esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Expectations  
Communication plays an important role in an FL setting, and various techniques, such as local updating, client selection, reduction in model updates, decentralized training, peer-to-peer learning, and compression schemes, are proposed to enhance the communication efficiency. This survey paper aimed to provide a bridge between the gap that was present on the topic of communication in FL. The paper mainly focused on the communication challenges and constraints in an FL environment, such as the bandwidth and limited computation. The challenges were proposed in the form of two RQs that both introduced the problems and provided a list of solutions that have been applied towards making communication more efficient. It is, after all, how all of the training of ML models occurs. Despite its importance, there is still limited research conducted compared to other aspects, such as privacy, security, and resources. For future expectations, it is critical to compose the aforementioned techniques effectively in order to overcome communication costs. Using a combination of techniques could be a future direction for research studies. In this case, the trade-off between the model's performance and communication could be studied. Additionally, more research is needed to improve the communication efficiency across FL settings.  
S.P.: supervision, conceptualization, methodology, investigation, writing--review and editing. O.S.: conceptualization, methodology, software, data curation, validation, investigation, visualization, writing--original draft. R.M.P.: supervision, methodology, validation, writing--review and editing. Q.Z.S.: methodology, writing--review and editing. G.S.: methodology, validation, writing--review and editing. L.Z.: methodology, writing--review and editing. M.N.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by U.S. SunTrust Fellow in Cybersecurity/Information Security Research Funding Program under Grant ST22-04.  
Not applicable.  
Data available on request from the authors.  
The authors have no conflicts of interest to declare for this manuscript.
## 4 Free-Space and On-Body Link Budget  
In body-centric communications, it is important to examine the transmission path loss (S\({}_{21}\)) of the antennas to ensure a reliable link budget. We measured S\({}_{21}\) for several cases of free-space and on-/off-body communications, in an open space (parking area) at UTP. This measurement operation allowed us to show a range of possible values of the path loss. The path loss in terms of the transmission coefficient (\(\left|S_{21}\right|\) dB) is the ratio of the input power (at transmitter antenna port) to the power received (at the receiver antenna port):  
\[PL_{dB}(r)=-\left|S_{21}\right|_{dB}. \tag{1}\]
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 2 Characteristics of Magnetic Field Detectors
### AMR Detector  
The AMR effect has been discovered by [PERSON] in Glasgow in 1857 [13]. He found out that single-domain ferromagnetic thin-films have a two per cent higher resistance in the direction of magnetization than in the orthogonal direction. Between these orientations, the resistance of an AMR thin filmstrip in a magnetic field can be described as a function of angle. In an AMR sensor, this effect is used for the detection and measurement of magnetic field magnitudes and directions. Frequently, the single-domain thin filmstrip is implemented in a Wheatstone bridge. The measured voltage difference depends on the magnitude and direction of the magnetic field [11].  
In our prototype implementation, a Sensitec AFF755 low noise AMR sensor in a SO8-housing is used (Figure 2) [14]. The typical sensitivity is specified as \(15\frac{\mathrm{mV/V}}{\mathrm{kA/m}}\). The noise level is given as \(10\frac{\mathrm{mV}}{\mathrm{\sqrt{Hz}}}\) for frequencies above 100 Hz up to the cut-off frequency, which is about 1 MHz. For an assumed bandwidth of 999.9 kHz this corresponds to a noise amplitude spectral density of \(N\approx 9.5\) uV without considering the noise of the instrumentation amplifier. The resulting detection threshold of the magnetic flux density of this AMR sensor is given as \(B_{\mathrm{min}}\approx 93\) nT if the full bandwidth is exploited. Accordingly, for 100 kHz bandwidth \(B_{\mathrm{min}}\approx 29\) nT.  
A flip coil is implemented between pin 1 and 8 for offset correction and detection of weak magnetic fields [15]. If a sufficiently high electrical current flows through the integrated flip coil for a short time, a magnetic pulse is generated. In practice, pulses with alternating signs are generated. After every pulse the current direction changes, thus the generated magnetic field is directed opposite to the actual ferromagnetic thin-film-magnetization. This bipolar magnetic pulse flips the magnetization of the ferromagnetic thin film between two stable states. Every magnetic pulse forces a new alignment of the internal magnetic domains. This principle ensures the full sensitivity of the AMR sensor for the detection of weak magnetic fields. The flipping of the magnetization direction can also be used for offset correction of the AMR sensor.
## 3 Results
### Sea-Ice Concentration Uncertainties
#### 3.3.3 Uncertainty Evaluation  
We evaluate the uncertainties by comparing them to the differences towards the Sentinel-2 reference SIC from Figure 8. Figure 14 shows that in the majority of cases, our uncertainty estimate is larger than the actual difference to the reference Sentinel-2 SIC. The uncertainties for the merged SIC are in most cases around 6%, go up to 15% and reach higher values only in exceptional cases. The differences are mostly close to 0% and hardly above 10%. In 79.2% of the cases, the uncertainty is higher than the difference. The uncertainties of the MODIS SIC are higher than those of the merged SIC. Uncertainties up to 20% occur frequently, but the majority is close to 5% and in 84.8% of the cases the uncertainty is higher than the difference. The AMSR2 SIC uncertainties are centred at about 5% and hardly exceed 8%. Here, our uncertainty estimates are higher than the differences in 72.8% of the cases. We conclude that the uncertainties are a conservative assumption of the actual difference to the reference SIC. In the majority of cases (\(\approx\)80%) the actual deviation from the real SIC should be lower than the uncertainty provided in the merged SIC dataset.
### Aim and Structure of the Article  
This article proposes a unified approach that combines RSO tracking and AN/mano-euvring algorithms with an aim to realize trusted autonomy in heterogenous DSS platforms for SA and CA. The remainder of the article is structured as follows. Section 2 presents an overview of various RSO tracking approaches and the equations corresponding to the proposed tracking algorithm. Section 3 summarises autonomous navigation (AN) and various trajectory optimization approaches for spaceflight applications and the equations for PSO algorithm. Verification case studies carried out in various scenarios are defined in Section 4. The results obtained from the case studies are discussed in Section 5. Section 6 comprises the conclusions and scope for future research.
## 5 Multi-Objective Reliability Optimization of Suspension Control Arm
### Multi-Objective Reliability-Based Optimization
The comparison of the performance indexes before and after multi-objective reliability optimization of the control arm is shown in Table 8. Compared with the initial steel control arm, after the deterministic optimization, the mass of optimized control arm was reduced by 8.89% from 3.16 to 2.88 kg and the life was reduced by 22.47% from 1.39 \(\times\) 10\({}^{6}\) to 1.08 \(\times\) 10\({}^{6}\), with a reliability of 90%. After the multi-objective reliability optimization of the control arm, longitudinal stiffness changed little, the first mode frequency and transverse stiffness increased, and the mass changed from 3.16 kg to 3.03 kg. The mass decreased by 4.1% while the life increased by 215.8%, with a high reliability of 97%.
# A Controlled-Environment Quality Assessment of Android GNSS Raw Measurements
## 2 GNSS Positioning and Basic Performance of Android Smartphones  
GNSSs are space-based positioning systems developed starting from the early 1970s, offering to the users the capability to locate with at least meter-level accuracy anywhere on Earth, provided that at least four satellites are in good line-of-sight conditions [12; 13]. GNSSs have been used for military, professional, and scientific reasons for a couple of decades and they entered the mass market only in the early 21 st century. With the advent of powerful and cheap processors for mobile phones, GNSS naturally entered the world of smartphones, enabling such devices with positioning capabilities.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 5 Open Issues
### Long Propagation Distances and Diverse Channel Characteristics  
Maritime environments involve large propagation distances, multi-modal communication (e.g., radio- or acoustic-based), and the impact of weather conditions and the relative motion of the sea surface. In order to overcome these issues, FL-aided solutions are needed to minimize the amount of data transmitted to centralized premises while keeping high local learning performance. For example, in URLLC collision avoidance scenarios with autonomous surface vehicles, decentralized FL has been adopted to mitigate the impact of environmental parameters on the delay performance [34].
## 2 Materials and Methods
### pH Assays  
For each bacterium strain tested, a suspension in a saline solution (0.9 % NaCl) was prepared from an isolated colony of the 18 h incubated agar plate and diluted to achieve turbidity equivalent to a 0.5 McFarland standard. The suspension was further diluted at 1:100 (equivalent to 0.025 McFarland or 1-2 \(\times\) 105 CFU/mL); 10 \(\mu\)L of this inoculum was then added to each well of a 96-round well-plate containing 100 \(\mu\)L per well of MHB medium, adjusted at different pH using different acids (see above) and incubated at 36 \({}^{\circ}\)C during 18 h. As a control for bacterial concentration and viability, 20 \(\mu\)L of the inoculum was plated in MHA (at neutral pH). As a control for bacteriostatic effect, two successive dilutions of the inoculum were done: 10 \(\mu\)L into 140 \(\mu\)L followed by 2 \(\mu\)L into 1 mL and then 50 \(\mu\)L of this suspension were plated on Muller-Hinton agar and incubated for 24 h at 36 \({}^{\circ}\)C. Bacterial growth in the liquid medium was then assessed by adding 40 \(\mu\)L of iodonitrotetrazolium chloride (INT, Sigma), 0.2 mg/mL per well, and incubating for an additional 30 min at 36 \({}^{\circ}\)C. Bacterial viability was determined by subculturing for 18 h in agar plates, with each condition showing no bacterial growth (no reduction of the dye to a pink colour), plus the first condition in which there was significant growth. Briefly, 2 \(\mu\)L of the selected wells were diluted into 1 mL saline solution and then 50 \(\mu\)L plated on Muller-Hinton agar and incubated for 24 h at 36 \({}^{\circ}\)C. It is expected that the number of colonies formed, on plates prepared from wells where a bacteriostatic effect happened, would be much lower than on the plates prepared from wells showing bacterial growth (INT positive ones) and equivalents to the ones obtained on bacteriostatic control plates. Each pH condition was tested in triplicate for each bacteria strain and each experimental set was carried out twice on different days.
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 4 Subsampling Methods for the IS-AR(1) Model  
If the size of the full data \(m\) is moderate, we can easily obtain the full-data MLE by maximizing (2) using Algorithm 1. However, it is computationally challenging to implement this algorithm when \(m\) is very large. In this case, we may resort to subsampling strategies, using a small fraction of the full data to obtain estimators. We propose three distinct methods: (1) optimal subsampling under A-optimality (**opt**), (2) information-based optimal subdata selection (**iboss**), and (3) sequential thinning (**thin**). We describe these procedures and propose practical algorithms in the following subsections.
## 3 Results  
1. Inverse dynamics:  
The RF moment at the knee joint obtained from the Anybody computation was in agreement with the EMG activity known from other studies [4, 6, 8]. The power of RF muscle was also computed and is reported in Figure 2 (red line). This variable synthetically represents the energetic contribution of RF to the knee joint movement. Figure 2 shows that in a normal situation the power is negative (energy is absorbed) from 40 to 71% of the gait cycle in correspondence with the knee flexion occurring at the late stance-early swing phase. From 75 to 85% of the gait cycle where the extension of the knee started, the power became positive. This indicates that the RF was transferring energy to the shank and was involved in active knee extension. The power of VA was computed in two different conditions: when RF was contributing as a part of the quadriceps muscle (green line in Figure 2) and without the contribution of RF (paralyzed muscle, blue line). The sequence of negative and positive power observed at early stance phase (out of interest in this study) was dominated by the VA contribution, so that no difference appears between presence or absence of RF (the green band is completely hidden by the blue band). In the phase of interest (late stance-early swing) the negative power of the VA was very small when RF was present (green line) and became higher, in absolute, than the power required by the RF in normal conditions when RF was paralyzed (blue line).  
The missing negative power of the paralyzed RF was replaced by a greater negative power of the VA. Between 75 and 87% of the gait cycle. Further, there was a positive power at the knee joint produced by the VA, which replaced the activity of the paralyzed RF. This indicates that the positive muscle  
Figure 2: Inverse dynamic simulation. Red line = power of RF with normal activity. Green line = power of all vasti with normal RF activity. Blue line = power of all vasti when RF is paralyzed (note that green line and blue line overlap during the stance phase, since RF power is negligible in this phase, and the vasti do not change their activity. Encircled: the need for positive power at the beginning of knee extension is evidenced. Below: Knee joint angle along the stride cycle, where flexion is positive and extension is negative.  
power of the knee extensors was necessary during this phase. It underlines that the initial extension of the knee in swing phase at 75% of the gait cycle (which corresponds to muscular power generation) was not only a passive motion induced by a double pendulum mechanism but also depended on knee extensor action, mainly the one of RF in normal conditions, or of VA in case of a paralyzed RF.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 3 Biomedical Applications of Magnetic-Plasmonic Nanocomposites
### Imaging and Contrast Agents  
Magnetite nanoparticles have been of great interest as new MRI contrast agents, largely due to their ease of synthesis, biocompatibility and superparamagnetism [68]. The magnetic plasmonic nanoclusters synthesised by [PERSON] et al. are proposed for use as multimodal imaging agents, utilising magnetic guidance under monitoring of the image in situ [40]. In this work, the molecular specificity of the composite nanoparticles is tested on SK-BR-3 breast cancer cells and A-431 keratinocytes, and it was shown that the cell targeting agent demonstrates high cell specificity and was preferentially taken up by cells with the complementary antigen (Figure 9).  
Another important consideration made by these authors was the choice of surfactant--this can have a major influence on the viability of the resultant nanoparticles use in biomedical applications. SDS (sodium dodecyl sulfate) is used in this synthesis in place of CTAB (which is cytotoxic). This SDS was easily replaced with methylated PEG thiol molecules, which enhance biocompatibility.  
Figure 8: (**a**) A plot illustrating the changes in tumour size in mice following a number of different treatments. The tumours were seen to shrink following treatment with the drug-carrying magnetic plasmonic nanoparticles; (**b**) Also shown are confocal microscopy images of cells treated with different compounds after a range of incubation times and conditions. Reprinted with permission from [45]. Copyright ACS, 2014.
## 5 Direct EAT Segmentation Methods
### Traditional Image Processing Methods  
Due to EAT's particular distribution, using traditional image processing methods to directly segment EAT automatically is quite challenging. Therefore, authors rely on  
Figure 11: A summary of the method presented in [PERSON] et al. [36]. The CT slice is fed into a U-Net-based CNN which segments the pericardium mask. The mask is refined using morphological processing and used to mask the input for a second U-Net based CNN which segments the EAT region.  
Figure 10: A summary of the method presented in [PERSON] et al. [7]. A CNN is first used to obtain a PAT mask, which is used to resample the input CT slice. The slice, converted to radial coordinates, is fed into a second CNN which delineates the pericardium line. The pericardium line and PAT mask are used to segment the image into EAT and PAT.  
semi-automatic approaches such as [PERSON] et al. [9]. The authors propose a time-saving GUI tool to enable experts to quickly segment EAT while reducing inter-observer variability. The experts manually determine the first and last slice of the heart region and then proceed to manually segment a region of interest on every 10 th or 20 th slice. The VoI of the remaining slices is then interpolated by the algorithm, with the ability of manual refinement if required. The algorithm further refines the segmentation with additional image processing steps like hole filling and morphological closing. The authors evaluate the method on 145 patients and a comparatively large number of slices (around 25,000 total slices), by slice count the largest datasets out of all the papers covered by this review. The authors report processing times of around 3.5 min for 60-slice scans and around 7.4 min for 235-slice scans, in both cases 4-8 times faster than manual segmentation. The method achieves a Dice coefficient of around 0.93. Considering the quantity of the used dataset, it is safe to say their method produces results equal to or better than inter-observer variability for this task, but still requires significantly more time than fully automatic methods. One potential improvement of the method would be to use a fully automatic method to perform the initial segmentation but allow experts to manually refine that automatic segmentation, thus saving even more time.
## 4 Exact Approaches
### Pulse Formulation  
The decision variables \(z_{jt}\) in the Pulse model refer to the possible instants \(t=r_{j},\ldots,\bar{d}_{j}-p_{j}+1\) when the order \(j=0,\ldots,n\) starts. It means that \(z_{jt}=1\) if and only if order \(j\) starts its production at time period \(t\), and \(0\) otherwise. Finally, for each pair of orders \(i,j=0,\ldots,n\) the binary decision variable \(u_{ij}\) equals \(1\) if order \(i\) precedes directly order \(j\). In this formula tion, the \(f_{jt}\) term represents the profit of an order \(j=1,\ldots,n\) at period \(t=r_{j},\ldots,\bar{d_{j}}-p_{j}+1\) minus the tardiness penalties: \(f_{jt}=e_{j}-w_{j}\max(0,t-d_{j})\).  
\[\textbf{maximize}\sum_{j=1}^{n}(\sum_{t=r_{j}}^{\bar{d_{j}}-p_{j}+1}z_{jt}(f_{ jt+p_{j}-1}-\sum_{t^{\prime}=0}^{p_{j}-1}c_{jt+t^{\prime}}-\sum_{\begin{subarray}{ c}i=0\\ i\
eq j\end{subarray}}^{n}u_{ij}\sum_{t^{\prime}=1}^{s_{ij}}c_{jt-t^{\prime}})) \tag{20}\]  
\[\sum_{j=1}^{n}z_{jt}\leq 1\qquad t=1,\ldots,T \tag{21}\]  
\[\sum_{\begin{subarray}{c}i=j\\ i\
eq j\end{subarray}}^{n}u_{ij}\leq\sum_{t=r_{j}}^{\bar{d_{i}}-p_{j}+1}z_{it} \qquad i=0,\ldots,n \tag{22}\]  
\[\sum_{\begin{subarray}{c}i=0\\ i\
eq j\end{subarray}}^{n}u_{ji}=\sum_{t=r_{i}}^{\bar{d_{i}}-p_{j}+1}z_{it} \qquad i=1,\ldots,n \tag{23}\]  
\[\sum_{t=r_{j}}^{\bar{d_{j}}-p_{j}+1}z_{jt}\leq 1\qquad j=1,\ldots,n \tag{24}\]  
\[\sum_{t=r_{i}}^{\bar{d_{i}}-p_{j}+1}tz_{it}+(s_{ij}+p_{i})u_{ij}-\bar{d_{i}}(1 -u_{ij})\leq\sum_{t=r_{j}+s_{ij}+1}^{\bar{d_{j}}-p_{j}+1}tz_{jt} \tag{25}\]  
\[i=0,\ldots,n\quad j=1,\ldots,n\quad i\
eq j\]  
\[\sum_{t=0}^{r_{j}-1}z_{jt}=0\qquad j=1,\ldots,n \tag{26}\]  
\[\sum_{t=(\bar{d_{j}}-p_{j}+1)+1}^{\bar{T}}z_{jt}=0\qquad j=1,\ldots,n \tag{27}\]  
\[z_{00}=1 \tag{28}\]  
The objective (20) is the maximization of the total profit including the tardiness penalties and environmental costs during processing and setup operations. Constraints (21) specify that at each time \(t\), the machine can start at most one job. Constraints (22) indicate that an accepted order has at least a successor. Constraints (23) impose to an accepted order \(j=1,\ldots,n\) to have exactly one preceding order. Constraints (24) restrict the starting time of each order to the interval defined from its release date to its deadline minus its processing time. Constraints (25) precise precedence relationship between two orders, guaranteeing that if order \(i\) precedes directly order \(j\), its starting time must be defined at least at a period after its release date \(r_{j}\) and the setup operation \(s_{ij}\), and after \(p_{i}\) the processing of order \(i\). Constraints (26) and (27) prevent each order to be processed before its release date and after its deadline. Finally, constraint (28) forces the dummy order to starts the sequence at time \(0\).
## 2 Results
### PTM Analysis  
We determined the putative sites for PTMs along the NaV1.5 protein and we found that the majority of them are in IDL domains, mostly in the four longest disordered regions (amino acid positions 20-66, 441-532, 993-1111-1125-1162, and 1932-1980). Overall, prediction of phosphorylation sites identified 38.2% of residues in ordered regions and 61.8% in IDRs (Figure 2A). Experimentally detected phosphorylated protein residues are annotated in PhosphositePlus. For NaV1.5, there are a total of 73 sites, of which 71.2% are in IDRs and 28.8% in ORs (Figure 2B). Potential glycosylation sites were predicted and yielded 114 residues: 76.3% in IDRs and 23.7% in ORs (Figure 2C). PEST sequences involved in protein degradation signaling were 91.5% in IDRs and 8.5% in ORs (Figure 2D). Finally, the prediction of ubiquitination sites identified seven candidates: three in ORs (42.8%) and four in IDRs (57.2%) (Figure 2E).