esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Progress on Membranes for Redox Flow Batteries
### Amphoteric Ion Exchange Membranes in VRB  
By combining excellent properties from both cation and anion exchange membranes, novel amphoteric ion exchange membranes were prepared and studied in VRB by [PERSON] and co-workers [113]. Radiation grafting is one useful method to fabricate membranes with modified properties. A polyvinylidene fluoride (PVDF) membrane was grafted with styrene and dimethylaminoethyl methacrylate (DMAEMA) followed by sulfonation and protonation to produce a novel PVDF-g-PSSA-g-PDMAEMA with cation and anion exchange properties. The optimization of the dose used or grafting yield and DMAEMA content was found to be important in producing membrane with low vanadium ions permeability and good conductivity. Higher grafting yield led to higher water uptake, ion exchange capacity as well as conductivity. Meanwhile, higher content of DMAEMA resulted in lower conductivity and lower permeability. The amphoteric ion exchange membrane showed lower vanadium ions permeability than that of Nafion 117. This is due to the repelling effect of the positively charged quaternary ammonium salt and the positively charged vanadium ions that diffuse. The Open circuit Voltage (OCV) decay of the amphoteric ion exchange membrane was slower when compared to Nafion 117 [113].  
In the same study, an ethylene-tetrafluoroethylene (ETFE) membrane was modified via a two-step radiation grafting process using styrene and Dimethylaminoethyl methacrylate (DMAEMA). ETFE-g-PSSA was initially synthesized by grafting the membrane with styrene; this was followed by sulfonation. This was done to prevent the decomposition of DMAEMA during sulfonation [113]. Inanother study by [PERSON] and co-workers [PERSON] and [PERSON] (2012), ETFE-g-PSSA was grafted using DMAEMA and then protonated in HCl, to produce a novel ETFE-g-PSSA-g-PDMAEMA amphoteric ion exchange membrane (AIEM). The AIEM exhibited lower permeability of vanadium ions and a high proton conductivity than Nafion 117. This could be due to Donnan exclusion effect because of which the charged cationic layer of the membrane repels the vanadium ions. The OCV decay was found to be much slower in the case of the amphoteric membrane when compared to Nafion 117 [PERSON] and [PERSON] (2012).
## 2 Theoretical Background
### Boundary Conditions and Solutions of Equations  
When a homogeneous fluid-saturated pore formation exists outside of the borehole, the boundary conditions are:  
At the borehole wall (r = a):  
\[\text{Shear stress continuity}:\hskip 56.905512 pt\tau_{r\theta}^{o}=\tau_{s}^{b} \tag{11}\]  
\[\text{Tangential electric field strength continuity}:\hskip 56.905512 ptE_{\theta}^{o}=E_{\theta}^{b} \tag{12}\]Axial magnetic field strength continuity : \(H_{z}^{o}=H_{z}^{b}\) (13)  
\(\tau_{s}^{b}\) represents the shear stress source, we take its amplitude as one-thousandth of the formation shear modulus, namely \(\tau_{s}^{b}=10^{-3}\times C^{o}\). Substituting Equations (5)-(10) into boundary conditions (11)-(13), the matrix Equation (14) is obtained.  
\[\left[M_{ij}\right]_{3\times 3}\left[A_{j}\right]_{3\times 1}=\left[B_{j} \right]_{3\times 1} \tag{14}\]  
\(\left[M_{ij}\right]_{3\times 3}\) is the coefficient matrix. The first column corresponds to the homogeneous general solutions of the borehole electromagnetic field, the second and third columns correspond to the homogeneous general solutions of the coupling wave fields in the porous formation. \(\left[A_{j}\right]_{3\times 1}=\left[A_{H},B_{sh}^{o},B_{em}^{o}\right]^{T}\) is the undetermined coefficient matrix. \(\left[B_{j}\right]_{3\times 1}=\left[\tau_{s}^{b},0,0\right]^{T}\) is a matrix related to the source. In order to solve the borehole fluid electromagnetic fields, the undetermined coefficient \(A_{H}\) should be solved first:  
\[A_{H}=\frac{Det[N_{ij}]_{3\times 3}}{Det[M_{ij}]_{3\times 3}} \tag{15}\]  
In Equation (15), matrix \(\left[N_{ij}\right]_{3\times 3}\) is obtained by replacing the elements in the first column of the coefficient matrix \(\left[M_{ij}\right]_{3\times 3}\) with the elements of \(\left[B_{j}\right]_{3\times 1}\). The expressions of each matrix element are given in Appendix A. Substituting \(A_{H}\) into Equation (5), we can obtain a complete analytical expression of the borehole fluid tangential electric field.
## 5 Numerical Simulation Study of the Iced Wind Turbine Flow Field
### Analysis of Results  
#### 5.2.1 Flow Field Pressure Analysis on the Icing Blade Surface  
The aerodynamic noise of the flow field is closely related to its pressure variation and distribution, and the analysis of the dynamic situation in the flow field can help to carry out further noise research. In this study, the pressure field was obtained when the rotational speed is 200 r/min and the incoming wind speed is 5 m/s by using LES as an example for analysis.  
From Figure 13, it can be seen that with the increase in icing mass, the surface pressure of the windward and leeward side blades increases with it, and the pressure distribution moves towards the blade tip. This is due to the fact that wind turbine blade icing changes the airfoil shape to make the surrounding flow field unstable, which in turn causes the pressure distribution to change. In addition, the turbulent fluctuating pressure generates noise on the airfoil surface, and the interaction between the blade surface and the boundary layer fluid also generates turbulent boundary layer noise.  
Figure 12: Mesh model.  
Figure 13: _Cont._As the angle of attack increases, the flow on the blade surface becomes more unstable and the separation point moves toward the leading edge and increases the turbulence intensity, leading to an increase in pressure surface pressure. In particular, at an angle of attack of 60\({}^{\circ}\), the pressure surface pressure maximum occurs in the separation zone and at the trailing edge. This indicates that the strongest aerodynamic noise is generated in these regions and is the main source of noise emission generation.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 5 Conclusions  
This article has presented a latency-optimized architecture of the SIMECK lightweight block cipher for securing low-resource embedded devices such as wireless sensor nodes. The proposed architecture supports a block and key lengths of 32 and 64 bits, respectively. We have created digits of the key and data blocks into smaller segments to optimize the latency or computational time. Each smaller segment is operated in parallel for cryptographic computations. The implementation results for evaluations are provided for modern 7-series FPGA devices.  
The results after the post-place-and-route level show that the Kintex-7, Virtex-7, and Artix-7 devices utilize 49, 51, and 50 Slices as hardware resources which indicates that the proposed architecture is an appropriate choice for many area-constrained cryptographic applications including the wireless sensor nodes. The minimum power consumption of 172 mW is achieved for Kintex-7 which looks a bit higher, especially for the embedded devices having built-in power sources (i.e., batteries) such as wireless sensor nodes. On the other hand, we are computing encryption and decryption at very high speeds such as 388 MHz on Artix-7. Using similar hardware resources, we believe that the power of our architecture can be optimized by paying cost over the system frequency. Moreover, some useful techniques have been described in [36] to optimize the consumed power especially for the low-resource embedded devices. Therefore, by employing these strategies, the power consumption of our design could be improved.  
For throughput and area parameters, at the same time, our design outperforms most relevant state-of-the-art chip accelerators of wireless sensor nodes such as [15; 28; 30; 31]. Consequently, the implementation results and comparison to the state-of-the-art show that the proposed architecture is an attractive alternative for securing the wireless sensor nodes.  
Conceptualization, H.T. and A.R.A.; methodology, A.A. ([PERSON]) and A.A. ([PERSON]); software, H.T. and A.A. ([PERSON]); validation, A.A. ([PERSON]) and A.A. ([PERSON]); formal analysis, A.R.A. and H.T.; investigation, A.A. ([PERSON]) and H.T.; resources, A.R.A.; data curation, H.T.; writing--original draft preparation, H.T. and A.A. ([PERSON]); writing--review and editing, H.T. and A.A. ([PERSON]); visualization, H.T.; supervision, A.R.A. and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at University of Tabuk for funding this work through research group no. S-0112-1443.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: Validation setup using a serial interface of FPGA with the python script.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure
### Spatial Deformation of Retaining Structure
#### 2.1.3 Waist Beam Deformation  
Assuming the height of the waist beam above the pile cap is \(h_{y}=\xi H\), where \(\xi\) is the height coefficient of the waist beam, and that the deformation curve of the waist beam is consistent with that of the crown beam, the deformation calculation formula of the waist beam can be expressed as Equation (3):  
\[\delta_{y}=\delta\sin(\frac{x}{l}\pi)(n_{1}\xi^{2}H^{2}+n_{2}\xi H+n_{3}) \tag{3}\]
## 4 Experiments
### Tracking Performance  
#### 4.3.1 Evaluation Results on the MOT17 Datasets  
Evaluation of our method is performed on the test set of the respective benchmark, without any training or optimization on the training set. The overall results accumulated over all sequences are shown in Table 1, including three sets with different public detectors in MOT17. Although our method does not show the state-of-the-art _MOTA_, high performance in both _IDF1_ and _MT_ metrics proves that our method can manage object ID consistently. This is what we aimed for. Specifically, most tracked (MT) and identity preserving (_IDF1_) (which compares groundtruth trajectory and computes trajectory by a bipartite graph, and reflects how long of a object has been correctly tracked) prove the effectiveness of our method on ID preserving, which outperform the other online trackers on MOT17. Different from online methods, offline methods do have both future and past information to further optimize the status of each object, which is usually expressed by a better overall performance. Despite this, our method still shows competitive ability on ID preserving, which is reflected by the _IDF1_ and _MT_ results. The overall results prove the ability of our methods to preserve the identity of objects. By adding another location estimation, provided by the single object tracker, as a second verification, the quality of the objects' representation is increased. Also the delay processing model helps in tackling the occlusion problem, which leads to better maintaining of the objects' identity after occlusion or deformation. To further illustrate the effectiveness of our method on results refinement and object ID preserving, we carefully select other online methods with a similar number of _FP_ and _FN_ (which leads to a similar _MOTA_ score) with ours, since ID consistency metrics are critically affected by FP and FN. According to Table 2, our method shows the best _IDF1_ and _MT_ results with the same level of _FP_ and _FN_, which proves our method's ability of maintaining the objects' identities in a similar environment. As for ID switches metrics, our method only achieve the second best results in Table 2, which is because most of the switches occur when using the SDP detector, which has additional small detections. For smaller detections, feature representation of the single object tracker is coarse. Thus, correlation filters do not perform well when handling occlusion and deformation. Since our method performs online tracking, using future information to refine trajectories is not an option either. Figure 6 shows some of the visualization tracking results of our method under different circumstances. Specifically MOT17-03 and MOT17-08 represents static camera and crowded surveillance, while MOT17-07 and MOT17-14 show a moving camera. Each frame is chosen for their complexity that can show the performance of our method well.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 7 Conclusions and Future Work  
In this paper, we have proposed a novel approach, namely a DiffServ-based dynamic cooperative MAC (DDC-MAC) protocol, to realize QoS media access control for the wireless network with the unique features in terms of large-range transmission, high dynamic topology, three-dimensional monitor region, and heterogeneous services.  
Its performance is analyzed by Markov chain-based modeling, and validated by ns-2 simulations. Both theoretical analysis and simulation experiments show that DDC-MAC can leverage cooperative communications and exploit spatial and user diversities. Therefore, it outperforms the existing protocols under the same channel assumptions and network scenarios. DDC-MAC also provides extended service ranges and a robust wireless communication link in NC-NET.  
As future research, we will pursue the following directions: (i) investigate protocols integrated with routing protocols; (ii) explore the performance with different slot intervals, prediction window and prediction techniques, and (iii) extend DDC-MAC to other NC-NET with different topologies and mobility models.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61473306, and partly by the Defense Advanced Research Project of China under Grant No. 9140A09040614 JB14001.  
[PERSON] and [PERSON] conceived and designed the MAC protocol; [PERSON] analyzed and developed algorithm; [PERSON] performed the experiments; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 3 Results and Discussion
### Emissions
#### 3.3.3 NOx  
Figure 9 displays the NOx emissions under different engine load conditions. In diesel exhaust emissions, NOx emissions are predominantly composed of NO, with lesser amounts of NO\({}_{2}\). Other NOx emissions, such as N\({}_{2}\)O, N\({}_{2}\)O\({}_{5}\) and NO\({}_{3}\), are negligible in most conditions [23]. [PERSON] and [PERSON] [23] reviewed the generation mechanism of NOx emissions emitted from diesel engines. They pointed out that NOx emissions from diesel engines are mainly related to Thermal NOx, Prompt NOx and Fuel NOx. In most combustion conditions for diesel engine, thermal NOx is considered to be the main contributor to total NOx emissions. Generally, oxygenated fuel can provide additional oxygen atoms to improve combustion efficiency, which is beneficial to generating more NOx emissions. However, not all oxygenated fuels show the above rules, such as ethanol. In addition to its high oxygen content, ethanol also has a high latent heat of evaporation, playing an interactive role in the formation of NOx emissions. In Figure 9, as many researchers reported, with the increase in engine load, NOx emissions for all the tested fuels show a gradual and obvious increase trend. This is because the increase in load causes more fuel to participate in combustion, and the combustion duration is also increased. However, with the addition of ethanol, NOx emissions of all diesel-ethanol-blended fuels are lower than that of diesel under the same load condition. The specific result is that, at 40 Nm, the NOx emissions of E5, E10 and E15 were, respectively, decreased by 4.16%, 7.43% and 9.36% compared to that of E0; at 60 Nm, the NOx emissions of E5, E10 and E15 were, respectively, decreased by 6.86%, 6.97% and 5.82% compared to that of E0; at 80 Nm, the NOx emissions of E5, E10 and E15 were, respectively, decreased by 4.36%, 5.13% and 3.06% compared to that of E0. This result can be interpreted as the high evaporation latent heat of ethanol causing a low temperature in the combustion chamber during the atomization process, which hinders the generation of a large amount of Thermal NOx emissions [15; 24].  
Figure 8: HC emissions.
# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 3 Results
### Respiratory Measurements of Subjects Moving Left and Right  
The respiration rate extraction accuracies of the conventional and proposed methods are compared for ten subjects seated in a chair and moving left and right.  
As depicted in Figure 11, the conventional method alleviates the effect of displacement of the chest movement associated with respiration, owing to the change in distance from the radar. Nevertheless, the result is inaccurate because the signal beyond the set range-bin is measured. In contrast, the proposed method can extract the specific velocity change in the chest owing to respiration, regardless of the measurement range. In addition, because the velocity change frequency associated with movement other than chest movement exists individually, the respiration rate can be accurately determined. We experimented with the motion conditions shown in Figure 5d. Table 5 shows the respiratory accuracy calculation values for all subjects obtained through the experiment.  
Since the proposed method outputs the respiration rate every second and the processing time is about 0.2-0.4 s, the method is sufficiently capable of real-time implementation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Conventional** & **Proposed** \\ \hline Subject \#1 & 69.62\% & 89.57\% \\ Subject \#2 & 69.71\% & 84.06\% \\ Subject \#3 & 71.13\% & 93.98\% \\ Subject \#4 & 80.95\% & 88.14\% \\ Subject \#5 & 59.57\% & 85.97\% \\ Subject \#6 & 65.59\% & 92.66\% \\ Subject \#7 & 47.46\% & 89.53\% \\ Subject \#8 & 58.85\% & 80.77\% \\ Subject \#9 & 41.72\% & 83.52\% \\ Subject \#10 & 50.11\% & 83.19\% \\ \hline Average & 61.47\% & 87.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of respiratory rate accuracy for the case shown in Figure 5c.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Conventional** & **Proposed** \\ \hline Subject \#1 & 84.66\% & 94.63\% \\ Subject \#2 & 67.09\% & 93.04\% \\ Subject \#3 & 61.09\% & 86.12\% \\ Subject \#4 & 49.62\% & 88.45\% \\ Subject \#5 & 74.14\% & 87.49\% \\ Subject \#6 & 56.91\% &
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
###### Abstract  
Multimodal medical image registration has an important role in monitoring tumor growth, radiotherapy, and disease diagnosis. Deep-learning-based methods have made great progress in the past few years. However, its success depends on large training datasets, and the performance of the model decreases due to overfitting and poor generalization when only limited data are available. In this paper, a multimodal medical image registration framework based on few-shot learning is proposed, named reverse-net, which can improve the accuracy and generalization ability of the network by using a few segmentation labels. Firstly, we used the border enhancement network to enhance the ROI (region of interest) boundaries of T1 images to provide high-quality data for the subsequent pixel alignment stage. Secondly, through a coarse registration network, the T1 image and T2 image were roughly aligned. Then, the pixel alignment network generated more smooth deformation fields. Finally, the reverse teaching network used the warped T1 segmentation labels and warped images generated by the deformation field to teach the border enhancement network more structural knowledge. The performance and generalizability of our model have been evaluated on publicly available brain datasets including the MRBrainSi3 DataNii-Pro, SRI24, CIT168, and OASIS datasets. Compared with VoxelMorph, the reverse-net obtained performance improvements of 4.36% in DSC on the publicly available MRBrainSi3 DataNii-Pro dataset. On the unseen dataset OASIS, the reverse-net obtained performance improvements of 4.2% in DSC compared with VoxelMorph, which shows that the model can obtain better generalizability. The promising performance on dataset CIT168 indicates that the model is practicable.  
multimodal registration; few-shot learning; generalizability; reverse teaching  
29 November 2022  
1
## 2 Materials and Methods
### Adsorption Column Modeling
#### 2.7.3 BDST Model  
The model of BDST depends physically on measuring the bed capacity at several values of breakthrough. This basic model disregards the resistant mass transfer of the intra-particles, as well as the external film resistance so that the adsorbate is adsorbed directly onto the surface of adsorbent. Considering such hypothesis, the model of BDST properly achieves and yields worthwhile modeling formulae for system parameters change. An altered version of the formula that indicates the service time at breakthrough (\(t\)) as being a static function concerning the process elements is the model of BDST [35; 40; 41]. The model of BDST is illustrated by Equation (7), as following:  
\[t=\frac{N_{0}^{\prime}}{C_{0}V}Z-\frac{1}{C_{0}K}\ln\left(\frac{C_{0}}{C_{b}}-1\right) \tag{7}\]  
where \(t\) represents the column's service time (h), \(N_{0}^{\prime}\) represents the adsorption capacity (mg\(\cdot\)L\({}^{-1}\)), \(V\) represents the linear velocity of flow for the bed feed (cm\(\cdot\)h\({}^{-1}\)), \(K\) represents a constant value for adsorption rate (L\(\cdot\)mg\({}^{-1}\cdot\)h\({}^{-1}\)), and \(C_{b}\) represents the desired concentration of solute at breakthrough (ultimate acceptable limit concentration, mg\(\cdot\)L\({}^{-1}\)). The BDST formula was effectively applied to characterize the adsorption of heavy metals using phosphate-treated RH [41] and copper ions on RH [35]. The formula of the Adams-Bohart equation, given in Equation (7), could be applied to determine the column's service time (\(t\)) of a bed depth \(Z\) having the values of \(N_{0}^{\prime}\), \(C_{0}\), and \(K\), which, essentially, can be assessed for laboratory columns operated over a range of velocity values (\(V\)). Applying \(t=0\) while solving Equation (8) for \(Z\) produces the following equation:  
\[Z_{0}=\frac{V}{KN_{0}^{\prime}}\ln\left(\frac{C_{0}}{C_{b}}-1\right) \tag{8}\]  
where \(Z_{0}\) represents the least height of column required to output the effluent concentration \(C_{b}\), which is, moreover, recognized as the critical depth of bed. A basic form of the BDST model is given as below:  
\[t=aZ+b \tag{9}\]  
where  
\[a=slope=\frac{N_{0}^{\prime}}{C_{0}V} \tag{10}\]  
and  
\[b=intercept=-\frac{1}{C_{0}K}\ln\left(\frac{C_{0}}{C_{b}}-1\right) \tag{11}\]  
With the view to associate the isotherm cogency with kinetic formula more definitely, the mean percent error (APE) with a base on the differences among the investigational data, plus the model's generated data will be applied through Equation (12), as follows [42]:  
\[APE\ (\%)=\frac{\sum_{i=1}^{N}\left|\left(A_{exp}-A_{cal}\right)/A_{exp}\right| }{N}\times 100 \tag{12}\]  
in which \(A_{exp}\) and \(A_{cal}\) present the experimental, as well as determined, amounts, respectively, while \(N\) is data point's number.