esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, [PERSON] and [PERSON] [13] established one simplified 2D theoretical model and finite element model to predict the metallic surface roughness resulting from the water jet peening. The velocity distribution and pressure distribution in the water jet area can be roughly obtained. However, the simplified 2D model has limitation in predicting the actual surface roughness on a solid metallic surface. Recently, [PERSON] and [PERSON] [14; 15] adopted a numerical modelling based on Arbitrary Lagrangian-Eulerian (ALE) formulation to analyze the interactional physics between a moving water jet and a coated target. The erosion phenomenon can be visually observed, which helps to understand the material removal mode. However, the ALE formulation is relatively expensive. In order to reduce the computational time, [PERSON] et al. (2008) [16] presented a numerical method by means of the hybrid code of Smooth Particle hydrodynamics (SPH) and FEA to predict the water jet penetration process. The relationship between the jet velocity and the erosion capacity is obtained based on the simulation results. It should be noted more corresponding water jet experiments should be added to validate the numerical simulation results. In the last few decades, the climbing robotic system, which can adhesive to the metal surface, were widely studied to realize the automation and high efficiency. The commonly used adhesion methodsfor climbing robots in the literature include vacuum adhesion [17], bio-inspired adhesion [18], gripping to the surface method [19], magnetic adhesion [20; 21], and so on. Each method has its advantages and disadvantages, and detailed analysis can be found in the literature [22]. Generally speaking, the magnetic adhesion method is more suitable for the climbing robot carrying coating removal instruments in the ship-building industry [23] due to its reliable adhesion, low cost, and low energy. Recently, combining the high pressure water jet technology and mechanical automation, one permanent magnetic tracked wall-climbing robot has been developed to realize the coating removal for big ships. In this paper, the influence of some processing parameters, such as water pressure, stand-off distance and travelling speed, on the actual coating removal effect is systematically investigated through experiments and numerical simulation. The rest of the paper is organized as follows. The structure of the wall-climbing robot is shortly introduced in Section 2. The experimental results and the numerical simulation results are presented in Section 3. The analysis in detail is discussed in Section 4. Finally, Section 5 contains the conclusions.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 1 Introduction
To systematically investigate the vibration propagation mechanisms of downhole impact tools and analyze the fatigue damage evolution under sustained impact loading, this study employs [PERSON]'s linear cumulative damage theory, integrating drill string vibration governing equations with material S-N curves. First, the dynamic response of the BHA under a periodic impact is derived using the FEM, enabling a quantitative analysis of transient von Mises stress distribution and impact vibration characteristics of the BHA. Concurrently, load uncertainty is incorporated and fatigue analysis modules are utilized to analyze the damage accumulation distribution in the BHA under varying loading conditions (amplitude and frequency), predicting the serviceable cycle life of the drilling tools. This provides theoretical references for optimizing impact parameters, extending the drilling tool service life, and enhancing the drilling speed in field applications.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Write Delay Comparison  
Write delay, or write access time (WAT), is measured as the time interval from when the word line reaches 50% of its full voltage swing to the point where the storage nodes Q and QB intersect [6]. WAT can be calculated as shown in Figure 7a. The WAT values for various comparison cells at different VDD voltages are presented in Figure 7b. Generally, affecting both the storage node and the internal node simultaneously during a write operation tends to reduce WAT [6]. Cells such as WEQUATRO, RHPD12T, RHBD14T, and RHHS16T, which flip the values both the storage node and the internal node through access transistors connected to each bit line, show shorter WATs compared to cells like RHD12T, RSP14T, and RHBD10T, which have only a pair of access transistors. In the cases of RHHS16T, RHPD14T, and RHBD14T, the NMOS pull-up transistor with a weak '1' characteristic is used for one of the nodes between the storage node and the internal node. Hence, it becomes easier for the access transistor to discharge the weak '1' at the '1'-storing node, resulting in shorter WATs for these cells than WEQUATRO and QUCCE12T. Furthermore, the proposed RHHS16T has the lowest pull-up ratio (=0.5/0.6) for the two access transistors. A lower pull-up ratio can make the strength of the access transistor discharge the '1'-storing node to the bit line pre-discharged to '0' stronger than the strength of the corresponding pull-up transistor to pull the '1'-storing node to VDD. Thus, appropriate pull-up ratio adjustment results in a shorter WAT. Consequently, as shown in Figure 7, the proposed RHHS16T exhibits the shortest WAT among all comparison cells.
## 5 Performance Evaluation
### Computation Overhead
's scheme [23], ours is lower slightly than the scheme in [23], and both of them are close to \(O(k)\cdot C_{prf}\). (2). Temporal aggregation  
In the proposed scheme, each node chooses the same nodes every billing period to satisfy with the Equation (5), so total temporal computation overhead in \(T\) serial time slots for every node is \(T\cdot(k\cdot C_{prf}+k\cdot C_{add}+C_{ma})+T\cdot C_{add}\). In [PERSON] et al. 's scheme [23], each node sends \(T\) fine-grained utility readings in each of the \(T\) time steps, so the overhead per node is \(T\cdot(C_{hash}+k\cdot C_{prf}+2k\cdot C_{add}+C_{enc})+T\cdot C_{mul}\). In fact, the temporal aggregation overhead of the scheme in Reference [23] is higher than it, as with the modification of Paillier encryption, spatial and temporal aggregations are not being synchronized. To compensate the lack, every user must add an additional random key \(R_{(i,T+1)}=\frac{r^{n}}{\prod_{d=1}^{T}n_{d}^{k}(i,d)}\) at \(T^{\text{th}}\) timestamp, which costs much overhead. However, our scheme has no extra cost and the third party's involvement. We set the fine-grained reporting interval to be 15 minutes, and billing period \(T=2880\) (roughly one month). Figure 5 plots the comparison of two schemes in terms of temporal computation overhead in every billing period for \(k\) ranging from 0 to 50. From Figure 5, we can see the temporal computation overhead per node grows with the increasing of \(k\) value in two schemes; however, our proposed scheme increases slightly compared with the scheme in References [23], as the latter costs much overhead on Paillier encryption, while our scheme achieves the same privacy protection effect as the asymmetric encryption with simple and low-cost modular addition. Figure 4: Variety of spatial computation overhead per node with the value k.
## 3 Results and Discussion
### Analysis of the Polymer Preparation
#### 3.1.2 The Effect of the Initiator  
Figure 7 shows the rise in temperature during the polymerization of different initiators.  
As shown in Figure 7, the reaction activities of K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) + NaHSO\({}_{4}\) and (NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{8}\) + NaHSO\({}_{4}\) were lower and the initiation temperatures higher, so it is not easy to increase the molecular weight of the polymer. However, the reaction activity of DTBT + NaHSO\({}_{4}\) was too high and a large number of free radicals were produced in a short time, resulting in too fast a polymerization rate and too low a molecular weight for the polymer. The insufficient reaction phenomenon was observed when the amount of the initiating system was reduced. Therefore, by selecting the K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) + DTBP + NaHSO\({}_{4}\) composite initiation system, the problem of the poor solubility of the AlBN initiator was solved, the problems of rapid heating in the reaction process of DTBT + NaHSO\({}_{4}\) and the excessive initiation temperature of K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) + NaHSO\({}_{4}\) were avoided and the molecular weight of the polymer was increased.  
According to the experimental results, the K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) + DTBP + NaHSO\({}_{4}\) system had the smallest temperature rise as the initiator.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### PFCs Removal Efficiency Through Pilot Test
#### 3.4.3 Removal Change by Chlorine Injection  
As shown in Figure 5, the highest amounts of PFOA were removed, at 9.7% with 20 mg/L of the injection, which was found to be negligible. The PFhxA removal was 3.33%, which means little to no removal, and the PFNA removal was 4.6% and its concentration somewhat increased with 10 mg/L of the injection and then again decreased. The other compounds, PFPeA and PFhxS, showed a removal of 5% or lower, which was negligible. Previous research result showed little to no effect of the chlorine injection on the PFCs removal regardless of the oxidant dose [9], which was similar to the results of this study.  
Since PFCs are stable and non-degradable, the use of a single chlorine disinfection process would not effectively remove them.  
Figure 4: Variations of PFCs concentration by O\({}_{3}\) dosages.  
Figure 5: Variations in PFCs concentration with different chlorine dosages.
## 3 Instantaneous Multi-Constellation Relative Positioning: Methodology and Performance
### Performance Assessment of Precise Positioning with Focus on E14 and E18 Galileo Satellites
#### 3.2.1 Data Selection & Experiment Design
The empirical ambiguity resolution success rate (ASR) was used for the evaluation of the ambiguity resolution process. This indicator was computed as a ratio of epochs with correctly fixed ambiguities with respect to the number of processed epochs. The quality of the fixed solution in the coordinate domain was based on the standard deviation (_std_) of coordinate residuals with respect to the target (reference) position. In the residual domain we characterised descriptive measures such as standard deviation. This description is also presented in residual histograms.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 6 Conclusions  
In this paper, a method for optimizing the structure of a reluctance coil gun has been proposed. Kicking real soccer balls used at the RoboCup in the Middle Size League has been chosen as a case study. After having presented the principles of coil guns, a mechatronic model coupling mechanic, electromagnetic and electric ones has been proposed and implemented. Simulation results have been explained and discussed so that this optimization method can be easily reproduced in another application.  
Results show that the output speed of the non-magnetic object propelled by the EML highly depends on the structure of the coil gun, the sequence for triggering it, the initial position of the iron plunger and the size of its non-magnetic extension. Among the results of this paper, we show that  
* Using a two-coil EML is 104% energetically more efficient than the reference situation of an existing coil gun [11], without adding too much mechanical, electrical and algorithmic complexity to the EML. As shown in Table 5, it is also ten times more efficient than a human considering the ball energy to launcher volume ratio.
* Having a high number of coils is not necessary for optimizing the energy transfer. In our case, having two coils in the EML is an excellent trade-off between energy transfer optimization and system complexity.
* Robustness in terms of sensitivity to the coil triggering instants decreases with the number of coils.  
Figure 31: Four-channel reluctance coil gun driver (on the right).  
**Author Contributions:** Writing--original draft preparation, V.G.; writing--review and editing, T.S., S.M., H.G. and V.H.; visualization, V.B. and H.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by [the Conseil Departemental du Var, France].  
**Acknowledgments:** The authors would like to thank Pole INPS of Toulon University and the Embedded Electronics Technology Platform of Toulon University named SMIoT (Scientific Microsystems for Internet of Things) [17] for its support in the achievement of this work, and the Conseil Departemental du Var, France, for funding this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Preliminaries
### Field Representation and Notations  
We assume that the target platform has a \(w\)-bit architecture. Let \(n=\lceil\log_{2}p\rceil\) be the bit-length of a Mersenne-like prime \(p=2^{n}-c\), where \(c\) is small. Let \(m=\lceil n/w\rceil\) be its word-length. Then, an arbitrary element \(a\in\mathbb{F}_{p}\) is represented by an array \((a_{m-1},\cdots,a_{2},a_{1},a_{0})\) of \(m\)\(w\)-bit words. The notations \(M_{1},S_{1},I_{1}\), and \(A_{1}\) represent multiplication, squaring, inversion, and addition (subtraction) over \(\mathbb{F}_{p}\), respectively. Similarly, the notations \(M_{2},S_{2},I_{2}\), and \(A_{2}\) represent multiplication, squaring, inversion, and addition (subtraction) over \(\mathbb{F}_{p^{2}}\), respectively. The notation \(A_{i}\) represents multi-precision addition without modular reduction and the notation \(M_{d}\) represents multiplication with a curve parameter.
## 4 Results
### Evaluation Index and Parameter Setting  
#### 4.1.1 Evaluation Index  
In this study, we have chosen precision (_Pr_), _F1_, and _IOU_ as evaluation metrics to assess the model's performance objectively. Higher values of these metrics indicate better image quality. The calculation formulas for these indicators are presented in Equations (4)-(7):  
\[\textit{Pr}=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{4}\]  
The precision calculation process is described in Formula (4), with _TP_ (True Positive) representing correctly predicted positive examples, _FP_ (False Positive) representing incorrectly predicted positive examples, and _Pr_ representing the proportion of true positive examples in the sample. Precision reflects the accuracy of the model's positive predictions, indicating how well it can differentiate between positive and negative examples. However, precision does not account for missed positive examples, which are captured by the recall rate formula. The recall rate formula is as follows:  
\[\textit{Re}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{5}\]  
where _FN_ (False Negative) represents the number of samples that the model incorrectly predicts as negative examples, the recall rate indicates the model's ability to identify positive examples accurately, showing how many true positive examples the model can detect. A higher recall rate signifies that the model correctly identifies a greater number of positive examples.  
\[F1=2\times\frac{\textit{Pr}\times\textit{Re}}{\textit{Pr}+\textit{Re}} \tag{6}\]  
The _F1_ score in Formula (6) is calculated as the harmonic mean of precision and recall, providing a balanced measure between the two. It ranges from 0 to 1, with 1 representing optimal performance. By combining precision and recall, the _F1_ score offers a more comprehensive evaluation of the model's performance compared to using either metric in isolation.  
\[IOU=\frac{\textit{TP}+\textit{TN}}{\textit{TP}+\textit{FP}+\textit{TN}+\textit {FN}} \tag{7}\]  
_IOU_ (Intersection over Union) is a metric utilized for assessing localization accuracy in object detection and image segmentation tasks. In Equation (7), TN denotes the prediction of the negative class as true negative. It quantifies the ratio of the intersection area to the union area of the predicted box and the true box. The _IOU_ value ranges from 0 to 1, where 1 indicates a perfect match between the predicted and true boxes. A prediction box with an _IOU_ exceeding 0.5 is commonly deemed a valid detection outcome, indicating a substantial spatial overlap between the prediction and ground truth.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Coding  
In general, to design a communication system is to split the system into several independent concatenated modules. These modules will realize the functionalities such as source/channel coding/decoding, modulation/demodulation, pre- and postequalization. The optimization of each module is carried out independently, either based on data-driven statistics features or based on mathematical models. However, the optimization of a single module cannot guarantee the overall optimization of the end-to-end communication of the entire physical layer. An intriguing approach is the end-to-end joint optimization for the physical layer [77]. The methodology is to treat physical-layer communication as an end-to-end signal-reconstruction problem, and to apply the concept of autoencoder to represent the physical-layer communication modules (the transmitter, the channel, and the receiver) by one deep neural network. Autoencoder is an unsupervised deep learning algorithm. The goal of the autoencoder is to find an optimized representation at its intermediate layer. This intermediate representation is robust to channel perturbations, allowing the output to be reconstructed with minimal error.  
The end-to-end learning method extended its applications in MIMO and OFDM cases [78; 79; 80] with satisfying results. The autoencoder establishes a unified physical-layer framework that can be used in complex communications scenarios, and can obtain a lower bit error rate than the classic method through learning with lower computational complexity. Most of these autoencoder approaches are optimized on the symbol-wise categorical cross entropy [80]. However, the communication system is defined by the bit error rate. Novel approaches developed in [80; 81] can optimize the bitwise mutual information between the input and output. Apart from that, VLC systems should consider more instability of complicated nonlinear distortions when searching for the optimal coding scheme. Considering recent works of VLC-based autoencoders, substantial efforts are still demanded to develop practical solutions in the wireless optical system.  
Figure 6: Overview of machine learning algorithms in the intelligent physical layer; Optimal coding: AutoEncoder [82; 83; 84; 85; 86]; Channel emulator: TTHnet [44], FFDNet [45]; MIMO: ANN [75], ICA [72], MIMO-MBNN [73], AANN [76]; Channel Equalization: ANN [50], GK-DNN [52; 54], LSTM [61], DBMLP [44], FSDNN [55], TFDNet [56], MPANN [63], PCVNN [60]; Optimal decision: SVM [68], K-means [64], ANN [70], GMM [67], DBSCAN [66].  
Figure 7: Channel equalization in VLC systems.
## 2 A Motivating Example  
Let us consider a multinational insurance company which is expanded over a number of countries across the globe. The company is associated, directly or indirectly, with many stakeholders, including its own employees, country's road and transport department, traffic polices, empanelled hospitals, common people, and many others. To facilitate operations like insurance record verification, insurance claims, etc., researchers have already proposed a number of blockchain-based solutions [14; 15; 16]. This is to observe that, in all these cases, the proposed systems are somehow dependent on either local database or third-party cloud storage to store insurance data. This is due to the fact that maintenance of a single blockchain and populating it with large volume of insurance data which is generated everyday across the globe may eventually slowdown the performance of the whole system, as its size grows.  
Interestingly, there are many situations when exact details of the data may not be required to answer queries issued by the stakeholders. For example, a traffic police needs to verify whether a car owner has valid insurance, an insurance company needs to verify whether the person already has Medicialim policy with any other company, etc. A common objective in all such cases is to obtain a decisive answer in the form of \"yes\" or \"no\" and the presence of insurance data in an abstract form (representing specific properties of interest) in the blockchain fulfills such functional requirements. This leads to a significant reduction of the overall size of the blockchain in the system, improving its performance thereof.  
Inspired from these facts, in the subsequent sections, we propose a hierarchical blockchain model where multiple blockchains at different levels representing data in various forms, from concrete (lowest level of the hierarchy) to abstract (towards higher levels), are considered. Such model may act as a powerful solution to deal with scalability, access control, data sharing, etc.
## 4 Meaningful Learning Analysis
### Methodology  
The methodology used in the recent VRLEs described in this work (e.g., Figure 2) is divided as follows: (i) assume a theoretical class --the process will vary in time depending on the real machine desired to simulate; (ii) estimate the time for individual use of the VRLE, which may take approximately 10-15 minutes (besides, students can reuse the VRLE in their free time); (iii) determine the resolution of the virtual exercises for small groups (2-3 students) including the VRLE [4; 9; 10] or traditional classroom exercises (using paper); and (iv) collect individual fulfillment of an specific survey one year later, which contains technical aspects of the different VRLEs used the previous year (e.g., the students who handled the VRLE in 2014 completed the technical survey in 2015, which allowed to know the level of knowledge of MSE machines that they still remembered a year later). For the reader to get an idea of the type of questions that have been raised in the survey, some examples are shown in Table 2. Three questions of a total of 30 have been selected; through these questions the students are asked about concepts of MSE simulated through a virtual environment (e.g., tensile testing [4], compression testing [6; 40], industrial radiology [9], ultrasonic testing [10], crystal lattices [11], ternary phase diagrams [14; 39], and hardness testing [41]). Although Table 2 does not include all the questions, it should be noted that the survey questions were the same during all the years considered in the present study, hence ensuring that the results from different years are comparable.  
For the sake of clarity, a scheme of the methodology followed in this study is presented in Figure 5. The implementation and evaluation of the VRLEs were carried out during the academic courses between 2015 and 2019. Students of MSE subjects of the degree in mechanical engineering taught at the Catholic University of Avila (Spain) participated in this study. Each year approximately 20 students participated. During the first four years (2015-2018) the study was based on the VRLEs created with the design process shown in Figure 1, which were used by students the previous years (i.e., 2014-2017) to fulfil the student survey requirement. However, more recently, in 2019, the study was based on the new design process (step-by-step protocol), as illustrated in Figures 2 and 4. In this case, the students used the updated VRLEs from 2018, which were designed as shown in Figure 4.  
To improve the retention of content, authors have decided to modify the design of the VRLEs by including a step-by-step protocol. Thus, the design process (and, specifically, the step-by-step protocol) is the main significant difference between the methodology used with the VRLEs of Figures 1 and 2.