esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiment  
### High Reynolds Number Simulation  
In this section, a 3D lid-driven cavity flow with a constant lid velocity U was simulated as a numerical test for our experiments.The 3D lid-driven cavity flow is a regularly shaped computational region, which can be used as a benchmark for verifying a parallel algorithm in terms of acceleration ratio and parallel efficiency. Therefore, the 3D lid-driven cavity flow is used in this paper, through which the results of the parallel algorithm designed in this paper are proved to be accurate. On this basis, it is further verified that the parallel algorithm designed in this paper also has good parallel speedup and parallel efficiency. In terms of units, for the sake of ease in experimental programming, we conducted a dimensionless analysis on all physical quantities and ensured that the dimensionless flow and heat transfer criteria remained consistent before and after the nondimensionalization process. Figure 9 shows the geometry of the lid-driven cavity flow. In Figure 9, B is the cavity width, D is the cavity height, L is the cavity length, and U is a constant lid velocity. In the experiments of this paper, the spanwise aspect ratio (SAR = L/B) is 0.5:1 or 1:1, and the depthwise aspect ratio (DAR = D/B) is fixed at 1:1.  
The RLBM algorithm using MPI is used to simulate the 3D lid-driven cavity flow with different Reynolds numbers. We prove the accuracy of the algorithm by simulating the Reynolds number of 100, 400, and 1000. Figures 10-12 show a comparison of the RLBM results of the lid-driven cavity flow with the results of [PERSON] et al. (2013) and [PERSON] et al. (2014). It can be found that the RLBM algorithm using MPI in this paper is accurate.  
Figure 9: Three-dimensional lid-driven cavity flow.  
Then, we added Sub-Grid Scale(SGS) to the RLBM algorithm using MPI to simulate the 3D lid-driven cavity flow with a high Reynolds number. Figures 13-16 show the comparision between the results of the RLBM-SGS algorithm using MPI under the high Reynolds number and the results of [PERSON] et al. [55]. It can be found that the RLBM-SGS has a good stability under the high Reynolds number.  
Figure 11: Comparison with benchmark values for (**a**) \(\mathbf{v}\) velocity at y = 0.5 [53]; (**b**) \(\mathbf{u}\) velocity at x = 0.5 at Re = 400 and SAR = 1:1 [53,54].  
Figure 10: Comparison with benchmark values for (**a**) \(\mathbf{v}\) velocity at y = 0.5 [53]; (**b**) \(\mathbf{u}\) velocity at x = 0.5 at Re = 100 and SAR = 1:1 [53,54].  
Figure 14: Comparison with benchmark values for (**a**) \(\mathbf{v}\) velocity at y = 0.5, (**b**) \(\mathbf{u}\) velocity at x = 0.5 at Re = 5000, and SAR = 0.5:1.  
Figure 13: Comparison n with benchmark values for (**a**) \(\mathbf{v}\) velocity at y = 0.5, (**b**) \(\mathbf{u}\) velocity at x = 0.5 at Re = 3200, and SAR = 0.5:1.  
Figure 15: Comparison with benchmark values for (**a**) \(\mathbf{v}\) velocity at y = 0.5, (**b**) \(\mathbf{u}\) velocity at x = 0.5 at Re = 7500, and SAR = 0.5:1.
## 3 Proposed System
### Position Correction with Particle Filter  
Define a set of \(N\) particles \(P=\{p_{1},p_{2},\cdots,p_{N}\}\). The coordinates of particle \(p_{i}\) at time \(t\) are \(\left(xp_{t}^{(i)},yp_{t}^{(i)}\right)\) and the weight is \(\omega_{t}^{(i)}\). The process of k-NN-based particle filtering algorithm is as follows.  
Figure 4: Configuration of the proposed system.  
Initial state At \(t=0\), determine the initial estimated location using the fingerprinting method with k-NN and generate \(N\) particles near the initial location, each randomly scattered and uniformly distributed. Let \(\omega_{0}^{(i)}=1/N\) be the initial weight.
2. Movement of particles Move \(N\) particles. Based on an average stride length, add noise at a distance of \(d=0.65\) m \(\pm\) 0.1 m and completely randomize the direction of movement. Delete particles whose coordinates are outside the wall by comparing the coordinates of the experimental environment with the coordinates of the particles after moving. The particle movement equation, with the movement angle \(\theta\), can be expressed as \[xp_{t}^{(i)} =xp_{t}^{(i)}+d\cos\theta\] (3) \[yp_{t}^{(i)} =yp_{t}^{(i)}+d\sin\theta\] (4) where \(\theta\in(0,2\pi)\).
3. Weighted by likelihood function In this study, the designed likelihood function is \(fp=\left(xp_{t}^{(i)},yp_{t}^{(i)}\right)\), where the new weights are obtained by the distance between the particle and the surrounding fingerprint points. The particle weights are \(\omega_{t}^{(i)}=fp\left(xp_{t}^{(i)},yp_{t}^{(i)}\right)\). At this time, the weight of the particle is defined as the magnitude of the likelihood.
4. Normalization For each particle, normalize as follows. \[\tilde{\omega}_{t}^{(i)}=\frac{\omega_{t}^{(i)}}{\sum_{i=1}^{N}\omega_{t}^{(i )}}\] (5)
5. Resampling The weight \(\omega_{t}^{(i)}\) of the resampled particles is set to \(1/N\). The state can be predicted and the result of the position estimation can be output.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method
### Differentiable Binarization  
The method of converting a probability map to a binary map using the standard binarization method of setting a fixed threshold is not differentiable and will result in a network that cannot be optimized at training time. To better separate the background from the foreground, a threshold map obtained from the network prediction rather than from a fixed threshold was added. In order to improve the inference speed, the threshold map was used only during training. In addition, to ensure that the gradient was differentiable during training, a differentiable binarization method was used by approximating the step function in the standard binarization, which is expressed in Equation (15):  
\[\hat{B}_{i,j}=\frac{1}{1+e^{-k(P_{i,j}-T_{i,j})}}, \tag{15}\]  
where \(\hat{B}_{i,j}\) is the value of point \((i,j)\) in the approximate binary map \(\hat{B}\); \(k\) is the enhancement factor for gradient scaling, which is empirically set to 50; \(P_{i,j}\) is the value of point \((i,j)\) in the probability map \(P\); \(T_{i,j}\) is the value of point \((i,j)\) in the threshold map \(T\).  
In the training phase, three feature maps, a probability map, threshold map, and binary map, were supervised to learn and update the parameters of each module. The automatically adjusted thresholds not only improved the accuracy, but also simplified post-processing and improved the performance of text detection. In the inference process, the probability map was used directly and the text detection results were obtained by fixing the thresholds, which simplified the inference process and improved the inference speed.
## 2 Related Works
Therefore, the development of a noise-resistant authentication protocol that utilizes polynomial modular coding and improves the cybersecurity of satellite IoV is an urgent task. The aim of the work is to increase the cybersecurity of satellite IoV by using the authentication protocol MPPC. The application of a modular polyalphabetic polynomial code allows for reducing the first-order error due to the correction of signal distortions caused by interference, as well as reducing the second-order error by increasing the speed of the authentication of the satellite RSU.
## 2 Materials and Methods  
### Titanium Disks and Group Formation  
Sistry disks with a 5 mm diameter and a 2 mm thickness were produced from commercially pure titanium grade 4, according to ASTM-F67 (Figure 1). The chemical composition is: titanium (Ti) \(\geq\) 98.6%, Iron (Fe) \(\leq\) 0.50%, Oxygen (O) \(\leq\) 0.40%, Carbon (C) \(\leq\) 0.080%, Hydrogen (H) 0.015%, and other 0.3%.  
Figure 1: Representative image of the titanium disk used in the study.  
All disks were manufactured by Implacil De Bortoli (Sao Paulo, Brazil) using titanium bars for dental implants. The disks were divided into four test groups according to the bur model used to wear the surface and a control group (n = 12 per group). The control group disks had a smooth surface, but had the same conditions (sterilization and physicochemical state) as commercialized implants. In one group, the disks were instrumented using a Tungsten Bur (TB group) tapered design at length of 4.4 mm and diameter of 1.2 mm of the active part, and granulometry of 91-126 um; in another group, the disks were instrumented using a Tungsten Carbide Bur (TCB group) tapered design at length of 6.5 mm and diameter of 1.0 mm of the active part, and granulometry of 30 um; in another group, the disks were instrumented using a Coarse-Grained Diamond Bur (CGB group) tapered design at length of 6.5 mm and diameter of 1.2 mm of the active part, and granulometry of 151 um; and finally, in one group, the disks were instrumented using a Fine-Grained Diamond Bur (FGB group) tapered design at length of 6.5 mm and diameter of 1.0 mm of the active part, and granulometry of 46 um. All tested burs were manufactured and commercialized by the same company (KG Sorensen, Cotia, Brazil) (Figure 2), and a new bur was used to wear each disk. Each disk was worn across the whole surface area (5 mm).  
The sample size was based on a power level of 85% to obtain a \(p\)-value of 0.05, calculated by using a software program (SigmaStat 4.0; Systat Software Inc., San Jose, CA, USA). To obtain a desired power level of 85%, with differences between the means and standard deviations of each group, the minimum sample size for each group under each condition was six samples.  
The disks were clamped into a mechanical lathe machine Model BV-20 Ferrari (Sao Paulo, Brazil) with the purpose of obtaining a uniform wear of all disks (Figure 3a). All disks had worn a thickness of 0.5 mm. Figure 3b shows a disk after the instrumentation. The wearing was performed under intense water cooling at a speed of 30,000 rpm. The average time for each disk to wear out was 60 s.  
After the instrumentation, all samples were washed in water, air-dried, and sterilized in an autoclave. Six disks for each group were used for the cell growth assay and six for atomic force microscopy (AFM) and scanning electron microscopy (SEM).  
Figure 2: Representative images of burs used in each group to wear the disks.
# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
## 4 Simulations and Discussion
The UAV trajectories for the different required data volumes are shown in Figure 9. Accordingly, the UAV trajectories planned for a required data volume of 0 bits ignore the communication corridors and interference. With growing amount of required data, the UAV trajectory stays in the communication corridor for a longer period of time. Figure 10 shows the variation in the amount of remaining data for different collection requirements. The larger the amount of required data, the longer the task takes to be completed, which is in line with the general rule. Figure 11 shows the variation in the in-flight energy consumption for different collection requirements. The lowest energy is consumed for zero collected data, and the highest energy is consumed for the collection of 1 Mbit. Figure 10: Variation in the amount of data remaining for different data collection requirements. Figure 9: UAV paths for different data collection volumes: (**a**) 0 bits; (**b**) 100 kbits; (**c**) 500 kbits; (**d**) 1 Mbits. Figure 12 displays the instantaneous communication rate of the UAV for different amounts of required data. The sky-blue dashed line indicates the communication threshold. Evidently, the greater the data demand exceeds the communication threshold area; for example, the red line in the figure is the instantaneous communication rate when the data collection volume is 0, which is basically less than the communication threshold. It does not need to consider the data collection task only needs to fly with the lowest energy consumption to focus. When the data collection volume is 1 Mbit (as shown by the green curve in the diagram), it will stay above the communication threshold as much as possible until the task has been completed. Figures 13 and 14 show the speed and flight altitude of the UAV during data collection for different required data volume, respectively. The UAV accelerates, decelerates, accelerates, and decelerates again, which is very much in line with the trapezoidal criterion we used during initialization. Therefore, the UAV should fly at full speed before entering the CFC, then decelerate appropriately to remain in the area long enough to collect the required amount of data in the CFC, and then fly at full speed to the end point. The flight  
Figure 11: Normalized flight energy consumption. Figure 12: Instantaneous communication rate. altitude also increases and then decreases, which is very much in line with the dynamics of a low-energy flight.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 6 Integration Mechanism for Fault Prediction and Scheduling Optimization  
This section explores the seamless integration of deep learning-based fault prediction with SMT-based scheduling optimization. By establishing a feedback loop between these two techniques, the system dynamically adapts job schedules in real-time based on current machine conditions. This integration minimizes the risk of faults while enhancing production efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Number of Features & 4 \\ Sequence Length & 10 \\ Hidden Size & 32 \\ Number of Layers & 2 \\ Learning Rate & 0.001 \\ Batch Size & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters used for the 1D-CNN model.
## 5 Conclusions  
In this study, a pair of effective cancellation coils connected in series with the \(B_{p}\) coil was designed for suppression of the residual magnetic interference in a SR. Simulation and experiment in a small-scale SR demonstrate that this method was effective in reducing the influence of the eddy effect. Simulation shows that with 70 turns CC, \(B_{r}\) decays much faster than without CC. Experimental results show that, with the same current in the \(B_{p}\) coil the modulus value of \(B_{r}\) with CC was 1851 nT when the current was completely switched off at two milliseconds, while the modulus value of \(B_{r}\) without CC was 28,310 nT. When compared with the modulus value of \(B_{r}\) at two milliseconds, the attenuation of modulus value of \(B_{r}\) with CC was 97.09% (the modulus value of \(B_{r}\) attenuated to 53.92 nT from 1851 nT) while the attenuation was 83.59% without CC at 50 ms (the modulus value of \(B_{r}\) attenuated to 4647 nT from 28,310 nT). It was thus demonstrated that with CC, the \(B_{r}\) was significantly reduced in the SR which makes great contribution to the improvement for suppressing the residual magnetic interference. Therefore, we can advance the measurement time of the ULF NMR and improve the SNR.  
Through theoretical derivation and experiment, the eddy-current magnetic field produced by \(B_{p}\) coil and CC in the SR was decoupled into two parts after the current was completely switched off at two milliseconds and the mathematical expressions were obtained. Therefore, it was possible to deeply analyze the relationship between the attenuation signal \(B_{r}\) and the structure of SR with CC in the future.  
Although a notable improvement was achieved for suppressing the residual magnetic interference in this work, further improvement on this scheme was still required, especially on the optimization of the size and structure of the cancellation coil, to obtain a better cancellation of the residual magnetic interference. In addition, the application of this method to a full-scale SR needs to be studied in the future.  
Conceptualization, D.Z. and J.L.; methodology, Y.T.; software, Y.T., D.Z. and M.S.; validation, Y.T. and D.Z.; formal analysis, Y.T., D.Z.; investigation, M.S.; data curation, Y.T.; writing--original draft preparation, Y.T. and M.S.; writing--review and editing, D.Z.; supervision, J.L.; project administration, D.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Opening Foundation of the State Key Laboratory of Functional Materials for Informatics, Grant Number SKL-2017-07, and National Key R&D Program of China, Grant Number 2016 YFB1200601-B12.  
**Acknowledgments:** The authors gratefully thank [PERSON] for thoughtful comments on the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Metadata Systems for Name Disambiguation
### International Standard Name Identifier (ISNI) [[http://www.isni.org/](http://www.isni.org/)]([http://www.isni.org/](http://www.isni.org/))  
ISNI is a draft standard from the International Standards Organization. Its purpose is to assign a unique number to personal names that appear as authors/contributors or subjects in print and online publications. The standard will be similar to the ISBN numbers that appear in books, with one exception. One purpose of the ISBN is to uniquely identify various editions of works. For example, a book's first edition gets a different ISBN number than its second edition. One work, then, can have many different ISBN numbers. One author, ideally, will only have one ISNI. Thus the ISBN serves to differentiate among various manifestations of a work and among other works, but the ISNI serves to bring together all instances of authorship with a single number.  
According to the project's web site, \"An ISNI is made up of 16 decimal digits, the last one being a check character\" [14]. Example: ISNI 1422 4586 3573 0476
## 3 Design and Optimization of the Proposed Extended DD Coil
### Design of the Extended DD Coil
According to [31], combined with the topology proposed in this paper, the magnetic flux transmission path from \(L_{in}\) to \(L_{s}\) can increase the output power of the system in a misaligned state in the \(X\)-axis direction. Overall, this design is suitable for solving problems related to when EVs are in a misaligned state in the door-to-door direction. Furthermore, this study utilized the FEM tool to simulate the LCT when the misalignment distance on the \(X\)-axis is increased to 150 mm. Particularly, the compensation inductors were designed to be a DD coil and extended DD coil, respectively, with an operating frequency of 85 kHz. The proposed fully integrated LCC-S compensation topology was incorporated, inputting the same circuit parameter values and resistance values as the calculated ones. Appropriate simulation step sizes were selected, and the magnetic coupler was partitioned along the Y-Z axis. Using contour plots, the magnetic flux distribution of the coupler can be clearly displayed, as shown in Figure 6.  
Figure 6: Different compensation inductor FEM simulation diagrams: (**a**) DD coil and (**b**) extended DD coil. Figure 6a shows the magnetic flux distribution of the LCT, in which the compensation inductor is adopted as the DD coil; specifically, the main transmission path of the magnetic flux is between \(L_{p}\) and \(L_{s}\). Due to the small face-to-face area of \(D_{1}\) or \(D_{2}\) with \(L_{s}\), the magnetic flux generated by \(L_{in}\) rarely passes through \(L_{s}\). However, when the compensation inductor is adopted as the extended DD coil in Figure 6b, the magnetic flux can be transferred not only between \(L_{p}\) and \(L_{s}\) but also between \(L_{in}\) and \(L_{s}\). Since \(D_{1}\) or \(D_{2}\) has a larger face-to-face area with \(L_{s}\), more magnetic flux will be transmitted, thereby increasing the magnetic field strength, as shown in the red circled area in Figure 6. Therefore, with the proposed magnetic coupling structure, more energy will be transferred from the transmitter to the receiver sides, thereby improving the misalignment tolerance performance of the system.
## 2 Energy Conservation Schemes in WSN
It aims to reduce the computational complexity of the LTSA method [22]. Compressive Sensing scheme also considers a random projection framework, since the signal to be compressed is projected in the sensing matrix to generate \(M\) CS coefficients. However, unlike [19; 21], we apply the CS scheme to compress environmental data gathered by a WSN. Furthermore, our work aims to investigate some network aspects, such as the energy consumption of sensor nodes and the impact of packets loss in the reconstruction of the monitored signals. A simple way to mitigate transmission losses (or packet erasures) is by using _acknowledgments_ (ACK) [23]. When a transmission is received, the sink may send an ACK, confirming the receipt of the measurement. If the sensor node receives the ACK, then the subsequent data can be sent. Otherwise, a retransmission is required. We consider a WSN without ACKs, since to retransmit packets means greater energy consumption. Authors in [24] investigate the characteristics of lossy links in WSNs, and apply the CS framework to overcome this problem. Experimental results show that the WSN can transmit with high quality, while reducing energy consumption because of the CS characteristics, i.e., if there is a sufficient number of received packets and if the sink is capable of identifying which coefficients (packets) were lost. The transmission over a noisy channel and the evaluation of the reconstruction error in the presence of packet erasures while saving sensor nodes energy, are considered in [25]. Two approaches for CS are studied in [25]: A cluster-based method that considers aggregation to compress data, and a consensus-based scheme in order to estimate the linear projections of sensor nodes; a basis pursuit denoising (BPDN) algorithm [16] is used for reconstruction by the sink node. In order to save sensor nodes energy in a surveillance application using a Visual Sensor Network (VSN), a routing framework called PRoFIT is proposed in [26]. In this VSN application scenario, sensing nodes divide images into bit-planes [27]. There are two layers that are created for captured images. The first one contains the most significant bit-planes (transmitted with higher priority) and the second layer bares less significant bit-planes (transmitted with lower priority). From the first layer, the image is reconstructed at the sink with a certain degree of detail, so that some immediate action can be taken based on the image content. If a more detailed reconstruction is required, the sink uses the second layer with remaining bit-planes. This strategy can be understood as a successive approximation scheme [28], in which bit-planes are incrementally transmitted, in order to provide a better reconstruction.
## 4 Computation and Communication Performance Modeling Approaches
### Communication Modeling Approach
#### 4.2.3 Transaction Level Model  
The Transaction Level model shown in the middle of Figure 4 is an improved Cycle Accurate model that uses events similar to the Message Level model to reduce the number of polling states and improve the simulation speed.  
On a real system with a configuration where most of the actors are executed in parallel, most of the time these actors are polling on the FIFO buffer waiting for data that can be processed. This causes lots of avoidable simulation steps decreasing the simulation speed tremendously. Therefore, not only in the Message Level but also in the Transaction Level model, this polling is implemented using events. When the current FIFO state is not as needed, the simulated actor waits for a channel exclusive event that the state of the FIFO buffer changed. This state update event is triggered after another actor accessed the FIFOs tokens, so the simulation kernel can easily skip the polling activity without simulating each individual polling attempt.  
Similar to the Cycle Accurate model, this model also considers a fixed delay offset for the shared memory access and an additional contention penalty depending on how many processing elements try to access the shared interconnection (See Figure 4\(n_{c}\)). In contrast to the Cycle Accurate model, the Transaction Level model assumes continuous bus access by the polling actors and ignores any computational overhead of executing the polling loop on the processing element the actor is executed on.