esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Theory of Multi-Rope Friction Hoisting System with Double Cages in Vertical Shaft
### Calculation Theory and Selection of the Structural Parameters of the Hoisting System  
#### 2.1.1 Calculation of Chord Length and Elevation Angle of Upper and Lower Ropes  
The calculation formulas for the chord lengths of the upper rope and lower rope \(L_{s}\) and \(L_{x}\) are shown in Equations (1) and (2).  
\[L_{s}=\sqrt{\left(H_{j}-E_{0}\right)^{2}+\left(L_{0}+S-\frac{D_{t}}{2}\right)^{ 2}-\left(\frac{D_{g}-D_{t}}{2}\right)^{2}} \tag{1}\]  
\[L_{x}=\left(H_{j}-H_{s}-E_{0}\right)^{2}+\left(L_{0}-\frac{D_{t}}{2}\right)^{ 2}-\left(\frac{D_{t}+D_{g}}{2}\right)^{2} \tag{2}\]  
The calculation formulas for the elevation angle of the upper rope and lower rope \(\beta_{s}\) and \(\beta_{x}\) are shown in Equations (3) and (4):  
\[\beta_{s}=\arctan\left(\frac{H_{j}-E_{0}}{L_{0}+S-\frac{D_{t}}{2}}\right)- \arctan(\frac{D_{g}-D_{t}}{2L_{s}}) \tag{3}\]  
\[\beta_{x}=\arctan\left(\frac{H_{j}-H_{s}-E_{0}}{L_{0}-\frac{D_{t}}{2}}\right) -\arctan(\frac{D_{g}+D_{t}}{2L_{x}}) \tag{4}\]  
where \(E_{0}\) is the vertical height from the center of the friction wheel to the level of the wellhead lock (rail surface), \(H_{j}\) is the vertical height from the center of the friction wheel to the lock of the wellhead, \(L_{0}\) is the horizontal distance from the center of the friction wheel to the right vertical rope, \(S\) is the center distance of the left and right vertical rope, \(D_{t}\) is the diameter of the upper sheave wheel and lower sheave wheel, and \(D_{g}\) is the diameter of the drum.  
Figure 2: Floor-standing multi-rope friction hoisting system with double cages. 1—Left cage; 2—hoisting rope; 3—upper sheave wheel; 4—lower sheave wheel; 5—friction wheel; 6—right cage; 7—balance rope.
## 7 Conclusions  
The present study is linked to the research area which deals with the development of a biofeedback system for anxiety awareness. The said system was used in relation to the students' anxiety awareness during online examination activities and attempts to integrate affective computing into education. It could be claimed that anxiety awareness can significantly support a distance learning activity, where there is no face-to-face contact and it is very difficult to identify the participants' emotions. The present research results can contribute to the design of a supportive intervention framework, which could be applied in the case of intensive autonomous or collaborative learning activities in order to deploy the said biofeedback, combined with a constructive reappraisal strategy and pedagogical methodologies. The anxiety level provided by biofeedback can be utilized as a trigger to activate the appropriate interventions.  
This research focused on two research goals.  
* The concurrent validity of the presented biofeedback system as regards Neurosky's Mindset and the STAI questionnaire (RG1).  
* The relationships between the students' TR dimensions, the anxiety detected by the biofeedback system and academic achievement (RG2). According to the results obtained and analyzed from both experiments, the following conclusions can be reached per research goal: RG1: The first research question is answered by the statistically significant correlations found between the biosignal measurements provided by the biofeedback device, the students' scores in the STAI state-questionnaire and the Neurosky measurements for the majority (80%) of students. Thus, we could support that the biosignal measurements of the biofeedback system are significantly related to those detected by a well-known commercial EEG device (Neurosky Mindset), and also to the responses given to a widely-used and accepted anxiety detection questionnaire (STAI). Thus the biofeedback system performance is quite satisfactory. Furthermore, the presented biofeedback device is less expensive than similar better-known commercial systems. RG2: The second research question is answered by the derived relationship model pertaining to the students' Technology Readiness (TR) beliefs, state anxiety, and performance. According to this model, less optimistic students feel more insecure, since this is a psychological condition that negatively affects their academic achievement. Moreover, students become more anxious or even stressed when they realize that their performance is declining.
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Data Acquisition of GF5 AHSI  
The GF5 HSI hyperspectral images were used with the acquisition date of 22 December 2019, which was synchronous with the sample collection date. The spatial resolution was 30 nm, the strip width was 60 km, and the spectral range was 390-2438 nm, with a total of 272 spectral bands, of which 143 bands were in the visible and near-infrared range (390-1000 nm), with a spectral resolution of 4.28 nm. 128 bands of short-wave infrared range (1000-2513 nm) had a spectral resolution of 8.42 nm. The sensor was affected by atmospheric water vapor absorption in the 1350-1400 nm and 1800-2000 nm bands, and the spectral data were discontinuous; therefore, 390-1317 nm, 1477-1739 nm, and 2007-2438 nm were chosen as the spectral ranges for this study. To eliminate the possible errors caused by atmospheric scattering and solar altitude angle on the spectral reflectance, ENVI 5.3 software was used to calibrate the image radiation and atmospheric correction. Then, ArcMap 10.6 software supplemented with GPS control point data was used to make fine geometric corrections to the image, and the corrected image was cropped according to the boundary of the study area to form the image of the regional basemap. Taking into account the influence of vegetation cover, different types of land, and the distribution of sample points on soil texture analysis, the 1:10,000 farmland  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Max (g/kg)** & **Min (g/kg)** & **Mean (g/kg)** & **Standard Deviation (g/kg)** \\ \hline Sand particles & 816.9 & 9.1 & 170.8 & 170.7 \\ Silt & 796.3 & 105.6 & 544.4 & 149.3 \\ Clay & 495.2 & 77.6 & 277.8 & 104.0 \\ Total & 816.9 & 9.1 & 331.0 & 213.1 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics of the soil texture content.  
Figure 1: (**a**) Location of the study area; (**b**) distribution of sampling points; (**c**) DEM and (**d**) current distribution of cultivated land.  
status map was used to mask the processing and generate a thematic image map of the farmland area, as shown in Figure 1d.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 1 Introduction
### Amorphous Metals: Preparation by Ablation Techniques  
One of the more interesting methods to produce metals with a highly disordered structure is ablation, induced either by laser or spark [29]. The approach to material synthesis here is bottom-up, where the target material is sublimated by high-energy plasma, which is also in some cases able to ionize the metallic vapor itself. The vapor then condensates, solidifying into primary particles of good local nanohomogeneity, and depending on the cooling rate of the particles, they either aggregate and crystallize or are frozen in the glassy state [30]. Laser ablation is usually performed in strong vacuum systems (<10\({}^{-3}\) Pa), while spark ablation can be performed under atmospheric conditions with a carrier gas. This carrier gas can be used to achieve a wide range of cooling rates, which is an obvious advantage for the synthesis of metallic glasses [31; 32]. Nowadays, plasma techniques such as laser or spark ablation are proving highly useful for laboratory studies of new systems. Spark ablation shows promising potential for scaling up and advancing research into industrial applications, such as coatings [33; 34], powders [35], and possibly even working parts, as evident by the already utilized spark plasma sintering [36].
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 5 Experiment
### Open Area Experiment  
We carried out the positioning accuracy of multi-sensor fusion positioning experiment based on fusion positioning equipment in the playground, as in Figure 11 of Beijing University of Posts and Telecommunications. We walked around the playground with positioning equipment and CHCNV E91. The fusion positioning results of SICI(CV), SICI(CT), BICI(CV), BICI(CT) and BICI-IMM algorithms are presented in Figure 12. The  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm** & **RMS Positioning Error (m)** \\ \hline BICI-IMM & 0.47 \\ BICI (CV) & 0.53 \\ BICI (CT) & 0.56 \\ SICI (CV) & 0.70 \\ SICI (CT) & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMS position error (m).  
Figure 10: The hardware of multi-sensor fusion positioning equipment.  
RMS positioning errors of SICI(CV), SICI(CT), BICI(CV), BICI(CT) and BICI-IMM fusion positioning algorithms are 1.559 m, 1.561 m, 1.49 m, 1.484 m and 1.27 m as shown in Table 4 and Figure 13. Compared with single model BICI and SICI algorithms, the positioning accuracies of the BICI-IMM multi-sensor fusion algorithm are improved by 18.5% and 14.4%, respectively. Therefore, the BICI-IMM multi-sensor fusion algorithm has better positioning accuracy in open area.  
Figure 12: Comparison of positioning results in the open area (local Cartesian coordinates coordinate system).  
Figure 13: The CDF of positioning error in open area.
###### Abstract
[10] studied the impact of potential confounding factors based on stratification--such as gender, age, reporting quarter--and compared the changes in IC values before and after stratification. [PERSON] et al. [11] combined the method of removing the masking factor and the stratification of the confounding factor, and proposed a method based on the competition index (ComIn) to identify the disproportionate strength of competitors. They compared the competition factor with the masking factor (MF) and the masking ratio (MR), and found that the ComIn can minimize the competition bias. However, when stratifying confounding factors, these researchers only considered the two major confounding factors, age and gender, and ignored the influence of drug category. Therefore, the improvement effect of stratification was not obvious in the results. Classification is an important subject in data mining. In recent years, researchers have begun to use the decision tree model to classify datasets. In order to verify the performances of data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making, [PERSON] et al. [12] used resampling to balance positive/negative class ratios, and used a decision tree and logistic regression to classify individuals according to risk, so as to improve balance in the classification of medical datasets. The results showed that resampling by decision tree can be effectively applied to biomedical data in order to optimize clinical decision making, and--at the same time--minimize the amount of unnecessary examinations. Since the mass production and use of penicillin by American pharmaceutical companies in 1942, hundreds of antibiotics have been synthesized. Antibiotic resistance affects the development of the world economy and threatens public health. Antibiotic-induced reactions account for half of spontaneous reports of adverse events in China [13]. Due to the high proportion of antibiotics in the Chinese spontaneous reporting database (CSRD), this paper proposes a decision tree-stratification method for the minimization of the masking effect in ADR signal detection by integrating the relevant factors of patients (age, gender) and drug category (whether or not antibiotics).
## 3 Results and Discussion
### Method Validation  
Optimum analysis conditions were established to be: PFBHA dopant concentration at 17 mg/mL, doping time 15 min, derivatization/extraction time 5 s, and doping and derivatization temperature 60 \({}^{\circ}\)C. The optimized parameters other than derivatization time (5 s) are similar to those in other studies on on-fiber derivatization SPME analysis of carbonyls (Table 1) although a much shorter derivatization time was used in this study to optimize 2-furral derivatization. Quality parameters, such as linearity, detection limit and precision, were determined based on the extraction method developed (Table 4). With optimization of important derivatization parameters, a very short derivatization time of 5 s generated detection limits comparable with previous studies, while a significantly low LOD on 2-furral was still achieved (Table 1). However, shorter derivatization time sacrificed uptake rate (recovery) of target aldehydes (Figures 1 and 5). Specifically, propanal and pentanal could not be detected with 5 s derivatization (Table 3). Minimum 2 min derivatization was needed for quantification of propanal and pentanal. Calibration curves of ethanal, hexanal, 2-furral and benzaldehyde were generated by plotting  
Figure 5: Time-profile of derivatization time from 2 s to 20 s.  
quantification ion peak areas against the corresponding carbonyl compound concentration in the standard sample. The analytical method developed was applied to ozonated FL solution/suspensions (Figure 6).
# Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
###### Abstract  
Image-based rail defect detection could be conceptually defined as an object detection task in computer vision. However, unlike academic object detection tasks, this practical industrial application suffers from two unique challenges, including object ambiguity and insufficient annotations. To overcome these challenges, we introduce the pixel-wise attention mechanism to fully exploit features of annotated defects, and develop a feature augmentation framework to tackle the defect detection problem. The pixel-wise attention is conducted through a learnable pixel-level similarity between input and support features to obtain augmented features. These augmented features contain co-existing information from input images and multi-class support defects. The final output features are augmented and refined by support features, thus endowing the model to distinguish between ambiguous defect patterns based on insufficient annotated samples. Experiments on the rail defect dataset demonstrate that feature augmentation can help balance the sensitivity and robustness of the model. On our collected dataset with eight defected classes, our algorithm achieves 11.32% higher mAP@.5 compared with original YOLOv5 and 4.27% higher mAP@.5 compared with Faster R-CNN.  
object detection; pixel-wise attention; feature augmentation; rail defect
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection
### Yolov5 Network
#### 3.1.2 Backbone Module  
(1)Focus structure  
The structure of the focus layer is shown in Figure 3. Taking the structure of Yolov5s as an example, the original 640\(\times\)640\(\times\)3 image is input into the Focus structure, using the slice operation, which first becomes a feature map of 320\(\times\)320\(\times\)12, and then goes through a convolution operation of 32 convolution cores, and finally becomes a feature map of 320\(\times\)320\(\times\)32. The number of convolution kernels for the other models will increase with the model depth.  
Batch normalization (BN) converts the distribution of data in each layer so that it has a zero mean and a variance of 1, so that the distribution of data in each layer is roughly the same, and the training will be easier to converge and prevent overfitting. If only the output data of layer A of the network is normalized and then sent to the next layer (layer B) of the network, the features learned by the network A of the layer will be affected. BN also uses transformation reconstruction and introduces the learnable parameters \(\gamma\) and \(\beta\), so that the network can learn to restore the original features of the target. The batch normalization is processed as shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Anchors** & **Size** \\ \hline
8 & [10,13,16,30,33,23] \\ \hline
16 & [30,61,62,45,59,119] \\ \hline
32 & [116,90,156,198,373,326] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The anchor setting of Yolov5.  
Figure 3: The structure of the focus layer.  
The Leaky ReLU function is a variant of the classical (and widely used) ReLU activation function whose output has a small slope to the negative input. As the derivative is always non-zero, this reduces the emergence of silent neurons, allowing for gradient-based learning.  
\[\text{LeakyReLU}(x)=\left\{\begin{aligned} & x,x>0\\ ax,x\leq 0\end{aligned}\right.\]  
(2). CSP structure  
CSPnet (cross stage partial network) is proposed to reduce the calculation of very large networks, mainly from the perspective of network structure design. The CSP module first divides the feature mapping of the base layer into two parts, and then merges them across the stage hierarchy, which can guarantee the accuracy while reducing the calculation amount.  
There are two kinds of CSPnet structures in the Yolov5 network. CSP 1 structure is applied to the backbone network, as shown in Figure 4.  
The benefits of adopting CSPnet include enhancing the learning ability of CNN, which adopts a lightweight structure to reduce memory cost while maintaining detection accuracy.
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 5 Methodology  
This investigation was performed with several compression and optimization tools and methods, which we describe below, explaining their advantages and disadvantages. We furthermore explain why some of the methods achieved better results than others we tried but rejected. Our tool for compression and decompression was written by us in C#, along with another tool written by us, which aims at comparing results and checking them visually. Examples of output files from the C# tool are shown in Figures A1-A5. The visual tool is explained in more detail below in Section 7.  
Accordingly, the algorithm for encoding a raw GNSS data file is (Algorithm 1):  
```
Input: Raw GNSS data Output: compressed binary data (1) Checking the correctness of the information from the GPS data by using GPRMC protocol like indecation in 3 rd parameter (V=invalid data). (2) If (information is incorrect) do: Remove incorrect information from the data file (3) Performing preprocessing compression using the difference method (very similar to the existing H.264 method). (4) If (its first time running algorithm) do: (a) Generate prefixes configuration (b) Execute mapping file (c) Generate Huffman configuration (5) Performing a more significant compression by the Huffman code.
```  
**Algorithm 2****Decompression GNSS data**
###### Abstract  
Visible Light Positioning (VLP) is widely recognized as a cost-effective solution for indoor positioning with increasing demand. However, the nonlinearity and highly complex relationship between three-dimensional world coordinate and two-dimensional image coordinate hinders the good performance of image-sensor-based VLP. Therefore, there is a need to develop effective VLP algorithms to locate the positioning terminal using image sensor. Besides, due to the high computational cost of image processing, most existing VLP systems do not achieve satisfactory performance in terms of real-time ability and positioning accuracy, both of which are significant for the performance of indoor positioning system. In addition, the accurate identification of the ID information of each LED (LED-ID) is important for positioning, because if the LED-ID is not recognized well, the positioning can only be achieved in a particular positioning unit and cannot be applied to a large scene with many LEDs. Therefore, an effective image-sensor-based double-light positioning system is proposed in this paper to solve the above problems. We also set up relevant experiments to test the performance of the proposed system, which utilizes the rolling shutter mechanism of the Complementary Metal Oxide Semiconductor (CMOS) image sensor. Machine learning was used to identify the LED-ID for better results. Simulation results show that the proposed double-light positioning system could deliver satisfactory performance in terms of both the real-time ability and the accuracy of positioning. Moreover, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which has never been considered before. Experiments confirmed that the proposed double-light positioning system can provide an accuracy of 3.85 cm with an average computing time of 56.28 ms, making it a promising candidate for future indoor positioning applications.  
visible light positioning (VLP); image processing; Complementary Metal Oxide Semiconductor (CMOS) image sensor; real-time ability and positioning accuracy; low complexity
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 3 Proposed Method
### Multi-Scale Enhanced Feature Extractor(MEFE Block)  
In the backbone network we design the multi-scale enhanced feature extractor module (MEFE Block) to optimize the initial feature extraction of the input image. The aim is to enhance the model's ability to detect different objects through the multi-scale feature extraction and enhancement mechanism.  
The MEFE module employs a Multi-Scale Parallel Convolution (MSPC) strategy, where different scale convolution kernels are used in parallel in the same layer to extract features, and each convolution kernel outputs a feature map. These feature maps from different scale convolution kernels are spliced in the channel dimension to form a new feature map. After that, further channel compression is performed by 1 \(\times\) 1 convolutional layers to reduce redundant information and enhance the feature representation. Finally, the importance of the features is automatically adjusted by introducing a SE mechanism to weight the features of each channel and output them.  
This design allows the MEFE module to capture both the local detail information of the image and the global contextual content, solving the problem of insufficient information acquisition by a single convolutional kernel due to the restricted sensory field. At the same time, the resulting computational overhead is effectively avoided compared to the stacked deep network approach. The MEFE Block is shown in Figure 3.
## 6 Simulation Results
### Simulation Results of the Proposed 2 WF-SSRT Topology
#### 6.3.3 Permanent dc Line-Line Fault on Line L\({}_{13}\) within the Substation Ring  
In the third case, again a permanent dc line-line fault is considered but within the SS ring as discussed in scenario-2 of Section 4.1.2. In the event of dc fault on L\({}_{13}\), dc breakers on either side of the fault will operate and open the ring. However, the power of units-1, 2, and 3 are smoothly and continuously being exported to ac networks from the MT-HVdc grid via GS-VSC\({}_{1}\), GS-VSC\({}_{2}\), and GS-VSC\({}_{3}\), since WFs are directly attached to respective substations after the operation of DCCBs. The direct voltage and power profiles are shown in Figure 15. Profiles are unaltered before and after  
Figure 14: Permanent dc line–line fault F\({}_{1}\) on cable L\({}_{12}\).  
Figure 13: Disconnection of GS-VSC1 within the SS ring of the 2 WF-SSRT.  
the operation of DCCBs in the event of fault F2, which gives conformability of the proposed topology with all three standards of GBSQSS.  
All the above three tests prove the superiority of the proposed 2 WF-SSRT over the simulated (literature best) WFRT and 2 WF-1 SST. 2 WF-SSRT fulfills the GBSQSS requirements and has enough protection to cope with the transients that occurred due to faults and disconnection of the GS-VSCs.