmax = 241 and 232, respectively); however, it was higher than for the routes of ethylene via methyl propionate, propylene, and tertiary butyl alcohol [14].  
The NuDIST score was calculated at 380.20 in the production of chitosan from shrimp exoskeleton. The level of risk according to this estimation is light following the analysis conducted by [PERSON] et al. [27] for the characterization of risk assessments. Compared to other works, the selected process is inherently safer owing to the organic nature of chitosan source (exoskeleton) and less drastic  
Figure 4: Contribution of chemicals to the highest values of flammability, explosiveness, and toxicity safety parameters.  
Figure 5: Rank of CSTS score estimated for chemicals in the large-scale production of chitosan from shrimp wastes.  
operating conditions. Figure 6 depicts a comparison in NuDIST estimation for the petrochemical industry case study [14] and this work. The routes of acetone cyanohydrin (ACH), ethylene through propionaldehyde (C2/PA), ethylene through methyl propionate (C2/MP), and propylene (C3) reported a score of 499, 574, 493, and 531, respectively, which represents a moderate level of risk.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 2 Problem Description
### Clipping-Enhanced Optical OFDM Technique  
CEO-OFDM stands out as a recently introduced technique specifically devised to mitigate the challenges associated with signal clipping beyond peak power limits as well as zero clipping. Empirical evidence supports its superior performance in terms of bit error rate (BER) and power efficiency when compared to conventional optical OFDM  
Figure 1: An illustration of the nonlinearity of the light source for OWC systems.  
methods, such as DCO-OFDM, ACO-OFDM, and U-OFDM [7]. This section offers a brief introduction to the fundamental principles underpinning CEO-OFDM, delves into its inherent limitations, and provides insight into the motivation of the proposed TAID algorithm.  
To mitigate the adverse effects of zero and peak power clipping, CEO-OFDM employs a distinctive structure, where each CEO-OFDM symbol comprises three distinct signal frames, as illustrated in Figure 2. These three frames are dedicated to transmitting the magnitudes of the positive, negative, and clipped portions of the original OFDM symbol. It is assumed that the peak radiation power is denoted as \(P_{\max}\), and any positive and negative signals exceeding this magnitude threshold are subject to clipping, being constrained to \(P_{\max}\). Importantly, the clipped portions are efficiently transmitted through frame 3, as shown in Figure 2. Notably, within the CEO-OFDM scheme, the occurrence of clipping distortion is confined exclusively to the signal within frame 3.  
After subjecting the discrete OFDM signals within frames 1 and 2 to hard clipping at \(P_{\max}\), it is noteworthy that, owing to the inherent effects of bandlimited Nyquist-Shannon interpolation [16], certain analog transmitted signals within frames 1 and 2 may still exceed the stipulated peak power constraint. In the context of this study, we adopt the assumption that the discrete signals across all three frames are uniformly clipped at a threshold of \(\beta P_{\max}\), where a suitable value of \(\beta<1\) can be determined to ensure that the corresponding analog transmitted signals remain within the prescribed range of \([0,P_{\max}]\).
## 1 Introduction  
The importance of renewable energy is driving a large growth in interest in geothermal systems and especially in shallow systems coupled to heat pumps. A system of groundwater pumping and reinjection performed by doublets of wells in shallow aquifers is known as Aquifer Thermal Energy Storage (ATES). In summer, groundwater is pumped into a (cold) well for cooling, and the heat is injected back with water into the aquifer at a higher temperature using a (warm) well. In winter, the system provides heat to the building from the pumped groundwater, and injection occurs at a lower temperature [1, 2, 3]. Both the efficiency and impact of ATES systems must be studied through the quantification of heat transfer in the saturated underground zone. Site-specific values for hydrogeological and heat transport properties/parameters are needed to design heat storage systems in aquifers. The seasonal heat storage considered in the shallow aquifers may be optimal or limited by the underground hydrodynamic conditions. If advection is driven by the hydraulic gradient and the hydraulic conductivity is high, heat and cold plumes are transferred rapidly far away from the site [4; 5; 6; 7] so that heat/cold cannot be stored efficiently for the next season for the concerned buildings. Reliable predictions are most often based on process-based numerical simulations of groundwater flow and heat transport. A yearly annual thermal balance between cold and heat demands could be required in cases of limited heat advection.  
The objective of this paper is to examine the cumulative effect of three adjacent open geothermal systems. A first model including a single aquifer, exploited by two of the three ATES systems, was developed previously by [PERSON] et al. [3], who studied the possible interference between these two systems. Here we add a deep aquifer separated from the upper aquifer by an aquitard (i.e., a low hydraulic conductivity layer) that is exploited by a newly added ATES system requiring a power of about 950 kW for both the cold and the warm seasons. The combined effect of the three installations is assessed with the wells of the new system established in the deeper aquifer in the fractured Paleozoic bedrock of the Brabant Massif. Heat exchanges are likely to take place through the intermediate aquitard formed by the weathered top of the bedrock, the overlying low permeability Cretaceous base deposits, and a Cenozoic clayey layer.  
Here, we will demonstrate that even though one unbalanced ATES system partly jeopardizes the thermal state of the upper aquifer [3], an adjacent but deeper ATES system can operate without too many negative interactions limiting either efficiency or durability. The numerical simulation of the 3D groundwater flow and heat transport conditions considering the upper and deeper aquifers separated by the aquitard provide an excellent tool for analysis and decision support. This model will be particularly useful for the future management of the three ATES systems.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 5 Simulations and Discussion
### Results and Discussion  
Figures 10 and 11 show the results of simulations 1 and 2, respectively. The line graphs in Figures 10 and 11 show comparisons of the number  
Overall, it can be seen from the graphs above that eEFA reduced the number of network frames compared with EFA in both unidirectional and bidirectional communication. While EFA still forwards the first sent frame to all DANH rings, eEFA filters the first sent frame for nondestination DANH rings. In addition, unlike EFA that uses the Locking message to discover passive QuadBox rings for both unidirectional and bidirectional communications, eEFA does not use any control message to detect the passive QuadBox rings for bidirectional communications. Therefore, eEFA saves the network bandwidth and improves the network performance.  
Figure 12 shows the percentage traffic reduction of eEFA compared to EFA. This graph clearly shows that the amount of traffic reduction decreased with an increase in the number of traffic frames sent in the communication. When the source DANH sent 10 frames to the destination DANH, the traffic reduction for unidirectional and bidirectional communication showed the maximum value of 26.21% and 19.65%, respectively. The traffic reduction rate decreased with a further increase in the number of sent frames, ultimately reaching values of 3.68% and 2.37% for unidirectional and bidirectional communication, respectively.  
The main contributions of the proposed eEFA approach are as follows:  
* eEFA filters all HSR frames for nondestination DANH rings, even the first sent frame.
* eEFA does not use any control message to discover passive QuadBox rings for bidirectional communications.  
By solving these issues, eEFA reduces more redundant unicast traffic than EFA, thus saving the network bandwidth and improving the network performance in HSR networks.
## 4 Experiments
### Experiment and Analysis
#### 4.2.2 Target Speed Measurement  
In this paper, the motion of the detected debris is categorized into two types: one for angled splashing debris with initial velocity and another for free-falling debris without initial velocity. Multiple sets of samples are collected for each category to measure the speed, and the average value is taken to enhance measurement accuracy. The results of the speed measurements are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **mAP@80/(\%)** & **mAP@75-95/(\%)** & **fps** & **Params/M** \\ \hline YOLOv5 & 72.48 & 39.93 & 78 & 50.70 \\ YOLOv8 & 72.15 & 41.24 & 93 & 41.61 \\ YOLOv11 & 71.39 & 40.94 & **111** & **24.20** \\ YOLOv11-Dd & **76.04** & **41.73** & 76 & 43.51 \\ \hline \end{tabular} Boldface denotes the best result for each evaluation metric.  
\end{table}
Table 3: Test results of different models.  
Figure 6: Comparison of detection results between YOLOv11-Dd and YOLOv11 (10\(\times\) magnification).  
Figure 7 illustrates a sequence of images of simulated debris splashing diagonally upward with initial velocity. From the figure, it is clearly observed that the speed of the target decreases during the upward motion and increases after reaching the peak, which is consistent with the basic laws of projectile motion. Moreover, the measured speed variation curve aligns closely with physical reality, thus validating the effectiveness and reliability of the pixel displacement-based speed measurement method for dynamic target tracking.  
The comparison in Table 4 shows that the proposed debris speed measurement model performs well across different testing scenarios. Notably, the 30 fps data outperforms the 60 fps data in terms of speed measurement accuracy. This is because, with a lower frame rate, the pixel displacement of the debris per unit time is larger, reducing errors caused by small offsets between the detected center of the bounding box and the true centroid position. Debris with initial velocity shows higher measurement accuracy due to larger displacement in a given time, which reduces the relative error. In contrast, the displacement of free-falling motion in the initial phase is very small, and even minor prediction errors by the detector can result in large speed measurement inaccuracies, thereby lowering overall accuracy.  
Based on the above analysis, future research will focus on improving the detection of low-speed small-displacement targets. This could involve introducing higher-resolution image perception modules or guiding the model to emphasize target contour features, which would further enhance speed measurement accuracy during the initial stages.
# Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
## 6 Conclusions  
This paper proposes a nonlinear FOPD controller for AUV motion control system based on saturation limitation. The proposed controller owns the advantages of improving system robustness and transient control performance, which is also easy to tune and implement. The stability of the controlled system is analyzed by Lyapunov method. The transient performance of regulation and tracking control can also be adjusted to satisfy different control requirements with the proposed controller. The simulations of both set-point regulation and trajectory tracking performance of AUV yaw and depth systems are quite satisfactory. The weights of ITAE and ISCO criteria in the optimization objective function can also be adjusted to fulfill different control requirements. With the increase of \(w_{2}\), almost all the evaluation indicators increase sharply at first and then slightly afterwards, which means both criteria need to be taken into account. The simulation results verify the effectiveness and flexibility of the proposed control method. The future work may include other fractional-order controllers designed for AUV motion systems in order to further improve the robustness and dynamic performance of the closed-loop system.  
Figure 5: Trajectory tracking performance of AUV yaw system.  
Figure 6: Trajectory tracking performance of AUV depth system.  
**Author Contributions:** Conceptualization and Revision, L.Z.; Writing-Review and Editing, L.L.; Stability Analysis, S.Z.; and Software, S.C.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grants 51979229 and 11902252, the China Postdoctoral Science under Grant 2019M50274, the Foundation Natural Science Foundation of Shaanxi Province under Grant 2019 JQ-164, and the Opening Foundation of Key Laboratory of Ocean Engineering (Shanghai Jiao Tong University) under Grant 1817.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Empirical Assessment Using Real-World Data
### Results
#### 4.3.1 Classification Stage
The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The ROC curve for our custom CatBoost model is shown in Figure 4, where the area under the curve (AUC) is 0.77. This AUC value is a measure of the model's ability to distinguish between the classes and is considered good, indicating a high level of separability. The closer the ROC curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The curve's significant elevation above the diagonal line of no discrimination suggests the model's strong classification capabilities. Training and validation loss curves are critical for understanding the model's learning over iterations. They provide information on whether the model is fitting well, overfitting, or underfitting. The plot of training and validation losses, depicted in Figure 5, shows a steady decline in both training and validation losses over the iterations, which is a positive indicator of a good model fit. The convergence of the two curves and the absence of a significant gap between them indicate that the model generalizes well to unseen data, avoiding overfitting. The losses decrease, suggesting that the model has learned the patterns within the data to a satisfactory degree, and that further iterations would yield diminishing improvements. These analyses collectively offer a comprehensive evaluation of the model's performance, highlighting its predictive power and learning efficiency. The ROC curve confirms the model's adeptness in classification, while the loss curves attest to the stability and reliability of the model's training process. Figure 4: ROC curve for custom CatBoost classification model.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 5 Energy Saving Protocol
### Network Nodes Nomenclature  
A network may be understood as a collection of nodes interconnected through communication links (routes, see Figure 1). Each node can be part of different routes and can also transmit different packets (measurement and control signals) from the source to the sink. Additionally, the maximum number of transmissions \(L\) can be independent for each route and each node. To represent the network nodes in the energy saving protocols described in this section, the following indexes are used (by omitting the particular network structure)  
\[\mathcal{N}_{U}^{\mathcal{R},\mathcal{S}}\in\Psi,\ \ U=\{1,2,\ldots,N_{node}\}\]  
where \(U\) indicates which node is being evaluated, \(N_{node}\) is the total number of nodes in the network, \(\mathcal{R}\) and \(\mathcal{S}\) respectively represent the number of routes and signals passing through \(U\) and \(\Psi\) denotes the set of all nodes belonging to the network. Furthermore, \(\mathcal{T}_{\mathcal{R}}\) represents the set of all routes, such that \(\mathcal{R}\subset\mathcal{T}_{\mathcal{R}}\). Similarly, \(\mathcal{T}_{\mathcal{S}}\) represents the set of all packets of signals, such that \(\mathcal{S}\subset\mathcal{T}_{\mathcal{S}}=\mathbf{K}^{y}\cup\mathbf{K}^{u}\). For example,  
\[\mathcal{N}_{10}^{[2,4],[g_{2},a_{1}]}\]  
means that the node 10 belongs to the routes 2 and 4, transmitting the packets with signals \(\mathcal{Y}_{2}\) and \(\bar{u}_{1}\).
# Digital Planning Technique for Surgical Guides for Prosthetic Implants before Orthodontic Treatment
###### Abstract  
Background: The use of digitalization allows clinicians to plan the position of dental implants in orthodontic patients treated with aligners in the beginning of their orthodontic treatment, instead of waiting until the end of orthodontic treatment, integrating implant restorations with orthodontic biomechanics. Aim: The aim of this case report is to describe a digital workflow for aligner treatment supported by implants, in which implants are placed at the beginning of the treatment and support the orthodontic treatment. Materials and Methods: Digital planning for orthodontic treatment with clear aligners and the preparation of surgical guides for implant surgery before orthodontic treatment are used to solve two multidisciplinary cases. Cone-beam computed tomography (CBCT) and virtual dental impressions are used for the virtual planning. Successively, a surgical guide for implant placement and aligners are used in the treatment plan. Results: The digital orthodontic setup with the integration of the root position allows the clinician to plan the position of dental implants on the final occlusion. The placement of the implant before the start of the orthodontic treatment allows the implants to be used as skeletal anchorage, thereby helping the orthodontic biomechanics. Conclusions: This study describes how to perform a digital workflow with orthodontic virtual planning and the design of surgical guides for implant placement. This technique is potentially promising for complex orthodontic cases.  
Keywords:aligneds; implants; surgical guide; orthodontics +
Footnote †: journal: _Sci._
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 5 Fundamental Consistency Checks
### Momentum and Angular Momentum  
Translational invariance implies momentum conservation only when \(\
abla\overline{S}=0\) (equilibrium):  
\[\frac{d\mathbf{P}}{dt}=T\
abla_{\mathbf{r}}\overline{S}. \tag{83}\]  
Similarly, angular momentum conservation requires rotational symmetry:  
\[\frac{d\mathbf{L}}{dt}=T\mathbf{r}\times\
abla_{\mathbf{r}}\overline{S}. \tag{84}\]  
These equations align with dissipative systems where entropy gradients drive momentum transfer.
## 4 Data Interpretation and Analysis
### Data Analysis and Model Estimation  
The questionnaire data were imported into SPSS 25.0 for reliability analysis, which showed a KMO value of 0.954 and a Cronbach's alpha value of 0.916. This indicates that the results of the questionnaire in this study are well suited for factor analysis. The indicator data was exported to SmartPLS 3.3 to estimate the model via the PLS algorithm. Figure 3 shows the R\({}^{2}\) values for the external loads, path coefficients and endogenous variables of the determined structural model. In terms of emotional response, for example, the external loadings for the emotional indicators are 0.628 (emotions in the face of crowds) and 0.467 (emotions in the face of unexpected events). The path coefficients then reveal the magnitude of the effect of the measurement model on the prediction model. In the case of competitive strategy, for example, contingency knowledge understanding (0.379) had the greatest influence, followed by emotion (0.340) and survivability (0.172). Finally, the R\({}^{2}\) value for competitive strategy was 0.709, which indicates that the three constructs above together explained 70.9% of competitive strategy.
## 3 Results and Discussion
Table 1 summarizes the maximum frequency shift and sensitivity achieved for the different metastructures studied in this work. As a final step, a comparison with other works found in the literature was carried out, in which similar designs in terms of complexity and dimensions were analyzed (note that we tried to keep \begin{table}
\begin{tabular}{c c c c} \hline \hline \(h_{a}\) (nm) & **Tripod Structure** & \(\Delta f\) **max (\%)** & **Mean Sensitivity (nm\({}^{-1}\))** \\ \hline  & Solid & 1.89 & \(3.54\times 10^{-5}\) \\  & Hollow \(a=26\)\(\mu\)m & 2.55 & \(3.72\times 10^{-5}\) \\
200–800 & Hollow \(a=30\)\(\mu\)m & 4.36 & \(6.40\times 10^{-5}\) \\  & Hollow + arms & 5.09 & \(7.32\times 10^{-5}\) \\  & Hollow + arms, rotated & 5.11 & \(7.35\times 10^{-5}\) \\ \hline
5–25 & Hollow + arms & 0.50 & \(1.27\times 10^{-4}\) \\  & Hollow + arms, rotated & 0.60 & \(1.42\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the most relevant quality parameters of the three tripod-metastructures studied in this work when working as thin-film sensors where the analyte thickness deposited varies between 200 and 800 nm (top), and for ultra-thin analytes between 5 and 25 nm (bottom). Figure 4: Reflection coefficient for different analyte thicknesses for the hollow tripod structure with arms (**a**), and the hollow tripod structure with arms, and rotated (**b**). Insets: zoom of the reflection coefficient for both metastructures and extremely thin film analytes (0–25 nm). Frequency shift as a function of the analyte thickness, with average sensitivity for the hollow tripod structure with arms (**c**), and the hollow tripod structure with arms, and rotated (**d**). the dimensions compatible with standard photolithography techniques). To make a fair comparison of all designs, we used a new sensitivity definition that also takes into account the operational frequency, and the analyte refractive index (otherwise, we would be benefiting those works in which the refractive index is higher) as \(S=(\Delta f\!/\!f_{0})/(h_{a}\!\cdot\!n_{a})\), with \(h_{a}\) the analyte thickness, and \(n_{a}\) the analyte refractive index. This comparison can be seen in Table 2. As shown, this work could be on the top of the ranking, achieving a high sensitivity for thicknesses of analyte several orders lower than other designs. A possible explanation for this better performance could be that the hollow tripod has a field distribution with more distributed hot spots along the unit cell and not concentrated at small areas, similar to what happened in our previous paper [4].