esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The current study is performed on an optimized pump geometry to forecast the impact of solid particle concentration, size, distribution, exit pressure heads, and cavitation on the erosion rate density of the pump casing. The following deductions have been made from the above-given results.  
The concentration of the solid particles substantially affects the magnitude of erosion characteristics of the pump casing and impeller blade, followed by the pump's exit pressure heat. On the contrary, the particle size and cavitation do not alter the erosion rates significantly but appreciably influence the regions of high erosion rate densities. The inlet concentration of the solid particles significantly impacts the erosion rate density, and it changes from 5% to 20% and doubles the erosion rate density at particular points.  
The maximum values for the erosions rates are found around the tongue region of the casing and in the vicinity of the exit section of the solute casing. It is found that variation in the erosion rate density, particle concentration, and wall stress changes substantially along with the pump casing (P\({}_{1}\) to P\({}_{11}\)) and its sensitivity at lower concentrations of the particles. The maximum value of the erosion rate density is found at 10% concentration, and a further increase in the particle concentration will decrease the erosion rate density.  
Particle size has no substantial effect on the magnitude of the erosion rate density; however, the location of the ERD peak changes with a change in the size of the particles.  
The value of the erosion rate density changes substantially (up to five times) by changing the exit pressure. The maximum values of the erosion rate density were found at the design value pressure head. The value of the erosion rate density decreases by decreasing the exit pressure head value.  
Results suggest that the blades' erosion density is significantly lower than the erosion rate density on the casing walls in all cases. The maximum value of the ERD is different for different blades depending upon their location. The maximum value of the erosion rate density can be observed near the leading edges for all blades. In contrast, erosion rate densities corresponding to the region between the leading and trailing edges are minimal.  
Conceptualization, K.A.; Data curation, B.A.; Formal analysis, B.A.; Investigation, M.A.; Methodology, A.M. and K.A.; Resources, B.A.; Software, A.M. and K.A.; Writing--original draft, K.A.; Writing--review & editing, M.A. All authors have read and agreed to the published version of the manuscript.  
Figure 22: Erosion density rate distribution for different concentrations of particles under the cavitation conditions.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy policy of the organization.  
The authors declare no conflict of interest.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 6 Performance Evaluation and Discussion
In this paper, the proposed communication technique is able to avoid redundant normal range data transmission by employing the algorithm demonstrated in Section 4, whereas the non-MI incremental relaying technique proposed in [10] allows all sensed data transmission, and thus incurs an energy penalty and is not applicable for long-term monitoring services for future rehabilitation monitoring services. Moreover, by investigating the communication link establishment criteria (depending on the predetermined MI threshold), a communication link is only available when the MI value becomes smaller than the predetermined threshold. Unlike the non-MI relaying scheme [10], the MI-based technique allows both a direct communication link and cooperative relaying between the implanted nodes and the coordinator. It is thus capable of supporting real-time patient monitoring services under emergency conditions. Moreover, a direct communication link promises lower propagation delay as compared with the incremental relaying method because data transmission does not need to be received and processed by the corresponding relay node. Future emerging implanted monitoring services can be designed by adopting the appropriate MI predetermined threshold value.
## 3 Results
### GPP Models Error Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Statistic** & \(\textit{D}_{\text{NDVI}}\) & \(\textit{D}_{\text{PRI}}\) & \(\textit{D}_{\text{PAR}}\) & \(\textit{D}_{\text{Tair}/\text{VPD}}\) & \(\textit{D}_{t}\) & \(\textit{D}_{\text{GPP}}\) \\ \hline \multirow{2}{*}{MOD1} & \(r_{\text{err}\,|\,D}\) & \(0.10\) & \(-0.06\) & \(0.04\) & \(0.24\) & \(0.02\) & \(-0.61\) \\  & \(p\) & \(0.00\) & \(0.00\) & \(0.03\) & \(0.00\) & \(0.16\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD2} & \(r_{\text{err}\,|\,D}\) & \(0.06\) & \(-0.03\) & \(0.55\) & \(-0.02\) & \(0.02\) & \(-0.49\) \\  & \(p\) & \(0.00\) & \(0.04\) & \(0.00\) & \(0.31\) & \(0.17\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD3-\(\textit{PRI}\)} & \(r_{\text{err}\,|\,D}\) & \(0.07\) & \(-0.01\) & \(0.56\) & \(-0.02\) & \(0.02\) & \(-0.47\) \\  & \(p\) & \(0.00\) & \(0.67\) & \(0.00\) & \(0.21\) & \(0.28\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD3-\(\textit{PRI}_{\text{515}}\)} & \(r_{\text{err}\,|\,D}\) & \(0.05\) & \(-0.03\) & \(0.56\) & \(-0.02\) & \(0.02\) & \(-0.51\) \\  & \(p\) & \(0.00\) & \(0.09\) & \(0.00\) & \(0.24\) & \(0.21\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD3-\(\textit{PRI}_{\text{norm}}\)} & \(r_{\text{err}\,|\,D}\) & \(-0.02\) & \(0.06\) & \(0.55\) & \(-0.02\) & \(0.02\) & \(-0.50\) \\  & \(p\) & \(0.16\) & \(0.00\) & \(0.00\) & \(0.34\) & \(0.16\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD3-\(\textit{CPRI}\)} & \(r_{\text{err}\,|\,D}\) & \(-0.08\) & \(-0.12\) & \(0.55\) & \(-0.02\) & \(0.02\) & \(-0.49\) \\  & \(p\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.14\) & \(0.19\) & \(0.00\) \\ \hline \multirow{2}{*}{MOD4} & \(r_{\text{err}\,|\,D}\) & \(0.11\) & \(-0.09\) & \(0.49\) & \(0.04\) & \(0.02\) & \(-0.47\) \\  & \(p\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.01\) & \(0.21\) & \(0.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Partial correlation coefficients and significance between \(\textit{GPP}_{\text{hh}}\) prediction errors and the Euclidean distance between each half-hourly input variable the day of the flight campaigns and the surrounding days. Figure 7: Mean error of MOD2-MOD4 vs. different variables. The adjusted linear model and Pearson correlation coefficient of the whole dataset are shown with the respective significance degree (**_p_ \(<\) 0.05; *_p_ \(<\) 0.10). In addition, data corresponding of the date of each flight are flagged. Similarly, Figure 8 shows the averaged errors from MOD2-MOD4 vs. the differences previously used in the partial correlations analysis (MOD1 errors are excluded since behaved differently). As can be seen, \(GPP_{\text{hh}}\) is underestimated in conditions of lower \(PAR\) (\(D_{PAR}<0\)), lower \(VPD\) and lower \(T_{\text{air}}\) than those found the day of the flights, as well as when \(GPP_{\text{hh}}\) and \(T_{\text{air}}/VPD\) are larger. However, the largest errors are found for \(T_{\text{air}}/VPD\) under conditions very similar to those of the flight. Footprint-weighted SVI and time distance to the flight show little effect.
## 3 Results and Discussion
### Molecular Modeling  
The eleven molecules were docked into the binding site of Tdp1 (PDB ID: 1 MU7, resolution 2.0 A) [53] with reasonable scores, as shown in Table S1 in the supplementary materials. Furthermore, two water molecules (H2O631 and H2O832) were kept in the binding pocket and all the ligands docked. The modelling shows that all the ligands show a similar plausible binding mode for both scenarios. The ligands have higher scores with water (see Table S1), which suggests that they contribute to the stability of the protein and binding. Indeed, the modelling predicts that the ligands interact with the water molecules. For the most interesting compound **46a**, the sulphur atom forms an H-bond with HOH631, this water molecule also interacts with Thr261, forming an H-bond bridge between the ligand the protein. The second water molecule HOH832 does not form direct H-bonds with the ligand but nudges the adamantane moiety closer to a lipophilic region. The adamantane moiety occupies a relatively lipophilic portion of the binding pocket whilst the thioamide occupies a hydrophilic portion as shown with Figure 4A.  
The calculated molecular descriptors molecular weight (MW), number of hydrogen bond donors (HDs), number of hydrogen bond acceptors (HAs), lipophilicity (Log P), polar surface area (PSA) and number of rotatable bonds (RBs) are given in Table S2 in the supplementary materials. HDs, HAs, and PSA are within the lead-like chemical space for all the ligands whilst the molecules have MW within the drug-like chemical space. The molecules are within the drug-like and known drug space (KDS) for Log P. The RBs are within the drug-like chemical space (for the definition of lead-like, drug-like and KDS regions see [54] and Table S3). Interestingly, it has been shown recently that orally bioavailable drugs tend not to follow the drug-like definition, in particular when it comes to MW [74]. The molecular weight of the ligands is between 303.5 and 335.6 g mol-1 and the Log P values lie in the range of 4.4 and 6.3. The ligands have low PSA values between 13.3 and 33.9. The known drug indexes 2a and 2b (KDIs2a/2b) [75] were also derived. The KDIs reflect the overall balance of the molecular properties  
Figure 3: Dose-dependent action of topotecan in combination with 5 \(\upmu\)M of **46a** on A-549 cells viability.  
of ligands based on KDS. KDI2a is additive with a maximum of 6.0 and for KDI2b the indexes are multiplied giving 1.0 as its maximum. The average for KDI2a for known drugs is 4.08 (\(\pm\)1.27) and the ligands lie in the range of 4.58 to 5.23 (i.e., they are well balanced compared to known drugs). The KDI2b gives a range of 0.18 to 0.43 with the average of know drugs being 0.18 (\(\pm\)0.20). The relatively low values can be attributed to the high Log Ps of **46a** and **47a**. The KDI2b index is less forgiving, since on one low number sinks the index significantly due to its multiplicative nature. Nevertheless, the ligands are relatively well balanced.
## 4 Experimental Setup and Result Analysis
### System Testing
#### 4.6.2 Wide-Angle Image Correction Implementation  
In this study, the trained wide-angle image correction network was deployed on the Jetson TX2 NX edge computing platform to support model inference and perform distortion correction. During system execution, the input image to be corrected is fed into the deployed network, and the output is the corresponding geometrically corrected image. The correction results are illustrated in Figures 22 and 23.
## 3 Data Fusion in the Integration of SINS/DVL/MCP/PS  
### Data Fusion with the Subsystems  
Considering the computational complexity of the data fusion process, the federated Kalman filter is used for data fusion. As shown in Figure 1, the subsystems are constructed with SINS and other aided systems, _i.e._, DVL, MCP, and PS. The standard Kalman filter is selected as the data fusion method for each subsystem. The algorithm of the Kalman filter and the state equations and measurement equation of subsystems are introduced as follows.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 3 Methodology
### Negative Sampling
#### 3.1.1 Gibbs Negative Sampling  
Gibbs negative sampling is introduced to tackle the challenge of a substantial rise in zero-loss negative samples caused by the subpar quality of negatives, resulting in gradient vanishing. It also addresses the issue of test set leakage. Ref. [16] utilizes the Metropolis-Hastings algorithm for negative sample generation in graph representation learning, which is well-suited for one-dimensional distributions. However, in the context of embedding knowledge maps, entities always appear in pairs within triples, manifesting as head entities and tail entities. Consequently, a two-dimensional distribution is more appropriate for generating samples. Drawing inspiration from this, our study explores Gibbs sampling, commonly used for multivariate joint distributions, as it offers a simpler approach. Specifically, we investigate the distribution characteristics of entities in the knowledge map. For the entity-relation pair \((h,r)\), the negative sample entities of higher quality are those \(T^{\prime}\) entities that exist in the observed facts in the form of \((\sim,r,t^{\prime})\). The formula for this is given in Algorithm 1.  
```
0: Conditional probability distribution \(P\), state transition times threshold \(n_{1}\), number of negative samples \(n_{2}\)
0: Random initialization state values \(x_{1}^{(0)},x_{2}^{(0)}\) for\(\mu\) in Search Grid do  Sample \(x_{2}^{(l+1)}\) from the conditional probability distribution \(P(x_{2}\mid x_{1}^{(l)})\) for\(t=1,2,\ldots,n_{1}+n_{2}-1\)do  Sample \(x_{1}^{(l+1)}\) from the conditional probability distribution \(P(x_{1}\mid x_{2}^{(l+1)})\) endfor  Calculate loss \(L_{\mu}\) of sample set: \[\left\{(x_{1}^{(n_{1})},x_{2}^{(n_{1})}),(x_{1}^{(n_{1}+1)},x_{2}^{(n_{1}+1)} ),\ldots,(x_{1}^{(n_{1}+n_{2}-1)},x_{2}^{(n_{1}+n_{2}-1)})\right\}\] if current loss is greater than historical maximum loss then  Replace the maximum loss with the candidate sample set endif endfor Output: Sample set \[N_{(h,r)}=\left\{(x_{1}^{(n_{1})},x_{2}^{(n_{1})}),(x_{1}^{(n_{1}+1)},x_{2}^{( n_{1}+1)}),\ldots,(x_{1}^{(n_{1}+n_{2}-1)},x_{2}^{(n_{1}+n_{2}-1)})\right\}\]
```  
**Algorithm 1** Gibbs Negative Sampling Algorithm  
Figure 1: Overall architecture of the model. Initially, entities are chosen from the knowledge graph for Gibbs sampling to derive high-quality negative sample triplets. Subsequently, shared weights between positive and negative samples are extracted, and the samples are partitioned separately along the directions of head and tail entities, resulting in two directional mapping matrices. Finally, the similarity between these mapping matrices is measured through metric learning.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
###### Abstract  
The Centre for Medical Radiation Physics introduced the concept of Silicon On Insulator (SOI) microdosimeters with 3-Dimensional (3D) cylindrical sensitive volumes (SVs) mimicking the dimensions of cells in an array. Several designs of high-definition 3D SVs fabricated using 3D MEMS technology were implemented. 3D SVs were fabricated in different sizes and configurations with diameters between 18 and 30 \(\upmu\)m, thicknesses of 2-50 \(\upmu\)m and at a pitch of 50 \(\upmu\)m in matrices with volumes of 20 \(\times\) 20 and 50 \(\times\) 50. SVs were segmented into sub-arrays to reduce capacitance and avoid pile up in high-dose rate pencil beam scanning applications. Detailed TCAD simulations and charge collection studies in individual SVs have been performed. The microdosimetry probe (MicroPlus) is composed of the silicon microdosimeter and low-noise front-end readout electronics housed in a PMMA waterproof sheath that allows measurements of lineal energies as low as 0.4 keV/\(\upmu\)m in water or PMMA. Microdosimetric quantities measured with SOI microdosimeters and the MicroPlus probe were used to evaluate the relative biological effectiveness (RBE) of heavy ions and protons delivered by pencil-beam scanning and passive scattering systems in different particle therapy centres. The 3D detectors and MicroPlus probe developed for microdosimetry have the potential to provide confidence in the delivery of RBE optimized particle therapy when introduced into routine clinical practice.  
silicon on insulator; proton therapy; particle therapy; RBE; 3D detector; microdosimetry +
Footnote †: journal: _sciences_  
_Applied_  
_sciences_
## 6 Evaluation and Analysis  
### Simulation Parameters  
This section evaluates the context-aware CL design proposed in Section 5. Unless otherwise stated, the following default simulation settings are used. A WSN with 100 nodes distributed in a \(10\times 10\) grid topology over an area of 200 m \(\times\) 200 m is simulated in OPNET. Each node has a transmission range of 20 m. A VB is formed in the centre of the network with four nodes, and 25 non-VB member nodes (\(N_{ami}\) = 25) are randomly selected to be AmI context publishers. The remaining nodes publish their sensor data to, and subscribe to receive AmI context from, the VB periodically. The VB forwards any matched sensor data to the AmI context publishers based on their subscription messages.  
The AmI context consists of _event, personal,_ and _activity contexts_ (Figure 5) whose content is randomly generated during the simulation. Six sensor data types are defined, along with five activities each requiring up to three sensor data types to be inferred. For each activity, the required number of sensor data types (e.g., 1, 2 or 3 types) and the type of sensor data (represented as Types 1-6) are randomly generated. For example, Activity one may require three sensor data types (Types 2, 3, and 6), while Activity two may require only two sensor data types (Types 1 and 5) in order to be inferred. This context structure may describe a context such as \"_a blind_ (personalDisability) _elderly_ (personalAge) _person is walking_ (activityType) _across the Queen Street_ (eventLocation) _traffic junction area_ (eventRadius)\".  
Each AmI context publisher subscribes to sensor data types needed to generate its context, which is then published at the rate of 1 frame every five seconds with a frame size of 512 bits. The sensor data publishing settings: sensor data publishing interval (\(D_{freq}\)) and sensor data size (\(D_{size}\)) will be varied in this evaluation. Data rate is set to 250 kbps at 2.4 GHz. The current drawn for radio transmission, and radio reception, is set to 17.4 mA at 0 dBm, and 19.7 mA, respectively, based on MICAz's specification [25]. The AmI context publishers are mobile users who move according to a random waypoint model with a speed of 1.2 m/s and pause time of 3.6 s [26,27].  
The IEEE 802.15.4 unslotted CSMA/CA and AODV are the default MAC protocol, and network routing protocol, used respectively, during the simulations. Delay threshold for context-aware DAAM is set to 1 s, which is the best setting obtained from our preliminary study. All results are the average of 10 runs over 180 s.
## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?  
In an expert finding system, the relationship between query topics and people is commonly known as the modelling task. Expert finding models have three main components-- (1) candidate, which represents a person who might be an expert, (2) document, which refers to the heterogeneous expertise resources, and (3) topic, which is a specified domain [4]. In the literature, there are several models that have been developed to capture the relations between query terms and expert candidates. These models are discussed in the following subsections, and the previous studies that used these models are represented in Table 8.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)
#### 3.2.3 Residual Energy  
One more factor is to calculate the energy status of the sensor node. All of the participating nodes must be alive and have a sufficient amount of energy for sending and receiving the data packets. A reliable link from source to sink must consist of all alive nodes.
###### Abstract  
A steel plate shear wall often uses partially encased composite (PEC) columns instead of edge frame columns. Such a steel plate shear wall not only bears the gravity load of the structure and resists the bending moment caused by lateral force by taking advantage of the high bearing capacity and bending stiffness of PEC columns, but also effectively anchors with the frame column to counteract the tension field generated by the steel plate. Therefore, the performance of the steel plate shear wall after buckling can be fully exerted and the seismic performance of the structure can be improved. In order to investigate the seismic performance of the structure, a 1/3-scale specimen test of steel plate shear wall with PEC columns is designed and fabricated, and a finite element model is established with the same size of test. It is found that the test and simulation results are in good agreement, which confirms the reliability of the simulation. Subsequently, 20 models with different parameters of steel plate shear wall with PEC columns are analyzed using ABAQUS. Finally, the failure mode, hysteretic behavior, skeleton curve, and bearing capacity of steel plate shear wall with PEC columns are obtained. The results show that PEC columns have a good anchoring effect on the diagonal tension field and can fully exert the plasticity of the infill steel plate, so that steel plate shear wall with PEC columns has superior seismic performance. Experiments also reveal that the crack type of damages appear in a steel plate shear wall with PEC columns, and, as a future work, the authors will explore the use of structural health monitoring methods, such as piezoceramic transducer-based method, to monitor such cracks.  
Keywords:partially encased composite (PEC) columns; steel shear wall; seismic performance; bearing capacity; hysteretic behavior +
Footnote †: journal: Applied Sciences
## 2 NOMA System Model
### Data Transmission  
The signal, \(s\), is transmitted through independent Rayleigh fading channels, \(H_{i}\), corresponding to each user, and the channels are contaminated with additive white Gaussian noise (AWGN), \(w_{i}\)with a power spectral density (PSD) of \(N_{0_{i}}\). The received signal, \(y_{i}\), at the \(UE_{i}\) terminal can be described by the following equation:  
\[y_{i}=H_{i}s+w_{i}. \tag{4}\]
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 4 Simulation Results
### Long-lived flows in a 16-ary fat-tree with oversubscription ratios
#### Fat-trees with degraded links  
We simulated asymmetries by degrading the bandwidth of a link from 1 Gbps to 100 Mbps for an 8-ary fat-tree and a 16-ary fat-tree both. We set the bandwidth of one link connecting a ToR switch and an aggregation switch to 100 Mbps. Short-lived flow tests were performed using flows with size 32 KB for the 8-ary fat-tree and long-lived flow tests using flows with size 100 MB for the 8-ary and 16-ary fat-trees both.  
Figure 7b shows the results for short-lived flow tests. We can see that the effect of a degraded link on flow completion time is larger than that of a disconnected link which is shown in Figure 6a. Note that performance of LBSP is less affected by a degraded link compared to that of the original packet scatter scheme. Figure 7c shows the results for long-lived flow tests. The impact of degraded links on performance of the original packet scatter scheme is enormous. For both 8-ary and 16-ary fat-trees, the original packet scatter scheme experiences over eleven times longer flow completion time than in a normal condition. Since there is no packet drop, the performance degradation is only due to out-of-order packets. LBSP can avoid such huge degradation of performance by selecting an alternative path group that does not contain the degraded link.