esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
## 4 Results and Discussion
### Modulus of Elasticity (\(E_{c}\))  
The modulus of elasticity \(E_{c}\) for all the specimens measured at 28 days was within the range of approximately 18-28 GPa, and the means were found to be 21.0, 23.9, and 25.2 GPa for group L,  
Figure 5: Typical compressive strength gain ratio of concrete.  
group M, and group H, respectively. The influence of \(W\)/C could be distinctly seen. A factor equally as significant as \(W\)/C in determining the \(E_{c}\) of concrete is the elastic modulus of the aggregate [39]. The type of coarse aggregate, that is, \(R_{G}\), was found to have particularly important effects on the results of the experiment. The use of coarse BAA (\(R_{G}\) = 0%) led to the \(E_{c}\) decreasing by approximately 12.6% (3.2 GPa) on average in group M and 17.9% (4.9 GPa) in group H, compared with the use of crushed granite (\(R_{G}\) = 100%). On the other hand, the deviation of \(E_{c}\) values according to the type of fine aggregate was rather low, \(\pm\)2.4% (0.6 GPa).  
A number of studies have reported high correlations between the mixing ratio of BAA and the \(E_{c}\) of the concrete. [PERSON] et al. [32] indicated that an increase in the mixing ratio of coarse BAA from 0% to 100% resulted in an abrupt decrease of \(E_{c}\) by up to approximately 65%, and [PERSON] et al. [40] stated that when using BAA for both fine aggregate and coarse aggregate, \(E_{c}\) dropped by up to 15.1%. When the BAA content is increased, more moisture accumulates around the aggregate than with natural aggregate because the porosity of the interfacial transition zone (ITZ) between the aggregate and cement paste increases. This ultimately results in a local increase in the \(W\)/C of the paste located around the aggregate, and thus the strength of the bond with the aggregate may be lowered [6; 22].  
Figure 6 shows the normalized modulus of elasticity, \(E_{c}/\sqrt{f_{c}}\), for concrete according to \(\rho_{c}\), and, in the same figure, the predictive values of ACI 318 [24] and the _fib_ model [21] and the values of the predictive model proposed by [PERSON] et al. [23]. \(E_{c}\) was normalized to exclude the effects of differences in strength, where \(f_{c}\) is the compressive strength at the same 28-day measurement point as the \(E_{c}\) measurement. The prediction based on \(\rho_{c}\) is reasonable because the \(E_{c}\) of concrete is very sensitive to variations in the types and physical properties of the aggregate used [39]. In Figure 6, predictions by ACI and _fib_ models show relatively close predictive values in the \(\rho_{c}\) range of less than 1850 kg/m\({}^{3}\), but an excessive increase of \(E_{c}\) in the range of 1950 kg/m\({}^{3}\) or above. The concrete \(\rho_{c}\) value of 1850 kg/m\({}^{3}\) is also the boundary reference value by which ACI 213R [19] distinguishes between LWAC and NWC. On the other hand, comparison with the prediction proposed by [PERSON] et al. [23] shows good agreement at most \(\rho_{c}\) intervals.
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 2 Design and Analysis of the Proposed Reconfigurable BSF
### State II: D-Bsf
#### 2.2.2 Design Approach of State II  
To arbitrarily control the equal-ripple level of \(S_{11}\) in the low-frequency passband of State II, the following equations should be maintained:  
\[\frac{\partial|F_{D}|}{\partial\theta}\big{|}_{\theta=\theta_{D1}^{S_{11}}}=0 \tag{45}\]  
\[|F_{D}||_{\theta=\delta_{D1}^{S_{11}}}=\frac{1}{\sqrt{10^{RL_{D1}/10}-1}} \tag{46}\]  
where (45) enables \(\theta_{D1}^{S_{11}}\) to satisfy the condition of the pole of \(S_{11}\), and (46) is used to set the ripple level corresponding to \(\theta_{D1}^{S_{11}}\) to \(RL_{D1}\).  
Similarly, the arbitrary control of the equal-ripple level for \(S_{11}\) in the center passband of State II can be implemented by following equations:  
\[\frac{\partial|F_{D}|}{\partial\theta}\big{|}_{\theta=\theta_{D2}^{S_{11}}}=0 \tag{47}\]  
\[|F_{D}||_{\theta=\theta_{D2}^{S_{11}}}=\frac{1}{\sqrt{10^{RL_{D2}/10}-1}} \tag{48}\]  
where (47) enables \(\theta_{D2}^{S_{11}}\) to be employed as the pole of \(S_{11}\), and (48) is used to set the ripple level corresponding to \(\theta_{D2}^{S_{11}}\) to \(RL_{D2}\).  
In addition, the same as in State I, (23) also needs to be satisfied to eliminate the unexpected resonance in State II, and the highest power of the numerator and denominator of \(F_{D}\) decreases by 2. In light of the number of design parameters, the D-BSF for State II has five design parameters (\(Z_{1}\), \(Z_{2}\), \(Z_{3}\), \(Z_{4}\), and \(Z_{5}\)). A unique solution achieving the desired \(RL_{D1}\) and \(RL_{D2}\) can be obtained by combining (23) and (45)-(48) and fixing two design parameters. In the following design, \(Z_{3}\) and \(Z_{5}\) are chosen to be fixed.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Effect of Particle Number on Experimental Results  
The number of particles is a crucial factor in the Particle Swarm Optimization (PSO) algorithm, directly influencing its optimization performance and convergence speed. Increasing the number of particles allows for a more comprehensive coverage of the search space, enhancing the likelihood of finding the global optimal solution and minimizing the risk of falling into local optima. Conversely, having too few particles can lead to insufficient search space coverage, which might result in premature convergence to suboptimal solutions. An appropriate number of particles facilitates faster convergence because particles can quickly share information and cluster around potential solution areas. However, excessive particles can decelerate convergence due to the increased computational load and the tendency for over-exploration.  
Table 4 demonstrates the average AQI prediction performance evaluation metrics for the next 2 h using the APSO-CNN-Bi-LSTM model with varying particle numbers. The results indicate that while the test error does not vary significantly with different particle numbers, optimal performance was achieved when the particle number was set to four. Specifically, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reach their minimum values of 49.6534 and 28.5812, with a model training time of 32.3 min.  
Choosing an appropriate number of particles in PSO is essential for balancing comprehensive search space coverage and computational efficiency. The results from Table 4 illustrate that a particle number of four provides an optimal balance for the APSO-CNN-Bi-LSTM model, yielding minimal prediction errors and reasonable training time.
# Seismic Response Mitigation of Base-Isolated Buildings
## 5 Numerical Study  
Numerical study is conducted on response mitigation of 5- and 10-story BI buildings equipped with TMDs at top and at different floor levels under earthquake ground motions. The shear-type buildings are modeled by considering only a lateral DOF at each floor level. The coupled governing differential equation of motion for the system is formulated and solved using [PERSON]'s integration method with linear acceleration assumption between consecutive time steps. For the sake of simplicity, the floor mass and stiffness are kept the same herein. The structural damping matrix is constructed by assuming 2% damping ratio in all the normal modes of vibration. The isolation period and the TMDs mass ratio are assumed to be 2.5 s and 5%, respectively. The total TMD mass is then divided into three TMDs for the cases of MTMDs and d-MTMDs. In addition, the isolation damping ratio, yield displacement, and normalized yield strength (yield strength per unit weight of the structure) are assumed to be 0.05, 5 cm, and 0.05, respectively [40]. The 5- and 10-story buildings have 0.5 s and 1 s fundamental period, respectively, when they are fixed at the base. Incremental dynamic analysis (IDA) of the above-mentioned buildings is conducted. A set of 40 earthquake ground motions are considered (see Tables 1 and 2). [PERSON] et al. [41] provide more details of the set of 40 ground motions for a theoretical site in Los Angeles, USA. The set is divided into two categories: (1) design-basis earthquakes (DBE) and (2) maximum considered earthquakes (MCE). The DBE are listed as LA01 to LA20 and the MCE are listed as LA21 to LA40 in Tables 1 and 2, respectively. In order to better understand the nature of the selected DBE and MCE earthquake ground motions, their displacement and acceleration response spectra considering 2% critical damping are shown in Figure 3. It can be seen that that selected list encompasses a wide range of amplitude and frequency content of ground shaking. Each ground motion in the set of 40 are scaled to PGA of 0.025 to 2 g with an increment of 0.025 g (80 times). As a result, a set of 3200 ground motions are obtained, which are used for IDA. Numerical simulation is carried out with MATLAB. The earthquake ground excitations are scaled based on the procedure given in [PERSON] et al. [42].
## 3 Experiments and Results
### Performance Analysis
#### 3.2.2 Test Scenario-2  
Figure 4 presents the computed classification (testing) accuracies for each learning algorithm with different \(p\) values in the horizontal axis. Here, the length of the shapelets \(m\) is variable (shown in the legends) and \(i_{max}\) is kept constant as 15. The objective of this test casework is to show the trend of accuracy with respect to \(p\) for different algorithms. It is evident that there is a trend of decreasing accuracy as the value of \(p\) increases signifying that the most distinctive subsequence resides in the starting region of any device. Therefore, it is suggested that the boundary to locate the subsequences should be confined near the starting point of a device operation.  
Figure 3: _Cont._
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 3 Positioning Errors Analysis of Machine Tool
### Periodic Error of Ball Screw and Linear Grating  
When periodic error of the ball screw and linear grating exists, the positioning errors of the \(x\)-axis can be expressed as:  
\[\delta_{xx}=d\sin\left(\frac{2\pi(-x)}{p}+\varphi\right) \tag{8}\]  
where \(p\) denotes the screw pitch of the ball screw or the grating pitch of the linear grating; \(d\) denotes the amplitude, \(\upmu\)m; \(\varphi\) denotes the phase angle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Num.** & **Error Types** \\ \hline
1 & Uniform contraction error of ball screw and linear grating \\
2 & Periodic error of the ball screw and linear grating \\
3 & Interference of measurement devices \\
4 & Opposite clearance \\ \hline \hline \end{tabular}
\end{table}
Table 1: The positioning errors of CNC machine tool.  
At this time, other errors are assumed to be zero, and the error vector \(E\) can be expressed as:  
\[E=\left(d\sin\left(\frac{2\pi x}{p}-\varphi\right),0,0\right) \tag{9}\]  
Substituting Equation (9) into Equation (1), we can obtain the periodic error equation of the ball screw and linear grating:  
\[\Delta R=d\cos\theta_{3}\sin\left(\frac{2\pi x_{0}\cos\theta_{3}}{p}-\varphi\right) \tag{10}\]
# Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
## 3 SEDGE Proposed Methodology
### Analysis Layer
#### 3.5.1 Data Processing: Firmware Deployment  
The edge firmware that establishes the system's component interconnection is developed in Python 3 and installed and maintained in the test sites using Git. The firmware is split into small components, each one assigned with a specific task to ensure system robustness and easier updates.  
The DevOps deployment methodology [36] is applied to the development of the set of system components, including the edge firmware, hardware, overlay applications, etc. The DevOps methodology offers continuous system delivery, integration, and deployment.  
The edge devices utilize work with Linux-based operating systems. A common example device is a Raspberry Pi 3/4, which was used in test sites. To ensure that the system is perpetually working without faults, all required actions, errors, and updates must be automatically handled and resolved. For the installation of the firmware components, two techniques are currently used. Components that are standard are installed using _docker-compose_, which creates dockerized containers in the system. An example configuration file can be seen in Figure 3.  
version: '3.7' services:  zwavejs2 mqtt:  container name: zwavejs2 mqtt  image: zwavejs/zwavejs2 mqtt:latest  restart: always  network mode: host  try: true  stop_signal: SIGINT  environment: - SESSION SECRET=mysupersecretkey  - ZWAVEJSEXTERNALCONFIG=/usr/src/app/store/.config-db  - TZ=Greece/Athens/  devices:  - /dev/zwave  volumes:  -  zwave-config:/usr/src/app/store  volumes:  zwave-config:  name: zwave-config ```  
The components, which are currently developed, are installed using the Linux service manager (systemctl) as system services. This way, it is easier to monitor the code for errors and warnings and continuously develop. An example service configuration file can be seen in Figure 4.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
## 3 Results
### Field Test  
The proposed DFT-based noise cancellation method was further validated with the experimental data collected in Xinjiang, China, in October 2017. Figure 11 shows the experimental site. In the field experiment, the rig was drilling at a depth of over 3000 m, with two triplex mud pumps installed. In the test, we chose an 811 FM pressure sensor, and the four sensors were distributed as shown in Figure 2. For the experiment, the sensor signal with the best waveform quality was chosen as the input received signal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Modulation Mode** & **Data Rate (bit/s)** & **BER** & **SIR** \\ \hline
1 & BFSK & 3 & 0 & 26.61 \\
2 & BFSK & 6 & 0 & 25.28 \\
3 & BFSK & 8 & 0 & 25.52 \\
4 & BFSK & 10 & 0 & 25.66 \\
5 & BFSK & 12 & 0 & 24.69 \\
6 & OOK & 3 & 0 & 18.05 \\
7 & OOK & 6 & 0 & 18.38 \\
8 & OOK & 9 & 0 & 16.62 \\
9 & OOK & 12 & 0 & 17.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Noise cancellation results with diverse information signals.  
Figure 10: Noise cancellation results of different Gaussian noises.  
Before logging, to validate the scenario of two pumps operating at different frequencies, their pure pump noise was sent at the wellhead and collected on the surface, the working frequencies of these two pumps were 0.6 Hz and 1.4 Hz, respectively, and their flow rates were nearly 2 m\({}^{3}\)/min. The signals received by the sensors were sent to a computer for noise cancellation. The information signal was modulated using the BFSK modulation mode with a symbol rate of 12 bits/s and a sampling rate of 1000 Hz. The experiment results are illustrated in the following figures (Figures 12-15). After mixing the real pump noise and the information signal, Figure 12 displays the received pressure signal. It is evident that the original signal is completely submerged by the pump noise and is hard to recognize. Figure 13 displays the output results after de-noising using the DFT method and shows a clearer and more observable pattern of the original signal. Similarly, Figure 14 compares the frequency spectrums of the original signal (blue, Figure 12) and the output signal (red, Figure 13) and indicates that the main peak amplitude of the mud pumps has been reduced by more than 80%. Figure 15 compares the pure pump noise block of the recovered signal (blue, part of Figure 13) with that of the input signal (red, part of Figure 12), demonstrating that the amplitude of the pump noise is nearly zero, consistent with the simulation test results.  
Figure 11: The experimental site in Xinjiang.
## 2 The Consensus Self-Organizing Models (COSMO)  
The COSMO method identifies the typical variability within a group of systems and to evaluate the likelihood of any individual being significantly different from the majority. In an ultrasonic testing system, a group of acoustic echo-signals were collected using pitch-catch method from N different locations (i.e., the total amount of samples) on a measured object. The spectral density of the acoustic signals were then obtained so that the difference between spectral density of two acoustic signals were compared [27; 28], and Hellinger distance \(d_{ij}\) was employed [22; 23; 24]:  
\[d_{i,j}=\frac{1}{\sqrt{2}}\sqrt{\sum_{i,j=1}^{m}\Bigl{(}\sqrt{p_{i}}-\sqrt{q_ {j}}\Bigr{)}^{2}}, \tag{1}\]  
where \(p_{i}\) and \(q_{j}\) are normalized histograms, representing spectral density of two acoustic signals. \(m\) is the number of sampled points of each acoustic signal. Two acoustic signals with different spectral density will yield a greater value of Hellinger distance than two similar ones. From the perspective of clustering analysis, samples with larger sum of distances to all peers are prone to be outliers.  
For all pairs of these histograms, Hellinger distance was computed, resulting in a symmetric distance matrix D:  
\[D=\left(\begin{array}{cccc}0&d_{1,2}&\cdots&d_{1,\text{N}}\\ d_{2,1}&0&\cdots&d_{2,\text{N}}\\ \vdots&\vdots&\vdots&\vdots\\ d_{\text{N},1}&\cdots&d_{\text{N},\text{N}-1}&0\end{array}\right).\]  
Within D, the row with minimum sum of distances is chosen as the most central pattern \(c\), representing the most typical testing sample within this group. Based on the most central pattern \(c\), the \(z\)-score is computed for each sample \(k\), representing the percentage of all samples that are further away from \(c\) than sample \(k\), that is,  
\[z(k)=\left|\frac{\left\{i=1,2,\ldots,\text{N}:d_{i,c}<d_{k,c}\right\}}{\text{N }}\right|\quad(k=1,2,\ldots,\text{N};)\enspace, \tag{2}\]  
where \(d_{k,c}\) is the distance between sample \(k\) and \(c\); \(d_{i,c}\) denotes the distance between sample \(i\) and \(c\); N is the total amount of samples. A sample with \(z\)-score close to 0 indicates a large distance to its peers. If there is no micro-crack at a particular position, the \(z\)-scores of a set of testing samples from this position should be uniformly distributed between \((0,1)\). We approximate the distribution of the average based on \(n\) samples using a normal distribution with mean 0.5 and variance 1/12n, i.e., [23; 24]  
\[z\sim N[\frac{1}{2},(1/12n)^{\frac{1/2}{2}}]. \tag{3}\]  
Therefore, the one-sided \(p\)-value, which is the probability that a single observation \(z\) picked from a normal distribution with parameters \((1/2,(1/12n)^{1/2})\) will fall in the interval \((-\infty,z]\), can be computed as:  
\[p=\frac{2}{\sqrt{2\pi}}\int_{-\infty}^{x}e^{-\frac{(z-1/2)^{2}}{2(1/\sqrt{2n}) ^{2}}}dz. \tag{4}\]  
Based on a uniformity test of \(z\)-scores over an area, the resulting \(p\)-value of this test is obtained to estimate whether the inspected contains any micro-flaws.
# A Decomposition Algorithm for Dynamic Car Sequencing Problems with Buffers
## 1 Introduction
* Parallel buffer: It consists of multiple parallel lanes (Figure 1), where cars from the upstream workshop are put into the parallel lanes, and then the first car of a lane is selected to join the downstream car production sequence according to the needs of the downstream workshop, and cars can only move in one direction in the lane, following the \"FIFO\" principle. Parallel line buffer is a more used class of buffer in automotive companies, and this paper also deals with solving CSP for parallel line buffer. We propose a decomposition algorithm to solve DCSPwB and reorder the car sequences in the paint shop using the post-painted car body buffer to find the optimal car sequences that satisfy all the hard constraints of the assembly shop as well as the appropriate trade-offs of various soft constraints, minimizing the violation of the constraint rules and the time cost of sequencing for the assembly shop. The whole method is divided into two parts: a heuristic inbound method based on simple rules and an outbound method based on a dynamic genetic algorithm and a greedy algorithm for delaying car outbound. We consider the time cost of the car sorting process as a soft constraint. The rest of this paper is organized as follows. A literature review is presented in Section 2. Section 3 provides a detailed description of DCSPwB and establishes a mathematical model. In Section 4, a decomposed algorithm is proposed to divide the problem into two phases: inbound and outbound. A simple rule-based heuristic inbound algorithm, a greedy algorithm based on the delayed car outbound method, and a dynamic-genetic-algorithm-based car outbound method are described. The numerical experiments in Section 5 verify  
Figure 1: Parallel buffer. the effectiveness of the proposed algorithms and investigate the effect of buffer capacity on the flexibility of car sorting. Section 6 describes the conclusions of this research.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 2 Cultivation Conditions
### Temperature, pH, and Salinity  
Temperature, pH, and salinity preferences vary among species and are usually reflected by their natural habitat. As shown in Table 1, commercially produced microalgae are cultivated at warm conditions and either neutral or slightly alkaline pH values. Additionally, it must be considered that temperature, pH, and salinity determine the solubility and volatility, and therefore the availability of nutrients and other substances present in the cultivation medium [17; 28].  
The pH of a nutrient solution can be adjusted by the addition of acids, bases, or CO\({}_{2}\). It should be controlled during the cultivation because it is important for photosynthesis. Depending on the pH value, dissolved CO\({}_{2}\) is available in water in different forms [29]. Most microalgae prefer taking up CO\({}_{2}\) or HCO\({}_{3}\)\({}^{-}\); the latter is available at a pH value between 6.5 and 10 [29].  
The highest tolerated salinity strongly differs among species (see Table 1). Extreme salt concentrations lead to various reactions in the microalgal cell, as the cell attempts to survive and adjust to osmotic pressure to establish a new steady state for growth [30]. Reactions include the restoration of turgor pressure, regulation of ion uptake and release, and accumulation of osmo-protecting solutes [31]. Under salt stress, photosynthetic activity is reduced due to an increase in respiratory activity and the inhibition of photosystem II [30]. Excess energy resulting from reduced growth can be stored in form of carbohydrates or lipids [32].