esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Active Antiresonance Assignment with No Additional Masses
### State-Feedback Approaches
#### 4.2.4 The Rank-One Acceptance Method with Regional Pole Placement
The closed-loop system is represented as follows, where \(\mathbf{C_{o}}\) is the output sensing matrix and \(\mathbf{F_{y}},\mathbf{G_{y}}\) are the gain matrices:  
\[\left\{ \begin{array}{c}\mathbf{M\ddot{q}}(t)+\mathbf{C\dot{q}}(t)+\mathbf{ Kq}(t)=\mathbf{B}\mathbf{f}(t)+\mathbf{B_{C}}\mathbf{u}(t)\\ \mathbf{u}(t)=-\left[\begin{array}{cc}\mathbf{F_{y}}&\mathbf{G_{y}}\end{array} \right]^{\mathsf{T}}\left\{\begin{array}{c}\dot{\mathbf{y}}(t)\\ \mathbf{y}(t)\end{array}\right\}\\ \left\{\begin{array}{c}\dot{\mathbf{y}}(t)\\ \mathbf{y}(t)\end{array}\right\}=\left[\begin{array}{cc}\mathbf{C_{O}}&0\\ 0&\mathbf{C_{O}}\end{array}\right]\left\{\begin{array}{c}\dot{\mathbf{q}}(t) \\ \mathbf{q}(t)\end{array}\right\}\end{array} ,\right. \tag{28}\]  
Collocated pairs are adopted, leading to \(\mathbf{C_{O}}=\mathbf{B_{C}^{T}}\), since stability is ensured if actuators and sensors do not introduce phase-lag [65]. The assignment of \(n_{z}\) zeros for \(p\)-th point receptance is formulated as the solution of the following condition:  
\[\left[\mathrm{adj}\left(\mathbf{I}+\mathbf{H}(z_{i})\mathbf{B_{C}}\left( \mathbf{G_{y}^{T}}+z_{i}\mathbf{F_{y}^{T}}\right)\mathbf{C_{O}}\right)\mathbf{ H}(z_{i})\right]_{pp}=0\ \ \ \text{with}\ i=1,\ldots,n_{z} \tag{29}\]  
where the \(pp\)-th receptance of the adjunct matrix is set to zero for all the prescribed antiresonances \(z_{i}\). Poles are assigned together with the zeros, and the gain matrices are imposed to be symmetric and positively semi-definite. Equation (29) leads to characteristic equations that are nonlinear in the control gains; hence, there may be one or more real solutions, or there may be no solution. On the other hand, the assumption of diagonal and positive semi-definite gain matrices simplifies the numerical solutions while ensuring that the control does not cause instability. The authors themselves recognize that using fully populated symmetric gain matrices would enlarge the achievable results by allowing more poles and zeros to be assigned than the number of actuators and sensors, or to accomplish secondary tasks, such as reducing the control effort or increasing robustness. To increase robustness of the closed-loop system in a prescribed frequency range from \(\omega_{\text{min}}\) to \(\omega_{\text{max}}\), the authors introduced a constraint in the solution of Equation (29) through the minimum singular (\(\underline{\sigma}\)) value of the matrix in Equation (30):  
\[\underline{\sigma}\left(\mathbf{I}+\mathbf{H}(j\omega)\mathbf{B_{C}}\left( \mathbf{G_{y}^{T}}+j\omega\mathbf{F_{y}^{T}}\right)\mathbf{C_{O}}\right)>r\\ \omega_{\text{min}}\leq\omega\leq\omega_{\text{max}}\ \ \, \tag{30}\]\(r\) is the minimum allowable value over the selected frequency range. The paper also provides the experimental application of the method to a T-shaped plate.
## 4 Conclusions  
In this work, the effects of BFS, MTS, DG and GA on the fluidity, mechanical properties and chloride ion penetration resistance of composite micronized powder cementitious materials were studied by orthogonal test. The ratio of the composite micronized powder with the highest 28 d activity was obtained. The hydration characteristics and microscopic morphology of cementitious materials, specifically the composite micronized powder which has the the optimal ratio mixed with OPC, were compared with those of pure OPC. The conclusions are as follows:  
1. The amount of MTS is the most significant factor among the four factors affecting the 7 d and 28 d activity index of the composite micronized powder, and the 28 d activity index decreases first and then increases with the increase in the amount of MTS. When the ratio is A4B4C4D2, it has the highest 28 d activity index, i.e., 35% BFS, 30% MTS, 0.3% GA, 5% DG and 30% WQMS.
2. The amount of BFS is the most significant factor among the four factors affecting the composite micronized powder fluidity and resistance to chloride ion penetration. With an increase in BFS doping, the fluidity ratio of composite micronized powder initially increases, then decreases. Simultaneously, the electric flux of the matrix decreases, resulting in an increased resistance to chloride ion penetration. When the ratio is A2B4C4D4, the composite micronized powder has the largest fluidity, i.e., 25% BFS, 30% MTS, 0.3% GA, 10% DG and 35% WQMS; when the ratio is A4B4C4D4, the matrix prepared with composite micronized powder has the best chloride ion permeation resistance, i.e., 35% BFS, 30% MTS, 0.3% GA, 10% DG and 25% WQMS.
3. The ratio with the highest 28 d activity index was selected as the best ratio of composite micronized powder, i.e., 35% BFS, 30% MTS, 0.3% GA, 5% DG and 30% WQMS. The hydration rate and accumulated heat release of the slurry mixed with 50% of the best ratio of composite micronized powder were lower than those of pure cement slurry; after 7 d and 28 d of curing, the microstructure of the mortar specimens mixed with 50% composite micronized powder of the best ratio appeared more compact and less porous than that of the pure cement mortar specimens cured at the same age.  
Conceptualization, Y.M.; Methodology, Y.M.; Software, D.H. and H.R.; Validation, D.H.; Formal analysis, Q.Z.; Investigation, Q.Z. and H.R.; Resources, X.L.; Data curation, P.W.; Writing--original draft, P.W.; Writing--review & editing, P.W.; Supervision, X.L.; Project administration, P.C.; Funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Natural Science Foundation, China (grant No. 2020 GXNSFBA297034), Key R & D projects in Guangxi (grant No. Gui Ke AB23026126) and Guangxi Key Laboratory of Green Building Materials and Construction (Nos. 22-J-21-20).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Proposed Method
### Network Details
#### 2.2.3 Prediction and Output Module  
The output of the previous module would be a feature map \(O\in\mathbb{R}\). In the prediction module, the feature map \(O\) would be firstly flatten to a 1D vector \(v_{in}\in\mathbb{R}^{\times 1}\). \(v_{in}\) is then connected to a fully connected (FC) layer, with a hidden layer of 160 neurons. The output of the FC layer is designed with 9 neurons, i.e., a vector \(v_{out}\in\mathbb{R}^{9\times 1}\), representing the 8 high-frequency components of loads \(f_{d_{1}},f_{d_{2}},\cdots,f_{d_{8}}\) and 1 low-frequency component \(f_{\text{gs}}\). Here, we only predict the load at current time stamp \(t_{now}\). The relation between \(v_{out}\) and \(v_{in}\) is given by  
\[v_{out}=a(W_{FC}v_{in}+b_{FC}), \tag{5}\]  
where \(W_{FC}\) is the weight matrix in the FC layer, \(b_{FC}\) the bias vector. The values of elements in \(W_{FC}\) and those of \(b_{FC}\) are learnable. The activation function \(a(\cdot)\) in this layer is also the ReLU function (Equation (4)).  
The prediction module would learn a mapping from the learnt feature maps of the decomposed components of the responses to the decomposed representations of the load. In the output module, the decomposed representations are transformed back to the time domain using  
\[f(t_{now})=f_{\text{gs}}+\sum_{p=1}^{8}f_{dp}, \tag{6}\]  
where \(f(t_{now})\) is the predict load at time stamp \(t_{now}\) in the time domain. It is noteworthy that the proposed method would take the responses in a time window to identify the load at a certain time stamp. When sliding the time window to a new time interval, the load at another time stamp would be identified, which make it possible for the propose method to meet the online identification demands by a overlapped moving time window.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layers** & **Kernel Size** & **Filter Number** & **Dilation Rate** & **Stride** \\ \hline Conv \#1 & 8 & 32 & 1 & 1 \\ Conv \#2 & 8 & 32 & 2 & 1 \\ Conv \#3 & 8 & 32 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of the conventional layers in the feature extraction module. There are in total 3 layers. While the kernel size, the stride and the filter numbers are set to be the same for all 3 layers, the dilation rate are different for a larger receptive fields for deeper layers.
# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
## 4 Experiment
### Setup  
Datasets: We used two publicly available datasets to validate the effectiveness of our method: The SemEval-2010 Task 8 dataset and the KBP37 dataset. Table 4 shows the statistics of each dataset. The SemEval-2010 Task 8 dataset was provided by [PERSON] et al. as a free dataset, containing 10,717 samples total, 8000 samples for training, and 2717 samples for testing. The dataset contains nine relation types where the relations are ordered. The directionality of the relations effectively doubles the number of relations, since entity pairs are considered to be correctly labeled if the order is also correct. So, finally, there are 19 relations (2 \(\times\) 9 + 1 other class). The KBP37 dataset includes 18 semantic relations and \"no relation\" classes. Similar to SemEval-2010 Task 8, the relations are directional; so, the actual number of relation types is 37. It contains 15,917 training instances and 3405 test instances.  
Evaluation: We adopt the evaluation scheme of the relation classification criteria: precision, recall, and micro F1 are used as evaluation parameters. For the SemEval-2010 Task 8 dataset and the KBP37 dataset, only the prediction of the subject-object order between entities in the predicted relation is considered a positive sample. Otherwise, it is a negative sample (e.g., a sentence with a relation label Message-Topic (e1,e2), if the prediction is Message-Topic (e2,e1), would represent an incorrect prediction).  
Platform Setup: The IDE used for the experiments in this paper is Pycharm2021 Professional Edition, PyTorch version 1.9.1, CUDA version 11.6, and CUDNN version 10.2. The model training and inference are performed on an NVIDIA A100-SMX with 40 GB GPU memory, and CPU memory of 16 GB.  
Implementation details: For a fair comparison with previous work, we used the SpanBert-base uncased as our base encoder for extracting features from the corpus in the dataset. In addition to using SpanBert-base uncased as an encoder, we also designed a model based on SpanBert-large uncased as an encoder. We set the learning rate to \(2\times 10^{-5}\), the training bitch size to 32, the test bitch size to 16, the number of epochs to 50, and the dropout to 0.1. The detailed settings of the hyperparameters are shown in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Relation** & **Train** & **Test** \\ \hline Semeval-2010 Task 8 & 19 & 8000 & 2717 \\ KBP37 & 37 & 15,917 & 3405 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of Semeval-2010 Task 8 and KBP37 datasets.
## 2 The Proposed Long-Distance, Real-Time LED-DCC System  
The schematic diagram of the proposed long-distance, real-time LED-DCC system is shown in Figure 1a. The system can be divided into a transmitter side with the LED display with VLC function and a receiver side with the smartphone's CMOS camera used as a photoelectric sensor array.  
On the transmitter side, the hidden data is encoded via the ABRC scheme [14] that replicates the original information bits multiple times in an alternating flip for synchronization between the transmitter and the receiver. To increase the LED-DCC light-emitting area for communication, as well as the transmission distance, each data bit in the encoded data frame is inserted in several nearby pixels of the video frame to be displayed on the LED display. Since each LED display point is modulated by the value of each pixel, each hidden data bit can be carried by multiple clustered LED-DCC points at the same time. Finally, the high-speed, modulated LED display points broadcast high-speed visible light signals at a baud rate, the same as the refresh rate of the LED display, which is imperceptible to human eyes.  
On the receiver side, the modulated signal transmitted through the VLC channel is captured by the smartphone's CMOS camera. To solve the problem of processing latency introduced by point clustering and improve the reliability of information extraction, a lightweight, back-forth, fast image processing algorithm is proposed to quickly locate the LED-DCC display area from the high-resolution video frame and extract the transmitteddata bits in a real-time mode. Finally, the extracted data frame is decoded with the ABRC scheme to recover the hidden data.  
In the long-distance, real-time LED-DCC system, two key technologies are proposed: a LED-DCC point clustering scheme for improving the system communication performance and a lightweight, back-forth, fast image processing algorithm for high-precision, real-time data reception.
## 4 Discussion
### Power Efficiency  
For small or micro-scale autonomous robotics, power efficiency is a key criteria in actuator and mechanism selection. To evaluate impact actuator efficiency, we first calculate a nominal efficiency for the piezoelectric bender, \(\eta_{f_{0}}\), as  
\[\eta_{f_{0}}=\frac{E_{p}d_{31}^{2}}{2e}\times 100\% \tag{8}\]  
where \(e\) is the dielectric constant of the piezoelectric material. For the PZT in use, ideal electrical-to-mechanical conversion efficiency is 7.5%. Conceptually, charge recovery mechanisms (as in [21; 22]) can increase efficiency substantially, but this provides a basis for comparison to the impact-driven system.  
The mechanical power transmitted to the mechanism is calculated from the power spectral density of the mechanism motion. To estimate power density from experimental results, we apply the approximation  
\[\begin{cases}\tilde{p}(kf_{0})=\frac{k_{exp}}{2N\cdot F_{s}|\Sigma_{n=0}^{N-1} \tau_{l}(n)e^{-i2\pi k\tilde{f}_{0}n}|^{2}}\\ k=0,1...N-1\\ f_{0}=\frac{F_{s}}{N}\end{cases} \tag{9}\]  
in which \(\tilde{p}(kf_{0})\) is the power density corresponding to frequency index \(kf_{0}\), \(k_{exp}\) is effective vertical stiffness of the mechanism (measured as 6.54 N/m), \(N\) is the number of samples, \(F_{s}\) is sampling rate, and \(n\) is the index of each sampled time series data point. Power transmitted is calculated from the band power  
\[P(f_{1})=\int_{f_{1}-w_{p}/2}^{f_{1}+w_{p}/2}|\tilde{p}(f)|df \tag{10}\]  
Figure 12: Displacement at tip of amplification mechanism tip, after a 347 Hz, 40 V input to the PZT bender is initiated at 1.35 s, showing quasi-static elevation of beam tip.  
where \(P(f_{1})\) is the mechanical power corresponding to the dominant frequency, \(f_{1}\), and \(w_{b}\) is the bandwidth, selected here to be 10 Hz. To calculate the energy efficiency, the mechanical output power is compared to electric power input, calculated from capacitance of the PZT actuator, \(C\), voltage, and input frequency  
\[\eta_{f_{1}}=\frac{P(f_{1})}{CV^{2}f_{d}}\times 100\% \tag{11}\]  
In the prototype system, capacitance is known to be 28.2 nF, and the voltage amplitude, as previously mentioned, is 40 V.  
Figure 13 shows the estimated experimental efficiency of electrical energy transmission to the dominant mechanical oscillation component as a function of driving frequency. Maximum efficiency of 0.9% is achieved at 17 Hz. This represents substantial energy losses from the theoretical border efficiency. However, it is similar to reported electrical-to-mechanical conversion efficiency for efficient shape memory alloy transducers, and large relative to electrothermal transducers, both of which tend to operate in similar frequency regimes [23].
# Acoustic Emission and Modal Frequency Variation in Concrete Specimens under Four-Point Bending
## 4 Results
Finite element models of the unloaded pre-notched beam were used to look for a first estimate of the crack advancement by comparing measured and calculated frequencies. The notch depth was increased from the initial value till the 95% of the beam depth. The following values of the relative notch depth \(\xi=a/h\) were considered: \(\xi=0.5\), 0.625, 0.75, 0.875, and 0.95. They correspond respectively to the following depth of the ligament, \(h\)-\(a\): 5, 3.75, 2.5, 1.25, and 0.5 cm. The corresponding fundamental frequencies are respectively equal to the following approximate values: 1120, 985, 915, 885, and 875 Hz. Figure 11a shows the numerical fundamental vibration mode and frequency for the previous values of the relative notch depth \(\xi\). Figure 11b proposes a comparison between the numerical values of the fundamental frequency and the parabolic approximation of the corresponding experimental values. Such a comparison represents a simple way to obtain a first evaluation of the crack advancement. At the same time, a more sophisticated analysis accounting for the nonlinear stress-strain relationship of concrete (both in tension and compression), together with the presence of the loadings (self-weight and external forces), would simulate better the experimental test, allowing for the study of linear vibrations at different loading levels. Further refinements could also be achieved by nonlinear modeling of breathing cracks [33; 42; 34]. However, the previous refinements are beyond the scope of the present study. Figure 10: Test 1: (**a**) experimental natural frequencies and cumulated AE energy vs. vertical displacement; (**b–d**) first, second, third natural frequency vs. cumulated AE energy (S\({}_{\rm AE1}\) = wideband sensor, S\({}_{\rm AE2}\) = resonant sensor).
# Crossover Rate Sorting in Adaptive Differential Evolution
## 2 Materials and Methods  
### Differential Evolution  
Like every evolutionary algorithm, the DE begins with initializing a population of solutions, randomly distributed in a search space. The initial population size is \(N\), and the individuals are \(D\)-dimensional points, \(x_{i}=(x_{i,1},x_{i,2},\ldots,x_{i,D})\), \(i=1,\ldots,N\), generated as follows:  
\[x_{i,j}=x_{l|b,j}+rand\times(x_{ub,j}-x_{lb,j}). \tag{1}\]  
where \(j=1,\ldots,D\), \(x_{ub,j}\) and \(x_{lb,j}\) are the upper and lower boundaries.  
The main loop of DE consists of three operations: mutation, crossover and selection. The mutation operator is a main feature of DE, and it generates donor vector \(v_{i,j}\) using individuals from the population and a scaling factor parameter \(F\) as follows:  
\[v_{i,j}=x_{r1,j}+F\times(x_{r2,j}-x_{r3,j}), \tag{2}\]  
where \(x_{i,j}\) is the \(j\)-th coordinate of the \(i\)-th solution, and indexes \(i\), \(r1\), \(r2\) and \(r3\) are different from each other. The presented strategy is called _rand/1_, and was presented in [19] together with several other strategies, such as _rand/2_, _best/1_, _best/2_. The scaling factor parameter was originally proposed to be set in range \([0,2]\); however, most studies nowadays set it in the \([0,1]\) range. The differential evolution is highly sensitive to this setting, thus requiring adaptive schemes to be applied.  
The crossover step combines the genetic information of mutant vector \(v_{i}\) and the target vector \(x_{i}\) to obtain an offspring, called trial vector \(u_{i}\). There are two popular crossover schemes in DE, namely exponential and binomial, the latter being used more frequently. The binomial crossover uses a parameter value \(Cr\) and works as follows:  
\[u_{i,j}=\begin{cases}v_{i,j},&\text{if }rand(0,1)<Cr\text{ or }j=jrand\\ x_{i,j},&\text{otherwise}\end{cases} \tag{3}\]  
where \(Cr\) is the crossover rate, and _jrand_ is a randomly generated index in \([1,D]\), required to make sure that at least one component is taken from the mutant vector. Without _jrand_, small \(Cr\) values may result in evaluating the same vector, which would be a waste of computational resource. However, recent studies show that evaluating the same vector may still be an issue in DE [20].  
Once the trial vector \(u_{i}\) is generated, it is checked to be within the upper and lower boundaries \(x_{ub,j}\) and \(x_{lb,j}\), and if not, a bound-constraint handling method is applied, for example, midpoint target.  
In DE, a greedy selection mechanism is applied, i.e., the trial vector \(u_{i}\) replaces the target vector \(x_{i}\) only if it has better fitness. This scheme can be described as follows:  
\[x_{i}=\begin{cases}u_{i},&\text{if }f(u_{i})\leq f(x_{i})\\ x_{i},&\text{if }f(u_{i})>f(x_{i}).\end{cases} \tag{4}\]  
Despite the efficiency of such an approach, some attempts were made to modify the selection step, for example, with neighborhood information [21] or by introducing novel algorithmic schemes with two populations [16].
## 3 Framework Overview  
To address the gap existing in literature, this study developed a novel, NLP-based framework for the automated extraction and time-cost prediction of contractual reporting requirements in construction. The framework consists of two modules, namely the (1) automatic extraction of reporting requirements module and (2) prediction of reporting time and cost module that are linked as illustrated in Figure 1.  
The first module, hereafter referred to as the extraction module, is responsible for identifying statements in contract or project specification documents that prescribe reporting requirements. Contract documents and project specifications are input into the NLP model. Relevant text is extracted from the documents and is transformed into a format that is compatible with the text classification models.  
Text classification algorithms are then used to classify contractual and project specification statements as either a (1) reporting requirement or (2) non-reporting statement. Both rule-based and ML-based text classification methods can be used to classify statements; application of either method will depend on the specific requirements of the user. While rule-based text classification is more time-consuming than ML-based classification due to the involvement of manual rule development, rule-based classification commonly results in higher precision and recall [26]. ML, on the other hand, makes it possible to automatically classify text, provided that sufficient learning opportunities are available [27].  
The second module, hereafter referred to as the prediction module, is responsible for generating relevant time-cost predictions. Reporting requirements output from the extraction module are used by practitioners to prepare a list of required reports and their associated submittal frequencies that are then input into the prediction module. Estimates of the time required to prepare a specific report are used as inputs. Then, a Monte Carlo simulation model, which uses random sampling to obtain numerical results or a probability distribution [31], is used to predict the cost associated with report preparation based on project duration and historical data.  
Although distinct, the practical functionality of these two modules increases considerably when used together. Manual extraction is very tedious and time-consuming, and contractors do not have enough time during the bidding stage to identify the contract requirements and plan accordingly. Because of the ability of the extraction module to quickly extract reporting requirements, the prediction module can now be applied in a more impactful stage of the project life-cycle (i.e., pre-construction bidding and planning stages). Specifically, the outputs of the prediction module can be used to (1) ensure that  
Figure 1: Proposed Framework.  
sufficient cost and time contingencies for report preparation are included in bid estimates, (2) engage in negotiations to reduce redundant reporting requirements before finalizing contracts, and (3) improve resource allocation.