esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Safety Performance of Prefabricated Construction Based on Complex Network Theory
## 3 Methodology
### Data Collection
#### 3.1.2 Obtain the Relationships between Safety Risks
A total of 154 online questionnaires were distributed, and 92 responses were received, with a recovery rate of 59.7%, of which 20 questionnaires were affirmed invalid due to incomplete filling or regular selection of influencing relationships, and 70 questionnaires were valid, with an effective rate of 76.1%. A response rate of 20% is generally considered satisfactory and 30% is good enough for the construction industry according to [PERSON] [83]. In this regard, the response rate of this study is deemed reasonable and satisfactory. The background information of the interviewees is shown in Table 2. Among them, the interviewees from the owner and the construction unit account for the largest proportion of 31.43% and 30%, respectively, those from the prefabrication plant account for 17.14%, design units account for 11.43%, and consultants account for a relatively small proportion of 1.43%. The interviewees are distributed among the main stakeholders of the prefabricated construction. Most of them have participated in the delivery of at least one prefabricated building project and have rich working experience, hence, lending further credence to questionnaire data. The answer results of the questionnaire were statistically analyzed through the frequency statistics method in Table 3. The corresponding figures in the boxes represent the frequency distribution of the questionnaire survey that the two safety risks are deemed to exert an influence on each other. At last, a total of 90 non-zero results were extracted, that is, 90 risk relationships were determined through the questionnaire survey. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Basic Information** & **Account** \\ \hline  & Owner & 31.43\% \\  & Design unit & 11.43\% \\  & Material and equipment supplier & 4.29\% \\ Your workplace & Prefabrication plant & 17.14\% \\  & Construction unit & 30.00\% \\  & Consulting unit & 1.43\% \\  & Other & 4.29\% \\  & Less than 5 years. & 28.57\% \\ Years have you worked in & 6–10 years. & 51.43\% \\ prefabricated construction & 11–15 years. & 14.29\% \\  & More than 15 years & 5.71\% \\  & 0 & 2.86\% \\ The number of prefabricated & 1 & 18.57\% \\ building projects you have involved & 2 & 32.86\% \\  & 3 & 35.71\% \\  & 4 or more & 10.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Background information of interviewees.
## 5 Evaluation Results
### Traffic Improvement Effect  
The improvement was smaller in any evaluation item when the number of vehicles coming in on the priority road was small. The reason is that there were fewer occasions for low-priority vehicles to require yielding. When priority vehicles yield, the density of vehicles on the priority road rises, and also more priority vehicles need to slow down or stop. This implies that there is a trade-off relation between the traffic efficiency of low-priority vehicles and that of priority vehicles. If the degree of degradation in the traffic efficiency of priority vehicles was greater than the degree of improvement in the traffic efficiency of low-priority vehicles, the overall traffic efficiency is reduced. If we are to improve the overall traffic efficiency, it is necessary to revise the conditions on which the yielding decision is based in such a way that vehicles do not agree to yield if the expected improvement in traffic efficiency is minimal.  
There were some situations in which low-priority vehicles did not see any improvement. The reason is that the present yielding algorithms do not allow multiple instances of yielding to taking place simultaneously. It will be necessary to improve the yielding algorithms so that multiple instances of yielding can proceed simultaneously.
## 2 Materials and Methods
### Analytical Methods
#### 2.2.2 Soil Analyses  
Soil samples were taken in the uppermost soil (0-5 cm) and in 30 cm depth, except samples for the tree _Canarium schweinfurthii_ and _Tristemma maurtiianum_. At that site, only the topsoil could be sampled due to an inaccessible underground structure (big stones, extremely compacted). The dried soil samples were sieved (2 mm mesh). Soil pH was measured in 0.01 mol/1 CaCl\({}_{2}\) (ISO 10390:2005-12) with a glass electrode (Multilab 540, WTW GmbH, Weilheim, Germany). To determine the specific bulk density, exact soil volumes of 100 mL using a ring corer were dried for 24 h at 105 \({}^{\circ}\)C. Soil texture defined by the proportion of the different fractions of sand, silt, and clay was determined by sieving (particles \(\emptyset>\) 63 \(\upmu\)m) and sedimentation for smaller particles (Sedimat 4-12, UGT, Muncheberg, Germany). The total contents of C and N were determined for ground soil (Achat millstone) by a CNS elemental analyzer (Vario EL, Langenselbold, Germany). To analyze the plant-available macro-and micronutrients in the soil, the Mehlich 3 extraction [24] was applied for P, K, Ca, Mg, Na, B, S, Cu, Fe, Mn, and Zn. This method is recommended for acid (carbonate free) soils. The Mehlich 3 soil extraction solutions were analyzed by the ICP-AES Spectrometer (CIROS, Spectro, Kleve, Germany). For the element contents of soil and leaf tissue, the average is quoted with the specific standard deviation.
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 1 Introduction
Therefore, the main objective of this work is to evaluate the SVM, Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGB) models combined with the DE algorithm for optimizing hyperparameters in the context of classifying three distinct singing registers based solely on their sounds. To achieve this purpose, the first planned step was to build a database made up of 350 6-s audio files containing the sounds of three different singing registers performed by an experienced singer, i.e., chest voice, mixed voice, and head voice (for additional details, refer to [PERSON] [23]). The latter three registers correspond to the voice resonances associated with low frequencies (chest voice), middle frequencies (middle voice), and high frequencies (head voice). Then, the Python Time Series Feature Extraction Library (TSFEL) [24] was leveraged to extract 14 features in the time domain from each of the considered signals. After constructing the corresponding feature matrix, the selected three ML models were optimized by the DE algorithm, and evaluated using five classification performance metrics. Relying on the above-presented experimental strategy, the main contributions of the present article are summarized here below as follows:* Building a database consisting of audio files collected from three singing registers in a controlled scenario;
* Proposing the usage of hybrid ML models, optimized by metaheuristics, and low-level audio descriptors for singing-voice classification;
* Assessing three different hybrid ML methods, i.e., SVM, MLP, and XGB, for classifying different singing registers via their collected sounds;
* Showing that optimizing the hyperparameters of ML models using DE algorithms can be a proper alternative for adjusting the internal parameters of the leveraged ML classifiers. The structure of the forthcoming part of the article is organized as follows: The next Section 2 presents the issue of categorizing singing registers, the process of audio recording, the features retrieved from the audio signals, the employed experimental data, and the ML models' implementation; The obtained results and their respective analysis, together with an evaluation of the ML models that includes a parametric analysis, feature importance analysis, and a discussion of the models' advantages and disadvantages, are presented in Section 3; Finally, Section 4 reports the concluding discussion and findings for the carried study.
## 2 Experiments  
The spatial-temporal properties of turbulent energy cascade were analyzed using the real experimental dataset collected by an autonomous vertical reciprocating profiler (AVRP), which was designed independently by the Ocean University of China, and the detailed mechanical structure and working principles of this AVRP platform have been given in the literature [15]. The deployed moored system (Figure 1) comprises several buoyancy elements, a 38 kHz acoustic doppler current profiler (ADCP), two groups of Sea-Bird Electronics CTD sensors (SBE37 SM-RS232, recorded the information about conductivity, temperature and depth), an AVRP instrument, a Doppler current meter (RCM11), two parallel acoustic releases (ARs), and an anchor block [15]. The CTD sensor mainly records the water depth, temperature, and conductivity in the vertical direction. Two groups of buoyancy elements are respectively installed at 600 m and 1500 m to determine the depth limitation of the profiler instrument. The RCM instrument is used to measure the flow velocity and flow direction. The acoustic releases are attached to receive the command to control the moored system. The anchor block provides gravities to make a balance for the moored system. Two orthogonal shear probes (PNS07) and a vibration sensor (MTI-300) are installed on the vertical VRP profiler, where shear probes are used to measure the shear velocity fluctuations, and an MTI sensor is utilized to record three orthogonal accelerations (horizontal acceleration (Ax), the lateral acceleration (Ay), and the vertical acceleration (Az)) and three attitudes of profiler (Heading (b), Pitch (\(\varphi\)), and Roll (\(\theta\))). The calibration procedure between two orthogonal shear data and three accelerations are elaborated by [PERSON] and [PERSON] [16]. The autonomous AVRP instrument was deployed at \(118^{\circ}09.83^{\prime}\) E \(20^{\circ}55.49^{\prime}\) N (Figure 2) in the South China Sea (SCS) to measure the turbulent microstructure shear, and the position of the AVRP (rectangle) is shown. The AVRP platform rises and sinks repeatedly along a mooring cable to measure the turbulence shear between 600 and 1500 m in deep sea, and it is a relatively low-noise environment. Therefore, the measured turbulence data are a little affected by the wave and tidal current. In this article, we mainly concentrate on the vortexes induced by the unsteady separation of turbulent flows. The formation and shedding of the turbulent vortex is an alternate process, in which the turbulent energy will transfer and dissipate.  
Figure 1: The sketch of the mooring system of a vertical profiler.
## 2 Materials and Methods  
### MFC Setup and Experimental Design  
Single-chamber, air-cathode MFCs were used for the enrichment of anodic respiring bacteria and suspended-cells (plankton) communities. The MFC was a bottle-type reactor (Adams & Chittenden Scientific Glass Ltd., Berkeley, Canada), 160 mL in capacity, with anode electrodes made of carbon felt with a surface area of 32.64 cm\({}^{2}\) (providing a specific surface area of 20 m\({}^{2}\) m\({}^{-3}\)).  
In every MFC reactor, two identical pieces of carbon felt were attached together and suspended at the center of the bottle. An air cathode was placed at the side port (circular), providing a specific surface area of 8 m\({}^{2}\) m\({}^{-3}\) (manganese-based catalyzed carbon commercial E4A electrode with a polypropylenemicroporous separator, Electric Fuel Ltd., Bet Shemesh, Israel). The anode and cathode of all close circuit MFCs were connected by Ti wires and an external resistor (1000 \(\Omega\)). Voltage across the resistor was monitored at 5 min intervals using a computerized data acquisition system connected to a PC. Before the experiment began, all MFC bottles were thoroughly washed with diluted hydrochloric acid and ultrapure water (Milli-Q system, Millipore, Merck, Rosh-Ha'ayin, Israel), and then autoclaved for 30 min at 121 \({}^{\circ}\)C. The carbon felt anodes were immersed in 100% ethanol (Merck, Israel) overnight and rinsed with ultrapure water before being placed into the reactor.  
In this study, four experiments were conducted similarly in terms of all parameters indicated above. Out of the four, three replicate experimental sets (named 2015, 2016, and 2016-2) were inoculated similarly using a diluted wastewater sample (10% _v/o_ of total reactor volume) as the microbial source. For experiment 2015, a sample was taken in March 2015. For experiment 2016, a sample was taken in February 2016, and finally for 2016-2, a wastewater sample was taken in September 2016. The fourth experimental set (2016-E) aimed to directly apply an enriched electroactive consortium (composed of \(>\)50% _Desulfuronomas_ spp.) from an active-operating MFC. The consortium was directly applied onto a clean anode in an MFC inoculated with a wastewater sample.
## 2 Materials and Methods
### HPLC-DAD Polyphenolic Profile  
Chromotographic analysis was performed using the Gilson chromatographic system (Gilson's Analytical-to-Semipreparative HPLC System, Gilson Inc., Middleton, WI, USA) including a binary gradient pump (Gilson 322), a column thermostat (Knauer, Berlin, Germany), an autosampler with a fraction collector (Liquid Handler GX-271) and a photodiode array detector (DAD, Gilson 172, Gilson, Middleton, WI, USA). The analytical column (Poroshell 120, EC C-18, 4.6 \(\times\) 150 mm, Agilent Technologies Inc., Santa Clara, CA, USA) was used for chromatographic separation. The column was maintained at 40 \({}^{\circ}\)C. The separation was done with the gradient mode of mobile phase: 0.1% (\(v/v\)) formic acid in water (phase A) and acetonitrile (phase B). A mobile phase flow of 1 mL/min was used. A following gradient program was applied: 10% B (1.5 min), 10-100% B (1.5-20 min), 100% B (20-25 min), and again 10% B to equilibrate the column. The injection volume of 10-fold diluted tincures was 10 \(\upmu\)L. The chromatograms were recorded at \(\uplambda=\) 254, 280, 320, and 360 nm. The phenolic compounds were detected in tincures by their UV-VIS spectra and by the comparison of their retention time values with the values of analytical standards. Literature data was also used as an identification aid.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 3 SOH Prediction Based on Transformer-LSTM Fusion Model
### Transformer-LSTM Fusion Model  
Long short-term memory (LSTM) [32] is an improved version of the recurrent neural network (RNN). Introducing gating mechanisms and memory units effectively overcomes the vanishing gradient and exploding gradient problems that are common in traditional RNNs when processing long sequences. LSTM is particularly good at capturing sequence data with long-term dependencies, and has achieved remarkable results in many tasks. Transformer [33]  
Figure 6: Comparison of SOH (red curve) with the 10 most strongly correlated features (green curve).  
Figure 7: The structure of the transformer–LSTM fusion model.  
is a neural network model based on the self-attention mechanism. With its multi-head attention mechanism and position encoding, it can fully focus on the information at each position in the sequence and is particularly suitable for sequence processing and parallel computing. Integrating LSTM with transformer leverages the strengths of both models and further improves the performance and efficiency of the model when processing sequence data.  
The advantages of the transformer-LSTM model in SOH prediction are mainly reflected in its combination of the transformer's self-attention mechanism and the LSTM's sequence modeling capabilities [34, 35, 36]. First, the transformer's self-attention mechanism can effectively focus on the contextual information in the battery degradation process and capture richer correlations between different time points. This enables the model to better understand the complex patterns in the battery degradation process, especially the changing trends at different stages of the degradation process. Second, the LSTM's long-term dependency modeling capability can effectively capture the long-term dependencies of battery performance over time and learn the dynamic changes in battery degradation. Crucially, the combination of transformer and LSTM enables the model to consider both advantages. Transformer is good at capturing global information and parallel computing, while LSTM can effectively handle time dependencies in sequences. This complementarity helps to improve the model's performance, especially when dealing with complex non-linear relationships in SOH prediction. Combining the two can more comprehensively learn the deep correlations between feature data and SOH, which can effectively improve prediction accuracy and enhance the robustness of the model.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 4 Signal Estimation  
Referring to the FG in Figure 3, which models the problem described in Section 2, we shall first show that the message passing procedure that is established by the SPA, as detailed in Section 3, along the horizontal edges of the FG coincides with two classical estimation problems and is solved by two equally classical signal processing algorithms. Namely, along the top line of edges in the FG, the forward-backward message passing of Gaussian estimates for the (continuous) samples \(s_{k}\) coincides with the celebrated Kalman smoother [24]. Along the bottom line of edges in the FG, a similar forward-backward message passing of (discrete) PMFs exactly follows the famous BCJR algorithm [25] (named after the initials of the authors), which is known to provide an MAP estimate of the channel states \(i_{k}\) at each time epoch, i.e., for the variable nodes along the bottom line of the FG. It is significant that the same BCJR algorithm was recently implemented in [11], in the context of a problem similar to ours, where memoryless Gaussian signal samples were estimated in the presence of Markov-Gaussian impulsive noise (to which our system model reduces in the special case \(a_{1}=0\) and \(M=2\)).  
It is indeed the memory in both the signal and the noise sequences that makes the loopy FG in Figure 3 \"unsolvable\" (in the sense of marginalizing the joint pdf) with the SPA. The problem arises in fact from the messages passed along the vertical edges of the FG, i.e., those that connect the top half (that of signal samples) to the bottom one (that of impulsive noise channel states). As discussed in the following in more detail, while the downward messages (22) result from products and convolutions of Gaussian pdfs, hence themselves Gaussian, this is not the case for the upward messages in (21), which are Gaussian mixtures, basically due to the presence of the discrete variables \(i_{k}\). That of mixed discrete and continuous variable nodes is a well-known scenario that makes the number of mixture elements, hence the complexity of messages, increase exponentially at every time step, so that mixture reduction techniques should be employed [19]. Mixture reduction can either be accomplished by making hard decisions on the impulsive noise channel states or otherwise by exploiting the soft information contained in the mixture messages \(p_{u}(s_{k})\), through more sophisticated approximation schemes [26].
## 4 Extended Parameter Analysis
### Finite Element Numerical Model
#### 4.1.2 Numerical Model Establishment  
Based on the aforementioned material constitutive relationship and parameter settings, the model components of the steel-reinforced cage and strengthened concrete pier column were established and assembled (Figure 18a,b). The model's boundary conditions were set as follows: (a) the connection between segments was simulated using a hard contact [25], indicating that the contact surface was only under compression. (b) The connections between the rebar and pier column concrete and the bolts and segment concrete were simulated using an embedded connection. (c) Based on the research by [PERSON] [72] and [PERSON] [25], the binding connection was used to simulate the connection between the filled  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\psi\) & \(\epsilon\) & \(\sigma_{b0}/\sigma_{c0}\) & \(K_{c}\) & \(\
u\) \\ \hline
30 & 0.1 & 1.16 & 0.6667 & 0.005 \\ \hline \end{tabular}
\end{table}
Table 5: Parameters of the plastic-damage model.  
concrete and its adjacent members, and the \"Coulomb friction model\" was adopted to simulate shear transmission. (d) The top and bottom surfaces of the models were simplified and coupled into one point; constraints were set in each direction of the point obtained from the top and bottom couplings, except in the Z-direction of the top coupling point. The axial pressure on the pier column was applied primarily through the top coupling point. The initial self-stress produced by the SSAWC was simulated by applying circumferential compressive stress to the surface of the pier column and the inner side of the segment.  
Regarding mesh division, when the grid spacing of the pier column body was <95 mm or the grid spacings of the filled concrete and segment sleeves were <65 mm, the finite element calculation results were difficult to converge, and the calculation cost was high. Thus, under the premise of comprehensive consideration of calculation accuracy and efficiency, the pier column body and filled concrete and segment sleeves adopted grid spacings of 100 mm and 70 mm, respectively (Figure 18c). Additionally, by calculating the finite element models with five different element sizes of the filled concrete and segment sleeves (i.e., 60 mm, 70 mm, 80 mm, 90 mm, and 100 mm), the maximum errors of the bearing capacity and ductility coefficient between different finite element models did not exceed 6% and 8%, respectively. Hence, the element size dependency of the finite element model was small and could be disregarded in this paper.
## 3 Results and Discussion
### Mechanical Characterization
#### 3.2.3 Short-Beam Shear Testing  
Among the different test options typically used to characterize fiber-reinforced polymers, the SBS test is a valid option to easily determine the ILSS. For this test, the span-to-thickness ratio is fixed at values in the order that the occurring shear stresses within the specimen are high compared to the normal stresses generated by the bending moment [33]. The average ILSS versus displacement curves obtained for the ULTEMM 9085 samples are shown in Figure 11 (also see Table S3). From these curves, it is immediately evident the effect of the material type, with the black resin showing higher SBS than the tan resin. For all the tested specimens, the readings of SBS varied in the range between 11.82 MPa and 16.74 MPa. The results of the ANOVA analysis for the experimental plan and the normal probability plot of the effects are shown in Table 6 and Figure 12, respectively. Model adequacy checking on the residuals from the analysis did not show any anomaly. As expected, the material type is clearly the influent factor (\(p\)-value \(<\) 0.0001) on the ILSS. Neither the geometry nor the  
Figure 9: Fracture surface morphology for a tensile sample (tan resin). Red ellipse highlights the adhesive failure on the transverse rasters. The green arrow highlights the yielded longitudinal rasters.