esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Electric Field Distribution around an Instrumented Total Hip Stem
## 2 Materials and Methods  
### Modification of a Total Hip Stem  
An electrode was integrated into a commercially available straight uncemented total hip stem (HIPSTARV40, Stryker, Duisburg, Germany) made from titanium alloy. For this, the implant was modified by milling a notch into its anterior side. The depth of the notch was 1.5 mm, which was decided based on a static analysis of the implant strength. A width of 3 mm was determined to allow for adequate insulation between the first electrode (Ti6 Al4V wire) and the second electrode (total hip stem). The insulating material was made from zirconium oxide (ZrO\({}_{2}\)), which is a biocompatible ceramic, primarily employed in the field of dentistry.  
The Ti6 Al4V wire and the insulator were adapted to critical regions of bone ongrowth of straight total hip stems. Clinically, sufficient ongrowth of bone tissue is observed at the lateral and medial parts of the central and distal areas of straight stems [20]. In particular, radiolucent lines are observed at the proximal part, which indicates a lack of bone ongrowth in this implant area. Due to this reason, the notch was positioned in such a manner so as to stimulate in particular the anterior and posterior parts in the proximal and central areas to achieve an additional effect on bone ongrowth by the electric stimulation. The electrodes were supplied by an alternating voltage to allow an electrical field to subsist around the implant. The alternating voltage also facilitated the usage of an inductively coupled system during the clinical application, thus avoiding the need for an internal power source. The energy was transferred by an alternating magnetic field generated by an external coil that was supplied by an external power source.
### Integration of AI-Based Cybersecurity Protection
#### 1.3.1 Software-Defined Networking
This solution used a combination of two detection methods: discrete wavelet transform and random forest. The second step is to take an automated mitigating action, dramatically shortening what would otherwise be a slow response time attainable by human operator. A combination of SDN together with network functions virtualization (NFV) was used for this purpose in [15]. Those authors used threshold-based classification, and on this basis a filtering action was taken such that the corresponding packets were dropped with a probability ranging from 10% to 100%. Another approach to automated protection was described in [16]. Those authors developed a framework called ArOMA that integrated traffic monitoring, anomaly detection and mitigation. This solution fully utilized the SDN advantage to integrate all these functions without the need for a dedicated hardware device or installation of specific software. Efficient detection and mitigation of (D)DoS attacks will be especially important in future large-scale networks integrating IoT devices. As presented by the authors of [11], utilization of SDN and automatically collected statistics stored on networking devices is more efficient and can achieve greater detection accuracy than traditional sampling-based detection methods.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
### Orthogonal Time Frequency Space (OTFS)  
OTFS has emerged as an alternative modulation scheme designed to tackle the challenges of multipath and high-mobility scenarios, such as those expected in SAGINs. It is particularly well-suited for doubly selective fading channels (time- and frequency-selective), offering improved robustness compared to traditional modulation schemes. Unlike OFDM, which operates in the time-frequency (TF) domain, OTFS modulates information symbols in the delay-Doppler (DD) domain, where the wireless channel characteristics are more effectively represented and managed [6].  
The delay-Doppler domain characterizes the wireless channel based on propagation delay (due to multipaths) and Doppler shift (due to relative motion between transceivers). Unlike the time-frequency domain, where the channel varies rapidly due to time and frequency fluctuations (Figure 2a), the DD domain provides a more structured and stable representation of the channel as shown in Figure 2b.  
Figure 2: 3D visualization of the wireless channel. (**a**) The TF domain (**b**) The DD domain. This stability arises because channel variations in the DD domain are expressed as discrete delays and Doppler shifts rather than continuous fluctuations. As a result, channel estimation and equalization are simplified. By operating in the DD domain, OTFS inherently aligns with the intrinsic properties of wireless channels, providing resilience against severe Doppler effects. In contrast, in the time–frequency domain, rapid variations in time and frequency create significant challenges for channel estimation.  
This capability makes OTFS particularly advantageous for high-mobility scenarios, such as those expected in SAGINs, including satellite communications, UAVs, vehicular networks, and high-speed trains. By effectively mitigating Doppler-induced distortions, OTFS offers significant performance improvements over OFDM, reinforcing its potential for next-generation 6G networks.
## 2 Theoretical Background
### Strategies for Coverage Optimization in WSNs  
As explained in Section 2.1 there are different types of coverage and factors that affect them. In order to cope with their particularities, various strategies have been proposed regarding the coverage problem in WSNs. The most popular of them are: grid based, virtual force based, and computational geometry based.  
Grid based methods consider grid points in order to determine the location of sensor nodes and calculate the existent area coverage as the ratio of grid points covered to the total number of grid points. The commonly used grids are of triangular, hexagonal, square, and honeycomb types.  
Force based strategy supposes the existence of virtual attractive and repulsive forces, which coerce sensor nodes to move towards or away each other until evenness is achieved so that full coverage is achieved.  
In computational geometry based strategy the area to be covered is divided into geometrical structures, such as Voronoi diagrams and Delaunay triangulation. A Voronoi diagram is a polygon,which is created by the lines of perpendicular bisectors that connect two neighboring objects. A Delaunay triangulation is a triangulation of an area such that no points in any triangle are located within the circumscribed circle of any other triangle in the area. Delaunay triangulation is the dual of Voronoi diagrams, because the circumcenters of Delaunay triangles are the vertices of the Voronoi diagram [27; 28].
## 5 Experiments
### The Influence of Parallel Corpus Size  
The method based on pretraining and fine-tuning is mainly aimed at improving the quality of low-resource neural machine translation. However, how many parallel corpora are low resource cases? When the scale of the parallel corpus becomes larger, does the model CPTT still work? In this section, we study these problems in the English-to-German translation task by changing the scale of parallel corpus. We collected all the parallel corpus data of WMT17 en-de, and then extracted different numbers of parallel corpus from them for experiments. The results are shown in Figure 6.  
As we can see, all models initialized by the pretrained model achieved significant improvements, but with the increase of the scale of the parallel corpus, these improvements become smaller and smaller. When the number of parallel corpus is small, especially less than 1 million, our CPTT model can obtain more improvements than the transformer. When the scale of parallel corpus is between 1 million and 3 million, the performance of CCPT and transformer are similar. When the scale of parallel corpus reaches 4.5 million, transformer can obtain more improvements than CCPT. We think that this is because when the scale of parallel corpus becomes larger and larger, the influence of pretraining model will become smaller and smaller. Therefore, when the scale of parallel corpus is large, the transformer will obtain better performance, otherwise CCPT may be a better choice.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Pretrained Model** & **zh-en** & **en-de** \\ \hline - & 9.72 & 19.03 \\ mlm\_src & 12.82 & 24.92 \\ mlm\_tgt & 11.77 & 24.40 \\ mlm\_srctgt & 13.79 & 25.87 \\ \hline \hline \end{tabular}
\end{table}
Table 6: BLEU scores for 6 layers LWCT model with different pretrained models in validation dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Encoder**} & \multirow{2}{*}{**Decoder**} & \multicolumn{2}{c}{**zh-en**} & \multicolumn{2}{c}{**en-de**} \\ \cline{3-6}  & & **Transformer** & **CPTT** & **Transformer** & **CPTT** \\ \hline - & - & 10.74 & 10.74 & 20.88 & 20.25 \\ - & mlm\_tgt & 10.82 & 11.73 & 20.90 & 20.33 \\ - & mlm\_src\&tgt & 11.00 & 11.65 & 21.08 & 21.26 \\ mlm\_src & - & 13.26 & 13.34 & 24.58 & 24.46 \\ mlm\_src\&tgt & - & 13.82 & 13.53 & 24.86 & 24.46 \\ mlm\_src & mlm\_tgt & 13.47 & 13.80 & 24.78 & 22.94 \\ mlm\_src\&tgt & mlm\_src\&tgt & 13.84 & 14.41 & 26.16 & 26.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: BLEU scores for encoder-decoder NMT models with different pretrained models in validation dataset. mlm_src&tgt is cross-lingual masked language model, mlm_src and mlm_tgt are monolingual masked language models.
## 4 Results
### Simulated Data
#### 4.1.3 Third Experiment  
In the third scenario (Figure 5), the first target moves horizontally from left to right, the second target moves vertically from bottom to top and the third target moves obliquely from bottom to top. The initial coordinates of the targets are target one: x = \(-\)80 m, y = \(-\)70 m, target two: x = 0 m, y = \(-\)140 m, target three: x = \(-\)65 m, y = \(-\)130 m. The measurements corresponding to the three targets, shown in Figure 5a, were obtained by applying the Hungarian data association algorithm. After applying Kalman filtering to each target, the trajectories in Figure 5b were obtained. The trajectories represented in Figure 5c are obtained after the application of the explicit polynomial fitting procedure.  
Figure 4: Partial and final results of the second experiment with simulated data, [6]: (**a**) spatial spreading of targets after data association; (**b**) the effect of Kalman filtering; (**c,d**) the effect of the explicit polynomial fitting method.  
These first three experiments prove that the explicit polynomial fitting procedure is efficient in the case of first order polynomial trajectories. We have compared the useful parts of the nine trajectories generated by simulation (three trajectories in each experiment), registered in the simulation phase, with the corresponding trajectories obtained after the application of the explicit polynomial fitting procedure proposed in this paper and we have remarked that those trajectories are identical.  
Hence, in the case of linear trajectories, the explicit polynomial fitting procedure can remove the entire uncertainty obtained at the output of Kalman filters.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 5 Experiments
### Learning Clusters  
Considering the averages of the three climate variables--the meridional wind (m/s), zonal wind (m/s), and the geopotential height (in geopotential meters)--at the pressure levels 250 hPa and 500 hPa, the quality of the clustering is evaluated using the validity index (see Equation (5)). We calculated the validity index for a number of clusters ranging from 2 to 24. After exhaustive tests, this range proved to be wide enough to achieve low validity and avoid small clusters. The result is shown in Figure 5. The number of clusters resulting in the lowest validity was \(K=19\), which results in an average of 6400 time series per cluster inside the considered region within 6\({}^{\circ}\) S-30\({}^{\circ}\) S and 33\({}^{\circ}\) W-83\({}^{\circ}\) W. For each of the \(K=19\) clusters, one obtains a different filter using the RLS adaptive filter.  
We employ K-means due to its simplicity and wide use [26,32]. It is important to mention that the K-means are not applied on the time series containing the climate forecasts but on their time averages, reducing the dimension of the clustering problem. Figure 6 shows the clusters provided by K-means. One can note that they are reasonably linked with our previous knowledge of climate resemblance in different subregions within the considered region. Other clustering approaches could be attempted based on self-organizing maps using directly the data points (a cell over the considered time-frame presenting several climate variables). One could use dimensional reduction mappings such as principal component analysis, independent component analysis, and other nonlinear dimension reduction methods to map the data points for an alternative clustering space, which might provide better results. However, as we develop in the sequel, k-means clustering proved effective for training adaptive filters for reducing climate forecast errors.
## 3 Results
### Length of Nonlinear Pair Generation Medium  
Second, we test the effect of the DSF length on the NLI patterns. In references [18; 19; 20], the authors assume that the wave vector mismatch goes to zero to ensure the satisfaction of the phase matching conditions. This study, however, considers the case where the wave vector mismatch is small but not negligible. In this case, we expect fine changes in NLI with varying the DSF length.  
The NLI system in this test consists of two equal-length DSF sections of various lengths and a \(50\)-m SMF section located between two DSF sections, and \(\lambda_{p}\) is fixed at \(1550.92\) nm. Figure 3a-c show the measured JSIs for various DSF lengths (\(L_{\text{DSF}}\)), and Figure 3d is the normalized diagonal JSI spectra for various DSF lengths against the frequency detuning from the pump frequency. As seen in Figure 3d, the peak frequency gets away from the pump frequency as \(L_{\text{DSF}}\) becomes longer. Figure 3e is the difference between the pump center frequency and the peak frequency of the first, second, and third islands (\(m=1\), \(2\), and \(3\), respectively) against the DSF length. The frequency shift rates with increasing \(L_{DSF}\) are approximately \(0.11322\) GHz/m, \(0.11953\) GHz/m, and \(0.14266\) GHz/m for \(m=1\), \(2\), and \(3\), respectively. The lines are the theoretical predictions using the identical parameters as in Section 3.1 except the DSF lengths and the pump wavelength. The frequency difference slowly grows as increasing \(L_{\text{DSF}}\) if we consider the phase shift induced in DSF (\(\Delta K_{\text{DSF}}L_{\text{DSF}}\)) (solid lines in Figure 3e), meanwhile, the difference is constant if we neglect the DSF phase shift (dashed lines). The measured results match well with the theoretical predictions with the phase shift in DSF. This slow frequency shift may induce the fine-tuning ability of the center frequency of islands.  
In addition, Figure 3d shows the envelope changes of the pair-generation spectrum for different DSF lengths. It is known that the pair-generation rate is proportional to the square of the SFWM medium length. Recently, we investigate the length dependence of the pair-generation bandwidth, showing that the longer the SFWM length is, the narrower the spectrum bandwidth is [21]. Therefore, a long DSF nonlinear medium can have a large maximum generation rate but a rapidly degraded generation rate at a frequency away from the pump frequency, as seen in Figure 3d. We should carefully select the DSF length for balancing both the bandwidth and pair-generation rate even though the \(L_{\text{DSF}}\) is the selectable parameter for the fine-tuning of peak frequency.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results  
### Experimental Setup  
Photoacoustic images (PAIs) were acquired from the CHAOS dataset by k-Wave simulation. During the experiments, the dataset was divided into a training set (75%), a validation set (5%), and a test set (20%), and data enhancement was performed on the training set by random rotations of 90 degrees and flipping on the x-axis, followed by super-resolution reconstruction using the EDSR-M network with 32 residual blocks. Finally, the pre- and post-reconstruction images were segmented using the DeepLabV3+ network with ResNet-50 backbone to assist in testing the reconstruction performance. The values of the main parameters of the network are shown in Table 1, and the implementation of these models was done with the MATLAB Deep Learning Toolbox (R2023b).
## 5 Conclusions  
In this work, we have presented a facile method for obtaining perfectly spherical polymer microspheres using \(o/w\) Pickering emulsions polymerization method of MM, BM, and tBA monomers, whereas three types of NPs were used as emulsion stabilizers, NP-Gly, NP-CN, and NP-SH. We have demonstrated that the key parameter influencing the size of the obtained microspheres is the surface and interfacial energy of the NPs. The surface and interfacial energy of the NPs determines their affinity toward one phase or the other and thus affects their interfacial immersion depth. Further, this immersion depth affects the curvature of the emulsion droplet and eventually of the polymer microsphere obtained after polymerization. Namely, the NPs least immersed in the emulsion droplet are NP-Gly. Further, the NP-Gly led to the formation of largest microsphere signaling that they have formed from the polymerization of a coarse Pickering emulsions, or suspensions. We have explained this by showing that NP-Gly is most strongly wetted by water, having the lowest interfacial energy with water \(\gamma_{NP/water}\) = 0.11 mJ/m\({}^{2}\) amongst the three NPs. This also predicts that NPs with moderate to high surface energy \(\gamma_{NP}\) = 67.96 mJ/m\({}^{2}\), a rather high surface polarity component \(\gamma^{p}_{NP}\) = 42.56 mJ/m\({}^{2}\), and an extremely low interfacial energy with water, \(\gamma_{NP/water}\) = 0.11 mJ/m\({}^{2}\) attach poorly to oils and generate coarse Pickering emulsions, more like suspensions. From the SEM images, we observe that most perfect spheres are obtained for the most polar NP-Gly, for all three monomers  
Figure 7: 2D surface plot relating the normalized population distribution of the microspheres (z-axis) as a function of energy of detachment \(\Delta\)GNP/P of the NPs (\(x\)-axis) and the size of the microspheres (\(y\)-axis). The filled circles represent the data points corresponding to the maximum of the size distribution of the microspheres, and the black line is a guide to the eye. investigated, completely covered by a monolayer of NPs, and we thus confirm that the polymerization _loci_ reside in the suspension droplet. On the other hand, as the surface energy of the NP-CN and NP-SH decreases, the polar contribution to surface energy also decreases, causing an overall increase of the interfacial energy with water and a deeper immersion into the emulsion droplet. These NPs generated finer Pickering emulsions and smaller microspheres after polymerization than NP-Gly. Although in the case of NP-CN and NP-SH with MM Pickering emulsions having been obtained, no microspheres were obtained after polymerization, which implies that the polymerization nucleation _locus_ has completely shifted into the water phase, consuming monomer from the colloidosomes, and generating polymer nanoparticles and microparticles that are devoid of NPs on their surface. Further work should focus on fine tuning the synthesis conditions for obtaining narrower size distributions of the large microspheres from Pickering emulsions stabilized by NPs bearing polar functional groups.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 2 Theory Background
### Parallel Network of BP and LSTM  
From Sections 2.1 and 2.2, the BP and LSTM networks each have their defects. The BP network has limited generalization ability for temporal multi-factor problems, and the LSTM network requires a certain length of sequence as training samples. Therefore, the prediction results of these two models alone do not have a high generalization ability. Considering the characteristics of the two models, this paper adopts the method of constructing a BP-LSTM hybrid model as shown in Figure 4 to input the sequential data into the LSTM network and the non-sequential data into the BP network, to give full play to the advantages of the two models and achieve a higher precision prediction of the models.
# Review of Advanced Medical Telerobots
## 2 Medical Telerobotic Topologies
### Multilateral Teleoperation
#### 2.3.3 Multi-Leader-Multi-Follower (ML/MF)  
Multi-leader-multi-follower (ML/MF) systems are the most complex teleoperation topology in terms of the number of leader and follower components involved in a task (see Figure 5). To provide an illustration of the kind of robots that enter this category, the da Vinci system is a decoupled ML/MF robot: the operator manipulates independently two handles that each controls one robotic arm. This topology takes advantage of (a) improved dexterity, grasp, and manipulation due to the use of multiple follower robots, and (b) more flexibility and cooperation due to the involvement of multiple operators commanding and monitoring the operation [249]. Such systems can make the execution of more complex tasks possible [296]. Potential other applications include the cooperation between several surgeons with more than two tools, with operators potentially being distant from each other and from the patient. An important feature needed for this topology is for avoiding collision between robots on the side of follower consoles since the control is shared between multiple operators [297]. The challenges for this topology can be categorized into two branches, (a) safety [233, 234, 235, 236], and (b) motion synchronization of the systems [240, 241, 242, 243]. For the safety of the system, different measures have been taken into account in the literature to avoid collision of the follower robots [233, 234, 235, 236]. One approach is through predictive models, which predict the trajectory overtime for the motion of the followers and uses it to ensure safety and avoid collision [233]. Another approach is to predict and render a model of the environment on the leader site instead of transmission of force or velocity signals, which can also improve the bandwidth of the overall system [234, 235]. The aforementioned controllers are mostly known as model-mediated controllers, which use the information about the environment to avoid collisions [233, 234, 235, 236, 237].  
In addition to motion synchronization and stability, force reflection strategy is another studied topic for this topology. In some scenarios, each operator controls a separate follower robotic arm, while force reflection provides information about the collision. In some other scenarios, environmental force is also reflected. It should also be highlighted that control, local autonomy, and task sharing for this architecture are challenging in the presence of distributed communication delay and uncertainties at each leader or follower side. Several advanced architectures have been introduced in the literature to address some of the mentioned issues, including distributed event-based controllers [238, 239], adaptive neural/fuzzy controllers [240, 241, 242], and Passivity-based approaches [243, 244, 245, 246, 298, 294, 296].