esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ray Tracking Method  
In order to investigate the influence of numerical aperture on focusing situation, an analysis of ray tracking for numerical apertures 0.45, 0.65, and 0.85 in air and glass were carried out, respectively. The analysis model of ray tracking is shown in Figure 6. Ray tracking is defined as the path that laser beam went through when focused in air or glass, which is helpful to discuss the power density. Thirteen rays of laser beam were initially set in the distance \(D\) as the diameter of laser beam at the objective lens. In this analysis, 9.0 mm, 6.0 mm and 5.8 mm were defined for the cases of N.A. 0.45, 0.65, and 0.85, respectively. In other words, the density of ray was 1.44 rays/mm, 2.17 rays/mm and 2.24 rays/mm, respectively. Moreover, the angle of incidence \(\alpha\) was \(26.74^{\circ}\), \(40.54^{\circ}\), and \(58.21^{\circ}\), respectively, for corresponding numerical aperture in air, which was calculated using Equation (2).  
\[\text{N.A.}=n\ sin\alpha \tag{2}\]  
where \(n=1.0\), which is the index of refraction of air [28]. Objective lenses with different numerical apertures were used to focus the laser beam, thus it was defined that the rays of laser beam intersected at one point. The focusing length of objective lens \(f\) were 9.0 mm, 3.6 mm, and 1.8 mm for N.A. 0.45, 0.65, and 0.85, respectively.  
Figure 5: Schematic illustration of calculation and measurement method of absorption rate.  
Figure 6: Analysis model of ray tracking.
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
## 5 Scalability
### Rate of Successful Delivery  
We begin by examining the success rate of the network in delivering messages successfully to the headend. We plot this rate against number of relays for the cases where each relay receives messages from 1, 2, 3 and 4 tags each.  
We can see the performance of the system in Figure 4. The system performs well when each relay is lightly loaded even when there is a large number of relays. With 20 relays and one tag per relay the probability of successful delivery is approximately 0.85. However, when the number of tags per relay increases there is a substantial reduction in successful delivery rate. With two tags per relay it is 0.76, for three it is 0.64 and for four it is less than 0.60. This indicates that a deployment consisting of large numbers of lightly loaded relays may perform better than smaller numbers of heavily loaded relays.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
###### Abstract
For each test scenario, the lateral load transfer ratio (LTR) was analyzed to assess the dynamic stability of trucks concerning the maximum safe slope angle. Additionally, a machine learning model was developed to predict LTR based on the planned path curvature and driving speed in an autonomous driving environment. The LTR metric represents the load transfer between a vehicle's left and right wheels, with values approaching 1 indicating that the load is almost entirely shifted to one side, increasing the risk of rollover. The machine learning models used in this study included multiple linear regression (MLR), random forest regression (hereafter, random forest), and recurrent neural networks (RNN). The experimental results compared the prediction accuracy of the machine learning models. The findings indicate that using curvature and vehicle speed directly as input features yielded lower accuracy compared to using lateral acceleration derived from these parameters. The RNN model demonstrated the highest prediction accuracy, highlighting its suitability for LTR estimation. These results suggest that machine-learning-based predictive models can enhance vehicle safety assessments by providing precise rollover risk predictions. Furthermore, the findings support the integration of machine learning into autonomous freight transportation systems, offering valuable insights for optimizing route planning and speed control to improve overall safety and efficiency.
## 5 Experimental and Results
Figure 24 shows an experimental setup for image data transmission. In this experiment, a scene is captured by a CCD camera, the image is modulated by a Manchester code, and LED array (4 \(\times\)4) is driven by the modulated signal of the image. An image with driving LED array is taken by the implemented CMOS sensor. The distance from the LED array to the CMOS sensor is set to 3 m. Since no tracking method is implemented in this experiment, the treatment of 25-channel outputs is simplified. A signal of one channel at the center of 25 LPR cells is selected. In this experiment, the horizontal and vertical addresses in the LPR cell array that produces the largest signal amplitude are estimated manually by a cut-and-try method. The LPR output is sent to a PC to reproduce an image with the transmitted signal. Figure 25(a) shows an image where an LED array is driven by the modulated signal of the captured image. Figure 25(c) is the reproduced image, which is transferred by the spatial optical communication. When the light communication pass is interrupted by hand as shown in Figure 25(b), errors in the reproduced image occur as shown in Figure 25(d). These results demonstrate that the proposed spatial optical communication system using the CMOS imager/LPR cell array successfully works. Figure 23: Bit Error Rate. Figure 24: Setup for image data transmission. In the experiment in Figure 25, the target light source is not moving. The final goal of the ISC chip is continuous communication for a moving target light source. To do this, investigations considering many aspects of the moving speed of the target light source and the ISC camera, the distance of communication, the sampling rate for the tracking and algorithm to use the 25-channel parallel LPR cell outputs are necessary, which are beyond the scope of the present paper.
## 4 Demulsification Mechanism
Figure 11: High resolution XPS spectra of Ti 2p (**a**), C 1s (**b**), N 1s (**c**), O 1s (**d**) in Ti-Mt-APTES before and after demulsification. Figure 12: Zeta potentials of Na-Mt and Ti-Mt-APTES at various pH levels. From the above analysis, the mechanism of demulsification is proposed and schematically illustrated in Figure 13. Negatively charged Na-Mt was modified to make the surface positively charged in an acidic environment and has amphiphilic properties. The Ti-Mt-APTES with hydrophilic titanium hydrate and hydrophobic grafted APTES can be well dispersed in the water phase. In acidic oil-in-water emulsions, Ti-Mt-APTES behaves like an amphiphilic flocculant. The seizing of oil droplets by Ti-Mt-APTES is divided into two aspects. On the one hand, in acidic oil-in-water emulsion systems, Ti-Mt-APTES can capture the major available oil droplets with a negative surface by electrostatic interactions, consistent with Figure 12. Moreover, the hydrophobic propyl on montmorillonite tends to adsorb hydrophobic oil droplets via hydrophobic-hydrophobic attraction, as confirmed in Figure 8:0 On the other hand, intercalation of titanium hydrate can adsorb oil droplets via van der Waals forces, promote oil adsorption from the acidic oil-in-water interface, and further improve emulsion separation. The oil droplets are attached onto Ti-Mt-APTES and prone to coalescing when the clays are close to one another. With the increase of coalescence and aggregation of oil droplets, they fall out and float to the surface, bringing about demulsification.
## 4 Conclusions  
A measurement system for the quasi-spectral determination of scattering parameters of veterinary tissue phantoms was constructed to examine artificial scattering media mimicking tissues--optical phantoms. The measurements were performed with the use of the three light sources: 635, 532 and 447 nm. These wavelengths were chosen because of their common applications in veterinary medicine. The data obtained from sample measurements correlate with those gathered from reference sources,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Phantom**} & \(\mathbf{R_{\rm d}}\) & \(\mathbf{T_{\rm d}}\) & \(\mathbf{\mu_{\rm a}}\) & \(\mathbf{\mu_{\rm s}}\)’ \\ \hline \multicolumn{5}{c}{**light source—635 nm**} \\ A & 0.37 & 0.57 & 0.07 \(\pm\) 0.02 & 1.67 \(\pm\) 0.05 \\ B & 0.39 & 0.48 & 0.18 \(\pm\) 0.03 & 2.67 \(\pm\) 0.1 \\ C & 0.23 & 0.25 & 0.92 \(\pm\) 0.03 & 1.83 \(\pm\) 0.1 \\  & **light source—532 nm** & & & \\ A & 0.38 & 0.57 & 0.05 \(\pm\) 0.02 & 1.75 \(\pm\) 0.05 \\ B & 0.40 & 0.47 & 0.19 \(\pm\) 0.03 & 2.75 \(\pm\) 0.1 \\ C & 0.24 & 0.22 & 0.98 \(\pm\) 0.03 & 2.04 \(\pm\) 0.1 \\  & **light source—447 nm** & & & \\ A & 0.39 & 0.56 & 0.06 \(\pm\) 0.02 & 1.83 \(\pm\) 0.05 \\ B & 0.42 & 0.48 & 0.14 \(\pm\) 0.03 & 2.86 \(\pm\) 0.1 \\ C & 0.25 & 0.22 & 0.97 \(\pm\) 0.03 & 2.11 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obtained measurement results of phantoms A, B and C.  
therefore verifying the effectiveness of the described system. Furthermore, the ability to tailor the optical parameters of phantoms allows future investigation.  
Conceptualization, A.M. and M.W.; methodology, A.M. and P.L.; validation, A.M. and P.L.; data analysis, A.M.; investigation, A.M., P.L. and M.K.; resources, M.K. and M.W.; writing--original draft preparation, review and editing, P.L., M.W., M.K. and A.M.; visualization, M.K. and A.M.; supervision, A.M.; funding acquisition, M.W. and A.M.  
This study was partially supported by DS Funds of the Faculty of Electronics, Telecommunications and Informatics of the Gdansk University of Technology.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM
## 2 Magnetic Saturation of Sensorless Control
The estimated position errors corresponding to the variation in the \(\gamma\)-\(\delta\) axes extended back EMF are illustrated in Figure 2. Note that \(\hat{\theta}_{e}^{\prime}\), \(\hat{\theta}_{e}^{\prime\prime}\), \(E_{ex}^{\prime}\), and \(E_{ex}^{\prime\prime}\) represent the estimated rotor position error and extended EMF, caused by the \(\gamma\)- and \(\delta\)-axes extended back EMF errors (\(\Delta e_{\gamma}\), \(\Delta e_{\delta}\)), respectively. As shown in Figure 2a, in the case of a constant value of the \(\gamma\)- and \(\delta\)-axes extended back EMF, \(e_{\gamma}\) and \(e_{\delta}\), the estimated extended EMF \(E_{ex}\) aligns with the \(q\)-axis of the synchronous reference frame. In Figure 2b,c, on the other hand, when errors occur in the \(\gamma\)-\(\delta\) axes extended back EMF resulting from an inductance variation, it leads to the generation of estimated rotor position errors. Usually, the \(\delta\)-axis extended back EMF component exhibits a significantly greater magnitude compared to the \(\gamma\)-axis component (\(e_{\delta}\gg e_{\gamma}\)); therefore, the variation in the \(\gamma\)-axis extended back EMF component significantly influences the speed estimation error compared to the \(\delta\)-axis component [14]. Consequently, \(\hat{\theta}_{e}^{\prime}\) is significantly larger than \(\hat{\theta}_{e}^{\prime}\) compared to \(\hat{\theta}_{e}\), when the magnitudes of \(\Delta e_{\gamma}\) and \(\Delta e_{\delta}\) are the same value, as shown in Figure 2b,c. In addition, to compare the magnitude of the two terms constituting the \(\gamma\)-axis back EMF variation \(\Delta e_{\gamma}\) in Equation (4), a simulation result of the comparison of \(\omega\hat{L}_{q}i_{\delta}\) and \(p\hat{L}_{d}i_{\gamma}\) under the condition of periodic load torque is represented in Figure 3. The term \(\omega\hat{L}_{q}i_{\delta}\) including the \(q\)-axis inductance is the main component in \(e_{\gamma}\) estimation; hence, it can be expressed as \(\Delta e_{\gamma}\approx\omega\Delta L_{q}i_{\delta}\). Consequently, since the dominant factor affecting position error estimation is \(\omega\Delta L_{q}i_{\delta}\), compensating fluctuations in the \(q\)-axis inductance, being the  
Figure 2: Comparison of estimated position errors (**a**) without extended back EMF errors, (**b**) with \(\gamma\)-axis extended back EMF error (\(\Delta e_{\gamma}\)), and (**c**) \(\delta\)-axis extended back EMF error (\(\Delta e_{\delta}\)). principal parameter influenced by magnetic saturation, can effectively enhance both the position error and speed ripple.
## 3 Results  
### Isolation and Species Identification of Turkey-Sourced Lactic Acid Bacteria  
A total of 1268 LAB isolates were obtained. From these, 24 turkey-sourced isolates were selected for the probiotic phenotypic assays (Table 1). These isolates represented eight different species and five different genera. The isolates were selected based on being species associated with positive probiotic performance from both previous research studies that our laboratory and other research groups have conducted [9; 19; 20]. A phylogenetic tree of the _Lactobacillus_ and _Lactobacillus_-like species, based on 165 rRNA sequences, was also created to better understand the relatedness of the isolates to known LAB species (Figure 1) and agreed with the ANI results.  
Figure 1: A phylogenetic tree of the different _Lactobacillus_ and _Lactobacillus_-like species included in our study (colored blue) based on full-length 16S rRNA sequences, compared to known species representatives (colored black). The scale is based upon 1628 nucleotide positions in the final dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Closest Species Match** & **ANI (\%)** \\ \hline MOF2W3B1T2 & _Lactobacillus crispatus_ & 98.0 \\ MOF2W6B4C10 & _Lactobacillus crispatus_ & 97.8 \\ MOF2W6B4C8 & _Lactobacillus crispatus_ & 97.7 \\ SNF2W3B2M1 & _Lactobacillus crispatus_ & 97.8 \\ SNF2W6B1L3 & _Lactobacillus crispatus_ & 97.9 \\ MOF2W10B1T3 & _Lactobacillus gallinarum_ & 97.7 \\ MOF2W1B3C5 & _Lactobacillus gallinarum_ & 97.7 \\ MOF2W3B5T3 & _Lactobacillus gallinarum_ & 97.9 \\ MOF2W5B1C1 & _Lactobacillus gallinarum_ & 98.1 \\ SNF2W3B1L8 & _Lactobacillus gallinarum_ & 97.8 \\ UMNLJ21 & _Lactobacillus jolnsonii_ & 97.5 \\ SNF2W1B5L4 & _Lactobacillus jolnsonii_ & 97.7 \\ SNF2W3B3L6 & _Lactobacillus jolnsonii_ & 97.8 \\ MOF2W3B1T3 & _Ligilactobacillus salivarius_ & 97.2 \\ MOF2W6B4C3 & _Ligilactobacillus salivarius_ & 97.2 \\ MOF2W3B1C8 & _Limosilactobacillus neuteri_ & 95.8 \\ SNF2W3B2M2 & _Limosilactobacillus neuteri_ & 96.0 \\ SNF2W3B1L7 & _Limosilactobacillus vaginalis_ & 98.8 \\ MOF2W1L6C & _Pedicoccus acidlactici_ & 98.6 \\ MOF2W1 LT10 & _Pedicoccus acidlactici_ & 98.7 \\ MOF2W5B3T3 & _Pedicoccus acidlactici_ & 98.5 \\ MOF2W1L8 & _Weissella paramesenteroides_ & 98.0 \\ MOF2W2 LT2 & _Weissella paramesenteroides_ & 98.2 \\ MOF2W3 LC2 & _Weissella paramesenteroides_ & 98.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of the isolates selected for our study and species identification using whole-genome ANI.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 3 Results
### Observation Results
#### 3.1.2 Received Power and \(C/N_{0}\)  
The signal powers received at the main and side lobes using a quad GNSS system with a reception threshold of 10 dB-Hz are displayed in Figure 9. A large number of green dots, i.e., side lobe signals, are concentrated below \(-195\) dBW and the main lobe signal rarely appears when the received power is below \(-202\) dBW. The maximum received power reaches only \(-180.59\) dBW due to the free space propagation loss.  
The received signal carrier/noise ratio, \(C/N_{0}\), was then calculated according to Equation (5) based on the received signal power above. The \(C/N_{0}\) is shown in Figure 10, where the received threshold is 10 dB-Hz. As the spacecraft travels along the LLO, received signals are marked by either red or green dots, indicating main or side lobe signals, respectively. For ease of comparison, the reception threshold of 15 dB-Hz is represented as a blue dashed line in Figure 10. It can be seen that the maximum \(C/N_{0}\) of the received signal is 34.8 dB-Hz. About 87% of the main lobe signals exceed the 15 dB-Hz threshold, while roughly 55% of the side lobe signals have a \(C/N_{0}\) below this threshold. Therefore, it can be expected that for lunar missions, the availability of side lobe signals will be greatly increased if the detected sensitivity can be further improved. Ultra-high sensitivity GNSS receivers are a necessary technology to achieve the autonomous navigation of lunar probes.
## 3 Hierarchical Graph Learning for Next POI Recommendation
### Hierarchical Graph Construction
#### 3.1.3 Global-Level Graph Construction  
For the construction of the global-level graph, we employ the Spatio-Temporal DBSCAN (ST-DBSCAN) algorithm [35], which extends the traditional DBSCAN algorithm to simultaneously consider both spatial and temporal attributes during clustering. We further cluster the nodes from the regional level to form higher-order cluster structures. These higher-order clusters serve as nodes in the global-level graph, capturing larger areas or functionally specialized districts within a city. These areas consist of region-level nodes that are spatially proximate and exhibit similar temporal visitation patterns.  
The edges in the global-level graph are established based on similarity between higher-order clusters. For edge construction, we employ cosine similarity to measure the relationship between global-level nodes. Specifically, we represent each global-level node by computing the average of the feature vectors from its constituent region-level nodes. This approach allows us to preserve the semantic information from lower levels while creating a more abstract representation at the global level.  
Given two global-level nodes A and B, with their respective feature vectors derived from averaging their constituent region-level nodes, we calculate their cosine similarity as follows:  
\[\text{Cosine}(A,B)=\frac{A\cdot B}{\|A\|\times\|B\|} \tag{1}\]  
If the calculated similarity exceeds a predetermined threshold, we establish an edge between these nodes. This method effectively captures the functional and semantic relationships between different large-scale areas within a city, enabling the model to leverage higher-level geographical patterns for more accurate recommendations.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Impedance Matching
In summary, to perform impedance matching for Antenna A, it is necessary to add a 2.59 cm-long segment of RG58C/U cable in series and a 1.61 cm-long open stub in parallel. For Antenna B, a 1.42 cm-long short-circuited stub and a 2.68 cm-long series cable segment are required. Using the 1.2 GHz band for our UAV communications offers a far cleaner slice of the spectrum than the heavily congested 2.4-2.6 GHz ISM band. In most countries, L-band frequencies around 1.2 GHz are reserved or lightly licensed for aeronautical telemetry and navigation. This means there are far fewer civilian users, and the risk of interference from consumer devices--Wi-Fi routers, Bluetooth headsets, microwave ovens--is dramatically lower. For military or other critical applications, spectral exclusivity simplifies coordination, reduces the complexity of spectrum-management protocols, and minimizes the need for aggressive frequency-hopping to dodge civilian traffic. From a propagation standpoint, lower frequencies enjoy intrinsic advantages in range and penetration. A 1.2 GHz signal experiences less free-space path loss than a 2.4 GHz signal and can better penetrate foliage, light vegetation, and even thin building materials. When drones operate in environments with tree cover, urban canyons, or varied terrain, this improved propagation translates into a more reliable beyond-line-of-sight link, which is essential for persistent situational awareness in contested or complex theaters. In terms of electronic warfare and security, an L-band link at 1.2 GHz is inherently more difficult for adversaries to detect and jam. Most off-the-shelf jamming and interception gear is calibrated for the popular ISM frequencies around 2.4 GHz. By contrast, a well-tuned 1.2 GHz radio, especially when paired with a directional monopole antenna, offers greater stealth and resilience. Coupled with military-grade frequency agility techniques, this choice significantly raises the bar against both jamming and spoofing attempts. Finally, while the quarter-wave monopole at 1.2 GHz is about 6.25 cm long--roughly twice the height of its 2.4 GHz counterpart--the slightly larger form factor is a minor trade-off for the performance gains. Modern composite materials such as carbon fibers allow us to integrate these antennas seamlessly into the UAV's structure without compromising aerodynamics or adding significant weight.
## 3 Results and Discussion
### Meat Substitute Formulations  
Figure 5 presents the cross-sectional structures of frozen-thawed doughs, revealing distinct morphological characteristics between pure tapioca flour systems and _L. cylindrica_ blend formulations at varying water concentrations.  
The upper panel displays tapioca flour:water doughs at concentrations ranging from 20% to 50%. At lower concentrations (20-30%), the cross-sections exhibit relatively uniform, dense structures with minimal void formation. As the flour concentration increases to 40%, the structure becomes more heterogeneous, with the appearance of larger air pockets and irregular void spaces. At the highest concentration (50%), the dough shows a more compact structure with reduced porosity, likely due to limited water availability for ice crystal formation during freezing.  
The lower panel illustrates _L. cylindrica_ blend doughs at concentrations from 25% to 35%. These samples demonstrate markedly different structural characteristics compared to pure tapioca flour systems. At 25% concentration, the cross-section reveals a highly porous, sponge-like structure with interconnected void spaces, suggesting extensive ice crystal formation and subsequent sublimation during the freeze-thaw cycle. The 30% concentration sample shows a more organized pore structure with distinct, uniformly distributed cavities throughout the matrix. At 35% concentration, the structure becomes denser with smaller, more regularly distributed pores, indicating reduced ice crystal formation due to lower water content.  
The structural differences between the two dough systems are particularly evident in their response to freeze-thaw treatment. Pure tapioca flour doughs maintain relatively compact structures with limited void formation, suggesting a strong gel network formation that resists ice crystal damage. In contrast, _L. cylindrica_ blend doughs exhibit more extensive  
Figure 5: Tapioca flour:water dough (20–50% concentration) (upper image); _L. cylindrica_ blends dough (25–35% concentration)—cross-section structures after freezing and 1 h thawing.  
structural modification, with pronounced honeycomb-like patterns, indicating weaker gel structures that are more susceptible to ice crystal disruption.  
The color variations observed across samples also provide insights into component distribution and water migration patterns. Darker regions in the _L. cylindrica_ blends likely indicate areas of concentrated seed press cake particles, while lighter areas suggest zones of higher starch concentration or ice crystal locations. The more uniform coloration in tapioca flour doughs indicates better component integration and more homogeneous water distribution.  
These freeze-thaw structural analyses demonstrate that the incorporation of _L. cylindrica_ seed press cake significantly alters dough architecture and water-binding properties, creating more open, porous structures that could influence final product texture and quality. The concentration-dependent structural changes suggest optimal formulation windows for specific textural outcomes in frozen dough applications.