esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 6 Conclusions  
This paper presents a novel method based on laser and machine vision to automatically measure tunnel deformation of multiple interest points in real time and effectively compensate for the environment vibration, and, moreover, it can overcome influence of dusty and dark environments in low visibility. An automatic and wireless real-time tunnel deformation monitoring system that is based on laser and machine vision, and can give early warnings for tunnel collapse accidents, is proposed. Compensation methods are proposed to reduce the measurement errors caused by laser beam feasibility, temperature, air refraction index, and wireless antenna attitude. Experimental results show that the proposed monitoring system has a high detection accuracy, which can compensate for the shortcomings of the traditional tunnel deformation monitoring method in highly automatic real-time monitoring. The proposed system can also avoid the conflict with tunnel construction, that is, it realizes real-time monitoring during construction. Moreover, the monitoring system has good stability and repeatability. The detection accuracy is better than 0.12 mm, the repeatability accuracy is less than 0.11 mm, and the minimum resolution is 10 \(\upmu\)m.  
[PERSON] conceived the method and revised the paper; [PERSON] designed the experiments and wrote the paper; [PERSON] and [PERSON] designed mechanical and electrical structure; [PERSON] and [PERSON] designed and conducted the field test; [PERSON], [PERSON], and [PERSON] developed system software and improved the experimental results.  
This research was financially supported by the National Natural Science Foundation of China (NSFC) (No: 51775378) and the National Key R&D Program of China (No.2017 YFF0108102).  
The authors declare no conflict of interest.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 3 Results
### Holdout Validation  
Holdout Validation was performed for each of the calculated land covers. This approach, involving the partitioning of data into training and testing sets, has been crucial for assessing and ensuring the effectiveness of our model across various years. Table 5 presents the OA and Kappa coefficients for each year. Remarkably, OA ranges from 92% to 95%, and the Kappa coefficient varies between 89% and 92%.
## 5 Results and Discussion
### Applications of Findings  
The findings from this research carry two major implications for achieving high-precision building dimension estimates. First, at the research level, they direct future studies to adopt selective augmentation approaches, rather than automatically applying every technique--especially emphasizing the strength of contrast augmentation for building detection and segmentation. Such selectivity not only reduces complexity in model experimentation but also aids in standardizing protocols around the most impactful augmentations. Second, in real-world applications, a contrast-focused augmentation strategy can optimize resource usage (e.g., through faster training and fewer variant images) while preserving accuracy. This is especially valuable for practical needs--from urban planning and infrastructure monitoring to insurance assessment and property valuation--all of which rely on rapid, reliable building detection or segmentation.
## 3 Results
The MRR fabrication with regard to (1) size, (2) coupling degree and (3) detuning is technologically feasible according to the following considerations. More specifically: (1) The MRR radius can be down to the very small micrometer scale using high-index contrast waveguide materials [40; 65]. (2) The matching between the MRR field transmission coefficient and amplitude attenuation factor for critical coupling is allowed to deviate by 3% from being perfect and still obtain a high PNCR [42]. This is possible by controlling the gap between the straight and bending waveguides using electrically-driven micro-electromechanical system (MEMS) microactuators [66]. (3) The position of the notches relative to the encoded data wavelength can be adjusted by physical means, such as the thermo-optic effect. In this case, electrical heaters are placed in the direct vicinity of the MRR to spectrally shift its resonance [65]. This method is particularly efficient for materials of large positive thermo-optic coefficient, such as silicon, since the required amount of detuning across the FSR can be achieved by supplying an electrical power of the order of a few mWs [65]. Overall, the MRR physical dimensions and tolerances can be determined by modeling the MRR using commercially available simulation software platforms based on coupled mode theory. This procedure is executed for a given waveguide structure material (hence effective index), fabrication technology (hence propagation loss) and incident light (hence wavelength) [67], and has been the subject of research elsewhere, thus being out of the scope of this paper. For example [68], a 20 \(\upmu\)m-radius MRR fabricated on a silicon-on-insulator substrate consisting of a 1-\(\upmu\)m-thick box layer and 0.45-\(\upmu\)m-thick device layer, with waveguide width of 0.5 \(\upmu\)m, round-trip propagation losses less than 2 dB/cm and gap between straight and bending waveguide of 250 nm, can efficiently act as notch filter, with PNCR 15 dB, on optical signals centered in the vicinity of 1550 nm. \[EP=\frac{1}{2}\text{erfc}\bigg{(}\frac{Q}{\sqrt{2}}\bigg{)}, \tag{10}\]1 MRR spectral response and action upon spectral components of maximum and minimum encoded logical ’1’s and ’0’s at directly modulated RSOA output stream. FSR: free spectral range. PNCR: peak-to-n notch contrast ratio. \(\Delta\lambda\): detuning (wavelength offset between encoded signal spectral position, \(\lambda_{enc}\), and shorter sideband transmission peak located FSR/2 away from notch, \(\lambda_{n notch}\)).
## 2 Experimental
### Data Analysis  
#### 2.4.1 Machine Learning  
Machine learning was performed using Scikit-learn version 1.0.2 and TensorFlow version 2.4.0 in Python version 3.7.6. Sensor responses \(r\) in all data points were converted to their standardized values for machine learning,  
\[x_{sk}=(r_{sk}-\overline{r_{s}})/\sigma_{s}, \tag{2}\]  
where \(x_{sk}\) is a standardization value of data number \(k\) on sensor number \(s\), and \(\overline{r_{s}}\) and \(\sigma_{s}\) are the mean and standard deviation of \(r\) on sensor \(s\), respectively.  
A PCA was performed to investigate the characteristics of the datasets obtained from the two sensor arrays. The PCA was performed only on single gases because the inclusion of double gases resulted in too many classes and made classification difficult (with 676 data points). The PCA was performed according to Equations (S1)-(S6) in the Supplementary Materials.  
The ANN model is shown in Figure 3. The concentrations of target gases were predicted using the ANN. The structure of the neural network comprised an input layer with eight nodes (\(x_{1}\)-\(x_{8}\)) for the standardization values of sensor responses from eight sensors as explanatory variables; two hidden layers with 64 nodes (\(b_{1}\)-\(b_{64}\) and \(b^{\prime}\)-\(b^{\prime}\)\({}_{64}\)) with rectified linear units (ReLU; \(z_{1}\)-\(z_{64}\) and \(z^{\prime}\)-\(z^{\prime}\)\({}_{64}\)); and an output layer with 13 nodes (\(y_{1}\)-\(y_{13}\)) for the concentrations of the 13 target gases as objective variables. The ANN performed using 5-fold cross validation. All data points (\(676+4056=4732\)) were randomly divided into five parts, one of which was used as test data (946 or 947 data points) and the rest as training data (3786 or 3785 data points). First, all coefficients for moving data to the next layer in the ANN model (corresponding to the black arrows in Figure 3) were optimized using training data. Subsequently, all data points of the test data were substituted into the input layer for collecting the predicted concentrations of the 13 target gases from the output layer. By repeating the ANN five times with 5-fold cross-validation, each data point was the test data only once. Among the hyperparameters, the batch size was set to 32, and the number of epochs was the value at which the average loss function of the test data in the 5-fold cross-validation was minimized.  
Figure 3: Model of ANN used in the study.
# An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole
## 4 Conclusions  
To solve the problem of laser welding defect detection for lithium battery poles and to realize the automatic welding defect detection in the industry, this paper proposed an end-to-end improvement algorithm based on the YOLOv5 model. Given the fact that small targets in laser welding defects are difficult to detect, we suggest replacing the original 3 \(\times\) 3 convolution kernel with a larger convolutional kernel; replacing the SPPF of the backbone network with our SPPSE module to improve the feature extraction capability; introducing the RepVGG module to enhance the accuracy and detection speed of the model; and finally, using SIoU as the bounding box regression loss function to reduce the negative impact of the mismatch between the predicted and real frames on the network. The experimental results showed that the improved model improved mAP by 1.1% to 97% relative to YOLOv5 on our BPLWD dataset, with a detection speed of 270 fps, and can meet the needs of the industry for high accuracy and speed of defect detection. Meanwhile, the model accuracy was improved by 3% in the MS COCO dataset and 2.4% in the PASCAL VOC dataset. The results prove that our improved model makes a good trade-off between detection accuracy and speed, which can meet the industrial requirements for defect detection when embedded into the image analysis module of the AOI system. In future work, we will focus on model compression and pruning to facilitate its subsequent deployment in industry.  
Figure 10: Comparison of the SIoU_Loss and CIoU_Loss curve.  
Figure 9: Comparison chart of mAP_0.5 and mAP_0.5:0.95 on different datasets. (**a**) MS COCO. (**b**) PASCAL VOC.  
**Author Contributions:** Conceptualization, Y.Y. and L.Z.; Methodology, Y.Z.; Software, Y.Z.; Validation, N.U.D., J.L. and Y.H.; Formal analysis, N.U.D.; Investigation, Y.Z.; Resources, Y.Y.; Data curation, J.L.; Writing--original draft preparation, Y.Z.; Writing--review and editing, L.Z.; Visualization, Y.H.; Supervision, L.Z.; Project administration, Y.Y.; Funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shenzhen Science and Technology Program (JCYJ20210324093806017), and Shenzhen-Hong Kong Joint Innovation Foundation (SGDX20190919094401725).  
Data Availability Statement: Data is unavailable due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 2 Related Work
In the literature [36], scholars propose a novel deep reinforcement learning method to optimize UAV trajectory controlling and users' offloaded task ratio scheduling and improve the performance of the UAV-assisted MEC system. It maximizes the system's stability and minimizes the energy consumption and computation latency of the UAV-assisted MEC system. In previous studies, most researchers focus on the partial offloading mode. For energy-saving goals, most of them focus on the energy consumption of the equipment itself, and there is very little work using deep reinforcement learning methods. In contrast, we increase the energy received by the wireless device on the basis of reducing the energy consumption of the wireless device. We propose a new objective problem of maximizing the residual energy of wireless devices in binary offload mode. Using deep reinforcement learning technology, the optimal MEC online computing offloading strategy is finally obtained.
## 3 An Efficient Speech CBSS Algorithm in Multipath Channels
### Evaluation Criteria
#### 3.3.1 SDR and SIR  
The estimated signal can be represented by four parts, namely, the real source part \(s_{ture}(t)\), the filtering distortion part \(e_{filt}(t)\), the interference part of other sources \(e_{inter}(t)\) and the false value \(e_{artif}(t)\), where \(s_{target}(t)=s_{ture}(t)+e_{filt}(t)\) indicates the part of estimated signal that belongs to the source signal (indicating that the sensor acquires information about the target source containing transmission effects); \(e_{inter}(t)\) indicates that the estimated signal does not satisfy the source signal and belongs to the mixed signal, which is the residual after the separation of other sources; \(e_{artif}(t)\) indicates the external noise generated by the algorithm. The specific definition equations for SDR and SIR are  
\[SDR=10\text{log}_{10}\frac{\left\|s_{target}(t)\right\|^{2}}{\left\|e_{filt}( t)+e_{inter}(t)+e_{artif}(t)\right\|^{2}}, \tag{13}\]  
\[SIR=10\text{log}_{10}\frac{\left\|s_{target}(t)\right\|^{2}}{\left\|e_{inter}( t)\right\|^{2}}. \tag{14}\]  
The SDR reveals the ratio of the true source to the other components. The larger the indicator value, the less the separated signal is affected by distortion, interference and artifacts. Furthermore, the larger the SIR value, the less the components of the separated signal are separated from other sources, and the better the performance of the separated signal.  
Figure 3: CBSS model for secure transmission of multiple speech signals.
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Adsorption Studies
#### 3.8.3 Effect of Agitation Time  
One of the parameters explored in the adsorption experiments was the different impacts of the agitation time on the adsorbent and adsorbate particles. An experiment was conducted to examine the effect of agitation time on chromium adsorption using pine sawdust adsorbent and cellulose fibres; the procedure was performed for 30 to 120 min at room temperature.  
Figure 13 shows the impact of time on the adsorption capacity of chromium by untreated pine sawdust and cellulose fibres. Both adsorbents exhibited an increase in chromium ion adsorption with longer agitation times. Within the first 60 min, there was rapid adsorption for both materials. After this initial period, equilibrium was reached within 90 min, and no further significant alteration in chromium removal occurred. This observation can be attributed to the abundance of available surface sites for adsorption during the initial stages. Furthermore, untreated pine sawdust showed a lower adsorption capacity than cellulose fibres, likely due to its lower surface area and porosity, limiting the availability of active adsorption sites [52].
## 4 BDS-3 Orbit Determination and Its Accuracy Analysis
However, the improvement of orbit accuracy using Scheme 2 was less distinct than Scheme 3 and Scheme 4, which indicated that the contribution of the improved cycle-slip detection and repair algorithm to the parameter estimation was less than the combination of different observation networks, given the fact that the combination offers a more reasonable global \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**3D RMS of One-Day Overlapping Arcs**} & \multicolumn{3}{c}{**SLR Residuals**} \\ \cline{2-7}
**Satellites** & & & \multicolumn{2}{c}{**NES**} & \multicolumn{2}{c}{**SS**} \\ \cline{3-7}  & **NES** & **SS** & \multicolumn{2}{c}{**Mean**} & **RMS** & **Mean** & **RMS** \\ \hline C31 & 31.0 & 34.6 & 20.4 & 55.1 & 21.0 & 60.5 \\ C32 & 36.0 & 39.4 & 35.8 & 49.6 & 38.3 & 53.6 \\ C33 & 40.3 & 43.4 & 29.7 & 61.5 & 31.7 & 65.8 \\ C34 & 50.1 & 55.5 & 46.2 & 70.9 & 46.3 & 73.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: BDS-3 orbit accuracy of the two methods based on one-year observations (cm). network structure. The orbit accuracy obtained using Scheme 3 was slightly worse than that of Scheme 4; this result is consistent with the theory discussed earlier. Furthermore, to analyze the reliability of the improved cycle-slip detection and repair algorithm, the data availability was taken into consideration based on the residuals of every epoch in Scheme 2. In Table 8, the threshold was set as 4\(\sigma\), where \(\sigma\) is the mean square error of observation residuals, to eliminate the observations with poor quality. From Table 8, the improved algorithm enhanced the data availability from 12.90% to 20.46% compared with the traditional Turboedit approach.
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 3 Framework of the Literature Survey
### Categories of LMD Problems  
Due to technological advancements and the effects of pandemics on LMD, much research has been reported on vehicle routing in LMD during the last eight years. Most of the literature on LMD can be grouped into these four categories based on the focus of the problems:  
1. Delivery with occasional drivers (crowdshipping): The logistics companies use occasionally available non-professional drivers in addition to their own fleet for parcel delivery.
2. Delivery to parcel locker: Customers' parcels are delivered to specific locations with many lockers, and the customers retrieve their parcels from assigned lockers.
3. Delivery using sidekicks: The delivery uses a main vehicle and one or more sidekick vehicles. The main vehicle brings parcels and sidekicks to intermediate places, whose locations are determined by each delivery plan, and the sidekicks deliver the parcels from the main vehicle to customers' locations.
4. Delivery to optional points: A customer offers multiple delivery locations, and the delivery system delivers the parcel to the best location.  
Some papers simultaneously consider two of the above categories. For example, occasional drivers are considered as the sidekicks [35]. Also, crowdshipping and delivery options are considered together in [36]. Some researchers consider different goals using the same approaches. Refs. [37; 38] employ drones to reduce emissions in green VRP, and [39] uses drones for cost optimization and delivery time reduction.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 3 Suspension Systems
### Alfa Pendular Vehicle Suspension  
The Portuguese AP tilting 4000 series train started its operation in 1999. This electric train has a total length of 158.9 m and reaches a maximum speed of 220 km/h. It is operated as a single unit comprising six cars, four motor units, and two trailers.  
The AP train has an active tilting system, which reduces the lateral acceleration perceived by passengers and, consequently, allows for the performance of curves at higher speeds than the balanced one while maintaining high passenger comfort levels [39]. Two bodies support each car, distanced by 19 m, and are 6 m from the bogies of consecutive cars.  
Figure 4a illustrates the primary AP suspension, while Figure 4b shows its secondary suspension system. The primary suspension is composed of four helicoidal springs (sets of two plus two) (1) and a vertical damper (2), both filtering the vibrations derived from the vehicle-track interaction in each wheel. Thus, each bogie of the AP tilting train has twelve flexi-coil springs combined with six dampers acting as a secondary suspension and sixteen springs coupled to four vertical hydraulic dampers performing as a primary suspension [42; 43; 44].  
The mechanical elements of the secondary suspension ensure the carbody-bogie connection. The secondary suspension (see Figure 4b) is constituted of twelve flexi-coil springs grouped within four units of three (3). Between spring units, on each side of the bogie, there is a vertical (4), a transversal (5), and an anti-yaw (6) hydraulic damper.
## 2 Theoretical Framework and Network
### Atmospheric Turbulent Channel Model  
Atmospheric turbulence can cause irregular changes in atmospheric refractive index with space and time. When LG beams carrying OAM propagate through free space, the phase and intensity of the beam will be distorted. In this paper, the power spectrum inversion method is used to simulate the influence of AT on the transmission process of LG beams. The modified Hill power spectrum proposed by Andrews LC is used to characterize atmospheric turbulence [24], and its expression is  
\[\Phi_{n}(\kappa)=0.033C_{n}^{2}\cdot\left[1+1.802\left(\frac{\kappa}{\kappa_{ l}}\right)-0.254\left(\frac{\kappa}{\kappa_{l}}\right)^{\frac{2}{6}}\right] \cdot\frac{\exp\left(-\frac{\kappa^{2}}{\kappa_{l}^{2}}\right)}{\left(\kappa_ {0}^{2}+\kappa^{2}\right)^{\frac{11}{6}}},0\leq\kappa<\infty \tag{3}\]  
where \(C_{n}^{2}\) is the refractive index structure constant, \(L_{0}\), \(l_{0}\) are the outer and inner scales of turbulence, respectively. \(\kappa_{l}=3.3/l_{0}\), \(\kappa_{0}=2\pi/L_{0}\), \(\kappa_{0}\) represents the wave number corresponding to the outer scale of turbulence, used to describe the characteristics of large-scale structures in turbulence, while \(\kappa_{l}\) represents the wave number corresponding to the inner scale of turbulence, used to describe the characteristics of small-scale structures in turbulence. The wavefront phase disturbance caused by atmospheric refractive index is simulated using \(\Phi_{\varphi}(\kappa)\), and the relationship between refractive index spectral density function and phase power spectrum can be expressed as  
\[\Phi_{\varphi}(\kappa)=2\pi\kappa^{2}\Delta z\Phi_{n}(\kappa), \tag{4}\]  
The phase perturbation in the frequency domain is expressed through the inverse Fourier transform as follows:  
\[\varphi(x,y)=\mathrm{IFFT}(C_{N\times N}\cdot\frac{2\pi}{N\Delta x}\sqrt{\Phi _{\varphi}(\kappa)}), \tag{5}\]  
where \(2\pi/N\Delta x\) is the wave number increment, \(N\) and \(\Delta x\) represent respectively the size of the phase mask and the spacing between grid points, \(C_{N\times N}\) is a complex random array with a mean of 0 and a variance of 1, \(\Delta z\) represents the interval between phase masks, \(\Delta z\). Indicate the interval between phase screens.  
According to Equations (1) and (5), the light field beam passing through the random phase screen is  
\[U_{p1+}^{l}(r,\theta,z)=U_{p}^{l}(r,\theta,z)\exp[i\varphi(x,y)] \tag{6}\]  
where \(U_{p1+}^{l}(r,\theta,z)\) represents the light field passing through the first phase screen. If it reaches the second phase screen, this process will be repeated until it reaches the receiving end. The turbulence model parameters for this study are set as shown in Table 1.