esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Putative Physiological Mechanisms Underlying Spirulina's Response to Salinity  
_Spirulina_ demonstrates several physiological responses for adapting to osmotic stress when exposed to changing salinity levels. These responses reflect an intricate relationship between stress detection systems and the adjustment of cells and the distribution of resources.  
_Spirulina_ could activate its osmoregulatory pathways to produce compatible solutes like glycine betaine, trehalose, and glucosylglycerol when salinity exceeds the optimal threshold [35]. The organic osmolytes act as protective agents by stabilizing proteins and cell structures, which stops dehydration while keeping enzymes operational in hyperosmotic conditions. The lack of substantial growth impairment between 22 and 36 ppt showsthat these solutes effectively preserve cell turgor and metabolic balance under moderate salt stress conditions.  
They could also detect changes in stress-responsive protein expression when subjected to salinity stress, which affects energy metabolic and translation processes, as well as redox homeostasis [36]. These changes might help the organism move its metabolic priorities from growth to maintenance and repair functions, which explains why photosynthesis and biomass growth become disconnected at high salinity levels.  
The gross primary productivity (GPP) shows a linear rise with increasing light intensity when salinity reaches \(\geq\)17 ppt, indicating regulatory changes in photosynthetic machinery operation. The photosynthetic machinery could alter pigment composition while readjusting light-harvesting complexes to enhance light absorption and energy conversion efficiency during stressful conditions [37]. The elevated photosynthetic efficiency under salt stress generates sufficient ATP and NADPH for cellular defense mechanisms such as ion transport and antioxidant production instead of promoting biomass growth.  
Salinity stress creates ionic stress by causing excessive Na\({}^{+}\) and Cl\({}^{-}\) accumulation, which disrupts enzyme function and membrane electrical stability [38]. _Spirulina_ could activate ion extrusion systems, which include Na\({}^{+}\)/H\({}^{+}\) antiporters and H\({}^{+}\)-ATPases, but this process requires substantial energy expenditure. High salt concentrations exceeding 40 ppt can damage thylakoid membranes, which impairs photosynthetic electron transfer, thus disrupting energy homeostasis.  
Filament size regulation together with morphological plasticity represents a stress-induced adaptation that causes a structural shift between small (<400 \(\upmu\)m) and large (400-799 \(\upmu\)m) filaments when salinity levels increase. The change in filament size distribution from small to large filaments at higher salinity levels indicates an adaptive response of the organism to stress. Large filaments decrease membrane surface area relative to volume, which leads to reduced ion flux and enhanced ionic regulation [39]. The morphological change provides better buoyancy and light-positioning capabilities that enhance survival rates in environments with variable nutrients and layering.
# Video over DSL with LDGM Codes for Interactive Applications
## 2 Channel Codes
### RS Codes  
RS codes have the MDS property, _i.e.,_ any \(k\) packets can be received to recover the k information packets, whereas around \(k\times 1.05\%\) of packets are needed [16] for full recovery in LDPC. RS codes are linear, cyclic codes, formed by sequences of \(m\)-bits symbols, each of which symbols belong to a finite Galois Field, _i.e.,_ GF(\(2m\)), where m takes values greater than two. \(n\) is set to the value \(2m-1\). If \(m\) is greater than eight the RS computational complexity can be prohibitive. Specifically, the total complexity is \(\mathrm{O}(\frac{k}{(n-k)}\times(\log(k))^{2}+\log(k))\), even when using a fast frequency-domain algorithm [25].  
RS FEC in the common intra-packet approach works by grouping \(k\) packets at a time. From each of the \(k\) packets, the first \(m\)-bits are extracted to form \(k\)\(m\)-bit symbols. These \(k\) symbols are employed to generate \(n-k\) redundant symbols by means of the RS algorithm. The redundant symbols are then packed as the first \(m\)-bit symbols of \(n-k\) parity packets. The intra-packet algorithm continues by extracting the next set of \(m\)-bit symbols and forming \(n-k\)\(m\)-bit redundant symbols and packing these as the next set of symbols in the \(n-k\) parity packets.  
In [26], the interleaving factor was increased by forming each \(m\)-bit symbol by extracting one bit from each of \(m\) packets in turn. This alternative inter-packet approach improves the error-recovery performance in \"bursty\" error conditions, as the loss of any one symbol affects only one bit per packet. However, whereas the latency budget of the intra-packet scheme is the time for \(k\) packets to arrive, in the inter-packet budget it is the time for \(k\times m\) packets to arrive at the sender.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
###### Abstract  
There is a significant amount of synergy between virtual reality (VR) and the field of robotics. However, it has only been in approximately the past five years that commercial immersive VR devices have been available to developers. This new availability has led to a rapid increase in research using VR devices in the field of robotics, especially in the development of VR interfaces for operating robots. In this paper, we present a systematic review on VR interfaces for robot operation that utilize commercially available immersive VR devices. A total of 41 papers published between 2016-2020 were collected for review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Papers are discussed and categorized into five categories: (1) Visualization, which focuses on displaying data or information to operators; (2) Robot Control and Planning, which focuses on connecting human input or movement to robot movement; (3) Interaction, which focuses on the development of new interaction techniques and/or identifying best interaction practices; (4) Usability, which focuses on user experiences of VR interfaces; and (5) Infrastructure, which focuses on system architectures or software to support connecting VR and robots for interface development. Additionally, we provide future directions to continue development in VR interfaces for operating robots.  
virtual reality; robotics; interfaces; human-robot interaction +
Footnote †: journal: Applied Sciences
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
## 3 Modeling Electromagnetic Propagation in a Rotating Machine
### Monopole Antenna Performance  
The HFSS simulation revealed that the metallic components of the machine significantly influenced the propagation factor (\(F_{p}\)) and the machine's carcass had a noticeable attenuating effect on signal transmission.  
In contrast, it was observed that the monopole antenna exhibited a superior propagation factor in comparison to the RN-171 antenna. Additionally, the monopole antenna radiated uniformly in a specific direction, contributing to a stronger transmitted signal.  
The existence of the electrical machine body significantly influences both the input impedance of the antenna and the \(S_{21}\) parameter, thereby affecting the transmission characteristics. This implies that optimizing antennas for their specific operational environment may offer more advantages than designing them exclusively for free-space propagation. Tailoring antennas to leverage the machine components enables a meaningful improvement in the efficiency of EM communication.
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 3 Results
### Summary  
Finally, in order to see the results clearly, we show both a table and a comparative figure with the results of all the models on three images from the test set.  
Table 3 shows a comparison between the five models we have trained and other state-of-the-art models on the DRIVE dataset competition. We include results in the validation and test DICE as well as the type of the model, the number of trainable parameters and the time to process one image. It shows how the ConvMixer model and its Light version obtain the best results of the five models we trained, slightly above the U-Net but with one and two orders of magnitude fewer parameters, respectively. We also saw that the ConvMixer-Light model takes only 4 s per image to train, while other models such as U-Net and UNETR take two and four times more on an NVIDIA T4.  
Figure 9 shows the prediction images and DICE scores made by all the trained models for three images of the test set. It can be seen how the segmentation performed by the ConvMixer models (both the normal and the Light version) are the ones that show a higher amount of cleaner blood vessels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Type** & **Params.** & **Val DICE** & **Test DICE** & **Process. Time (s)** \\ \hline U-Net & CNN & 31M & 81 & 82 & 8.1 \\ UNETR & ViT & 104M & 80 & 80 & 16.0 \\ Swin-Unet & ViT & 27M & 76 & 77 & 6.5 \\ ConvMixer & CNN & 2.97M & **82** & **83** & 11.0 \\ ConvMixer-Light & CNN & **0.27M** & **82** & 82 & **4.2** \\ \hline IterNet [32] & CNN & 13.6M & - & 82.18 & - \\ BCDU-Net [33] & CNN-RNN & 20.7M & - & 82.24 & - \\ LadderNet [34] & CNN & 1.5M & - & 82.02 & - \\ RV-GAN [35] & GAN & \textgreater{}14M & - & **86.90** & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table summary of the results in validation and test DICE of the models we trained (top panel). The models and results of some of the leaders of the DRIVE dataset competition are also shown (bottom panel).  
Figure 8: Two validation images, its real mask and the ConvMixer-Light prediction.
## 4 Comparison between Theoretical and Simulation Results of \(C_{2\varphi}\)  
The statistics characteristic of \(C_{2\varphi}\) has been discussed in the previous sections. It provides a threshold setting that indicates the lock/unlock condition of FLD. In order to validate the validity of the theoretical analysis, we will compare between theoretical and simulation results.  
The probability density functions of \(C_{2\varphi}\) under lock and unlock conditions can be obtained from (19) and (20), respectively. At the same time, Monte Carlo simulation experiments that software receiver [23] acquires and tracks simulated GPS L1 signal are carried out. The parameters of the simulation experiment are shown in Table 1.  
The simulated GPS signal with 4.996 MHz intermediate frequency is generated by the software program. Four signals with different \(C/N_{0}\) values and a noise signal are simulated. The software receiver is used to process the signal and the probability density function (PDF) of FLD output can be retrieved under different situations.  
The comparison between theoretical and simulation results are shown in Figures 1-3. Figures 1 and 2 show the PDF of \(C_{2\varphi}\) with different average length \(M\) when signal presented. However, both of the figures indicate that maximum probability point of the horizontal axis moves closer to one when increasing the \(C/N_{0}\) values, which means that \(\Delta\varphi_{i}\) become smaller. Normally, \(C_{2\varphi}\) decreases when reducing \(C/N_{0}\). Maximum probability points in probability density curves change dramatically with different \(C/N_{0}\) values in Figure 1 but no evident differences in Figure 2. It means that probability density curve could not indicate \(C_{2\varphi}\) changing with different \(C/N_{0}\) because of interference by noise  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Signal & GPS L1 Signal \\ Signal \(C/N_{0}\) & High \(C/N_{0}\): 48 dB-Hz, 44 dB-Hz, 40 dB-Hz, 36 dB-Hz \\  & Low \(C/N_{0}\): No Signal \\ Signal sampling rate & 12 MHz \\ Coherent integration length & 1 ms \\ Doppler frequency & 0 Hz \\ Simulation times & 5000 times, 5 s (\(M=1\)\(M=20\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of software receiver.  
when \(M=1\) in Figure 2. So, \(M\) should be set larger. Figure 3 shows \(C_{2\varphi}\) obeys uniform distribution when no signal presented.
## 2 Laser Layout and Features
### Main Beamline  
The stretched pulse from the front end, with an energy of approximately 600 mJ, undergoes further amplification in the third high-energy multi-pass amplifier (MP3) stage. This stage features a 4-pass amplifier pumped by ten synchronized Propulse+ Nd:YAG systems supplied by Amplitude Technologies, each delivering 2 J, 532 nm pulses at a 10 Hz repetition rate. The amplifier features a cryo-cooled 50 \(\times\) 50 \(\times\) 25 mm\({}^{3}\) Ti:Sapphs crystal. The 800 nm output pulse reaches 6 J of energy before compression. Our system can operate at different repetition rates (up to 10 Hz), but it was finally set to deliver pulses from 1 Hz to single-shot at full-power operations due to the types of the performed experimental campaigns able to spoil the required system vacuum constraints. Before final compression, the amplified pulse is initially expanded to approximately 65 mm before passing through an attenuator system. This system includes a rotating half-wave plate and two high-performance broadband polarizers, enabling the output energy to be precisely adjusted down to 1% of the total in 1% steps. Prior to reaching the high-power compressor, the beam undergoes further expansion by a refractive telescope, achieving a final clear aperture diameter of around 100 mm.  
The optical high-power compressor (shown in Figure 1), configured in a 4-pass folded layout utilizing a retro-reflector and housed in a vacuum chamber maintained at 10\({}^{-6}\) mbar, finally compresses the pulse length down to 30 fs without any residual spatial chirp or additional spatio-temporal couplings [25]. This system employs four gold-coated gratings (HORIBA Jobin Yvon, Kyoto, Japan) with 1480 grooves/mm, measuring 135 mm \(\times\) 175 mm for the first and fourth gratings and 135 mm \(\times\) 230 mm for the second and third gratings, achieving a throughput efficiency of approximately 70%.  
After compression, pulse optimization and metrology are performed by inserting a one-inch mirror along the transport line to the TET interaction chamber, which allows for the selection of a specific section of the beam. The temporal measurement is performed by means of an APE SPIDER and its optimization can be performed using a closed loop between the temporal diagnostics and the Dazzler system. A couple of remotely controlled mirrors direct the beam from the high-power compressor to the TET area, where a dedicated experimental chamber is set up. The laser pulse is then focused using a gold-coated, 15-degree Off-Axis Parabolic (OAP) mirror with a focal length of 1 m, achieving a transverse diameter of 18 \(\upmu\)m, as shown in the Section Laser Diagnostics Section 3.2.1.  
The TET area is dedicated to experiments on laser-matter (gas and solid targets) interaction, where particles are accelerated and secondary radiation sources are generated, as well as on high-power laser guiding for external injection scheme experiments and plasma-target characterization experiments. Here, experiments are also carried out to develop user-oriented applications, within the EuAPS and EuPRAXIA frameworks.
# A 112 Gb/s DAC-Based Duo-Binary PAM4 Transmitter in 28 nm CMOS
## 4 Results  
The transmitter designed in this paper used Cadence Virtuoso IC617 simulation software for circuit design and layout postsimulation. The transmitter used a 28 nm CMOS process. In the whole design, 64 channels of parallel PRBS7 were used as input data. The 14 Ghz clock signal generated by the PLL provided the clock signal to each transmitter module through the CML2 CMOS module and the frequency division circuit. The whole layout of the transmitter is shown in Figure 17. The overall module size was 269 \(\upmu\)m \(\times\) 162 \(\upmu\)m, and the power consumption of the transmitter was 226 mW, the energy efficiency was 2.02 mW/Gb/s. The 112 Gb/s duo-binary PAM4 eye diagram obtained after layout imitation is shown in Figure 18. The six eye heights of the duo-binary PAM4  
Figure 16: Integral nonlinearity.  
Figure 15: Differential nonlinearity.  
were 82.3 mV, 80.2 mV, 78.6 mV, 81.1 mV, 82.1 mV, and 83.4 mV, respectively, and the level mismatch rate was 96.7%.The width was 11.7 ps, 10.5 ps, 8.9 ps, 10.3 ps, 11.6 ps, and 11.4 ps, respectively. The minimum opening height obtained was 78.6 mV, the minimum width was 8.9 ps, and the level mismatch rate was 96.7%. Figure 19 shows the test results of the bathtub curve. Through the error code test on the transmitter with an ideal receiver, the bit error rate was lower than \(10^{-12}\) and the eye width was about 0.4 UI. From the layout postsimulation results, it could be concluded that the transmitter designed in this paper had good performance and met the design indicators. Table 1 summarizes the performance of this transmitter and compares it with other transmitters with similar rate and process [29; 30; 31]. It can be found that under the same process, it had a higher eye height and
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 3 BILSTM neural network
### Pre-Processing of Data
#### 2.3.2 Interpolation Processing of Trajectory Data  
In this paper, a section of the trajectory of the vessel with MMSI No. 636017973 sailing at sea on 2 August 2020 was obtained via ShipHub. Since the observation moments are sparse and not uniform enough, the time intervals of the data features input to the model are unequal, so the model has difficulty in extracting the navigation characteristics of the target ship from these unevenly spaced navigation data. For this kind of non-uniform time-series data with unequal time intervals, the common processing method is to use an interpolation algorithm to fit the historical trajectory data and then to sample uniformly to predict the sailing trajectory based on the obtained trajectory data with equal time intervals.  
The interpolation method used in this paper is the constant directional Mercator algorithm, which is used to calculate the range D and heading C of two waypoints \((\text{lat}_{1},\text{lon}_{1})\), \((\text{lat}_{2},\text{lon}_{2})\) at the same time so that the average speed v between the two points can be obtained; the waypoint \((\text{lat}_{1},\text{lon}_{1})\) is used as the starting point, and the waypoint is carried out with speed v to obtain the waypoint of the same time interval, whereby the range D and heading C are calculated as follows:  
\[\text{C}=\arctan\biggl{(}\frac{|\text{D}\lambda|}{|\text{DMP}|}\biggr{)}= \arctan\biggl{(}\frac{|\text{lat}_{1}-\text{lat}_{2}|}{|\text{MP}_{2}-\text{ MP}_{1}|}\biggr{)} \tag{21}\]  
\[\text{D}=\text{D}\varphi\text{sec}\,\text{C}=(\text{lon}_{1}-\text{lon}_{2}) *\text{sec}\,\text{C} \tag{22}\]  
The interpolation of the track points in this paper is performed with a time interval of 30 s as a way of obtaining the ship trajectory with a uniform time distribution. The plots of some of the track segments before and after the interpolation of the target-ship track points are shown in Figure 5:
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 8 Ultrasound
### Classification  
To classify the quality and prevent calculation errors in fetal ultrasound videos, a scheme was proposed by [PERSON] et al. [68]. The proposed approach was realized with two CNN models, which were denoted as C-CNN and L-CNN. The purpose of the L-CNN was to find the fetal abdominal region in the ultrasound frame. The C-CNN evaluated the frame quality based on the output of L-CNN. Perception ultrasound by learning songraphic experience (PULSE) [69] was a research project at the University of Oxford that combined state-of-the-art machine learning techniques with probe and eye movement data during an ultrasound. While working on this project, [PERSON] et al. [70] proposed a model for efficient ultrasound analysis consisting of a teacher and student module. The teacher module was trained on ultrasound images and sonographer eye movement data during ultrasound video, while the student model was trained just over the ultrasound images. The purpose of the model was to classify fetal abdomen, head, and femur frames. VGG-16 was used as a feature extractor in the model, and MobileNet V2 was explored for the student module. [PERSON] et al. [71] combined a radiologist's domain expertise to propose a contrast-enhanced ultrasound (CEUS) video classification model for breast cancer. The researchers explored 3D CNN with a domain-knowledge-guided temporal attention module and a channel attention module. Due to these modules, the proposed model is able to focus on critical time slots of CEUS videos and learn features more efficiently, which helps to improve the classification performance of the model. The model achieved an accuracy of 86.3% and a sensitivity of 97.2% on a dataset containing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Database** & **Result** & **Limitations** & **Advantages** \\ \hline [PERSON] et al., & & SSD with VGG16 & 92 videos and & Precision = 0.851, & High false & Real-time \\
\begin{tabular}{} \end{tabular} } \\  & Detection of & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(Cont\).  
221 cases. [PERSON] et al. [72] proposed a fused model with hand-crafted and deep features to classify atypical hepatocellular carcinoma. The deep features are extracted by the 3-D-CNN, and the hand-crafted features are extracted by state-of-the-art methods. Then they are combined and given to a classifier such as Support Vector Machine (SVM) for classification.  
The limitations for the classification task in ultrasound videos include data from the same source, such as videos captured by the same machine or by the same technician. Ideally, the training dataset should be larger, heterogeneous, and more balanced in terms of classes for learning accurate deep models.