esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application Analysis
### Measured Responses and Analysis
As the speed of the vehicle group increases, the displacement and acceleration of the bridge increase significantly, but the fluctuation is also more noticeable. Therefore, the coupling effect of vehicle-bridge cannot be ignored, and the driving speed is an important factor for comprehensive analysis. Figure 11:2 Acceleration of bridge with different uniform mass and road roughness. Figure 11:3 Maximum displacements of bridge with different vehicle speeds and frequencies. Figure 24 illustrates the bridge acceleration signals collected by the health monitoring system with or without vehicle effect. The total amplitude of the measured ambient vibration signal and electromagnetic interference signal typically falls in the range of \(\pm\)1.0 \(\times\) 10\({}^{-3}\) m/s\({}^{2}\), while the acceleration amplitude caused by a vehicle is generally in the range of \(\pm\)0.07 m/s\({}^{2}\), consistent with the results of the numerical analysis. Considering the monitoring needs, pavement flatness, environmental factors, along with the preliminary monitoring results, the bridge health monitoring threshold is generally determined as follows: If the acceleration amplitude is below \(\pm\)5.0 \(\times\) 10\({}^{-3}\) m/s\({}^{2}\) or the displacement amplitude is less than \(\pm\)1.0 \(\times\) 10\({}^{-4}\) m, the modal analysis function is not initiated. Otherwise, suitable data are selected, and modal analysis is carried out by using the stochastic subspace method to obtain the frequencies, modal shapes, and damping ratios of the first three order modes. If the acceleration amplitude is less than \(\pm\)1.0 \(\times\) 10\({}^{-2}\) m/s\({}^{2}\) or the displacement amplitude is less than \(\pm\)1.8 \(\times\) 10\({}^{-4}\) m, the damage detection program is not initiated. Otherwise, appropriate data are selected for damage detection, determining the possible damage location and degree, and the corresponding important data are stored for long-term analysis. Following this monitoring strategy, only essential data are recorded and analyzed, effectively saving computational resources while ensuring the effectiveness of health monitoring. Figure 11:4 Maximum acceleration of bridge with different vehicle speeds and frequencies. Figure 11:5 Bridge acceleration signals with or without vehicle effect. For example, in a scenario where the health monitoring system operates continuously for 24 h, the memory space in the original database is 504.15 megabyte. However, after the implementation of the monitoring strategy proposed in this paper, the actual memory space is reduced to only 57.06 megabyte. The valid data used for the actual analysis aligns more closely with the conclusions.
## Materials and Methods
### Statistical Analyses  
Statistical analysis was carried out using the statistical package Statistica 10 for Windows (StatSoft). To test the normality of the data set of the VOCs, skewness and kurtosis were performed. Most of the data were normally distributed. For this reason, one-way ANOVA was performed on the grape juice and skin volatile composition and on wine sensory data to find differences between the samples. Mean data, where required, were separated using the LSD test (\(p<0.05\)). The entire data set was also analyzed by using a non-parametric method, the Kruskal-Wallis one-way analysis of variance, which does not assume a normal distribution of the residuals. The results obtained from the application of the two different methods (parametric and non-parametric) were identical. Principal component analysis (PCA) was performed on the volatile compounds and sensory data to extract the most relevant information [32], followed by a Pearson correlation (\(p<0.05\)) to highlight any significant relationships between the data. Finally, Partial Least Squares (PLS) regression was applied to determine the relationship between the predictors (independent variables: volatile compounds) and the responses (dependent variables: sensory attributes).
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 2 WAMS-Based Power System Modeling
### Multi Area Dynamics  
In addition to the single area dynamics, we can also consider the multi area dynamics. In real power systems, a single area can represent a power station or a specific region of a large power system. These power stations or regions are then connected together to form an interconnected grid. The control center monitors the whole grid and gives specific commands in emergency scenarios and this is known as centralized disturbance estimation. The general equation for the whole power system becomes  
\[\dot{x}\left(t\right)=Ax\left(t\right)+Bu\left(t\right)+F\Delta P \tag{13}\]  
where  
\[x\left(t\right)=\left[x_{1}\left(t\right)\quad x_{2}\left(t\right)\quad x_{3} \left(t\right)\quad\cdots\quad x_{n}\left(t\right)\right]^{T} \tag{14}\]  
\[u\left(t\right)=\left[u_{1}\left(t\right)\quad u_{2}\left(t\right)\quad u_{3} \left(t\right)\quad\cdots\quad u_{n}\left(t\right)\right]^{T} \tag{15}\]  
\[\Delta P=\left[\Delta P_{1}\quad\Delta P_{2}\quad\Delta P_{3}\quad\cdots\quad \Delta P_{n}\right]^{T} \tag{16}\]  
If we neglect the tie line dynamics, for an \(n\) area power system the overall matrices are given in the Appendix A. The matrices with tie lines neglected have the following dimension: \(A\in\mathbb{R}^{3n\times 3n}\), \(B\in\mathbb{R}^{3n\times 3}\) and \(F\in\mathbb{R}^{3n\times 3}\). The entire elements are real numbers. If we consider the tie line dynamics, we have \(A\in\mathbb{R}^{4n\times 4n}\), \(B\in\mathbb{R}^{4n\times 4}\) and \(F\in\mathbb{R}^{4n\times 4}\).
# Defect Processes in Halogen Doped SnO2
## 3 Results
### Electrical Properties
This is in stark contrast with the intercalation of interstitial halogens that create some localized perturbations upon the upper valence band and the conduction band of the material. That implies free electrons' presence contributing to the n-type conductivity, in accordance with experimental observations [45; 46; 47; 48]. The halogen substitutional doping is seen to induce states inside or near the valence band. We believe that this can be explained because in the substitutional doping, the halogen orbitals are not mixing with the nearest Sn-5p or O-2p orbitals. As a result, no hybridized mid-gap states arise. In Table 2 we have summarized our results. According to these calculations, the minimum value of the calculated bandgap is 2.61 eV for the case of I\({}_{\text{o}}\) doping. It is therefore suggested as the most effective of the halogen SnO\({}_{2}\) dopants for photocatalytic applications. Therefore, it is suggested that this type of doping can be used to develop better photocatalysts. In Figures 3 and 4, the pDOS for the doped SnO\({}_{2}\) are shown. From the pDOS, we can gain important information about the gap states and the contribution of each element's orbitals to the valence and conduction band. As seen in Figure 3a, which shows the pDOS of the plain SnO\({}_{2}\), the valence band is mainly consisting of O-2p and Sn-5p orbitals, whereas the conduction band is created from the Sn-5s, Sn-5p, O-2s, and O-2p. This is in good agreement with previous theoretical studies [41; 42]. Referring to Figure 3b, we see that F doped SnO\({}_{2}\) has a similar valence and conduction band structure with the undoped SnO\({}_{2}\) with a small deviation coming from the contribution of fluorine in the doped oxide. Focusing on the gap states formed near the valence band in the F-doped material, calculations show that it is created due to the hybridization of O-2p with F-2p orbitals (refer to Figure 4a). This is the same phenomenon that we predicted to occur in the case of TiO\({}_{2}\)[43] upon doping with interstitial F atoms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Halogen Dopants** & \multicolumn{2}{c}{**Band Gap**} \\ \hline  & **Interstitial Doping** & **Substitutional Doping** \\ \hline F & 3.12 & 2.90 \\ \hline Cl & 2.85 & 2.70 \\ \hline Br & 2.90 & 2.71 \\ \hline I & 2.62 & 2.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Band gap values for every halogen doping case. Figure 3: \(Cont.\)  
Similarly, in Figure 4b-d the rise of gap states is mainly created by the O-2p and Cl-2p and O-2p and Br-4p and I-5p. This probably indicates that the formation of mid-gap states in halogen doped n-type metal oxides such as SnO\({}_{2}\) and TiO\({}_{2}\) may have a universal origin. In any case, further investigation is needed, which is beyond the scope of the present manuscript.
# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 3 Design and Implementation of Multi-Core Accelerator Architecture
### FMesh, the Bridge of FAcc Communicatio
#### 3.2.1 The FRouter Implementation  
Within the FMesh network, each FCore is interfaced with a dedicated router named FRouter. Figure 6 illustrates the port design of FRouter, where the data port transmits flow control units from upstream to downstream routers, and the is_valid signal indicates whether the incoming flit is valid for the downstream FRouter.  
The FRouter architecture employs a minimalist design comprising two modules: Input module and Output module. As depicted in Figure 7, the Input module integrates an  
Figure 5: The FMesh architecture.  
Figure 6: The port design of FRouter.  
input buffer and a routing computation unit (rc_unit). The FRouter receives data from five directional ports (north, south, east, west, and local), storing incoming flits in the input buffer. Valid flits are identified via the is_valid signal, then forwarded to both the rc_unit and Output module. The rc_unit computes the output port direction based on predefined routing policies, while the Output module transmits data according to these computed directions. To simplify the design and minimize input buffer depth, each FRouter processes valid data from only one active port per cycle, determined by the is_valid signal. If multiple valid ports are detected, data transmission follows a prioritized sequence of west \(\rightarrow\) north \(\rightarrow\) east \(\rightarrow\) south. When the computed out_port is designated as local, the flit is further classified as either an instruction (routed to the instruction memory, IM) or data (routed to the general-purpose register file, GPR).
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 3 Experimental Results
### Simulated Example  
The compression rate is one commonly used criterion to evaluate compressive sensing recovery. It is defined by \(cr=\frac{N-M}{N}\). The noise level is a second important criterion for industrial vibrations signals. In fact, mechanical vibration signals are very often acquired by accelerometers that are mounted on operating mechanical components. Because of the harsh acquisition environment, mechanical signals are always contaminated with high levels of random noise as well as external interference, resulting in small signal-to-noise ratio. Therefore, it is important to investigate the ability of the reconstruction methods to reduce the noise and interfering components while providing an accurate estimate of the signal. For this reason, the simulation part is organized, as follows. First, we consider different compression rates and different Gaussian noise levels, and we aim to numerically investigate the contribution brought by the proposed structure models to the reconstruction quality. This is performed by comparing these models with their univariate versions. Finally, we study the shrinking properties for interfering components.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Supply Chain Management
#### 4.2.1 Transparency and Information Sharing in Supply Chains
The study introduces an innovative solution that employs blockchain technology alongside a cyber-physical system (CPS) to authenticate agricultural produce's quantity and quality. This approach aims to increase the reliability of the supply chain. CPS's distinguishing aspect in agricultural management lies in its combination of hardware and software solutions, ensuring comprehensive coverage and dependability throughout various stages. Figure 7 encapsulates the CPS-based information management architecture within the agricultural supply chain. The subsequent sections provide an in-depth examination of each component and the dynamics of information flow. Information traverses from farms, warehouses, factories, and stores to sensor servers and back. In this context, the term \"sensor server\" refers to a central database that stores data collected by monitoring devices positioned in agricultural fields. In a CPS setting, it is notable that sensors and monitoring devices are generally small, with restricted data transmission capacities and a limited operational scope. Furthermore, fields are often equipped with numerous monitoring devices to ensure adequate coverage, rendering individual manual interaction with each device impractical. This necessitates the adoption of an intermediary layer, such as a server, to aggregate data from all devices, enabling immediate user access to this information. In Figure 7, the physical components--farms, warehouses, factories, and stores--represent the tangible environment (field), where bidirectional information flow is facilitated by monitoring devices capable of both gathering essential data and receiving operational commands from the sensor server. Figure 7: Information management based on cyber–physical systems in agricultural supply chain systems [95]. The user refers to individuals overseeing the physical environment, enabling them to retrieve data from the sensor server. \"Higher-Level User\" encompasses entities seeking an overarching view of the agricultural supply chain, including managers, shareholders, customers, and governmental bodies. Nonetheless, due to concerns over privacy and competitive dynamics, there may be a reluctance to share comprehensive field data with higher-level users. Moreover, these higher-level users, often located in remote regions, might not have the capability for direct, dedicated network connectivity to the field. This is where cloud computing and related technologies become pivotal, serving as an intermediary that collates and abstracts essential information from disparate locations to offer a global perspective to higher-level users. Ultimately, this blockchain-centric solution represents a significant stride towards establishing a more equitable and accessible market for small-scale agricultural stakeholders.
## 4 BPSK FDA System Design
### Baseband FDA Far-Field Behavior  
Traditionally, a BPSK modulated signal has the general form [43]  
\[x(t)=A(t)\cos\left(2\pi f_{0}t\right), \tag{6}\]  
where \(A(t)\in\{-1,1\}\) is the sign of the BPSK fast-time coefficient at fast time \(t\), and \(f_{0}\) is the carrier frequency.  
In our BPSK FDA system with a single down-conversion frequency \(f_{0}\) at the receiver, the baseband signal depends on the coherent summation of all radiated frequencies at the far-field point \((R,\theta)\). Thus, we evaluate Equation (4) with \(N=2\)  
\[\begin{split} F_{bb}(t;R,\theta)&=\ e^{-j2\pi f_{0}R/ c}\ +\\ &\ e^{j2\pi\left(\Delta ft-(f_{0}+\Delta f)\frac{R}{c}+(f_{0}+ \Delta f)\frac{d\sin\theta}{c}\right)},\end{split} \tag{7}\]  
then take the real component of the signal  
\[\begin{split} F_{re,bb}(t;R,\theta)&=\ \cos\left(2\pi f_{0}R/c \right)\ +\\ \cos\left(2\pi\bigg{[}\Delta ft-(f_{0}+\Delta f)\frac{R}{c}+(f_{ 0}+\Delta f)\frac{d\sin\theta}{c}\bigg{]}\right).\end{split} \tag{8}\]  
Figure 2: Simulated baseband behavior at a nominal far-field location \((R,\theta)=(10\ \text{m},0^{\circ})\) for \(N=2\) and \(N=4\) element FDA with \(f_{0}=720\) MHz and \(\Delta f=10\) kHz.  
Equation (8) can be expressed as a single cosine wave with a range-periodic amplitude offset (\(R_{p}=\lambda_{0}\)) and temporal periodicity at fixed far-field locations (\(T_{p}=1/\Delta f\)). There is also a temporal shift in the maxima of each period given by  
\[\begin{split}\phi_{shift}&=2\pi\bigg{[}(f_{0}+\Delta f )\bigg{(}\frac{R}{c}-\frac{d\sin\theta}{c}\bigg{)}\bigg{]}\mod 2\pi\\ T_{shift}&=T_{p}\bigg{(}1-\frac{\phi_{shift}}{2\pi} \bigg{)}\end{split}. \tag{9}\]  
These equations help determine how data should be modulated onto the BPSK carrier waves. For visualization, we show one period of a BPSK-modulated waveform in Figure 3, generated using Equation (5) with \(N=2\), \(f_{0}=720\) MHz and \(\Delta f=10\) kHz at the far-field location \((R,\theta)=(2\) m, \(-25^{\circ})\). Note that we still assume perfect frequency and phase offset compensation at this point in the analysis.  
The design of this transmitted modulation window is based on the BER analysis shown in Figure 4. The bit at the maxima of the normalized amplitude is assumed (for now) to have an energy per bit (\(E_{b}/N_{0}\)) of 12 dB. The energy per bit then scales with the squared amplitude of the waveform. The chosen modulation window is shown via the highlighted regions in Figure 4, where the BER with respect to minimum \(E_{b}/N_{0}\) for varying modulation window sizes is shown. From Figure 4, it can be seen that the 50-bit window provides good balance between window size and BER.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 6 Conclusions  
We studied a specific sub-domain of the continuous queries of moving objects, namely the pruning optimization of historical continuous queries based on a threshold. First, we optimized the processing overhead of a single pruning check using a pruning strategy based on extended MBR overlap. Secondly, a 3 DR-tree extension structure called E3 DR-tree was proposed for traversing the query trajectory, and based on this, the best-first traversal algorithm was introduced, so that the accurate pruning results  
Figure 10: Performance of different distance thresholds: (**a**) Number of the accessed nodes, (**b**) selectivity, (c) pruning latency, (**d**) query latency.  
could be obtained with accessing as few nodes as possible. Finally, a large number of experimental results verify the effectiveness of our method. In the future work, we plan to apply this method to the distributed environment, and combine it with a distributed index, to further improve the query performance based on parallelization techniques. Moreover, we will improve this method to solve another sub-problem of historical continuous query--\(k\)-nearest history continuous query.  
Conceptualization, J.Q. and Q.L.; data curation, J.Q.; methodology, J.Q.; supervision, L.M.; validation, J.Q. and Q.L.; writing--original draft, J.Q.; writing--review and editing, L.M. and Q.L. All authors have read and approved the final manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61802425.  
The authors declare no conflict of interest.
#### Notation  
Notations \((\cdot)^{T}\) and \((\cdot)^{H}\) represent the transpose and Hermitian (conjugate transpose) of a matrix, respectively. The operators \(\mathbb{P}(\cdot)\) and \(\mathbb{E}(\cdot)\) represent the probability and the expectation of an event, respectively, and \(\text{Re}(\cdot)\) and \(\text{Im}(\cdot)\) refer to the real and imaginary parts of the complex number, respectively. Function \(\log(\cdot)\) represents a natural logarithm. The symbols \(|\cdot|\) and \(\|\cdot\|_{0}\) represent the absolute value and zero norm (i.e., the number of nonzero elements), respectively. The indicator function \(\mathbb{1}\left\{\mathcal{X}\right\}\) equals to \(1\) if the event \(\mathcal{X}\) is true and is \(0\) otherwise. The sign function \(\text{Sign}(x)\) is defined as \(1\) for \(x\geq 1\) and \(-1\) otherwise. The Q-function is defined as \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-ix^{2}/2}du\). Finally, \(\mathbb{R}\) and \(\mathbb{C}\) represent sets of real and complex numbers, respectively.
## 2 Materials and Methods
### Extraction of Pasta Volatiles  
To identify and quantify the volatile compounds emitted by the different types of pasta tested in the behavioural bioassays, the static head-space solid-phase micro-extraction (HS-SPME) technique was used according to [PERSON] et al. (2009) and [PERSON] et al. (2019) [34; 35]. Pastas were stored in their unopened packages at room temperature until use. Before analysis, 10 g of each pasta sample was ground in an electric mill for 20 s at 6000 rpm (Waring(r) laboratory blenders, Fisher scientific, Goteborg, Sweden) and placed in a 20 mL headspace vial (Supelco Co., Bellefonte, PA, USA), sealed with a PTFE/silicon septum (Supelco Co., Bellefonte, PA, USA) for analysis. The vial was then conditioned at 50 \({}^{\circ}\)C for 30 min in a water bath prior to SPME headspace sampling.  
Extraction was performed using SPME fibres (Supelco Co., Bellefonte, PA, USA) coated with either 50/30 \(\upmu\)m of divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS). The fibres were conditioned before use by heating them in the injection port of the GC system, according to the manufacturer's recommendations, at 270 \({}^{\circ}\)C for 1 h. Then, the SPME needle was introduced through the septum, and the fibre was exposed in the vial to the headspace of the pasta sample for 90 min. A temperature of 50 \({}^{\circ}\)C was maintained during headspace sampling. After the extraction time, the fibre was recovered and transferred to the injection port of the GC, where the compounds were thermally desorbed at 250 \({}^{\circ}\)C for 4 min. A fibre cleaning step of 10 min at the conditioning temperature with the split valve opened was performed in the GC injector after every chromatographic run to remove any absorbed residue. Before the acquisitions, a blank test was performed under the same experimental conditions to check for possible impurities. Each sampling was performed in triplicate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Two-Choice Bioassay** & **First Choice** & **Second Choice** & \multicolumn{2}{c}{**Student’s \(t\)-Test**} & **Response Index** \\ \cline{1-1}
**First vs. Second Choice** & **(\(\pm\)SE)** & **(\(\pm\)SE)** & **t-Value** & **_p-Value_** & **(\(\pm\)SE)** \\ \hline F vs. Control & 64.50 \(\pm\) 2.25 & 1.25 \(\pm\) 0.25 & \(<\)0.001 & 28.111 & 63.25 \(\pm\) 2.25 \\ H vs. Control & 20.00 \(\pm\) 1.08 & 3.50 \(\pm\) 0.65 & 0.001 & 13.863 & 16.50 \(\pm\) 1.19 \\ F+H (1:1) vs. Control & 80.00 \(\pm\) 1.47 & 2.00 \(\pm\) 0.41 & \(<\)0.001 & 63.687 & 78.00 \(\pm\) 1.22 \\ F vs. H & 73.75 \(\pm\) 1.55 & 4.75 \(\pm\) 0.48 & \(<\)0.001 & 35.242 & 69.00 \(\pm\) 1.96 \\ F+H (1:1) vs. F+H (3:1) & 40.75 \(\pm\) 2.84 & 39.25 \(\pm\) 4.33 & 0.828 & 0.236 & 1.50 \(\pm\) 6.34 \\ F+H (1:1) vs. F+H (1:3) & 39.00 \(\pm\) 1.78 & 41.50 \(\pm\) 1.32 & 0.464 & \(-\)0.837 & \(-\)2.5 \(\pm\) 2.99 \\ \hline \end{tabular}
\end{table}
Table 2: Behavioural responses of _S. granarius_ adults in two-choice bioassay. In a row, significant differences between first and second choices are indicated by Student’s \(t\)-test.
## 3 Results and Discussion
### Vertical Variation of Ammonia Concentration and Air Temperature
#### 3.3.1 Ammonia and Temperature in Cross Section A  
In this section, the maximum variation of ammonia is 10 ppm; from a concentration of 15.4 ppm emitted by the source, it decreases to 5 ppm at the top of the farm and occurs when the wind direction is at 45\({}^{\circ}\). Nevertheless, in this wind direction, the lowest ammonia concentration is presented compared to the concentration found in the other wind directions, with a decrease from 15.4 ppm to only 7 ppm. Regarding the air temperature, when the wind direction is at 45\({}^{\circ}\), it presents greater variation and a higher temperature compared to the other two cases: the temperature ranges between 19.8 and 21.6 \({}^{\circ}\)C with the wind direction at 0\({}^{\circ}\), and with the wind direction at 90\({}^{\circ}\), the temperature reaches a maximum of 21.3 \({}^{\circ}\)C (Figure 6A).