esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model
### Inference Network  
Our objective is to maximize the log marginal likelihood \(L(\theta)=\text{log}\,p(x_{1:T}|s_{1:T})\,\). Owing to the nonlinear characteristics of parameterized deep neural networks, accurately calculating the data's log likelihood in variational autoencoders (VAE) presents a challenge. Nevertheless, to optimize the parameters of the VAE through maximum likelihood estimation, we use variational inference to approximate challenging posterior distributions and select an efficient variational approximation \(q_{\phi}(z_{t}|z_{t-1},x_{t},h_{t})=N\big{(}z_{t};u_{\theta_{x}},\sigma_{q} \big{)}\), as shown  
Figure 2: Recurrent variational state-space model. (**a**) Generative model; (**b**) Inference network.  
in Figure 2. Specifically, the deep neural network takes data points as input and generates the corresponding mean and diagonal covariance matrix of the Gaussian variational approximation. The formula is as follows:  
\[\begin{array}{l}u_{\phi}(t)=\text{NN}_{3}(z_{t-1},x_{t},h_{t})\\ \log\sigma_{\phi}(t)=\text{NN}_{4}(z_{t-1},x_{t},h_{t})\end{array} \tag{8}\]  
Then we maximize the variational evidence lower bound (ELBO); the maximization over \(\theta\) in Equations (3)-(5) is actually a maximization over the parameters \(\varphi\) (use below the notation \(\mathcal{F}_{i}(\theta,\phi)\)). ELBO can be decomposed in two terms [22]:  
\[\begin{array}{ll}\mathcal{F}_{i}(\theta,\phi)&=\mathbb{E}_{q_{\phi}(z_{1:T} ;h_{1:T}|x_{1:T},s_{1:T},h_{0})}\left[\log\frac{p_{\theta}(x_{1:T}|z_{1:T})p_{ \theta}(z_{1:T}|h_{1:T})}{q_{\phi}(z_{1:T}|x_{1:T},h_{1:T})}\right]\\ &=\mathbb{E}_{q_{\phi}(z_{1:T}|x_{1:T},h_{1:T})}\left[\log\frac{p_{\theta}(x_{ 1:T}|z_{1:T})p_{\theta}(z_{1:T}|h_{1:T})}{q_{\phi}(z_{1:T}|x_{1:T},h_{1:T})} \right]\\ &=\sum_{t=1}^{T}\mathbb{E}_{q_{\phi}(z_{t}|x_{t},h_{t})}\left[\log\frac{p_{ \theta}(x_{1}|z_{t})p_{\theta}(z_{t}|h_{t})}{q_{\phi}(z_{t}|x_{t},h_{t})}\right] \\ &=\sum_{t=1}^{T}\left(\mathbb{E}_{q_{\theta}(z_{t}|x_{t},h_{t})}[\log p_{ \theta}(x_{t}|z_{t}))-\text{KL}\big{[}q_{\phi}(z_{t}|x_{t},h_{t})\big{|}\big{|} p_{\theta}(z_{t}|h_{t})\big{]}\right)\\ \end{array} \tag{9}\]  
The reconstruction term allows the likelihood and the inference network to accurately reconstruct data, thereby maximizing the autoencoding capability of VAEs. The regularization term, as a penalty, discourages posterior approximations that deviate significantly from the prior distribution. Given that both the generative and inference models are defined using neural networks, we can effectively compute gradients of \(\theta\) and \(\phi\) using the back-propagation algorithm [23]. To obtain low-variance, differentiable, and unbiased estimates of the lower bound, the reparameterization technique is also employed [24], which allows backpropagation to flow through latent variables \(z\) and approximates complex expectations via Monte Carlo integration. Notably, due to the Gaussian nature of \(q_{\phi}(z|x)\) and \(p_{\theta}(z)\), the KL divergence term can be analytically computed [25].
## 1 Introduction
Alongside the scheduling regime, the paper also presents the gains from employing adaptive FEC as opposed to statically determining the redundancy overhead. In fact, adaptive rateless FEC is a significant feature of the proposed scheme. For codec-dependent aspects of the scheme, the reader is referred to [10] by the authors. In general, the contribution of this paper is that a resilient video streaming scheme needs to consider both error resilience and congestion resilience, as error resilience alone is unable to protect against packet drops before the wireless channel is even reached. Section 2 describes the design decisions made by the authors in arriving at the video streaming system, such as the decision to employ rateless channel coding, which at the application layer of the protocol stack has largely superseded [11] fixed rate codes, especially those codes with high decoding complexity. Section 3 of the paper critically reviews recent work on packet scheduling, especially for use in multimedia applications. Section 4 details the methodology of both the error resilience and the congestion resilience aspects of the video streaming scheme. This is followed in Section 5 by an evaluation of selected aspects of the proposed scheme, as simulated for a Worldwide Interoperability for Microwave Access (WiMAX) wireless link, especially the performance of adaptive, rateless coding and the video-content response for packet scheduling. Finally, Section 6 draws some conclusions about the proposed video-streaming scheme.
## 4 The Floor Impact Noise Tests of SBR Latex-Modified Mortar
### Heavy-Weight Floor Impact Noise  
#### 4.2.1 Bang Machine  
The test results on the heavy-weight impact noise of the mock-up using SBR latex-mixed mortar with different glass transition temperatures, as shown in Figure 11, indicate that the characteristics of heavy-weight floor impact noise for the mock-up using Tg 4 \({}^{\circ}\)C SBR latex are similar to those of the mock-up using general mortar. This suggests that the Tg 4 \({}^{\circ}\)C SBR latex-mixed mortar does not significantly differ from general mortar in terms of heavy-weight impact noise reduction.  
Even with slight differences, the mock-up using Tg 4 \({}^{\circ}\)C SBR latex showed a better reduction effect of about 0.5 dB at frequencies between 63 Hz and 125 Hz compared to general mortar. This indicates a marginally improved performance in reducing heavy-weight impact noise within this frequency range.  
As seen in Figure 13, the characteristics of heavy-weight impact noise differ significantly between the mock-up specimen using Tg \(-\)16 \({}^{\circ}\)C and Tg 4 \({}^{\circ}\)C SBR latex-mixed mortars. This indicates that the two types of SBR latex-mixed mortars have distinctly different effects on reducing heavy-weight impact noise.  
Figure 12: Comparison of light-weight impact noises for different mixture ratios of SBR latex mortar specimens.  
Figure 13: Comparison of heavy-weight impact noises for different mixture ratios of SBR latex mortar specimens using bang machine.  
The mock-up specimen using the Tg \(-16\)\({}^{\circ}\)C SBR latex-mixed mortar showed a significantly better reduction performance, achieving about a 10 dB improvement at the 63 Hz frequency band, which is critical for controlling heavy-weight impact noise, compared to the mock-up using the Tg 4 \({}^{\circ}\)C SBR latex-mixed mortar. It can be inferred that the mock-up using the Tg \(-16\)\({}^{\circ}\)C SBR latex-mixed mortar achieves higher reduction performance at the 63 Hz frequency band and subsequently maintains higher performance at 125 Hz compared to other specimens, likely due to the energy balance.
# Galileo Single Point Positioning Assessment Including FOC Satellites in Eccentric Orbits
###### Abstract  
On August 2016, the Milena (E14) and Doresa (E18) satellites started to broadcast ephemeris in navigation message for testing purposes. As the Galileo constellation is not yet complete. It is very important to have two more satellites available since the position accuracy increases as the number of visible satellites increases. In this article, we examine how the inclusion of the Milena (E14) and Doresa (E18) satellites impacts the position accuracy. The analysis was carried out on 20 days of 1-Hz observations collected by a receiver placed in YEL2 IGS (International GNSS service) station. Two different scenarios are considered: the first excludes the measurements coming from the analyzed satellites, while the second one includes them. The analysis was conducted by using a suitable software tool developed in the MATLAB(r) environment able to compute satellites position from both the broadcast and precise ephemerides, to assess DOP (Dilution Of Precision) parameters and to compute single-point positioning for all Galileo frequencies. The analyses are conducted by using both broadcast and precise ephemeris. The inclusion of the two satellites improves the system availability, varying it from 94.1-97.94%, the DOP parameters, and the percentages of achieved positioning solutions by about 5% regardless of the frequency used. Nevertheless, in the positioning domain, when the broadcast ephemerides are used, the inclusion of the satellites worsens both the horizontal and vertical accuracy of the solution. The deterioration of the horizontal accuracy goes from 0.17 m with E5a frequency measurements to 0.74 m with E1 measurements. The reduction of vertical accuracy goes from 0.68 m for E5a to 1.2 m for E1 measurements. However, if precise ephemerides are used, both the horizontal and the vertical accuracy remain stable, actually for the E5b frequency, the DRMS (Distance Root Mean Squared) improves by almost 0.5 m. The results achieved show that the real drawback to overcome is related to the quality of broadcast ephemeris as, when precise ephemeris are used, the number of solutions achieved is increased by about 5% with an accuracy similar to that obtained when the satellites are excluded.  
G ailieo; Milena (E14); Doresa (E18); broadcast ephemeris; precise ephemeris; sp3; single-point positioning 26 April 201926 June 201926 April 201926 June 201926 April 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 201926 June 2019
## 2 Materials and Methods
### Analytical Procedures  
#### 2.3.1 SDS-PAGE and Immunoblot Analysis  
Frozen gill and liver samples were homogenized in 3 mL/g of cold lysis buffer [20 mM \(\upmu\)g-glycerophosphate, 50 mM NaF, 2 mM EDTA, 20 mM Hepes, 0.2 mM Na\({}_{3}\)VO\({}_{4}\), 10 mM benzamidine, pH 7, 200 mM leupeptin, 10 mM transepoxy succinyl-L- leucylamido- (4 guanidino) butane, 5 mM dithiothreitol, 300 mM phenyl methyl sulfonyl fluoride (PMSF), 50 \(\upmu\)g/mL pepstatin, 1% \(v/v\) Triton X-100], and extracted on ice for 30 min. The samples were then centrifuged (10,000\(\times\)\(g\), 10 min, 4 \({}^{\circ}\)C) and the supernatants were collected and boiled with 0.33 volumes of SDS/PAGE sample buffer (330 mM Tris-HCl, 13% \(v/v\) glycerol, 133 mM DTT, 10% \(w/v\) SDS, 0.2% \(w/v\) bromophenol blue). Protein concentrations were determined using the BioRad protein assay (BioRad, Hercules, CA, USA). Thereafter, equivalent amounts of proteins (50 \(\upmu\)g) were separated either on 10% and 0.275% (\(w/v\)) acrylamide and bisacrylamide slab gels and transferred electrophoretically onto nitrocellulose membranes (0.45 \(\upmu\)m, Schleicher and Schuell, Keene, NH, USA). All nitrocellulose membranes were dyed with Ponceau stain in order to assure a good quality of transfer and equal protein loading. Non-specific binding sites on the membranes were blocked by 5% (\(w/v\)) non-fat milk in TBST [20 mM Tris-HCl, pH 7.5, 137 mM NaCl, 0.1% (\(v/v\)) Tween 20], during a 30-45 min incubation at room temperature. Subsequently, the membranes were further incubated overnight with primary antibodies, including polyclonal rabbit anti-HSP70 (Cat. No. 4872, Cell Signaling, Beverly, MA, USA), polyclonal rabbit anti-HSP90 (Cat. No. 4874, Cell Signaling, Beverly, MA, USA), polyclonal rabbit anti-Na, K-ATPase (Cat. No. 3010, Cell Signaling, Beverly, MA, USA), polyclonal rabbit anti-p38 MAP kinase (Cat. No. 9212, Cell Signaling, Beverly, MA, USA), monoclonal rabbit anti-HSP60 (Cat. No. 12165, Cell Signaling, Beverly, MA, USA), monoclonal rabbit anti-phospho-p38 MAPK (Cat. No. 4511, Cell Signaling, Beverly, MA, USA) and monoclonal rabbit anti-phospho-p44/42 MAPK (Cat. No. 4370, Cell Signaling, Beverly, MA, USA) and monoclonal rabbit anti-p44/42 MAPK (Cat. No. 4695, Cell Signaling, Beverly, MA, USA). The next day and after washing in TBST (3 time periods for 5 min each), the blots were incubated with horseradish peroxidase-linked secondary antibodies and washed again in TBST (3 time periods for 5 min each). The bands were then detected, using enhanced chemiluminescence (Chemicon, MA, USA) and were exposed to Fuji Medical X-ray films. Films were quantified by laser-scanning densitometry (GelPro Analyzer Software, Media Cybernetics/Image Studio Lite Software Ver 5.2).
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
## 2 Methods  
We take the linear regression model as a starting point that incorporates multiple data sources; let us say \(S\) sources. For the \(i\)-th source, let \(\mathbf{X}^{i}\) denote a data matrix of dimensions \(n\times p_{i}\), where \(n\) is the number of samples and \(p_{i}\) represents the number of variables, with \(p_{i}\geq 2\). Furthermore, let \(\mathbf{y}\) be the \(n\)-dimensional response vector, where each entry corresponds to the value of a sample. Then, we formulate the model in the following way:  
\[\mathbf{y}=\sum_{i=1}^{S}\mathbf{X}^{i}\mathbf{\beta}^{i}+\mathbf{\varepsilon},\]  
where \(\mathbf{\varepsilon}\) denotes the noise term, while \(\mathbf{\beta}^{i}\in\mathbb{R}^{p_{i}\times 1}\) represents the vector of unknown parameters corresponding to the \(i\)-th data source. To enable analysis at both the feature and source levels, we introduce an additional parameter vector \(\mathbf{\alpha}=(\alpha^{1},\ldots,\alpha^{S})\in\mathbb{R}^{S}\) (a weight for each source), which incorporates the learned models into the regression setup:  
\[\mathbf{y}=\sum_{i=1}^{S}\alpha^{i}\mathbf{X}^{i}\mathbf{\beta}^{i}+\mathbf{\varepsilon}. \tag{1}\]  
The analysis at the feature level is prominent for deriving meaningful insights from the dataset, understanding the importance of individual features, and taking thoughtful action on data preprocessing and model specification. However, source-level analysis enables the identification of differences between groups, the detection of unique patterns linked to specific sources, and the ability to make data-driven decisions from these findings.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 3 Method
### Dataset
#### 3.4.3 MPII Dataset  
The MPII [8] dataset is a collection of approximately 25,000 images containing over 40,000 people with annotated body joints. Among these, 12,000 instances were reserved for testing, while the rest were used for training. These images have been systematically classified according to human daily activity. The dataset covers 410 human activities, with each image having an activity label. The images were extracted from YouTube videos. Additionally, to facilitate comparison with other methods, the dataset resizes input images to 256 \(\times\) 256.
## 2 Self-organizing Dynamical Systems
### Explicit Parameter Dynamics  
We will now derive the explicit parameter dynamics for a robot with two sensors and two actuators that will be used in the next Section. In order adapt the controller according to Equation 9, we must invert the matrix \(Q\). This is easy in the two dimensional case such that explicit expressions for the parameter dynamics can be obtained. We observe that the parameters occur also in the derivative of the output functions \(g^{\prime}\) which can be treated as  
\[\frac{\partial}{\partial c_{ik}}g^{\prime}_{j}=\delta_{ij}g^{\prime\prime}_{i }\frac{\partial z_{i}}{\partial c_{ik}}=\delta_{ij}g^{\prime\prime}_{i}x_{k},\]  
where \(\delta_{ij}\) is the Kronecker delta. In the case \(g\left(z\right)=\tanh(z)\), we have \(g^{\prime\prime}\left(z\right)=-2 gg^{\prime}\) such that  
\[\frac{\partial}{\partial c_{ik}}g^{\prime}_{j}=-2\delta_{ij}g^{\prime}_{i}y_{i }x_{k} \tag{10}\]The explicit formulas are  
\[\varepsilon^{-1}\Delta c_{ij} =\zeta_{i}v_{j}-2\zeta_{i}\rho_{i}y_{i}x_{j} \tag{11}\] \[\varepsilon^{-1}\Delta h_{i} =-2\zeta_{i}\rho_{i}y_{i}\]  
where \(\zeta_{i}=g_{i}^{\prime}\mu_{i}\), \(\mu=a^{T}Q^{-1}\xi\), \(\rho=cv\), and \(v=L^{-1}\xi\). All quantities refer to the same time step \(t\) and a suitable regularization has to be used if \(L\) is singular.  
We may interpret the first term \(\zeta_{i}v_{j}\) as a general driving term which is seen to increase the sensitivity of the linearized sensorimotor loop. The second term contains the effects of the nonlinearity (10) and essentially keeps the output away from saturation which implies sensitivity to the inputs. This term may be interpreted as an (anti-)Hebbian learning contribution with strength \(2\zeta_{i}\rho_{i}\). The dynamics of the threshold \(h\) is driven exclusively by this term.  
The learning parameter \(\varepsilon\) is chosen such that the parameters change at about the same time scale as the behavior, _i.e._ parameter configurations corresponding to generated behaviors are not preserved by the control system. Hence, the system can be said to show rather adaptation than learning. This flexibility should not be considered as a drawback of the approach. It is straightforward to store controller configurations in a long-term memory if the behavior persists for some time or receives any external reward. The stored behaviors can be re-used at later encounters with similar sensory inputs or can be accessed for planning or for further optimization of a particular behavior.
## 3 Materials and Methods
### Photogrammetric Processing  
All images in this study were processed using Agisoft Metashape Professional software, version 1.8.0 [15], and the parameters chosen for the different steps are listed in Table 2. Agisoft Metashape is a software tool based on Structure from Motion (SfM) for bundle adjustment and Multiview Stereo (MVS) for dense image matching. This software was the appropriate choice for the reconstruction of DEMs and orthophotos for the scene. The images were automatically processed in this software, except for some interventions in some details, such as the identification and measurement of ground control points (GCPs) and checkpoints (CPs), which were manually performed by the user.  
Figure 5: Preparation of GCPs and CPs. (**a**) initial iron sheets. (**c**) final Colored iron sheets. (**b**,**d**) samples of CPs.  
The processing of UAV images in Agisoft Metashape software was performed in the following main steps [2]:  
1. Import images, GNSS data, and IMU data.
2. Align images for interior and exterior orientation and generate a sparse point cloud.
3. Import and mark GCPs.
4. Optimize the camera.
5. Build a dense point cloud using image matching methods.
6. Create of a mesh and texture.
7. Generate a DEM.
8. Create an orthophoto.  
In this study, eleven sets of the dense point cloud, DEMs, and orthomosaic were created from the combinations of the acquisition images; one set was processed based on the direct georeferencing method (without GCPs), and the other ten sets were processed by indirect georeferencing using five GCPs. In all cases, the dense point cloud was used to create DEMs, and the last one was used to create the orthophoto for each image. Then, the orthophoto images were aligned to create the orthomosaic for the study area.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 4 Discussion

Table 8: Results predicted by ANN and FEM in a statistical comparison of Spar mooring responses. Figure 17: The mooring line response error between ANN and FEM for 12.65 m wave height and 11.39 s wave period. Based on the above discussion, the ANN model can predict the surge, pitch, and heave response of a spar hull and the maximum mooring line response using untrained wave height and wave period data as well as structural parameters. It has been observed that the ANN model is highly efficient and considerably reduces the time needed to predict response time histories of spars and mooring lines. The responses of the spar platform predicted by the ANN model correlate well with the FEM results. However, an ANN model that has already been trained and formed the networks for a spar platform cannot predict the response for other different types of platforms.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 4 Results
### Field Test  
The farm selected as a case study is a berry farm located in Huelva (Andalusia, Spain), one of the most important berry producing areas in Europe and where 100% of the crop is irrigated under plastic (Figure 6). In climatic terms, this is a semi-arid zone with an annual rainfall of approximately 630 mm. The validation tests were carried out on a blueberry crop that covers two plots of approximately 4 hectares each.  
The devices were installed in this crop for 6 months (January-June, 2018) (Figure 7). Figure 8 shows the snapshots of real data from the sensors.  
Figure 5: Node battery voltage.  
Because the parameters that are be measured in the crop do not show large variations in short time intervals, it was decided that if measurements are taken every 30 min, a correct balance between battery life and adequate precision would be obtained, allowing us to evaluate the state of the crop in real time. Thus, if we use the data previously measured in the different operating modes, we can calculate the average of the device for a time between measures of 30 min in the following way:  
\[Consumption=\frac{9.4\;\mathrm{mA}\times 4\;\mathrm{s}+22\;\mathrm{mA}\times 1 \;\mathrm{s}+0.036\;\mathrm{mA}\times 1795\;\mathrm{s}}{1800\;\mathrm{s}}=0.069\; \mathrm{mA}\]  
Taking into account the calculations performed previously, and assuming that the actual duration is 60% of the theoretical calculation, the estimated duration of the battery for a time between data collection of 30 min would be the following:  
\[Duration=\frac{2000\;\mathrm{mA}\times 0.6}{0.069\;\mathrm{mA}}=17391\; \mathrm{h}=724\;\mathrm{days}\]  
Figure 6: Experimental site for the field test.  
Figure 7: Installed device taking measurements every 30 min.  
With this autonomy, the end-device would be able to operate for two years without the need for battery change or any maintenance.