esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 5 Results
### Data Ingestion Systems  
Table 1 summarizes the comparative performance of Apache Kafka and RabbitMQ under various message loads, utilizing both producer (Algorithms A1 and A3) and consumer (Algorithms A2 and A4) components. Kafka was configured with Zookeeper for resource management, while RabbitMQ was orchestrated using Kubernetes within Docker.  
The algorithms were optimized specifically for Kafka and RabbitMQ to enhance throughput, latency, and durability by leveraging the architectural advantages of each system. Kafka producer (Algorithm A1) dynamically manages message loads through an adjustable message count parameter (max_messages), along with a logging mechanism for progress tracking, which is particularly valuable for handling high data volumes in CM environments. The consumer algorithm (Algorithm A2) focuses on sequential processing with robust error-handling, which is beneficial for applications that require strict data ordering.  
For RabbitMQ, the producer (Algorithm A3) employed in-memory messaging to maintain a low-latency dispatch suitable for real-time applications. The consumer (Algorithm A4) incorporates a callback mechanism for immediate message processing and a prefetch count of one to facilitate rapid adaptability under a dynamic workload. This setup supports high-speed message handling although it does not enforce strict message ordering. The performance metrics for Kafka and RabbitMQ are listed in Table 1.  
The Kafka setup demonstrated particular strengths in managing high-volume data streams by leveraging the dynamic message load management and progress-tracking features of the producer algorithm. These abilities make Kafka well suited for CM environments that deal with large-scale data processing, Kafka consumer algorithm emphasizes sequential processing with robust error handling, catering to applications that require strict data ordering. In contrast, the RabbitMQ configuration exhibits advantages in real-time, low-latency applications. The producer algorithm uses in-memory message management for swift dispatch, whereas the consumer algorithm employs a callback mechanism that enables rapid adaptation to changing workloads. This setup allows RabbitMQ to excel in high-speed message-handling scenarios without strict message ordering provided by Kafka.  
The results of this study, which are graphically represented in Figure 5, demonstrate higher throughput values for Kafka producers than RabbitMQ producers across varying user counts. These findings suggest that Kafka exhibits superior performance in managing structured, sequence-sensitive data, particularly in producer operations, thereby making it more suitable for applications that require ordered message handling. Conversely, RabbitMQ demonstrated higher consumer throughput across all message rates, highlighting its efficacy in real-time high-throughput scenarios where immediate message consumption is prioritized over strict message ordering.
_applied sciences_  
_Article_  
Microstructure and Mechanical Properties of Manganese Bronze Submitted to Cold Work and Subsequent Heat Treatment  
[PERSON] 1,2,*, [PERSON] 1, [PERSON] 1,3, [PERSON] 1,  
[PERSON] 4 and [PERSON] 3  
Department of Research and Development, Termomecanica Sio Paulo S.A.,  
Sao Bernardo do Campo 09612-000, Brazil; [EMAIL_ADDRESS] (H.P.H.); [EMAIL_ADDRESS] (V.I.G.S.); [EMAIL_ADDRESS] (M.R.G.S.)  
Department of Engineering, Modeling and Applied Social Sciences, Federal University of ABC,  
Santo Andre 09210-580, Brazil  
Department of Engineering, Mackenzie Presyterian University, UPM, Sao Paulo 01302-907, Brazil;  
[EMAIL_ADDRESS]  
Department of Mechanics, Federal Institute of Education, Science and Technology of Sao Paulo,  
Sao Paulo 01109-010, Brazil; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
**Featureued Application: Manganese bronzes are applied in marine environments, for example, gate valve stems due to their characteristics of high mechanical strength and corrosion resistance. Each element present in the chemical composition of the alloy contributes to the properties required in this application, such as: lead contributes to machinability, iron improves tensile strength, tin increases corrosion resistance and manganese contributes to elastic properties. The study of the increase in mechanical properties is crucial for a better understanding of alloy hardening, as well as for the optimization of properties and manufacturing processes.**  
The present study evaluated the influences of different temperatures during heat treatment on the microstructure and mechanical properties of the manganese bronze alloy. During heating, there was a decrease of stored energy in the form of crystal defects due to the mechanisms of rearrangement and the annihilation of dislocations, followed by nucleation and grain growth. Initially, the samples were drawn using 34% cold work. Then, the specimens were heat-treated for one hour with different temperatures ranging from 200 to 750 \({}^{\circ}\)C, increasing by 50 \({}^{\circ}\)C for each sample. The chemical composition characterization was determined by X-ray fluorescence spectrometry (XRF). The mechanical property characterization involved the Vickers hardness, tensile strength, yield strength, and elongation. For the microstructural analysis of the samples, optical microscopy and scanning electron microscopy were used. The results showed an increase of elongation and decrease of the Vickers hardness and tensile and yield strengths with the increasing annealing temperature. The Hollomon model was used to investigate the strain-hardening behavior in all specimens. The (\(n\)) strain-hardening coefficient and the (\(K\)) strength coefficient were calculated, and the correlation with the increase of temperature occurred with the increase of n and variation of \(K\) after the recrystallization temperature.  
**Keywords: manganese bronze; mechanical properties; heat treatment; recrystallization temperature; Hollomon model**
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
###### Abstract  
Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Nino-Southern Oscillation (ENSO) cycle.  
spatiotemporal climate forecasting; periodicity; convolutional LSTM; geopotential height; sea surface temperature +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
###### Abstract  
Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure's health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge's health status. In particular, this algorithm involves the training of \(N\) models, each of them able to estimate the quantity measured via a sensor by using the others' \(N-1\) measurements. Hence, the system can be represented by the ensemble of the \(N\) models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.  
structural health monitoring; neural networks; bridge monitoring; iterative models; artificial intelligence +
Footnote †: journal: _Future Internet_
###### Abstract  
Shock load from industrial wastewater is known to harm the microbial activities of the activated sludge in wastewater treatment plants (WWTPs) and disturb their performance. This study developed a system monitoring the activated sludge activities based on the relative oxygen uptake rate (ROUR) and explored the influential factors with wastewater and the activated sludge samples collected from a typical WWTP in the Taihu Lake of southern Jiangsu province, China. The ROUR was affected by the concentration of toxic substances, mixed liquid suspended solids (MLSS), hydraulic retention time (HRT) and pH. Higher toxin contents significantly decreased the ROUR and the EC\({}_{50}\) value of Zn\({}^{2+}\), Ni\({}^{2+}\), Cr(VI), Cu\({}^{2+}\), and Cd\({}^{2+}\) was 13.40, 15.54, 97.56, 12.01, and 14.65 mg/L, respectively. The ROUR declined with the increasing HRT and MLSS above 2000 mg/L had buffering capacities for the impacts of toxic substances to some extent. The ROUR remained stable within a broad range pH (6-10), covering most of the operational pH in WWTPs and behaving as an appropriate indicator for monitoring the shock load. A toxicity model assessing and predicting the ROUR was developed and fitted well with experimental data. Coupling the ROUR monitoring system and toxicity model, an online early-warning system was assembled and successfully used for predicting the toxicity of different potential toxic metals. This study provides a new universal toxicity model and an online early-warning system for monitoring the shock load from industrial wastewater, which is useful for improving the performance of WWTPs.  
potential toxic elements (PTEs); relative oxygen uptake rate (ROUR); toxicity; wastewater +
Footnote †: journal: Applied Sciences
## 3 The HADES Framework
### The Port of Cartagena  
The Port of Cartagena constitutes two separate and independent docks: the Cartagena basin and the Escombreras basin. The distance between the two basins is 1.5 miles by sea and 5 km by road (Figure 1).  
Figure 1: Port of Cartagena. Source: Google Earth.  
The liquid bulk terminal is in the Escombreras dock. It has 13 quays, of which 8 are single client; that is, they are only operated by one terminal operator, and 5 are multi-client, operated by more than one terminal operator--that is, the quay infrastructure is shared by several terminal operators that have placed loading arms in the same quay infrastructure (Figure 2).  
The maritime traffic of liquid bulk is fundamental in this port. According to the data provided by the Cartagena Port Authority in 2019, 75.8% of the tons moved in the port are liquid bulk, mainly crude oil and its byproducts [33] (see Figure 3). These figures put the Port of Cartagena in second place in liquid bulk traffic in Spain, only behind Algeciras, and ninth in the highest liquid bulk activity in Europe, according to statistics from the ESPO (European Sea Ports Organization, www.espo.be (accessed on 30 March 2021)).
## 2 Materials and Methods
### Desalination Experiments  
The MCDI system used to examine the desalination performance is described in Scheme 1. Each electrode consisted of graphite as the current collector, an AC electrode, and an IEM. An anion-exchange membrane (AMV, Selemion, Tokyo, Japan) and cation-exchange membrane (CMV, Selemion, Tokyo, Japan) were placed on the anode and cathode, respectively. The pair of electrodes were separated by a nylon spacer with a thickness of 110 \(\upmu\)m. A feed water (10 mM NaCl solution) was supplied to the cell using a peristaltic pump (Minipuls 3, Gilson, Middleton, WI, USA). The treated solution directly flowed through a conductivity meter (3573-10C, Horiba, Kyoto, Japan) and was subsequently discarded (i.e., single-pass mode). CDI experiments were also performed with the same configuration as used in MCDI except for the presence of the IEM. Voltage was applied using a battery cycler (WBCS3000, WonATech, Seoul, Korea) for 10 min each at 1.2 V in the charging step and 0 V in the discharging step. Data were obtained from the third cycle and an average value was calculated from a minimum of three operations for reproducibility.  
The MCDI desalination performance was evaluated in terms of SAC and the average salt adsorption rate (ASAR). The SAC was calculated by integrating the decreased conductivity, as given in Equation (1) [33]:  
\[SAC\left(mg\ g^{-1}\right)=\frac{Q\cdot M_{w}\cdot\int\ (C_{in}-C_{out})dt}{m} \tag{1}\]  
where \(C_{in}\) and \(C_{out}\) are the influent and effluent concentrations of NaCl (mM); Q is the flow rate (L min\({}^{-1}\)); MW is the molecular weight of NaCl (58.443 g mol\({}^{-1}\)), and m is the electrode weight (g). The average values of SAC were statistically analyzed by t-test.  
ASAR was obtained using Equation (2) [34]:  
\[ASAR\left(mg\ g^{-1}\ s^{-1}\right)=\ \frac{Q\cdot M_{W}\cdot\int\ (C_{in}-C_{out})dt}{m\int\ dt} \tag{2}\]  
CDI was conducted additionally under constant current charging operations to evaluate the capacity and kinetics of the electrodes using a CDI Ragone plot. Data for MCDI were obtained with different applied currents (1-3.5 mA cm\({}^{-2}\)) with a step of 0.5 mA cm\({}^{-2}\) during the charging step and 0 V was applied for discharging. More details are described in the literature [35].
## 3 Results and Discussions
### Reflection Characteristics  
Figure 16 depicts the relationship between \(H_{\text{i}}\)/\(L\) and \(k_{r}\) (in front of the flap) for patterns E and A. For pattern E, \(k_{r}\) increases as \(h\)/\(h_{\text{f}}\) decreases and \(H_{\text{i}}\)/\(L\) increases. It is analogous to the general reflection tendency of a vertical wall because the flap does not rotate at all in pattern E. Consequently, the flap becomes the vertical wall in front of the reflection wall. In pattern A, \(k_{r}\) tends to increase as \(H_{\text{i}}\)/\(L\) increases similar to pattern E.  
Figure 17 depicts the relationship between \(k_{e}\) and \(k_{r}\) for patterns A. In contrast, it is difficult to determine the correlation between \(k_{e}\) and \(k_{r}\), as depicted in Figure 17. In general, a large rotation angle of the flap is accompanied by high \(k_{e}\) so that less reflected waves are generated due to a large portion of the incident waves getting captured in the flap in terms of the conservation of wave energy. However, the case of high \(k_{e}\) with low \(k_{r}\) or vice versa is regarded as the flap tending to be tilted to one side, depending on the installation position (e.g., shore side for \(0.25<D\)/\(L<0.5\); offshore for \(0.5<D\)/\(L\)). The tilted flap to one side causes a different energy dissipation mechanism of the incident and reflected waves from the reflection wall for rotating the pendulum. As a result, \(k_{r}\) is not proportional to \(k_{e}\), as depicted in Figure 17. The mechanism of energy dissipation depending on the motion of the flap and the relationship between \(k_{e}\) and \(k_{r}\) should be evaluated in further studies.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### Model Predictive Controller Simulation Results  
The output current of the grid-connected inverter when a MPC was used in the current control loop is shown in Figure 11a. As with the case of PR and hysteresis control, during the step change in the reference current, there were no distortions. Figure 11b shows the change in the active power of the grid-connected inverter corresponding to the change in the output current. The response  
Figure 10: Simulation results with hysteresis controller. (**a**) Three-phase output current of inverter. (**b**) Active and reactive power output.  
Figure 9: Simulation results with PR controller. (**a**) Three-phase output current of inverter. (**b**) Active and reactive power output.  
speed of the output power was the fastest among PI, PR, and hysteresis control, which was suitable for virtual inertia emulation, as discussed previously. The frequency of a weak grid with lower inertia can decrease/increase quickly, and the additional required inertia has to be emulated almost instantaneously to prevent significant changes in frequency [51]. The fast response of the MPC technique can be exploited for this effect. However, the absence of a modulator and the variable switching operation means both the active and reactive power have a higher output ripple. Moreover, the high switching frequencies generated may lead to increased system losses.
## 4 Discussion  
When considering the rock as a composite of numerous unit particles, for a cylindrical rock with cross-sectional area \(A\) and height \(h\), the internal particles of the rock obey the following equations of motion subsequent to being excited by ultrasonic vibrations:  
\[\frac{\partial^{2}u}{\partial t^{2}}=a^{2}\frac{\partial^{2}u}{\partial x^{2}}+ \frac{1}{\rho A}q(x,t) \tag{6}\]  
where \(u\) and \(q(x,t)\), respectively, represent the displacement and external force of particles inside the rock unit; \(a\) stands for rock wave velocity; \(t\) stands for time. When the rock is freely vibrating, the equation of vibration of its internal particles should be expressed as:  
\[\frac{\partial^{2}u}{\partial t^{2}}=a^{2}\frac{\partial^{2}u}{\partial x^{2}} \tag{7}\]  
The solution of Equation (3) can be formulated as follows:  
\[u(x,t)=U(x)(A_{1}\cos\omega t+B_{1}\sin\omega t) \tag{8}\]  
where \(U(x)\) represents the mode function; \(A_{1}\) and \(B_{1}\) denote constants; \(u(x,t)\) represents the displacement of section unit particles at height \(x\) of the rock. The mode function can be obtained as follows:  
\[U(x)=C_{1}\cos\frac{\omega x}{a}+D_{1}\sin\frac{\omega x}{a} \tag{9}\]  
where both \(C_{1}\) and \(D_{1}\) denote constants. In the ultrasonic vibrational excitation test in this study, the rock is in a state where the lower end is fixed and the upper end is free, and the corresponding boundary condition is formulated as follows:  
\[\left\{ \begin{array}{l}U(0)=0\\ \frac{dU}{dx}|_{x=h}=0\end{array} \right. \tag{10}\]  
Combining this with Equation (5), we can obtain the following expression for the angular frequency of each order of the rock sample:  
\[\omega_{i}=\frac{(2i-1)}{4h}a,i=1,2,\ldots \tag{11}\]  
where \(i\) denotes the order of natural frequencies, and the principal modes corresponding to each order of angular frequency can be written as follows:  
\[U_{i}(x)=D_{i}\sin\frac{(2i-1)\pi}{2h}x,i=1,2,\ldots \tag{12}\]  
The above mode function is normalized according to Equation (9) and the regular mode is obtained as shown in Equation (10):  
\[\int_{0}^{1}\rho AU_{i}(x)^{2}dx=1 \tag{13}\]  
\[\widetilde{U}_{i}(x)=\sqrt{\frac{2}{m}}\sin\frac{(2i-1)\pi}{2l}x,i=1,2,\ldots \tag{14}\]  
When the exciting force is applied to the rock, the motion equation of its vibration can be expressed by the regular equation shown in Equation (11):  
\[\widetilde{\eta_{i}}+{\omega_{i}}^{2}\eta_{i}=\int_{0}^{l}q(x,t)\widetilde{U} _{i}(x)dx,i=1,2,\ldots \tag{15}\]In the ultrasonic vibrational excitation test, a longitudinal forced vibration of a rock can be induced by applying a simple harmonic excitation force to the central region of its upper free end. This can be considered as a concentrated force acting on the sample end and can be expressed in the form of the following impact function:  
\[q(x,t)=F(t)\delta(x-h) \tag{16}\]  
where \(F(t)\) represents the force applied to the rock, consisting of a simple harmonic excitation force and a static load force, as follows:  
\[F(t)=F_{0}\sin\omega t+F_{s} \tag{17}\]  
Here, \(F_{0}\) denotes the excitation force amplitude and \(F_{s}\) represents the static load force.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 4 Performance Analysis of SSPSWs
### Ductility Performance  
A comparative analysis incorporating a non-slotted steel plate shear wall (SPSW) with identical framing (SPSW-TF) and 12 mm plate thickness revealed a distinctive ductility performance: the SSPSWs exhibited an average ductility coefficient \(\mu\) = 8, substantially exceeding the SPSW's \(\mu\) = 3.44. This 57% enhancement in deformation capacity quantitatively demonstrates the SSPSWs' superior energy dissipation characteristics over those of conventional solid-walled systems, as evidenced by the parametric comparison in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Frame** \\ **Configuration** \\ \end{tabular} } & \multirow{2}{*}{**No.**} & \multirow{2}{*}{\begin{tabular}{c} **Initial** \\ **Stiffness** \\ **(kN/mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield Load** \\ **(kN)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ultimate** \\ **Displacement** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Ultimate Load** \\ **(kN)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ductility** \\ **Coefficient** \\ \end{tabular} } \\ \hline \multirow{3}{*}{B-J} & SPSW & 641.25 & 12.57 & 6095.36 & 43.29 & 6901.26 & 3.44 \\ \cline{2-7}  & SPSW-TF & 99.34 & 38.03 & 2180.39 & 312.83 & 2791.57 & 8.22 \\ \cline{2-7}  & SPSW-TB36 & 91.53 & 41.11 & 2150.25 & 343.95 & 2761.54 & 8.37 \\ \cline{2-7}  & SPSW-FF & 160.95 & 27.46 & 2568.48 & 258.89 & 3229.30 & 9.43 \\ \cline{2-7}  & SPSW-FTR & 134.58 & 29.21 & 2837.38 & 278.33 & 2917.72 & 9.53 \\ \cline{2-7}  & SPSW-FT10 & 147.82 & 29.41 & 2438.88 & 263.76 & 3104.54 & 8.97 \\ \cline{2-7}  & SPSW-FB36 & 145.38 & 29.31 & 2663.98 & 250.14 & 3587.77 & 8.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of performance parameters.  
Figure 10: Stress distribution of steel plate shear wall with slit: (**a**) SPSW-TF, (**b**) SPSW-TB36, (**c**) SPSW-FF, (**d**) SPSW-FT10, (**e**) SPSW-FT8, and (**f**) SPSW-FB36.