esa-sceva/satcom-chunk-collection huggingface dataset:
# The Less Known Cyclins--Uncovered
## 13 Cyclin Y  
Cyclin Y is encoded by the CCNY gene located on the short arm of chromosome 10 at position p11.21. The protein is one of the most conservative members of the cyclin superfamilies, which are known for their key role in the regulation of the cell cycle and transcription. It binds and activates two cyclin-dependent kinases: Cdk14/PFTK1 and Cdk16 [107; 108; 109].  
Cyclin Y in normal cells along with Cdk14 influences the transduction of signals in the Wnt pathway by phosphorylation of low-density lipoprotein receptor-related protein 6 (LRP6) (Figure 4). [PERSON] et al. showed that the complex of cyclin Y with Cdk14 through phosphorylation of LRP6 prevents the degradation of \(\beta\)-catenin, which allows the signal transmission. Wnt receptor phosphorylation and signaling reach the maximum level in the G2/M phase, so as the expression of cyclin Y, which may suggest a relationship between these two elements [110].  
In normal cells, cyclin Y plays an important role in the regulation of the spermatogenesis process. While studying the complex of cyclin Y and Cdk16, [PERSON] et al., 2012 [108] showed that it is required in the final stages of cell differentiation in the spermatogenesis process. By studying mice with Cdk16 knockout, the authors concluded that they are infertile. Although they contained all cell types at various stages of spermatogenesis, the study of sperm cells showed many irregularities in their structure, which ultimately led to infertility [108].  
Furthermore, the expression of cyclin Y plays a key role in tumorigenesis. [PERSON] et al., 2010 [111] demonstrated that nonsmall cell lung cancer (NSCLC) is characterized by cyclin Y overexpression. Moreover, the expression of cyclin Y positively correlates with the histological subtypes of NSCLC, and the size of the tumor. It was also shown that the decrease in cyclin Y expression in the NSCLC cells leads to inhibition of tumor proliferation and growth [109]. Increased expression of cyclin Y has also been detected in the ovarian tumor. [PERSON] et al., 2016 [112] showed that cyclin Y enhances the proliferation rate, migration and invasion of ovarian cancer cells by regulating the Wnt signaling pathway [112].  
Cyclin Y is also overexpressed in glioma and larynx cancer. The studies conducted by [PERSON] et al., 2009 [113] proved that lowering the expression of cyclin Y results in inhibition of cell proliferation and colony formation, and the arrest of cell cycle progression in glioma cells. [PERSON] et al., 2013 [114] came to similar conclusions after studying the expression of cyclin Y in laryngeal cancer [113; 114]. Moreover, the overexpression of cyclin Y is observed in breast, colon and liver cancers [109; 115; 116].
## 4 Simulation Results and Analysis
This improvement is attributed to the transfer learning technology employed by HPTR, which leverages anti-jamming experiences from similar UACNs. In the initial phase of transmitter strategy optimization, HPTR mitigates the inefficiencies of blind random exploration by incorporating additional experience. Furthermore, the hybrid strategy-based reinforcement learning algorithm optimizes the transmitter's strategy to confuse the jammer, preventing it from executing precise attacks, thereby enhancing anti-jamming communication performance. Compared with PTR, the proposed HPTR method requires approximately 500 times more slots to converge to the optimal policy. The policy is then used until the network state or attack strategy changes for faster convergence due to the anti-jamming experience accumulated by HPTR via transfer learning. **Author Contributions:** Conceptualization, L.Y. and Y.C. ; methodology, L.Y. and H.W. ; software, Y.C. and H.W. ; validation, L.Y., Y.C. and H.W. ; formal analysis, Y.C. ; investigation, L.Y., Y.C. and H.W. ; data curation, Y.C. ; writing--original draft preparation, L.Y., Y.C. and H.W. ; writing--review and editing, L.Y. and H.W. All authors have read and agreed to the published version of the manuscript. Figure 3: Anti-jamming performance of the transmitter under different interference power levels: (**a**) transmitter utility, (**b**) bit error rate, and (**c**) SINR. **Funding:** This research was supported by the Fujian Provincial Natural Science Fund (2022J011226 and 2023J01155), and open project funding project of Fujian University Key Laboratory of Intelligent Ecotourism and Leisure Agriculture under Grant LN202401. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Acknowledgments:** We are grateful for the insightful feedback and thoughtful comments offered by the journal editors and reviewers, which have been instrumental in enhancing our work. **Conflicts of Interest:** The authors declare no conflict of interest.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 2 Challenges in CubeSat Communications
Of these segments, space segment routing includes data routing between satellites over one or more reliable paths to satisfy system requirements, while access routing controls satellite access to a ground station or a mobile ground user. The election of the access satellite is based on link efficiency regarding elements, such as signal strength and delay. However, the boundary routing is responsible for enhancing the interoperability between the space segment and the terrestrial network, where communication capability mismatch is present. The capability mismatch at the borders between the satellite and ground networks requires a resilient routing protocol to provide reliable communication. Thus, highly sophisticated routing techniques are essential for coping with these routing challenges.
## 3 Data and Methodology
### Laboratory Testing of Rock Masses
#### 3.4.1 Laboratory Physical and Mechanical Tests  
Rock samples were obtained from on-site cores, following the specifications outlined in the Standard for Geotechnical Test Methods (GB/T50123-2019) [27] and the Standard for Testing Methods for Engineering Rocks (GB/T 50266-2013) [28]. Subsequently, tests were conducted to determine the physical and mechanical properties of the specimens, including water content, natural density, particle density, water absorption, uniaxial compressive strength, tensile strength, shear strength, etc. A total of 30 groups of tests for rock physical parameters and 489 tests for mechanical parameters were carried out. Since the properties of rock masses are generally controlled by fractures and indoor experiments may not fully account for their influences, subsequent analyses will be combined with rock mass quality evaluation methods to ascertain rock mass parameters.
## 3 Results and Discussion
### Adsorption of 4-Ethylphenol and 4-Ethylguaiacol
#### 3.2.2 Kinetic Studies of 4-Ethylphenol and 4-Ethylguaiacol on Granular and Powder ACs  
The influence of the ACs physical forms on the 4-EP and 4-EG removal from the aqueous phase was evaluated. For this purpose, AC Norit 1240W was used in a granulated and powdered form. When it was placed in solutions with high concentrations of both phenolic compounds, both ACs forms (granular and powder) exhibited almost the same maximum adsorption capacity.  
In the diluted solutions of 4-EP and 4-EG, the Norit AC in powder form presented a maximum adsorption capacity of more than double that displayed by the granular form (0.33 and 0.15 mmol g\({}^{-1}\), respectively). These results highlight the external transport contribution, as a limiting step, to the phenomenon of adsorption on dilute solutions. When using granular AC, the adsorptive molecules (4-EP and 4-EG) were too far from direct contact with the inner surface, and adsorption has no place, which does not happen when it was sprayed. Nevertheless, when AC powder is used in diluted solutions containing a deficient mixture, diffusion has been referred to as the limiting step in the adsorption process [26].
## 3 Results and Discussion
### Correspondence Analysis Applied to RATA  
The previous section shows that there are differences in the flavor substances of yellow wine at stages F2, F3, and F4 by PCA, which will certainly cause differences in the sensory characteristics of yellow wine. Therefore, the data from RATA were analyzed using CA analysis to visualize the different odor and taste characteristics of yellow wine at the three stages. It showed that, for odor, the most heterogeneous attributes in the group are mushroom, metal, paraffin, and glue, suggesting that these odors are not representative. Honey was the odor with the least variability between groups (Figure 6A). F2 was characterized by the odors: floral, grass, banana, and cereal. The pear, pungent, and cream odors mainly characterize the F3 through the RATA. Overall, F3 exhibits a distinctly fruity aroma. Orange, pineapple, and grape aromas can also be used to describe its characteristic scent. Similarly, the F4 was characterized by wine, lemon, fat, apple, and maltose terms. As for taste, the descriptors for three samples promote the detection of a classification pattern. The salt and  
Figure 5: Descriptors created with RATA method grouped by odor and taste. Respective sum of frequencies from different fermentation periods in yellow wine are: (**A**) odor; (**B**) taste. The mean intensity ratings of three samples obtained for RATA method grouped by odor and taste (**C**) is odor, (**D**) is taste; a,b,c indicate significant differences between groups (\(p\) < 0.05).  
sweet tastes clearly characterize F2. F3 and F4 were characterized by spicy, bitterness, and sour taste, respectively (Figure 6B).
# Design and Implementation of a Compiled Declarative Language for Game AI Control
## 4 Bots Performance, Game Analysis and Credibility AI Assessment
### System
#### 4.1.2 Server  
The server was programmed in Go due to its high performance and low resource usage, such as CPU and RAM. This efficiency enables the simultaneous reception of large volumes of data from multiple games without issues. Additionally, the server offers a REST API with several endpoints for storing game data and retrieving stored information for future analysis.
# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 2 Related Works
### Transformer-Based ReID  
Researchers [5; 6; 7; 8; 9; 10] have recently employed transformer-based models for feature extraction, introducing multi-head attention modules which make transformer-based models well-suited to address challenges in this area. Research on transformer-based ReID has seen substantial progress in recent years. Compared to Convolutional Neural Networks (CNNs), Vision Transformers exhibit superior global information processing capabilities. Through leveraging self-attention mechanisms, ViTs excel at capturing long-range dependencies and global contextual information, thereby enabling the model to effectively extract inter-regional feature correlations when dealing with variations in pedestrian poses, occlusions, and images from different viewpoints. TransReID [6] was the first model to apply ViT in ReID, in which ViT offers a more flexible feature representation; through dividing images into multiple patches and directly applying self-attention to these patches, the model learns features without the constraints imposed by local receptive fields, providing a more adaptable expression for subtle differences in pedestrian identities. DC-Former [5] employs multiple class tokens to represent diverse embedding spaces. This approach incorporates SDC in the output of the final transformer block, thereby encouraging class tokens to be distanced from one another and embedded into distinct representational spaces. Part-Aware Transformer (PAT) [9] introduces a mechanism that adaptively identifies and highlights multiple key body parts of persons during the training process. Furthermore, through incorporating occlusion-aware loss functions and training strategies, the model is able to better comprehend and adapt to the effects of occlusions, thereby enhancing its robustness in recognizing individuals under such challenging conditions. PVT [10] uses a pose estimator to detect key points in the human body and uses these points to locate intermediate features. These intermediate features of key points are input into the pose-based transformer branch to learn point-level features.  
However, the attention mechanism of ViT may be influenced by occluded objects, resulting in attention being dispersed to the occluded area. Features of these parts are ineffective for ReID tasks, leading to a decrease in the performance of ViT-based models when considering occluded scenes.
## 5 Experimental Results and Analysis
### Experiments with the Controller under Different Step Signals  
Figure 17 presents the position curves of the controller for various step signals based on experimental data. The curves for the ADRC, RBF-ADRC, and FRBF-ADRC controllers under 1 rad, 1.5 rad, and 2 rad step signals illustrate the response speed of each controller at specific times. Additionally, the comparison data displayed in Table 4 can be derived from Figure 17. The FRBF-ADRC controller demonstrates a superior performance compared to the other two controllers. Furthermore, for each controller, a higher position correlates with a slower response time.  
Figure 16: Curve diagram of step signal sudden loading position in experiment.  
Figure 15: Step signal position curve in the experiment.
## 6 Performance Evaluation
### Parameter Analysis
#### 6.3.4 Request Model Analysis  
Considering that the random requests are rare in real life, in order to simulate more realistic data requests, a series of experiments based on Poisson distribution are developed. On the one hand, the number of requests from each vehicle follows a Poisson distribution with \(\lambda=2.5\), and hence 100 vehicles totally have 235 requests. On the other hand, we set different numbers of contents as 100, 200, and 300, and the number of requests for each content follow the Poisson distributions with \(\lambda=2.35\), 1.175, and 0.783, respectively. If content has a large number of requests, it is regarded as popular content. The popularity distributions in the three scenarios with 100, 200 and 300 contents are shown in Figure 10, and the performances of the four schemes are illustrated in Figure 11.  
Figure 11: _Cont._  
Figure 10: Popularity distribution of contents.  
In Figure 10, we see that, given the number of requests in the entire network, when there are 100 pieces of content, nearly 70% of them have more than one request, and the most popular content has seven requests. By contrast, when there are 300 pieces of content, nearly half of them do not have requests, and the majority of the remaining contents have only one request for each. Therefore, a variety of contents leads to a sparse popularity distribution.  
From Figure 11, a larger number of contents degrade the access ratio and bring in more replicas. Compared with the results using random requests in Figure 5, our scheme DAS with the requests following the Poisson distribution has a drop on the number of replicas because a replica of popular content may respond to multiple requests.  
In general, from the parameter analysis, we conclude that DAS has a stable advantage over compared schemes on the metrics of delivery ratio and storage efficiency.
## 3 Materials and Methods
### Periodic Review (R, s, S) Inventory System  
Inventory in DC is controlled by a periodic review (R, s, S) inventory system with various deterministic review periods. Since demand is stochastic, the order size to the warehouse varies, with S - s being the minimum size. Observed market demand, product deliveries, and inventory levels are non-negative integer values. The starting inventory level on day one for each simulation experiment is set to order-up-to level S.  
Lead time represents the interval between placing and receiving an order, directly influencing the overall SC performance [54]. In a periodic review inventory system, the inventory position at the time of ordering should account for demand fluctuations over the period of (R + LT). The inventory simulation model does not allow the launch of a replenishment order until the previous one is delivered, preventing the problem of over-ordering. Deliveries from the warehouse to DC before the openings on the following day  
Figure 3: Periodic review (R, s, S) inventory system model used for supply chain simulation.  
Figure 2: Model of supply chain.  
are referred to as replenishments with zero lead time (LT = 0) or instantaneous deliveries. In a periodic review (R, s, S) inventory system, LT and R are independent, i.e., lead time can be shorter, equal, or longer than the review period.  
The warehouse can fulfill orders from DC in all scenarios, i.e., the FR from warehouse to DC is assumed to be 100%. Fill rates of 99.9% and 89.9%, with a tolerance of 0.01%, are set as targets for satisfying market demand. Excess demand is being lost, resulting in penalty costs. The targeted FR is a service-based performance criterion that keeps inventory levels as low as possible while respecting the rest of the model constraints. The FR assumes the level of demand that is fulfilled directly from the stock. The SC model does not consider partial deliveries, backlogging, or additional safety stock.  
To ensure the lowest inventory levels for the SC's operating conditions, a brute force search algorithm is used to determine the lowest values of s and S that fulfill the targeted FRs and satisfy the SC's operating conditions. In a periodic review (R, s, S) inventory system, order-up-to level S is always higher than reorder point s for at least one unit. The simplified algorithm of OptimInventory 4.2 software [55] used for the simulation of inventories and replenishments starts the search with characteristic inventory levels S = 1, s = 0, and calculates the actual FR within given SC operating conditions. If the measured FR is lower than required, increase S for 1, set s to 0, and increase until S = s + 1. This search repeats until the combination of (s, S) results in the required FR. OptimInventory software is used in the analysis of periodic review (R, s, S) inventory systems in the works of [37; 40; 56; 57; 58; 59; 60]. In total, 6.42 \(\times\) 10\({}^{11}\) simulation scenarios were tested to determine 192000 SEs. A dual AMD EPYC 7763X workstation with 128 cores and 256 GB RAM was used, with a total computational time of approximately 41 h.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 3 Analysis of Dynamic Parameters
Figure 5 shows the averaged RMS of calibration parameters' differences between using the extrapolated calibration parameters for one hour and the estimated ones as well as the averaged RMS of calibration parameters' differences between using the last set of calibration parameters and estimated ones for each arc. From Figure 5, we can see that the extrapolated calibration parameters are much closer to the estimated ones, which means the precision of the predicted orbit by using extrapolated calibration parameters should be higher than that of using the last set of calibration parameters. The accelerometer bias in the y direction shows the largest difference since this direction is the least sensitive for the accelerometer instrument, which is pointing to the cross-track direction of the LEO orbit while the bias in the x direction shows the smallest difference. In the following LEO orbit prediction analysis, we will use the extrapolation calibration parameters for orbit prediction with strategy 2. Figure 6 shows the dynamic parameters in the EMP method. It shows that the dynamic parameters are not stable, especially for the Sa and Ca terms, which are used for compensating the unmodeled part of the along-track component. They are quite different from day to day. This also implies that the non-gravitational force models used in the POD are not accurate enough and the dynamic parameters have to be estimated in order to absorb the unmodeled part of non-gravitation. It is not appropriate to  
Figure 4: Accelerometer bias parameters for strategy 2. The data between black dashed lines are excluded because of the change of the controller onboard or abnormal behavior of accelerometer. Black lines are the linear fitting line. The corresponding fitting results are also shown in each panel. use the extrapolate method to obtain dynamic parameters for orbit prediction like the ACC_B method since these parameters fluctuated and are hard to predict. So, the last set of dynamic parameters was adopted to predict the orbit in the following parts for the EMP method. Figure 5: Averaged RMS of calibration parameters’ differences between using the extrapolated calibration parameters and the estimated ones as well as the averaged RMS of calibration parameters’ differences between using the last set of calibration parameters and the estimated ones for each arc. “W/O Extro” means without extrapolated and “W Extro” means with extrapolation. Figure 6: Dynamic parameters estimated from Strategy 3.
## 3 Results  
In this section, we describe extensive experiments evaluating the performance of the proposed method. Four regulatory network datasets were used in the experiments. Our proposed method was compared with four network inference algorithms based on information theory: CLR, ARACNE, MRNET, LDCNET, and two methods based on distance correlation: REL-DC and MRNET-DC. For the ARACNE algorithm, we chose the default threshold to discriminate the final regulatory relationship. The CLR and MRNET algorithms were implemented using the optimal threshold selection method from article [27]. The code for all the algorithms was implemented in R and Matlab, respectively. The distance dependence was calculated using the existing dcor function in R. The REL, CLR, ARACNE, and MRNET algorithms were implemented using the existing R package MINET [28]; the LDCNET and DCNTC algorithms were edited and implemented in Matlab.  
All of the experiments were performed on four network datasets, including simulated and real data. The datasets can be obtained from previous studies.  
The DREAM3-10 gene dataset [5], contains 10 samples for 10 genes. It is from the DREAM (\"Dialogue for Reverse Engineering Assessments and Methods\") project, and represents a yeast gene network. The true network is composed of 10 nodes and 10 edges.  
The DREAM3-50 gene dataset [5], contains 50 samples for 50 genes. It also belongs to the DREAM project and represents a yeast gene network. The true network is composed of 50 nodes and 77 edges.  
The DREAM3-100 gene dataset [5], contains 100 samples for 100 genes. It also belongs to the DREAM project and represents a yeast gene network. The true network is composed of 100 nodes and 166 edges.  
SOS [29; 30], contains nine samples for nine genes. It is an SOS DNA repair network in Escherichia coli. The true network is composed of 9 nodes and 24 edges.  
To contrast the reasoning methods objectively, it is necessary to measure their performance quantitatively. The predictive results are defined as follows: false-positive (FP), true-positive (TP), false-positive rate (FPR), true-positive rate (TPR), positive predictive value (PPV), accuracy (ACC), and Matthews coefficient constant (MCC) [31]. Mathematically, they are defined by:  
\[TPR=\frac{TP}{TP+FN} \tag{7}\]  
\[FPR=\frac{FP}{FP+TN} \tag{8}\]  
\[PPV=\frac{TP}{TP+FP} \tag{9}\]  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{10}\]\[MCC=\frac{(TP*TN-FP*FN)}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}} \tag{11}\]  
where TP, FP, TN, and FN are the elements in the formula. These are accounts of true-positives, false-positives, true-negatives, and false-negatives, respectively.