esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 4 Prototype of UAV-EMOR and Use Scenarios
When the security manager receives notice of an emergency situation on the Security Manager app, he/she can check the extent of the danger and the size of any fires through real-time video and photos taken by indoor and outdoor drones. As shown in Figure 8d, if an abnormal temperature is detected, the control server registers the 'Unsafe' status, then transmits this status information to all the smart sensors in the building, to the indoor and outdoor drones and to the security manager smartphone. Once the security manager checks the detailed information of a fire situation, he/she can declare an emergency command by pressing the 'Emergency' button, which will set in motion the fire emergency response procedure.
## Appendix A  
The decision function can be obtained as \(\mathrm{y}(x)=\mathrm{sgn}\big{(}\omega^{T}\cdot x+b\big{)}\). To maximize the interval, it can be reduced to the following quadratic programming problem:  
\[\begin{array}{c}\max\frac{2}{\|\omega\|}\\ \mathrm{s.t.}y_{i}\big{(}\omega^{T}\cdot x_{i}+b\big{)}\geq 1\\ i=1,2,\cdots,m\end{array} \tag{1}\]  
Then, the language function can be introduced:  
\[L(\omega,\mathrm{b},\alpha)=\frac{1}{2}\|\omega\|+\sum_{i=1}^{m}\alpha_{i} \Big{(}1-y_{i}\Big{(}\omega^{T}\cdot x_{i}+b\Big{)}\Big{)} \tag{2}\]  
Then, the dual problem can be identified:  
\[\max_{\omega}\left[\sum_{i=1}^{m}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{m}\sum_{j=1 }^{m}\alpha_{i}\alpha_{j}y_{i}y_{j}x^{T}x_{j}\right] \tag{3}\]  
Then, \(\alpha\) can be determined, and, by solving \(\omega\) and \(b\), the model can be obtained:  
\[\mathrm{f}(x)=\omega^{T}x+b=\sum_{i=1}^{m}\alpha_{i}y_{i}x_{i}^{T}x+b \tag{4}\]  
where \(\alpha_{i}\) satisfies the Karush-Kuhn-Tucker condition:  
\[\left\{\begin{array}{c}\alpha_{i}\geq 0\\ y_{i}f(x_{i})-1\geq 0\\ \alpha_{i}(y_{i}f(x_{i})-1)=0\end{array}\right. \tag{5}\]  
Because some of the points appear at the interval of the two lines and do not satisfy the constraint condition of the function interval, a slack variable is introduced to represent the degree to which the sample does not satisfy the constraint:  
\[\begin{array}{c}\min\limits_{\omega,b,\varepsilon_{i}}\frac{1}{2}\|\omega\| ^{2}+C\sum_{i=1}^{m}\varepsilon_{i}\\ s.t.y_{i}\big{(}\omega^{T}\cdot x_{i}+b\big{)}\geq 1-\varepsilon_{i}\\ \varepsilon_{i}\geq 0,i=1,2,\cdots,m\end{array} \tag{6}\]  
where \(\varepsilon_{i}\) denotes the slack variable and \(C\) denotes the penalty factor (\(C>0\)).  
In the case of linear inseparability, the sample can be mapped to a higher dimensional eigenspace, so that the sample is linearly separable in said eigenspace. The model corresponding to the optimal hyperplane divided in the feature space can be expressed as follows:  
\[\mathrm{f}(x)=\omega^{T}\cdot\phi(x)+b=\sum_{i=1}^{m}\alpha_{i}y_{i}\phi(x_{i })^{T}\phi(x)+b \tag{7}\]  
Finally, a kernel function is introduced to avoid the dimensional disaster in Equation (7). Common kernel functions include linear, polynomial, and Gaussian functions (also knownas the radial basis function, RBF). Accordingly, the SVM nonlinear regression model can be expressed as follows:  
\[\mathrm{f}(x)=\sum_{i=1}^{m}\alpha_{i}y_{i}k(x,x_{i})+b \tag{10}\]
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 4 Wireless Communication System Using Chaotic Filter
### Threshold Calculation
#### 4.3.2 Computational Complexity Analysis  
In this subsection, we study the computational complexity of different wireless communication systems in terms of floating-point operations (flops). A flop is defined as one addition, subtraction, multiplication, or division of two floating point numbers, a comparison, and the usually accompanying fetch and store [27]. The flop count can be obtained by simply adding up all of the described operations. With this definition, one complex addition and one complex multiplication can be viewed as 2 and 6 flops, respectively.  
The main differences between the proposed system and the traditional non-chaotic system are the pulse-shaping filter, corresponding matched filter and symbol decision algorithm. Thus, the number of flops of these three parts are discussed and shown in Table 1. \(N_{s}\), \(N_{o}\), \(N_{f}\) and \(L\) are the length of the symbol, the oversampling rate, the tap number of the filter, and the multi-path number, respectively. The cost of executing pulse shaping for one symbol is \(N_{f}\) multiplications and \(N_{f}-1\) adds. Therefore, by omitting the lower-order terms, \(2N_{o}N_{f}\) flops are required to carry out pulse shaping after oversampling with the sampling frequency \(N_{o}\) for all three methods. Matched filtering has the same computational complexity as pulse shaping. To decode one symbol, in a non-chaotic communication system, the computational complexity of the decoding algorithm mainly caused by MMSE equalization is \(24L^{3}\) flops [28]. In CBWCS, for each optimal threshold calculation, one convolution needs \(38N_{s}\) mathematical operations, \(N_{s}-1\) multiplications and \(N_{s}-2\) additions, while in the proposed method, because the threshold has a simpler impulse response expression, the flops of one convolution are reduced to \(17N_{s}\) mathematical operations, \(N_{s}-1\) multiplications and \(N_{s}-2\) additions. Multiplying by the multi-path number and omitting the lower-order terms, the computational complexity of threshold calculation in CBWCS and the proposed system are \(40N_{s}L\) flops and \(19N_{s}L\) flops, respectively. The reduction of \(52.5\%\) flops in threshold calculation means that the proposed method has a performance advantage in terms of overall computational complexity.  
In threshold calculation, because the response of \(R(t)\) will rapidly decay to zero, \(\theta_{opt}\) only depends on the current and future \(N_{p}\) non-zero coefficients of \(R(t)\). In CBWCS, for \(N_{p}=5\), the requirement is satisfied, and in the proposed method, \(N_{p}=6\). Thus, for \(L\geq 3\), the RRC with MMSE has the highest computational complexity, while the proposed method has the key advantages of involving less computational complexity and a simpler structure of the decision threshold.
# Second-Harmonic Generation in Mie-Resonant GaAs Nanowires
## 3 Second-Harmonic Generation: Scattering and Absorption Efficiencies
The field distributions for pump and SH in this scenario are reported in Figure 6. While the pump takes on the typical shape of a TM\({}_{1}\) mode, the SH is coupled to a superposition of modes, with the stronger components being the TE\({}_{1}\) and the TE\({}_{2}\), i.e., the modes associated with the \(b_{1}\) an \(b_{2}\) Mie coefficients, respectively.
## 2 Method Analysis
### Noise PSD Estimation Algorithm Based on Continuous Spectrum Update
Let \(\alpha^{\prime}_{i,d}(k,l)\) denote the time-frequency dependent smoothing factor of node \(i\), it can be calculated by the following equation:  
\[\alpha^{\prime}_{i,d}(k,l)=\alpha_{d}+(1-\alpha_{d})P_{i}(k,l), \tag{32}\]  
where \(\alpha_{d}\) is the fixed smoothing factor of \(\alpha^{\prime}_{i,d}(k,l)\). Let \(S_{f}(k,l)\) describe the variation of noise power spectrum, the \(S_{f}(k,l)\) of node \(i\) is computed by the following equation:  
\[S_{i,f}(k,l)=S_{imin1}(k,l)/S_{imin2}(k,l), \tag{33}\]  
When the noise power spectrum varies little, \(S_{imin}(k,l)\) is used to estimate noise PSD, otherwise the noise PSD is estimated with the time-frequency dependent smoothing factor \(\alpha^{\prime}_{i,d}(k,l)\). Then, the noise PSD is estimated through by the following equation:  
\[\sigma_{i}^{2}(k,l)=\left\{ \begin{array}{c}S_{imin}(k,l),\;1/c<S_{i,f}(k,l) <c\\ \alpha^{\prime}_{i,d}(k,l)\sigma_{i}^{2}(k,l-1)+\left[1-\alpha^{\prime}_{i,d} (k,l)\right]|Y(k,l)|^{2},\;\mathrm{other}\end{array} \right., \tag{34}\]  
where the \(\sigma_{i}^{2}(k,l)\) is the noise PSD of node \(i\), and \(c\) is the decision threshold constant.
# Multiparameter Analysis of the Ultrasonic Transducer Transfer Function Using a Genetic Algorithm
## 3 Selection of Matching Layers Using a Genetic Algorithm
Figure 3 shows two characteristics of a transducer with one matching layer, which is operated in water. In the first case, the layer impedance was Z\({}_{1}\) = 4,300,000 Rayl, and in the second case it was Z\({}_{2}\) = 7,250,000 Rayl. The impedance value of the matching layer significantly influences the shape of the transfer function. The effective transducer transmission bandwidth depends on the assumed limit value of the unevenness of the characteristics. Typically, the limit value is assumed to be \(-3\) dB with respect to the maximum value. This is the value assumed in the analysis of the transfer function for the transducer with a layer with impedance Z\({}_{1}\). At \(\Delta\)k\({}_{1}\) = 3 dB, the bandwidth is B\({}_{1}\). In the case of a transducer with a layer impedance Z\({}_{2}\), the assumed value was \(\Delta\)k\({}_{2}\) (greater than \(\Delta\)k\({}_{1}\)) and the obtained bandwidth was B\({}_{2}\) (greater than B\({}_{1}\)). This example serves as an  
Figure 3: Relationship between the bandwidth and the unevenness of the characteristics. illustration that the interpretation of the transfer function is not unambiguous and depends on the used assumptions. A note should be made here that the analysis of the transfer function should also involve the values that the function acquires within the transducer's operating bandwidth. This can serve as the basis for the evaluation of the effectiveness at which the transducer transmits the acoustic wave to a particular medium. In this case, a compromise is also necessary: an increase in the transducer's transmission bandwidth is at the cost of a decrease in its effectiveness. Having involved the above factors, the evaluation of the transducer on the basis of its transfer function was performed by assuming the following relationship (it is considered as a fitness function for the evaluation of the individuals in the genetic algorithm):  
\[\text{FP}=\alpha_{1}\;\text{B}+\alpha_{2}\;\text{K}_{\text{mean}} \tag{4}\]  
where B is the bandwidth (calculated at a defined value \(\Delta\)k) and K\({}_{\text{mean}}\) is the mean value of the transfer function calculated for bandwidth B. An appropriate selection of the values of the coefficients \(\alpha_{1}\) and \(\alpha_{2}\) allows the expected shape of the transducer's transfer function to be manipulated. Increasing the value \(\alpha_{1}\) causes an increase in the transducer's bandwidth, while increasing the value \(\alpha_{2}\) causes an increase in its effectiveness.
## 3 Overview of Wavelet Transform and Diversity Combining  
### Overview of Wavelet Transform  
The wavelet transform is one of the most popular methods of time-frequency transformations; it has multiple bases and changes the time-frequency resolution. The heartbeat pulse wave is an almost periodic signal, defined as a signal that is periodic to within any desired level of accuracy. In a normal state, a subject-dependent pulse shape repeats in time, and the shape and period of the pulse can vary slightly. Hence, wavelet transform is suitable for analysis of PPG and SDPPG signals.  
The discrete wavelet transform performs decomposition of signals into multi-resolution sub-band representations with two disjoint digital filters: a low-pass filter of impulse response \(g[n]\) in (1) and a high-pass filter of impulse response \(h[n]\) in (2). The two filters are a quadratic mirror filter, which satisfies the half-band condition [14]. Output signals down-sampled by 2 via the low-pass filter and the high-pass filter are called approximation coefficients (\(A[n]\)) and detail coefficients (\(D[n]\)), respectively. Hence, the original signal \(S[n]\) consists of the approximation and detail coefficients, as seen in (3).  
\[A[n]=(x*g)[n]=\sum_{k=-\infty}^{\infty}x[k]g[2n-k] \tag{1}\]  
\[D[n]=(x*h)[n]=\sum_{k=-\infty}^{\infty}x[k]h[2n-k] \tag{2}\]  
\[S[n]=A[n]+D[n] \tag{3}\]  
The approximation is repeatedly decomposed into two coefficients, approximation (\(A_{\text{N}}\)) and detail (\(D_{\text{N}}\)) coefficients, where \(N\) is the decomposition level of wavelet transform, and the outputs are down-sampled by 2. At the level of the transform, the signal is decomposed into low and high frequencies. Figure 6 shows the example of the 6 th level wavelet transform and the corresponding equation is expressed as:  
\[S=A_{6}+D_{1}+D_{2}+D_{3}+D_{4}+D_{5}+D_{6}\]  
Figure 5: SDPPG signal distorted by movement noise.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 5 Experimental Results and Analysis  
### Selection of Quantitative Evaluation Metrics and Evaluation Methods  
In this study, we constructed diverse evaluation metrics including retrieval accuracy, response generation quality, and user satisfaction to comprehensively verify the performance of the technical manual-based question-answering system. Retrieval performance was measured using semantic similarity metrics such as BERTScore and cosine similarity, as well as structural consistency metrics like ROUGE-L. Additionally, the system was verified through qualitative evaluation including experts familiar with large-scale technical manuals such as those used in automotive or defense settings.  
Given that procedural accuracy is particularly important in domains where operational steps must be followed precisely, we performed a manual review to qualitatively assess whether response sequences preserved correct procedural order. Through this multidimensional evaluation framework, we assessed not only technical metrics but also the system's real-world applicability and usability.
# Development and Assessment of a 4D Printing Technique for Space Applications
## 4 Discussion
### Programming  
Our investigation into the programming experiments for 4D printed hinges provides valuable insights into the intricate relationship between temperature variables, programming device design, and the subsequent performance of the SMP hinges.  
Examining Figure 7, we observed a noticeable deviation between the measured surface temperature and the temperature set on the printer's interface. This divergence is expected, given the influence of exposure time on achieving the desired temperature during programming. It is crucial to acknowledge that the thermal imaging camera used for surface temperature measurements introduces a margin of error due to variations across the programming device and hinge. Despite this inherent imprecision, the measured temperatures fell within a reasonable range relative to the set temperature on the printer's interface.  
Analyzing Figure 8, a clear correlation emerged between the temperature during folding/bending and the quality of bending results. Higher temperatures during bending yielded superior outcomes, aligning with the principles of shape memory effects and supported by the DMTA results gathered by [PERSON] et al. [12] for PEEK (see Figure 14 PEEK_neat).  
A notable distinction between the first and second iteration programming device was evident, with the second iteration consistently outperforming the first. The second iteration achieved a 100% programming ratio more frequently and at lower temperatures, underscoring the positive impact of design modifications. In Figure 9, an independent assessment of the programming results, irrespective of the measured surface temperature, vividly showcased the enhanced performance of the second version of the programming device.  
The results presented in Figures 10 and 11 highlight the profound influence of programming on hinge recovery. Inadequate programming led to insufficient recovery, emphasizing the critical importance of precision in the programming phase. Conversely, when the programming was optimal, as seen in experiment 2, recovery became almost independent of the recovery temperature. This observation underscored the interdependence of programming and the subsequent recovery processes in achieving the desired shape memory effects.
## 3 Protection Method Combining the Stator and Rotor Currents
### Protection Criterion
To ensure the rapidity of the protection, the selected area calculation time window is 0.02 s (one power frequency cycle). The stator phase current of the VSPSGM is measured by the electromagnetic current transformer, and the rotor phase current is measured by Hall effect or optical current transformer. Both of them are synchronously measured and the sampling rate is consistent. One power frequency cycle is sampled \(N\)+1 times. In a power frequency cycle, \(g[1]\), \(g[2]\), \(\ldots\), \(g[N\)+1] can be obtained by the difference between \(h^{2}f(i_{\text{d}r},i_{\text{q}r})\) and \(f(i_{\text{d}s},i_{\text{q}s})\). The area calculated by the trapezoidal rule is:  
\[S_{op}=\sum_{i=1}^{N}\frac{0.01}{N}(|g[i]|+|g[i+1]|) \tag{18}\]  
Furthermore, the protection criterion can be expressed as:  
\[S_{op}>S_{set} \tag{19}\]  
where \(S_{op}\) is the protection action value, \(S_{set}\) is the protection setting value. To ensure the reliability of the proposed protection method, the protection setting value should avoid the maximum action value \(S_{op,normal}\) of the VSPSGM under normal operation. The steps of the proposed method are as follows:  
1. The instantaneous values of the three-phase stator and rotor currents are measured synchronously, and the dq-axis stator and rotor currents are obtained by using the Clark transform. This process is the product operation of the three-dimensional matrix, the computational burden is small, and can be realized quickly. 2. According to Equations (12), (15) and (16), \(g(t)\) is calculated by using the dq-axis stator and rotor currents, which is no longer equal to 0 after a rotor winding short-circuit fault occurs. This process is a simple algebraic operation; the computational burden is small and can be realized quickly. 3. The protection action value \(S_{op}\) is calculated according to Equation (18), and the protection outlet mode is determined according to the size of the protection action value. This process requires the protection device to have a memory storage function, which is easy to implement. The existing protection device can store the intermediate  
Figure 3: \(f(i_{d,s},i_{q,s})\) and \(h^{2}f(i_{d,r},i_{q,r})\) obtained by Clark transform. calculation results in RAM, and can quickly exchange data with the CPU. Thus, the protection action value in Equation (18) can be obtained quickly by invoking the stored results. In addition, this process is a discrete integral operation, its algorithm is not complicated and can be realized quickly. In summary, the computational burdens of the proposed method are small, and the conventional protection device can realize its function.
## 3 Results and Discussions
### Effect of Solid Concentration on MP Retention
In addition, MPs isolated from the sludge samples were analysed according to the following size ranges: 19.3 \(\pm\) 3.9% (>500 \(\upmu\)m), 47.5 \(\pm\) 6.8% (500-250 \(\upmu\)m), 29.0 \(\pm\) 5.1% (250-100 \(\upmu\)m), and 4.2 \(\pm\) 1.9% (100-20 \(\upmu\)m) (Figure 7b). The percentage of MP retention in the range between 100 and 500 \(\upmu\)m increased by around 7% compared to the initial sewage sludge. After the pre-treatment (screening and degreasing system) in a WWTP, the majority of MPs contained in the wastewater had a size between 100 \(\upmu\)m and 500 \(\upmu\)m, which is beneficial for their removal in the biological step (Figure 8) [1; 56]. It is noteworthy that the percentage of MPs smaller than 100 \(\upmu\)m in the settled sludge was around half of the initial amount, indicating that the efficacy of retaining these smaller MPs is much lower compared to MPs with sizes closer to those of the flocs. The main characteristics of the MPs remaining in the supernatant samples are reported in Table S2. In the tests carried out with coagulant and flocculant agents, it can be observed that fibres and fragments represent around 75% and 20%, respectively, in the supernatant samples. In contrast, fibres and fragments accounted for 59% and 40%, respectively, in the supernatants of samples without chemical treatment. Therefore, it seems that the use of chemical agents improves the removal of fibres. With respect to the average size of the MPs recovered in the supernatant samples, no important differences were observed between the tests with and without chemical agents. Figure 6: Number of MPs retained in the settled sludge (given per mass of TSS) versus MP concentration in the supernatant. Figure 7: Comparison of the abundance of MP shapes (**a**) and sizes (**b**) depending on the different sludge concentrations.
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 7 Conclusions  
In this paper, a hybrid position-force PID controller, a type-I fuzzy controller, and a type-II fuzzy controller were designed and implemented to grasp an object through a three-fold arm with an independent two-finger gripper. From the results, it can be concluded that the hybrid PID controller generated oscillations in stages 2 and 3 of manipulating the object due to switching between the position PID and the force PID. On the other hand, few oscillations were generated over the three stages of object manipulation with the type-II fuzzy controller. Moreover, it was able to calculate values close to the references in terms of position for both fingers; but in terms of force, it was able to calculate the appropriate value only for the left finger. As for the type-I fuzzy controller, it obtained the best performance, because few oscillations were generated in the three stages of object manipulation, and its \(t_{r}\) and \(t_{s}\) values are practically the same. Despite the type-II fuzzy controller having a lower \(t_{r}\) in the position controller, it had a higher \(t_{s}\) in the force controller and it had more oscillations. The type-I fuzzy controller was able to calculate values close to the reference values in terms of position and force for both fingers.  
Conceptualization, S.H.-M., A.M.-H. and E.R.P.-H. Formal analysis, E.R.P.-H., E.J.R.-R. and H.V.-L. Investigation, S.H.-M. Supervision, A.M.-H. and E.R.P.-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that they have no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller** & **IAE** & **ITAE** \\ \hline Hybrid PID & 10,278.5 & 47,901.75 \\ \hline Type-1 FLC & 684 & 6366.08 \\ \hline Type-2 FLC & 4864.25 & 31,878.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison among the hybrid PID controller, and the type-I and type-II fuzzy controllers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Controller** & **Position MR** & **Position ML** & **Force SR** & **Force SL** \\ \hline \multirow{4}{*}{\(t_{r}\)} & PID & 2.93 & 6.53 & 11 & 3.31 \\ \cline{2-6}  & Fuzzy I & 3.009 & 3.000 & 3.00 & 3.00 \\ \cline{2-6}  & Fuzzy II & 2.705 & 3.31 & 3.91 & 3.31 \\ \hline \multirow{4}{*}{\(t_{s}\)} & PID & 3.67 & 10.76 & 7.5 & 4.5 \\ \cline{2-6}  & Fuzzy I & 3.31 & 3.5 & 3.3 & 4.2 \\ \cline{2-6}  & Fuzzy II & 3.61 & 3.9 & 6 & 4.5 \\ \hline \multirow{2}{*}{Setpoint} & \multicolumn{2}{c}{3.4 rad} & 1.8 rad & 60 mV & 60 mV \\ \hline \multicolumn{6}{l}{Response} & Overdamping & Overdamping & Overdamping \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances \(t_{s}\) and \(t_{r}\) in a hybrid PID controller, and in type-I and type-II fuzzy controllers.