esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Speech-to-Text Results  
The availability of STT models has increased significantly in recent years, ranging from free, open-source models to cloud-based services. Although standard benchmarks exist to evaluate their performance, assessing specific requirements that differ from the conditions of available benchmarks is often necessary. This consideration is particularly relevant when working with children aged four who are non-English speakers, in this case, Spanish speakers. Therefore, various models and services were evaluated using the dataset we captured to determine which model performs better regarding the word error rate (WER). WER is a popular metric used by automatic speech recognition systems. It tells you how many mistakes a system makes in recognizing spoken words. A lower WER indicates a more accurate system with fewer word recognition mistakes.  
From Table 3, it is evident that STT systems face challenges when processing voice instructions from small children. This difficulty can be attributed to various factors. One factor is the language used (Spanish); another is the limited amount of data available for young children compared to adults, leading to generally poorer STT performance in this age group. Additionally, speech development in young children is a critical factor. For instance, children in Spanish-speaking countries often struggle with words containing the \(R\) sound. The comparison reveals that the Whisper Large model and Google STT using context performed the best. However, their performance still falls short of the results obtained in standard benchmarks, highlighting the complexities involved in working with children's speech.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Class** & **Instances** & **mAP50** & **mAP50-95** & **GFLOPs** \\ \hline \multirow{4}{*}{YOLOv8X} & all & 400 & 0.983 & 0.812 & \\  & cube & 161 & 0.959 & 0.768 & \\  & cylinder & 89 & 0.995 & 0.862 & \\  & star & 150 & 0.995 & 0.805 & \\ \hline \multirow{4}{*}{RT-DETR} & all & 400 & 0.776 & 0.501 & \\  & cube & 161 & 0.819 & 0.497 & \\  & cylinder & 89 & 0.951 & 0.632 & \\  & star & 150 & 0.558 & 0.372 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: Top view of the working area with objects detected using YOLOv8N.  
Figure 6 illustrates the relationship between runtime and WER. Although the Whisper Large model offers favorable WER performance, its slower processing speed may not be practical for real-time applications, especially in contexts where children are involved and quick interaction is crucial. The Vosk model presents a balanced trade-off between speed and accuracy, but it does not match the accuracy of Google's context-aware STT, which outperforms other models in both speed and precision. Additionally, it is advantageous that Google's model does not require high-end hardware, making it more accessible and cost-effective, at least for this application.  
Runtime vs. WER for different STT models
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 5 Performance Comparison
### Communication Overhead  
The communication overhead of the proposed ITP-AKA and related protocols [11; 12; 13; 17] is determined on the basis of messages that are transmitted among the participating entities of respective protocols. In the proposed ITP-AKA, during the login phase, message \(\{M_{1},TID_{k},auth_{k}\}\) needs \((256+160+256)=672\) bits, whereas the messages \(\{M_{1},M_{2},TID_{k},TID_{f},auth_{k},auth_{f}\}\), \(\{N_{1},N_{2},Q_{k},Q_{f},auth_{Csp_{1}},auth_{Csp_{2}}\}\) and \(\{N_{1},Q_{k},auth_{Csp_{1}}\}\) transmitted during the authentication and key-establishment phase need \((256+256+160+160+256+256)=1344,(160+160+160+160+256+256)=1152\) and \((160+160+256)=576\) bits, respectively. So, the accumulative communication overhead of ITP-AKA is \((672+1344+1152+576)=3744\) bits. Furthermore, the communication overhead of all related protocols [11; 12; 13; 17; 18] is computed in the same way. The communication overheads of [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [11], [PERSON] et al. [17] and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Execution Time** & **Parameter** & \(\mathcal{U}_{k}\) & \(\mathcal{CSP}\) & \(\mathcal{FN}_{f}\) \\ \hline Hash function & \(E_{hash}\) & 0.002 ms & 0.00063 ms & 0.192 ms \\ \hline Point multiplication & \(E_{m}\) & 0.010 ms & 0.00087 ms & 0.388 ms \\ \hline Encryption/decryption & \(E/D\) & 0.008 ms & 0.00076 ms & 0.259 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cryptographic Operations used for Analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **Arduino** & **Cloud Server** & **Mobile Device** \\ \hline Platform & - & Online cloud server & Android \\ \hline System & Microcontroller:ATmega328 & PythonAnywhere & Mediatek P60 \\ \hline RAM & SRAM: 2 KB (ATmega328) & — & 6 GB \\ \hline Clock Speed & 16 MHz & — & 2.0 GHZ \\ \hline IDE & Arduino IDE & Python console & Android Studio \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of devices.  
Ever [18] are 4064 bits, 4832 bits, 4254 bits, 2560 bits and 1120 bits, respectively. The comparative analysis of communication overhead reveals that the communication overhead of proposed protocol is less than all related competing protocols except [17,18]. The complete communication overhead comparison among the proposed and related competing protocols is presented in Table 5. Table 6 demonstrates that the designed protocol provides more security and functionality features than the related competing protocols.
# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 2 Methodology
### Methods  
#### 2.2.1 RM Paint Functionalization and Evaluation of the Self-Cleaning Ability  
Four rates of nano-TiO\({}_{2}\), 0.5%, 1%, 2%, and 3% (wt%), were incorporated into the RM paint in the functionalization process. The incorporation was carried out using a low-shear mixer (15 min, 1000 RPM) [33] at room temperature. Glass slides (2.5 \(\times\) 7.5 cm\({}^{2}\)) were coated with the reference paint (0%) and the functionalized ones. The paint application ratio was 750 g/m\({}^{2}\), in accordance with standard EN 1436:2009 [34]. Two specimens were prepared for each RM paint condition (test results to be averaged).  
Each sample was polluted with 500 uL of Rhodamine B solution (100 ppm) and then allowed to dry for at least 24 h at room temperature. Then, the specimens were irradiated at intervals of 0, 3, 6, 12, 24, and 48 h with a lamp that simulates sunlight (Osram Ultra Vitalux 300 W) (OSRAM, Munich, Germany) with a light intensity of 11 W/m\({}^{2}\) measured with a Quantum Photo Radiometer HD9021. The lamp was placed at a distance of 25 cm from the samples (Figure 2).  
The self-cleaning property was evaluated by monitoring the pollutant degradation on the sample surfaces through visual analysis and by calculating the color variation and photocatalytic efficiency using colorimetry, according to [20; 35]. The latter technique was performed with spectrophotometric and AI-enhanced CIELAB color space measurements.
## 4 Results and Discussion
### Electrical Performance of the PV Power Plant  
The solar radiation and module temperature are the main influencing factors affecting PV power production [33]. PV current curve does not always follow the irradiance curve directly throughout the day as expected since the photo-generated current is directly proportional to solar irradiance. However, the connected load also determines the amount of current released by the PV module. PV open circuit voltage tends to decrease with an increase in the module temperature [32]. The current and voltage distribution of the PV, charge controller and battery over the reporting period are presented in Figure 14a-c.  
In Figure 14, the negative battery current depicts charging whereas load supply (discharging) was represented with a positive current. Comparison of Figures 11 and 14 indicate that the PV voltage increases with the increase in solar irradiance in all systems. At midday which corresponds with peak module temperature, a slight dip of the systems' voltages was also observed. Furthermore, the PV current appeared to have increased with the solar radiation but dropped by approximately half around midday. Based on the typical operation of MPPT charge controllers, the systems' charge controllers continuously monitor the PV power level and batteries state of charge. A  
Figure 13: Measured data from CR1000 data logger during a rainy day in February 2018 at the University of Fort Hare, South Africa.  
induced by the charge controller; having detected a low battery and available PV power at sunrise [32]. This created a dummy load for the PV and the resulting large amount of current flow.  
At midday, the charge controller sensed a fully charged battery and switches to Float or Sleep mode. At these modes, the charge controller prevents the PV with a smaller amount of load, thereby reducing the quantity of current flow. A similar phenomenon occurs in the three systems but varies according to the battery size (Ah), state of charge and connected load. The observed electrical performance of the PV systems is in line with the findings of [34]. In summary, during this one week, the designed DAS was noticed to be consistent and was able to observe all the meteorological and electrical sensors with high precision and can resist high temperatures and severe circumstances surrounding the environment like wind and heavy rain.
## 6 Dynamic Path Optimization with Charging Selection
### Charging Problem Model  
Then, if the SoC value of AEV \(k\) is less than the charging threshold \(\Omega\), for a set of fixed values of \(x_{ij}^{k}(t)\), we can convert the original problem (29) into problem,  
\[\min_{\begin{subarray}{c}y_{Gij}^{k,c}(t)\\ y_{ij}^{k,c}(t)\end{subarray}}w_{r}\sum_{\begin{subarray}{c}cs_{i}\in V_{Gs} \\ (i,t)\in\Theta_{e}\end{subarray}}\left(\left(p_{t}\Big{(}\tau_{cs_{i}}^{k,w}(t )+\tau_{cs_{i}}^{k,c}(t)\Big{)}+p_{cs}C_{a}^{k}\Big{)}y_{cs_{i}}^{k,cs}(t)+p_{ r}\tau_{ij}^{k,c}\lambda y_{ij}^{k,cs}(t)\right),\] \[\text{s.t.} \tag{36}\] \[\text{(\ref{eq:23}),(\ref{eq:24}),(\ref{eq:25}),(\ref{eq:26}),( \ref{eq:27}),(\ref{eq:28}).}\]  
For the charging selection problem (36), based on the decision variable \(x_{ij}^{k}(t)\) determined by the DRT-A* algorithm, we propose a DRT-CS algorithm. We make charging selections according to the real-time charging infrastructures service status information and consider the travel and charging costs to adjust the selection, as shown in Algorithm 3.  
```
1:Input: Charging infrastructures, SoC, t
2:Output: The optimal charging infrastructure
3:Load charging infrastructures service status information
4:Load path, and travel cost via Algorithm 2
5:for charging infrastructure meet Equations (26)-(28)do
6: Calculates Equation (22)
7:if the sum is minimum then
8: Return the optimal charging infrastructure
9:endif
10:endfor
11:if Optimal charging infrastructure changes then
12: Update t
13: Go to line 4
14:endif
```  
**Algorithm 3**: DRT-CS algorithm.
## 3 Materials and Methods
### Mapping in Dense Vegetation by the Fusion of FPCS Components from Different Data Sources Using GEE  
Frequently used multispectral data sources, such as the ASTER, the Landsat-8 OLI, and the TIRS, and Sentinel-2 MSI collections, are publicly available as part of the Google Cloud public data program [48; 49; 50]. The satellite data are stored as pixels rather than images in GEE [50], which means that one can acquire data from the GEE data catalogue at the pixel level and not at the image level. Mosaicking can be performed at a pixel level, e.g., retaining the best pixel with the minimum cloud cover among the same image collected at different times. Furthermore, cloud storage allows web-based access for processing, applying various algorithms and analysis by utilizing Google distributed computing architecture with computing efficiency exponentially higher than that of standard desktop computers [49].  
A pixel in RS images holds the mixed spectral response from the earthly objects present in the corresponding location. The mixture gets more complicated, when the spatial resolution is low. Due to inconsistent findings and contradictory observations in TIR imageries, it is difficult to interpret and understand TIR imageries in highly vegetated areas. Therefore, high-resolution SWIR bands perform better than lower-resolution TIR bands in a medium-to-low vegetation region [11]. Integrating multiple datasets can help compensate for the lower resolution of TIR and the shallow penetration of SWIR for granitic rock mapping [7; 51]. Such an operation is generally known as a fusion [52], broadly categorized as pixel-level (pan-sharpening before processing), object-level (integrating features from pixels), or product-level fusion (after processing of individual data sources) [53]. The application of PCA and the product-level fusion of selective PCs in the lower-dimension space have been reported for computational efficiency [54]. Similar to the weighted FCCs of different band ratios [55], PCs can be assigned a unique weight and linearly combined to get weighted FCCs.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 4 Lane Detection and Tracking
### Special Point Analysis of Lane Line Detection  
#### 4.2.1 ROI Search Based on the Previous Image  
The difference between the two adjacent frames is not small, but it is related to the speed of the vehicle. To improve the real-time capability of lane line detection, the search scope of the next frame can be set based on the last search. Therefore, if the lateral velocity of the vehicle is low, the scope of the DROI search is smaller than the previous area. Additionally, to avoid incorrect detection and misdetection of lane lines due to lessening the range of search, the on and off conditions of the local search should also be set simultaneously.
## 3 Software Design of Frequency Stabilization System
### Incremental PI Control (S3)  
PID control is also known as proportional (\(k_{p}\)), integral (\(k_{i}\)), and differential (\(k_{d}\)) control. The principle is to give feedback on the system error \(e(t)\) between the expected value and  
Figure 8: Fitting curve of locking point scanning voltage with corresponding frequency and wavelength.  
Figure 7: 6 error fragments of the error signal.  
the actual output value \(u(t)\) to the controlled object after the combination of proportion, integral, and differential so that the actual output value \(u(t)\) is always close to the expected value, to realize the tracking control of the controlled object. The relationship between the output \(u(t)\) and the error signal \(e(t)\) is as follows:  
\[u(t)=k_{p}e(t)+k_{i}{\sum_{t=1}^{t}}e(t)+k_{d}[e(t)-e(t-1)] \tag{1}\]  
The above equation can be rewritten in the form of an incremental PID, and the function relationship is as follows:  
\[\begin{array}{ll}\Delta u(t)&=u(t)-u(t-1)\\ &=(k_{p}+k_{i}+k_{d})\cdot e(t)-(k_{p}+2k_{d})\cdot e(t-1)\\ &+k_{d}\cdot e(t-2)\\ u(t)&=\Delta u(t)+u(t-1)\end{array} \tag{2}\]  
It shows that the actual output \(u(t)\) is based on \(u(t-1)\) at time \(t-1\), and the change \(\Delta u(t)\) at time \(t\) is increased, i.e., incremental. \(\Delta u(t)\) is only related to the error of the last three moments. The calculation is fast, and there is no cumulative error. The incremental PI control program is shown in Figure 9. After obtaining the voltage reference value, it is transmitted to the initial amplitude of PZT through local variables, and the closed-loop control is performed through a shift register in the While loop. Here, \(k_{p}\) = 1.1 \(\times\) 10\({}^{-3}\), \(k_{i}\) = 1.3 \(\times\) 10\({}^{-4}\), \(k_{d}\) = 0, i.e., incremental PI control. When the conditions \(e(t)\) are greater than 6 \(\times\) 10\({}^{-5}\) (reflect error change), or the SAS signal is less than 0.1 (indicating that the PD does not detect the incident light) and is not satisfied, it means that the closed-loop control is only normally working, and the frequency stabilization system is locked at the required frequency.
## 3 Results
### Wavelength Tunability and Temperature Tolerance of the DFG Laser
These results highlight the broad wavelength tunability of our DFG laser source, which is critical for applications requiring precise mid-IR control. Using the Full Width at Half Maximum of the curve for the leftmost peak in Figure 6 corresponding to a temperature of 16 \({}^{\circ}\)C and a peak signal wavelength of \(\sim\)1540 nm, the idler output bandwidth spans 3447-3457 nm or \(\sim\)10 nm. These results highlight the broad wavelength tunability of our DFG laser source, which is critical for applications requiring precise mid-IR control. As previously discussed in Figure 5, while variations in the PPLN length influence the Full Width at Half Maximum (FWHM) of the DFG output, they do not alter the center wavelength of the peak. This indicates that changes in the PPLN length affect the temperature tuning sensitivity rather than extending the wavelength range or tunability. Therefore, the  
Figure 6: Simulated DFG output power as a function of the signal wavelength for a 40 mm PPLN at varying temperatures. The graph demonstrates the broad wavelength tunability of the DFG laser source, with a peak power of approximately 42 mW across a signal bandwidth of 1539 to 1552 nm. position of the peak remains almost constant across different PPLN lengths, demonstrating that length adjustments alone do not enhance tunability in terms of wavelength range. Instead, the PPLN length primarily impacts the FWHM, which can be optimized depending on the requirements for stability or sensitivity in specific applications. Overall, these findings reinforce the importance of optimizing both the temperature and PPLN length to achieve the desired mid-IR wavelengths, underscoring our design's capability for applications in greenhouse gas (GHG) detection and free-space optical communication.
## 3 Components of VPN/Overlay Architecture
### IPsec  
IPsec, or Internet Protocol Security, manages encrypted Virtual Private Network (VPN) tunnels at the OSI Layer 3 level. It forms part of a suite of protocols standardized by the \"Internet Engineering Task Force\", the organization responsible for advancing Internet technologies. Originally developed for IPv6, IPsec has since been adapted for use with IPv4. It offers three primary functional implementations, as described in [23]. IPsec utilizes AH and ESP protocols to ensure the integrity and authenticity of transmitted data. The AH protocol authenticates the data source and safeguards against packet modification during transmission through the Packet Accelerator Extension. Additionally, it includes a sequence number in the header to prevent packet replay attacks. Meanwhile, the ESP protocol verifies data integrity and identity and encrypts the payload data. However, ESP authentication excludes the outermost IP header, necessitating additional encapsulation for correct delivery, particularly across networks employing Network Address Translation (NAT), such as private xDSL networks. Our choice for this article was to use Libreswan implementation on linux systems, as shown in Table 1. IPsec operates in two modes: tunnel and transport. The IP packet header remains unchanged in transport mode, with the transport protocol inserted between the header and the data area. This mode provides security between the source and destination addresses and is suitable for _Host-to-Host_ or _Host-to-Router_ connections. Conversely, tunnel mode adds a new complete IP header to the data packets, concealing original addresses and data. This encapsulated packet is forwarded between encrypted endpoints defined by the new outermost IP header. Tunnel mode is commonly used for _Site-to-Site_, _Host-to-Site_, and _Host-to-Host_ data transmission scenarios.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion  
### Evaluation of the SPME Arrow Procedure  
The main factors affecting the extraction of VOCs from milk samples were evaluated. The well-established one-factor-at-a-time approach was followed for optimizing adsorption and desorption conditions. Regarding the extraction step, the effects of fiber type (100 \(\upmu\)m PDMS, 120 \(\upmu\)m CAR/PDMS, or 120 \(\upmu\)m DVB/PDMS SPME Arrow fibers), sample volume (1-10 mL), extraction temperature (25-60 \({}^{\circ}\)C), extraction time (5-80 min), salt addition (0-20% _w/w_ of NaCl), and stirring rate (0-1000 rpm) were investigated. Regarding the thermal desorption step, the desorption time (1-5 min) and desorption temperature (220-300 \({}^{\circ}\)C) were investigated. The evaluation of the experimental parameters was performed in terms of the normalized peak areas of 2-heptanone, \(\alpha\)-pinene and dodecane, due to their abundance, as well as the total volatiles in milk samples. Since the reproducibility of the measurements was satisfactory, each experiment was performed with one repetition.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 2 NOx Formation Mechanism
### Prompt NO Route  
The prompt NO route is also called the Fenimore pathway. It occurred at the fuel-rich flame, which mainly occurred in the fuel consumption area. The prompt NOx pathway is first observed in the experiment; the whole pathway is very complex. The current research supposed that the prompt NOx has a relationship with the radical [CH\(\cdot\)], and the beginning of the chain reaction is as follows:  
\[\text{CH}\cdot+\text{N}_{2}\rightarrow\text{HCN}\cdot+\text{N}\cdot \tag{4}\]  
\[\text{C}\cdot+\text{N}_{2}\rightarrow\text{CN}\cdot+\text{N}\cdot \tag{5}\]  
Equations (4) and (5) are the initial chain reactions; then, [HCN\(\cdot\)] and [CN\(\cdot\)] oxidized, generating [NCO\(\cdot\)]; [NCO\(\cdot\)] was hydrogenated to product [NH\(\cdot\)]; and [NH\(\cdot\)] was finally transferred to NO.  
The prompt NOx route mainly occurred under fuel-rich conditions because the radical [C\({}_{2}\)H\({}_{2}\cdot\)] is the predecessor of radical [CH\(\cdot\)] [27]. Through the measurement, the presence of high concentrations of [CH\(\cdot\)] occurred at the regions of peak NOx, which proved that the prompt NO route generated the NOx [28]. Similarly, the prompt route was strongly coupled to the concentration of peak [CH\(\cdot\)] and flow residence time within the CH\(\cdot\) layer [29].