esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 3 Results
### Clock Performance
C34 shows the best performance, with a  
Figure 8: Modified Allan deviation of BDS-3 (C31–C34) satellite clocks for the time period DOY 204–223 (2016). For comparison purposes, the performance of the BDS-2 satellite clocks is also shown in the plot. G(2), I(2) and M(2) represent BDS-2 GEO (C01–C05 ), IGSO (C06–C10) and MEO (C11, C12, C14) satellites, and I(3) and M(3) represent BDS-3 IGSO (C31, C32) and MEO (C33, C34) satellites, respectively. MADEV of about \(1.5\times 10^{-13}\) at an integration time of 1000 s, and \(4.1\times 10^{-14}\) at an integration time of 10,000 s. In addition, the mean frequency stability of 1000 s, 10,000s, and one day, according to different satellite types, is listed in Table 7. For IGSO, BDS-3 shows an improvement of 19.2%, 38.8%, and 33.3% for frequency stability of 1000 s, 10,000 s, and one day, respectively. For MEO, the corresponding improvement is 36.8%, 30.7%, and 55.0%, respectively. In summary, BDS-3 satellite clocks outperform previous BDS-2 clocks in terms of frequency stability thanks to new RAFS and PHM.
Design and Optimization on a Novel High-Performance Ultra-Thin Barrier AlGaN/GaN Power HEMT With Local Charge Compensation Trench  
[PERSON]  
1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China 1,  
the turn-on resistance (Ron) with the breakdown voltage (BV) [17; 18]. In other words, this LDD could be used to enhance the device's Baliga Figure-of-Merits (BFOM) in a form that has been adopted in recent GaN power device research and is depicted in Equation (1), where \(BV\) is the breakdown voltage and \(R_{on}\) is the on-state resistance [19; 20]:  
\[\text{BFOM}=\frac{BV^{2}}{R_{on}} \tag{1}\]  
Nevertheless, to redistribute the E-field, LDD requires etching or an ion implantation process to form a special alloy barrier region which is beside the gate to modulate the 2 DEG density underneath. This process always causes lattice damages and, hereby, low electron mobility [8; 15; 21]. Fortunately, a novel HEMT architecture that utilizes the charge compensation from the Silicon Nitride (SiN) to the AlGaN ultra-thin barrier (UTB) has been presented [22; 23]. In this architecture, one can modulate the concentration of 2 DEG induced in an AlGaN/GaN interface without etching the barrier just by adding or removing the SiN layer which supplies the compensation charges. In other words, to realize the enhancement-type HEMT, one can partially remove the SiN on the UTB at which the gate metal will be deposited. Therefore, by such virtue of UTB, a LDD-like technique could be introduced naturally into the HEMT without lowering the quality of the heterojunction. Furthermore, recent experiment results have already demonstrated the prospect of UTB-HEMT in power applications [24; 25].  
In this paper, we proposed a novel UTB HEMT with a local charge compensation trench (LCCT) to further improve the performance of AlGaN/GaN HEMT. Such LCCT-HEMT avoids the lattice damage in the barrier, and in the meantime, LCCT could modulate the 2 DEG concentration to smoothen the reverse E-field by introducing extra negative charges [26; 27]. In this way, BFOM that is two times higher, compared with conventional UTB HEMT, could be obtained in the proposed device with a properly designed LCCT according to simulation that is calibrated by experimental data. These results reveal the potential of proposed HEMT in high-power applications.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design  
In this study, we propose the Multi-Constraint Optimization for Network Topology Generation (MCOTG) method to resolve the single-objective optimization problem. The single-objective optimization problem in our model concerns the generation of an optical communication constellation network that is optimized for specific constraints, including visibility time window constraints and degree constraints for each satellite. The MCOTG method is designed to efficiently navigate this problem space by considering multiple constraints during the topology generation process. The method starts with an initial network topology and iteratively refines it by evaluating link weights based on several attributes and by removing redundant links. This process facilitates the optimization of the network topology under the specified constraints, hence addressing the single-objective optimization problem. Moreover, by considering the effects of link propagation delay, link bandwidth, and link lifetime on inter-satellite communication, the MCOTG method ensures that the resultant network is not only optimized for the constraints but also for performance and robustness. Therefore, we believe that our MCOTG method is a feasible solution to the single-objective optimization problem.  
The MCOTG comprises three primary components: network topology initialization, link weight determination, and redundant link removal. As illustrated in Figure 2, the algorithm considers the impact of multiple attributes on satellites. Initial links are established between satellites that satisfy visibility time window constraints. For these links, a multi-attribute decision-making method is employed to determine attribute weights, minimizing the influence of subjective factors on indicator weight decision-making. Subsequently, link weights are calculated, providing a basis for link removal decisions. During the link removal process, if a satellite possesses an excessive number of potential nodes, smaller-weight potential links are removed first to ensure the constellation network topology's connectivity. The process continues until all satellites satisfy degree constraints, finalizing the optical communication constellation network topology.
# Time Series Classification with Shapelet and Canonical Features
## 3 Definition and Background
### Time Complexity Analysis  
Training time complexity: RSFCF is an ensemble classification model composed of \(r\) time series trees. Building a time series tree first requires dataset transformation and then training on the transformed dataset. The time complexity of the latter is \(O(n\cdot\log(n)\cdot k\cdot a)\)[14], where \(n\) is the number of training set instances, \(\log(n)\) is the average depth of the tree, and the converted dataset has \(O(k\cdot a)\) attributes (\(k\) is the number of randomly selected Shapelets, \(a\) is the number of features used when transfroming the dataset). Randomly selected Shapelets vary in length and the computational complexity of each feature varies, making it difficult to analyze the precise time complexity of the dataset transformation process, but still making reasonable estimates possible. The transformation of time series \(\mathbf{T}_{i}\) over Shapelet \(\mathbf{S}_{j}\) consists of two processes: (1) finding the subsequence \(\mathbf{S}^{\prime}\) most similar to \(\mathbf{S}_{j}\) on the Euclidean distance measure in \(\mathbf{T}_{i}\); (2) calculating the value of \(\mathbf{S}^{\prime}\) on \(a\) randomly selected features. Process 1 requires \(2\cdot_{shift}\) normalized Euclidean distance calculations with complexity of \(O(m)\) (\(m\) is the length of Shapelet \(\mathbf{S}_{j}\)), so the time complexity of the process is \(O(shift\cdot m)\). On the basis of the Catch22 feature set, we introduce three features of linear computational complexity (mean, variance and slope), and randomly select \(a\) features of 25 features to calculate the feature values, since the average computational complexity of the 22 Catch22 features is approximately linear (\(O(m^{1.16})\)) [15], so the computational complexity of process 2 is also approximately linear in the mean sense, and the total time complexity of the two processes can be approximated as \(O(shift\cdot m)\). Since \(n\) time series need to be transformed with \(k\) Shapelets, the time complexity of the dataset transformation is \(O(n\cdot k\cdot shift\cdot\overline{m})\), where \(\overline{m}\) is the average length of the Shapelets. Overall, the time complexity of training an RSFCF classification model is:  
\[O(r\cdot(\underbrace{n\cdot k\cdot shift\cdot\overline{m}}_{\text{data transformation}}+\underbrace{n\cdot\log(n)\cdot k\cdot a}_{\text{time series tree training}}))\]  
Classification time complexity: The time complexity of transforming test dataset is \(O(k\cdot shift\cdot\overline{m})\), after which a traversal of the average \(\log(n)\) nodes completes the classification of each tree. Therefore, the time complexity of the classification process is:  
\[O(r\cdot(\underbrace{k\cdot shift\cdot\overline{m}}_{\text{testset transformation}}+\underbrace{\log(n)}_{\text{classification}}))\]
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 3 Experimental Details
### Mixture Proportions  
Seven mixtures were prepared for testing in this research: A reference mixture (R0) without GGBS and six slag mixtures, designated as S4R2, S4R4, S5R2, S5R4, S6R2, and S6R4. S4, S5, and S6 refer to slag fineness of 4000, 5000 and 6000 cm\({}^{2}\)/g, respectively. R2 and R4 refer to the replacement ratios of 20% and 40%, respectively, by weight of the cementitious material. The water to cementitious material ratio (w/cm) was kept at 0.35 for all mixtures. All concretes were designed to have a target compressive strength level at 28 days of 62 MPa. The dosage of superplasticizer was adjusted to produce a designed slump of 250 \(\pm\) 20 mm and a slump flow of 600 \(\pm\) 50 mm for all mixtures. The mixture proportions are shown in Table 2.
## 3 Materials and Methods
### Feature Detection and Warping
#### 3.2.1 Determining Correspondences with NBB  
The NBB [5] network tackles the cross-domain correspondence issue by searching for pairs of neurons that represent similar features in two different images, called neural best buddies. The idea behind this technique is to pass pairs of images through a hierarchy of convolutional layers that narrow down the regions of interest by each level. The network will then keep track of neural best buddies with activations higher than a given threshold value. To choose the \"best\" matching pair between a specific object image and a geometry style image, we look for the pair of images that returns the most points matching certain criteria after running the NBB on them, which can be seen in Equations (1) and (2).  
\[P_{NBB}(I_{O}(k))=\{NBB(I_{O}(k),I_{S_{NBB}}(k))\big{|}card\big{(}NBB(I_{O}(k), I_{S_{NBB}}(k))\big{)}\ =\ max(card(NBB(I_{O}(k),I_{S})),\forall I_{S}\in I_{S_{NBB}}\big{)}\} \tag{1}\]  
\[\begin{array}{c}NBB(A,B)=\{(a\_i,b\_i)|\ V(a\_i,b\_i)\ \geq t_{a_{NBB}},\\ \sqrt{(a_{i_{x}}-b_{i_{x}})^{2}+\big{(}a_{i_{y}}-b_{i_{y}}\big{)}^{2}}\geq t_ {d_{NBB}}\\ card(NBB(A,B))\leq t_{P_{NBB}}\}\end{array} \tag{2}\]  
where \(I_{O}(k)\) represents the \(k\)-th image detected that contains an object of interest, \(P_{NBB}(I_{O}(k))\) represents the final set of matching points chosen, \(I_{S_{NBB}}\) represents the set of geometric-style images, \((a_{i},b_{i})\) a pair of matching points given to two images \(A,B,V(a_{i},b_{i})\) the activation value for said points when considering the entire network, \(t_{a_{NBB}}\) the activation threshold for considering the two points as a pair, \(t_{d_{NBB}}\) the distance threshold for considering the two points as a pair, and \(t_{P_{NBB}}\) represents the maximum number of pair points we want to choose. If more than the number of desired points gets chosen, then we only keep the points with the highest activation.
## 2 Sensors
### State-of-the-Art Research on BSN Status
#### 2.1.2 Main Researched Sensors
Compared with the Livepod, the accuracy of existing research models needs to be improved. Compared with human energy expenditure detection, behavioral recognition is much more complex, because it employs not only accelerometers, but also other inertial sensors to recognize direction and angle of movement and other action parameters. Furthermore, it needs to get activity information from raw sensor data, thus the signal processing process is more complex [45]. Generally, accelerometers work together with gyroscopes and magnetometers to achieve a more accurate measure, but it's not absolute. In Reference [46], a kind of inertial measurement unit (IMU) called EcoIMU which only utilizes accelerometers is presented, which can accurately measure linear acceleration and angular velocity. It is built from a pair of triaxial accelerometers, expanding the application scope of accelerometers from relative motion tracking to absolute motion tracking. Moreover, the use of accelerometers significantly lowers the power consumption and the cost of an IMU, which conforms to the desirable low-power characteristics of BSNs. Reference [22] indicates that behavioral recognition often requires multiple sensor nodes. The more nodes it utilizes, the higher accuracy it obtains, however the more difficult it becomes. Therefore, in order to identify motions accurately with only a few nodes, finding the best deployment location for sensor nodes has become a hot topic of research in the field of BSN sensors in recent years.
## 2 Related Work
### Vision-Based Approach  
First, with a vision-based system, users can use their body parts as a 3D cursor, which is useful for intuitively selecting and manipulating 3D objects in mid-air. For example, [PERSON] et al. [8], implemented a 3D kinematics tool that enables users to manipulate 3D objects using gestures in 3D space, using Microsoft Kinect. Furthermore, one gesture-based interface was released for 3D modeling in virtual reality (VR) with Leap Motion [9]. Vision-based interfaces are natural and intuitive,so users do not have to learn manually because they already use their hands and body parts in this manner. Moreover, since users feel that they directly control the 3D space, this approach can provide an immersive effect [10].  
However, the use of a camera to accurately detecting hands or body parts remains challenging. For example, occlusion problems might occur depending on the installation position, direction and posture of the camera and detection performance might vary significantly as a result. Occlusion problems remained even in a study that showed considerable improvement in hand tracking performance [11]. In terms of AR, 3D object manipulation based on freehand gestures is not suitable for applications that require high-level accuracy. Moreover, the method of registering a 3D object with the user's hand exhibits a significant limitation. Although it is possible to register relatively precisely within reach of the user's hands and arms [12], registering to an area further away requires additional gestures, which is associated with a longer learning curve [9].  
On the other hand, HoloLens uses the bare hand as an auxiliary means with a click function but also considers the position of the hand in object registration interaction. The distance that objects can be registered in mid-air is limited to the range of the hand, thus sharing the problem of vision-based hand interfaces. The limitation of remote object manipulation when using the bare hand has been actively discussed in the early VR interaction research but even after several decades, no dominant solution (or method) has yet emerged [13]. In relatively recent studies, [PERSON] et al. [14], attempted to solve this problem by modifying the GoGo interaction [15] and implementing a switchable interface that allows a virtual hand to reach a distance with a simple gesture change. As a result, intuitiveness, which is the greatest advantage of vision-based interfaces, is reduced and usability is also decreased.
## 1 Introduction
The spatial attention module makes use of the space of the feature map, while the channel attention module concentrates on the most significant input images and improves or suppresses various channels for various tasks to optimize the networks' capacity for representation. In addition to being a useful addition to channel attention, the relationship creates a spatial attention map that identifies the most crucial regions of the network for processing and may be utilized for adaptive feature optimization with the input feature map. Inspired by previous works, this article presents a new deep prediction network frame for predicting the electricity load. Taking advantage of the powerful image data-processing capabilities of CNN, the proposed model first converts the time series into images via the method of the statistically interpretable GAF, which substantially increases the prediction performance of the power load data with strong nonlinear features and environmental fluctuations, and then uses a deep CNN to extract nonlinear features from the electricity load data. The types of attention mechanism, i.e., the channel attention (CA) and spatial attention (SA) modules, are applied to extract the data's hierarchical features to minimize information loss, and residual connections are adopted in the appropriate convolutional layers to ensure the proper convergence of model training and parameter updates. The novel concepts and main contributions of this paper are as follows:  
1. A novel deep convolutional attention mechanism model is proposed to solve the issue of electricity load forecasting with strong nonlinear features, which optimizes the deep characteristics of the power load data using the convolution layer, residual connections, and the CA and SA mechanisms;
2. The proposed deep learning structure is designed to reduce the randomness of the data and guarantee the robustness of machine learning. It can also easily extract the intrinsic properties of the data thanks to the Gram matrix principle, which is used to convert time series data into figure data;
3. From different time scales, the proposed model can directly output the multi-step prediction results, reduce the prediction error, and achieve more excellent prediction performance results. The remainder of the paper will be structured as follows: the background theory of the proposed prediction approach is provided in Section 2, the fundamental structure of the proposed model is described in Section 3, the experimental data, evaluation metrics, and results are presented in Section 4, and the final conclusion is given in Section 5.
# Study on the \"Two-Zone\" Heights in Lower Slice Mining Under Thick Al Alluvium and Thin Bedrock
## 2 Mining Situation and Geological Characteristics
### Hydrogeological Conditions  
The data from 10 boreholes in the mine were selected, and a borehole histogram was created using CDR 2019 software for a comparative study of the profiles. Four comparative profiles were generated. Following research and comparison, the loose layer was divided into three layers, from top to bottom:  
(1) The strong water-rich rock group in the upper section has a thickness ranging from 174.96 m to 246.40 m, with an average thickness of 209.76 m. This section contains numerous aquifers and abundant water.  
Figure 1: Traffic location of coal mine.  
Figure 2: East panel layout and bedrock thickness contour map.  
2. The middle section consists of relatively weak water-rich rock, with a thickness ranging from 126.91 m to 187.56 m and an average thickness of 157.22 m. The number of aquifers in this section is significantly lower than that in the upper section, and the continuity is poor, with lower water content.
3. The thickness of the lower section ranges from 25.96 m to 158.15 m, with an average thickness of 84.72 m. This section has the fewest number of aquifers, and the aquifer is lenticular with low water content.  
To confirm the accuracy of the aquifer division mentioned above, the sand-mud ratio of 34 boreholes in the east and north wings of the mine was analyzed. Subsequently, sand-mud ratio contour maps of the upper, middle, and lower sections were created, as illustrated in Figure 3. The sand-mud ratio within the upper section of the eastern area ranges from 0.10 to 1.78, while the middle section exhibits ratios between 0.05 and 0.97. In the lower section, the sand-to-mud ratio varies from 0 to 0.42. The area with a high sand-mud ratio indicates the presence of more aquifers like sand and gravel, while a low sand-mud ratio suggests the prevalence of muddy aquifers. Therefore, the sand-mud ratio diagram confirms the accuracy of the division of the three water-bearing (aquitard) layers. Specifically, the upper section of the loose layer in the East First Panel is a strong water-rich rock group, the middle section is a relatively weak water-rich rock group, and the lower section is a relatively aquitard rock group. This lower section effectively blocks the entry of the overlying aquifer water.  
Figure 3: Near-north–south loose layer (aquifuge) group section comparison.
# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### The Physician Method
#### 2.3.5 Ultrasound Findings  
Musculoskeletal ultrasonography (MSUS) has established its role in the diagnosis of RA. High resolution gray scale (GSUS) and power Doppler (PDUS) assist the diagnostic performance of 2010 ACR (American College of Rheumatology)/EULAR (European League Against Rheumatism) classification criteria in early detection of RA.  
Synoxia is the inner layer of the articular capsule. It is a highly vascularized layer of serous connective tissue. It absorbs and secretes synovial fluid and is responsible for the mediation of nutrient exchange between blood and joint. In RA, a patient's ultrasound often reveals inflammation of the synoxia, the so-called synovitis, with the typical accumulation of fluid in the synoxia or synovial thickening. The presence of the power Doppler gives us information about the current activity of inflammation. Older inflammation sites most of the time do not have a power Doppler.  
The Outcome Measures in Rheumatology (OMERACT) US Working Group formulated the definitions of pathological findings in ultrasound in RA patients and their quantification (Figure 10). The definition and grading of synovitis in RA are presented in Table 2[17].  
Figure 10: EULAR-OMERACT score examples [17].