esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 5 Conclusions  
This paper proposed a novel pointing and stabilizing manipulator that integrated both coarse and fine actuators in parallel. The manipulator is designed to provide small optical space payloads on large spacecraft with independent and large-scale pointing adjustment as well as pointing stabilization at the same time.  
A theoretical analysis of the manipulator characteristics was first conducted. Singularity analysis showed that the division of pointing labor is feasible with parallel coarse and fine actuators. Through dynamic analysis, the feed-through characteristic of disturbance transmissibility and the'mobile-base' effect on active control was revealed. The paper further presented a specific operation scheme of the novel manipulator including a large-scale adjustment algorithm and an active vibration isolation algorithm. According to the simulation, using the proposed adjustment algorithm, the prototype could arbitrarily adjust the roll-pitch-yaw angles of the payload pointing within the range of \(\pm 8.5^{\circ}\) and error below \(2\times 10^{-8}\) degrees, and using the proposed active vibration isolation algorithm, the prototype could reduce the pointing direction jitter by over 95%, which reached the level of \(10^{-7}\)rad from the original level of \(10^{-5}\)rad. Therefore, it could be accepted that the parallel integrated scheme of coarse actuators and fine actuators is feasible. The simulation also confirmed some key conclusions of theoretical analysis.  
The proposed parallel integrated scheme suits the pointing requirements of future small optical payloads on large spacecraft. The active feedforward method could also be extended to other vibration isolation manipulators. Next, we intend to build an experimental system to validate the solution in practice and achieve the given performance requirements.  
Conceptualization, Z.X.; Methodology, H.Z.; Software, H.Z.; Validation, H.Z.; Investigation, H.Z.; Resources, A.X. and E.Z.; Data curation, A.X. and E.Z.; Writing-original draft, H.Z.; Writing-review & editing, Z.X., A.X. and E.Z.; Project administration, Z.X.; Funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 11972343.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 5 Experimental Setup and Results
### System Validation Dataset and Criteria  
To validate the proposed system, ASL is selected mainly due to the availability of a full dataset, which is needed to create an effective training set. This dataset is used globally; thus, the number of end-users will increase. Other parameters considered are that this dataset can be modified and redistributed, open source, use its own unique grammar, and how many people are using this dataset. An in-depth review of the sign language dataset with the aim of creating a new dataset is outside the scope of what is presented in this manuscript.  
For any new proposed system, the first step is always to select known input test data and determine the expected output. Through this approach, the system can be validated, limitations can be understood, and accuracy can be defined. Once this is done, the second step is to expand the input test dataset and process it through the system. This process will help enhance the capabilities of this new system.  
Using a known dataset is the key to validating the proposed system. A fully working and validated system will then open the door for further research by processing new datasets through the system.  
Here, a validation strategy is defined in which some parameters are fixed to evaluate the proposed system. The first is to select a dataset for hand gestures. For this, the images are used from Kaggle [48]. The hand gesture images used for validation of the proposed system are of American Sign Language (ASL). This process is used to review the proposed system for accuracy, speed, ease of communication, and effectiveness of the machine learning algorithm. The images used are in color and RGB form, although other forms can also be used.  
Alphabets A to Z and numbers 0 to 9 are used for validation. Within the dataset, there are 1820 images of alphabets A to Z and 700 images of numbers 0 to 9. These images show different variants of these alphabets and numbers. The number of images is appropriate for the evaluation of the algorithm. Images of some alphabets and numbers selected randomly are shown in Figure 8. These images provide a variety of gestures that are detected and evaluated.  
Figure 8: A subset of alphabets and numbers from the Kaggle dataset.  
The dataset is split into two groups. The first includes 70% of the data, which is used for training, while the remaining 30% is used for testing. This is a standard split for evaluating machine learning-based algorithms. In other combinations, researchers use 60% of the data for training, while 20% each is used for validation and testing, respectively.  
For alphabet classification, 1274 images, i.e., 70% of 1820, are used for training, while the remaining 30%, i.e., 546 images, are used for testing. Similarly, for the evaluation of numbers, 70% of images, i.e., 490 out of 700, are allocated for training, while the remaining 210 images are used for testing.
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
###### Abstract  
Traditional manual measurement of Cobb angle is a time-consuming process and leads to different results. To address this issue, this paper proposes a deep learning-based method of locating the vertebral center points. The whole X-ray can be input into the network for prediction, without worrying about the detection of cervical vertebrae with similar characteristics to the thoracic and lumbar vertebrae. First, key points predicting and noise points filtering operations are employed to obtain vertebral center points for fitting. Then, the spine curve is fitted, and the slope of the normal line of the spine curve is adjusted according to an empirical formula. Finally, the Cobb angle allowed by the error is calculated. Through the reliability analysis of the traditional manual measurement method and the automatic detection method in this paper, ICC (intraclass correlation coefficient) with the two observers was 0.897 and 0.901, respectively, and MAD (mean absolute deviation) was 3.13\({}^{\circ}\) and 3.04\({}^{\circ}\) respectively. This indicates that the automatic detecting method by computer has good reliability. Therefore, this method can be used to detect scoliosis quickly and effectively.  
scoliosis; Cobb angle; deep learning; key point detection Article
# Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing
## 2 Methodology
### Two-Plane Parametrization of Light Field  
In geometric optics, the fundamental carrier of light is a ray, which can be described by plenoptic function with five parameters, including three coordinates and two angles [9]. The description can further be reduced to four dimension with the assumption that the radiance of the ray remains constant from point to point along its length [10]. Figure 2a shows the two-plane based four-dimensional parametrization of the light field. In two-plane parametrization, a ray is parameterized as \(L(u,v,s,t)\), where \((u,v)\) are the coordinates of the view point in \(UV\) plane, and \((s,t)\) are coordinates of the image point in the \(ST\) plane. Without loss of generality, the four dimensional representation can further be simplified to two-dimensional \(L(u,s)\) in Cartesian ray-space denoted as _US_ plane as shown in Figure 2b. The ray depicted two planes is reduced to a point in the Cartesian ray-space _US_ plane. The blue view point \(P_{1}\) and red view point \(P_{2}\) in Figure 2a represent corresponding color streak in Figure 2b, respectively. An arbitrary view point in a light field corresponds to a 2D slice in the _US_ plane. Hence, by the use of the two-plane parametrization, the multi-view of the scene can be obtained by selecting different slices in _US_ planes, and the all-in-focus image is just a special slice, of which \(u\) and \(v\) are the center of the \(UV\) plane [10]. Focal stacking is essentially a process of refocusing the scene at a series of different depths. Based on the two-plane parametrization, the irradiance of an arbitrary point in the image plane can be obtained by integrating the light passing through the entire \(UV\) plane, and the process of digital refocus is generally 2D slices shift in spatial dimension and integration in the angular dimension. This will be discussed in more detail in Section 2.2.  
When considering the instruments used to capture the light field, camera array matches well with the principle that is presented in Figure 2. That is, each camera samples different views of the scenes corresponding to the \(UV\) plane. For the light field captured by micro lens array, the aperture is \(UV\) plane and the sensor is \(ST\) plane. Different views can be reconstructed by selecting corresponding pixel under each micro lens and putting them together.  
Figure 1: Framework of the proposed method.
## 3 Results and Discussion
### Characterization of Cellulose Fibers
#### 3.1.1 Compositional Analysis
For instance, researchers [65] observed that grass clippings subjected to alkali and bleaching treatments had an \(\alpha\)-cellulose content of only 59% compared with 52% in the untreated feedstock. Despite the initially modest \(\alpha\)-cellulose content in grass clippings, chemical-thermal treatment induces substantial compositional changes in the feedstock. Our findings reveal that alkaline treatment reduced moisture content to 4.6% and ash content to 3.14%, accompanied by a notable increase in \(\alpha\)-cellulose content to 86.16%. Subsequent bleaching treatment decreased the ash content to 1.03%, while \(\alpha\)-cellulose content further increased, reaching 87.37%. However, interpreting the obtained results became more complicated when analyzing the data using the Kappa number. Surprisingly, Kappa number values increased after the bleaching treatment, contrary to the anticipated decrease in lignin content. Notably, the Kappa number findings align with those reported by [PERSON] et al. [66], who observed Kappa number values ranging from 12 to 17 for various biomass samples. In the context of our study, the observed increase in the Kappa number following chemical-thermal treatment may be attributed to side reactions leading to the formation of chromophores and subsequent elevation of the Kappa number. For instance, [PERSON] et al. [67] highlighted that such reactions are more likely to occur with prolonged reaction times and high levels of hydrogen peroxide, characteristic of biomass processing methods.
## 7 Conclusions  
In this paper, the Inventory Routing Problem with priorities and heterogeneous fleet has been proposed. A mixed-integer model is proposed based on previous formulations [9,40] and was extended to consider facilities from the health sector, which have priority over industrial customers according to Mexican law, as well as multiple vehicles with different capacities. Test instances were also proposed considering deterministic consumption rates and a certain percentage of customers with priority. The results show that solutions from the IRPPHF differ importantly from those of the IRP. The proposed formulation was used to solve instances of up to 30 clients. Results from the computational experiments with the benchmark instances show that the inclusion of customers with priority increases the total cost mainly by the routing cost, while the inventory costs, in general, remain the same. Routing cost increases depending on the number of vehicles used in each period, and the location and consumption rates of the customers with priorities. Additionally, a case study is presented to assess the performance of the proposed model in a real-life scenario. The results show that the use of computational tools such as the one presented in this paper can lead to substantial cost savings (up to 30%). These kinds of tools can support the route design process and help managers to make better decisions. For future research, additional constraints and features can be included in the model to make it closer to reality. The use of articulated vehicles, multiproduct distribution, and non-deterministic consumption rates are among the features that might be considered in future research. The inclusion of such features could allow not to underestimate elements of the problem like the total distribution cost, as mentioned in [42]. Finally, in order to improve the solution process, the use of cutting plane algorithms and column generation might be studied.  
Conceptualization, P.A.A.-T.; Data curation, N.M.A.-M.; Formal analysis, N.M.A.-M. and E.R.-y-R.; Investigation, P.A.A.-T.; Methodology, P.A.A.-T., N.M.A.-M. and E.R.-y-R.; Project administration, P.A.A.-T.; Software, P.A.A.-T. and N.M.A.-M.; Validation, P.A.A.-T., N.M.A.-M. and E.R.-y-R.; Visualization, N.M.A.-M.; Writing--original draft, P.A.A.-T., N.M.A.-M. and E.R.-y-R.; Writing--review & editing, P.A.A.-T., N.M.A.-M. and E.R.-y-R. All authors have read and agreed to the published version of the manuscript. This research was funded by Fundacion Universidad de las Americas Puebla.  
We thank you to the company for prividing us all the information and data needed to develop this research.  
The authors declare no conflict of interest.  
Figure 7: Routes for the case of study.
## 3 The Experimental Results and Analysis
### The Static Measurement Experiment  
Multiple static objects are measured to verify the feasibility of the proposed method. Figure 9 shows one of the experimental results for a knife model measurement. Figure 9a,b show the knife model and its corresponding single-shot deformed pattern, and Figure 9c,d show the three frames binarized deformed patterns decomposed from the single-shot deformed pattern and the corresponding sinusoidal phase-shift deformed patterns, respectively. Figure 9e is the wrapped phase modulated by the height of the knife model, and Figure 9f is the 3D reconstruction result. It can be seen from Figure 9 that the proposed method can reconstruct objects accurately and verify the practicability of the proposed method. Due to the single-shot quaternary grating projected by DLP4500, the projection fringe refresh rate is as high as 1428 Hz, which will have broad application prospects in real-time and fast online measurement.
## 4 Results and Analysis
### Field Measurement Results and Analysis  
In this paper, only the results from measuring the S-line are presented due to the accuracy of these measurement results. According to the monitoring results, the S-Line shows that the ground subsidence increased slowly as the working face advanced. When the working face was fully mined and had reached its full mining impact, the maximum subsidence reached its peak, at 47 mm at the S8 measuring point for the 4 th measurement, due to the presence of a railway on the surface above this measuring point. Based on the predictions of the numerical simulation, the maximum surface inclination deformation value from the S-line ground subsidence value at the end of mining and with the backfill of the working face was 0.18 mm/m, which occurred at the S5 measuring point; the maximum curvature deformation value was 0.009 mm/m\({}^{2}\), which can be basically ignored; the maximum horizontal movement deformation value was 38.9 mm; and the maximum horizontal deformation value was 1.32 mm/m, which also occurred at the S5 measuring point. These deformation values meet the defense standards of the 2017 \"Regulations on Coal Pillar Retention and Compressed Coal Mining for Buildings, Water Bodies, Railways, and Main Shafts and Lanes.\" The cumulative subsidence value of the S-Line measuring points is shown in Figure 18.  
Figure 16: (**a**) Maximum surface subsidence and (**b**) horizontal movement.  
Figure 17: (**a**) Horizontal deformation and (**b**) inclination deformation.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 4 Implementation and Experimental Results
### Sensitive Analysis
#### 4.3.1 Analysis of Minimum Support Count  
When constructing a Product Relationship Network (PRN), the minimum support count in the Apriori algorithm is an important threshold since it is used to remove the links between nodes with weak association values. A high threshold value produces a network with few links. However, determining an appropriate minimum support count is a difficult task. To address this issue, the concept of \"accumulated percentage of active links\" is proposed to help users determine the threshold value. Note that an active link is defined as the link between two products whose association is greater than the minimum support count in the PRN.  
Figure 7 shows the relationships among the settings of minimum support counts, the number of active links, and the accumulated percentage of activate links in TBN. There are 5995 possible active links among 110 products when the minimum support count is set at 0. If the minimum support count is set at 15, the accumulated percentage of active links will be 30% (= 1799/5995). That is, store managers expect that at most 30% of items (i.e., around 33 items) might be exchanged. Similarly, when the minimum support count is set at 20, the accumulated percentage of active links will be 12% (= 719/5995), which indicates that at most 12% of items (i.e., around 13 items) might be moved. Based on the information in the figure, managers can determine the appropriate minimum support count with consideration of re-shelving cost. Similarly, Figure 8 shows the relationship among the settings of minimum support count, number of active links, and accumulated percentage of activated links in CBN.  
Figure 6: Fifteen re-shelved opportunity items.  
Based on the two figures, it is clear that the number of active links falls as the minimum support count increases. However, if the minimum support count is set the same for TBN and CBN, the number of active links in CBN is larger than that in TBN because most association values in the links in CBN are stronger than those in TBN. In addition, when the minimum support count is low, the centrality value will be affected by a large number of nodes with weak associations, thus reducing the significance of the reassignment result. Conversely, when the minimum support count is high, the centrality value is derived based on a smaller number of nodes with strong association, thus increasing the significance of the reassignment result.
## 4 Transcriptional Regulation during Salinity and Drought Stresses
### Myb  
The MYB family of transcription factors is one of the largest TF families in plants. A growing body of evidence has demonstrated the regulatory effects of the MYB family on many biological and biochemical activities such as defense and stress response regulation ([PERSON] et al., 2019), primary and secondary metabolism regulation ([PERSON] et al., 2010; [PERSON] et al., 2019) and the regulation of other plant-related developmental mechanisms. The MYB family of TFs is characterized by the presence of a conserved MYB DNA-binding domain.  
A transcriptional regulatory network analysis of MYB TF family genes in rice was conducted by [PERSON] et al [69] in order to identify new links and characterize the interaction between the MYB family and the downstream target genes. In their study, they observed around 40 putative target genes for OsMYB that contain at least one MYB-binding region in their promoter. Out of the 40 target genes, 27 were found to have a high number of MYB-binding regions involved in drought-inducibility (MBS, CAACTG and TAACTG). This finding implies that the _OsMYB_ genes are strongly involved and have great regulatory roles in drought response at the transcriptional level [69]. Studies have shown that _AtMYB2_, _AtMYB74_ and _AtMYB102_ MYB genes are upregulated by drought stress [70], which indicates that they play a role in the stress response regulation to protect the plant against the harmful effects of water deficit. Additionally, a study by [PERSON] et al. [71] investigated the possibility of _MYB44_ interacting directly with ABA signaling in cotton species. They concluded that the _MYB44_ gene encodes for a TF that interacts with ABA receptor proteins such as PYLs, which bind to PP2 Cs. Hence, the MYBB44 has been determined to be a regulator in the ABA-dependent signaling pathway that is related to the activation and repression of the SNF1-related protein kinases 2 (SnRK2s) in which it phosphorylates several stress-inducible genes. _AtMYB20_ enhances salt tolerance by repressing the expression of PP2 Cs [71; 72].
## 3 Methodology
### Inference  
Similar to the models proposed by [PERSON] et al. (2018) and [PERSON] et al. (2018), addressing the challenges of error correction in multi-error texts remains a substantial hurdle, leading to suboptimal effects. In an effort to overcome these challenges and mitigate the tendency toward excessive error correction, this paper introduces a novel polling error correction strategy during the inference phase.  
Specifically, during inference, each input sentence undergoes an iterative process of character detection and correction, deviating from the conventional all-at-once correction approach. In each iteration, only erroneous characters within a specified window size around each correction position from the preceding iteration are eligible for correction. If a position undergoes modification in every iteration, it is reverted to its original character without any correction. Drawing from experimental insights, the paper opts for two iterations, with a window size set to 5.  
This iterative correction process involves counting a specified number of low-probability words as the targets for the subsequent error correction after each prediction. The corrected information from the previous round is then incorporated into the next round, enriching contextual information and facilitating more nuanced error correction. This iterative strategy proves conducive to elevating the overall quality of error correction.
## 2 Materials and Methods  
### Specimen Design and Selection  
V-structure specimens were scaled down from a representative MRAP to 1:8.3 scale [5]. Geometrically similar scaling of a TM-57 anti-vehicle landmine (with an effective explosive diameter of 316 mm) and a popular armoured personnel carrier (approximate width of 2.5 m, ground clearance of 0.41 m) fixed the specimen width at 300 mm. The V-structures were manufactured by folding nominally 2 mm thick Domex 700 MC steel into the required geometry using a CNC plate bender. Three variants were produced: (i) a 105\({}^{\circ}\) internal angle with an external tip radius of 32 mm, (ii) a 105\({}^{\circ}\) internal angle with an external tip radius of 62 mm, and (iii) a 120\({}^{\circ}\) internal angle with an external tip radius of 32 mm. An example of the folded profile of a typical blast test specimen is shown in Figure 1.  
The V-structure design and mounting system were based on numerical simulations reported in [4], with a cross-cut width of 300 mm and a ridge length of 300 mm. The sheet thickness was measured to be 2.08 \(\pm\) 0.06 mm. The 105\({}^{\circ}\) V-structures had a mass of 2.31 \(\pm\) 0.01 kg, and the 120\({}^{\circ}\) V structures were 2.19 \(\pm\) 0.004 kg. The two vertical sides of the V-structures were machined with six equi-spaced clearance holes for M12 bolts, positioned 25 mm from the edge with 50 mm centre-to-centre distances.