esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Lectures  
### L-01. Newborn Screening for Creating Synthesis Defects  
[PERSON]  
Division of Biochemical Genetics, Department of Pediatrics, University of British Columbia, Canada  
GAMT (Guanidinoacetate Methyltransferase) Deficiency is one of the three known inborn errors of creatine metabolism, affecting creatine synthesis (GAMT and AGAT deficiency) and transport (SLC6A8 deficiency).  
Less than 100 patients have been diagnosed worldwide with GAMT deficiency. Clinical features include a spectrum of intellectual disability (ID), autistic behavior, epilepsy, and spasticity/dystonia/ataxia. Treatment with creatine and ornithine supplements, and/or arginine restricted diet, results in partial correction of cerebral creatine deficiency and in reduction of guanidinoacetate accumulation. Elevated guanidinoacetate concentration in body fluids is a specific diagnostic marker. Based on evidence that early recognition prevents ID and neurologic manifestations, GAMT deficiency is a candidate for newborn screening. GAA is reliably measured in blood spots via a two-tier test protocol, integrating a standard tandem mass spectrometry flow injection (FIA-MS/MS) method (as used for acylcarnitines/amino acids) as a first-tier assay, and an LC-MS/MS quantification step as a second tier. GAMT gene sequencing in positive samples is a third tier option.  
AGAT deficiency presents with a spectrum of ID and myopathy. Less than 20 patients have been described worldwide. Based on observations in early treated patients, treatment with creatine supplement prevents clinical manifestations. In the absence of a specific biomarker, newborn screening for AGAT deficiency has not yet been implemented. SLC6A8 deficiency might be another candidate for newborn screening, once effective therapies have been developed.
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 8 Discussion
If the input is not available, most of existing works depend on a pre-trained segmentation model to predict the visible segmentation mask. * **Feature extraction:** The pixels in the visible region of an image are rather important and contain essential information for various tasks; hence, they are considered as valid pixels. Contrary to this, the invisible pixels are invalid ones; hence, they should not be included in the feature extraction/encoding process. However, the vanilla convolution process cannot differentiate between valid and invalid pixels, which generates images with visual artifacts and color discrepancies. Therefore, partial convolution and a soft gating mechanism are implemented to enforce the generator to focus only on valid pixels and eliminate/minimize the effect of the invalid ones. On the other hand, dilated convolution layers can replace the vanilla convolution layers to borrow information from relevant spatially distant pixels. Additionally, contextual attention layers and attention mechanism are added to the networks of the GAN to leverage the information from the image context and capture global dependencies. Among the various architectures of GAN, three types are most commonly used in the reviewed works in this article, namely CGAN, WGAN-GP, and PatchGAN. The application of CGAN is mostly in amodal content completion tasks, because the GAN is encouraged to complete an object of a specific class. WGAN-GP stabilizes the training of GAN with an EM distance objective function and a weight clipping method. Therefore, it is a preferred architecture to ensure GAN convergence. On the other hand, PatchGAN is used in designing the discriminator, as it attempts to classify patches of the generated image as real or fake. Consequently, the image is penalized for style consistency between pixels that are spatially more than a patch diameter away from each other. Finally, handling occlusion is fundamental in several computer vision tasks. For example, completing an occluded facial image helps in better recognizing the face and predicting the identity of the person. Similarly, inferring the full shape of pedestrians and vehicles as well as the occlusion relationship between them can lead to a safer autonomous driving. Furthermore, in surveillance cameras, amodal completion helps in target tracking and security applications.
## 4 Experiments
### Results
#### 4.2.2 Evaluating TACMA  
**PSP** in [19] is one of the state-of-the-art solutions for AVE localization. It adopts a threshold-based attention style module to aggregate information from all the similar audio and visual features. This module is highly reliant on applying additional supervision for the _background_ to force the network to learn appropriate features for thresholding.  
**Comparison with state-of-the-art.** By training the proposed TACMA based on fixed representations produced by AV-BT, we decoupled multimodal data representation and late fusion in the AVE localization task. From this perspective, it increases the difficulty of adapting from the source domain to the target domain while the solutions we compare are all combined late fusion networks with feature encoders, and are trained in an end-to-end manner supervised with full labels. Nevertheless, **our overall method still achieves state-of-the-art performance** on the AVE localization task, as shown in Table 3. This result suggests that channeling attention according to both the time difference and seman  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Audio only [6] & 59.5 \\ Visual only [6] & 55.3 \\ AVEL [6] & 71.3 \\ Ours (Audio only) & **63.5** \\ Ours (Visual only) & **61.0** \\ Ours (Audio-Visual) & **75.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of AV-BT. Our results are in **bold**. Note that we use no extra module for fusion at this point, but simply add audio and visual features together to make classifications. For comparison, the result of AVEL in [6] is obtained by end-to-end fully supervised training and they used the same strategy of addition before prediction.  
tic similarity between features encoded from AV-BT can further improve the quality of fused features.  
**Detailed comparison with AV-BT.** In addition to comparing with the latest methods, we would also like to know the detailed performance improvement of TACMA compared to AVBT. Hence, we show the confusion matrices of our solutions in Figure 5. From Figure 5 we can observe that most of the categories can be well represented by AV-BT. However, there are still many misclassifications between events and the _background_. After further fusing information with TACMA, the misclassification of events as the _background_ improves for almost all categories, indicating that TACMA can grasp more event-related information since it dynamically considers all connections between different modal features to integrate useful cues, as shown in the bottom row in Figure 5. In addition, for certain categories with very few samples, TACMA also improves the accuracy based on AV-BT. For example, index 11: \"Truck\", index 12: \"Shofar\", index 13: \"Motorcycle\", index 22: \"Cat\" and index 23: \"Horse\".
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 3 Results  
### The Selected Optimal Dry Soil Spectral Variables for CLQ Estimation  
The GBDT algorithm was used to select the optimal spectral variables for estimating CLQ. The feature importance (FI) of the soil spectral variables is shown in Figure 5. Twenty spectral variables were selected (FI \(>\) 0.003). Then, the VIF was used to eliminate the collinearity of the above selected spectral variables and the optimal spectral variables (X1:FD478, X2:FD872, X3:FD966, X4:FD1796, X5:SD502, X6:SD614, and X7:SD1007) were obtained, as shown in Table 1.
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
## 3 Results
### Quantitative Analysis of LIF Image Color Components
#### 3.4.3 Two-Color Histogram Comparison  
The analyses above focused on quantifying the difference between individual color channels over time, but did not utilize the possible advantage of the compound information provided by the combination of multiple color channels extracted from the moss images. In order to express differences in the looped shapes resulting from two-color comparison  
Figure 10: Red color histogram differences (**A**), DTW (**B**), and red versus green DTW (**C**) for each trial on each day of the experiment relative to their day-0 control plotted against the cumulative Cu dose. The 2 \(\sigma\) (dashed line) and 3 \(\sigma\) (dotted line) confidence intervals are shown for the mean of trial 1. (**D–F**) show a curve fitting through all data points corresponding to dose (**A–C**). As seen in (**D–F**), the root mean squared errors with 95% confidence were 0.0464, 0.0406, and 0.06059, respectively.  
(Figure 8), we applied DTW analysis of the minimum cumulative difference in red vs. green figure shapes from all trials between days 1 and 10 to their respective day-0 color shapes. DTW analysis results for red versus green comparisons are shown in Table A3 and Figure 10C. We also plotted these results against cumulative Cu doses (Figure 10C), duplicating the same approach for single-color analysis. Minimum cumulative differences for all trials treated with Cu were outside of the mean \(\pm\) 3\(\sigma\) interval of control trial 1. Figure 10 suggests that the DTW two-color analysis provides better separation and may be more sensitive than either single-color analysis method.
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides
### Electrodeposition Method
A thin film thermoelectric power generator device containing 242 pairs of n-type Bi\({}_{2}\)Te\({}_{3}\) and p-type Sb\({}_{2}\)Te\({}_{3}\) thin-film legs on the bottom substrate was fabricated using the electrodeposition process [102]. Apart from the traditional substrates for electrodeposition, [PERSON] et al. recently reported using a free-standing poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) film as the working electrode for electrodeposition of Bi\({}_{2}\)Te\({}_{3}\) films for preparation of a sandwich-structured Bi\({}_{2}\)Te\({}_{3}\)/PEDOT:PSS/Bi\({}_{2}\)Te\({}_{3}\) composite film (Figure 8) [103]. The as-prepared hybrid films exhibit a high electrical conductivity of 403.5 S cm\({}^{-1}\) and a Seebeck coefficient of 15.3 \(\upmu\)V K\({}^{-1}\) with a favorable low thermal conductivity of 0.169-0.179 W m\({}^{-1}\) K\({}^{-1}\). In addition to films, electrochemical deposition method can also be applied to produce nanowire or nanotube arrays using a hard-template-assisted method. [PERSON] and coworkers prepared potentiostatically electrodeposited Bi\({}_{2}\)Te\({}_{3}\) nanowire arrays embedded in an ordered porous anodic alumina membrane (AAM) template. Compared to the normal direct-current electrochemical deposition, the pulsed electrodeposition with a pulse delay time for the recovery of the ion concentration always leads to a crystalline structure with high orientation and good uniformity [104]. [PERSON] and coworkers fabricated highly oriented single-crystalline Bi\({}_{2}\)Te\({}_{3}\) nanowire arrays by pulsed electrochemical deposition with an alternate pulse time of 3 ms and a delay time of 10 ms into the pores of the AAM template [91]. [PERSON] et al. reported the tunable crystalline structure of the electrodeposited Bi\({}_{2}\)Te\({}_{3}\) nanowires (with a Seebeck coefficient value of 12-33 \(\upmu\)V K\({}^{-1}\)) by varying the delay times [92]. Figure 8: Schematic diagram of Bi\({}_{2}\)Te\({}_{3}\) electrodeposited on the flexible PEDOT:PSS electrode. The insets are the SEM images of PEDOT:PSS and the cross section of the Bi\({}_{2}\)Te\({}_{3}\)/ PEDOT:PSS/Bi\({}_{2}\)Te\({}_{3}\) film. Reprinted with permission from ES publisher [103]. PEDOT:PSS: poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate).
## 4 Conclusions  
In conclusion, we have shown that our proton acceleration scheme might be a considerable improvement over alternative acceleration schemes, since it provides proton energies higher than anticipated based on TNSA with thin planar targets. Moreover, it requires lower energy laser system and relax the constraint of very high contrast ratio.  
The targets were engineered by depositing snow on sapphire substrates, generating a microstructured morphology that can be controlled by two means: selective growth was achieved by deposition of artificial nucleation centers, while the lengths and shapes of the structures were controlled by varying the water vapor deposition rate. As in the acceleration mechanism considered here, the dynamic plasma generated by the pre-pulse is beneficial, aimed to understanding the properties of this plasma, we measured the ODT of the microstructured snow surface.  
A multi-variables PIC simulations study was conducted to understand the above acceleration mechanism. Our findings are that the notably increased proton energy may be was attributed to a combination of several mechanisms, such as localized enhancement of the laser field intensity near the tip of the whisker, mass-limited like phenomenon and Coulomb explosion of the positively charged whisker. All these mechanisms are function of the shape, dimensions and local density profile of the  
Figure 8: Electrons (**left**) and protons (**right**) spatial distributions obtained by simulation of the interaction of a laser pulse of intensity \(2.5\times 10^{19}\) W/cm\({}^{2}\), with ellipsoidal shape constant density whisker. X, Y axes are in \(\upmu\)m. Color map scale is logarithmic normalized related to the critical density.  
snow whisker, the geometry of the irradiation and the laser intensity. Our simulations predict that 150 MeV protons needed for medical applications can be produced using a 100 TW laser system.  
Further research will focus on experiments with more controlled and also periodic microstructured targets and explore the properties of their dynamic plasma generated by the prepulse. In addition, we are aimed to investigate whether this proton acceleration scheme could be applicable in proton therapy and other scientific uses.  
The authors would like to acknowledge [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for their contribution to the results presented in Figure 1.  
The experimental data were collected by [PERSON], [PERSON] and [PERSON]. The theoretical analysis and PIC simulations were conducted by [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 7 Performance Evaluation
### Simulation Scenarios  
Three characteristics are important to set up scenarios in our simulation. The first one is the behavior of the light variable. Sometimes, the light variable changes easily and leads to different results in the prediction, due to the variation of correlation between gathered variables. It can be presented in two forms, constant and not constant. Temperature and humidity variables are usually correlated, _i.e._, when one increases the other decreases and vice versa. Therefore, their behavior is constant, with their values changing simultaneously and slowly. Application versions from 2 to 4 use prediction and can increase prediction error when one or more variables change their values quickly.  
The second one is the topology which can increase the energy consumption in random deployments. Usually, all application versions suffer the same effects on energy consumption, since the topology will not affect the prediction.  
The last one is the network density which also influences the energy consumption, but does not affect the prediction. When the network density is high, _i.e._, many nodes close to each other, the energy consumption increases due to packet reception by broadcast. The application versions from 2 to 4 should suffer the same effects of network density, but it has to be checked whether the communication between sensor nodes with the lowest prediction error can optimize energy consumption.  
Then, in order to explain the simulation scenarios, we summarize the characteristics in Table 2. These characteristics try to emulate the circumstances of the real world so that we can simulate scenarios close to a deployment of sensor nodes for environment applications. We have defined six different scenarios that have been run 30 times each. All scenarios use four application versions and number of nodes ranging from 4 to 100 (to measure scalability). Scalability is important to check energy consumption in all application versions. All scenarios that obtained results from experiments have confidence interval of 95%.  
The Link Layer Model tool of TinyOS 2.x was used to create the grid and random topologies. In each scenario several nodes densities are used and summarized in Table 3. The energy consumption model adopted is the same of [PERSON] _et al._[18], where the radio spends 1.67 \(\upmu\)J/Byte sending and 1.89 \(\upmu\)J/Byte receiving data by using micaz  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{3}{*}{**Scenarios**} & \multicolumn{6}{c}{**Features**} \\ \cline{2-8}  & \multicolumn{2}{c}{**Light variable**} & \multicolumn{2}{c}{**Topology**} & \multicolumn{2}{c}{**Network density**} \\ \cline{2-8}  & **Constant** & \multicolumn{2}{c}{**Not constant**} & **Grid** & **Random** & **1 node/5 m** & **Ranging** & **Fixed** \\ \hline
1 & X & & X & & X & & \\
2 & & X & X & & X & & \\
3 & & & X & & X & & X & \\
4 & X & & & X & & X & \\
5 & & & X & & X & & X \\
6 & X & & & X & & & X \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of the simulation scenarios.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 2 SAR Interferometric Processing  
This section briefly introduces the basic interferometric concepts and the the key computationally relevant processing steps in SAR interferometry.
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 3 Experimental Setup
### Data Preparation  
The transmitted data were generated as a \(1.224\times 10^{6}\) bit long PRBS using the Mersenne Twister pseudo-random number generator with the order of 623, establishing a minimum detectable BER of \(8.17\times 10^{-7}\) corresponding to a single error in the demodulated data. There data were saved for later comparison with the demodulated data. The TR-PPM  
Figure 8: Pulse waveforms after the envelope detector for experiment setup with 20 km SMF with (**a**) \(\tau=100\) ps, (**b**) \(\tau=200\) ps, (**c**) \(\tau=500\) ps.  
Figure 6: Magnitude response of the ARoF system.  
Figure 7: Pulse waveforms after the envelope detector for back-to-back experiment setup with (**a**) \(\tau=100\) ps, (**b**) \(\tau=200\) ps, (**c**) \(\tau=500\) ps.  
waveforms were generated offline in MATLAB 2024a with pulse durations varying from 500 ps to 100 ps and time bin durations ranging from 200 ps to 50 ps and then loaded into the AWG. The number of pulse positions was adjusted for each configuration to achieve optimal data rates for the given parameters [36]. Additionally, these waveforms were generated with and without ECC. Error correction was implemented using Gray code alone and Gray code with CCSDS \"Blue book\" 131.0-B-5 Section 4 standard-compliant RS ECC, which can correct up to 16 error bytes in the encoded data. In the case of RS encoding, the waveform was generated with the number of positions \(M=256\), so that one byte of data takes up an entire TR-PPM frame and is not split across multiple frames.
## 3 Results and Discussion
### In Vitro Release  
Figure 10A-C illustrates the cumulative kinetics as a percentage of the drug released, whilst Figure 10D-F reports the release rates of the drug over experimental time. The rate of each stage might depend on the formation of hydrogen bonds among the carbonyl group of drugs with the -OH group of silica and aqueous solvent (SBF) [65]. SAy samples showed the fastest release (wt%) and rate (mg/min) (Figure 10A,D). In particular, the SA5 hybrid released all the drug within 1 h, while SA10, SA15, and SA20 released up to 90 wt% of the drug within 6 h, and the release was completed after 12 h for the SA10 hybrid and 24 h for SA15 and SA20. An increase in P amount of 6 wt% (Figure 10B,E) led to a slight decrease in the release rate. Indeed, SP6A5 released 90 wt% of the drug in 6 h and released all the amount after 24 h. An increase in caffeic acid amount (from 10 to 20 wt%) corresponds to a decrease in wt% and rate release, which seems to be more controlled. Furthermore, the data reported in Figure 10C,F, related to SP12 Ay hybrid systems underline that an increase in polyethylene glycol and caffeic acid amount led to a drug-release rate lower than the samples without the P, which is also confirmed by the graphs shown in Figure 11. According to [66], which investigated the properties of silica matrix synthesized via sol-gel route by using different TEOS:H\({}_{2}\)O ratios, a silica matrix with TEOS:H\({}_{2}\)O = 1:2 ratio has a nanometric scale (12.50 nm \(<\) x \(<\) 32.01 nm), a high surface area (347.31 m\({}^{2}\)/g \(<\) x \(<\) 549.92 m\({}^{2}\)/g), a total pore volume ranging from 0.41 to 0.51 cm\({}^{3}\)/g, and isotherm hysteresis type H2. These features could explain the reason why there is a faster release of the drug in the matrices without P. Moreover, the presence of P positively affects the release of the drug increasing the control of release over time. Finally, the kinetic study suggests a two-step mechanism: the first fast step occurs by dissolution and diffusion of the caffeic acid entrapped onto the material surface, while the second slow step occurs with the dissolution and diffusion inside the material clusters.  
Figure 10: _Cont._Figure 10: **(A)** Say, **(B)** SP6 Ay, and **(C)** SP12 Ay drug-release plots. **(D)** SAy, **(E)** SP6 Ay, and **(F)** SP12 Ay release rate plots of the hybrid biomaterials.