esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Results and Analysis  
#### 5.4.1 Evaluation Metrics  
This paper adopts the mean average precision (mAP) as the evaluation metric. It is the area under the precision and recall (true-positive rate) curve, calculated according to Equation (5), at different intersection-over-union (\(IoU\)) thresholds. mAP_0.5, at a 0.5 intersection-over-union (\(IoU\)) threshold, is commonly used as the evaluation metric. In addition, mAP_0.5:0.95, which is the average mAP over multiple \(IoU\) thresholds, can affect the model, resulting in better performance. Therefore, both metrics are considered in the training and testing procedures to evaluate the performance of charging station detection.  
\[IoU=\frac{\text{Area of Overlap}}{\text{Area of Union}} \tag{5}\]  
\[AP=\int_{0}^{1}\left(Precision\times Recall\right)\,d(Recall) \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
Here, \(TP\), \(FP\), and \(FN\) are the true-positive, false-positive, and false-negative results of the predicted bounding box, respectively.
## 2 Reconfigurable RFID Sensor for Detecting Solvent Vapors/Gas  
### Operating Principles of the Chipless RFID Sensor  
Generally, in a chipless sensor system based on electromagnetic transduction, two main modules can be identified. A reader sends an interrogation signal and a sensor tag responds with a backscattered signal in a spectrum containing the sensed data [31]. The chipless RFID sensor reader initiates the operation by sending an interrogation signal to the chipless RFID sensor tag. The chipless RFID tag, which serves as a sensing unit, then immediately reflects the impact signal, which generates a backscattered signal containing the sensed information. The scattered signal is then received and analyzed by the reader. The electromagnetic transduction employs an RF transducer to convert the physical parameters that are being sensed into RF parameters that can be easily detected by remote systems. When an RF sensor is deployed in an environment to detect the presence of or changes in a specific natural phenomenon, its RF parameters such as the resonant frequency or RF impedance will change accordingly. A change in RF parameters subsequently affects the sensor response such as its radar cross section (RCS). This in turn affects the spectral signature of the scattered signal, which carries information about the physical parameters that are being sensed such as temperature, the presence of gas and its type, moisture, etc. The modified scattered RF signal is then received by the RFID reader and the transferred information is measured and analyzed.
## 2 Results and Discussion  
The microarray and RT-qPCR platforms have facilitated a better understanding of transcriptional changes due to treatment of MRSA with eugenol, as correlated with expression levels. To determine the genomic effect of eugenol on MRSA, we first exposed exponentially growing cells of MRSA to 0.0012 mg/mL eugenol (dissolved in 10% DMSO), and concurrently demonstrated growth inhibition of MRSA at 60 min. Tables 1 and 2 below list some of the important metabolic pathways that fluctuated upon treatment of MRSA with eugenol.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Probe Name** & **Gene ID** & **Gene Name** & **Functional Class** & **Protein Name** & **Pathway Description** & **Fold Change** & \(p\)**-Value** & **Regulation** \\ \hline CUST\_3705\_P4H28639740 & SAHV\_0568 & Carbohydrate metabolism & \begin{tabular}{c} Hypothetical protein \\ 3-heavoles-6-phosphate \\ synthase \\ \end{tabular} & Pentose phosphate pathway & 2.5 & 0.02 & Up \\ \hline CUST\_5451\_P4H28639740 & SAHV\_1404 & Complement & Hypothetical protein & & 5.4 & 0.003 & Down \\ \hline CUST\_1524\_P4H28639740 & SAHV\_1631 & Obj & Ribosome biogenesis & GTPases & GTP-binding protein & 3.9 & 0.03 & Up \\ \hline CUST\_1991\_P4H28639740 & SAHV\_2118 & \begin{tabular}{c} Amino acid metabolism; \\ cysteine and methionine \\ metabolism \\ \end{tabular} & \
\begin{tabular}{c} \(S\)-ribosylhomocytinase \\ \end{tabular} & Cysteine and methionine metabolism & 2.1 & 0.01 & Down \\ \hline CUST\_54540\_P4H28639740 & SAHV\_1404 & Complement & Hypothetical protein & & 4.6 & 0.04 & Down \\ \hline CUST\_2569\_P4H28639740 & SAHV\_0045 & Complement & Hypothetical protein & & 2.2 & 0.02 & Down \\ \hline CUST\_6663\_P4H28639740 & SAHV\_1949 &
\begin{tabular}{c} Complement \\ \end{tabular} & Hypothetical protein & & 2.9 & 0.04 & Down \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of genes expressed in response to eugenol treatment of methicillin-resistant _Staphylococcus aureus_, and their fold changes, \(p\)-values, and functional classifications; FC: fold change.
## 1 Introduction
In contrast, heat sinks, heat pipes, and active cooling techniques are primarily designed to enhance heat dissipation to the ambient environment rather than directly controlling the internal thermal resistance of the system. In terms of cost, thermal vias are considered a cost-effective solution, as they can be implemented using standard substrate fabrication processes without requiring additional components. Also, circuit layouts and substrate dimensions remain unchanged with the implementation of vias, enabling their integration into various device form factors. Therefore, the integration of vias is a practical and scalable thermal management solution in compact, high-power LED devices. The optimization of via configurations is essential for enhancing heat dissipation efficiency [31; 32; 33; 34; 35]. For instance, the heat transfer performance of vias improves when positioned close to heat sources, such as directly beneath LEDs rather than in peripheral regions. However, the number of vias is constrained by the minimum manufacturable feature size of the holes and the required spacing between them. In addition, increasing via density can introduce fabrication complexity, imposing further constraints on their practical implementation. Furthermore, localized heat accumulation caused by low substrate thermal conductivity or weak interlayer adhesion may offset the benefits of increasing via number and density. In detailed thermal via configuration, parameters such as dimensions, location, number, and even misalignment due to manufacturing tolerances are interdependent and thus require comprehensive analysis. However, previous studies have often addressed these factors individually using simplified models. Furthermore, these parameter studies have rarely been evaluated in relation to the associated fabrication cost and complexity. As discussed later, the marginal thermal gains from increasing the number of vias gradually decrease, which reduces efficiency relative to the increased fabrication complexity. In this study, we analyze the temperature reduction in high-power LEDs under various thermal via designs using three-dimensional (3D) finite element analysis (FEA). We investigate the peak value and distribution of temperature in the LED and the substrate under various via configurations, including their number, pitch (center-to-center distance), and positional misalignment. We found that the LED temperature saturates as the number of vias increases. For a given number of vias, comprehensively adjusting their pitch further reduces the LED temperature. Misalignment of the via array significantly degrades heat dissipation performance, particularly at lower via densities. This study provides a systemic approach to designing thermal via layouts while accounting for fabrication reliability in high-power LED systems. Such an approach can also be applied as a versatile framework for pixel-level thermal management in a wide range of thermally constrained applications.
## 4 Method  
This section provides a detailed explanation of the proposed method. Figure 2 illustrates the overall framework, which consists of three main modules: color correction, atmospheric light estimation, and transmission estimation. The first module restores the lost color information by utilizing the statistical features of sand-dust images, effectively removing the color veil. The second module computes a local atmospheric light map using the dark channel prior and adjusts the illumination of the refined atmospheric light map. The third module estimates the transmission using the saturation line prior. Due to the illumination adjustment in the second module, the boundary constraints of the saturation line prior have been modified. As a result, a clean image can be obtained using the atmospheric scattering model.
## 5 Implementation
### Fire Spreading Based on a Large-Scale Cellular Model
: \(M_{HS}=<X,Y,S_{DES},\sigma_{Ext},\sigma_{Int},\lambda,ta,S_{CS},F,g,\phi>\)__  
* \(X=\{X_{in}\}\cup\{M_{state}\}\) is the set of inputs where \(X_{in}\) represents vector value of neighbor cells;
* \(Y=\) the set of outputs;
* \(S_{DES}=(wait,\ done,\ stable,\ unstable)\) is the set of DES model states;
* \(\sigma_{Ext}:(Y_{CS},M_{state})\rightarrow(stable,unstable)\), \(Y_{CS}\rightarrow\ done\);
* \(\sigma_{Int}:Y_{CS}\rightarrow(stable,unstable)\), \(done\rightarrow\ wait\);
* \(\lambda:done\rightarrow\ Y_{CS}\);
* \(ta:wait\rightarrow\ \infty,\ done\rightarrow\ 1\);
* \(S_{CS}=\) set of \(\sigma_{v}\) which used in internal transition;
* \(F=\{f_{i,1},f_{i,2},...f_{i,n}\}\) is the set of threshold sensor;
* \(g=\) the set of outputs which represent continuous value;
* \(\phi=\{\phi_{1},\phi_{2},...\phi_{n}\}\) is the set of differential equation. Figure 8: DEVS based fire-spreading model of a large-scale hybrid system. X is the set of inputs, represented by the vector value from neighbor cells, and Y is the set of outputs. In addition, \(S_{DES}\) is the set of the discrete event system model states: _wait_, _done_, _stable_, and _unstable_. These states are updated by \(\sigma_{Ext}\) and \(\sigma_{Int}\) to check if the current state is stable or unstable to synchronize their operations. \(\lambda\) is the output function from \(S_{DES}\times Y_{CS}\). \(ta\) is the time advance function in the DES model. For a CS, \(S_{CS}\) is the set of states, and \(F\) is the set of the threshold sensor for the conversion of the continuous state of the event. Meanwhile, \(g\) is the output function, and \(\phi\) represents the differential equations for a CS.
## 3 Results and Analysis  
### Welding Process Stability  
The transient current and voltage waveforms in DP-GMAW with a thermal frequency of 3, 4, and 5 Hz are shown in Figure 4. All of the welding processes were successfully finished. According to Figure 4, the transient electrical waveform, which consists of an array of thermal strong pulses and an array of thermal weak pulses in one cycle, are repeatable and smooth. The difference between the peak current of thermal strong pulse and the peak current of thermal weak pulse is 40 A, according to [PERSON] [18]. Neither short circuit nor broken circuit appears in the smooth and periodic curve waveforms, which indicates a smooth current transition during DP-GMAW welding. Besides, the stability of transient voltage waveform correlates to the regularity of transfer droplet [21].  
The transient input energy and transient resistance waveforms in DP-GMAW welding with thermal frequency of 3, 4, and 5 Hz are shown in Figure 5. Both input energy and dynamic resistance are the product of transient current and transient voltage. Similarly it is found that their waveforms consist of an array of strong pulses and an array of weak pulses in one period of the DP-GMAW process. It should be noticed that when the waveform of current is on wave crest (wave trough), the waveform of input energy was also on wave crest (wave trough). In addition, it can be seen that the transient dynamic resistance waveform mainly varies in the narrow range of 0.1\(\sim\)0.4 \(\Omega\). Also, when the thermal frequency is varied from 3 Hz to 5 Hz, the transient input energy waveform fluctuates in the concentrated range of 1\(\sim\)7 kW with good repeatability, which indicates that the process of DP-GMAW welding is stable without short circuit and arc extinguishing.  
Figure 4: Transient electrical signal waveforms of DP-GMAW: (**a**) 3 Hz; (**b**) 4 Hz; and, (**c**) 5 Hz.
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 2 Related Works
The author of [28] proposed a maximal weighted area (MWA) algorithm to solve the three-dimensional placement problem of UAV base stations. Unfortunately, these works did not discuss the completion time or the time restriction of collecting data. Different from the above-mentioned conventional works, in this article, we focus on the 3D optimization of the flight altitude of the UAV, examining the completion time reduction effect by adopting the three-dimensional trajectory and a scheduling mechanism to minimize the time required for the data collection process.
## 3 Methodology
### Mel Frequency Cepstral Coefficients (MFCCs)  
We extracted features as a set of measured, non-redundant and derived values from heart signal which has been used in our study. As we used two types of features, MFCCs, DWT and the combination of both, MFCCs features are enormously used in signal processing and recognition. They were first calculated and used in speech analysis by [PERSON] and [PERSON] in the 1980's. Human can recognize small changes in pitch of sound signal at lower frequencies and the MFCC scale is linear for those frequencies having range <1 kHz. The power aspect of MFCCs is that they are able to represent the signal information efficiently. The MFCCs feature are almost similar to the log filter bank energies and include the mel scale which makes it an exact copy on smaller scale of what humans understand, the frequency in mel scale is given by Equation (1):  
\[Mel\ (f)=2595\ log\ (1+\frac{f}{700}) \tag{1}\]  
In MFCC algorithm, using Hamming window an audio signal is reshaped to smaller windows hence splitting of signal to frames is done. Using Fast Fourier Transform, spectrum is computed for each frame, using filter bank each spectrum is being weighted. Finally using Logarithm and Discrete Cosine Transform, the MFCC vector is computed [12; 13]. Compared to other features, MFCCs have good performance against noisy signal when used in speech signal processing hence can be used in biomedical signal processing [12]. During feature extraction, we resampled each signal frequency to 8 KHz, the length of the each signal's feature extracted is 19 and the length of frame in each sample is 240 and the step size is 80. The following diagram represents the extraction of MFCCs features Figure 3a.  
Figure 2: (**a**) Proposed heart sound signal classification algorithm using SVM; (**b**) Proposed heart sound signal classification algorithm using DNN.
# Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
## 2 Materials and Methods
### Recognition of Corn Kernel
The results is shown in Figure 4. Obviously, the region of kernels in Figure 4 is highly consistent with the white region in Figure 4.  
```
0: Original RGB image \(\mathbf{X}\in\mathcal{R}^{n_{1}\times n_{2}\times 3}\), dictionaries \(\mathbf{D}^{k}\), \(\mathbf{D}^{e}\), and \(\mathbf{D}^{u}\) threshold \(\beta\), \(\gamma^{\prime}\). 1: Initialize \(\mathbf{C}_{1}=\mathbf{0}\in\mathcal{R}^{n_{1}\times n_{2}}\)
2:for each spatial pixel \((i,j)\)do
3: Denote \(\mathbf{x}=\mathbf{X}(i,j,:)=\mathbf{x}=[x^{\prime},x^{\beta},x^{b}]^{T}\);
4:if\(\|\mathbf{x}\|_{2}>\beta\) and \(x^{\prime}>\gamma^{\prime}\)then
5: Normalize \(\mathbf{x}\) by \(\mathbf{x}_{0}=\mathbf{x}/\|\mathbf{x}\|_{2}\);
6: Compute \(\mathbf{p}^{k}=\mathbf{D}^{k^{T}}\mathbf{x}_{0}\), \(\mathbf{p}^{e}=\mathbf{D}^{e}\mathbf{x}_{0}\), and \(\mathbf{p}^{u}=\mathbf{D}^{u}\mathbf{x}_{0}\);
7: Compute \(\alpha^{k}=\|\mathbf{p}^{k}\|_{\infty}\), \(\alpha^{e}=\|\mathbf{p}^{e}\|_{\infty}\), and \(\alpha^{u}=\|\mathbf{p}^{u}\|_{\infty}\);
8:if\(\alpha^{k}=\max\{\alpha^{k},\alpha^{e},\alpha^{u}\}\)then
9:\(\mathbf{C}_{1}(i,j)=1\);
10:endif
11:endif
12:endfor
13:Recognizing result \(\mathbf{C}_{1}\). ```  
**Algorithm 2** Recognizing corn kernels using learned dictionaries. **Remark 1**. : _It should be noted we only use Algorithm 2 to recognize corn kernels rather than ear damages. This is because ear damage is close to kernel gap in terms of RGB information, such that it is easy to confuse with damages with gap. Hence, we first recognize kernels, and then occupy the region of kernels and their gap using corroding and expanding operations. Finally, we find the damage based on RGB information within the region that is not occupied._
# Waveform Design for 5G and beyond Systems
## 1 Aims and Objectives  
Currently, 5G communication systems are being commercially deployed in many countries. It is also true that several new applications are expected to be realized within the pre-defined 5G capabilities. To accommodate these various new applications, 5G traffic has very diverse requirements for data rate, delay, and reliability. Such challenging requirements are met when the deployment of 5G is based on proper radio access technology. Consequently, multiple OFDM numerologies were adopted in the 5G NR (New Radio) standard, i.e., legacy OFDM of the 4G system was scaled up to cope with the requirements for enhanced mobile broadband, enhanced machine-type communications, and ultra-reliable low latency communications.  
The concept of using multiple OFDM numerologies likely meets the multiple requirements for a 5G system to some extent. However, this cannot be an ultimate solution, because the traffic is radically accruing more varied characteristics and requirements when compared to the initial stage of 5G, which focused mainly on the high-speed multimedia data application. For instance, applications such as vehicular communications and robotic control require high reliability and ultra-low delay. In addition, various emerging machine-to-machine (M2M) applications have sparse traffic, with a small amount of data to be delivered. The state-of-the-art OFDM technique has some limitations when addressing the aforementioned requirements at the same time. Meanwhile, numerous waveform alternatives, such as FBMC, GFDM, and UFMC, have been explored to overcome this situation. However, they also have their pros and cons due to their intrinsic waveform properties. Hence, it is very timely to come up with modifications/variations/combinations to the waveform alternatives to OFDM, together with the state-of-the-art OFDM technique, or a new innovative waveform design for 5G systems and beyond. This Special Issue aims to provide the latest research and advances in the field of waveform design, along with the related issues of 5G systems and beyond.
## 2 Materials and Methods
An average of 34 finite elements, corresponding to a length of 30 cm of the shingle's lower surface, were considered. The dry-out rate (Dry-out rate_w_m) was determined as the derivation of Dry-out_w_m with respect to time. Finally, the total moisture mass (liquid, vapour, ice) was determined for the lower and upper shingle halves and referred to the dry mass of the wood (EMC_w_m and EMC_w_u_m). Both values were an average of 136 finite elements (15 \(\times\) 300 mm) each. First of all, the model values were determined with the air stream in the ventilation layer as measured for all four cardinal orientations of the roof surface. Then, the air speed was set to 0.06 m/s (corresponding to the value determined with closed eaves) during the course of the whole year to evaluate the characteristics of a construction with permanently closed eaves (ventilation out of operation and thermal effects on the air speed neglected). Finally, the spruce sheathing in the model was removed, and AtAT and AtAM were considered as boundary conditions at the shingles' lower surface, which represents a shingle roof on a cold attic. All model values were determined as hourly values, and the measurements were averaged on an hourly basis for comparison with the model. This resulted in 8760 values for each measurement. The model was validated by comparing real and modeled VAT, SST, VAM, and VVP using a linear regression analysis between real and modeled values in the course of a year (24 November 2017 until 24 November 2018). \[p_{\mathrm{s}}(\vartheta)=C_{1}*e^{\frac{C_{2}*\vartheta}{C_{3}+\vartheta}}, \tag{1}\]  
\[\vartheta_{\mathit{dev}}=\frac{C_{3}*\ln\left(\frac{\varphi*p_{\mathrm{s}}( \vartheta)}{C_{1}}\right)}{C_{2}-\ln\left(\frac{\varphi*p_{\mathrm{s}}( \vartheta)}{C_{1}}\right)}, \tag{2}\]  
where \(p_{\mathrm{s}}\) is the saturation vapour pressure for air temperature \(\vartheta\) in Pa, \(\vartheta_{\mathit{dev}}\) is the dew-point temperature in \({}^{\circ}\)C, \(\varphi\) is the relative air moisture content, \(C_{1}\) is a constant (610.5), \(C_{2}\) is a constant (21.875 for \(-50<\vartheta<0\)\({}^{\circ}\)C and 17.269 for \(0\leq\vartheta<50\)\({}^{\circ}\)C), and \(C_{3}\) is a constant (265.5 for \(-50<\vartheta<0\)\({}^{\circ}\)C and 237.3 for \(0\leq\vartheta<50\)\({}^{\circ}\)C.  
\[\mathit{EMC}=\frac{1800}{W}*\left(\frac{K\mathit{h}l}{1-K\mathit{h}l}+\frac{ \left(K_{1}*K*\mathit{h}+2*K_{1}*K_{2}*K^{2}*\mathit{h}^{2}\right)}{\left(1+K_ {1}*K*\mathit{h}+K_{1}*K_{2}*K^{2}*\mathit{h}^{2}\right)}\right),\]  
\[ \begin{gathered} W=330+0.452*(T*1.8+32),\\ K=0.791+0.000463*(T*1.8+32)-0.000000844*(T*1.8+32)^{2},\\ K_{1}=6.34+0.000775*(T*1.8+32)-0.0000935*(T*1.8+32)^{2},\\ K_{2}=1.09+0.0284*(T*1.8+32)-0.0000904*(T*1.8+32)^{2},\end{gathered}  \tag{3}\]  
where \(\mathit{EMC}\) is the equilibrium moisture content as a percentage, \(\mathit{h}\) is the relative humidity expressed in decimal form, and \(T\) is the air temperature in \({}^{\circ}\)C.
## 6 Conclusions  
Among a variety of bio-signals, PPG-related signals such as PPG and SDPPG are widely used for health care applications because of their easy and comfortable measurement. However, PPG and SDPPG signals are vulnerable to movement noise in measurement. Compared to PPG, an SDPPG signal has more information for diagnosing diseases, but is more sensitive to movement noise. Deterioration of SDPPG can result in incorrect diagnosis.  
To remove movement noise, an algorithm was developed using wavelet transform and a selection combining technique. Since SDPPG is almost periodic with subject-dependent pulse shape and a band-limited signal, wavelet transform is used for detecting movement noises. SDPPG signals originating from one source (heart) can be measured at different locations so that a diversity combining scheme can be adopted to reduce movement noise. Contrast to previous algorithms, the proposed algorithm improves not only PPI performance but also the shape of SDPPG, which results in better diagnosis. In experiments, movement noise could be reduced by up to 70.89%. The proposed approach is also applicable to PPG and other bio-signals that have periodicity and measurement diversity.  
Figure 14: Performance comparison: (**a**) noise ratio at output, and (**b**) noise reduction ratio with respect to the amount of noise on Channel 1.  
**Author Contributions:** Conceived the method and wrote the manuscript, D.B.; Designed experiments, D.B. and S.K.; Results analysis, D.B., S.M.S., and S.K.; Writing-review & editing, S.M.S.; Supervision, S.K.  
**Funding:** This work was supported by the 2015 Research Fund of the University of Ulsan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 1 Introduction
In general, wildfire activity is dominated by smoldering combustion overnight and in the early morning, and peaks in the late afternoon period centered around 18:00 local time known as the “peak burn period”. Notably, currently available active-fire low-earth-orbiting satellite observations from instruments such as SLSTR (Sentinel-3a/b), MODIS (TERRA/AQUA), and VIIRS (NPP/JPSS) fail to observe wildfires during the most active portions of the day.