esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 3 Methodology  
This section provides a detailed description of the proposed algorithm, the Nighttime Unsupervised Domain Adaptation Network (NUDN) for object detection based on distillation. Let D denote the daytime source data \(\rm{D_{d}}=\{I_{d},C_{d},B_{d}\}\), where \(\rm{C_{d}}\) represents source data images, \(\rm{C_{d}}\) denotes source data class labels, and \(\rm{B_{d}}\) denotes bounding box labels. The subscript \(\rm{d}\) indicates the daytime source. The nighttime target data is denoted by \(\rm{D_{n}}\), where \(\rm{D_{n}}=\{I_{n}\}\), as the target domain contains unlabeled samples. The subscript indicates the nighttime target.  
The architecture of our NUDN is illustrated in Figure 2. Our network comprises a student network and a teacher network. The student network is a multi-domain model trained with LightImg-enhanced daytime images and unlabeled nighttime images. The teacher network only needs to learn from nighttime images and subsequently generate pseudo-labels for the student, with weights being the exponential moving average (EMA) of the student [8; 13]. Following the pre-training phase, the teacher begins generating pseudo-labels while the student initializes feature extractors and detectors.  
Model initialization is achieved through pre-training, which involves generating candidate object proposals via the Region Proposal Network (RPN). The feature vectors obtained from ROI pooling are used by the ROI-level classifier to predict category labels. The training loss is composed of the losses from both the RPN and the ROI classifier:  
\[L_{Pre}=L_{rpn}+L_{roi} \tag{1}\]  
During each iteration, the teacher first generates pseudo-labels from the nighttime images. These pseudo-labels are filtered through a confidence threshold to ensure the student learns from high-quality pseudo-labels. Subsequently, the bounding boxes in the pseudo-labels are combined with regions proposed by the student's Region Proposal Network (RPN). The combined region proposals are then used to generate predictions from the student's RoI network.
## 6 Result of Case Studies
### Model 3--LNG Cargo Containment Boxes with Hull Structure
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rise Time** [msec]} & \multicolumn{2}{c}{**Area = 0.13 m\({}^{2}\)**} & \multicolumn{2}{c}{**Area = 0.40 m\({}^{2}\)**} & \multicolumn{2}{c}{**Area = 0.53 m\({}^{2}\)**} & \multicolumn{2}{c}{**Area = 0.80 m\({}^{2}\)**} & \multicolumn{2}{c}{**Area = 1.20 m\({}^{2}\)**} \\ \cline{2-10}  & **TIRF** & **DIR** & ** TIRF** & **DIR** & ** TIRF** & **DIR** & ** TIRF** & **DIR** & ** TIRF** & **DIR** \\ \hline
1 & 12.6 & 13.5 & 13.5 & 14.0 & 13.6 & 14.2 & 13.6 & 14.3 & 15.0 & 15.7 \\
2 & 12.5 & 14.5 & 13.7 & 15.0 & 14.1 & 15.3 & 14.0 & 15.3 & 15.2 & 16.8 \\
5 based TIRF method can be used for the structural evaluation of an LNG CCS that consists of layered composite materials and complex boundary conditions. Based on our findings, we have concluded that the TIRF can be applied for evaluating the structural capacities of box-type LNG CCS, provided that appropriate partial safety factors are used.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 4 Discussion
Technical integration challenges include hardware compatibility requirements, data standardization protocols, and connectivity infrastructure limitations in rural environments. Furthermore, model generalization across varying geographical, climatic, and varietal conditions necessitates comprehensive field validation to ensure robust performance under diverse operational scenarios. Additionally, real-field conditions often present more unpredictable variations in lighting, weather conditions, crop growth stages, and background complexity than controlled experimental datasets. This domain gap between laboratory conditions and practical field deployments represents a significant challenge that warrants further exploration in future work.
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 3 Methods and Validation
### Grid Independence Study  
For complex flow phenomena, the selection of grid scale generally relies on the experience of basic flow models; however, more reliable results can be obtained through  
Figure 8: Mesh generation for supersonic cavity-compression corner and geometric parameters.  
Figure 10: Root-mean-square distribution of flow velocity at different stations.  
Figure 9: (**a**) Iso-contours of time averaged \(Ma\) and (**b**) time averaged eddy viscosity.  
grid convergence analysis. The DDES method imposes high requirements on the mesh size, thus this section enhances the encryption of the flow field area near the window. The mesh density along the wall of the back step is increased, with a mesh scale close to the wall set at 50 \(\upmu\)m. After encryption, there are approximately 700,000, 1 million, and 1.3 million grids near the window, respectively.  
Firstly, three types of meshes with varying levels of encryption were utilized to compute the characteristics of the window flow field without jet flow, specifically referred to as the Case1 (_Ma_ = 5, without jet) example. The grid independence was evaluated by analyzing the longitudinal density fluctuation in the flow field as an indicator. As illustrated in Figure 11, no significant disparity is observed in the pulsation trend among the three sets of numerically simulated grid results. From a magnification perspective, it can be noted that the sparse mesh fails to adequately capture detailed variations occurring at locations where the density changes more abruptly. Conversely, medium and refined meshes demonstrate superior capability in capturing and yielding similar outcomes for fluid dynamics.  
The spanwise distribution of flow velocity in the shear layer formed by a cooling jet and distant incoming flow under the Case3 (_Ma_ = 5, jet flow = 0.05 kg/s, \(\theta\) = 15\({}^{\circ}\)) working condition is depicted in Figure 12. It can be observed that there is a significant variation in velocity along the spreading direction, with slightly lower speeds at the center compared to both ends of the window. Based on the calculation results obtained from three different density grids, it is found that the results obtained using medium-density and encrypted grids exhibit similarity, with a maximum error of 3 m/s, accounting for less than 1%. Therefore, for flow simulation near the optical window, employing medium and encrypted meshes is deemed feasible. In order to ensure accuracy while reducing computational efforts, this paper adopts a medium grid for subsequent calculations.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 7 Discussion
Second, serving cells from eNBs located further away are reported, especially after losing the connection to the currently serving cell. The first issue might arise when the connection to the currently serving cell remains stronger than the connection to an adjacent cell, e.g., due to multipath or interference effects. The second issue can be caused by side and back lobes of directional antennas, which seem to establish a better connection for a short amount of time. Furthermore, despite being located in the serving cell sector, the UE might select a stronger cell from a nearby eNB if there is no more LoS to the current eNB. Thus, in line with the ideas of [PERSON] et al. [25], it can be concluded that the performance of the cell reselection process in the NB-IoT technology should be improved.
## 4 A Case Study
### Statistical Analysis of Mining-Induced Seismicity  
To analyze the advanced mining influence range, mining-induced seismicity data from working face 205, covering 8 August 2020 to 4 January 2021, were selected. During the period, the mining distance c was 188.5 m. A continuous statistics method was adopted, setting the distances between the mining phases \(c_{0}\), \(c_{1}\),\(\cdots\), and \(c_{m-1}\) at 0 m. The periodic weighting distances at working face 205 from August 2020 to January 2021 ranged from 10.9 m to 23.1 m. Consequently, eight phases were defined based on the daily advance records, with the corresponding advancing distances \(a\) for each phase listed in Table 1.  
Figure 4 shows the spatial distribution of mining-induced seismicity across the eight phases. It is evident that as the working face advances, the mining-induced seismicity also moves forward, with high-energy events primarily concentrated in the advanced area close to the working face. The maximum distance between the mining-induced seismicity and the working face is 500 m. Hence, the statistical area \(L\) was set to 500 m, and it was divided into 100 advanced subareas (\(n=100\)), each with a distance \(b\) of 5 m.  
The mining-induced seismicity data from the eight phases were analyzed using the method described in Section 3. Figure 5 displays the curves of the total seismic number and the total seismic energy. Both curves generally follow the pattern of typical abutment stress before the working face, as shown in Figure 1. The seismic activity curves initially increase and then decrease as the distance from the working face grows. The peak seismic activities, including both the seismic number and the seismic energy, occur at a distance of 45-75 m ahead of the working face.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase No.** & **Duration** & **Advancing Distance/m** \\ \hline
1 & 8 August 2020–13 August 2020 & 24.0 \\
2 & 14 August 2020–22 August 2020 & 24.4 \\
3 & 23 August 2020–15 September 2020 & 23.3 \\
4 & 16 September 2020–28 September 2020 & 23.6 \\
5 & 29 September 2020–19 October 2020 & 24.9 \\
6 & 20 October 2020–3 November 2020 & 24.2 \\
7 & 4 November 2020–27 December 2020 & 21.0 \\
8 & 28 December 2020–4 January 2021 & 23.1 \\  & Total & 188.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advancing schedule of working face 205 from 8 August 2020 to 4 January 2021.  
Figure 3: Layout of (**a**) working face 205 and (**b**) the deep borehole ZY1 with optical fiber for monitoring overburden strata strain.  
Figure 4: Spatial distribution of mining-induced seismicity at working face 205 during (**a**) Phase 1, (**b**) Phase 2, (**c**) Phase 3, (**d**) Phase 4, (**e**) Phase 5, (**f**) Phase 6, (**g**) Phase 7, (**h**) Phase 8.  
Figure 5: _Cont._
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 3 CAN Network Elements
### Spectral Converters
#### 3.3.2 Experimental Analysis of Spectral Converter Performance
Figure 12: Representative optical spectrum (**a**) emerging from the DP-MZM subject to the optimum operating conditions for the 1-st converted optical component; (**b**) emerging from the DP-MZM subject to non-optimized operating conditions. Figure 13: BERs versus optical launch power for the first and third converted optical components each obtained under its optimum operating conditions. A BER curve for the spectral conversion-free input optical signal is also plotted for comparison.
## 7 Cross-Layer Design Schemes
### MAC and Physical Layer Schemes
Normally AMC is used to change the transmission rate at the physical layer, improve BER, or vary bandwidth. Time slotted and unslotted sleep modes were used in the MAC layer. Energy consumption was expressed by the product of power level and time slot length. Simulation results showed that the cross-layer scheme achieved minimal energy consumption, thereby extending node lifetimes, while the spectral efficiency in the whole SNR range was maximized.
###### Abstract
This method utilizes an insulated sub to isolate the positive and negative electrodes [12,13]. However, due to the limited length of the insulated sub (less than or equal to 1 m), it prevents currents from flowing deeper into the formation, which reduces the effective measurement distance. Furthermore, this method is still in its research and development stage and has not yet been implemented in formal commercial applications in field cases [14]. During the operation of connecting the relief well and the accident well, it is not feasible to place the excitation source of the ranging system into the accident well. Therefore, an access-independent excitation method must be adopted in the drilling well, where both the excitation source and the receiving device are located. The key challenge lies in improving the effective ranging range and measurement accuracy, while also reducing the safety risks, operating costs, and operating time. The WellSpot tool currently utilizes a single-electrode excitation mode in the drilling well, requiring the insertion of the excitation electrodes through the cable and relying on ground systems for the high-power excitation current. However, this setup does not allow for real-time ranging or synchronous operation during drilling [8]. To address these limitations, this paper explores a new method based on dual-electrode current excitation for MWD. This synchronous excitation approach enables real-time measurements during drilling operations and is particularly suitable for connecting relief wells with their inherent safety risks. In hazardous drilling incidents, such as blowouts, the safety risks near the wellhead of the accident well are significant. Therefore, it is necessary to run a ranging excitation device and measuring tool into the relief well. Currently, the primary ranging system used for communication between the relief well and the accident well is the WellSpot tool developed by the VM company. This tool adopts a drilling well unipolar current incentive that requires cable ranging and drilling intersection operation. In order to achieve the synchronous operation of ranging and drilling, as well as to improve the ranging range and measuring accuracy, this paper proposes a new method for MWD based on dual-electrode current excitation. The research results provide a theoretical basis for developing and optimizing the parameters of distance MWD systems for relief wells.
## 4 Performance Evaluation
### Tail Pulling Detection Performance  
We designed a stretch sensor in the tail of Pepita. The tail is made of silicon, and a linear hall effect sensor is used to measure the variations of distance from a magnet when the tail is stretched. The wiring inside the silicon was coiled to avoid ruptures. With this approach, it was possible to work with the structure and material of the tail to design a simple interaction. The purpose of this experiment was to test the performance of the sensor to detect pulling behavior among other gestures.
## 3 Results and Discussion
### Process Design and Economic Analysis
Sensitivity analysis was provided for key assumptions in reference to the unit production cost and is presented in Figure 5. The baseline unit production price was 2.75 $/kg and the baseline number of batches was \(10^{7}\). Those values were then tested in the range of \(-20\%\) to \(20\%\). Results show that the main impact on product cost has the medium price, which confirms the need for seeking alternative carbon sources and media based on recycled waste materials. Additionally, the drying process has some impact, mainly because of the dryer purchase price, as well as the amount of trehalose and energy used. Trehalose cost, HPMC cost, and fermentation time both show the expected impact on the price. Little to no change was observed for coating, mainly since this process utilizes the same equipment as the drying step and, therefore, only the resource and energy demand are considered. Figures 6 and 7 both show the probability distribution based on 10,000 trials for the unit production cost and annual number of batches, accordingly. The probability of achieving unit production prices under 3.01 was calculated at 91.88%. The probability of achieving the desired annual number of batches in the range of 102-112 was certain in 86.79%. The variability of the main product cost and number of batches per year has been demonstrated in Figure 8.  
Figure 5: Tornado plot depicting the impact of variables on unit production cost. Figure 6: Probability distribution of the unit production cost, based on 10,000 trials. Figure 7: Probability distribution of the annual number of batches, based on 10,000 trials.
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 3 Theoretical Background
### Prediction Accuracy  
Network traffic prediction in the next time step depends on the historical traffic volumes. Having measurements of the past throughput \(\{r_{t-1},\ldots,r_{t-n}\}\), we aim to predict the value of \(r_{t}\). Hence, we use a predictor \(\hat{r}_{t}\), which takes the past throughput  
\[\hat{r}_{t}=\phi(r_{t-1},\ldots,r_{t-n}). \tag{1}\]  
The problem is to determine \(\phi\), which minimizes the difference between \(\hat{r}_{t}\) and \(r_{t}\). To quantify the difference, we opted for the Normalised Root Mean Square Error (NRMSE) as our metric:  
\[\text{NRMSE}=\frac{1}{\overline{r}}\sqrt{\frac{\sum_{t=1}^{N}(r_{t}-\hat{r}_{ t})^{2}}{N}}, \tag{2}\]  
The NRMSE is the squared difference between predictions \(\hat{r}_{t}\) and measured values \(r_{t}\) divided by the number of measurements \(N\). The NRMSE value is an indicator of how well a model fits empirical data. The lower the NRMSE value, the more accurate the prediction. To allow for a comparison of the results across traces with different throughput levels, we normalized the results by an average of observed values \(\overline{r}\).
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
###### Abstract
It may be worthwhile to incorporate computational intelligence or data-based learning approach that enhances classification performance for reliable decision-making processes. In this study, we investigate signal processing methods in conjunction with an optimization algorithm to isolate the damage of a bearing system, and identify the types of defects. The typical signal of bearing failure appears as a mixture of periodic shock waves superimposed on the noise from normal bearings. In order to distinguish the characteristics of the damaged and healthy bearing signals, a technique for resolving the fault signal needs to be applied first. The time-frequency decomposer EEMD was used to decompose the acceleration measurement of a bearing with defects. Specifically, EEMD is applied to segments, which are divided into uniform time intervals, and the statistical features of each signal are extracted to manifest the characteristics of the bearing condition. This process generates the EEMD-based feature vector of the time segment signal, which is used for defect detection. The statistical properties of time-series data, such as standard deviation, root mean square (RMS), kurtosis, zero-crossing rate, and entropy, have different ranges of values. Thus, the characteristic values having different ranges vary in sensitivity to defects, because the weights of defects are different from each other. In order to prevent bias of weightings in parameters, we normalized the same characteristic values to generate equivalent vectors. The feature vectors are then visualized using two-dimensional reduction techniques, PCA, and Isomap. It is expected that the feature vectors having the same defect case are located at a distance adjacent to each other to form one defect group. However, depending on the nature of defect, the generated feature vector may have both defect-sensitive and insensitive characteristics and weightings, which may deteriorate the classification performance. In order to solve this problem, particle swarm optimization [15], which is one of the most reliable optimization algorithms, was used to determine a linear weight to the feature vector, and to optimize its coefficients. We used the Dunn index [16; 17], which is typically used in classification analysis, to indicate whether the objective function of PSO is well distinguished from other groups. The dimension of the optimized results was reduced by two to visualize the classification. PCA, Isomap, and non-optimized results were all compared for discussion. Finally, the aforementioned techniques that have been applied to a mathematical model for simulating bearing defects were verified using actual data, i.e., experimentally measured defective bearing signals.
## 7 Discussion  
### Validity of Results  
The method proposed in this article constructs an RLC oscillation circuit, which achieves accurate measurement of residual magnetism and synchronous demagnetization by converting the energy between the storage capacitor and the magnetic field of the transformer core. In order to prevent the induced current on the secondary side from affecting the residual magnetism of the iron core and causing any interference to the power grid during demagnetization, the method proposed in this article is mainly aimed at transformers under power outage conditions. The experimental and simulation results show that the measurement error of this method is less than 5%, and the residual magnetism after demagnetization is less than 3 Wb, which is significantly better than traditional methods. Meanwhile, this article validated the feasibility of this method in large transformers. The main sources of error are concentrated in the process of voltage flux conversion, such as possible noise interference, long oscillation of voltage leading to inaccurate integration intervals, and accuracy limitations of the measurement equipment. By optimizing data collection equipment and signal processing algorithms, errors can be further reduced and measurement accuracy can be improved.  
Figure 21: Dynamic hysteresis curve plot (software interface. The blue curve represents the demagnetization hysteresis loop, the red curve represents the current curve, and the yellow curve represents the voltage curve).
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Mission Planning Algorithm Based on ACO-GA
#### 3.2.2 Selection Method Based on ACO  
Traditional genetic algorithm selection operations effectively implement the survival of the fittest in the population, preserving individuals with higher fitness values for crossover and mutation operations in the next generation. Common selection methods include roulette wheel selection, probability selection, and tournament selection, all of which involve transmitting some individuals unchanged to the next generation. However, individuals with high fitness values may be heavily replicated, reducing solution efficiency and potentially causing the algorithm to converge to local optima. In the ACO, each ant determines its next task point based on the transfer probability of its task points. This transfer probability is calculated based on the residual pheromones and heuristic information along the path, which accelerates convergence. Each ant operates independently and communicates through pheromones, providing certain advantages for increasing solution diversity.  
For the ant \(k\), the probability of its transfer from task point \(i\) to \(j\) at time \(t\) is calculated using:  
\[p_{i,j}^{k}(t)=\left\{\begin{array}{l}\frac{[\tau_{i,j}(t)]^{\alpha}\cdot[ \eta_{i,j}(t)]^{\beta}}{\sum\limits_{\begin{subarray}{c}\epsilon\in k\\ 0,others\end{subarray}}([\tau_{i,j}(t)]^{\alpha}\cdot[\eta_{i,j}(t)]^{\beta}},j \in J_{k}(i)\\ 0,others\end{array}\right. \tag{8}\]  
where \(\tau_{i,j}(t)\) is the size of the pheromone from task point \(i\) to \(j\), its initial value is usually a small constant, and its value is updated after all ants have obtained the path. \(\eta_{i,j}(t)\) is a heuristic factor that represents the desired degree from task points \(i\) to \(j\), the value of which is usually the reciprocal of the distance between the two task points. The size of \(\alpha\) and \(\beta\) determines the importance of the above two parameters.  
Therefore, two strategies were adopted in the selection operation: half of the solutions were generated using ACO, and then the tournament selection method was used until the number of solutions reached \(N\!P\).