esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 4 Methodology
### Hard Example Mining Approach  
RetinaNet is a one-stage object detection algorithm, which directly regresses and classifies the bounding box of the object with fast detection speed. In order to deal with the sample imbalance problem, FL is used as the classification loss function in training. FL mainly solves the problem of positive and negative sample imbalance and also deals with the problem of hard examples. However, there are a small number of hard examples in the terahertz image dataset for which the effect of FL is limited.  
OHEM provides a method to specifically mine hard examples for training. It is designed for a two-stage object detector and cannot be used directly for RetinaNet; therefore, we modified the process. We first train the basic detector, then used the detector to compute loss to select hard examples, and used these hard examples to reinforce training the detector. HEM is implemented by converting the selection hard RoI to selection hard examples. The HEM training process is shown in Figure 5, where \(Data_{B}\) denotes the base terahertz image sample set, \(Data_{T}\) denotes the training sample set, and \(Data_{H}\) denotes the hard example sample set.  
The training process is mainly divided into two stages. The first stage is the training of the basic detector, and the second stage is the hard example mining and the intensive training of the model. In the first stage, the base terahertz image sample set is randomly disrupted and input to RetinaNet combined with focal loss to train the network directly until convergence. Once the basic detector training is complete, move on to the second stage. First, all training samples are passed through RetinaNet to calculate the loss, and then the top 10% samples with large loss are selected as hard examples according to the order of loss from largest to smallest. Finally, the hard example \(Data_{H}\) and the base terahertz sample \(Data_{B}\) are input into the training data generator, which is randomly disrupted to generate new training data \(Data_{T}\) and continue to train the detector. The pseudo-code for the whole process is shown in Algorithm 1.  
Figure 5: Training process of RetinaNet and Focal Loss combined with HEM.  
In the second stage, there are two methods of adding hard examples to the training data generator. The first is to ensure the training set size is constant and add hard examples after removing the easy classification samples (namely \(\text{HEM}_{E}\)). The second is to directly add hard examples to expand the training set (namely \(\text{HEM}_{A}\)). The following experiments show that using the second method to add difficult examples to the training set is more effective.
# Design and Modelling of an Amphibious Spherical Robot Attached with Assistant Fins
## 3 Dynamic Model of the Robot
### Coordinate Frames  
Coordinate frames attached to those three rigid bodies are shown in Figure 5. An inertial frame \(U\)-\(XYZ\) is set with its \(X\) axis pointing to the east of the locus, its \(Y\) axis pointing to the north and its \(Z\) axis pointing vertically upward. The frame \(O\)-\(XYZ\) is parallel to the inertial frame, with its origin located at the center of spherical shell \(O\). Let \(O_{S}\)-\(X_{S}Y_{S}Z_{S}\) be the coordinate frame anchored to the spherical shell, whose origin is located at the point \(O\) and \(Y_{S}\) axis is always parallel to the main axis. This \(O_{S}\)-\(X_{S}Y_{S}Z_{S}\) frame is used to describe the configuration of the spherical shell. Let \(O_{D}\)-\(X_{D}Y_{D}Z_{D}\) be the body frame of the IDU platform, with its origin located also at point \(O\), its \(Y_{D}\) axis coinciding with the \(Y_{S}\) axis and its \(Z_{D}\) axis parallel to the rotator's spinning axis. In addition, the \(A\)-\(X_{A}Y_{A}Z_{A}\) frame, located at the center of mass of the rotator, is used to describe the configuration of the rotator; its \(Z_{A}\) axis lies along the rotator's spinning axis.  
Figure 4: Control block diagram of the robot.  
Figure 5: Coordinate frames of the robot.  
In general, eight variables \(\mathbf{\eta}=(x,y,z,\phi,\theta,\psi,\alpha_{s},\alpha_{r})\) are needed to describe the states of the whole robot, among which, three variables \((x,y,z)\) represent the center of the spherical shell, three Euler angles \((\phi,\theta,\psi)\) represent the orientation of the IDU platform, the angle \(\alpha_{s}\) represents the angle between the body frame of the spherical shell \(O_{S}\)-\(X_{S}Y_{S}Z_{S}\) and the body frame of the IDU platform \(O_{D}\)-\(X_{D}Y_{D}Z_{D}\); lastly, the angle \(\alpha_{r}\) is the spinning angle of the rotator. As these eight variables are independent of each other, they are chosen as the generalized coordinates of the robot system.
## 4 Results
### Synthesis of Strain Results
#### 4.1.2 Maximum Shear Strain (MaxSS)  
The MaxSS parameter exhibits a wide distribution of values within the North Aegean Sea region, as depicted in Figure 5. These values range from \(10\) to \(1650\) nano-strain, with both peak values observed in the Samothraki Plateau. The regions that show the highest values are documented in the eastern part of the Aegean Sea (western Turkey), the CAT (Central Aegean Trough) and the NAT, as shown in Figure 5a. The MaxSS is characterized by slightly reduced values within the vicinity of the NAB, in the transitional zone that links the NAB and the CAT, and the broader region of northwestern Turkey. The regions showing great values are primarily associated with shear faulting. The extension may exhibit a greater magnitude in regions with possible oblique-to-normal dip-slip faulting. An example of such a scenario can be observed in the eastern Aegean Sea region, suggesting the occurrence of normal and oblique-slip faulting. Local kinematic variations (from pure normal dip-slip to strike-slip faulting) can be observed in regions with moderate values.  
Concerning the Strymon basin, the MaxSS value analysis shows a range of approximately \(1\) to \(2750\times 10^{-9}\) nano-strain (Figure 5a). The highest MaxSS values are recorded in the eastern region of the Strymon basin, with comparable values noted in the northwestern margin [17], respectively, as depicted in Figure 5a. Furthermore, high MaxSS values are documented in the southern region of the western boundary of the Strymon basin. The results of the geostatistical analysis, as presented in Figure 5b, provide the distribution of MaxSS. In particular, the analysis reveals that higher values of MaxSS are concentrated along the WNW-ESE axis of the Strymon basin [17]. Notably, the adjacent basins (such as the Drama basin) exhibit exceedingly low, nearly negligible values. Consequently, the significant shear values highlight the tectonic structures that are actively distributed along the boundaries of the Strymon basin, with particular emphasis on the eastern one.  
Finally, the Thessaly area exhibits a range of MSS values from 5 to 730 nano-strain. According to the analysis of MSS values (Figure 5a,b), the regions with the highest values, exceeding 400 nano-strain, are adjacent to the western boundary of the Thessalian basin [72]; additionally, high MSS values are observed in the southern part of this region. Moreover, intermediate values (approximately 200-400 nano-strain) have been detected all along the western margin, while the northern part is characterized by low MSS values, measuring less than 100 nano-strain.
## 4 Key Characteristics Analysis  
### Capture Characteristic  
Since EASIST can accurately control the motor speed, it is possible to merge the capture processes of EMPU and ERSD, as shown in Figure 10. ERSD immediately drives the claw to approach the probe rod along the lateral when EMPU starts driving the ERSD to approach the probe rod along the longitudinal. Thus, the total capture time can be reduced by more than half.  
The driving speed trajectory of EMPU and ERSD needs to be planned to minimize the capture time and reduce the capture force. Both EMPU and ERSD have the following characteristics when executing a capture task:  
* The claw displacement is determined.
* The claw accelerates from rest.
* The claw impacts the probe rod at a low speed.  
For example, the capture process of EMPU can be described as follows. As shown in Figure 11, EMPU drives ERSD to start from rest and accelerates flexibly to a certain speed \(v_{\text{max}}\) and runs at this speed uniformly for a fixed displacement. EMPU enters the deceleration stage and decelerates to \(v\) in the remaining distance when ERSD approaches the probe rod. At the same time, it reaches the end of the displacement and completes the longitudinal capture task.  
In the motor control system, a suitable trajectory planning algorithm is helpful to reduce mechanical wear, increase the service life, and improve the control accuracy of the system. Compared with other trajectory planning algorithms, the cosine function has advantages of the smooth connection point, infinite continuous derivative, and simple  
Figure 11: Velocity curve planning [42].  
Figure 10: EASIST capture stage diagram.  
calculation [42; 44; 45]. The cosine function is used to construct the longitudinal capture velocity trajectory as follows:  
\[\left\{\begin{array}{ll}v_{\rm{ya}}(t)=\frac{1}{2}v_{\rm{max}}(1-\cos t_{\rm{a }}^{\prime})&0\leq t<t_{\rm{ma}}\\ v_{\rm{yc}}(t)=v_{\rm{max}}\\ v_{\rm{yd}}(t)=\frac{1}{2}(v_{\rm{max}}+v_{2})-\frac{1}{2}(v_{2}-v_{\rm{max}}) \cos t_{\rm{d}}^{\prime}&t_{\rm{ma}}+t_{\rm{c}}\leq t\end{array}\right.. \tag{11}\]  
where  
* \(t_{a}^{\prime}=(t/t_{\rm{ma}})\pi\), \(t_{d}^{\prime}=[(t-t_{\rm{ma}}-t_{\rm{c}})/t_{\rm{md}}]\pi\);
* \(t_{\rm{ma}}\) is the required time of acceleration;
* \(t_{\rm{md}}\) is the required time of deceleration;
* \(t_{\rm{c}}\) is the time of constant velocity.  
And the lateral capture velocity can be structured by normalizing the longitudinal speed according to the position of the probe rod [45], as shown below:  
\[v_{x}=\lambda v_{y}. \tag{12}\]  
For the position \((x,y)\) of the probe rod at the time of capture, the normalization factor \(\lambda\), can be obtained from the following equation:  
\[\lambda=\frac{x}{y}. \tag{13}\]
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 6 Deployment of DCoS  
DCoS functions as an intelligent scheduling layer within the transaction processing pipeline. It builds upon a correctness enforcement layer that guarantees serializability through partitioning methods and runtime pipelining (RP). Figure 7 depicts the hierarchical interaction among partitioning, CC protocols, and scheduling methods. DCoS has the following characteristics:  
**Adaptive execution under varying contention levels.** In low-contention scenarios, coarse-grained execution is sufficient. Transactions that do not access hot data and are free from contention can be executed entirely within a single partition. This approach eliminates coordination overhead and avoids the complexity associated with fine-grained control mechanisms. When contention is detected, typically through cross-partition accesses to hot data items, DCoS responds by activating a fine-grained scheduler based on DRL. The scheduler reorders and interleaves contention-intensive transaction pieces in a way that minimizes conflicts and improves concurrency.  
**Complementarity with existing CC protocols.** DCoS is designed to serve as a complement to existing correctness mechanisms. Rather than replacing CC protocols that optimize lock acquisition order or validation timing, DCoS enhances them by operating at the scheduling layer. DCoS focuses on improving execution order to reduce conflicts and transaction aborts. DCoS assumes the presence of a base CC protocol, such as RP, to enforce correctness, while it adaptively applies fine-grained scheduling only when it is beneficial.  
_Limitations and Practical Considerations_  
DCoS introduces several practical considerations:  
Figure 7: Transaction processing of DCoS under varying contention levels.  
**Assumption of known access patterns.** DCoS relies on a one-shot transaction processing model where access patterns are known ahead of execution, which may limit its applicability in dynamic or ad hoc workloads with unpredictable access patterns.  
**Accuracy in hot data and contention detection.** The integration of a DRL-based scheduler requires offline training. The effective activation of fine-grained scheduling depends on the accurate identification of hot data items and contention-intensive transaction pieces.  
A promising direction for future work is the tighter integration of DCoS-style scheduling with enhanced CC mechanisms, enabling adaptive selection of scheduling strategies tailored to workload characteristics. Furthermore, exploring lightweight online learning and transfer learning techniques [32] could reduce training overhead and improve scheduler adaptability, broadening DCoS's applicability across diverse OLTP environments.
## 4 Experimental Results and Analyses
### Experiment on Shot Boundary Detection
#### 4.2.2 BBC Planet Earth Dataset  
The dataset for the BBC's educational TV series Planet Earth [48] has 11 videos. Sample images are shown in Figure 9. All of the videos are approximately 50 min in duration.  
Their information is listed in Table 12. Here, we select the first 10 min of each for the experiments. The results are also listed in Table 12.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 3 Proposed SC-SR Algorithm
### Sparse Representation Model for Image Denoising
The weight \(w_{ij}\) of the two patches was computed as follows based on their covariance matrixes \(\mathbf{Cov}_{i}\) and \(\mathbf{Cov}_{j}\):  
\[w_{ij}=\frac{1}{\mathbf{A}_{i}}e^{-pd(\mathbf{Cov}_{i},\mathbf{Cov}_{j})}, \tag{20}\]  
\[\mathbf{A}_{i}=\sum_{j=1}^{L}e^{-pd(\mathbf{Cov}_{i},\mathbf{Cov}_{j})}. \tag{21}\]  
Based on the iterative soft-thresholding (IST) method [19], the solution to Equation (18) was computed by:  
\[\mathbf{\alpha}_{i}^{(t+1)}=S_{\tau}\Big{(}\mathbf{v}_{i}^{(t)}-\mathbf{\chi}_{i}\Big{)}+ \mathbf{\chi}_{i}, \tag{22}\]  
\[\mathbf{v}_{i}^{(t)}\mathbf{\alpha}_{i}^{(t)}-\frac{1}{c}=\mathbf{D}^{T}\Big{(}\mathbf{D}\mathbf{ \alpha}_{i}^{(t)}-\mathbf{y}_{i}\Big{)} \tag{23}\]  
where \(S_{\tau}\) is a soft-thresholding function; \(t\) denotes the iteration frequency; and \(c\) is a constant to guarantee the strict convexity of the optimal problem and \(\mathbf{D}^{T}\mathbf{D}<c\)[20]. After \(J\) iterations, we can get preferable sparse coefficients. All patches can be estimated by \(\mathbf{\hat{y}}_{i}=\mathbf{D}\mathbf{\alpha}_{i}^{(J)}\). Since every pixel admits multiple estimations, its value can be computed by averaging all estimations. When the sparsecoefficients for all patches within the image were obtained, the whole original image \(x\) was obtained by Equation (8). By using the weighted average sparse coefficients of the similar patches to restrain the sparse decomposition process of the image patches, the accuracy of the sparse coefficients was is improved. As a result, the reconstructed image was closer to the original image. The proposed algorithm is completely demonstrated in Algorithm 1. It makes full use of the NSS prior both in the spatial domain and sparse transform domains. Clustering in the spatial domain with mid-level visual cues could better exploit image structure and edges prior in comparison with directly clustering the size-fixed local patches, which also contributes to the preservation of image edges and textures. As for the sparse transform domain, to achieve a better solution, the weighted average of the sparse coefficients for the patches similar to a local patch was utilized to constrain the sparse coefficient for the local patch. These are all devoted to the high denoising performance of the proposed algorithm. ```
1. Input image \(\mathbf{Y}\) with white Gaussian noise. 2. Set parameters: noise variance \(\delta\), superpixles number \(N_{s}\), cluster number \(K\), the patch size \(b\times b\), the number \(L\) of the first most similar patches, regularity parameters \(\beta,\rho,u,\gamma,\lambda\ \eta,c\), \(J\). 3. Adopt SLIC to generate \(N_{s}\) superpixles. 4. Utilize sparse subspace clustering method to group superpixels into \(K\) cluster to form sub-datasets \(\{\mathbf{M}_{k}\ \}_{k=1}^{K}\). 5. **Outer loop**: For \(k=1\): \(K\)
```  
**Algorithm 1** The proposed algorithm called SC-SR
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification  
### Introduction of the Case Study  
The case study involved a 26-story reinforced-concrete building with four basement levels, requiring an excavation depth of 15.8 m over an area of approximately 1400 m\({}^{2}\). The excavation utilized a diaphragm wall as a temporary retaining structure, which was 32 m deep and 80 cm thick. A top-down construction method with internal supports was employed for the foundation excavation, following a six-cut and five-support approach. The supports were spaced horizontally at approximately 6 m, and vertically at around 3 m. The support system included the following: H300 steel for the first stage, with a load capacity of 30 tons per support; H350 steel for the second stage, with a load capacity of 80 tons per support; 2-H350 steel for the third stage, with a load capacity of 120 tons per support; and 2-H400 steel for the fourth and fifth stages, with a load capacity of 150 tons per support.  
For this project, four geological boreholes were drilled: two to a depth of 33 m and two to a depth of 50 m. Based on the results from these borehole investigations, in situ tests, and laboratory analyses, the site strata were categorized into seven main layers. The intermediate piles were made of H350 steel, each measure 32 m in length. Within an area extending five times the excavation depth, several neighboring buildings were located: a 12-story building with two basement levels to the east, a 4-story building with one basement level to the north, a 4-story building with one basement level to the west, and a 5-story building with one basement level to the south. Safety monitoring was conducted throughout the excavation process to ensure the stability of these adjacent structures and monitor the load and displacement behavior of the retaining support system.
# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 2 Background
### Major Motivations  
Evolutionary algorithms (EAs) have been widely applied to multi-objective feature selection [18; 31; 32], but most studies merely apply EAs without considering the relationships among features. We recognize that, with the increasing complexity of real-world data, especially the rise of big data, features in high-dimensional datasets are likely to encompass various complex relationships [33] which might not be merely linearly positive or negative correlations. These potential high-dimensional relationships could significantly impact the exploration process of feature selection. We can attempt to capture such relationships and examine their contributions to evolutionary optimization. The motivation to integrate knowledge-guided strategies stems from the observation that not all features contribute equally to the outcomes of predictive models. Some features are more interdependent than others and can be utilized to enhance the search process. By incorporating knowledge of feature correlations directly into the evolutionary process, this method can more effectively guide the search, reduce computational overhead, accelerate the search speed, and improve the quality of the selected feature subsets.  
Furthermore, in multi-objective optimization, particularly when applied to feature selection, balancing exploration of the search space and exploitation of promising areas are crucial. This balance is difficult to achieve with a single algorithm because of the high risk of convergence to local optima [34; 35]. Traditional static cooperative algorithms cannot adapt to changes in the problem landscape that might occur during the search process. A dynamic, adaptive approach can better respond to such changes, optimizing resource allocation based on the performance of competing methods, thereby ensuring that the most effective strategies are employed as conditions evolve. These motivations guide the development of the KCCEA, focusing on creating a versatile, efficient, and effective tool for feature selection that addresses the unique challenges posed by modern, high-dimensional datasets in various domains. The ultimate goal is to provide a method that not only performs well in academic benchmarks but also offers practical, real-world applicability and scalability.
# Experimental Study of a Novel Prototype of Dual-Cycle Two-Phase Cooling System for a Data Cabinet
## 4 Conclusions  
This study proposes a dual-cycle two-phase cooling system (DTCS) based on data cabinets, which aims to achieve the efficient cooling of data cabinets and provides an innovative and efficient cooling solution for large-scale data centers. Through in-depth analysis of the operational characteristics and energy consumption of this dual-cycle cooling system, the main conclusions are as follows:  
1. Even under extreme conditions where the outdoor temperature reaches 42 \({}^{\circ}\)C, the chip cooling system can still stabilize the chip core temperature at 76 \({}^{\circ}\)C, enabling uninterrupted natural cooling throughout the year. In addition, this study found that appropriately increasing the control temperature can significantly reduce the heat transfer temperature difference at the system level, thereby improving cooling efficiency.
2. For two pump-driven cooling systems, excessive flow rates can lead to a significant increase in resistance in the two-phase zone. This increased two-phase resistance will widen the evaporation-condensation temperature difference of the system, adversely affecting overall efficiency. This phenomenon is particularly pronounced in low-pressure pump-driven two-phase cooling systems.
3. While ensuring stable server operation, precisely controlling the chip temperature, and appropriately increasing the ambient temperature of the data center can significantly reduce the energy consumption of the cooling system, thereby decreasing the overall energy consumption of the data room.
4. The DTCS demonstrates excellent cooling performance, with an annual average pPUE of 1.078. In most cities in China, the pPUE of this system can be maintained below 1.1, fully demonstrating its enormous potential and application value in energy conservation and emission reduction in data centers.