esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pressure Sensor Concept and Design
### Membrane Sensor (MS)
#### 3.2.3 Effect of Interstitial Pore Pressure: Stress-Strain Analysis and Geometry Validation  
The membrane sensor was designed to sustain 400 MPa of pressure, as mentioned above. Elastic calculations were performed using Abaqus software to know the maximum stress reached in each sensor. A passage hole of 1.5 mm for wires connection and a groove to receive the joint of 3 \(\times\) 3 mm in depth and height were created to meet the real design (Figure 6). Sensors mechanical properties were selected by considering a steel material, a Young's modulus \(E\) equal to 210 GPa and a Poisson ratio \(\
u\) equal to 0.3.  
Table 2 presents the maximum stresses and strains obtained from the numerical simulations performed on the three sensors. Figure 7a,b presents an example of the stress and strain distribution on the s2 sensor back face (see Table 1 for the geometry) under 400 MPa of pressure. The strain was  
Figure 5: Loading and boundary condition used to simulate the behavior of the pressure sensor: (**a**,**b**) sensor with all nodes or half nodes fixed; (**c**) water collecting cap inner surface displacement due to the confinement pressure; and (**d**) mechanical effect on the sensor transmitted from the water collecting cap lateral surface due to confining pressure.  
Figure 6: Sensor modeling with Abaqus FEA: applied load with boundary condition.  
averaged on the center of the sensor back face where the gauge was glued. The results show that the thicker the sensor was, the lower the stress was. Moreover, the sensor strain tended to increase when the thickness decreased. Regarding the boundary conditions effect, the stress and strain values increased when the number of constrained nodes decreased.  
Finally, the sensor geometry was selected carefully to meet two major aspects. The first one was based on the sensor's ability to deflect properly while remaining in the elastic domain. Calculations showed that \(s_{1}\) has a very high Misses stress, while \(s_{2}\) and \(s_{3}\) sensors display a maximum stress that does not exceed 1300 MPa. The second aspect is the sensor sensitivity with respect to its thickness. The \(s_{1}\) sensor was eliminated since it could not resist the desired load despite its high sensitivity. On the contrary, the \(s_{3}\) sensor displayed a high resistance capacity but had a low sensitivity, in particular for low and intermediate pressure. Thus, the \(s_{2}\) sensor was selected for the rest of this study as it responded the most to the technical requirements (Figure 7c). A steel called _TOOLLOX44_ having a high elastic resistance \(\sigma_{e}\) equal to 1400 MPa was hence chosen for the material fabrication.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CH\({}_{4}\) Combustion
#### 3.2.5 Effect of Gas Concentration on NO Production  
We investigated the influence of NH\({}_{3}\)/CH\({}_{4}\) doped gas concentration changes on the amount of NO production under different excess air coefficients and combustion temperatures. The experimental results are shown in Figure 7. The proportion of ammonia in the mixed combustion gas used was 50%; the excess air coefficients were set at 0.9, 1.0, and 1.1; and the combustion temperature was set at 1500 \({}^{\circ}\)C. When \(\alpha\geq 1\), the amount of NO produced by combustion increased when the fuel gas concentration was doubled. When the excess air coefficient was 1.0, the increase rate was small, and when the excess air coefficient reached 1.1, the amount of NO emissions increased significantly. After the concentration of gas was doubled, the free radicals such as HNO and OH produced by the reaction increased, so that the amount of NO produced also increased. However, when \(\alpha<1\) and the fuel concentration increased, NO production changed from a minimal amount to zero. This may be the reason that, when the excess air coefficient \(\alpha<1\), the methane in the fuel gas may undergo incomplete combustion to produce CO, and the reduction reaction with NO occurs. On the other hand, the concentration of O/H free radicals decreases during the combustion of ammonia under the condition of rich combustion, and the proportion of H in O/H free radicals increases, resulting in a large number of H free radicals. This promotes the binding of NH\({}_{x}\) (\(x=1\), 2, 3) free radicals to H and its oxidation (Reactions 12-14). NO production is too low, hence the change is not obvious and difficult to observe.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 5 Performance Evaluation and Discussion
### Scenario 1: Impact of the Clutter Parameter  
* Training manner 1: Different clutter parameters for training and test  
We considered the clutter parameter of [60%, 6 m, 2 m] and the clutter parameter of {40%, 2 m, 2 m}. Two training data sets are generated. One is the data set purely generated based on the clutter parameter of {60%, 6 m, 2 m}, and another one is the data set purely generated based on the clutter parameter of {40%, 2 m, 2 m}. Two AI models are trained based on these two data sets, respectively. For the test, data sets of different clutter parameters are used.  
Figures 10 and 11 show the evaluation results for the direct AI-based positioning and the AI-based ToA prediction. If the same clutter parameter is set for the training data set and the test data set, optimal positioning accuracy could be achieved for both direct AI-based positioning and AI-based ToA prediction as shown in scenario 0. While, once the clutter parameter for the test data set is different from that of the training data set, the inference performance degrades sharply. In the direct AI-based positioning, take the AI model trained by clutter parameter of {60%, 6 m, 2 m} data set as an example. The positioning error @90% is less than 0.5 m when using clutter parameter {60%, 6 m, 2 m} test data set, while the positioning error @90% is up to 7 m when using clutter parameter {40%,  
Figure 8: Performance of direct AI/ML-based positioning with the same clutter parameters between training and test.  
Figure 9: Performance of AI/ML-based ToA prediction with the same clutter parameter between training and test.  
* Training manner 3: Fine-tuning  
In this case, the model is firstly trained by data set with a clutter parameter of {60%, 6 m, 2 m} or a clutter parameter of {40%, 2 m, 2 m} and then fine-tuned by a data set with the same clutter meter with that of the test data set. The data set for fine-tuning only contains a small number of samples.  
Figures 14 and 15 summarize the results for fine-tuning. Compared with results without fine-tuning in Figures 10 and 11, the positioning accuracy is improved reasonably. For example, for the AI model trained by clutter parameter {60%, 6 m, 2 m} and fine-tuned by data set with clutter parameter {40%, 2 m, 2 m}, when applying this model in the scenario with clutter parameter of {40%, 2 m, 2 m}, the positioning error is reduced from to around 7 m to around 1.5 m in both direct AI-based positioning method and AI-based ToA prediction method. However, compared with the results of the AI model trained with mixed data set in Figures 12 and 13, there is still some gap. The possible reason is fewer samples from the applied scenario for fine-tuning.  
Figure 12: Performance of direct AI/ML-based positioning with mixed clutter parameters for training.  
Figure 13: Performance of AI/ML-based ToA prediction with mixed clutter parameters for training.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 5 Conclusions  
With the development of 3D laser scanner, point clouds have become the primary data format to represent the 3D world, and point clouds registration technology is an important step. In order to overcome the problems of slow calculation speed and low registration accuracy in the process of point clouds registration, this paper proposes a point clouds registration algorithm based on improved voxel filter combining ISS-USC features. First of all, the source point clouds and target point clouds are sampled by the improved voxel filter, respectively, to simplify the point clouds. Secondly, the ISS feature points detection algorithm is used to extract the feature points, and the USC descriptor is used to describe the feature points. Next, coarse registration is carried out by RANSAC algorithm, and the incorrect corresponding point pairs are eliminated to obtain a good initial registration position. Finally, ICP algorithm based on KD tree is used for fine registration. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the proposed algorithm has a fast registration speed. The registration time is reduced by 50%, and the registration accuracy is higher. Considering that there are still threshold adaptation problems of some parameters in the proposed algorithm and the construction of covariance matrix that need to be further optimized, the proposed algorithm will continue to be improved in future scientific research work.  
Figure 18: Registration results of the proposed algorithm with two scenes. (**a**) Scene1 registration result. (**b**) Scene1 registration result.  
**Author Contributions:** Conceptualization, Y.D., A.W. and J.M.; software, Y.D. and A.W.; validation, A.W. and X.Z.; formal analysis, Y.D. and J.M.; investigation, A.W. and J.M.; data curation, J.M. and X.Z.; writing--original draft preparation, Y.D.; writing--review and editing, J.M. and Y.D.; supervision, A.W.; project administration, J.M.; funding acquisition, J.M. and A.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is financially supported in part by Natural Science Research Program of Jiangsu Colleges and Universities under Grant No. 20 KJA470002, Excellent Teaching Team of \"Qinglan Project\" of Jiangsu Colleges and Universities, and Science and Technology Research Program of Nantong under Grant No. JC2020094 and MS22020022.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud
#### 5.3.3 LSTM-Based SDN Controller for Load Prediction and Balancing  
With the rise of new delay-sensitive and resource-hungry applications and services, optimizing resource allocation and load balancing in the edge cloud environment has become crucial. SDNs play a key role in enabling load balancing between computing nodes as well as distributed SDN controllers. [PERSON] et al. proposed a pre-emptive load balancing approach between controllers for delay-sensitive applications within a distributed SDN-enabled edge cloud architecture [50]. In this proactive mechanism, they use a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model, which is capable of remembering information from prior stages to make predictions with high accuracy. The load on an SDN controller at time \(t\), defined as \(L(t)\), is a sum of requests received from the data plane components, neighboring SDN controllers, and other network entities communicating with this controller. To predict and balance the load across distributed SDN controllers, a root controller is considered. This root controller is associated with other controllers but does not directly manage any data plane components, as shown in Figure 10.  
To optimize the load balancing and cost, this model is formulated as follows [50]:  
\[\begin{split}\underset{X,M}{minimize}\omega\times LB+(1-\omega) \times T_{m}\end{split} \tag{3}\]  
Figure 10: Distributed SDN edge cloud network architecture [50].  
Figure 9: Architecture of SDN-enabled resource allocation framework for edge cloud networks [53].  
In the above Equation (3), weight factor \(\omega\ \in[0,\ 1]\), \(LB\) is the load balancing factor, and \(T_{m}\) is the total migration cost. The algorithm optimizes load balancing in the control plane and minimizes latency.  
This is still an active research area and many other research and industry communities are also working on it to find an efficient solution. For example, [PERSON] et al. [65] proposed a dynamic load balancing algorithm for home edge computing using SDNs. This work addresses the problems of controller overload using clustering and load balancing. The authors in [67] proposed a model for resource optimization and load balancing that takes into account user preferences, SLA, and cost.
## 3 Methods
### Relationships between Plant Growth Indicators and NDVI  
Linear relationships between NDVI, ground coverage (GC) and plant height were established for each crop type separately using least-squares. Each observation for GC and plant height was a mean per plot. The NDVI was determined as the mean of all pixels within a plot. The 26 August 2014 image was selected as it corresponds to the period of maximum green biomass and for this date, _in-situ_ data were also available. The image of 10 October 2014 was probably a better choice, as ground coverage was larger in that period for cotton, sorghum and millet but field data were not collected for these crops at this image acquisition date.
## 1 Introduction
We propose a loss function based on Lab space and form a fused loss function with the loss function in RGB space, which more comprehensively considers image color recovery. 4. Numerous pieces of experimental evidence show that the proposed CCD-Net achieves a better dehazing effect, enhances the color quality of images, and consumes less computational resources compared to other competing methods. The remaining sections of this paper are structured as follows: Section 2 reviews related work, covering both prior-based and deep learning-based dehazing methods. Section 3 introduces the proposed CCD-Net, detailing its overall framework, dehazing branch, and color-correction branch, along with the design of the loss function. Section 4 describes the experimental setup, and presents a comparison with state-of-the-art dehazing methods and an ablation study to evaluate the effectiveness of different components in our approach. Finally, Section 5 provides the conclusion of the paper.
## 4 Results
### Verification of Results  
#### 4.3.1 Multi-Period Monitoring and Verification  
To verify the reliability and accuracy of the conclusions obtained by the research method and to analyze whether the selected experimental days can represent the typical situation of the scenic area on weekends, this study conducted four monitoring sessions at FangQiao rest area over a span of 43 days (from 2 November to 14 December 2024). The four selected days had similar meteorological conditions (moderate temperature and clear  
Figure 9: Main routes to the three locations.  
weather), and all data were collected during peak visitor hours (afternoon), with each day's monitoring lasting for 2 h to ensure spatiotemporal comparability of the observations.  
As shown in Figure 10, the seat occupancy rates of service facilities obtained from the four independent monitoring sessions exhibited a stable distribution, with values of 62.50% (Day 1), 65.83% (Day 2), 71.67% (Day 3), and 63.33% (Day 4). The corresponding proportions of elderly visitors during these periods were 8.67%, 2.89%, 1.08%, and 8.53%, respectively. Statistical analysis revealed that the average seat occupancy rate over the four days was 65.83% \(\pm\) 3.82% (95% confidence interval), with a coefficient of variation (CV) of only 5.7%. The average proportion of elderly visitors was 5.29% \(\pm\) 3.72%, and no significant differences were observed among the data from the experimental days, indicating stable observation results.  
According to the evaluation model for the adequacy of aging-friendly service facilities established in this study, FangQiao rest area, under average conditions, exhibited a seat occupancy rate within the 30-70% range (65.83%), with non-elderly visitors accounting for over 90% (94.71%) of the occupancy. This categorized the rest area as having a low adequacy level in terms of aging-friendly service facilities, which aligned with the results from the selected experimental day (seat occupancy rate: 62.50%; non-elderly proportion at full occupancy: 91.33%). This demonstrates that the selected experimental days effectively represented the general operational state of the scenic area on weekends, exhibiting typicality and universality, and the research findings are reliable.
## 2 Non-Linear Finite Element Analysis  
An explosion event typically happens in a very short duration of less than 1 s which indicates that the loading is time-dependent and requires a dynamic approach in the structural analysis. To solve the dynamic problem, a few techniques for structural dynamic analysis for blast resistant design have been developed; namely, the conventional equivalent static load or an elastic approach, a single degree of freedom (SDOF), and non-linear FEA. Offshore steel structures or petrochemical facility structures are generally constructed as a structural module with complex configurations and highly occupied by equipment and pipelines. This complexity leads to the classification of the structure as a multiple degree of freedom (MDOF) system and requires the non-linear MDOF analysis to provide the most comprehensive approach in the structural response computation.  
The basic concepts and problem-solving techniques for MDOF systems can be found in many references [16; 17; 18; 19]. A simple structure consisting of a MDOF system with the general dynamic equation of motion given in Equation (1).  
\[M\ddot{u}(t)+C\dot{u}(t)+F_{int}(t)=F_{ext}(t) \tag{1}\]  
\(M\) is the mass matrix, \(C\) is the damping matrix, \(u\), \(\dot{u}\), \(\ddot{u}\) are the displacement, velocity, and acceleration vectors, respectively, \(F_{int}\) is the internal forces vector, and \(F_{ext}\) is the external forces vector. For dynamic events like the explosion case with a very high loading rate within short duration, the calculation requires small time increments to obtain a high-resolution for accurate solutions. Additionally, this technique is stable for small-time increment steps and requires relatively small computational cost per increment [20]. Therefore, the explicit method is efficient and suitable to be used in transient dynamic cases such as blasts, explosions, and impacts [21].
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 2D Electrochemical Process Operational Conditions
#### 3.1.3 Effect of Electrolyte Concentration on CBZ Removal  
The influence of the electrolyte (NaCl) concentration (0.01 M and 0.1 M) on the CBZ removal was studied. The results presented in Figure 3 showed that the higher NaCl concentration (0.1 M) provided a faster removal of CBZ. This was due to the increase in conductivity of the solution. A low electrolyte concentration results in a poor conductivity of the solution and the inability to produce sufficiently strong oxidizing substances [45]. Besides this, if NaCl is used as electrolyte, Ti-based anodes generate chlorine species (Cl\({}_{2}\), HOCl, and ClO\({}^{-}\)), which help the degradation of organic material by indirect and direct oxidation [34]. Therefore, 0.1 M was selected as the optimum electrolyte concentration.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Integration of \(\Delta SM_{SMAP}\) with Seismic Records
#### 3.3.4 Accumoli, Italy  
The Accumoli earthquake with a moment magnitude recorded as 6.0 occurred on 24 August 2016 in the central part of Italy. As depicted earlier in Figure 5, the central part of Italy did not experience any rainfall event in the SMAP event window. However, grids with \(\Delta SM_{SMAP}>0.05\)cm\({}^{3}\)/cm\({}^{3}\), shown in Figure 13a, indicate a considerable effect from the seismic event on the soil moisture content of this region. Some of these grids were located within the MMI contour of 6 and inspected by reconnaissance teams [86]. Some other grids with \(\Delta SM_{SMAP}>0.05\)cm\({}^{3}\)/cm\({}^{3}\) were far from the epicenter of the earthquake and located outside of the visually inspected zones, but Figure 14 shows that these regions experienced ground failures as well.  
Figure 13: \(\Delta SM_{\text{SMAP}}\) (9 km \(\times\) 9 km) in grids with (**a**) available soil moisture data, (**b**) \(MDI>0.04\), and (**c**) \(MDI>0.02\) cm\({}^{3}\)/cm\({}^{3}\) integrated with seismic records for Accumoli, Italy 2016 earthquake.
## 5 Simulation Results and Discussions
### System Efficiency  
We define the system efficiency as \(\varepsilon\)= \(\mathcal{R}/\mathcal{R}_{\text{\tiny opt}}\), where \(\mathcal{R}_{\text{\tiny opt}}\) represents the exhaustive optimal sum data rate. Figure 5 gives the system efficiency with different numbers of NACK-UEs and different numbers of ACK-UEs. From the simulation result, we can see the lowest value of \(\varepsilon\) is around 0.94 and the efficiency is stable above 0.94 over different parameters of NACK-UEs and ACK-UEs. It indicates that the proposed algorithm provides high system efficiency, and its performance is close to theoretical optimum. Moreover, the efficiency decreases slightly as the number of NACK-UEs increases. This is due to the limited number of adjustment for bidders such that the combinatorial auction is restricted to only achieving an approximate global optimization.
###### Abstract  
Purpose: To investigate the relationship between the apolipoprotein E (APOE) \(\varepsilon\)4 allele and retinal structural and vascular characteristics in older adult participants from several research studies. We also studied the relationship between these structural and vascular characteristics with multifocal visual evoked potential (mfVEP) indices, neuropsychological parameters and MRI brain volumes in these participants. Methods: In this study, 109 participants with a mean (SD) age of 67.1 (9.0) years were recruited. Participants were classified as APOE \(\varepsilon\)4 carriers or non-carriers based on the presence or absence of the \(\varepsilon\)4 allele. Baseline measurements included peripapillary retinal nerve fibre layer optical coherence tomography (RNFL OCT), and OCT-angiography (OCT-A) for evaluation of the retinal layer thickness and vessel density (VD) parameters. A multifocal visual evoked potential (mfVEP) test, including amplitude and latency, was used to assess the visual pathway function. Finally, cognitive function was evaluated using a battery of neuropsychological tests. OCT-A images were analysed in ImageJ to quantify VD in the superficial and deep vascular plexus and the size of the foveal avascular zone (FAZ). The relationship between carriers of APOE \(\varepsilon\)4 allele and these ocular parameters was analysed using generalised estimating equation (GEE) models and data adjusted for age, sex and inter-eye differences as within-subject variables (\(p<0.05\)). Results: Twenty-four participants were APOE \(\varepsilon\)4 carriers. Temporal RNFL thickness was decreased in APOE \(\varepsilon\)4 carriers (\(p<0.01\)). Vessel density between carriers and non-carriers was not significantly different at either the superficial or deep level. The FAZ area was significantly smaller in \(\varepsilon\)4 carriers in both superficial (\(p<0.01\)) and deep layers (\(p<0.003\)). Conclusions: Retinal abnormalities were present in participants with increased genetic risk of dementia due to presence of the \(\varepsilon\)4 allele. These findings provide preliminary evidence for their potential role in the diagnosis of dementia.  
APOE; OCT; dementia; MRI; retina 2023