esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermophilic Methane Production from Hydrothermally Pretreated Norway Spruce (_Picea abies_)
## 3 Results
### Syringe Tests
#### 3.4.1 Influence of Organic Loading on AD of Hydrolysate  
Increasing OL of the hydrolysate pretreated at 170 \({}^{\circ}\)C had negative effect on the methane yield (Figure 2). Methane yields at OL of 6 and 10 gCOD/L were 0.56 gCOD/gCOD (195 NmL/gCOD) and 0.53 gCOD/gCOD (186 NmL/gCOD) respectively. At higher loadings of 20 and 30 gCOD/L, the methane yield decreased even more to 0.48 (167 NmL/gCOD) and 0.43 gCOD/gCOD (152 NmL/gCOD) respectively.  
The methane yields from hydrolysate pretreated at 140 \({}^{\circ}\)C (Figure 3) had a similar trend as that of hydrolysate pretreated at 170 \({}^{\circ}\)C. During the lower OLs of 6 and 10 gCOD/L, the methane yield was 0.83 (290 NmL/gCOD) and 0.80 gCOD/gCOD (282 NmL/gCOD) respectively while decreasing to 0.71 (247 NmL/gCOD) and 0.6 gCOD/gCOD (211 NmL/gCOD) for 20 and 30 gCOD/L respectively. The methane yield of hydrolysate pretreated at 140 \({}^{\circ}\)C was significantly higher than hydrolysate pretreated at 170 \({}^{\circ}\)C at all OLs (Figure 4), as also observed during AD in AMPTS II.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **G\({}_{\mathbf{0}}\) (NmL CH\({}_{\mathbf{4}}\)** & **R\({}_{\text{max}}\) (NmL CH\({}_{\mathbf{4}}\)** & **\(\lambda\) (Days)** & **R\({}^{\mathbf{2}}\)** & **Cumulative Methane Yield** \\  & **gCOD \({}^{-1}\))** & **GCOD \({}^{-1}\)Day\({}^{-1}\))** & **GCOD \({}^{-1}\))** & **(NmL gCOD\({}^{-1}\))** \\ \hline H170 & 168.0 & 34.0 & 0.4 & 0.962 & 162 \\ H140 & 187.4 & 99.8 & 0.3 & 0.962 & 189 \\ H170 syn & 302.9 & 42.2 & 0.8 & 0.960 & 285 \\ H140 syn & 295.2 & 60.1 & 0.6 & 0.960 & 295 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Regression results of cumulative biogas yield with the modified Gompertz model.  
Figure 2: Methane yield of H170 under thermophilic condition under different organic loadings (OLs).
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 4 Experiments and Results
### Results  
The predicted anomaly points using our proposed model and the labeled anomaly points are shown in Figure 4. Also, the precision, recall, F1-score, and detection accuracy of our proposed model and other autoencoder-based models are shown in Table 1. From these results, three observations were made.  
* _Observation 1_. Our proposed model can detect anomaly points around the peak well except the peak with a very long bandwidth.
* _Analysis_. Figure 4 shows that our proposed model can detect anomaly points around the peak well except the peak with a very long bandwidth. This is because the peaks with very long bandwidth hardly occur in our dataset, which makes our model hard to predict the anomaly points around the peak with very long bandwidth. However, except for the peak with a very long bandwidth, our model can predict anomaly points well since our anomaly detection model is based on LSTM that can capture long-range correlations efficiently.
* _Observation 2_. 1D-CNN autoencoder performs worse than RNN-based autoencoders (biGRU-AE, biLSTM-AE, GRU-AE, LSTM-AE) in terms of precision, recall, F1-score, and detection accuracy.
* _Analysis_. Table 1 shows that 1D-CNN autoencoder performs worse than RNN-based autoencoders (biGRU-AE, biLSTM-AE, GRU-AE, LSTM-AE) in terms of precision, recall, F1-score, and detection accuracy. This is because CNN in 1D-CNN autoencoder use only time or frequency domain data as input, which results in the insufficient extraction of time-series data features. Also, CNN can extract spatial features but cannot extract temporal features of time-series data since CNN does not retain a memory of previous time-series patterns, which is not suitable for time-series data. However, LSTM or GRU in RNN-based autoencoders is a special RNN structure that can effectively process time-series data and enhance the generalization ability of a model via a gate structure. Therefore, RNN-based autoencoders outperform 1D-CNN autoencoder.
* _Observation 3_. LSTM autoencoder performs best among other models in terms of precision, recall, F1-score, and detection accuracy.
* _Analysis_. Table 1 shows that the LSTM autoencoder performs best among other models in terms of precision, recall, F1-score, and detection accuracy. Also, LSTM-based models (biLSTM-AE, LSTM-AE) outperform GRU-based models (biGRU-AE, GRU-AE). This is because GRU in GRU-based models doesn't have a memory unit for controlling information flow, and it just exposes the full hidden content without any control while LSTM in LSTM-based models uses a memory unit to control information flow. Hence, LSTM-based models can remember longer sequences and have greater expressive power than GRU-based models. Also, bi-directional RNN models perform worst than one-directional RNN models. This is because the anomaly points are located after each peak of the BC pressure signal. Hence, the direction information is very important. However, bi-directional RNN models consider both directions, which makes our
## 1 Introduction
A promising tool for this kind of noise mapping is given by geo-statistics, which provides a set of statistical techniques specifically designed for spatial problems. These methods are aimed at predicting the values of a certain phenomenon over an area where several points have been sampled [12]. The predictions are based on statistical models that can incorporate additional information as explicative variables. Moreover, the prediction error can be estimated using uncertainty propagation [13; 14]. The use of an equally distributed sampling grid allows for a better determination of the mean value for the underlying statistical distribution of the sampled data obtained at the measuring points. For this reason, the use of geostatistical interpolation, as performed by the Ordinary Kriging (OK) method [12], can be applied, since the mean value of the measured distribution is already known. The goal of this paper is to analyze the suitability of a hardware system for noise measuring, based on Raspberry Pi nodes with a condenser microphone as an acoustic sensor and a data collection system, combined with geo-statistical techniques which are applied to the problem of noise mapping in a small city in the Valencian Community in Spain. The advantages provided by geographic information systems (GIS) are also used to locate precisely the measurement locations within a heterogeneous environment. A predictive model is developed by analyzing the noise pollution measurements obtained from a small set of points over a specific city. This model allows for obtaining punctual level estimates and calculating the spatial-temporal variation of the noise pollution level. To this end, the OK interpolation method will be considered. The main objective of this paper is then to evaluate the performance of the Wireless Acoustic Sensor Network (WASN) in combination with this spatial-temporal interpolating method at estimating the values corresponding to unsampled spatial locations, provided that the sampling grid has been correctly designed. This applied study is oriented to find the noise climate in different points for predicting global behaviors. As a result, the method allows for establishing environmental local policies for noise control and zonification at a municipal level, evaluating the local impact of the sound sources.
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 4 Preliminary Networking Assumptions  
This work is based on different RPL characteristics and network assumptions. It is assumed that an RPL network is initially established with a single-DODAG or multi-DODAG topological setup. Each DODAG is formed with a single sink node and multiple non-sink RPL nodes. The RPL operates in a storing mode for all the initiated DODAGs. All the nodes run an RPL implementation that is based on the RFC-6550 RPL specification. The implementation is considered to support multi-instance configurations in addition to the slight modifications introduced in this work.  
It is also assumed that all the nodes are deployed in a homogeneous and stationary IoT network setup using small-sized IoT devices. They are powered with rechargeable batteries, which makes it critical to take into account energy efficiency and resource utilization. Although they would be resource-limited, the devices have enough memory to locally cache routing and multi-DODAG information. In addition, each device has a communication module for low-power wireless connectivity. Wireless connections are established among the devices in the network to enable RPL communications. A multi-hop topology is formed among the nodes in each DODAG over the wireless links. This would result in more than one multi-hop network path established up to the sink node across the network.  
The deployment of the network is assumed to target a specific IoT application. Examples of possible applications are a smart city, e-health, and smart agriculture. Accordingly, IoT devices are equipped with the necessary smart hardware components such as sensors and actuators to enable the collection of application-specific IoT data. Data communications are carried out periodically based on configured time intervals. The data are transmitted in UDP packets over the established routing paths. Forwarding the data further to/from the Internet is only performed at the sink nodes, which act as Internet gateways.
## 1 Introduction
### Motivation  
LQR has been considered one of the most effective controllers for structural control; however, it requires structural states for feedback which may not be measurable in real application. As a result, a state observer or estimator is necessary which could increase the computational effort and lead to time delay in the control loop. Furthermore, state estimation error could reduce the control performance or even lead to inferior structural response. Last but not least, control performance of LQR depends on the weighting matrices in the cost function. Well-determined weighting matrices lead to effective control performance with limited force capacity of active devices. Therefore, the objectives of this study are (1) to optimize the weighting matrices of LQR that leads to effective control performance; (2) to replace  
Figure 1: Illustration of the mathematical model of a building with an active mass damper.  
the LQR with a controller that intends to emulate the control performance of the LQR without a state estimator; and (3) to demonstrate the control performance of the LQR and the controllers that emulate the LQR through numerical and experimental approaches. In this study, machine learning-based control models are proposed and trained to emulate an LQR with weighting matrices optimized by applying a novel and simple metaheuristic algorithm named symbiotic organisms search (SOS). A 10-story benchmark building controlled by an AMD is adopted for numerical studies. The AMD is controlled by an LQR in the modal configuration optimized by the SOS with respect to the objective function considering the square root of sum of squares (SRSS) of maximum acceleration of the selected modes. The LQR with optimized weighting matrices is expected to achieve effective seismic control performance, which, however, may be affected by the performance of state estimator. As a result, two neural network models are adopted for data training including the multilayer perceptron (MLP) model and the autoregressive with exogenous inputs (ARX) model to emulate the control performance of LQR. However, state estimation is not critical in the neural network models which reduces the effect of state estimation error on the seismic control performance of LQR. Afterwards, the numerical simulation results are compared and discussed thoroughly. Finally, shake table testing is performed for experimental validation in the structural laboratory and the results are summarized and concluded.
## 2 Materials and Methods
### Scope and Purpose  
This project endeavors to enhance the immersive visualization of hydrological processes with a specific focus on the continental United States. The primary objective is to enable the visualization of hydrological phenomena and pertinent information throughout the region, thereby providing valuable insights for stakeholders involved in water resources planning. These stakeholders encompass a broad spectrum of individuals and organizations impacted by hydrological events.  
While the project primarily caters to planners and decision-makers, its utility extends to a wider audience. The application dynamically generates 3D models to represent terrain and relevant infrastructure, adapting in real-time as users navigate the interface to display terrain tiles, visualizing the landscape of the continental United States. All 3D modeling and generation for this process is executed locally, leveraging client-side hardware resources and eliminating the need for resource-intensive server-side preprocessing. The application draws upon data from sensors maintained by the United States Geological Survey (USGS) to present real-time information on local weather conditions, stream height, and discharge. By implementing this framework, we contribute to an ongoing endeavor to revolutionize the field of hydrological visualization, facilitating collaborative planning and response to climate-related disasters.  
The innovation of this research lies in its departure from static, 2D graphs and maps used in hydrology, which often separate spatial, temporal, and contextual information across multiple platforms. In contrast, this XR-based framework consolidates multivariate, real-time data into a single immersive environment, allowing users to engage with complex hydrological systems in situational context. Unlike traditional systems that rely on abstract representations and require prior domain expertise for interpretation, the proposed solution offers intuitive, spatially embedded interactions that improve accessibility, comprehension, and stakeholder engagement and positions the framework as a foundation for future applications in digital twin environments, climate resilience planning, immersive training, and public education on environmental risks.  
Through the development of this application, we aim to address the following key questions: (a) What data sources are most effective for creating immersive hydrological visualizations? (b) How can XR be used to enhance hydrological data visualizations? (c) How can user immersion be created, maintained, and leveraged in data visualization? (d) What are the future prospects and potential improvements that can be made in the use of XR systems for hydrological data visualization?
## 1 Introduction  
Carbon-based nanofillers, such as carbon nanotubes (CNTs), graphene nanosheets (G) or/and graphene nanoplatelets (GNPs) are being widely investigated as reinforcements of epoxy resins [1; 2; 3; 4]. Their excellent electrical, thermal and mechanical properties induce interesting expectations of improving the behavior of these thermosetting resins. Among other advantages, the high aspect ratio of these nanofillers together with their hydrophobic character allow them to act as efficient barriers against water transport through the resin. Some authors [5; 6; 7] have reported that the reduction of water permeability is associated with both the reduction of the free volume and the restriction of the molecular dynamics of the polymer chain surrounding the nanofiller. Several works [5; 6; 7; 8; 9; 10] have been published related to the reduction of the maximum water absorbed caused by the addition of CNTs or GNPs. This effect can be enhanced by the increase of the nanofiller content added into the matrix and its dispersion degree.  
On the other hand, it is well known that the absorption of water into thermosetting resins causes a plasticization effect [11; 12]. This is due to the fact that water increases the chain segments mobility, which implies a decrease of the glass transition temperature and even the reduction of modulus and mechanical strength. It is expected that the nanofillers addition acts in two different ways: Reducing the rate and/or the maximum content of water uptake and improving the mechanical properties of non-aged composites.  
Recently, [PERSON] et al. [5; 6] studied the moisture uptake and its effect on the thermo-mechanical properties in composites reinforced with CNTs and reduced graphene oxide. They concluded that the CNTs addition decreases the diffusivity rate but the maximum water absorption remains unchanged. However, the addition of graphene oxide nanoparticles reduces the water absorption capability of the epoxy resin. This means that the geometry of graphitic nanofiller is significant as the GNPs seems to be more efficient. CNTs usually have a higher specific surface area than GNPs. Theoretically, the high specific area of the nanofiller should increase the barrier properties but the higher aspect of CNTs decreases this phenomenon. In addition, graphene nanoparticles tend to a self-orientation, showing a preferential orientation through the in-plain direction, which enhances barrier properties.  
In this paper, the influence of GNPs geometry on the moisture uptake of epoxy/GNPs nanocomposites is analyzed. In addition, we analyze the plasticization effect caused by water in the neat epoxy resin and nanocomposites in order to evaluate the influence of the GNPs addition.
## 2 Materials and Methods
### Method Validation
#### 2.2.1 Linearity of Calibration  
Two dilution series from 25 to 250 mg L\({}^{-1}\) and 250 to 500 mg L\({}^{-1}\) were prepared by diluting the standard stock solution with 20 vol.% MeOH in water. Three calibration setsfor each dilution series were prepared and each standard concentration was measured three times.
# RIS-Assisted Cooperative Time-Division Multiple Access
## 1 Introduction
The authors focused on the joint optimization problem of transmit beamforming and RIS control, aiming to maximize the energy efficiency under minimum rate constraints of the users. However, the RIS-assisted opportunistic communication to serve two UEs in the same time slot can still have a unique challenge because RISs can inadvertently amplify interference signals between each other. Since time slot-based resource allocation methods are used in most communication systems, including LTE and NR, studying RIS-based opportunistic transmission methods in TDMA systems has a valuable contribution from the perspective of developing a technique that can be applied to many commercialized communication systems. From the perspective of practicality, guaranteeing the quality-of-service (QoS) of scheduled UEs is more important than maximizing the sum capacity of scheduled UEs. In this paper, we propose RIS-assisted cooperative time-division multiple access (RC-TDMA), in which the required signal-to-interference-plus-noise ratio (SINR) of all users is guaranteed as much as possible by opportunistic use of RIS for cooperative transmission. In the proposed RC-TDMA, each user has a time slot dedicated to each user. The time slots are divided into two groups: non-cooperative time slots and cooperative time slots. If any scheduled users in the time slots are expected to be satisfied with QoS, it is called a strong-channel-conditioned UE; these time slots become the candidate time slots to perform cooperative transmission using the RIS. Conversely, suppose any scheduled users in the time slots are not satisfied with QoS, then it is called a weak-channel-conditioned UE. In that case, these users become candidates for additional data service in the cooperative time slots. Thus, a pair of users, i.e., strong- and weak-channel-conditioned UEs, can be simultaneously served in the time slot dedicated to the strong-channel-conditioned UE. Thus, the RIS is only turned on and operated in the cooperative time slot for the cooperative transmission. We adopt a random phase shift-based RIS, which is constant for the given time slots. It allows us to estimate the effective channel between BS and UE without computational complexity or overhead. Therefore, in this study, we propose scheduling for non-cooperative and cooperative time slots, user pairing (i.e., weak-channel-conditioned UE and strong-channel-conditioned UE) for a scheduled cooperative time slot, and transmit beamforming vector design for both UEs in the cooperative time slots. The simulation and numerical results demonstrate that the proposed RC-TDMA ensures QoS for all UEs as much as possible. Thus, the proposed scheme minimizes the remaining required capacity indicating the amount of capacity that was not achieved compared with the required capacity. Furthermore, it is shown that the proposed scheme can increase power efficiency compared with the conventional TDMA that does not use the RIS when the RIS consumes much less power than BS power consumption.
## 8 SCOH Transceiver Structures
### Optical Field Representation  
In this subsection, we present the associated concepts and implementations of various potential SV-DD transceiver structures. We begin this part by presenting the Jones space and Stokes space for optical field representations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Detection Schemes**} \\ Feature & **Coherent** & **Self-Coherent** & **IM-DD** & **Reference** \\ \hline Maximum DoFs per polarization & 2 & 2 & 1 & [12,13,4/7,139] \\ Adopted method & Heterodyne or homodyne & Heterodyne or homodyne & Direct & [10–13,10/7] \\ Modulation parameters & 1 and Q or amplitude and & I and Q or amplitude and & Intensity & [10–13,10/7] \\ Polarization sensitivity & & phase & phase & [10–13,10/7] \\ Carrier phase sensitivity & & & & [10–13,10/7] \\ Electrical filter usage for selecting the WDM channel & & & & [11–13,10/7,139] \\ CD is a linear distortion (offers more effective CD\({}^{-1}\)) & & & & [11–13,4/7,107,139] \\ Requirement of LO laser at Rx & & & & [12–13,4/7,107,139] \\ Requirement of polarization control or diversity at Rx & & & & [11–13,4/7,139] \\ DSP complexity & & & & [11–13,10/7] \\ Implementation cost & & & & [11–13,10/7] \\ Footprint & & & & [11–13,10/7] \\ Power consumption & & & & [11–13,10/7] \\ Spectral efficiency & & & & [11–13,10/7] \\ Reach & & & & \\ \hline \hline \end{tabular}  
* Feature fully supported/yes/high,
* Feature partially supported/medium,
* Feature not supported/no/low;
* The highlighted cell signifies the most advantageous option; DoF: degree of freedom; CD chromatic dispersion; CD\({}^{-1}\): chromatic dispersion compensation; _I:_ in-phase and \(\mathcal{Q}\): quadrature; Rx: receiver; LO: local oscillator; WDM: wavelength-division multiplexing; DSP: digital signal processing. **Note:*
* In this table, a SCOH system is assumed to be a good compromise between the COH and IM-DD. However, some SCOH schemes have comparable features to that of the COH scheme while still leveraging different notable features of the IM-DD. This depends on the applied detection dimensions and other adopted techniques, as detailed in Section 8.  
\end{table}
Table 8: Different detection schemes’ comparison.