esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussions
### Simulation Scenarios and Results
#### 6.3.3 Simulation Scenario 3: Energy Consumption Evaluation

Table 12: Comparative analysis of improvement ratios: HND and LND in the proposed protocol versus counterparts. and SSDFH can immediately take over data aggregation and forwarding tasks, minimizing data loss, reducing downtime, and maintaining network performance. This redundancy is essential for critical applications, such as healthcare monitoring or industrial automation, where consistent data transmission is vital for decision-making and operational efficiency. More to the point, the routing strategy in the proposed protocol with multi-hop routing, which is based on a novel RDFCF, helps to reduce the communication distances between SNs and the sink node, which reduces energy consumption and enhances load balancing for PDFHs that are located farther away from the sink node. Table 13 displays the improvement ratios over a range of total rounds to evaluate the proposed protocol's potency compared to other protocols. Figure 12: Network energy consumption vs. number of rounds: A comparison with counterpart protocols for Scenarios 1 and 2 (PSO-ECC [39], LDIWPSO [40], OFCA [41], and NPSOP [42]). (**a**) Scenario #1: Network area: 100 \(\times\) 100 m\({}^{2}\) with 100 nodes, 10% DFHs. (**b**) Scenario #1: Network area: 100 \(\times\) 100 m\({}^{2}\) with 100 nodes, 5% DFHs. (**c**) Scenario #2: Network area: 500 \(\times\) 500 m\({}^{2}\) with 300 nodes, 10% DFHs. (**d**) Scenario #2: Network area: 500 \(\times\) 500 m\({}^{2}\) with 300 nodes, 5% DFHs.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
###### Abstract  
The cell cycle and biological processes rely on RNA and RNA-binding protein (RBP) interactions. It is crucial to identify the binding sites on RNA. Various deep-learning methods have been used for RNA-binding site prediction. However, they cannot extract the hierarchical features of the RNA secondary structure. Therefore, this paper proposes HPNet, which can automatically identify RNA-binding sites and -binding preferences. HPNet performs feature learning from the two perspectives of the RNA sequence and the RNA secondary structure. A convolutional neural network (CNN), a deep-learning method, is used to learn RNA sequence features in HPNet. To capture the hierarchical information for RNA, we introduced DiffPool into HPNet, a differentiable pooling graph neural network (GNN). A CNN and DiffPool were combined to improve the binding site prediction accuracy by leveraging both RNA sequence features and hierarchical features of the RNA secondary structure. Binding preferences can be extracted based on model outputs and parameters. Overall, the experimental results showed that HPNet achieved a mean area under the curve (AUC) of 94.5% for the benchmark dataset, which was more accurate than the state-of-the-art methods. Moreover, these results demonstrate that the hierarchical features of RNA secondary structure play an essential role in selecting RNA-binding sites.  
protein-RNA interaction; RNA-binding sites; deep learning; graph neural network; hierarchical pooling network; RNA secondary structure
## 4 Discussion
### Spatial and Temporal Distribution of Chl-a  
4.2.1. Spatial Difference of Chl-a  
The average distribution of the Chl-a concentration in the coastal waters of the Guangxi Beibu Gulf is shown in Figure 11. The concentration of coastal sea surface chlorophyll was high in the nearshore waters and low in the offshore waters, and it gradually decreased from north to south. Nutrients in the Beibu Gulf in Guangxi are mainly transported from coastal land sources (Shen and Zhang, 2017), and the pollution inputs from the Naniu River are the largest. The Naniu River basin is large, bringing together industrial, agricultural, and urban sewage from adjacent land, which carries a large amount of nutrients. According to the Marine Environment Quality Bulletin of the Guangxi Zhuang Autonomous Region, in 2016, the inflow \(\mathrm{COD_{Cr}}\) of the Naniu River in 2016 was 174,049 t, and the total amount of ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen was 9868 t. The estuarine and coastal zones are transitional zones between land and sea with relatively shallow water depths, so river inflows with high-input terrigenous nutrient content mix with offshore salt water. The annual average SST is about 20.3-29.9 \({}^{\circ}\)C. High light intensity and high SST promote the growth and reproduction of phytoplankton and algae, resulting in an increase in the Chl-a concentration ([PERSON] and [PERSON], 2017).  
Figure 11: Distribution of the Chl-a concentration in the coastal waters of the Beibu Gulf in Guangxi.  
The runoff diluted water lifts the level in the nearshore sea, and the offshore sea level is relatively low; nutrients then plume out to sea from the estuary. The Beibu Gulf in Guangxi has a tropical and subtropical monsoon climate. The monsoon strengthens the emergence of a current alongshore area, which is also affected by the Coriolis force, and the seaward runoff moves westward along the coast. Therefore, from the perspective of the source, the concentration of Chl-a in the coastal waters of the Beibu Gulf in Guangxi was higher in the nearshore water and lower in the offshore water due to the input of terrestrial nutrients. From the perspective of transport, the nutrients in the Beibu Gulf in Guangxi diffuse from the estuary to the offshore sea. Under the influence of the monsoon, the nutrients flow westward along the coast, and the concentration of Chl-a gradually decreases when moving westward.
## 2 Friction between Main Cable and Saddle  
### Determination of Nominal Friction Coefficient  
The friction between the saddle and the main cable is mainly composed of the friction \(f_{\rm b}\) of the contact surface between the main cable and the bottom of the saddle groove and the friction \(f_{\rm s}\) of the contact surface of the side wall of the groove (Figure 3). In case of insufficient safety reserve, some suspension bridges will also adopt the method of adding a diaphragm in the saddle groove to improve the friction. The friction coefficient between the saddle and the main cable is different along the longitudinal direction. In practical engineering design, the friction coefficient \(\mu\) takes a fixed quantity that combines various factors. The nominal friction coefficient is the comprehensive characteristic value of the above friction.  
The contact section of the main cable and cable saddle are shown in Figure 4. In the figure, B and C are respectively, the left and right tangent points of the main cable on the saddle. The tension of the main cable at tangent points B and C are T1 and T2, respectively. The central angle of the circle corresponding to the tangent point is \(\theta\).  
Figure 3: Schematic of force on cable-bearing trough.  
Taking a micro segment \(dl\), the tension at both ends of the micro segment is \(T+dT\) and \(T\), respectively, and the corresponding center angle is \(d\theta\). The vertical pressure \(dN\) caused by micro section tension is  
\[dN=2T\sin\frac{d\theta}{2}\approx T\sin(d\theta)\approx Td\theta \tag{1}\]  
The friction resistance \(df\) of the micro section is  
\[df=\mu dN=\mu Td\theta \tag{2}\]  
There is a relationship between the tension difference \(dT\) at both ends of the micro segment and the friction \(df\) of the micro segment.  
\[dT=\mu Td\theta \tag{3}\]  
The following equation can be obtained by integrating Equation (3) along the BC arc length  
\[\int_{T_{2}}^{T_{1}}\frac{1}{T}dT=\int_{0}^{\theta}\mu d\theta \tag{4}\]  
The friction coefficient \(\mu\) is obtained by integrating Equation (4)  
\[\mu=\frac{\ln T_{1}/T_{2}}{\theta} \tag{5}\]  
The friction between the main cable and the bottom of the groove accounts for most of the total friction between the main cable and the saddle. The transverse horizontal pressure on the contact surface between the main cable and the side wall of the groove is not considered in the calculation. When the critical slip state between the main cable and the saddle is reached, the nominal friction coefficient \(\mu_{n}=\mu\). The nominal friction coefficient is determined by the material friction coefficient and saddle structure.
Remote Sens._**2013**, \(5\), 2534-2553; doi:10.3390/rs5052534  
_Remote Sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
**Evaluating Ecohydrological Impacts of Vegetation Activities on Climatological Perspectives Using MODIS Gross Primary Productivity and Evapotranspiration Products at Korean Regional Flux Network Site**  
**[PERSON] and [PERSON] ***  
Water Resources and Remote Sensing Laboratory, Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea;  
E-Mail: cysur(@)hanyang.ac.kr  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-2220-0324; Fax: +82-2-2220-1945.  
_Received: 15 April 2013; in revised form: 26 April 2013 / Accepted: 9 May 2013 / Published: 21 May 2013_  
**Abstract:** Accurate assessments of spatio-temporal variations in gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) play a crucial role in the evaluation of carbon and water balance as well as have considerable effects on climate change. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to quantify the mean annual GPP and ET at Korean regional flux network site. We found that the seasonal mean values of WUE were 2.86 to 2.92 g\(\cdot\)C\(\cdot\)g\(\cdot\)H\({}_{2}\)O\({}^{-1}\) in the dormant season and 1.81 to 1.88 g\(\cdot\)C\(\cdot\)g\(\cdot\)H\({}_{2}\)O\({}^{-1}\) in the growing season during 2007 and 2008. The WUE was relatively stable during the growing season and tended to vary in the dormant season. Remote sensing data obtained by the MODIS satellite were appeared to be effective to improve our understanding of the spatio-temporal variation of ecohydrological parameters which have not yet been investigated in a number of previous articles. Based on the results of this study, we summarize the interactions between carbon and water circulation in terrestrial ecosystems and how their ecological procedures generated by the photosynthesis of vegetation influence in climatological perspectives.  
**Keywords:** gross primary productivity (GPP); evapotranspiration (ET); moderate resolution imaging spectroradiometer (MODIS); water use efficiency (WUE); ecohydrological impacts
## 3 Method Descriptions
### Data Sets  
The real-time monitoring system for voiding dysfunction classification was developed by training and testing the deep learning model using the collected datasets, named Ten Chen Medical Group (TCMG) voiding database. The following subsections describe the dataset details, signal preprocessing, and model training and testing.  
The TCMG datasets used for deep learning model training and testing [23,24] were collected using measures and standards approved by the institutional review board (IRB) of the cooperating hospital. The hospital used the developed system to acquire patients' uroflowmetry data. The TCMG voiding database included data from 76 males aged 30 to 80 who required uroflowmetry [25] for voiding symptoms. The exclusion criteria were small bladder capacity (<50 mL), external genital malformations, and inability to complete uroflowmetry. For each participant, at least one or two uroflow measurements were performed, and each was recorded for no longer than 30 s. An average of approximately 24.67 seconds of signal length was collected per patient. The total signal length in the database was 31.25 min. Each patient's data was annotated with his diagnosis through a uroflowmetry test [25] for voiding dysfunction diagnosis. The uroflowmetry is a key diagnostic information for voiding dysfunction. The physicians labeled all the voiding patterns and clinical impressions. There were six typical voiding patterns labeled from 0 to 5 (0 indicates normal voiding while the others indicate abnormal patterns). A total of 38 patients were diagnosed with decrease, 4 patients with flattened, 5 patients with intermittent, 9 patients with saw-tooth, 2 patients with tall and peak, and 18 patients with normal.  
As shown in Figure 1, the six typical voiding patterns are labeled classified as: 0 (normal flow, which is the bell-shaped pattern with an adequate peak flow rate); 1 (Tall and peak flow, i.e., the urine flow rate reaches a high flow peak quickly and decline soon); 2 (decreased flow, i.e., a prolonged urine flow time with a low peak flow rate presented); 3 (saw-tooth flow, i.e., the urine flow rate fluctuates greatly but does not drop to zero); 4 (intermittent flow, i.e., the urine flow rate drops to zero and rise again later); and 5 (flattened flow, i.e., the urine flow rate is gradual and constant until the end). In this study, the physicians visually compared the voiding patterns based on the raw vibration signal data. For example, for Patient 39, the physician labeled the raw data as \"decreased flow\", i.e., a prolonged urine flow time with a low peak flow rate was presented, which is consistent with the observed raw data. Thus, there is a correlation between the datasets and the patients' urinary features.
## 4 Analysis of RT Solutions
### Baseline Lengths  
For Phase 1, both RTK and RTN solutions were obtained to compare the effect of the baseline lengths. Two temporary bases were established for the reference: FLNV (about 2.1 km away from the project site) and FLFP (\(\sim\)30.6 km). All RT vectors were referenced to the base coordinates determined by the OPUS-Projects using the data collected independently during the RT sessions in order to be consistent. The multiple-epoch RTK solution shows that the relative precision for short baselines (FLNV) as a function of the duration time decreases slowly. The sigma (standard deviation) values given by the RT solutions are generally worse than the calculated precision. The RT solutions from long baselines (FLFP) do not show any particular bias (but with relatively low precision) when compared with the reference solution (Figure 10). The site on the west, however, consistently provides a biased solution for both short (several millimeters) and long baselines (up to 2 cm). Since there are no major obstacles for signal blockage on the west, it is attributed to RT survey procedures for the site. In addition, the statistics of RT solutions from the receiver are overly optimistic for long baselines.  
Single-epoch network RTK (sRTN) solutions were used to validate the effect of the baseline lengths for RTN in Phase 1. The long baseline solutions were obtained for 2.5 h after almost two and a half days of short baseline solutions, and additional short baseline solutions of 11 h followed (Figure 11). Since the bias for RT solutions can also occur due to a biased reference station height it is not possible to determine whether the bias is from solution or reference station unless the verified reference station coordinates are provided. In addition, special care should be taken not to confuse the antenna reference point with the phase center in the transmitted coordinates of the reference station. It should be noted that there is a significant error in the ellipsoidal height near the end of the session, which is common for all sites. This corresponds to the epoch switching from long baseline to short baseline solutions,  
Figure 9: Errors and baseline lengths by antenna type (TRMR8_GNSS NONE) for Phase 2.  
which lasts about 30 min. Therefore, the RTN system may not work immediately after switching the reference stations.  
Figure 11: Effect of the baseline length on network RT. The long baselines are intentionally offset by 10 cm for clarity.  
Figure 10: Estimated error bar plot of RTK by duration time and site. The long baselines are intentionally offset by 5 cm for clarity.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 3 Data Collection and Methodology
### Message Tracing Model  
#### 3.3.1 Valid Transaction Filtering  
IBC Recv Tx and IBC Ack Tx data are entered into the valid transaction filtering process generating IBC Recv Tx and IBC Ack Tx data--multiple relayers relay transactions between the Cosmos Hub and the Osmosis Network. The Relayer searches the sending chain's most recent ledger state for inter-blockchain communication transactions, then broadcasts them to the receiving chain by appending an IBC UpdateClient transaction for proof of state. Signers certify the up-to-date status of the transmission chain in an IBC UpdateClient transaction [20]. Entering the wallet address of the Relayer who will relay the transaction in the signer box and adding an IBC UpdateClient transaction turns the transaction for one fungible token transfer into a transaction with a new hash. In the transaction of the transfer of the same fungible token, an inter-blockchain communication transaction is established for each Relayer, as shown in Figure 8. The receiving blockchain contains all created transactions.  
Each Relayer generates an IBC transaction for every fungible token transfer, resulting in several transactions involving the same token in the recipient blockchain. The receiving chain combines these transactions into a single block, generating an IBC Recv Tx for each receiving IBC Transfer Tx. The sender chain is notified that the fungible token transfer successfully delivered an IBC Ack Tx. The first transaction that reaches the Proposer's mempool is validated by the inter-chain transactions in the Cosmos Hub and Osmosis blocks. A token voucher is deposited to the recipient's wallet, and the Relayer who originated and disseminated the transaction receives a reward from the transaction charge [20,23]. In addition, the permitted IBC Recv-Packet transaction is constructed and sent to the sender blockchain.  
Figure 7: Transaction data collecting process.  
Transactions with a positive fee field (Fee > 0) are separated from the IBC Recv Tx and IBC Ack Tx data and preserved as legitimate IBC Recv Tx and Valid IBC Ack Tx data. These transactions were completed successfully at a low transaction cost. As a result, they are considered valid. The filtering technique is depicted in Figure 9. Because IBC Transfer transactions propagate and reach a consensus within the Cosmos Hub without the assistance of relayers, there are no duplicate transactions in the IBC Transfer Tx data.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 5 Results and Discussion
### Effect of Initial Solid Volume Fraction
#### 5.1.3 Total Pressure Drop  
The variation of the total pressure drop with initial SVF is listed in Table 3. The total pressure drop, \(l_{v}\), is composed of three parts, as illustrated in Equation (5). \(l_{s}\) is related to the local SVF and the density of manganese nodules. \(l_{l}\) depends on the seawater velocity, which influences the friction loss between seawater and the pipe wall [30]. For a given  
Figure 8: Effects of initial SVF on average SVF (\(V_{mix,0}=4.0\) m/s, \(d_{s}=15.0\) mm).  
Figure 7: Effects of initial SVF on average slip velocity (\(V_{mix,0}=4.0\) m/s, \(d_{s}=15.0\) mm).  
particle size and the initial mixture velocity, the total pressure drop increases with initial SVF, which can be reasonably explained by the energy balance during the transport process. Liquid and particles derive their kinetic and potential energy mainly from the pressure drop through the pipe. As the initial SVF increases, the sum of the kinetic or potential energy of particles increases. Moreover, increased frequencies of collisions cause more energy loss of the two-phase flow. Therefore, high initial SVFs are responsible for the low pressure energy of the liquid at the outlet and a corresponding high pressure drop due to the enlarging of the difference of pressure energy between inlet and outlet. It is noteworthy that, in CFD code, relative pressure in the fluid domain is adopted so that the outlet pressure may be less than 0 Pa. However, in practical applications, the gauge pressure at the outlet of the pipe should be larger than 0 Pa, so fluid machinery is required to provide additional energy for conveying the mixture to prevent reverse flow. According to Equations (6) and (7), it seems that hydraulic transport with a higher SVF is more economical. However, increasing the initial SVF can cause a decrease of particle velocity. When particle velocities are lower than three times the settling velocity, this may incur retention and even blockage. Therefore, the selection of appropriate parameters, such as the initial SVF, the initial mixture velocity, and particle diameter, is vital for the transport of the mixture.
# Experimental Investigation of Crystal Blocking in Drainage Pipes for Tunnels in the Kast Region
## 3 Testing Design
### Experimental Conditions  
In this experiment, the effect of longitudinal drainage pipes (LDP) with different diameters, spacing, and materials on the development pattern of crystalline blockage is investigated with and without fillers. The test conditions are shown in Table 1.  
Figure 5: Schematic diagram of experimental steps.  
Five parameters are considered in this experiment. The first one is filled with sand. The unfilled sand condition is designed to contrast with the filled sand condition. For the no-filled sand condition, the test chamber is filled with the ionic solution. Clearly, the flow rate in the drain for the no-fill sand condition is greater than that for the sand-fill condition. Therefore, this comparison can reflect the effect of flow rate on crystal blockage. The second influencing factor is the longitudinal drainage pipe (LDP). Two longitudinal drainage pipes are installed at the ends of the circular drainage pipes, respectively. They are used to collect the water from the circular drainage pipes. Obviously, there are more connections if the longitudinal drainage pipe (LDP) is installed. Therefore, this comparison could reflect the influence of connection conditions on the crystal blocking.  
It is believed that the pipe diameter had a significant effect on the crystal clogging law, so the pipe diameter is selected as the third influencing factor to be studied. Therefore, on the basis of previous scholars' research, a drainage pipe with a diameter of 8 mm is selected for comparison experiments to study the analysis of the effect of pipe diameter on the crystal clogging law.  
The section space of two adjacent drainage pipes is another important factor which determines the crystalline clogging law of drainage pipes. Obviously, the total number of circular drainage pipes will increase with the decrease in the spacing of the circular drainage pipes, which can improve the drainage efficiency of the drainage system. Therefore, the distance between the original two circular drainage pipes is considered to be reduced by half and used to study the effect of spacing change on the crystallization clogging law. Finally, there is no doubt that the pipe material can also have a significant impact on the crystal clogging law. Based on the research of previous studies, it is found that polypropylene (PP) and polyamide (PA) have similar properties. Therefore, in this paper, polypropylene (PP) is used instead of polyamide (PA) for comparative analysis to investigate the effect of drainage pipe materials on the development law of crystalline blockage.