esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The highest growth rate and productivity is achieved in cultivation systems with lower microalgae layer thickness. Small culture depth, high flow velocity, and higher slope angles create intensive turbulence, which optimizes the change of light and dark periods of individual cells and results in a higher efficiency of light utilization [19; 20]. Cultural thickness also plays an important role in the carbonation efficiency of TLC. High culture thickness also limits algal growth due to the incomplete absorption of CO\({}_{2}\) because of the short retention time of the gas bubbles [21]. Optimizing culture mixing by selecting the suitable values of these design parameters is necessary to prevent photoinhibition in TLC reactors and consequently enhance biomass production. However, studies investigating the culture mixing in TLC reactors are lacking. The advancement in numerical techniques and the availability of high-quality computers have allowed us to predict complex flow characteristics inside photobioreactors, especially for the cases where the use of an experimental setup is restricted by technical constraints [22]. Recently, [PERSON] et al. [10] performed a numerical simulation to evaluate the effect of volume flow and slope angle on cultural mixing for the main cultivation channel of TLC (i.e., the upper channel). Their results confirmed that mixing could be improved at high volume flows and slope angles. However, their studies do not include a detailed evaluation of hydrodynamic and cell mixing in each compartment of the TLC reactor. Despite its high potential for efficient biomass production, TLC in numerical studies has remained neglected. Moreover, the hydrodynamic is different in each compartment of the TLC; the residence time of the algae cell, mixing efficiency, energy consumption, and flow field will be different in each compartment. Therefore, this study investigates the hydrodynamic performance and algae cell mixing in each compartment of the reactor separately by employing computational fluid dynamics (CFD). The numerical calculations have been performed by considering different geometrical aspects (channel width, water depth, and inclination of the surface) of the reactor. The results in terms of mixing efficiency and the residence time of algae cells have been discussed to evaluate the mixing phenomenon. For the hydrodynamic characteristics of the TLC reactor, the velocity magnitude and the hydraulic power consumption have been evaluated.
## 3 The Results of the Statistical Analysis of the Work of the Optical and Electrochemical Methods
### Analysis of an Electrochemical Method for Measuring the Concentration of Exhaust Gas
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Time** & \begin{tabular}{c} **SO\({}_{2}\)** \\ **(mg/m3)** \\ **(p)** **(AMS)** \\ \end{tabular} & \begin{tabular}{c} **SO\({}_{x}\)** \\ **(mg/m3)** \\ **(p)** **(AMS)** \\ \end{tabular} & \begin{tabular}{c} **NO\({}_{x}\)** \\ **(mg/m3)** \\ **(MS)** \\ \end{tabular} & \begin{tabular}{c} **O\({}_{2}\) (\%)** \\ **(P)** \\ **(AMS)** \\ \end{tabular} & \
\begin{tabular}{c} **CO** \\ **(mg/m3)** \\ **(I500)** \\ \end{tabular} &
\begin{tabular}{c} **CO** \\ **(mg/m3)** \\ **(AMS)** \\ \end{tabular} \\ \hline
9-25 & 1052 & 1583 & 663 & 347 & 8.1 & 7.2 & 41 & 25 \\
9-30 & 1005 & 1582 & 675 & 343 & 8 & 7.2 & 44 & 19 \\
10-23 & 863 & 1552 & 662 & 358 & 8 & 7.3 & 31 & 12 \\
11-21 & 878 & 1637 & 577 & 314 & 7.7 & 6.9 & 453 & 410 \\
14-10 & 895 & 1623 & 488 & 318 & 8.1 & - & 353 & 470 \\ \hline \end{tabular}
\end{table} 
Table 5: Measurements performed using the POLAR (P) and the AMS (reference oxygen concentration is 6%). Figure 12: POLAR measurements: (**a**)—for SO\({}_{2}\), NO\({}_{\times}\), CO; (**b**)—for O\({}_{2}\). Figure 13: Measurement graphs for the main markers: (**a**)–SO\({}_{2}\), (**b**)–NO\({}_{\times}\), (**c**)–O\({}_{2}\), (**d**)–CO. The representation of the statistical values from the \(t\)-test and the ANOVA test is demonstrated in Figure 14 (the values for CO were not considered as they were very low). According to the statistical analysis, the relative measurement error for CO was 1.5%, for NO\({}_{x}\) it was 82.4405%, for SO\({}_{2}\) it was 41.17%, and for O\({}_{2}\) it was 11.61% (Figure 15). The error distribution demonstrates that the deviation is very high for this device.
## 6 Limitations and Future Directions
### Future Directions  
Implementing and evaluating RAG in generating SQL queries for medical databases opens several avenues for future research. Key areas include developing robust retrieval mechanisms that interface with ICD taxonomies and medical ontologies, ensuring the system can fetch and interpret relevant terms efficiently. Establishing metrics to evaluate the accuracy and reliability of term resolution and its overall impact on query correctness is also critical. Furthermore, conducting user studies can help refine the system's ability to disambiguate complex queries and adapt to real-world requirements. While the effort to translate natural language into medical SQL queries is commendable and beneficial for laypersons, it is likely that medical staff will derive even greater benefits from such systems once they are familiar with the domain-specific jargon (e.g., ICD taxonomies). While the study focused on general natural language to SQL translation, we acknowledge that medical professionals familiar with domain-specific jargon may benefit from a more specialized system. This has been noted as a potential direction for future research, where evaluating LLMs on translating structured medical queries into SQL could provide a more realistic assessment of their utility in clinical settings.  
Additionally, expanding the dataset is a crucial aspect of future work. The limitation of using 1,000 queries from the TREQS dataset is acknowledged, and efforts to address this through the incorporation of additional datasets and the generation of synthetic queries are planned to enhance robustness and generalizability. These enhancements will provide a more comprehensive evaluation framework for SQL query generation in medical databases.  
The potential of DeepSeek-R1 is also recognized, and its inclusion in future work is planned. Evaluating its performance in comparison with other state-of-the-art models will contribute to a deeper understanding of model capabilities and trade-offs in the context of SQL query generation for healthcare applications.  
By addressing these challenges, retrieval-augmented generation, coupled with dataset expansion and broader model evaluations, has the potential to significantly improve the accuracy and reliability of SQL queries generated for medical databases.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 3 Research Method
### PDR on Smartphone
For the other cases, the related data of gyroscope fluctuate in a small range (around zero). In this way, we separate a pedestrian's movement into segments by these detected turns. Figure 4: The vector module of accelerator readings for walking. As the motion in each segment is linear, we use a KF to alleviate the accumulative error of orientation. To improve the orientation regarding each segment, we set up the prediction equations of KF that includes heading orientation (\(\theta\)) and angular rate (\(\varphi\)) (see Equation (15)). \(\hat{X}_{k}\) is the state vector at time \(k\), \(P_{k}\) represents the covariance matrix at time \(k\), and \(Q\) represents stochastic error. \(\Delta t\) indicates the time of the change of angular rate. As this filter is applied to linear motion, the change of angular rate at the last moment can be regarded as the same to the current one. The predicted value of \(\theta\) is set to the last \(\theta\) plus the changed angle calculated by \(\Delta t\) and \(\varphi_{k-1}\). The state vector \(\hat{X}^{{}^{\prime}}_{k}\) can be computed by applying the update equations of this KF. Then, the filtered heading orientation \(\theta_{k}\) of time \(k\) is obtained. As the motions of two segments are in different orientations, the covariance matrix \(P_{k}\) would be reset to an identity matrix after each turn, which avoids the influence of the last segment on the next one. \[ \begin{split}\hat{X}_{k}=\begin{bmatrix}\theta_{k}\\ \varphi_{k}\end{bmatrix}=F_{k}\hat{X}_{k-1}=\begin{bmatrix}1&\Delta t\\ 0&1\end{bmatrix}\begin{bmatrix}\theta_{k-1}\\ \varphi_{k-1}\end{bmatrix}\\ P_{k}=F_{k}P_{k-1}F_{k}^{T}+Q\end{split}  \tag{15}\]
## 2 Testing Spacecrafts and Data Collection
### Data Collection and Quality Analysis  
The high-quality GPS data are of great significance to LEO satellites, so we analyzed the data quality before POCD. Onboard GPS data with a 10 s sampling rate from day of year (DOY) 273 to 278, 2018, were collected. First, the number of observed and useful satellites was discussed. In this paper, the number of observed satellites means the sum of all satellites tracked by the LEO receiver at one epoch and the useful number is defined as the sum of all satellites for which both code and phase dual-frequency data are simultaneously available. From the top panel of Figure 1, we can see that the average observed satellite number of CS01 is much higher than that of GRCC. The largest observed number of GRCC and CS01 is 10 and 12, respectively. The proportion of tracking 6 to 10 GPS satellites for GRCC is 99.81% and the proportion of tracking 10 to 12 GPS satellites for CS01 is as high as 99.38%. In the performance of useful satellites, GRCC is better than CS01, as seen in the bottom panel of Figure 1. The percentage of 4-6 useful satellites for GRCC and CS01 in an epoch is 2.10% and 4.40%, respectively. However, the percentage of 7-12 useful satellites for GRCC and CS01 in an epoch is 97.89% and 95.39%, respectively.  
As an important index of assessing systematic errors in an LEO receiver [16; 22], multipath errors for GRCC and CS01 are analyzed. The code multipath errors of P1 and P2, recorded as MP1 and MP2, are determined by TEQC [32]. Figure 2 shows the variation in the code multipath error with the elevation angle for GRCC and CS01 in DOY 273, 2018. Whether GRCC or CS01 satellite, both MP1 and MP2 tend to be smaller, with a larger elevation angle, indicating one elevation-dependent pattern, and the average MP1 series was found to be smaller than MP2. However, the multipath errors of GRCC can decrease faster than CS01 with increasing elevation angle (\(\geq\)20\({}^{\circ}\)). Another noticeable phenomenon is that the average multipath errors for CS01 are larger than GRCC, where the root mean square (RMS) of L1 and L2 multipath error for GRCC is 0.12 m, 0.17 m, respectively, and the corresponding values for CS01 are 0.26 m, 0.38 m, respectively. In addition, we note that it is difficult for CS01 to track GPS satellites at low elevations (\(\leq\)5\({}^{\circ}\)). The reason may be related to the manufacturing characteristics of the receiver itself.  
Figure 3 displays the average signal to noise ratio (SNR) series with elevations for GRCC and CS01. As for GRCC, the general variations between SNR and elevations are quite stable. However, the SNR of CS01 tends to be larger with increasing elevation. Overall, the data quality of GRCC is somewhat superior to that of CS01. However, taking manufacturing costs into consideration, the performance of CS01 is still satisfactory compared to GRCC.  
Figure 1: Average percentage of observed (**top**) and useful (**bottom**) GPS satellites for GRCC and CS01.
## 5 Experimental Evaluations and Results
### Time Complexity Analysis  
The training time of deep learning models increases with the number of parameters and weights. We observe the training time of the deep learning models used in our study  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Method** & **Dataset** & **Size** & **Specificity** & **Sensitivity** & **Precision** & **Accuracy** & **F\({}_{1}\) Score** \\ \hline [PERSON] [12] & Self collected & – & 0.6890 & **0.9810** & – & 0.8400 & – \\ [PERSON] [55] & – & – & – & – & 0.8927 & – & 0.9530 \\ [PERSON] [51] & Self collected & 1300 & 0.8519 & 0.7766 & 0.7804 & 0.8215 & 0.7784 \\ [PERSON] [56] & Self collected & 630 & 0.9590 & 0.9760 & – & 0.9630 & 0.8960 \\ [PERSON] [52] & PEIR-VM & 24,648 & 0.8273 & 0.7402 & 0.7439 & 0.7921 & 0.7420 \\ [PERSON] [54] & NIH dataset & 27,558 & 0.9720 & 0.9470 & – & 0.9590 & 0.9590 \\ [PERSON] [53] & Self collected & 2550 & 0.9292 & 0.9344 & 0.8995 & 0.9313 & 0.9166 \\ [PERSON] [11] & NIH dataset & 27,558 & 0.9500 & 0.8860 & 0.9466 & 0.9180 & 0.9153 \\ Proposed & NIH dataset & 27,558 & **0.9778** & 0.9633 & **0.9682** & **0.9682** & **0.9682** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the proposed and the compared methods. Size represents the number of images in the dataset. The best results are marked in bold.  
for 25 epochs to evaluate their time complexity; the results are shown in Figure 7a. We recall that 173,700 images of the test dataset have been used in training. The results show that our model takes around 25 min for training, which is less than all the compared models. The reason for this computational efficiency is its small number of convolutional layers with a small number of parameters that eventually require less time for training. The VGG-19 and Inception models exhibit the best training times amongst the rest of the models. We also evaluated the testing time of compared models for complete testing data containing 8272 images, the results are presented in Figure 7b. The proposed model takes 5 s for a complete testing dataset, less than the other compared models. Among the rest of the models, the SqueezeNet model takes 6 s for testing, and it took around 30 min for training.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 7 Industry Implementations
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **PIC Benefit** & **Ref.** & **PIC Research Opportunities** \\ \hline \multirow{3}{*}{Speed} & Ultra-fast data rates \textgreater{}800 Gbps per channel enable faster interconnects between compute elements in Cloud fabrics. & [3] & Next-gen optical I/O and photonic chiplets for AI and edge computing acceleration \\ \hline \multirow{3}{*}{Energy Efficiency} & Energy usage as low as \textless{}1 pJ/bit reduces cooling and operational costs in large-scale data centers. & [113] & Photonic-electronic co-design and ultra-low-power modulation for Fog devices \\ \hline \multirow{3}{*}{Latency} & Sub-nanosecond switching using integrated optics minimizes processing delay for real-time Fog applications. & [114] & Hybrid photonic systems for near-instantaneous edge decision-making \\ \hline \multirow{3}{*}{Scalability} & High bandwidth density \textgreater{}1 Tbps/mm using CMOS-compatible photonics supports chiplet-based architectures. & [113] & Co-packaged optics and modular PIC platforms for distributed Cloud–Fog scaling \\ \hline \multirow{3}{*}{Capacity} & WDM technologies achieve up to 15.3 bps/Hz spectral efficiency for multi-terabit transmission. & [115] & Dense integration of wavelength-selective components and photonic memory \\ \hline \multirow{3}{*}{Flexibility} & Reconfigurable optical paths and SDN compatibility allow for dynamic adaptation to workload changes. & [74] & Software-defined photonic routing and dynamic reconfiguration architectures \\ \hline \multirow{3}{*}{Reach} & Supports coherent transmission over distances up to 3000 km, suitable for Cloud interconnects. & [116] & Long-haul photonic integration, tunable lasers, and quantum-secured links \\ \hline \multirow{3}{*}{Wireless Communication} & PIC-enabled RF-photonic interfaces can support mmWave and 5G/6G edge connectivity. & [67] & Integrated RF-optical transceivers and photonic beamforming for wireless Fog networks \\ \hline \multirow{3}{*}{Optical Fiber Communication} & PICs enhance WDM and coherent optics, enabling multi-terabit, long-distance fiber transmission. & [116] & Advanced modulation schemes and programmable PICs for elastic optical networks \\ \cline{1-1} \cline{2-3}  & & & \\ \end{tabular}
\end{table} 
Table 5: Key benefits and performance metrics of integrated photonics in Fog and Cloud computing.
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## Methodology  
### Heat Transfer Equation for Laser Heating on Thin Plates  
The temperature \(u(x,y,t)\) on a 2D rectangular plate for a continuous laser beam source satisfies the following partial differential equation with initial and boundary conditions:\[\frac{f-q}{\Delta z} =\rho c_{p}\frac{\partial u}{\partial t}-\
abla\cdot(\kappa\
abla u) \tag{1}\] \[q(x,y,t) =h\cdot(u(x,y,t)-u_{\infty})\] \[u(x,y,0) =u_{\infty}\] \[u(0,y,t) =u(a,y,t)=u(x,0,t)=u(x,b,t)=u_{\infty},\]  
where \(a\times b\times\Delta z\) are the plate dimensions, \(\rho\) is the plate density, \(c_{p}\) is the specific heat and \(\kappa\) is the thermal conductivity. \(q=q(x,y,t)\) is the heat loss due to convection at the plate surface, \(h\) is the convection coefficient and \(u_{\infty}\) is the ambient temperature.  
Finally, the heat source \(f=f(x,y,t)\) is defined as a square-shape moving laser beam:  
\[f(x,y,t)=\begin{cases}\frac{P(1-R)}{\pi r^{2}},&\left\|\vec{x}-\vec{x}_{0}(t) \right\|_{\infty}<\frac{r\sqrt{\pi}}{2}\\ 0,&\text{otherwise},\end{cases} \tag{2}\]  
where \(R\) is the plate reflectivity, \(P\) is the laser power, \(r\) is the laser radius and \(\vec{x}_{0}(t)=[x_{0}(t),y_{0}(t)]\) is the location of the laser spot at time \(t\). \(\vec{x}_{0}\) is the parametric curve that defines the laser trajectory, discretized as a sequence of piecewise linear trajectories as described in References [23; 24]. The function \(f\) describes the laser power on the plate according to the distance (infinity norm) \(\|\vec{x}-\vec{x}_{0}(t)\|_{\infty}=\max(x-x_{0}(t),y-y_{0}(t))\) of each plate point \(\vec{x}=[x,y]\) to the laser spot \(\vec{x}_{0}(t)=[x_{0}(t),y_{0}(t)]\). Figure 1 presents an scheme of the laser heating problem on thin metal plates.
## 2 Materials and Methods  
In this section, we first briefly describe the antenna-induced phase bias model and then present a new antenna calibration method, which was specifically designed for the low-cost antennas with centimeter-level PCCs.  
In general, an antenna calibration process consists of two steps: data collection and phase pattern fitting. The purpose of data collection is to collect measurements of PCCs or PCC combinations, where other components contained in the carrier-phase observations have been resolved or eliminated as much as possible. The purpose of phase pattern fitting is to fit a PCC model with a set of functions like spherical harmonics and then derive the PCC values at the grid points on antenna hemisphere.  
In the following, Section 2.2 concentrates on the data collection process, introducing a PCC measurements construction procedure based on triple-difference; Section 2.3 concerns the phase pattern fitting process, proposing a weight setting method based on the area of a spherical quadrilateral; and Section 2.4 investigates the duration of data collection with respect to GPS and BDS.
# Two Interval Upper-Bound \(Q\)-Function Approximations with Applications
## 6 Application in Designing a Quantizer with Equiprobable Cells for Gaussian Sources
Similarly, we determined the representation levels from the condition that \(Q(y_{i})\) were in the middle between \(Q(t_{i-1})\) and \(Q(t_{i})\), forming in that manner a system of \(N/2\) integral equations, that could be solved as was previously described, that is, by solving the following system of equations:  
\[Q^{com\_1}(y_{i})=\frac{N-2i+1}{2N},\quad i=1,2,\ldots,N/2. \tag{27}\]  
The results we obtained for the design parameters using the \(Q\)-function approximation are presented in Table 9. In addition to the design parameters, Table 9 also tabulates the values for the distortion and signal-to-quantization-noise ratio estimated by running a simulation of the proposed quantization. In the Matlab software package, we generated and quantized according to Equation (21) \(M=\)10\({}^{8}\) Gaussian variables. By comparing the generated and quantized values, as Equations (22) and (23) specify, we estimated the quantization performance for bit rates of 2, 3 and 4 bits/sample. The approximation of the \(Q\)-function has a wide range of applications. We listed two. It can be applied in all areas where data are modeled by a Gaussian PDF. Further, the theory of quantization, besides big data theory, can be used in neural networks, MIMO systems, signal transmission, 5G networks, as well as in Internet-of-things source coding. The Gaussian PDF is the most common data model encountered and also has applications in signal processing. A further application of the proposed approximation can be considered in areas described in [33; 34; 35; 36; 37; 38]. Some of these applications will be the focus of our future research.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 2 Related Technology and Works
### MulteFire  
MulteFire [4] is a standalone LTE system designed to operate entirely in the unlicensed band, in which both the control signal and data transmission are delivered in the unlicensed band. It is distinguished for improving LTE performance and simplifying LTE deployment in the unlicensed band. Basically, the MulteFire builds on 3 GPP standards and utilizes the LBT mechanism to coexist with Wi-Fi or LAA users. One of the most important application areas of MulteFire is industrial IoT, which demands fast transmission and broad bandwidth for collecting a large volume of data to support intelligent industrial operations using advanced data analysis tools. Due to the use of LTE technology, MulteFire enjoys certain advantages, including high capacity, better coverage, seamless mobility, industrial-grade reliability, and LTE-based security. In [8], the challenges and solutions of deploying the MulteFire in the unlicensed band are discussed.