esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Disaster Response System (ADRS)
### Emergency Broadcast System  
In our prototype, we designed and implemented an emergency broadcast system that simulates the Central Weather Bureau to publish warning messages. We adopt PubNub's real-time push service [25] to push warning messages to Tibbo devices and smart phones in a building. As Figure 2 shows, PubNub can deliver hundreds of thousands, even millions, of real-time messages to devices at the same time by utilizing cloud infrastructure and services. In our implementation, we used a smart phone to act as an emergency broadcast system that publishes disaster warning messages. For this, we first registered a PubNub account to get its own subscribe key and publish key. We then used the publish key and the subscribe key to publish and retrieve warning messages with PubNub APIs (Application Programming Interfaces), shown in Table 1.  
Figure 3a shows the user interface of the emergency broadcast system on an Android smart phone. This mobile emergency app (application) provides key functions to publish warning message, such as  
\begin{table}
\begin{tabular}{c c} \hline
**API name** & **Description** \\ \hline PubNub & To create PubNub service, put parameters as publish\_key and subscribe\_key \\ Publish & To publish messages, put parameters as channel\_name, messages \\ Subscribe & To subscribe messages, put parameters as channel\_name, callback function \\ \hline \end{tabular}
\end{table}
Table 1: PubNub APIs [25] used in the active disaster response system (ADRS).  
Figure 1: Active disaster response system flow chart.  
publish, subscribe, unsubscribe, history records, and so on. Figure 3b shows a warning message about an earthquake received by an end user.
# Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight
## 6 Optimization Results and Analysis
### Sensitivity Analysis for Optimal WGP  
Given the optimized WGP listed in Table 3, the effect of a perturbing single parameter on lift-to-weight ratio and power density is analyzed to gain insight of the reason why the optimized WGP can produce power-efficient lift to maintain the insect's aloft hovering. It is necessary to dissect how the most power-efficient lift-to-weight ratio changes with one parameter of WGP while keeping other parameters unchanged and equal to their optimal values. As shown in Figure 8a-c, the lift-to-weight ratio and power density consistently and monotonically increase with wing length (\(R_{\text{eff,vari}}\)), average chord (\(C_{\text{aver,vari}}\)) and wing-root offset (\(x_{\text{r,vari}}\)), although there is an interplay between WGP via \(AR\) except for the parameter for non-dimensional location of pitch axis (\(\hat{x}_{0,\text{vari}}\)). This indicates that the conflict between lift-to-weight ratio and power density is unavoidable because additional  
Figure 7: The approximate hovering steady wingbeat motion (**a**); pitch and flapping power output of single wing (**b**,**c**) for optimal wing geometry parameters (WGP).  
lift production must result in an increase of power consumption. Thus, the optimal values for these parameters, which are loosely restricted by approximate nonlinear equality constraint (\(LtoW\equiv 1\)), were found by the resulting simplex algorithm. The lift-to-weight ratio and power density consistently and monotonically decrease with \(\hat{x}_{0,\text{vari}}\) (Figure 8d), which arises from the fact that lift changes with \(\hat{x}_{0,\text{vari}}\) due to variation of rotational circulation coefficient (\(C_{\text{R}}\) ) with \(\hat{x}_{0,\text{vari}}\)[(64; 32)] although the inertial power shall reduce with the pitch axis shifting to trailing edge. Thus, the optimal value of \(\hat{x}_{0,\text{vari}}\) with maximum rotational circulation coefficient is a reasonable value under primary condition of lift balancing weight.  
Furthermore, the sole optimization results of WGP for 2D translational aerodynamic coefficients (Table 3) are also used to plot instantaneous power output (as seen in Figure S1 of Supplementary Materials for compactness). The sensitivity analysis of the single parameter is shown in Figure S2. No obvious distinction is observed in the visualization results involving 3D and 2D translational aerodynamic coefficients although there is a small difference in amplitude, which directly indicates that the adopted 3D translational aerodynamic force coefficients have little effect on the eventual optimization results of WGP and indirectly shows that the variation of wing shape only brings little influence on lift and power density by probable way of influencing translational aerodynamic coefficients.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 3 Methods: A Robust Analog Filter Design Using Evolutionary Algorithms
### Evaluation Criteria  
For all evolved filters, we used the following criteria to evaluate their robustness and functional performance. For each candidate filter, we run SPI = 10,000 simulations, each with different perturbations of its component parameters. This SPI number is chosen so that the statistical values from a set of simulations remain stable. The perturbation is modeled as Gaussian noise \(N(\mu,\sigma)\) with the mean \(\mu\) set to 0 and the standard deviation \(\sigma\) set to 10-50% of parameter values, in steps of 10%.  
There are two ways to calculate the robustness of a candidate design. One approach is the definition of robustness fitness as used by [PERSON] (2016) and defined in Equation (4). This Type-I robustness \(f_{robust1}\) definition uses the average (or sum) of the total 101-point deviation from the target frequency response curve for SPI simulations as the final robustness. The benefit of this robustness definition is that it incorporates requirements on both performance and its variation: increasing mean performance and reducing performance variation both lead to increased robustness. This measure is especially good for evolving robustness during which the absolute performance is also important in addition to the stability of functions. The disadvantage of this definition is that it cannot distinguish the robustness if a specified level of performance is satisfied by all solutions being compared. For such a case, it is more desirable to use the standard deviation of the 101-point deviations from the target frequency response curve for all 10,000 simulations as the robustness criterion. We call this robustness definition Type-II robustness. Note that this is not the other form of robustness (to component failure) but rather another definition of robustness to parameter variation. The ideal case is to use a multi-objective method to separate performance and its variation completely, as suggested by [PERSON] (2016) and [PERSON] et al. (2018). We defined this Type-II robustness criterion as in Equation (5). In this paper, both criteria will be evaluated.  
\[f_{robust1I}=\sqrt{\frac{\sum\limits_{k=1}^{ SPI}(f_{raw}^{k}-\bar{f}_{raw})^{2}}{SPI}} \tag{5}\]  
where \(\bar{f}_{raw}\) is the average 101-point-deviation from the target frequency response curve; \(f_{raw}\) is the 101-point-deviation of a single simulation from target frequency response curve; SPI is the number of simulations used to evaluate the fitness of an individual.
## 4 Experiments
### Experimental Results  
Table 4 presents the solving accuracy of **PROP** and all baseline algorithms across six datasets. For each dataset, our experiments constructed both Chinese and English versions using Baidu Translate (available at: [[https://fanyi.baidu.com](https://fanyi.baidu.com)]([https://fanyi.baidu.com](https://fanyi.baidu.com)), accessed on 15 March 2024). **PROP** was compared with the baseline algorithms on both the Chinese and English versions of the datasets.  
Table 4 discloses three key findings.  
* **PROP** achieves an average accuracy of 90.4% on Chinese datasets and 90.3% on English datasets, outperforming the best Seq2 Seq-based algorithm (**JieAlg**) by 6% and 10.9%, and outperforming the best LLM-based algorithm (**LiuAlg**) by 1.1% and 0.7%. The differences between the Chinese and English versions are mainly due to translation errors, which are reflected in the performance of all algorithms.
* **PROP** (90.4%) achieved an improvement of 31.9% and 34.6% on the Chinese and English datasets, respectively, compared to **YuAlg** (58.5%), despite both using a relation-flow approach to solve AWPs. This significant improvement indicates that **PROP**'s relation-flow approach, empowered by LLM, has a broader solvable range for AWPs.  
* **PROP** (90.3%) outperformed **YasuAlg** (86.1%) and **LiuAlg** (89.6%) on the English dataset by 4.2% and 0.7%, respectively. This demonstrates that symbolic reasoning based on relation sets effectively reduces errors compared to LLM directly reasoning based on natural language or invoking code interpreters.  
To further verify that the relation-flow approach can enhance the problem-solving performance of LLMs, this experiment compared **LiuAlg** (directly using the LLM for solving) with **PROP** (using relations for solving) by adopting different LLMs: gpt-3.5-turbo and qwen-Max-0428. To reduce the cost, this paper tests their solving accuracy across three small datasets in the Chinese version, and the results are presented in Table 5. From this table, it can be seen that for the same LLM, combining it with the relation-flow approach can effectively improve solving accuracy. Additionally, larger scale and more advanced training methods can provide LLM with more comprehensive knowledge to decompose complex scenes.
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 2 System Model
### 802.11b Packet Structure  
The 802.11b protocol specifies that the transmitted packet contains PLCP Preamble, PLCP Header, and PSDU. Among them, PSDU has variable length and contains a MAC frame in the CRCScatter system. The MAC frame consists of a MAC header, a frame body, and the frame check sequence (FCS). The frame body field carries the transmitted data, and the CRC32 sequence in the FCS field can detect bit errors in the MAC frame. The part of the packet other than the data segment contains packet control information; if these fields are modified by tag, then the receiver may not be able to demodulate correctly. To receive and demodulate properly, the CRCScatter tag only modulates the frame body field in the packet. The received backscatter packet structure is shown in Figure 3.  
Figure 3: The structure of received backscatter packet.  
Figure 2: CRCScatter system model.
## 4 Computational Testing Experiments
For the small-sized group, low values of RDI and MAD are indications that all VNS algorithms give good performances. In Table 4, computational times (CPU times) of instances are also calculated. We can find the CPU time of CPLEX exponentially increases as the number of jobs increases. Meanwhile, CPLEX could not search an optimal solution for problems over 7-8 jobs in the given time limit. In Table 5, the performance results of the large-sized group are summarized. The average RDI and MAD of VNS_D is lower than those of VNS_S. The results indicate that VNS algorithms with a local search with dynamic case selection probability significantly improve the performance of the algorithms compared to VNS algorithms with a local search with static case selection probability. The CPU times of each VNS algorithm are short enough to obtain the best solution. The observed differences between two local search schemes are more statistically significant as tardiness factors decrease. This result  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Test Problems**} & \multicolumn{3}{c}{**CPLEX**} & \multicolumn{3}{c}{**VNS\_S**} & \multicolumn{3}{c}{**VNS\_D**} \\ \hline \(\delta\) & \(J\) & \(M\) & \(T\) & \(C\) & **Obj. ** & **CPU** & **RDI** & **MAD** & **CPU** & **RDI** & **MAD** & **CPU** \\ \hline \(0.1\) & 5 & 3 & 2 & 4 & 918 & 2.52 & 0.00 & 0.00 & 0.02 & 0.00 & 0.00 & 0.05 \\  & 5 & 4 & 3 & 3 & 1068 & 1.55 & 1.00 & 0.00 & 0.02 & 1.00 & 0.00 &  
indicates that VNS_D gives a better performance for the proposed scheduling problem as the due date becomes more tightly controlled. To compare the performances of the VNS algorithms with the other meta-heuristics, we tested GA-based algorithms with the same problem sets. The performance results are also presented in Table 5. The tested GA is a single-stage algorithm with independent dispatching rules. It uses a chromosome representing two string arrays for machine and truck scheduling and one string array with job batching. In this article, VNS algorithms proposed give better performance than GA in any job size and any tardiness factors. The results indicate that VNS algorithms improve the performance by broadly exploring the solution space compared with GA.
## 4 Simulation Tests Using Watermark Replay Channel Simulator
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Spreading** & **BER** & **Transmission** & **BCH code** & **Transmission** & **BCH Code** & **Transmission** \\
**Sequence** & **No ECC** & **Rate** & **Rate** & **Rate** & **Rate** & **Rate** \\  & & **No ECC** & **BER \textless{} 0.01** & **BER \textless{} 0.01** & **BER \textless{} 0.001** & **BER \textless{} 0.001** \\ \hline \multicolumn{8}{c}{**Bandwidth = 1 kHz**} \\ \hline m-sequence & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline Kasami code & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline Gold code & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 2 kHz**} \\ \hline m-sequence & \textless{}0.001 & 7.84 bps & — & 7.84 bps & — & 7.84 bps \\ \hline Kasami code & \textless{}0.001 & 7.84 bps & — & 7.84 bps & — & 7.84 bps \\ \hline Gold code & \textless{}0.001 & 7.84 bps & — & 7.84 bps & — & 7.84 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 4 kHz**} \\ \hline m-sequence & 0.35 & 15.69 bps & 12/2047 & 0.09 bps & \textgreater{}13/4095 & \textless{}0.05 bps \\ \hline Kasami code & 0.22 & 15.69 bps & 7/63 & 1.74 bps & \textgreater{}10/511 & 0.31 bps \\ \hline Gold code & 0.35 & 15.69 bps & 12/2047 & 0.09 bps & \textgreater{}13/4095 & \textless{}0.05 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 5 kHz**} \\ \hline m-sequence & 0.37 & 19.61 bps & 12/2047 & 0.11 bps & \textgreater{}13/4095 & \textless{}0.06 bps \\ \hline Kasami code & 0.33 & 19.61 bps & 12/2047 & 0.11 bps & \textgreater{}13/4095 & \textless{}0.06 bps \\ \hline Gold code & 0.37 & 19.61 bps & 12/2047 & 0.11 bps & \textgreater{}13/4095 & \textless{}0.06 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 8 kHz**} \\ \hline m-sequence & 0.40 & 31.37 bps & \textgreater{}13/4095 & \textless{}0.1 bps & \textgreater{}13/4095 & \textless{}0.1 bps \\ \hline Kasami code & 0.38 & 31.37 bps & \textgreater{}13/4095 & \textless{}0.1 bps & \textgreater{}13/4095 & \textless{}0.1 bps \\ \hline Gold code & 0.40 & 31.37 bps & \textgreater{}13/4095 & \textless{}0.1 bps & \textgreater{}13/4095 & \textless{}0.1 bps \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of simulation tests using Watermark software; NCS1 channel.
## 3 The Proposed Control Strategies
### Case 2: Circulating Currents with a dc Component and Sinusoidal Second-Order Frequency  
More detailed circulating currents could achieve more accurate control laws for the proposed control strategy. In this subsection, the circulating currents consist of the dc component and sinusoidal second-order frequency as shown in (9):  
\[i_{cirk}=I_{dc\_cir}+I_{mci\prime 2}\cos\biggl{(}\omega_{2}t+n\frac{2\pi}{3}+ \alpha_{ci\prime 2}\biggr{)}. \tag{9}\]  
Figure 2: Proposed control strategy under zero circulating currents: (**a**) harmonic components of control laws and (**b**) the control laws.  
In this state, the relationships (1), (2), (6), and (9) are used to drive the following upper and lower arm currents:  
\[\begin{array}{c}i_{u(l)k}=0.5\sum\limits_{j=1,5,7}^{\infty}I_{mj}\cos\Bigl{(} \omega_{j}t+n\frac{2\pi}{3}+\beta_{j}\Bigr{)}\pm\\ \frac{i_{tt}}{3}\pm I_{dc\_cir}\pm I_{mci2}\cos\Bigl{(}\omega_{2}t+n\frac{2\pi} {3}+\alpha_{ci2}\Bigr{)}.\end{array} \tag{10}\]  
As can be seen from (10), the second-order harmonic appears in both the upper and lower arm currents. This means that asymmetric currents pass through the upper and lower arms of the considered MMC that should be perceived in the proposed shift level PWM (SLPWM).  
These currents can be incorporated into proposed control laws by substituting (10) into the dynamic Equations (3) and (4):  
\[\begin{array}{c}v_{u(l)k}=\frac{v_{dc}}{2}-R_{t}\frac{i_{dc}}{3}-R_{t}I_{dc \_cir}-L_{t}\frac{i_{dc}}{3}\pm\left[(0.5L_{t}++L)\sum\limits_{j=1,5,7}^{ \infty}I_{mj}\omega_{j}\sin\Bigl{(}\omega_{j}t+n\frac{2\pi}{3}+\beta_{j} \Bigr{)}\right]\\ +L_{t}I_{mci2}\omega_{2}\sin\Bigl{(}\omega_{2}t+n\frac{2\pi}{3}+\alpha_{ci2} \Bigr{)}\mp\left[(0.5R_{t}+R)\sum\limits_{j=1,5,7}^{\infty}I_{mj}\cos\Bigl{(} \omega_{j}t+n\frac{2\pi}{3}+\beta_{j}\Bigr{)}\right]\\ -R_{t}I_{mci2}\cos\Bigl{(}\omega_{2}t+n\frac{2\pi}{3}+\alpha_{ci2}\Bigr{)}\mp \left[\sum\limits_{j=1,5,7}^{\infty}v_{mj}\cos\Bigl{(}\omega_{j}t+n\frac{2\pi} {3}\Bigr{)}\right]_{.}\end{array} \tag{11}\]  
The proposed control strategy under circulating currents with a dc component and second-order harmonic is illustrated in Figure 3. As can be deduced from this figure, the harmonic components of the designed control technique are divided in two parts. The first part belongs to the same harmonic components as those due to zero circulating currents. In this part, the MMC output currents and voltages cause these harmonic components.  
Figure 3: _Cont._  
The second part is formed by the second-order harmonic for circulating current, as depicted in Figure 3. In addition, the dc component of circulating currents will impact on the proposed control laws according to Figure 3b. More detailed circulating currents used in the proposed control laws can lead to more accurate PWM reference waveforms.
## 8 Do Coumarins Have an Impact on Food Safety?
Although the use of coumarin from dietary sources is generally considered harmless, there have been rare reports of its hepatotoxicityin humans [301; 302]. It should be noted that cinnamon is the most significant source of coumarin in our diet, and that the coumarin concentration in _Cassia cinnamon_ was found to be substantial, ranging from 16 to 25 mg/kg [303]. However, there are other significant sources of coumarin in alcoholic beverages, such as _Hierochloe odorata_ (holy grass) [121; 304]. Nevertheless, and for security reasons, the use of synthetic coumarin as a food flavouring agent was discontinued in 1954 due to its hepatotoxic properties, leading to its banning [285; 302]. In Europe, the limits set for coumarin content in food products are in the range of 5 to 50 mg/kg. Coumarin's tolerated daily intake (TDI) is set at 0.1 mg/kg of body weight [305], and this value was theoretically calculated from the no-observed-adverse-effect level (NOAEL) for hepatotoxicity in both animal and human data gathered regarding coumarin pharmaceutical use [301; 302]. It is noteworthy that the influence on food safety mainly depends on the coumarin concentration in specific food products and their compliance with regulatory criteria, rather than the presence of coumarins in general. To ensure food safety, as with any food component, moderation is key, and it is advised that people follow dietary guidelines and limits set by local authorities for this purpose. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**n-**} & \multirow{2}{*}{**Test Type/Route**} & \multirow{2}{*}{**Organism**} & \multirow{2}{*}{**Dose**} & \multirow{2}{*}{**References**} & \multirow{2}{*}{**Remarks**} \\  & **Octaol/Water** & & & & & & \\  & **(log KOW)****a** & & & & & & \\ \hline \multirow{4}{*}{coumarin} & \multirow{4}{*}{1.39} & LD50/oral & Rat & 293 mg/kg & [287] & May cause \\  & & LD50/oral & Mouse & 196 mg/kg & [288] & irritation. Harmful \\  & & LD50/intraperitoneal & Mouse & 220 mg/kg & [289] & if swallowed: \\  & & LD50/subcutaneous & Mouse & 242 mg/kg & [288] & causes liver injury \\  & & LD50/oral & Guinea pig & 202 mg/kg & [290] & and somnolence \\ \hline \multirow{4}{*}{umbelliferone} & \multirow{4}{*}{1.03 (TOXNET)} & \multirow{4}{*}{LD50/intravenous} & \multirow{4}{*}{Mouse} & \multirow{4}{*}{450 mg/kg} & \multirow{4}{*}{[291]} & Causes liver effects, \\  & & & & & & \\  & &
# Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites
## 2 Experimental Procedure
### Acoustic Emission Features  
The acoustic emission descriptors considered for this study are explained in this section. First, the most commonly used parameter, namely, peak amplitude, was used. It represents the largest voltage peak of the recorded AE signal (\(U_{max}\)) relative to the reference voltage (\(U_{ref}\)) set at the pre-amplifier. It is measured in decibels (dB) and it can be expressed as:  
\[A\ =20\log\frac{U_{max}}{U_{ref}}. \tag{1}\]  
Figure 2: Experimental setup with AE sensors mounted on the specimen.  
Unlike the peak amplitude, which only represents the largest voltage peak, the average signal level (ASL) measures both the negative and positive amplitudes in an acoustic signal with equal weights. In simple words, it provides the average level of the signal in decibels. It can be expressed in volts as:  
\[\text{ASL}_{\text{v}}=\ \frac{1}{T}\int_{t_{0}}^{t_{0}+T}|V_{i}|dt=\ \frac{1}{N}\sum_{n=1}^{N}|V_{i}(n)|\, \tag{2}\]  
where \(N\) is the total number of discretized signal points, \(V_{i}\) is the transient voltage, and \(T\) is the duration of the signal. The ASL in dB can be expressed as:  
\[\text{ASL}=20\log\left(\frac{\text{ASL}_{\text{v}}}{1\ \text{uV}}\right). \tag{3}\]  
The I-Frequency measures the characteristic frequency of the acoustic signal before the peak amplitude is recorded. It is the ratio of the total number of acoustic counts before the peak amplitude (P.Counts) and the duration of the signal for that same instant (Rise time).  
\[\text{I}-\text{Frequency}\ =\ \frac{\text{P.Counts}}{\text{Rise time}}. \tag{4}\]  
R-Frequency is the exact opposite measure of the I-Frequency. It measures the characteristic frequency of the signal after the peak amplitude. It is the ratio of the number of counts after the peak amplitude (Ringdown Counts) and the duration of the signal after that same instance:  
\[\text{R}-\text{Frequency}=\ \frac{\text{Ringdown Counts}}{\text{ Duration}\ -\ \text{Rise time}}. \tag{5}\]  
A-Frequency is the ratio of the total number of counts recorded above the threshold of acquisition to the duration of the signal:  
\[\text{A}-\text{Frequency}\ =\ \frac{\text{Counts}}{\text{ Duration}}. \tag{6}\]  
The peak frequency of an AE signal is the frequency with the largest magnitude when the signal coefficients are analyzed in their frequency domain using a fast Fourier transform (FFT).  
These are the parameters that were considered for this study. The method for selecting the best parameters among the aforementioned parameters and the procedure followed for clustering the selected parameters are presented in the next section.
# Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
## 2 Materials and Methods
### Classification Methods and Explainable Techniques  
Different classifiers were used to predict hate speech on the Google Jigsaw data set, namely, artificial neural network (ANN) [29], multilayer perceptron (MLP) [30], decision trees, KNN, random forest, multinomial naive Bayes, logistic regression, and long short-term memory (LSTM). Explainability was described on the HateXplain Dataset using BERT and LIME. We briefly discuss LSTM, BERT, and LIME in this section.