esa-sceva/satcom-chunk-collection huggingface dataset:
# Recurrent Embedded Topic Model
## 3 Fundamentals
### Inference and Estimation  
In Equation (2), the parameters \(\rho\) and \(\alpha\) determine the word distribution; furthermore, \(\delta_{d}\) is a Gaussian seed during the generative process. The inferential problem to be solved in this model is calculating the posterior distribution of the variables for any given document, although this distribution is computationally unrealistic to achieve (due to the integral, as we are sampling a continuous variable), such as is shown in (2) [17].  
\[p(\mathbf{w}_{d}\mid\alpha,\rho)=\int p(\delta_{d})\prod_{n=1}^{N_{d}}p(w_{dn} \mid\delta_{d},\alpha,\rho)\mathrm{d}\delta_{d} \tag{2}\]  
Like LDA, ETM uses variational inference to get around calculating the previous equation and going through proven approximations [37]. They used amortized inference where the variational distribution of \(\delta_{d}\) depends on \(w_{d}\) and the variational parameters \(v\), where a Gaussian distribution whose mean and variance comes from a neural network parametrized by \(v\) (when \(w_{d}\) is ingested it outputs the mean and variance of \(\delta_{d}\)). The ELBO (Evidence Lower Bound) is the function of the model parameters and variational parameters that is optimized throughout the training of the model, as shown in (3) [17].  
\[\begin{split}\mathcal{L}(\alpha,\rho,v)&=\sum_{d=1} ^{D}\sum_{n=1}^{N_{d}}\mathbb{E}_{q}[\log p(w_{nd}\mid\delta_{d},\rho,\alpha)] \\ &-\sum_{d=1}^{D}\mathrm{KL}(q(\delta_{d};w_{d},v)\|p(\delta_{d})).\end{split} \tag{3}\]  
This equation helps \(\delta_{d}\) to place mass on topic proportions to explain the observed words where the Kullback Leibler (KL) divergence helps them get closer to the prior \(p(\delta_{d})\). This metric supports the maximization of the expected complete log-likelihood \(\sum_{d}\log p(\delta_{d},w_{d}\mid\alpha,\rho)\)[17].  
The next Algorithm 1 assumes that \(\mathrm{NN}(x;v)\) refers to a neural network (linear) where \(x\) is the input and \(v\) are the variational parameters of the network:  
Figure 3: LSTM model, single cell. Adapted from [36].  
```
1:Initialize model and variational parameters
2:for epoch do
3: Compute \(\beta_{k}=\text{softmax}\big{(}\rho^{\top}\alpha_{k}\big{)}\) for each topic \(k\)
4: Choose a minibatch \(\mathbf{d}\) of documents
5:foreach document \(d\) in \(\mathbf{d}\)do
6: Get normalized bag-of-word. \(\mathbf{x}_{d}\)
7: Compute \(\mu_{d}=\text{NN}\big{(}\mathbf{x}_{d};\
u_{\mu}\big{)}\)
8: Compute \(\Sigma_{d}=\text{NN}(\mathbf{x}_{d};\
u_{\Sigma})\)
9: Sample \(\theta_{d}\sim\mathcal{LN}(\mu_{d},\Sigma_{d})\)
10:foreach word \(w\) in the document \(d\)do
11: Compute \(p(w\mid\theta_{d})=\theta_{d}^{\top}\beta_{\cdot}w\)
12:endfor
13:endfor
14: Estimate the ELBO and its gradient (backprop.)
15: Update model parameters \(\alpha_{1:K}\)
16: Update variational parameters \(\big{(}\
u_{\mu},\
u_{\Sigma}\big{)}\)
17:endfor
```  
**Algorithm 1** ETM
## 5 Experimental Results
### Experimental Platform and Algorithms' Parameter Settings  
For a fair comparison, all of the experiments are conducted on the same machine with an Intel 3.4 GHz central processing unit (CPU) and 4 GB memory. The operating system is Windows 7 with MATLAB 8.0.0.783(R2012b).  
On the test functions, we compare PTBO with classic PSO, DE, and six other recent popular meta-heuristic algorithms, which are BA, CS, BSO, WWO, WCA, and SMS. The first three algorithms belong to the second category, i.e., swarm intelligence, and the remaining three algorithms belong to the third category, i.e., intelligent algorithms simulating physical phenomena. The parameters adopted for the PTBO algorithm and the compared algorithms are given in Table 7.
## 4 Classification and Core Components of DT Systems in Transportation Infrastructure
### Basic Framework of DTs of Urban Transportation Infrastructure
#### 4.3.3 \"Form-Condition-Mechanism-Tendency\" Concept  
Alongside the \"4 horizontal + 4 vertical + N\" framework, the \"Form-Condition-Mechanism-Tendency\" concept further refines the DT system's ability to manage urban transportation infrastructure as follows:  
* Form (Digital Reconstruction): This component describes the infrastructure's physical shape, structure, and geometric properties. High-precision 3D modeling is essential to mirror the physical entities accurately. This phase also develops digital representation rules and databases that retain the unique attributes and characteristics of the infrastructure.
* Condition (Precise Mapping): This layer represents the infrastructure's current operational state, dynamic responses, and performance under specific conditions, such as wear and tear, health status, and potential risk factors. By integrating performance monitoring and holographic sensing technologies, the system provides real-time insights into the health of the infrastructure.
* Mechanism (Mechanistic Deduction): This element analyzes the infrastructure's detailed functioning and performance, including reliability and safety. DTs simulate and predict the infrastructure's response to environmental and load conditions, offering deep insights into performance dynamics.
* Tendency (Integration and Feedback): The tendency phase employs data integration to forecast future trends and identify potential issues, aiding urban planners in anticipating and addressing future challenges. By synthesizing insights from previous layers, the DT delivers a comprehensive view of the infrastructure's performance trajectory.  
Incorporating these concepts into the DT framework equips urban transportation systems with a more detailed understanding of infrastructure performance and potential challenges, thereby ensuring continuous optimization and sustainable management.
## 6 Castalia Simulator
### Simulation Setup  
We have evaluated our TPC approaches in the Castalia simulation environment. We use Version 3.0 of Castalia on top of Version 4.1 of OMNeT++. A body sensor network of three nodes in a star topology has been defined as our basic simulation scenario in the configuration file, omnetpp.ini, resembling the experimental scenario conducted in our previous work. The sink node always operates at a maximum transmission power level (0 dBm) for ensuring that the sensor nodes receive the beacon. On the other hand, the sensor nodes transmit at a power level selected by the TPC algorithm. The nodes send constant length packets of 25 bytes at a predefined rate of 20 data packets per second to the sink node. Each simulation lasts 52 s, and results are obtained from the average of 10 simulation results.  
In Castalia, the radio collision model is configured according to the InterfModel parameter, which can take three different levels: Level 0, 1 and 2. In Level 0, the simulator assumes no collision at all. In Level 1, it considers that the collision happens if more than one node is sending data at the same time and the signal is over the signal interference ratio (SIR). In Level 2, it considers the strongest signal and adds all other signals as noise; then, based on the SIR, the signal is received or ignored [28]. We have used for this study Level 0 with the purpose of reproducing the the experimental scenario most exactly as possible, where each link was tested independently, with no inter-link interference.  
We feed the Castalia simulator with the traces of path loss calculated from the RSSI measures obtained with the Shimmer nodes in our experimental scenarios. We define different path loss map files corresponding to each human posture and each transmission power level.  
Due to the large number of simulations required, we made extensive use of scripting to automatically generate the parameter description files and to measure and collect the results from the trace files that list for each sensor node the temporal evolution of the signal detected at the node location.  
We present the simulation parameters for the nodes: the CC2420 radio file with the default values provided by the simulator; 802.15.4 MAC with two transmission attempts; the temporal variation model to recreate the dynamics of the path-loss fluctuations; and the no collisions model. Table 4 shows the threshold values and other simulation parameters.
## 4 Parametric Study
### Analysis Results
All the analysis results are plotted as a function of PLR, as shown in Figure 16a-c. From these figures, it can be seen that \(f_{all}\) is almost linearly increased with increasing PLR. For each curve radius, the linear regressions are performed and R\({}^{2}\) is larger than 0.97. From  
Figure 14: Effect of curve radius (\(e_{G}\) = 0): (a) \(L_{G}\) = 9 m; (b) \(L_{G}\) = 15.2 m; and (c) \(L_{G}\) = 30 m.  
Figure 13: Effect of girder length (\(e_{G}\) = 0): (a) \(R\) = 1500 m; (b) \(R\) = 700 m; and (c) \(R\) = 300 m.  
Figure 15: Effect of lateral displacement of girder (\(L_{G}\) = 9 m): (a) \(R\) = 1500 m; (b) \(R\) = 700 m; and (c) \(R\) = 300 m.  
these regression results, the minimum PLR requirement for each curve radius is obtained, as shown in Figure 16a-c. It should be noted that target \(f_{all}\) values are shown in Table 1, and they are 72 MPa, 58 MPa, and 27 MPa for \(R\) = 1500 m, \(R\) = 700 m, and \(R\) = 300 m, respectively. From the results, the minimum PLR requirements for \(R\) = 1500 m, \(R\) = 700 m, and \(R\) = 300 m are 18 kN/m/track, 15 kN/m/track, and 14 kN/m/track, respectively. These values are approximately 6% and 7% greater than those from the theoretical approach shown in Section 2 for \(R\) = 1500 m and \(R\) = 700 m, respectively. In the case of \(R\) = 300 m, the minimum PLR requirement obtained from the FE analysis is 40% greater than that from theoretical approach. This is because the effect of the bridge not considered in the theoretical approach is much greater at very small curve radius. In the case of ballasted track, CWR track buckling is mainly affected by the ballast properties [20] and the Track Maintenance Guideline in Korea [17] specifies the minimum PLR of the ballast as 10 kN/m/track and 18 kN/m/track for conventional and high-speed railway, respectively, to prevent the buckling of CWR track. From the analysis results, the minimum 18 kN/m/track of PLR is needed for CWR track with \(R\) = 1500 m on the open-check steel plate girder bridge, which is the same value for the ballasted track in high-speed railway.
## 3 Methodology
### Comparative Training Protocol  
To ensure the reliability and fairness of performance evaluation across different federated learning strategies, a standardized training protocol was employed. Each experimental run was repeated across multiple random seeds, allowing the statistical averaging to reduce the influence of stochastic variation in dataset partitioning and model initialization. This multi-seed framework enhances the robustness of the findings and provides a more accurate estimation of each model's generalization ability.  
The following key hyperparameters were fixed across all configurations to ensure comparability:  
* ROUNDS: Refers to the number of global aggregation steps in the federated learning process. Each round consists of a cycle in which clients train locally and contribute their updated models to the aggregation procedure.
* LOCAL_EPOCHS: Specifies how many times each client iterates over its local dataset during a single federated round. This controls the degree of local learning before model updates are exchanged.
* BATCH: Denotes the number of training samples processed in each mini-batch during local training. A fixed batch size of 32 was used to balance learning stability and computational efficiency.
* NUM_SEEDS: Indicates the number of different random seeds used to initialize experiments. Multiple seeds were employed to ensure that the results would not be biased by any specific random configuration, thereby improving the statistical validity of the evaluation.  
The comparative study incorporated two principal federated learning topologies: centralized and decentralized. In the centralized federated setting, the training process is orchestrated by a central server that collects model updates from all clients and performs weighted Federated Averaging. In this scheme, clients with larger datasets contribute more significantly to the global model update.  
In contrast, the decentralized configuration eliminates the need for a central coordinator. Clients are organized in a ring-based topology, where each client sequentially updates the shared model using its own data and passes the updated parameters to the next node. Aggregation is performed via unweighted averaging, treating all clients equally regardless of dataset size.  
This decentralized design aligns closely with real-world constraints in privacy-sensitive or bandwidth-limited environments, where a central aggregator may be infeasible or undesirable. Each client retains its own validation subset to monitor overfitting locally, reflecting the practical necessity of maintaining autonomy over private data. Through this protocol, the study ensures a rigorous and equitable comparison of learning strategies in federated fall-detection settings.
## 7 Conclusions  
In this paper, we proposed a robust rank reduction algorithm for steering vector estimation with the method of iterative parameter optimization and vector perturbation. In this algorithm, a bank of perturbed steering vectors was introduced as candidate array steering vectors around the true steering vector. The candidate steering vectors are responsible for performing rank reduction, and the reduced-rank beamformer forms the beam in the direction of the signal of interest (SoI). The perturbation vectors and the vector \(\mathbf{q}_{d}\) were introduced in order to break the correlations among the columns of the projection matrix, and the rank number can be controlled. Additionally, we devised efficient stochastic gradient (SG) and recursive least-squares (RLS) algorithms for implementing the proposed robust IOVP design. Finally, we derived the automatic rank selection scheme in order to obtain the optimal beamforming performance with low computational complexity. The simulation results for a digital beamforming application with a large array showed that the proposed IOVP and algorithms outperformed in convergence and tracking the existing full-rank and reduced-rank algorithms at comparable complexity.
# 2-Methoxy-4-Vinylphenol as a Biobased Monomer Precursor for Thermoplastics and Thermoset Polymers
## 3 Results
### Thermoplastic Applications
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Name** & **Type 1** & _p (\%) 2_ & **Composition** & _M\({}_{n}\) (D)_ & _T\({}_{\mathrm{g}}\)_ & _T\({}_{\mathrm{d}g}\)_ \\  & & **(wt.\%) 3** & **(g/mol) 4** & **(\({}^{\circ}\)C) 5** & **(\({}^{\circ}\)C) 6** \\ \hline P(MVP-AcOMe) & S & 91 & N.A. & 25,000 (1.5) & 45.9 \(\pm\) 0.2 & 289 \(\pm\) 12 \\ \hline P(MVP-AcOH) & S & 89 & N.A. & N.A. ** & 56.1 \(\pm\) 0.3 & 115 \(\pm\) 1 \\ \hline P(MVP-Bz) & S & 75 & N.A. & 14,600 (1.7) & 47.1 \(\pm\) 0.2 & 250 \(\pm\) 1 \\ \hline PS & E & \textgreater{}99 & N.A. & 170,000 (1.6) & 105.8 \(\pm\) 0.3 & 381.0 \(\pm\) 0.3 * \\ \hline P(S\({}_{90}\)-MVP-AcOMe\({}_{10}\)) & E & 94 & 88:12 & 140,000 (1.4) & 99.5 \(\pm\) 0.5 & 349 \(\pm\) 1 * \\ \hline P(S\({}_{90}\)-MVP-Bz\({}_{10}\)) & E & 92 & 66:34 & 75,000 (1.4) & 101.2 \(\pm\) 0.8 & 344 \(\pm\) 2 * \\ \hline P(S\({}_{90}\)-MVP-AcOMe\({}_{5}\)-MVP-AcOH\({}_{5}\)) & E & 81 & 99.0:9:0.1 & 1,000,000 (1.5) & 93.7 \(\pm\) 2.9 & 354 \(\pm\) 8 * \\ \hline \hline \end{tabular}  
* Polymerization technique, i.e., solution (S) or emulsion (E). 2 Monomer conversion based on \({}^{1}\)H-NMR analysis. 3 Ratio between the building blocks determined from \({}^{1}\)H-NMR analysis. 4 Obtained from the DMF-SEC with PS calibration standards. 5 Determined from the second heating cycle from DSC. 6 Determined at 5 wt.% reduction from TGA.
* Determined at 15 wt.% reduction from TGA. *
* Could not be measured due to its polarity.  
\end{table} 
Table 1: Physicochemical properties of the MVP-derived biobased polymers. Figure 1: TGA thermograms of solution (a) and emulsion (b) polymers. the calculated theoretical \(T_{\text{g}}\) (Equation (S3)). This is because PS comprised the majority of the composition (Figure S22). Additionally, the degradation patterns of all emulsion copolymers exhibited similar trends to PS with slightly lower \(T_{\text{deg}}\). According to the literature, PS degrades at around 375 \({}^{\circ}\)C which is similar to the major degradation observed in Figure 2[46]. Moreover, an initial mass loss was observed around 100 \({}^{\circ}\)C, possibly due to some residual styrene still being present in the dried polymer (Figure S17). To summarize this part of the work, we propose simple synthetic approaches to produce biobased derivatives of FA that can later be polymerized using different techniques toward thermoplastic applications. This was exemplified by the synthesis of homo- and copolymers of the biobased monomers in solution and emulsion polymerization. The thermoplastics exhibited good emulsion properties without compromising the thermal and morphological profile of PS, thus demonstrating their potential in partially replacing PS for future coating applications.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 1 Introduction
The proposed methodology for computing TDI is based on real data, a linear temperature model, and a forecasting model for water utilization applying ensemble learning and Monte Carlo simulation. The proposed TDI was developed and evaluated with data from the electrical system in Montevideo, Uruguay. This is a relevant case study since electricity is the main resource used for heating and other domestic activities in Uruguay, far superior to natural gas and other sources. In Montevideo, as in other main Uruguay cities, more than 90% of households have a thermostat-controlled electric water heater (according to the 2019 household survey by National Statistics Institute [5]). Since the electric water heater is one of the most energy-intensive household appliance (accounting for 34% of residential energy consumption, on average), it is an ideal candidate for remote load control as a demand management technique. The experimental analysis was performed both on real and simulated scenarios built using data from real electric water heaters in Uruguay, gathered in the ECD-UY dataset [6]. ECD-UY includes utilization of time series data about power consumption of several appliances, including water heaters, and aggregated consumption for representative households in the main Uruguayan cities. Results reported for the considered case studies demonstrate that the proposed index managed to capture the impact of thermal discomfort, fulfilling the goal of sorting electric water heaters to be properly managed by applying a direct control strategy, and allows special cases to be defined where a particular water heater is required to never be interrupted for security reasons. This article extends our previous conference article \"Demand response control in electric waterheaters: evaluation of impact on thermal comfort\" [7], presented at III Iberoamerican Congress on Smart Cities. The main contributions beyond those of the previous conference article include: (i) an extension of the developed temperature model for electric water heaters, only measuring the electrical state of the device; (ii) an improved procedure to predict water utilization by applying data analysis to real electricity consumption data from the ECD-UY dataset; (iii) the definition of an index to approximate the discomfort associated with an active demand management interruption of the water heater; and (iv) an extended evaluation of the proposed methodology for several cases of active demand management interruption of water heaters over different realistic scenarios. The article is organized as follows. Section 2 presents the formulation of the demand management problem through direct control of electric appliances. Section 3 reviews related works. Section 4 describes the proposed approach to define a TDI for direct control of electric water heaters. Section 5 reports the experimental validation of the water utilization forecasting, the temperature model, and the proposed TDI for realistic case studies. Finally, Section 7 presents the main conclusions and lines for future work.
## 4 A320 Cabin Interference Path Loss Measurement
### Experiment Results and Analyses
The frequency band was set in the GPS antenna's operating frequency range, or 1985-2005 MHz. Average IPL values at various window positions were calculated and compared with the simulation results. These data in Figure 9 demonstrate that the position of the PED influences the IPL value. As the distance between the PED and GPS antenna increases, the IPL shows a gradual increase. Additionally, a comparison of IPL values for two different frequency bands reveals that for the 1575.42 MHz frequency, the IPL values range from 65 to 80 dB. In contrast, for the 1985-2005 MHz band, the IPL values range from 75 to 90 dB. This indicates that an increase in the measurement frequency corresponds to a noticeable rise in IPL values. Figure 10 illustrates the IPL value cloud map derived from measurement data within the 1985-2005 MHz frequency band. It provides a more intuitive visualization of the variation tendency in IPL values. Several factors contribute to the discrepancy between simulation and experiment, including simplifications made in the aircraft model, such as the exclusion of seats, passengers, and luggage racks. Based on the power balance theory, some researchers [29] introduced the quality factor and mean absorption cross-section to quantify the impact of seats and passengers on \"power escape through windows\". Variations in aircraft type and the number of seats can also influence the results. The study found that under unoccupied  
Figure 8: PED emission waveform and measurement results. aircraft conditions, lossy media such as seats can reduce the power received outside the window by approximately 5-7 dB. In the experimental setup, the estimated loss from the internal aircraft cable may introduce some uncertainty into the results. Therefore, a statistical analysis was conducted on the difference in average IPL values between the simulation and the measurement results, as shown in Figure 11.  
To further validate the consistency between the simulation and experimental results, a statistical analysis was conducted on the average IPL differences. As shown in Figure 11, the mean difference was 2.997 \(\pm\) 0.678 dB, with a median of 3.28 dB, a maximum of 6.22 dB, and a minimum of 0.19 dB. The distribution indicates that most deviations are concentrated around 3 dB, with minimal variation. Although a discrepancy exists between the IPL values obtained from the simulation and the experimental measurements, the error is considered acceptable. These results suggest that the model and simulation methods used to calculate IPL are both valid and feasible, thus supporting the reliability of the methodology in this study. Figure 10: IPL value cloud map. Figure 9: Comparison of IPL simulation and experimental results.