esa-sceva/satcom-chunk-collection huggingface dataset:
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 4 Design and Fabrication of the CMR  
### Design of the Crawling Part  
To design the CMR, we considered the magnetic force and torque generated by the interaction of the magnets (CMs, RM, and FM) of the CMR as well as the magnetic torque  
Figure 5: (**a**) CMR with needle covered to reach a target area. (**b**) Uncovering motion of CMR to expose the needle. (**c**) Crawling motion to puncture the tissue with external precessional magnetic field. (**d**) CMR with covered needle and collected sample heading backward to return to the starting point.  
generated by the external magnetic field. The magnetic field generated by the permanent magnet is represented by a point-dipole model as follows:  
\[\textbf{B}_{point-dipole}=\frac{\mu_{0}}{4\pi}(\frac{3\textbf{R}(\textbf{m} \times\textbf{R})}{R^{5}}-\frac{\textbf{m}}{R^{3}}) \tag{4}\]  
where \(\mu_{0}\), \(R\), and \(R\) are the permeability of air, a vector from the permanent magnet to a point, and the magnitude of \(R\), respectively.  
For the crawling part, the magnetic torque exerted on the CM by the external magnetic field should be greater than the magnetic torque exerted on the CM by other CMs for the crawling body to oscillate freely along the y-axis due to the external magnetic field. Since this is mostly determined by the distance between CMs, it is necessary to design the intervals between crawling bodies. Figure 6 shows the FM, RM, and CMs as magnets 1, 2, 3, 4, and 5, in order, and the distances between each pair of magnets are defined as d\({}_{12}\), d\({}_{23}\), d\({}_{34}\), and d\({}_{45}\). To determine the spacing between the crawling bodies, the magnetic torque due to the external magnetic field must be greater than the internal magnetic torque between the CMs. In this paper, we ignored the influence of magnets 1 and 2 on magnets 3, 4, and 5. One major reason is that the volumes of the magnets 1 (FM) and 2 (RM) are smaller than those of the magnets 3, 4, and 5 (CMs), because we sought to use a small external magnetic field for navigation. Therefore, the spacing between the crawling bodies should be determined to satisfy the following torque constraint:  
\[\textbf{T}_{\textbf{e}}-\textbf{T}_{\textbf{i}}\geq 0 \tag{5}\]  
where \(\textbf{T}_{\textbf{e}}\) and \(\textbf{T}_{\textbf{i}}\) are the external magnetic torque and internal magnetic torque exerted on CM, respectively. Utilizing Equation (5), the minimum lengths of d\({}_{34}\) and d\({}_{45}\) between the crawling bodies can be determined as follows [20]:  
\[d_{m}^{\text{min}}=\sqrt[3]{\frac{9|\textbf{m}|\mu}{10\pi\textbf{B}_{e}}} \tag{6}\]  
Once we determined the size of the CM, as listed in Table 1 and the external magnetic flux density generated from MNS as in Figure 2 to be 8 mT for the crawling motion, the calculated result for d\({}_{34}\) and d\({}_{45}\) was 13 mm for this study. We also determined the length of the legs to be 12.5 mm, because they can rotate 180\({}^{\circ}\) through the slots between the CMs to change the direction of movement.  
Figure 6: Distances between the magnets in the CMR.
# Magnetic Properties of Gd-Doped Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
## 3 Results and Discussion
### Size and Gd Doping Effects on the Band Gap  
Finally, we studied the band gap energy \(E_{g}\) of Fe\({}_{3}\)O\({}_{4}\) NPs as a function of size and ion doping. [PERSON] et al. [32] reported that, with decrease in the crystallite size, the optical band gap energy \(E_{g}\) increases. The result for the band gap width \(E_{g}\) of Gd-doped Fe\({}_{3}\)O\({}_{4}\) NPs is presented in Figure 5. We find a blue shift for \(E_{g}\) with increasing the doping with RE ions. The direct optical band gap \(E_{g}\) of pure Fe\({}_{3}\)O\({}_{4}\) NPs is around 2.2 eV [12], which may vary with particle size, composition and structure. We observe that \(E_{g}\) for a pure Fe\({}_{3}\)O\({}_{4}\) NP is slightly larger than that of a bulk one (2.0 eV [12]), which is related to surface effects (not shown here).  
Figure 4: Gd doping concentration dependence of the Curie temperature \(T_{C}\) of a Fe\({}_{3}\)O\({}_{4}\) NP, \(d=20\) nm, \(J_{d}=0.8J_{b}\).  
Figure 3: Gd doping concentration dependence of the spontaneous magnetization \(M_{s}\) of a Fe\({}_{3}\)O\({}_{4}\) NP, \(d=20\) nm, \(J_{d}=0.8J_{b}\).  
The band gap energy \(E_{g}\) is enhanced with increasing Gd dopants due to the tensile strain which appears by the ion doping. As we discussed in Section 3.1, the growth of the crystal lattice volume and the appearance of an antiferromagnetic ordering between the spins of Gd and Fe in the octahedral sites reduces the spontaneous magnetization \(M_{\rm s}\) for small doping concentration \(x\) (see Figure 3). Then, it is clear from Equation (8) that, with increasing \(x\), the band width \(E_{g}\) increases.  
Our model could explain also the enhanced band gap energy by doping with other RE ions, as experimentally found by [PERSON] et al. [12] for Dy, Nd, La doped Fe\({}_{3}\)O\({}_{4}\) NPs. The RE ions have, similar to that of the Gd ion, an ionic radius larger than that of the Fe ion, but they have no biological role.
## 5 Simulation Method and Results  
### Simulation Method  
The data used in the simulation are all the same. We use the newly constructed 1000 Korean topography data, composed of 200 data for each of the five types of topography classes described in Section 2.2. The classification performance of single-step DNN or CNN-based classifiers and two-step NN classifiers are examined. In the case of a two-stage ML classifier, since either CNN or DNN can be used for stage 1 and stage 2, respectively, we test all four combinations. Table 4 shows the simulation parameters of DNN. Five-fold cross-validation is performed by dividing the total 1000 data into 800 training data and 200 test data, and the number of epochs is set to 200, considering the learning error curve.  
Figure 6: The decision process of the proposed NN-based topography class classification.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DNN Parameters** & **Value** \\ \hline Number of input nodes & 100 \\ \hline Number of hidden layers & 3 \\ \hline Number of nodes for the 1 st hidden layer & 500 \\ \hline Number of nodes for the 2 nd hidden layer & 200 \\ \hline Number of nodes for the 3 rd hidden layer & 40 \\ \hline Number of output nodes & 2, 4, 5 \\ \hline Activation function of hidden layers & Sigmoid \\ \hline Activation function of the output layer & Softmax \\ \hline Cost function & Cross Entropy \\ \hline Dropout & 0.2 \\ \hline Training method & Stochastic Gradient Descent (SGD) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure of DNN for performance evaluation.  
Table 5 shows CNN-related parameters applied to the experiment. As with DNN, five-fold cross-validation is performed by dividing the total number of 1000 data into 800 training data and 200 test data.  
In addition, for performance comparison with the proposed ML-based classifier, we present the experimental results of a correlator-based classifier [22] and a method in which persons directly classify topography.
## 4 Evaluation and Experiments
### Experiments
#### 4.2.2 Experimental Preparation  
We use three monitoring nodes in our experiment. Each monitoring node has the same hardware composition as mentioned in the preceding sections. Each monitoring node communicates with the gateway node separately. In one monitoring node, two sensor nodes are connected. One sensor node has a pH sensor and water temperature sensor, and another has a dissolved oxygen (DO) sensor and conductivity sensor. To compute the energy consumption conveniently, the monitoring and gateway  
Figure 18: Monitoring system management interface.  
nodes are powered by two common 5 V, 800 mAh rechargeable batteries. The battery has an internal protection circuit to guard against overdischarge, undervoltage, and overcharge conditions. Five groups of water samples are adopted in our test. The water samples are from real water regions of Huangpi, Jiangmenkou, Gulao, Xihucun, and Rongjiang, which correspond to five grades of water quality in GB3838-2002. The definition of five grades of water quality in GB3838-2002 [31] is presented in Table 1.  
A battery tester is used to online test the remaining capacity of the battery.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 4 Experimental Results
### Performance Evaluation for Body-to-Body Occlusion Subset  
To evaluate the improvement in pose estimation achieved by the proposed method in body-to-body occlusion scenes, we selected a total of 383 samples from RGB-D images in the NTU RGB+D 120 dataset. For performance comparison and analysis, joint-wise AP is measured during the occlusion experiments. However, in the COCO dataset skeleton format, the nose joint exists in the NTU RGB+D 120 dataset, but its annotated location varies significantly when capturing side profiles of individuals. Therefore, the AP measurement for the nose joint is excluded from this experiment to ensure fair evaluation.  
The results in Table 8 demonstrate that the proposed method improves detection accuracy by achieving higher AP scores for most joints compared to existing SOTA multi-person pose estimation benchmarks. Additionally, in terms of mean Average Precision (mAP)--computed as the average AP across all joints--the proposed method outperforms OpenPose and HigherHRNet by 22.2 percentage points and 23.4 percentage points, respectively. However, for wrist joints, which are particularly challenging to detect due to their small region size, the RGB-D network, which integrates depth information, achieves better performance than OpenPose but lower AP scores than HigherHRNet, which benefits from high-resolution feature maps that enhance precise joint localization. These results suggest that incorporating depth information into the pose estimation network does not necessarily provide fine-grained feature details for joint localization. However, in inter-person occlusion scenarios, the proposed method effectively leverages depth variations across multiple  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature Fusion Method** & **AP** & **p50** & **p75** & **APM** & **APL** & **AR** \\ \hline RGB image only & 0.39 & 0.83 & 0.32 & 0.22 & 0.41 & 0.39 \\ Depth image only & 0.28 & 0.69 & 0.28 & 0.11 & 0.28 & 0.29 \\ Early fusion & 0.14 & 0.40 & 0.07 & 0.06 & 0.14 & 0.13 \\ Late fusion & 0.40 & 0.84 & 0.31 & 0.23 & 0.40 & 0.40 \\ Ours (\(R\) = 3) & 0.51 & 0.86 & 0.51 & 0.33 & 0.52 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different feature fusion methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **\#Params** & **GFLOPs** & **AP** & **P50** & **P75** & **APM** & **APL** & **AR** \\ \hline \multicolumn{8}{c}{Subset of 8921 images as in NTU RGB+D 120 dataset} \\ \hline HigherHRNet [18] & 28.7M & 73.1 & 0.49 & 0.88 & 0.49 & 0.35 & 0.50 & 0.51 \\ OpenPose [27] & 52.3M & 411.1 & 0.39 & 0.83 & 0.32 & 0.22 & 0.41 & 0.39 \\ Ours (\(R\) = 3) & 68.9M & 593.7 & 0.51 & 0.86 & 0.51 & 0.33 & 0.52 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result on subset test set of NTU RGB+D 120 dataset.  
stages to reinforce the heatmap representation of occluded joints, enabling more accurate joint predictions from the camera's perspective.
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 3 High Performance MRAM Writing
### Reliability Issues
#### 3.4.2 Dielectric Breakdown  
High current flowing or voltage pulse is usually required to guarantee MTJ switching. The high electric field and significant self-heating effect cause a risk of dielectric breakdown in MTJ oxide barrier, which lead to MTJ dielectric breakdown, as well as performance degradation (by soft breakdown) and functional failure (by hard breakdown) of hybrid CMOS/MTJ circuits.  
The MTJ oxide barrier thickness is lower than FD-SOI CMOS oxide, which is around 1 nm [46]. The probability of breakdown occurrence in CMOS devices follows the Weibull distribution [47]. Figure 7 illustrates the breakdown probability with operation time duration of MTJ and nNOS/pMOS transistors. The curves of FD-SOI transistors show agreement with the experimental data in [47]. The stress voltage applied on MTJ oxide barrier is around 400 mV, whereas CMOS transistor is biased with 1 V gate voltage.  
Figure 6: **(a)** DC simulations (4 times) of the perpendicular magnetic anisotropy (PMA) STT-MTJ/CMOS hybrid circuit to validate the correctness of the spice model, with 3\(\sigma\) variations of both the MTJ and the CMOS access transistor. **(b)** Monte Carlo statistical simulation of the write operations: parallel (P) to anti-parallel (AP) and back to parallel (P). Write error may occur if the write duration is less than the required value.  
High stress voltage facilitates the switching but induces short time to breakdown. Thus, an optimum tradeoff of power design can be obtained according to the requirements of writing probability and endurance [49].
## 3 Communication and Channel Modeling
### Communication Performance
#### 3.2.2 Secrecy, Security and Reliability  
The secrecy and reliability indexes of UAV-enabled, RIS-assisted communication have been considered in several studies. These studies typically focus on wireless communication systems in the presence of eavesdroppers, with an emphasis on enhancing physical layer security through techniques such as beamforming, artificial noise injection, and signal rerouting. The secrecy performance of integrated satellite UAV-relayed RIS-assisted networks, with multiple vehicle eavesdroppers, has been investigated in [66]. In these systems, the UAV forwards the legitimate signal to the destination user through RIS, which ensures reliable transmission. The maximal ratio combining (MRC) eavesdropping technique is used to intercept the legitimate signal. Based on the proposed secrecy system model and the MRC eavesdropping scheme, the study analyzes the secrecy outage probability (SOP) across various SNRs, evaluating the effects of key parameters on the SOP. The theoretical analysis is validated through Monte Carlo (MC) simulations.  
UAV trajectory optimization has been explored for robust and secure RIS-assisted communication in the presence of eavesdroppers [67; 68]. The objective function of the joint trajectory and communication optimization problem aims to maximize the worst-case secrecy rate. For the RISeUAV, it has been demonstrated that a well-planned flight path significantly enhances the security gain of physical layer security [69]. Moreover, the flexibility of the RISeUAV is shown to be particularly effective in improving security when the transmission environment is favorable, such as higher transmit power or a weaker wiretap link. Additionally, the 3D positioning of the RISeUAV and the partitioning of the RIS (i.e., dividing the RIS into several sections and controlling the phase shift of each section) have been optimized to increase the physical layer security for legitimate users while enhancing the impact of artificial noise on illegitimate users [70].
## 2 Reaction Mechanisms and Kinetics of Electrocatalytic Nitrate Reduction
### Advances in Electrocatalyst Preparation Techniques for Efficient Nitrate Reduction
#### 3.1.3 Thermal Treatment (Calcination)  
The calcination treatment requires thermal treatment at controlled temperatures for a defined time [89]. [PERSON] et al. [90] prepared a Copper-modified titanate cathode (CalcTi/Cu) at 350 \({}^{\circ}\)C for 30 min for nitrate reduction in boiling water. They demonstrated that Nitrate reduction efficiency with Cal-Ti/Cu (65.37%) is much higher than Ti (5.3%) and Ti/Cu (5.21%) at 80 \({}^{\circ}\)C. Furthermore, the calcination of electrocatalysts increases surface area, active sites, and corrosion resistance, preventing surface deactivation [90]. Another study by [PERSON] et al. [91] prepared Ni foam/Carbon nanotubes (CNTs)/Cu electrodes at different temperatures (350 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 450 \({}^{\circ}\)C). The maximum nitrate removal was achieved at 450 \({}^{\circ}\)C due to better binding of CNTs to Ni foam and improved CNT film properties with this specific temperature of calcination [91].
### Contributions
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Protocol** & **Approach** & **Flaws/Limitations** & **Achievements** & **Year** & **Reference** \\ \hline DBR & The pressure sensor is used in orders to select the lowest depth node & The low depth node die quickly, because of high data burden & It has a good result for throughput and latency & 2008 [12] \\ \hline SMC & Uses amplify and forward cooperative technology & More consumption of energy because it introduces relay cooperation & High packet delivery ratio & 2016 [16] \\ \hline IACR & Depth threshold is determined to the master node, select only a node which has the highest residual energy and lowest depth & High consumption in energy and greater delay due to redundant packets transmission & Good result in throughput and network lifetime & 2015 [18] \\ \hline Co-PAMP & Used multiple gliders for the avoidance of the void zones in the network & High consumption of energy and high latency due to cooperation involved in data forwarding & High throughput efficiency & 2017 [21] \\ \hline Co-DR & Forwarder and relay selection is based on depth, utilize cooperation technique & Unbalanced energy consumption, high latency because the cooperation take extra time to reach the packet to the destination & High packet delivery ratio & 2014 [22] \\ \hline EECOR & A sender checks the set of relay nodes basis of its depth, FLRS technique is applied to choose the optimal relay & The forwarder set among all the nodes consume more energy with a cost of latency & Good energy efficiency and high throughput & 2017 [23] \\ \hline DEAC & Uses cooperative technique while forwarding the data & High consumption of the energy due to cooperative routing & Delivers the data packet with a high efficiency & 2016 [24] \\ \hline ACE & Relay node is chosen if it has the lowest depth and enough energy & High consumption in the energy, high latency, low network lifetime & High throughput, less packet drop, greater reliability & 2014 [25] \\ \hline RRCRP & Uses mobile sinks (MSs) either in a vertical or horizontal direction, also uses diversity technique & High energy consumption in a sparse network technique & Adopted an efficient delivery for the packets & 2017 [26] \\ \hline ODBR & A single-path routing scheme, only depth information is enough for data forwarding & maximizes the packets drop and reduces the network reliability & Good energy consumption and network life-span & 2016 [27] \\ \hline DNAAR & A criterion is used to choose the best forwarder, the high value of the criterion leads to select the forwarder node & It has high latency, because of the frequent checking of the channel condition & DNAAR has good packet delivery ratio, residual energy, dead and alive nodes & 2018 [28] \\ \hline \end{tabular}
\end{table} 
Table 2: The state-of-the-art of routing protocols in underwater wireless sensor networks (UWSNs).
## 4 Future Direction and Limitation  
Future Directions: One method could be to use Machine Learning (ML) techniques to dynamically alter routing parameters based on current network conditions. Machine learning approaches such as reinforcement learning may allow for the prediction and optimization of AODV and GPSR performance in particularly dynamic conditions, such as metropolitan regions with frequent vehicle movements. Moreover, future simulations may also include 5G and Vehicle-to-Everything communication technologies to improve data exchange rates and reduce latency in-vehicle networks [27]. As noted in the section on practical applications, further research might investigate ways to connect our routing protocols with UAVs and Connected Autonomous Vehicles to improve coverage and communication reliability in mixed traffic scenarios.  
Current Limitations: The limitation of this work is that it relies on static mobility models in certain instances. Though we considered vehicle movement along with urban and highway situations, actual traffic behaviors are often very unpredictable and dynamic [16]. Future research may include more realistic mobility models or connect with SUMO to enhance vehicle behavior simulation [13]. Another disadvantage is the reliance on basic routing protocols, which ignores the promise of advanced protocols to manage the increasing complexity of traffic and communication for autonomous vehicles. Furthermore, the ability of AODV and GPSR to scale in large vehicular networks with many vehicles remains a difficulty, and future research may focus on improving these protocols to handle bigger networks more effectively [26].
## 3 Methodology
### Design of Privacy-Preserving Techniques Using DL  
Privacy-preservation techniques based on DL are designed as multi-layer neural networks. DL models have the ability to transform actual high-dimensional initial data into low dimensional data. This can be achieved by training multiple neural networks in such a way that the high-dimensional input data are reconstructed. DL-based architectures are of two types: generative and discriminate. Figure 7 represent our proposed three layered architecture for an intrusion detection system using DL techniques. In generative architecture, joint probability distributions are estimated using the actual distribution of each class, whereas discriminative architecture calculates the conditional probability distribution based on the data observed. AutoEncoders (AE) are the most commonly used DL-based privacy-preservation methods. A Variational AutoEncoder (VAE) technique is used in this paper compared to classical and denoising AE models. The VAE can produce new samples based on the data's previous distribution, however, classical and denoising AEs are deterministic to features' latent structure and cannot generate new samples. Under the above scenario, three main types of threats are relevant; information Leakage, tampering, and sabotage. The sabotage threat is not going to be addressed in this work, in practice, this risk can be mitigated, for example by enhancing the physical security of the device deployed, but cannot be completely removed, given the assumption that the adversary has physical access, so they can destroy the device or shut it down. A more serious security challenge, in this case, is information leakage. Take, for example, drones that are frequently flown into hostile territories. These devices are very likely to be shot at if discovered, which can cause financial losses. However, if a drone is captured by an adversary, this can have more dire consequences. Extracting information from such a device (e.g., origin, mission, and likely destinations) is much more valuable to the enemy than destroying it. Another threat, in this case, is false alarms, caused by noise and or environment variations. The latter may trigger undue tamper response mechanisms such as powering down the system or deletion of sensitive data, which undermines the system's availability and disrupts its operation. Identification of threats mechanisms is explained through Algorithms 1-3. Our proposed algorithm allow only authorized users to access the EMR through Fog Computing.