esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### DEAP Dataset  
DEAP is widely used for emotion recognition and stress assessment [38]. Therefore, we used the DEAP dataset to validate the proposed scheme. It contains the physiological signals and subjective ratings collected from the 32 healthy participants while watching the emotionally evocative music videos. Each participant watched the 40 one-minute music videos that were selected to induce various emotional states of valance and arousal. The music videos are rated by participants based on their valence and arousal values.  
DEAP dataset uses a BioSemi AcriView system to record EEG data. The original sampling frequency for the EEG signals was 512 Hz. In the preprocessing, the original data is down-sampled at a sampling rate of 128 Hz to avoid the computational complexity and improve the time-domain and frequency-domain feature extraction without compromising the quality of signals. Also, feature extraction can be effectively performed on down-sampled data. The original data is down-sampled, EOG removed, filtered, and segmented.  
Figure 2: Proposed Methodology for Enhanced Stress Classification.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 2 Cultivation Conditions
### Nutrient Requirements  
Microalgae are diverse and so are their biomass compositions and nutrient requirements. A successful commercial microalgae production requires that the most important nutrients are provided in sufficient amounts and at more or less balanced ratios. Otherwise, growth or doubling times will be reduced due to the deficiency in one element ([PERSON]'s law of the minimum) [29]. Carbon, hydrogen, and oxygen, which make up 65-85% of the cell dry weight, are fixed through photosynthesis [6]. Required elements must be provided by the cultivation medium. The biomass composition varies among species, but the average composition of planktonic microalgae was found to be 7.6% N, 2.5% Na, 2.3% K, 1.4% S, and 1.1% P [6]. This can be a general guideline for the nutrient composition of the cultivation medium [6]. However, microalgae are capable of adapting their nutrient uptake to availabilities, which can also be exploited in biotechnology if secondary metabolites are desired [29]. Nitrogen starvation can trigger the production of \(\beta\)-carotene in _Dunaliella_ or the accumulation of storage lipids as well as polysaccharides in various microalgae [7; 42]. A limitation of nitrogen or the combination of nitrogen and phosphorus depletion leads to astaxanthin production in _Haematococcus_ and polyhydroxybutyrate (PHB) accumulation in many cyanobacteria [10; 44].  
Various cultivation media have been developed for different species or strains and their specific applications. Nevertheless, media are usually not optimized for the use in mass cultivation but slightly modified laboratory media are often used [45]. Most have excess nutrients, and thus growth is not at its optimum and their use is neither economical nor sustainable [45]. Furthermore, mainly artificial and limited nutrient resources are currently used. To reduce the ecological impact of microalgae cultivation and increase its sustainability, the utilization of alternative nutrient sources such as anaerobic effluents are mandatory in the long run. This would also reduce costs and dependencies from producing countries and strengthen regional nutrient supply.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 3 Architecture
### Topology Management  
SDNPS maintains two kinds of topologies: subscription topology and physical topology.  
For the former, every cluster (to be more exact, the representative broker) has global subscription information for all topics, which constitutes to the subscription topology. Each new (un)subscription event should be broadcast to all of the clusters, so that every cluster can receive it and then update its subscription topology. The representative broker of the cluster, which originally generates  
Figure 1: System architecture overview.  
Figure 2: Topology/routing management.  
(un)subscription events, also answers for forwarding this (un)subscription information to the server. This is the subscription topology.  
For the latter, on the one hand, a controller detects the physical link states of its own cluster, which forms an intra-cluster topology; on the other hand, it maintains the reachability information to the neighbor clusters they selected by periodically sending out heartbeat information, then applying network virtualization [32] technology to abstract the physical network to the virtual network with tuples (source cluster, destination cluster, and bandwidth), which forms the cluster-level topology. When centralized routing computation is adopted, every controller delivers its cluster-level topology formatted with JSON (JavaScript Object Notation) to the server. Then, the server forms an inter-cluster topology by aggregating and abstracting these data and computing inter-cluster routing paths. When distributed routing computation is adopted, every controller maintains this inter-cluster topology through a distributed protocol and computes inter-cluster routing paths, respectively. In this paper, we adopt the centralized routing computation for brevity. In subsequent work, we will adopt distributed computation for better flexibility and reliability. This is the physical topology.  
Each update of the cluster topology or subscription topology would trigger a routing recalculation, so as to add or delete paths among relevant switches.
## 4 Materials and Methods
### Bioassays of A. grandis with Bt Proteins  
The bioassays were based on the method of incorporating the test substances into an artificial diet [16]. The proteins, supplied as a lyophilized powder, were resuspended in sterile deionized water, and the concentration in this solution was determined by the Bradford method, using bovine serum albumin (BSA) as a standard. The aliquots required to obtain the doses corresponding to concentrations of 100; 50; 25; 12.5; 6.25, 3.13, and 1.56 \(\upmu\)g\(\cdot\)g\({}^{-1}\) were incorporated into the artificial CBW diet in a total volume of 30 mL. After blending, the mixture was poured into Petri dishes (\(\mathcal{O}\) 90 \(\times\) 15 mm) for solidification, and then 35 newly hatched _A. grandis_ larvae were placed at equal distances on the diet surface. The control for natural mortality consisted of an artificial diet, without the protein addition. The plates were incubated at 27 \({}^{\circ}\)C, with 40% relative humidity, and a photoperiod of 12:12 h light:dark for seven days, at which time the number of live and dead larvae on each plate was counted. All assays were performed in triplicate.  
Mpp23 Aa/Xpp37 Aa proteins that induced very high mortality (100%) at the lowest dose (1.56 \(\upmu\)g\(\cdot\)g\({}^{-1}\)) were selected for an additional bioassay at concentrations of 4, 2, 1, 0.5, 0.25, and 0.13 \(\upmu\)g\(\cdot\)g\({}^{-1}\), according to the methodology described above.
###### Abstract  
Direct current (DC) distribution is one of the most important enabling technologies for the future development of microgrids, due to the ease of interfacing DC components (e.g., batteries, photovoltaic systems, and native DC loads) to the grid. In these power systems, the large use of controlled power converters suggests the need of a careful analysis of system stability, as it can be impaired in particular conditions. Indeed, in DC power systems, a destabilizing effect can arise due to the presence of inductor/capacitor (LC) filtering stages (installed for power quality requirements) and high-bandwidth controlled converters, behaving as constant power loads (CPLs). This issue is even more critical when the CPL is potentially fed only by the battery, causing the DC bus to be floating. In this context, Lyapunov theory constitutes a valuable method for studying the system stability of DC microgrids feeding CPLs. Such a theory demonstrates how the region of asymptotic stability (RAS) shrinks as the state of charge of the battery diminishes (i.e., as the bus voltage decreases). Once the accuracy of the RAS is validated by comparing it to the real basin of attraction (BA), numerically derived using continuation methods, a smart power management of the CPL can be proposed to preserve the system stability even in the presence of a low bus voltage. Indeed, a suitably designed criterion for limiting the load power can guarantee the invariance of RAS and BA for each equilibrium point. An electric vehicle was used herein as a particular DC microgrid for evaluating the performance derating given by the power limitation.  
islanded DC microgrid; battery-only operation; constant power load; CPL; floating DC bus; stability criterion; power management
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
## 4 Experimental Analysis
### Experimental Comparison of General Input Mesh Data  
The training data are generated by the original mesh cartoon elephant, and the number of levels of the training data is 2. The number of levels of the mesh subdivision after the training is completed is also 2. Then, multiple coarse meshes are subdivided. Two indicators are calculated for each shape and listed in Table 1. The experimental results in Figure 6 show that, for the general input mesh data, both GNS and neural subdivision can obtain a smooth subdivision visual effect.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Hausdorff Distance \(\downarrow\)**} & \multicolumn{2}{c}{**Mean Surface Distance \(\downarrow\)**} \\ \cline{2-6}  & **LOOP** & **NS** & **GNS** & **LOOP** & **NS** & **GNS** \\ \hline Cat & 0.013005 & 0.010555 & **0.008341** & 0.001844 & 0.001422 & **0.000556** \\ [PERSON] & 0.011471 & 0.011188 & **0.007268** & 0.002715 & 0.001711 & **0.000698** \\ Dog & 0.015791 & 0.013866 & **0.010673** & 0.00313 & 0.002616 & **0.001048** \\ Gorilla & 0.016909 & **0.009626** & 0.010328 & 0.004218 & 0.00144 & **0.001154** \\ Horse & 0.021246 & 0.012571 & **0.010154** & 0.00431 & 0.001538 & **0.001028** \\ [PERSON] & 0.01776 & 0.013847 & **0.008048** & 0.003208 & 0.002445 & **0.00093** \\ Victoria & 0.016212 & 0.011919 & **0.007197** & 0.002646 & 0.001229 & **0.000733** \\ Wolf & 0.022053 & 0.014519 & **0.009069** & 0.003207 & 0.002004 & **0.000996** \\ \hline mean & 0.016805 & 0.012261 & **0.008884** & 0.003159 & 0.001801 & **0.000893** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of quantitative analysis of LOOP subdivision, NS, and GNS on the same dataset (bold numbers mean lower distances).  
Figure 6: \(Cont.\)  
As shown in Table 1, for two popular evaluation metrics, GNS outperforms neural subdivision and LOOP subdivision in most sample sizes. From the average point of view, the Hausdorff distance and average surface distance of the GNS mesh and the ground-truth mesh are 27.53% and 43.01% lower than those of NS, respectively.
## 3 Results and Discussion
### Oscillatory Shear Index (OSI)  
The OSI reflects the oscillatory behavior of the WSS throughout the cardiac cycle and is a dimensionless constant [20; 21]. OSI values range from 0 to 0.5, with 0 representing no oscillation and 0.5 representing full oscillation [22]. An OSI > 0.2 is considered as an atherogenic region [23]. The OSI is defined as:  
\[\text{OSI}=\frac{1}{2}\left(1-\frac{\left|\int_{0}^{\text{T}}\text{WSSdt} \right|}{\int_{0}^{\text{T}}\text{WSS}|\text{dt}}\right) \tag{8}\]  
where WSS is the instantaneous WSS (Pa) magnitude, and T (s) is the cardiac cycle time. The maximum OSI values in the different models are recorded in Figure 13. In models 1 to 4, the OSI values were 0.385, 0.340, 0.388, and 0.396, respectively. The largest OSI value was in model 4, and the smallest OSI value was in model 2. The OSI differences were insignificant in the different models except for the significantly smaller values in model 2. This finding suggests that the WSS oscillations were most severe when the plaque was located away from the LCX, followed by the plaque located close to the LCX and away from the heart side, and the oscillations were least severe when the plaque was located close to the heart side. The findings obtained by the OSI were the same as those of the TAWSS, which further validates the effect of the plaque circumferential distribution on the coronary blood flow.  
Figure 13: The oscillatory shear index (OSI) for the different models.
## 4 Investigation of the Influence of Major Parameters on the Pipeline Damping Performance
### Effect of Boundary Conditions on Damping Performance of the Pipeline System  
To investigate the effect of the boundary conditions on the damping performance of the pipeline system, for the six-cavity damper, two boundary conditions were selected: fix-free and fix-fix, with 1 mm steel balls shown in Figure 17.  
Figure 16: Influence of damper cavity on damping effect.  
Figure 17: Two kinds of boundary conditions. (**a**) Fix-free; (**b**) Fix-Fix.  
Figure 15: Comparison of frequency response with different cavities at the (**a**) 20%, (**b**) 80% filling rate.  
Figure 18a shows the fix-free pipeline with an additional particle impact damper, where the first- and second-order resonant frequencies at 20% and 80% are offset from the bare pipe by 41 and 75 Hz, respectively. The first- and second-resonant amplitudes are reduced by 48.1%, 89.2% and 42.6%, 59.6%, respectively. When the boundary condition is fix-fix, the corresponding resonance amplitudes are reduced by 68.8% and 88.8%, respectively. The particle impact damper has superb damping performance regardless of the fix-free or fix-fix boundary conditions.  
Figure 19 shows that the RMS value of the pipeline with the particle impact damper displays a decreasing trend with the filling rate for both boundary conditions. Compared to the bare pipeline, the RMS values of the fix-free condition with 20% and 80% fill rates decreased by 42.68% and 58.14%. Similarly, the RMS values of the fix-fix pipe decreased by 35.65% and 46.87%, respectively. This indicates that different boundary conditions do not affect the damping effect of the particle impact damper, and the particle impact damper provides a good damping effect under different boundary conditions. When the filling rate is 0, it can be seen that the root mean square value of the fix-free bare pipe is larger than that of the fix-fix bare pipe, which is because there is no fixed configuration and the structure is less rigid, so it is more affected by vibration. At the filling rates of 20% and 80%, the root mean square value of the fix-free pipe is slightly lower than that of the fix-fix pipe, mainly because the particle impact damper is attached to the end of the fix-free pipe with the largest vibration amplitude. The more serious the pipe vibration, the more serious the particle collision in the particle impact damper. Therefore, the more energy consumed by the collision, the better the vibration reduction effect.  
Figure 18: Comparison of damping performance with different boundary conditions; (**a**) fix-free, (**b**) fix-fix boundary condition.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 1 Introduction
Summarizing, the objectives of the paper are:  
* to develop a Finite Volume Method for a CubeSat thermal analysis with a high level of geometrical fidelity;
* to identify critical geometries that play fundamental roles in the thermal behaviour of the HERMES payload;
* to design, set up and execute a thermal test for the HERMES payload, providing all the details for the equipment and procedures to provide a baseline for any reader that has no experience with such type of test.
### Contribution  
The main contribution of this study is that it evaluates the impact of data pre-processing on hate speech detection in a multilingual context. The impact of data pre-processing is evaluated in terms of prediction accuracy (percentage of correctly predicted tweet categories), prediction efficiency (runtime), and method scalability with respect to the size of the input dataset.  
To the best of our knowledge, the study by [PERSON] et al. [7] is the only study that addresses the detection of hate speech in a mix of different languages, including pure English and code-mixed Hindi-English. In this study, we propose an efficient method to accurately detect hate speech in a mix of English and Hindi-English tweets. Our method first subjects all tweets to 10-step data cleansing (not performed by [7]); then, it uses a CNN as well as a combination of CNN and LSTM algorithms with character-level embedding to build a hate speech prediction model. We perform extensive experimental analysis on real-life tweets by evaluating the performance of our proposed method in terms of accuracy, efficiency (runtime), and scalability, and by comparing it with a closely related method proposed by [7]. Experimental evaluation suggests that our method has an improved accuracy and a significantly improved runtime.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods  
Feature-based (FB) methods focus on extracting representative features from the received signal, which are then used by a classifier to determine the modulation type (as illustrated in Figure 3). These methods typically exhibit lower computational complexity, making them suitable for practical applications. In modulation format recognition tasks, the emphasis is on extracting time-domain and frequency-domain features, along with higher-order cumulants, moments, and instantaneous features from the signal. These features capture the characteristics of signals across different modulation formats and serve as a foundation for subsequent classification [18].
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Modelling Methodology  
MATLAB/Simulink and its SimPowerSystems (SPS) toolbox are used for modelling [15]. In this study, a detailed model is a priority for consideration since it can better present the dynamic behaviour of the circuit assembly.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 3 System Description
### Hardware of the Transmitter
#### 3.1.4 Power Amplifier  
The required linearity of the power amplifier is highly dependent on the type of modulation used to transmit the data. Hence, the best choice for sounding the channel is a family of pseudonoise (PN) sequences, due to their optimal auto-correlation and cross-correlation properties. Since the envelope of these sequences is nearly constant, the linearity requirements of the amplifier are not critical. However, this type of sequences is not the best option for data transmission and some non-constant envelope modulations have been tested [9; 18]. In that case, the linearity of the transmitter is a critical issue, as the non-linear response leads to constellation rotations and noise distortion. A power amplifier of the manufacturer BONN (model BLWA 0103-250) is used, which can reach up to 250 W with an input power lower than 4 dBm. That means a gain of 50 dB, so the amplifier can be fed by the ADC without any additional stage.  
The gain fidelity and the linearity were measured using a calibrated network analyzer with a 60 dB attenuator. A variation of less than 1.8 dB from 3 MHz to 30 MHz can be appreciated in Figure 7a, which is an acceptable result for this amplifier. As far as the linearity is concerned, the gain is measured in detail (module of \(S_{21}\)[22]) for values of input power between \(-2\) dBm and 6 dBm, as shown in Figure 7b. We can observe variations of the gain from 48 dB to 49.7 dB when the input power varies from \(-2\) dBm to 4.4 dBm. Hence, even if an Input Back-Off (IBO) is applied to keep the average power below the compression point, the gain will not be constant if the signal presents a non-constant envelope. So, in our case, it is worth clipping the signal to keep the average power as constant as possible instead of applying an IBO. This has also been verified by some results showing that the Bit-Error Rate (BER) with strong clipping is the best option for OFDM modulations [9].  
Figure 6: Block diagram of the remote controlled VSWR meter.
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 1 Introduction
The BDS-3 includes the migration of its civil B1 signal from 1561.098 MHz to a frequency centered at 1575.42 MHz, which is the same as the GPS L1 and Galileo E1 civil signals, and its transformation from a quadrature phase shift keying (QPSK) modulation to a multiplexed binary offset carrier (MBOC) modulation similar to the future GPS L1C and Galileo E1. [PERSON], et al. [22] analyzed the navigation signals transmitted by BDS-3 M2S satellite with the help of a 7.3-m high-gain antenna. [PERSON], et al. [21] evaluated the contribution of inter-satellite link measurement on BDS-3 precise orbit and clock determination and the performance of BDS-3 satellite clock prediction. In this contribution, we initially assess the characterization and performance of four BDS-3 satellites based on collected GNSS tracking data. The carrier-to-noise density ratio and multipath combinations of BDS-3 observations are firstly investigated and compared with those of BDS-2 satellites to analyze signal characteristics, pseudo-range multipath and errors. Then, precise orbit and clock determination is carried out by jointly processing for BDS-2 and BDS-3 satellites. Orbit results are evaluated by orbit overlap comparison and SLR validation. The performance of the onboard clocks is analyzed and compared with those of BDS-2 satellites by the modified Allan deviations computed using the clock solutions. The paper is organized as follows: In the \"GNSS Processing\" section, we describe the data collection, processing strategy, and post-fit residuals for BDS-3 precise orbit and \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Satellite** & **PRN** & **Type** & **Launch Date** & **Carrier Rocket** &
\begin{tabular}{l} **Mean Longitude** \\ **(Inclination)** \\ \end{tabular} \\ \hline I1S & C31 & IGSO & 30 March 2015 & Long March-3C & 92.8\({}^{\circ}\) E (55.5\({}^{\circ}\)) \\ I2S & C32 & IGSO & 29 September 2015 & Long March-3B & 96.5\({}^{\circ}\) E (55.5\({}^{\circ}\)) \\ M1S & C33 & MEO & 25 July 2015 & Long March-3B & (55.5\({}^{\circ}\)) \\ M2S & C34 & MEO & 25 July 2015 & Long March-3B & (55.5\({}^{\circ}\)) \\ M3S & C35 & MEO & 1 February 2016 & Long March-3C & (55.5\({}^{\circ}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The status of the experimental BDS-3 satellites (February 2016). clock determination. In the \"Result\" section, the results of the analysis of measurement quality and validation of orbit accuracy, as well as the assessment of clock performance, are shown. Conclusions and future work are given in the last section.