esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In response to the need for cutting-motion monitoring in unmanned drilling operations, this paper proposes a high-precision detection approach that integrates small-object detection with speed estimation. The method is built upon an improved YOLOv11 detection algorithm and establishes an end-to-end solution for cutting-speed measurement by leveraging the mapping relationship between image pixel displacement and actual physical displacement. Firstly, to address challenges such as the small size and weak features of cuttings, we optimize the YOLOv11 detection head by introducing a multi-scale feature alignment and fusion strategy, significantly enhancing the small-object detection capabilities. The experimental results demonstrate that the proposed improvements boost detection performance by 4.65% compared to the original model, achieving a final mAP@80 of 76.04%. Secondly, during the object tracking phase, the ByteTrack ensures stable tracking, while the speed estimation module maps pixel displacement to real-world coordinates using camera calibration, enabling precise velocity measurement. The proposed model was trained and evaluated on a custom-built dataset. The experimental results show that, while detection accuracy decreases under low-speed motion with high frame rates compared to high-speed motion with low frame rates, the method still achieves an average speed estimation accuracy of 92.12%. It also outperforms the baseline model in detection accuracy, tracking stability, and computational efficiency. However, the dataset used in this study was generated under idealized conditions and does not fully represent the complexity of real-world environments. Future research will focus on expanding and enriching the dataset to cover more realistic and challenging working conditions, enabling more targeted validation. In addition, we plan to further optimize the YOLOv11-Dd detection architecture by reducing model complexity while maintaining detection accuracy. Special improvements will also be made for low-speed target scenarios to enhance the system's overall robustness and adaptability.  
J.Y., development and design of methodology; creation of models. Y.B., management and coordination of research activity planning and execution. Z.X. and H.Z., conducting the research and investigation process, specifically performing the experiments and data collection. Z.L., financial support for the project leading to this publication. Y.L., oversight and leadership of research activity planning and execution, including mentorship external to the core team. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the project of the National Natural Science Foundation of China (NSFC). Specifically: Project No. 52374164, PI: [PERSON]; Project No. 62306320, PI: [PERSON].  
**Institutional Review Board Statement:** Not applicable.
## 2 Results
### Volatile Compounds in Proso Millet Wine
#### 2.2.1 Esters  
Esters were the largest group in terms of the number of aroma compounds identified in the sample. A total of 14 esters were detected in samples, and the subtotal concentration of esters varied from 5192.56 to 11482.29 \(\mathrm{\SIUnitSymbolMicro g}\)/L in four different SPME fibers. The formation of esters was significantly influenced by the SPME fibers. There were 10, 9, 11, and 12 esters detected in PA, PDMS, CAR/PDMS, and DVB/CAR/PDMS, respectively. Esters showed a higher degree of similarity using different fibers. Among them, isoamyl lactate, ethyl benzoate, ethyl benzeneacetate, ethyl 3-phenylpropanoate, dimethyl phthalate, and ethyl hexadecanoate were detected in all fibers. Most of these esters were the main ester which was detected in high concentration, such as ethyl benzeneacetate and dimethyl phthalate, which exceeded 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/L in four fibers. Ethyl hexadecanoate exceeded 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/L in CAR/PDMS and DVB/CAR/PDMS fibers. Dimethyl phthalate is usually a contaminant coming from plastic containers where samples were stored, and in wine [26]. Esters also represented the largest group in rice wine. Actually, all ethyl esters have been detected in rice wine except ethyl dodecanoate [6; 9]. Esters are mainly produced by the esterification of alcohols with fatty acids or the biosynthesis of alcohol acetyl transferase using higher alcohols and acetyl-CoA as substrates [27]. Esters can impact the quality of alcohol beverages for desirable fruity and flowery flavors [23]. Among 14 esters, only the OAVs of four esters were greater than 1; these four could have a significant impact on sample aroma. Ethyl 3-phenylpropanoate, which had an OAV in the SMPE of different fibers that exceeded 5, contributed fruity and sweet notes to wine. The OAV of ethyl dodecanoate and ethyl hexadecanoate, in CAR/PDMS and DVB/CAR/PDMS, were greater than 1 and ranged from 1 to 2; they contributed to the aroma of the sample. Ethyl benzoate and ethyl decanoate, which had OAVs just greater than 1 but only in CAR/PDMS, also presented as being fruity and sweet.
## 5 Restoring Force Model
### Hysteresis Curve Model
#### 5.2.2 Hysteresis Curve Model Verification  
According to the hysteresis rules proposed in Section 5.2.1, the calculated hysteresis curve models of the five strengthened specimens RP-0, RP-1, RP-2, RP-4, and RP-5 were established and compared with the experimentally measured hysteresis curves. A certain error existed in the initial stiffness between the measured and calculated hysteresis curve models (Figure 30). This is primarily due to the adopted trilinear model ignoring the cracking point (i.e., elasticity limit) before yielding the strengthened pier column. The initial stiffness of the calculated model is taken as the slope of the line connecting the yield point and the origin, which degrades after the pier column cracks. Therefore, the initial stiffness of the measured hysteresis curve models was slightly higher than that of the calculated models. A slight deviation occurred after the yield stage between the measured and calculated hysteresis curve models; however, the models were in basic agreement. Thus, the hysteresis curve model and the hysteresis rules proposed in Section 5.2.1 can relatively accurately and reasonably reflect the hysteresis characteristics of the specimens strengthened using the IPCSAM. In addition, the proposed restoring force model has good applicability as a reference for practical seismic strengthening pier designs.  
Figure 30: Comparison of the theoretical and experimental hysteresis curve models for the strengthened specimens (**a**–**e**).
## 3 Results and Discussion
### Emulsion Structure
#### 3.3.4 Confocal Laser Scanning Microscopy  
CLSM images of the fish broth are shown in Figure 5, with red and green fluorescence indicating the signals of protein and fat, respectively. The results of the CLSM showed a trend consistent with the optical micrographs. The fat droplets of the boiling water group first became bigger before 60 min, and then became progressively smaller. At subsequent times, the amount of fat and protein dissolved in the fish broth samples of the thermo-ultrasonic treatment group increased and dispersed more uniformly. The fat globules in the fish broth were surrounded by proteins. The longer the thermo-ultrasonic treatment was applied, the more nutrients were present in the emulsified system of the fish broth, which also became homogeneous and stable. Generally, proteins are amphiphilic emulsifiers with hydrophilic and hydrophobic regions. Proteins are adsorbed at the oil-water interface to form a stable interfacial film, which can reduce interfacial tension to stabilize a fish broth emulsification system [PERSON] et al. (2018). The storage state of the fish broth samples with thermo-ultrasonic treatment for 120 min was the most stable.  
Figure 4: Optical micrographs of fish broth prepared by boiling water (**A**) and thermo-ultrasonic treatment (**B**) with different treatment times (**1–5—**0, 30, 60, 90, 120 min; Bar = 50 \(\upmu\)m).
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
###### Abstract  
The middle rock pillar in ultra-small-spacing tunnels is significantly narrow, and the stability of the primary support and lining are easily influenced by the blasting vibration wave from an adjacent tunnel. Therefore, understanding the vibration frequency characteristics is essential for the blasting vibration control. Based on the blasting works on a double-track roadway tunnel (Jiuwuij tunnel in Guizhou, China), this study investigates the dominant frequency attenuation in the preceding tunnel with the middle rock pillar spacing ranging from 4.0 m to 9.4 m. The results show that the ranges of the dominant frequency distributions on the primary support and lining are widely within 200 Hz, but there are varieties in their propagation laws. The distribution of the dominant frequencies on the primary support is broader than that on the lining and the dominant frequencies are concentrated on a specific range when the lining is far from the blast face beside a particular value, which is not present on the primary support. As the presence of cavity and changing medium between the lining and the primary support, it made a significant contribution to the filtering the vibration waves. Furthermore, on the primary support, the high-frequency part of the vibration waves attenuates rapidly with distance, and then, the practical prediction equations describing dominant frequency attenuation were proposed. The comparison on frequency characteristics per delay for the millisecond delay blasting shows that multiple delay sequences blast contributes to a multi-structured amplitude spectrum of blast vibration waves; and the varies of the equivalent explosion sources dimensions and numbers of free surfaces in each blast delay resulting in diverse vibration waveforms. Finally, the dominant frequencies determined by different methods were compared, and the results show a nonlinear relationship between the ZCFs and DFs. The above research conclusion expands the understanding of blasting vibration in tunnel engineering, particularly in the frequency distribution.  
blas vibration; dominant frequency; ultra-small-spacing tunnel; prediction formulas +
Footnote †: journal: Applied sciences
## 3 Results
### Soil Moisture Assimilation Performance
When the ESA-CCI soil moisture is added in the _\"sm +co\({}_{2}\)\"_ experiment, the model performs better in modeling both soil hydrology and CO\({}_{2}\) concentrations, as shown in Figures 2 and 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Name** & **Location** & _“prior\"_ & _“co\({}_{2}\)”_ & _“sm +co\({}_{2}\)”_ \\ \hline ALT & 82.3\({}^{\circ}\)N, 62.3\({}^{\circ}\)W & 17.5 ppm & 1.54 ppm & 2.07 ppm \\ \hline BHD & 41.4\({}^{\circ}\)S, 174.9\({}^{\circ}\)E & 15.56 ppm & 1.13 ppm & 0.89 ppm \\ \hline BRW & 71.3\({}^{\circ}\)N, 156.6\({}^{\circ}\)W & 17.72 ppm & 1.71 ppm & 2.54 ppm \\ \hline CHR & 2.0\({}^{\circ}\)N, 157.3\({}^{\circ}\)W & 15.9 ppm & 1.12 ppm & 1.12 ppm \\ \hline KUM & 19.5\({}^{\circ}\)N, 154.8\({}^{\circ}\)W & 18.03 ppm & 1.28 ppm & 1.77 ppm \\ \hline MLO & 19.5\({}^{\circ}\)N, 155.6\({}^{\circ}\)W & 17.76 ppm & 0.97 ppm & 1.42 ppm \\ \hline SMO & 14.2\({}^{\circ}\)S, 170.6\({}^{\circ}\)W & 15.77 ppm & 0.89 ppm & 0.31 ppm \\ \hline SPO & 90.0\({}^{\circ}\)S & 15.39 ppm & 0.80 ppm & 1.29 ppm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The RMSEs for _“prior”_, “co\({}_{2}\)”_, and “sm +co\({}_{2}\)”_ experiments with respect to SIO CO\({}_{2}\) concentration observations, respectively.
# Towards Intelligent, Programmable, and Open Railway Networks
## 3 AI Technologies within the Railway Sector  
Use cases of the deployment of AI technologies in railways and perspectives thereof are described in [21]. ML can be effective in image recognition in the fight against terrorism. Data collection and processing using AI algorithms may help to predict abnormal behavior in passengers [22; 23]. ML can be used for predictive maintenance, e.g., on rolling stocks, infrastructure, and other operational decision support. Rolling stock maintenance is important for railway service availability, punctuality, and reliability, and ML algorithms may predict wheel tread defects or recognize anomalies in train bearing temperature, etc. [24]. Train punctuality and safety, among the other things, depend on the reliability and availability of railway infrastructure, including tunnels, bridges, tracks, cuttings, and embankments [25; 26]. ML can be used in the optimization of traffic management, train path allocation, passenger flow management at railway stations, detection of abandoned luggage, etc. [27].  
In addition to the application areas listed above, AI/ML techniques may be used for autonomous train control and train driving, transferring the responsibilities from manual operators to an onboard control system [28]. A review on the application of AI in high-speed railway automatic train control is provided in [29]. The existing research is mainly focused on speed control, trajectory control, and intelligent monitoring and prediction of abnormal conditions.  
Some of the RBC functions are time sensitive, while others are delay tolerant. Actions related to ATC and ATP, as well as some measures related to preventive maintenance based on ML models, must be implemented in near real time, while others are non-real time. The proposed distributed intelligent railway network architecture disaggregates the RBC's functionality into time-sensitive control functions and time-insensitive control functions.  
The idea for the disaggregation of the RBC's functionality is inspired by the concept of an open radio access network (O-RAN) that is aimed at the provisioning of an open multi-vendor platform and embedding ML to optimize the RAN's performance [30].  
The next section provides more details on the proposed disaggregated railway network architecture, which encourages programmability, openness, virtualization, and intelligence.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 3 Numerical Simulations
Relieving the influence of the amplitude and phase inconsistency, we set the amplitude error as a random positive value \(a_{2}\)\(\sim\)U (0, 3\({}^{1/2}\)), which is a uniform distribution with a variance is 0.5 dB, and the phase error was set as a random value \(p_{2}\)\(\sim\)U (\(-\)3\({}^{1/2}\), 3\({}^{1/2}\)), a uniform distribution with zero-mean and a variance of 0.5 ps. The amplitude and phase inconsistency are added on 16 AEs, and the simulation results are plotted in Figure 7.  
Figure 6: The multi\(-\)beam pattern in amplitude and phase inconsistency with the variances are 1 dB and 1 ps. (**a**) single\(-\)beam mode; (**b**) dual\(-\)beam mode; (**c**) four\(-\)beam mode. As shown in Figure 7, the power of the main lobe is 22.40 dBm, and the angle of deflection is 0.2\({}^{\circ}\) in the single-beam mode. The powers of the dual main lobe are 16.27 and 15.91 dBm; meanwhile, the deflection angles are 0.1\({}^{\circ}\) and 0.2\({}^{\circ}\) in the dual-beam mode. As for the four-beam mode, the powers are 10.62, 10.37, 10.28, and 10.23 dBm, and the angles of deflection are 0.3\({}^{\circ}\), 0\({}^{\circ}\), 0.1\({}^{\circ}\), 0\({}^{\circ}\). Compared to the multi-beam patterns in Figure 6, the qualities of the beam are uprising. Thus, when the reconfigurable structure is fabricating and deploying, the amplitude and phase difference between the optical path needs to be balanced and compensated.
## 2 Signal Model  
The 3D TWI scene of the moving target is shown in Figure 1. The planar array of the radar includes \(M\) transmitting antennas and \(N\) receiving antennas, with a total of \(M\times N\) channels. Both the transmitting and receiving channels are realized via TDM through microwave switches, which is called TDM MIMO 3D TWR. There will be a delay between the channels, due to switching. During the signal acquisition process of the moving target, the position of the target has changed from \(P_{1}\) to \(P_{M\times N}\) within the single-frame (containing \(M\times N\) channels) time, as is shown in Figure 1. The echo of the moving target can be described as:  
\[S_{j}(t)=S_{0}(t-\tau_{j}) \tag{1}\]  
where \(S_{0}(t)\) is the transmitting signal, and \(\tau_{j}=\tau_{S\_j}+\tau_{M\_j}\) is the echo delay of the target calculated by channel \(j\), \(j=1,\ldots,M\times N\). \(\tau_{S\_j}\) is the time delay of the starting position measured by channel \(j\). \(\tau_{M\_j}\) is the time delay introduced by the target movement within the time when the switch is switched to channel \(j\). \(\tau_{S\_j}\) can be expressed as:  
\[\begin{array}{c}\tau_{S\_j}=(\sqrt{\left(x_{T\_j}-P_{x}\right)^{2}+\left(y_{T \_j}-P_{y}\right)^{2}+\left(z_{T\_j}-P_{z}\right)^{2}}\\ +\sqrt{\left(x_{R\_j}-P_{x}\right)^{2}+\left(y_{R\_j}-P_{y}\right)^{2}+\left(z _{R\_j}-P_{z}\right)^{2}})/c\end{array} \tag{2}\]  
where \(\left(x_{T\_j},y_{T\_j},z_{T\_j}\right)\) and \(\left(x_{R\_j},y_{R\_j},z_{R\_j}\right)\) are the positions of the transceiver antennas of the \(j\)th channel, respectively. \(\left(P_{x},P_{y},P_{z}\right)\) are the coordinates of the initial position of the target. \(c\) is the propagation speed of the electromagnetic waves.  
The \(\tau_{M\_j}\) will introduce different effects to different channels. The echoes of the through-wall stationary and moving targets are given in Figure 2. It can be seen that the echo-delay trend measured using different channels has changed significantly, which is known as range migration [1]. There will be high sidelobes and position deviation when the echo \(S_{j}(t)\) is used for imaging directly. Therefore, the echo of the moving target needs to be compensated before the imaging.
# Effect of Pseudomonas Fluorescens on Isofetamid Dissipation and Soil Microbial Activity
## 3 Results
### Abundance of Bacteria, Archaea and Ammonia Monooxygenase Bacteria Genes  
Figure 4 presents the log\({}_{10}\) copy numbers of bacterial (16S), archaea (16S) and ammonia-oxidizing bacteria (amoA) genes.  
In the case of the bacterial gene, it was observed that on D1, the abundance in all the study variants ranged from 7.0 (IS) to 7.08 log\({}_{10}\) gene copies/g dm, and no significant differences were found between the study variants. In the samples collected on D21, an increase in the abundance of the bacterial gene 16S was observed, ranging from 7.49 (C) to 8.35 log\({}_{10}\) gene copies/g dm (IS+P). It was observed that in the IS+P variant, the number of bacterial genes was significantly higher than in C and IS and than on the first sampling date (D1), by 10% and 20%, respectively.  
In our study, the number of archaea differed significantly depending on the sampling date and the research variant. On D1, the lowest number of archaea was recorded in the IS (3.17 log\({}_{10}\) gene copies/g dm) and IS+P (3.25 log\({}_{10}\) gene copies/g dm) variants. A significantly higher number of archaeal genes was recorded for C (5.37 log\({}_{10}\) gene copies/g dm). On the second date (D21), the number of archaeal genes was significantly higher in all research variants and ranged from 3.61 (C) to 6.88 log\({}_{10}\) gene copies/g dm (IS+P).  
The number of AOB genes on D1 ranged from 2.5 (IS+P) to 4.25 log\({}_{10}\) gene copies/g dm (C). On D21, the numbers of the studied gene were significantly higher than on D1 in IS and IS+P samples (3.89 and 4.66 log\({}_{10}\) gene copies/g dm, respectively).  
Figure 4: Abundance of (**A**) bacterial genes, (**B**) archaea and (**C**) ammonia-oxidizing bacteria (amoA) in control (C), soil with isofetamid (IS) and soil with isofetamid and _P. fluorescens_ (IS+P). Data are presented as means \(\pm\) SE; different letters and error bars indicate statistically significant differences assessed by [PERSON]’s test at \(p<0.05\).  
Relationships Between Isofetamide Dissipation, Soil Enzyme Activity and Abundance of Bacteria, Archaeons and Ammonia Monooxygenase Bacteria  
Figure 5 shows the relationships between the biological parameters studied, taking into account the experimental variants. A biplot analysis showed the relationship between enzyme activity, isofetamid concentration (expressed as dissipation) and TP, TOC and TN contents in the soil. Considering the correlations between the variables, it can be seen that there was a positive correlation between enzyme activity and TN, TP and TOC contents in the soil, while a negative relationship occurred between the number of AOB and archaea genes and isofetamid concentration. A Pearson correlation analysis showed a positive average significant correlation between IS concentration and TOC (r = 0.42) and a negative correlation between IS concentration and the number of archaea genes (r = \(-\)0.72), AOB (r = \(-\)0.92) and \(\beta\)-Glu activity (r = \(-\)0.40).
## 3 Results and Discussion
### Effect of Breed and Finishing Diet on Volatile Compounds
#### 3.2.1 Hydrocarbons: Lineal, Branched and Cyclic
**} \\ \cline{3-10}  & & **D1** & **D2** & **D1** & **D2** & **SEM** & **B** & **D** & **B** \(\times\)**D** \\ \hline Isobutane & 488 & 57 & 0.24 ab  & 0.19 a  & 0.30 b  & 0.22 ab  & 0.015 & ns & * & ns \\ Pentane, 2-methyl- & 516 & 71 & 0.25 a  & 0.21 a  & 0.51 b  & 0.029 & *** & ns & ns \\ Pentane, 3-methyl- & 525 & 57 & 2.24 b  & 1.41 a  & 3.67 c  & 3.30 c  & 0.173 & *** & * & ns \\ Pentane & 500 & 43 & 1.30 b  & 0.87 a  & 1.14 ab  & 0.92 a  & 0.060 & ns & ** & ns \\ Cyclopentane, methyl- & 564 & 56 & 1.47 a  & 1.13 a  & 3.13 b  & 2.75 b  & 0.160 & *** & ns & ns \\ Bicylcol(3.2.0)hepta-2.6-diene & 790 & 91 & 2670 b  & 2210 a  & 2981 b  & 86.640 & ** & * & ns \\ Octane & 800 & 85 & 1.95 ab  & 1.63 a  & 2.18 b  & 1.44 a  & 0.091 ns & ** & ns & ns \\ Nonane & 900 & 57 & 0.65 b  & 0.43 a  & 0.76 b  & 0.75 b  & 0.033 & *** & * & ns \\ Cyclopentane, 1,1-dimethyl- & 927 & 69 & 0.70 c  & 0.34 a  & 0.59 b  & 0.032 & ns & *** & * \\ Cyclopropane & 1041 & 55 & 25.64 b  & 16.03 a  & 19.75 a  & 20.20 a  & 1.027 ns & * & ** \\ Undecane, 5,5-dimethyl- & 1069 & 71 & 0.27 a  & 1.53 b  & 0.13 a  & 0.07 a  & 0.102 & *** & *** & ** \\ Cyclobutane, butyl- & 1119 & 84 & 0.86 b  & 0.36 a  & 0.74 b  & 0.40 a  & 0.039 & ns & *** & ns \\ Dodecane & 1200 & 71 & 0.20 b  & 0.40 c  & 0.22 b  & 0.07 a  & 0.023 & *** & ns & *** \\
**Total lineal hydrocarbons** & & & 4.35 b  & 3.53 a  & 4.60 b  & 3.40 a  & 0.135 & ns & *** & ns \\
**Total branched hydrocarbons** & & & 2.76 a  & 3.15 a  & 4.31 b  & 3.95 b  & 0.156 & *** & ns & ns \\
**Total cyclic hydrocarbons** & & & 2698 b  & 2228 a  & 3006 b  & 2825 b  & 86.732 & ** & ns & ns \\
**Total hydrocarbons** & & & 2705 b  & 2235 a  & 3015 b  & 2832 b  & 86.831 & ** & ns & ns \\ \hline Butanoic acid, 4-hydroxy- & 1038 & 86 & 4.49 b  & 3.98 b  & 4.20 b  & 2.95 a  & 0.170 & * & ** & ns \\ Hexanoic acid & 1074 & 60 & 5.73 b  & 0.76 a  & 9.46 c  & 4.36 b  & 0.524 & *** & ** & ns \\
2-Furancarboxylic acid, & & 1131 & 99 & 1.44 c  & 1.81 d  & 1.02 b  & 0.092 & *** & *** & ns \\ tetrahydro-3-methyl-5-oxo- & & & 11.65 c  & 5.20 a  & 15.48 d  & 8.33 b  & 0.642 & *** & *** & ns \\ \hline Glycold &  
\begin{table}
\begin{tabular}{r c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Volatile Compounds**} & \multirow{2}{*}{**LRI**} & \multirow{2}{*}{\(m\)/z**} & \multicolumn{2}{c}{**JN**} & \multicolumn{2}{c}{**BU**} & \multicolumn{2}{c}{**Sig.