esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
With regard to biofilm-eradicating activity, fusidic acid, ozonated olive oil and silver dihydrogen citrate were effective for both _S. aureus_ and _S. epidermidis_, while Betadine (0.1% \(w/w\)) with polyhexanide (0.1% \(w/w\)) and Hydroactive colloid gel were effective only for S. epidermidis. However, these dressings alone are often not sufficient to eradicate infection, and the possibility of combining an antibiotic or AMPs may improve the outcome of wound healing. Again, in vivo studies are required to evaluate efficacy. We suggest the early use of dressings with silver derivates, PVP-iodine or ozonate olive oil, considering their widespread availability. An important role in the treatment of wounds infected with biofilm-producing staphylococcal strains is played by APDT, which was proven, in association with RLP068/CI, [152] to be faster than teicoplanin in reducing bacterial load and more effective in wound healing on human fibroblast cultures. In vivo studies are needed to evaluate its efficacy in wound healing. In the literature, we found a case report of APDT with ALA on a patient with a chronic ulcer infected with _S. aureus_ resistant to conventional therapy, with excellent clinical results on wound healing and a long period without relapse (29 months) [158]. This suggests that in the most difficult cases, APDT may be a valid treatment option. There are promising studies in the literature demonstrating in vitro anti-biofilm staphylococcal action of APDT with various photosensiers and blue light, [171; 172; 173; 174; 175; 176; 177; 178; 179] but which are lacking in vivo histological evaluation of wound healing. Other promising treatments have not been investigated because there is no evaluation of wound healing, such as antibodies against staphylococcal antigens [174; 175; 176; 177; 178], nanotechnology [179; 180; 181; 182], and new genetic approaches [183; 184]. In addition, bacteriophages are viruses with a predatory action against specific bacteria [185]. Their effectiveness in eradicating biofilms has been evaluated in combination with other molecules, such as antibiotics [186; 187; 188]. It was also shown that the bacteriophage phiPLA-RODI can be useful in eradicating _S. aureus_ biofilms 24 h per day both in vitro and in an ex vivo pig model, especially in combination with a phage-derived lytic protein CHAPSH3b. The latter resulted in a reduction in the _S. aureus_ population up to 7 h after exposure, followed by bacteriophage activity, which limits bacterial regrowth [185]. All of these methods have been shown to have an anti-biofilm effect, but no studies were conducted to assess their impact on wound healing. Currently, only RIP and APDT with ALA were used in human patients, albeit only in case reports. In our opinion, these are promising and will help to overcome bacterial resistance, especially in difficult multifailure cases. We hope that other therapies will soon be proved in humans, allowing the treatment of chronic wounds with difficult tissue healing.
## 4 Payload Development
### Thermal Testing  
Similar to RSONA, RSONA II used passive thermal control by covering the payload with aluminum polyamide. Based on measured temperatures recorded during previous flights, it was determined that internal payload components were likely to experience temperatures of approximately \(-20\)\({}^{\circ}\)C. As a result, a temperature profile was created for RSONA II, with a minimum temperature of \(-20\)\({}^{\circ}\)C. The payload underwent a 16-h operational assessment in a thermal chamber to evaluate any alterations in its performance. Following the thermal test, it was ascertained that all components were functioning properly with no performance degradation in the systems.
## 2 Results
### Photochemical Processes
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Bacterial** \\ **Strains** \\ \end{tabular} } & \multirow{2}{*}{**Sampling Site**} & \multirow{2}{*}{**\begin{tabular}{c} **Limit Salt** \\ **Tolerance** \\ **(mM)** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Plant-** \\ **Solubilization** \\ \end{tabular} } & \multirow{2}{*}{**Siderophore**} & \multirow{2}{*}{**\begin{tabular}{c} **ACC Deaminase** \\ **(nm mg\({}^{-1}\) h\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{**IAA (\(\mu\)g mL\({}^{-1}\))**} & \multirow{2}{*}{**\begin{tabular}{c} **N-** \\ **Fixation** \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} _Basillus_ \\ _argushattai_ \\ _SP20_ \\ \end{tabular} } & Horta dos & \multirow{2}{*}{856} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{–} & \multirow{2}{*}{–} & \multirow{2}{*}{0.80 \(\pm\) 0.01} \\  & Peixinhos, & Portugal & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} _Silentrophomonas_ \\ _rhizophila_ \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Horta dos** \\ _rhizoplusthia_ \\ \end{tabular} } & \multirow{2}{*}{856} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{-} & \multirow{2}{*}{15.02 \(\pm\) 0.31} & \multirow{2}{*}{+} & \multirow{2}{*}{–} \\  & Peixinhos, & Portugal & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} _Pentalomonas_ \\ _organibulans_ \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Tagus Estuary, \\ Portugal \\ \end{tabular} } & \multirow{2}{*}{1711} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{\({26.9}\) \(\pm\) 14.13} & \multirow{2}{*}{39.55 \(\pm\) 1.01} & \multirow{2}{*}{–} & \multirow{2}{*}{0.40 \(\pm\) 0.01} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} _Salimicola_ \\ _end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Tagus Estuary, \\ Portugal \\ \end{tabular} } & \multirow{2}{*}{1711} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{+} & \multirow{2}{*}{71.35 \(\pm\) 6.86} & \multirow{2}{*}{–} & \multirow{2}{*}{0.54 \(\pm\) 0.01} \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the bacteria isolates and their plant growth-promoting properties [24].
## 4 Data Collection  
To assess the possible impact of redesign interventions on rural single carriageway roads, a case study of an Italian national road is considered. The first step involves collecting data for the existing road under investigation. In particular, traffic data, accident data and geometric-functional characteristics of the existing road are required. Furthermore, specific details of the planned redesign interventions are needed.  
All time-varying data, such as traffic, must be evaluated for each year of the total time interval considered. Given that the road section under consideration spans approximately 20 km, it is necessary to consider a long time period of 9 years (from 2014 to 2022) to gather a sufficiently large database.
## 3 Results and Discussion
### Characteristics of the Quartz Crystal Microbalance Sensor
#### 3.1.1 Effect of Dissociation on Quartz Crystal Microbalance Frequency Responses
The \(C_{tot}^{sat}\) is determined as follows [25]:  
\[C_{tot}^{sat}\ =\ \frac{C^{sat}(\mathrm{HA})}{\alpha}\ =\ \frac{C^{sat}( \mathrm{HA})}{[\mathrm{HA}]/\left([\mathrm{HA}]\ +\ \left[\mathrm{A^{-}}\right]\right)}=\ C^{sat}( \mathrm{HA})\left(1\ +\ \frac{K_{a}}{[\mathrm{H^{+}}]}\right) \tag{1}\]where \(C^{sat}\)(HA) is the water-solubility of the nondissociated palmitic acid and [HA], [A\({}^{-}\)] and [H\({}^{+}\)] are concentrations of the nondissociated palmitic acid, dissociated palmitic acid, and proton, respectively. \(C^{sat}\)(HA) is 7.2 mg/L [26] at 20 \({}^{\circ}\)C. \(K_{a}\) can be based on the pKa (8.7) of the palmitic acid determined by [PERSON] et al. [23] at 20 \({}^{\circ}\)C. The total solubility of the palmitic acid (nondissociated and dissociated forms) increases as a function of pH and shows a logarithmic increase up to the solubility product of the palmitate salt under alkaline conditions (pH > 9). Because there is no information about the solubility product of the palmitate salt, the theoretical maximum total solubility is unknown. According to this estimation, although the temperature is a little different, the total solubility values are 22 mg/L at pH 9.0 and 727 mg/L at pH 10.7. This means that 100 mg/L palmitic acid is completely dissolved into water (100 mg/L) at pH 10.7. Interestingly both the total solubility and frequency-shift rate at pH 9.0 were approximately one-fifth of those obtained at pH 10.7. It is clear that the increase in the concentration of the ionized form of palmitic acid, caused by pH elevation, can enhance the response of the QCM sensor. The dissociated forms of fatty acids are water-soluble and have a hydrophobic carbon chain, likely leading to effective absorption onto the PS-DVB film, which has a hydrophobic nature. On the basis of the above facts, we can conclude that pH adjustment as a pretreatment for QCM measurements is a useful strategy.
###### Abstract  
This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy.  
Keywords:computed tomography; deep learning; magnetic resonance imaging; neural network; pseudo-CT +
Footnote †: journal: Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, 28933 Madrid, Spain
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 3 Methodology
### Structure of TD-LSTM Networks  
In the previous section, we discussed how to choose keyframes for the model. This reduces training time and capture time-dependent features. Figure 4 shows the structure of our proposed TD-LSTM networks. As illustrated in the top part of Figure 4, there is a set of historical observations of ocean temperature. The red dot indicates the temperature at which time t is to be predicted. We used green dots to reflect the closeness of time. As time distance increases, the relationship between temporal closeness becomes weaker and green becomes shallower. We used blue dots to reflect the period of time, they have the same time interval. The closeness and periodic points together reflect the trend. We combined these points that reflect closeness, period, and trend, and put them in sequentially into the LSTM units in the middle of Figure 5. The lower part of Figure 5 shows the output value corresponding to each LSTM unit state. When the input at time \(t-1\) is completed, the LSTM network gives the predicted value at time \(t\). The predicted value is compared with the real value, and the Loss value is calculated to optimize the network.  
Figure 4: TD-LSTM networks architecture.  
Figure 5: The average of MSE.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### [PERSON] et al. [99] studied GNSS IWV from 54 stations over North America. Diurnal variations were significant, explaining over the 50% of the subdaily variance, specially in summer, with weaker diurnal cycles in the rest of the seasons. Peaks are found around 1000-1400 LST in winter, and from midafternoon to to midnight in summer. The semidirurnal cycle is rather weak.  
[PERSON] et al. [116] proposed a model to spatially interpolate GNSS ZTD and used interpolated values from meteorological stations for pressure and \(T_{m}\) in California. The interpolation method is iterative and tries to decouple turbulent- and elevation-dependent ZTD components. Validation with MODIS as a reference yielded RMSE of 1.70 mm.  
[PERSON] et al. [117] studied temporal series of six GNSS stations in Eastern US, where it was found that the variability of IWV is dominated by the annual cycle, with a missing daily cycle, while RH from weather stations is more characterized by the daily cycle, without an annual cycle.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 4 DSP Techniques on Compensation of MDL Effects in FMF Links  
Transmitted signals in SDM links are also impaired by different gain or loss between different spatial modes, which phenomenon could be collectively referred to as MDL effect. MDL mainly arises from the imperfections of inline components such as splices and micro-bends of fibers, amplifiers and multiplexers.  
MDL causes different OSNR degradations between different modes, which finally reduce the system capacity. For an optical MIMO system modeled as Equation (1), if there is not any prior information of the SDM links at transmitter, the capacity, \(C\), of a MIMO-SDM system with equal transmitted power between different modes is given by Equation (10) [51; 52]:  
\[C=\sum_{i=1}^{N_{R}}log_{2}\bigg{[}1+\frac{P}{N_{T}N_{0}}\lambda_{i}\bigg{]} \tag{10}\]  
where \(P\) is the total power of all transmitted signal, \(N_{0}\) is the noise power of each received channel, and \(\lambda_{i}\) is the eigenvalues of the \(N_{R}\ \times\ N_{R}\) positive semidefinite Hermitian matrix \(\mathbf{HH^{+}}\) that satisfy \(\lambda_{i}\ \geq\ 0\ (i\ \in\{1,\ 2,\dots,\ N_{R}\})\).  
Equation (1) indicates that the available MIMO system capacity is the summation of the capacity of respective parallel \(N_{R}\) single channels, each of which has a power gain or loss of \(\lambda_{i}\)[52]. Since MDL could be defined as the ratio between the maximum eigenvalue (max \((\lambda_{i})\)) and the minimum eigenvalue (min \((\lambda_{i})\)) of \(\mathbf{HH^{+}}\), the capacity of MIMO system is naturally reduced due to the presence of MDL.  
On the other hand, in terms of DSP, unlike DMGD which could be compensated totally by MIMO detection, MDL has a serious damage on linear MIMO equalization performance as a result of the non-unitary transfer matrix induced by MDL. Much effort on DSP techniques has been explored to mitigate the MDL-induced capacity loss or to suppress MDL-impaired non-unitary channels.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 3 Numerical Comparative Performance Analysis
### Computational Scheme and Boundary Conditions  
A density-based coupled implicit solver was used to solve the governing equations. A second-order upwind discretization method was used in Navier-Stokes equations, and a first-order upwind discretization method was used in turbulence equations. Convergence was evaluated by comparing the inlet and outlet mass flow rates. The variation in solution residuals was monitored and used to check the convergence as well.  
A mass flow boundary condition was used at the inlet of the fluid domain to ensure consistency with the experimentally tested propellant feed conditions. The specified mass flow rate of the gas mixture was 1.75 \(\times\) 10\({}^{-5}\) kg/s, a quarter of 7 \(\times\) 10\({}^{-5}\) kg/s, as the computational domain was a quarter of the microthruster experimentally tested. The temperature of decomposed gas mixtures at the inlet was set to the adiabatic decomposition temperature of hydrogen peroxide, which was 898.92 K for 85 wt%, 1021.61 K for 90 wt%, and 1144.23 K for 95 wt%. The turbulent intensity and turbulent viscosity ratio at the inlet were set to 2% and 5%, respectively. The pressure outlet boundary condition was specified at the sea level atmospheric condition. The wall was specified as a non-slip and smooth wall boundary with zero surface roughness. The symmetry boundary condition was used at the surfaces where the full microthruster geometry was divided into quarters.  
At the outside surface of the solid domain, where the solid meets the air, the heat transfer coefficient was assumed to be 25 W/m\({}^{2}\).K, considering both thermal radiation and free convection. The contact between the fluid domain and the solid domain was modeled as a coupled wall for the heat transfer between the two domains. Since the solid domain completely enclosed the fluid domain, the fluid domain met the solid domain at the inlet. At that location, the solid domain side wall was specified as an adiabatic wall with a zero heat transfer coefficient. This was inevitable to maintain a constant temperature at the inlet and prevent an invalid boundary condition setup, and accordingly, it was expected that the temperature distribution near this area would be somewhat unrealistic, as the fluid lost its heat at the fluid-solid interface next to the inlet. The geometric boundary conditions are illustrated in Figure 4.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Self-Supervised Loss Functions
#### 3.5.1 Geometric Equivariance Loss (\(L_{equiviv}\))  
This is a primary self-supervisory signal ensuring that keypoint predictions on templates transform consistently with input image transformations applied to those templates. Its objective is to ensure that if an input template image batch (\(I\)) is subjected to a geometric transformation (\(T_{\theta}\)) (such as a rotation or an affine transformation, specifically excluding horizontal and vertical flips in our setup), resulting in \(I^{\prime}\), the heatmaps predicted from\(I^{\prime}\) are equivalent to the heatmaps predicted from the original template image batch (\(I\)) warped by the inverse transformation (\(T_{\theta^{-1}}\)). Let \(H_{trans}\) be the heatmaps predicted from the transformed image batch (\(I^{\prime}\)). Let \(W_{\theta^{-1}}\) be the operation that warps a heatmap grid according to the inverse transformation (\(\theta^{-1}\)), using bilinear interpolation. The loss (\(L_{equix}(T_{\theta})\)) for a specific transformation (\(T_{\theta}\)) penalizes the L2 norm of the difference between the transformed prediction and the inversely warped original prediction as follows:  
\[L_{quix}(T_{\theta})=\frac{1}{B\cdot K\cdot H_{out}\cdot W_{out}}\sum_{b=1}^{B }\sum_{k=1}^{K}\sum_{h=0}^{H_{out}-1}\sum_{w=0}^{W_{out}-1}(H_{trans,b,k,h,w}- W_{\theta^{-1}}(H_{orig})_{b,k,h,w})^{2} \tag{4}\]  
where \(b\) is the batch index, \(k\) is the keypoint index, and \(h,w\) are the spatial coordinates on the heatmap. In our experiments, this loss is applied for rotation and affine transformations in Stage 1 and for affine transformations only in Stage 2.
## 3 Experimental Results and Discussion
### Pushover Tests on Structural Frames
#### 3.2.3 Diagram of Bending Moment versus Rotation  
Figure 18 shows the diagrams of bending moment versus rotation of the connections between Walls 1 and 2 and the slab. The bending moment is defined as the product of the horizontal force and the vertical distance between the force and the centroid of the low-cost energy dissipation system. This is, in fact, a \"global bending moment\" of the frame, and not the real moment of the connection between the wall and the slab. The rotation is defined as the variation of the inner angle between the wall and the slab.  
The behavior observed in Figure 18 agrees with the one shown in Figure 16. The first hysteresis cycles (up to a drift of 2%) reveal a linear-elastic behavior of the connections. The loading and the unloading branches are very similar, and the areas enclosed by the hysteresis loop are very small.  
Beyond a drift of 2%, the structure begins to show a plastic behavior. In each hysteresis loop, the slope of the curve bending moment versus rotation progressively decreases, and the area enclosed by the hysteresis loop progressively increases. Consequently, the energy dissipation capacity of the connection between the wall and the slab increases. Additionally, the permanent rotation corresponding to null bending moment increases in each cycle, which denotes that the connection suffers damage in each cycle.  
An unexpected behavior is observed during the last cycle in the diagram concerning Wall 1. In particular, there is an interruption of the data capture from rotations around 0.03 rad. This can be  
Figure 18: Diagrams of bending moment vs. rotation. Pushover test on structural frame. (**a**) Bending moment versus rotation of the connection between wall 1 and slab; (**b**) bending moment versus rotation of the connection between wall 2 and slab.  
explained because the measurement range of the transducers used to calculate rotations was exceeded. As a result, the transducers are detached from the concrete surfaces and there is no data collection.  
When both connections are compared, it is observed that the one placed in Wall 1 (and consequently subjected to positive bending) shows less stiffness than the one in Wall 2 (subjected to negative bending). This finding agrees with the results of Figure 17. Moreover, the permanent rotation corresponding to null bending moment in the connection of Wall 1 is larger than the one in Wall 2.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art  
This section presents the state of the art in satellite communication constellation management, including several key components. First, satellite communication constellations, technologies, and architectural structures are analyzed. Then, satellite and constellation management systems, particularly those that address the satellite scheduling problem, are discussed. Envisioned operations management in STIN are described. Finally, the gaps and challenges identified in the existing literature are summarized, highlighting areas that require further research and development.