esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multimodal Imaging System  
The centerpiece of the multimodal imaging system (Figure 1) is the modular upright microscope Leica DM R (Leica Microsystems, Wetzlar, Germany) for transmitted and incident light microscopy. Table 1 specifies the numbered parts used throughout. Figure 1: (**a**) Schematic drawing of the multimodal imaging system; (**b**) light path of the expanded Raman spectroscopy. See Table 1 for the description of the used numbers. Compared to the original device configuration, the setup is expanded by an additional light path (red path) for Raman spectroscopy (see Figure 1b). For this purpose, the commercially available MultiSpec(r) Raman System (te5 AG, Steinbach, Germany) is used. The integrated temperature-stabilized 785 nm diode laser (1) with an adjustable output power between 50 mW and 500 mW is connected to the setup via a 100 \(\upmu\)m (core diameter) optical fiber (2). A plano-convex lens (3) generates a collimated laser beam. The laser line bandpass filter (4) eliminates spectral noise by transmitting only the laser wavelength. Subsequently, a dichroic mirror (5) reflects the monochromatic light downwards at a 90\({}^{\circ}\) angle. After passing the microscope stand, the laser light is focused by a 50\(\times\) objective (6) onto the sample. The tissue section is mounted on a microscope slide (7) and placed at the motorized microscope stage (8). The backscattered elastic and inelastic light is collected by the aforementioned microscope objective (6) and penetrates upwards through the microscope stand. The dichroic mirror (5) and the adjacent longpass filter (9) block the Rayleigh scattered laser light as well as the higher energetic anti-Stokes scattered light. Only the long-wave (Stokes) shifted light passes the filters and is focused by an achromatic lens (10) onto a 100 \(\upmu\)m fiber. This fiber acts as a pinhole for the confocal setup and is coupled to a further 600 \(\upmu\)m fiber (11) in order to achieve an entire illumination of the Raman spectrometer's entrance
## 8 Twistronics in Epitaxial Graphene on SiC
(**a**) LEED image acquired at 67.9 eV from 4H-SiC(\(000\overline{1}\)) with 10 layers of graphene, showing only graphene spots and diffuse arcs. (**b**) Auger peak intensity ratio versus number of graphene layers on SiC(\(0001\)) substrates. Inset: Auger spectra obtained after different process. (**c**) Atomic force microscope (AFM) image of graphene on 6H-SiC(\(0001\)) with a nominal thickness of 1.2 ML formed by annealing in Ar. (**d**) STM image showing three domains with different thicknesses and twist angles. Zone A: bilayer, \(\theta=14^{\circ}\); zone B: 2-3 layers, \(\theta=6.0^{\circ}\); zone C: 3 layers, \(\theta=6.0^{\circ}\). Panel (**a**) adapted with permission from Ref. [89], Copyright American Physical Society 2008. Panel (**b**) adapted with permission from Ref. [90], Copyright Elsevier 2007. Panel (**c**) adapted with permission from Ref. [91], Copyright AAAS 2006. Panel (**d**) adapted with permission from Ref. [92], American Physical Society 2015.  
grown on the C-face SiC [40; 92; 105]. In 2018, [PERSON], et al. [106] observed a very intense and very flat band on bilayer graphene on graphene epitaxial on 6H-SiC. [PERSON] et al. also verified the existence of a nearly dispersionless electronic band near the Fermi level on 3C-SiC by angle resolved photoelectron spectroscopy [107]. Although the precision control of the twist angle of epitaxial graphene remains a technique problem yet to be solved, the epitaxial graphene on SiC already becomes a promising platform for the study as well as application of twistronics in graphene. We suggest more twistronics studies on this system in the aspects including but not limited to the transport measurement, local electronic structure and functional devices.
## 2 Related Work  
### The Processing of the SORT  
The overall SORT process involves the detection, estimation, data association, and creation and deletion of tracked identities.  
**Detection**: First, Faster-RCNN is used for detection and feature extraction. Because the detection objects in this study are objects, other objects are ignored, and only objects that are more than 50% likely to be a object are considered.  
**Estimation**: The SORT model's estimation model describes the model of the object and enters the movement model of its representation and transmission target in the next frame. First, the Kalman filter is used to predict the target state model (including size and position) of an object detected at time \(T\) at time \(T+1\). An object's state model can be expressed as follows:  
\[x=\left[u,v,s,r,\dot{u},\dot{v},\dot{s}\right]^{T} \tag{1}\]  
where (\(u\), \(v\)) represents the coordinates of the object's center at time \(T\); (\(s\), \(r\)) represents the region and aspect ratio of the object's bounding box at time \(T\); and \((\dot{u},\dot{v})\) and \((\dot{s})\), respectively, represent the center point and speed of the object at time \(T\). When the object in the next frame is detected, the object's bounding box \((\dot{u},\dot{v})\) is used to update the object's status. If no correlations between the objects are detected, the prediction model is not updated.  
**Data association**: The detection result is used to determine the object's target state; that is, the bounding box \((\dot{u},\dot{v})\) of the object at time \(T\) is used to predict the new position of the object at time \(T+1\). First, the model predicts the bounding box \((\dot{u}^{T+1},\dot{v}^{T+1})\) of the object at time \(T\) and the \(i\)th object at time \(T+1\) (\(u_{i}^{T+1},v_{i}^{T+1}\)), and calculates the Mahalanobis distance between them. Thereafter, the model uses the Hungarian algorithm for matching to enable multi-object tracking. When the intersection area (intersection over union [IOU]) is less than the threshold value, the object is regarded as the tracking target.  
**Creation and deletion of tracked identities**: When an object enters or leaves the screen, its identity information must be added or deleted from this system. To prevent erroneous tracking, the model must detect objects to be tracked within a few frames of their entrance to determine whether the object must be newly added to this system. Furthermore, the IOU of the object in each frame and in the next frame is calculated; if its value is less than the threshold value, the object is determined to have left the screen, and the object's identity information is deleted.
## 3 Materials and Methods
### Methods
#### 3.2.4 Mixed Pooling Module  
In the original U-Net network, the skin lesion image is down-sampled by the encoder and then directly input to the decoder to restore the features and obtain the segmented image. However, this kind of operation will make the deep feature map unable to obtain enough receptive field, resulting in a lack of deep information. The Mixed Pooling Module [37] (MPM), which is composed of pyramid pooling [38] and strip pooling [39], can effectively enlarge receptive fields, obtain deep semantic information and capture remote dependencies. The two sub-modules of the hybrid pooling module capture the short-distance and long-distance dependencies between different locations at the same time. Among them, the stripe pooling layer is used to capture the local context information of the feature graph and establish the long-distance dependency relationship through repeated aggregation. Pyramid pooling is used to capture global context information and establish short-distance dependencies. Figure 6 shows the structure diagram of the Mixed Pooling Module.  
Figure 5: Multi-level split receptive field module.  
In Figure 6, the skin disease feature map is \(X\in R^{\text{C}\times H\times\text{W}}\), C represents the width of the feature map, H represents the height of the feature map, W represents the number of channels of the feature map, Pool_S3 and Pool_S5 represent spatial pyramid pooling with convolution kernel sizes of 3 \(\times\) 3 and 5 \(\times\) 5, and Pool_H and Pool_W represent horizontal pooling and vertical pooling. In order to improve the extraction of feature information, each branch of the hybrid pooling module is carried out separately. Feature map \(X\in R^{\text{C}\times H\times\text{W}}\) uses Pool_H and Pool_W to compress the feature map in horizontal and vertical directions, respectively, to obtain feature map \(X_{1}\in R^{1\times\text{W}\times\text{C}/4}\) and \(X_{2}\in R^{H\times 1\times\text{C}/4}\), then, restores the feature map by up-sampling, captures the long-distance dependence, and splices the two new feature maps to obtain \(Y_{1}\). Feature maps \(X_{3}\in R^{H/3\times W/3\times\text{C}/4}\) and \(X_{4}\in R^{H/5\times\text{W}/5\times\text{C}/4}\) are compressed by Pool_S3 and Pool_S5. After up-sampling, the size of the feature map is reduced to obtain a new feature map and then \(Y_{2}\) is spliced with the original feature map. \(Y_{1}\) and \(Y_{2}\) are spliced to obtain the final output. The hybrid pooling module realizes the fusion of global information and local information of the feature map and establishes the long-short distance dependence relationship.
Figure 3 highlights the current state of scientific and technological progress in 4D printing, showing both strengths and weaknesses. Many areas lack clear, predictable prospects for practical applications, with only a few niches showing promise. The color intensity in Figure 3 (yellow: low exploration needed, orange: medium, and red: high) indicates the need for further exploration in areas such as AM, smart materials, design, and stimuli.  
Active materials, which are the focus of about 70% of scientific publications, are usually polymers that use macromolecular reptation to achieve shape changes [3]. However, there are several challenges beyond the intrinsic response time of the bulk material:  
* The relationship between energy stimulation and material behavior, governed by [PERSON]'s and/or Fourier's laws, often leads to response times exceeding a minute for centimeter-sized objects;
* Limited mechanical performance, such as [PERSON]'s modulus.  
Figure 1: Interaction between additively manufactured smart materials and stimuli in 4D printing [3].  
Figure 3: Research maturity in 4D printing (yellow: low exploration needed, orange: medium exploration needed, and red: high exploration needed).  
Figure 2: Example of a thermally stimulated shape memory polymer [4] (reproduced with permission from the authors). (**a**) 3D printed multi-material grippers with different designs (I–V). (**b**) Illustration of shape changes between as printed shapes and temporary shapes of multi-material grippers.  
To address these challenges, objects need to be built using multi-material AM and specific structures that counteract [PERSON]'s and [PERSON]'s laws. The shift from traditional manufacturing to 4D printing involves:  
* Reassessing manufacturing strategies, moving from continuous to discrete approaches;
* Ensuring the printability of voxels with different materials using local and global methods;
* Refining the design of the AM process.  
It is also essential to consider methods for stimulating active voxels. Viable methods for localized activations include light (with challenges like shadow effects and optical fiber integration), electricity (with concerns about thermal dissipation), and pneumatic methods. The literature often lacks substantial discussion on these practical aspects.  
Moreover, it is crucial to understand the interaction between stimuli and voxelized 4D objects as the effects of successive or simultaneous stimulation with varying amplitudes over time and space. This understanding is vital for addressing the inverse design problem. Discussing these aspects in isolation risks oversimplification and limited analysis, as their deep interconnection often seen in the literature requires a comprehensive approach.
# Influence of the Slope and Gate Offset on Movement Variability and Performance in Slalom Skiing
## 4 Discussion and Conclusions
Unlike previous research that employed linear statistical measures such as standard deviation and tempo calculation, this study introduces non-linear tools like SampEn in the field of alpine skiing, offering a novel approach to explore the nature of human movement and its connection to coordination development [18]. In this sense, a skier's MV must be understood and perceived during the execution of the task, within the other biomechanical variables of the process [68]. The conclusion of this study is that determining MV through entropy can be used to assess the skier's ability to adapt to changing conditions. Introducing variations in on-snow training, such as different slopes and gate offsets, can increase MV, suggesting that skiers are adapting to the new stimuli. Best-performing skiers exhibit lower MV, indicating greater consistency of movement patterns. Skiers with higher MV may be in the process of developing their motor adaptations, as they explore new movement options.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
###### Abstract  
An improved YOLOv8s-based method is proposed to address the challenge of accurately recognizing tiny objects in remote sensing images during practical human-computer interaction. In detecting tiny targets, the accuracy of YOLOv8s is low because the downsampling module of the original YOLOv8s algorithm causes the network to lose fine-grained feature information, and the neck network feature information needs to be sufficiently fused. In this method, the strided convolution module in YOLOv8s is replaced with the SPD-Conv module. By doing so, the feature map undergoes downsampling while preserving fine-grained feature information, thereby improving the learning and expressive capabilities of the network and enhancing recognition accuracy. Meanwhile, the path aggregation network is substituted with the SPANet structure, which facilitates the acquisition of more prosperous gradient paths. This substitution enhances the fusion of feature maps at various scales, reduces model parameters, and further improves detection accuracy. Additionally, it enhances the network's robustness to complex backgrounds. Experimental verification is conducted on the following two intricate datasets containing tiny objects: AI-TOD and TinyPerson. A comparative analysis with the original YOLOv8s algorithm reveals notable enhancements in recognition accuracy. Specifically, under real-time performance constraints, the proposed method yields a 4.9% and 9.1% improvement in mAP0.5 recognition accuracy for AI-TOD and TinyPerson datasets, respectively. Moreover, the recognition accuracy for mAP0.50.95 is enhanced by 3.4% and 3.2% for the same datasets, respectively. The results indicate that the proposed method enables rapid and accurate recognition of tiny objects in complex backgrounds. Furthermore, it demonstrates better recognition precision and stability than other algorithms, such as YOLOv5s and YOLOv8s.  
c 2023 13 8161. [[https://doi.org/10.3390/app1348161](https://doi.org/10.3390/app1348161)]([https://doi.org/10.3390/app1348161](https://doi.org/10.3390/app1348161))  
1
# The BCL Method for DSSC: Basis and Applications +
## 5 The \(\mathbf{I_{T}}\) Parameter: A Measure of the Electronic Density on the Anchor Group
### Results  
#### 6.2.1 Acceptor-Donor Properties of a Ligand  
In the above analysis, and in accordance with Equation (33), one the factors that could increase the electron injection is related to the softness and polarizability of the anchor ligand. Keeping this objective in mind, a suitable characterization of the ligands in order to determine their acceptor/donor character should be helpful to understand how they influence the performance of the DSSC. The forthcoming classification is based in three theoretical parameters: orbital hardness (n\({}^{+}\)), polarizability (\(\alpha_{0}\)), and electrophilicity (E).  
The first index is a measure of the resistance to deliver charge, the second is the deformation of the electronic cloud in presence of an electric field, and finally, the third is a measure of the power of the stored electronic charge. The results of this study are relevant for the design of new ligands for complexes supporting a DoAlAc architecture.  
Figure 27 displays the six types of ligands incorporated in this study, which are currently employed in DSSC devices. They are ordered according to their decreasing orbital hardness. This theoretical index measures the resistance of the SOMO orbital to deliver electronic charge. Ligands with high orbital hardness are good donors and are hard in the sense of [PERSON]'s acid-base theory.  
Accordingly, thiocianate is the hardest ligand and is followed by phenyl pyridine. Softness is measured in terms of the calculated average polarizability and shows that carboxylate groups and highly delocalized ligands increase the polarizability and become good acceptors according to the Electrophilicity index.  
Figure 26: Ionization potential for iodide species, and electron affinity for ruthenium complexes.  
The acceptor capacity is related to the ability of the SOMO to distribute electronic charge along the carbon skeleton. Dcbpy and tctpy belong to this category. On the other side, thiocyanate, because of its low polarizability and null electrophilicity, is a good donor.
## System Model
### Network Model  
In this study, it is assumed that channel bandwidth \(\omega\) is equally allocated among tasks. According to [59], the data transmission rate from the UE with task \(m\) to the BS \(n\) is obtained by (4):  
\[r_{m,n}=\omega\times\log_{2}(1+\frac{p_{m}h_{m,n}}{\mathcal{N}_{e^{2}}}) \tag{4}\]  
Figure 2: System model of mobility-aware task offloading in MEC enabled 5G network.  
where \(\mathcal{N}_{\sigma^{2}}\) represents the noise power, \(p_{m}\) is the transmission power of UE \(m\), and \(h_{m,n}\) denotes the wireless channel gain from UE with task \(m\) to BS \(n\) at location \(l_{m}\), respectively. It is assumed that users are constant during task offloading. As each task \(m\) can be divided into multiples sub-tasks, the channel gain \(h_{m,n}\) will remain constant. The wireless channel gain \(h_{m,n}\) is described as (5):  
\[h_{m,n}[dB]=127+30\times\log d[Km] \tag{5}\]  
where \(d=\parallel l_{n}-l_{m}\parallel\) refers to the distance between task \(m\) at location \(l_{m}\) and BS \(n\) (in kilometers). The channel model is as suggested in [60], which is for ultra-dense heterogeneous networks, and the fading follows a Rayleigh distribution.  
Additionally, the time and energy costs of offloading a sub-task of \(m\) to BS \(n\), respectively, can be presented using (6) and (7):  
\[t^{o}_{m,n}=\frac{d_{sub}}{r_{m,n}} \tag{6}\]  
\[e^{o}_{m,n}=p_{m}t^{o}_{m,n} \tag{7}\]  
where \(p_{m}\) specifies power required to transmit \(m\). The \(p_{m}\) is assumed as given for each task, and it depends on many parameters, including antenna gain.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 5 Discussion  
Regarding the various bearing fault signals obtained under different conditions, the diagnoses results are shown in Table 7. Note that the fault is supposed to be diagnosed successfully only if the FCO and its first three harmonics or above are identified effectively, as mentioned in [28].  
From the diagnosis results, it is clear that the FOSCK is capable of detecting bearing faults in all cases. It is also concluded that: (1) the fault impact has broadband characteristics and causes different resonances; (2) the Protrugram and FOSCK have better robustness against random impulse disturbance; (3) the FOSCK and FK can diagnose the outer race fault effectively under a large range of the speed fluctuations conditions, which verifies the OSCK index proposed in Section 2 is sensitive to the speed fluctuation while not being affected by the size of the speed; (4) the comparison between the results of experimental studies #1 and #2 shows that both of the Protrugram and FOSCK can suppress the influence of acceleration changes; (5) under the same fault severity conditions, the energy of the inner race fault is dispersed due to the modulation, the local SNR is lower, in which case the envelope order spectra obtained by the existing methods fails to provide any bearing fault related signature. However, the FOSCK is capable of detecting bearing inner race fault in all cases. The results of performance comparison of the FK, the WPTK, the Protrugram and the FOSCK are summarized in Table 8. Besides, considering the FOSCK is robust to the random shock and heavy noise, the method can be applied for exacting random impulses caused by earthquake, in which the random impulses have similar characteristics with bearing fault impulses [29].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Location** & \multicolumn{3}{c}{**Outer Race Fault**} & \multicolumn{3}{c}{**Inner Race Fault**} \\ \hline
**Experimental Study** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** \\ \hline FK & Y & N & Y & N & N & N \\ WPTK & Y & N & N & N & N & N \\ Protrugram & Y & Y & N & N & N & N \\ FOSCK & Y & Y & Y & Y & Y & Y \\ \hline \hline \end{tabular}  
* Y means Yes and N means No.  
\end{table}
Table 7: Bearing fault diagnosis result.  
Figure 38: The envelope order spectra of the signal obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Interference** & **Random Shock** & **Large Speed Fluctuation** & **Different Acceleration** & **Heavy Noise** \\ \hline FK & N & Y & P & N \\ WPTK & N & N & P & N \\ Protrugram & Y & N & Y & N \\ FOSCK & Y & Y & Y & Y \\ \hline \hline \end{tabular}  
* Y means Yes, N means No and P means Pending.  
\end{table}
Table 8: Method robustness.
# Form Finding of Asymmetric Cable Network Reflector with Flexible Ring Truss
## 2 Form-Finding Method for Asymmetric Cable Network Reflector
### Equilibrium Equation
\tag{12}\]  
Further, considering similar sets of equations in the \(y\)-coordinate and \(z\)-coordinate, we obtain the following set of equations:  
\[ \begin{array}{c}C^{T}\cdot Q\cdot(C\cdot x+C_{f}\cdot x_{f})=0\\ C^{T}\cdot Q\cdot(C\cdot y+C_{f}\cdot y_{f})=0\\ C^{T}\cdot Q\cdot(C\cdot z+C_{f}\cdot z_{f})=0\end{array}  \tag{13}\]  
Using Equation (13), the column vectors of the positions of the free nodes \((x,y,z)\) can be obtained as:  
\[ \begin{array}{c}x=-\widetilde{Q}^{-1}\cdot\widetilde{Q}_{f}\cdot x_{f}\\ y=-\widetilde{Q}^{-1}\cdot\widetilde{Q}_{f}\cdot y_{f}\\ z=-\widetilde{Q}^{-1}\cdot\widetilde{Q}_{f}\cdot z_{f}\end{array}  \tag{14}\]  
where,  
\[ \begin{array}{c}C^{T}\cdot Q\cdot C=\widetilde{Q}\\ C^{T}\cdot Q\cdot C_{f}=\widetilde{Q}_{f}\end{array}  \tag{15}\]  
We assume that \(x\), \(y\), and \(z\) are the positions of the free nodes. When the supporting structure is undeformed, the \(x\) and \(y\) positions of the rear reflector are the same as the front reflector because the tension ties between the front and rear surfaces are vertical. Since the front and rear surfaces are placed asymmetrically, the front and rear parabolic surfaces are described using the global coordinates as follows:  
\[ \begin{array}{c}z_{1}=\frac{x^{2}+y^{2}}{4f_{1}}-\eta_{1}\\ z_{2}=-\frac{x^{2}+y^{2}}{4f_{2}}-\eta_{2}\end{array}  \tag{16}\]  
where \(f_{1}\) and \(f_{2}\) denote the focal length of the front and rear reflectors, respectively. The symbols \(\eta_{1}\) and \(\eta_{2}\) denote the depth of the front and rear reflectors, respectively. For a certain cable network reflector, the parabolic surface is given during the design process. Therefore, if the positions in the \(x\)-coordinate and \(y\)-coordinate can be calculated, the positions in the \(z\)-coordinate can be obtained using Equation (16).