esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass
#### 3.2.2 Adsorption Kinetics  
The measured adsorption kinetic data were fitted to the linear forms of [PERSON] PFO and PSO equations (Equations (5) and (6)). The two models were applied to estimate the adsorption constants (\(k_{1}\) and \(k_{2}\)) as well as the correlation coefficients (R\({}^{2}\)) values for Pb\({}^{2+}\). The results are presented in Figure 3  
Figure 2: Effect of contact time on the adsorption capacity and removal percentage of lead(II) ions onto LPD. Conditions: 55 ppm Pb\({}^{2+}\) concentration, 50 mL Pb\({}^{2+}\) solution volume, 0.2 g LPD adsorbent dose, pH 6, 25 \({}^{\circ}\)C adsorption temperature, and 150 rpm agitation speed.  
and Table 4. According to the data obtained, the values of \(q_{e}\) (6.91 mg/g) and \(R^{2}\) (0.969) of the PSO model are more acceptable, describing kinetic behavior with \(q_{e}\) value closer to the experimental one (\(q_{e}\). Exp.? 4.12 mg/g) compared with that from PFO (15.10 and 0.946, respectively), indicating that the PSO model fits the experimental data better [31]. Therefore, the rate-limiting step of Pb\({}^{2+}\) adsorption seems to be controlled by a chemical adsorption process via, for example, exchanging of electrons between the adsorbent and adsorbate [32], however, the physical adsorption process cannot be ignored, at least in the last stage of adsorption period. The initial adsorption rate (\(h\); mg/(g\(\cdot\)min)) can be calculated from the data of the PSO model, as expressed in Equation (10). However, the calculation indicates low value of \(h\) (0.095 mg/(g\(\cdot\)min) at the applied adsorption conditions (Table 4).  
\[h=k_{2}q_{e}^{2} \tag{10}\]
###### Abstract  
In this paper, we consider partial path selection (PPS) for a multi-hop decode-and-forward cooperative system with limited channel state information (CSI) feedback, where the PPS utilizes local CSI only for a subset of hops on each of all independent paths between a source and a destination to reduce the energy consumption for CSI feedback. To enhance the end-to-end performance of the PPS, we propose a novel PPS method with local CSI chosen by the correlation between the end-to-end signal-to-noise ratios (SNRs) based on global and local CSI under Nakagami-\(m\) fading channels. For each path, we pick a subset of hops to report CSI with the highest correlation for a given CSI feedback overhead requirement, which can achieve the similar end-to-end outage probability to the best path selection with global CSI. We provide an exact and closed-form expression for the SNR correlation coefficient and present an impact of the SNR correlation on the end-to-end outage probability.  
multi-hop decode-and-forward relaying; limited channel feedback; SNR correlation; relay selection; Nakagami fading channels +
Footnote †: journal:
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 5 Experiments
### Experimental Results
#### 5.4.3 Different Components' Influences (RQ3)  
To evaluate the influences of the two components of CKGAT (i.e., the knowledge-aware GAT-based attentive embedding layer and the attention aggregator) on recommendation accuracy, we have designed the following two variant methods of CKGAT:  
* **CKGAT**w/o**Att**. This variant method is achieved by removing the attention aggregator from CKGAT. The purpose is to verify the influence of the knowledge-aware GAT-based attentive embedding layer on recommendation accuracy.
* **CKGAT**w/o**G**AT**. This variant method is achieved by removing the knowledge-aware GAT-based attentive embedding layer from CKGAT. The purpose is to verify the influence of the attention aggregator on recommendation accuracy.  
Table 2 shows the top-N recommendation results of CKAN, CKGAT, and the two variant methods on the four datasets, with the best results among all the methods highlighted in bold.  
Figure 7: Recommendation diversity performance comparison on the MovieLens 20M dataset. (a) DIV@10; (b) DIV@20.  
We have the following observations from the results:  
1. CKGAT is significantly better than the two variants \(\text{CKGAT}_{\text{w/o Att}}\) and \(\text{CKGAT}_{\text{w/o GAT}}\) across all evaluation metrics on all the datasets, with the exception of the NDCG@10 metric on the Book-Crossing dataset. This shows that CKGAT can more effectively capture users' preferences and improve recommendation accuracy by integrating the knowledge-aware GAT-based attentive embedding layer and the attention aggregator to learn user embeddings and item embeddings.
2. The two variant methods are superior to CKAN (the basic method of CKGAT) across all the evaluation metrics on all the datasets, with the exception of the NDCG@10 metric on the MovieLens 20M dataset. This shows that using the knowledge-aware GAT-based attentive embedding layer to capture the topological proximity structures of entities in multi-hop ripple sets as well as using the attention aggregator to distinguish the importance of ripple set embeddings can refine the representations of users and the representations of items.
3. The recommendation accuracy of \(\text{CKGAT}_{\text{w/o Att}}\) is better than \(\text{CKGAT}_{\text{w/o GAT}}\) in terms of most evaluation metrics on all the datasets. This shows that the topological proximity structures of the entities in multi-hop ripple sets play a more important role in learning the user representations and item representations.
## 1 Introduction
compared 14 different working fluids and found that they had different dynamic characteristics [36]. [PERSON] et al. designed an ORC for exhaust heat recovery of a hybrid powertrain and three different driving cycles (FTP-75, EUDC, and US06) were used to evaluate the extent of fuel reduction [37]. The operation characteristics of the expander has a great influence on the performance of an ORC system [38]. [PERSON] et al. designed a predicted method for the efficiency of a radial-inflow turbine and compared with a constant turbine efficiency method. The results indicated the system performances of these two different methods were different evidently [39]. Although several investigations were performed with regard to the dynamic performance of an ORC operating together with an ICE, most of them employed a scroll expander or a turbine. The single-screw expander is a positive displacement expander, which has a compact volume and a good force balance, leading to low noise and a small vibration during the working process. In this study, the dynamic performance of an ORC using a single-screw expander for exhaust heat recovery is optimized based on the working conditions of the ICE and the transient operation characteristics of the ICE-ORC combined system is investigated. First, a combined simulation model consisting of the ICE and the ORC with a single-screw expander is set up in GT-POWER and the operation characteristics of the ORC system under the rated power condition of the ICE is studied. The optimal operation conditions of the ORC system are obtainedaccording to the working conditions of the ICE. Then, a control strategy of the ORC system based on a feedforward method is designed in MATLAB. The dynamic operation performance of the ICE-ORC system is simulated under a transient driving cycle. The results indicate that the designed control strategy can operate properly and the efficiency of the entire system can be increased under some driving conditions. The transient performance under the highway conditions is better than that of the urban driving conditions. The results can provide a reference for the practical application of ORC system for waste heat recovery of ICE on vehicles.
###### Abstract  
The aim of this study was to determine the effects of the month of lactation and udder health of high-yielding Polish Holstein-Friesian (PHF) cows on daily milk production and the content of K, Ca, Na, Mg and Zn in milk. The experimental materials comprised 380 milk samples collected from 38 cows. The highest average daily milk yield (49.1 kg) was noted in cows in the second month of lactation and, considering udder health, in cows whose milk contained 201,000 to 400,000 somatic cells per mL on average (denoting risk of mastitis). The K content of milk increased (\(p<0.05\)) in successive months of lactation, whereas the opposite trend was observed in the Zn content of milk. The average content of Ca (842 mg-dm\({}^{-3}\)), Na (344.5 mg dm\({}^{-3}\)) and Mg (98.5 mg dm\({}^{-3}\)) in milk was below the lower limits of the normal physiological ranges. Milk from cows with healthy udders (\(\leq\)200,000 somatic cells per mL) had the highest concentration of K, whereas the levels of Na and Zn were highest in milk from cows with clinical mastitis (\(>1\) mln somatic cells per mL). Udder inflammation was accompanied by an increase in the levels of Na and Zn in milk. The high content of Na and Zn in milk can be an additional indicator of mastitis in cows.  
potassium; calcium; sodium; magnesium; zinc; daily milk yield; primiparous cows; multiparous cows; somatic cell count (SCC) +
Footnote †: journal: Applied Sciences
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers
#### 3.2.6 Pnn  
PNN has been derived from the neural network's methodology. This method is highly sensitive in one path, and it has a greater value than the other path. The sensed parameter variables should have a normalized scale within themselves; otherwise, they must be normalized to an acceptable level before they are used. If smaller datasets are used in applications and classification schemes, then the PNN algorithm is an efficient one.  
The PNN is used to calculate the Probability Distributed Function (PDF) by the Parzen Window (PW and this PW is based on Kernel Density Estimation (KDE). The PNN isa multi-layered algorithm based on Feed Forward Network (FFN) at four levels as follows:  
* Input layer: As the PNN is based on the neural network, it has multiple layers of operation in it. The starting point of this algorithm is the input layer. The neurons act as a predictor variable among the available network structures. In WSN, the sensor node's communication is considered as neurons. In PNN, N number of neurons as groups are available and N--1 neurons are considered as logical variables. For each group, the range of the variable is standardized by adjusting with the offset variable. These calculated values are utilized as input to the following layer of neurons.
* Pattern layer: The next layer of PNN is the pattern layer. At the training phase, each dataset contains a neuron. In every case, the predictor variables and expected target values are stored. After the target case is set, an individual neuron computes the Euclidean Distance (ED) from the midpoint of all other neurons in the layer. The entire process is repeated to get the best ED. Finally, the calculated ED is used in the process with the radial kernel function and sigma values.
* Summation layer: The pattern layer's output is given as input to the summation layer for further processing in PNN. Each category of output variable has one existing neuron pattern. All the hidden neurons aggregate the collected target data on every training case. These are weighted average values of the pattern neurons. All the collected values are aggregated on the pattern neurons which are forwarded to the output layer.
* Output layer: Data collected from the summation layer are passed to the output layer with the weighted votes for comparisons. The result of these comparisons generates the weighted votes of the target category for each target class. This is referred to as the calculated output of the second layer.
## 3 Results
### Thematic Categories
#### 3.2.5 Precise Adjustment to Objectives  
Six studies analyzed the applicability of these systems for maintaining biological constants within a more precise target range [51; 52; 53; 54; 59; 61]. Five of these studies reported that the application of CDSSs kept patients' constants stable, improving the evolution of the patients who were submitted to cardiac surgery [51; 52; 53; 54; 59].  
[PERSON] et al. reported that CDSSs helped to maintain the stability of the biomedical parameters of the patients in the study group compared to those of the patients in the control group, resulting in a statistically significant relationship (\(p<0.01\)) [54]. Moreover, [PERSON] al. determined the applicability of a CDSS for administering or adjusting insulin doses. The results showed that the recommended doses were accepted to a greater extent (83%) than higher doses (78%), with a statistically significant relationship (\(p<0.001\)) [52].  
Another field of application deals with hemodynamic parameters. Monitors such as pulse wave cardiac output lithium dilution cardiac output (LIDCOMPLUS) provide computational intelligence to facilitate clinical decision making. Two studies proved LIDCOMPLUS to be useful for patients undergoing cardiac surgery to maintain hemodynamic constants by assessing continuous monitoring and suggesting measures needed to keep them in range. In vasoactive drug infusion, the system modifies the doses or proposes a dose modification, measuring the improvement in the quality and efficiency of healthcare [53; 59]. Likewise, VenAssit achieved a range of 84% by stabilizing six parameters such as plateau pressure, tidal volume, respiratory rate, Wob, tidal end CO\({}_{2}\), and O\({}_{2}\) saturation [51].  
Finally, [PERSON] et al. used a target-directed therapy (TGT) to diagnose and administration a real-time pharmacological treatment regularly used. The results showed that there was no difference in the mean percentage time spent in the target zone (36.7% in control group and 35.5% in the intervention group), and there were no differences in the fluid balance, but there was a high level of concordance between both groups [61].
## 4 Conclusions  
In summary, three sets of MIPs for gossypol were prepared by bulk polymerization (MIP1), sol-gel surface layer imprinting (MIP2), and a sol\(-\)gel process (MIP3). MIP1 showed the highest adsorption capacity up to 564 mg g\({}^{-1}\). The obtained MIP2 and MIP3 exhibited faster adsorption kinetics towards gossypol compared with MIP1. Noticeably, MIP2 prepared with sol-gel surface layer imprinting combined the merits of the sol\(-\)gel process and surface imprinting using the silica gel as the supporting material. It only took 40 min for MIP2 to reach adsorption equilibrium. The selectivity of MIPs was also satisfactory. All above mentioned results suggested the MIP2 was suitable as a sorbent for rapid recognition and separation for gossypol, and MIP1 was desirable for gossypol binding at high concentrations. Both types of MIPs had potential value in different practical applications, such as the removal of gossypol from cotton seed, in food safety control, and drug monitoring.  
Conceptualization, Y.Z. and L.W.; Methodology, L.W. and Y.L.; Software, A.Y. and L.Z.; Validation, Y.L., A.Y. and L.Z.; Formal Analysis, L.W. and Q.L.; Investigation, L.W., K.Z. and Y.L.; Resources, Y.Z. and L.Z.; Data Curation, L.W., K.Z. and Y.L.; Writing-Original Draft Preparation, L.W.; Writing\(-\)Review & Editing, Y.Z.; Visualization, A.Y. and L.Z.; Supervision, Y.Z.; Project Administration, Y.Z.; Funding Acquisition, Y.Z. and A.Y. All authors contributed substantially to the work reported. This work was financially supported by the National Natural Science Foundation of China (21472235, 21464015), Xinjiang Tianshan Talents Program (2018 xgystsyc2-3), Young Elite Scientist Sponsorship Program by CAST (2017 QNRC001) and Western Light program of CAS (2017-XBQNXZ-B-001). The authors declare no conflict of interest.
## 4 Suggestions for Future Trends  
Motivated by the challenges presented in Section 3.5, and follows on the flow of the received signal and according to the Observation-Orientation-Decision-Action (OODA) rule, we think future trends for the RFI mitigation techniques can be summarized into three keywords: Cognitive, Integrated, and Adaptive. These three aspects form an integral system, and support each other for improving the RFI-rejection performance. Figure 11 depicts an assumed system architecture for future SAR systems incorporating the RFI mitigation capability. The whole flow is a dynamic, learning-based process involving long-term planning, decision-making correction, and long-term incremental learning.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 4 Simulation and Experiment  
In this section, we describe out algorithm simulation experiments to verify the efficiency of ILSN by using KCF, MCNN, SiamRPN, DaSiamRPN, and SlamRPN++ as comparison algorithms. Furthermore, to test the feasibility of the ILSN, an experimental platform of the UAV system for dim target tracking was established. Additionally, the composition of the software and hardware system is described in detail. On this basis, the physical test experiment of the algorithm performance was completed.
## Materials and Methods
### Input Data
\begin{table}
\begin{tabular}{c c c c c} \hline
**NO** & **Scenarios** & **Projects** & **GCMs** & **SRES** \\ \hline
1 & CNRM-CM3-A2 & CMIP3 & CNRM-CM3 & AR4-A2 \\
2 & CCSM3-A2 & CMIP3 & NCAR-CCSM3 & AR4-A2 \\
3 & CNRM-CM3-B1 & CMIP3 & CNRM-CM3 & AR4-B1 \\
4 & CCSM3-B1 & CMIP3 & NCAR-CCSM3 & AR4-B1 \\
5 & CNRM-CM5-RCP45 & CMIP5 & CNRM-CM5 & AR5-RCP45 \\
6 & CCSM4-RCP45 & CMIP5 & NCAR-CCSM4 & AR5-RCP45 \\
7 & CNRM-CM5-RCP85 & CMIP5 & CNRM-CM5 & AR5-RCP85 \\
8 & CCSM3-RCP85 & CMIP5 & NCAR-CCSM4 & AR5-RCP85 \\ \hline \end{tabular}
\end{table} 
Table 1: Details of eight future climate scenarios from CMIP3 and CMIP5 The data set of the global CO2 concentration under the IPCC AR4 and AR5 scenarios during 2017-2100 were downloaded from the IPCC DDC ([[http://www.ipcc-data.org/](http://www.ipcc-data.org/)]([http://www.ipcc-data.org/](http://www.ipcc-data.org/))) and RCP Database ([[http://www.iiasa.ac.at/web-apps/tnt/RcpDb](http://www.iiasa.ac.at/web-apps/tnt/RcpDb)]([http://www.iiasa.ac.at/web-apps/tnt/RcpDb](http://www.iiasa.ac.at/web-apps/tnt/RcpDb))), respectively. The average of the references estimated from the ISAM and BERN-CC models was used in AR4. The CO2 concentrations under the AR4 and AR5 scenarios over 10 years were interpolated into yearly CO2 concentration values using the Fourier-fitting method. The results are shown in Figure 2. The observed CO2 concentration increased from 317.64 ppm in 1961 to 404.21 ppm in 2016, with an annual rate of 1.52 ppm. Under the AR5 RCP85 scenario (AR5_RCP85), the CO2 concentration increased to 935.87 ppm in 2100, which was the highest for the scenario without the inclusion of climate policy [58]. This scenario was followed by the AR4 A2 scenario (AR4_A2), with a value of 846 ppm in 2100. B1 (AR4_B1) and RCP45 (AR5_RCP45) are the medium stabilization scenarios, and the CO2 concentration increased to 544.50 ppm and 538.36 ppm in 2100, respectively. Figure 1: Time series of the MAT (**a,b**) and MAP (**c,d**) in China projected by different climate models under the AR4 (**a,c**) and AR5 (**b,d**) scenarios. Figure 2: Times series of global atmospheric CO2 from 1961 to 2100 in four IPCC SRES.
## 3 Methodology
### Algorithm Operation: Solving the Model with Extended INSGA-II
#### 3.5.3 Route Evaluation  
INSGA-II maintains two populations concurrently: the operational population and the archival population. The operational population is the source of parent chromosomes used for evolutionary manipulation in Section 3.5.2. The archival population stores a fixed number of globally high-quality chromosomes. The operational population is sufficiently small to perform genetic operations with less computational effort, while the archival population is sufficiently large to achieve a good distribution of convergence points. Both populations are constantly renewed as the routes evolve. After an evolutionary operation, new offspring are generated from the parent chromosomes. The best individuals from the parents and offspring are selected as the updated operational population, which is the parent population for the next iteration by a fast non-dominated sorting approach based on the crowding distance assignment in the NSGA-II framework [61]. The parents, offspring, and original archival chromosomes are stratified and ranked using the same strategy as the operational population, and a specified number (\(P_{A}\)) of chromosomes are selected as the updated archival population according to the ranking.  
Better populations can be obtained by route evaluation with the archiving strategy. On the one hand, elite solution losses due to crossover and mutation operations are retained in the archival population, thereby reducing the risk of convergence distortion; on the other hand, preserving a larger archive population of non-dominated points entails preserving more information about the Pareto front accumulated during the last generations to guide population evolution.
## 3 Energy Efficient Routing
### Performance Evaluation  
We now evaluate the performances of EOLSR in various network configurations. Each configuration is characterized by a node number and a density. We recall that network density denotes the average number of neighbors per node + 1. For each configuration, we run 10 simulations and the result depicted in the curves is the average of these 10 simulations. Based on the simulation parameters given in Table 2, we compare the network lifetime obtained with EOLSR, and two other strategies:  
* MinEnergy that selects the route consuming the minimum energy for an end-to-end transmission, without considering node residual energy. Notice that in this algorithm, the EMPRs of a node are its 1-hop neighbors that minimize the energy consumed to reach a 2-hop neighbor.
* MaxPacket that selects the routes that maximize the number of packets that can be transmitted from a source to it destination. It adopts the same selection of EMPRs as EOLSR but takes the route maximizing \(min_{i\in sender(flow)}M1E(i)\).  
Simulation results concerning network lifetime [3] are illustrated in Figure 2. In all cases, MaxPacket provides the lowest network lifetime. This can be explained by the fact that the selected route does not minimize the energy consumed by an end-to-end transmission. MinEnergy does, but it always uses the same nodes: those belonging to the route of minimum energy. EOLSR that selects (1) the EMPRs according to their residual energy and this of their neighbors and (2) the routes of minimum energy built from these nodes, obtains better results.  
In this paper, we show that the increase in network lifetime is not a factice one. It really allows the application to transfer more useful data as depicted in Figure 3. These new simulation results justify both the selection of EMPRs according to Equation 3 and the selection of routes of minimum energy cost, two design choices of EOLSR.  
Figure 2: Network lifetime obtained with EOLSR, MinEnergy and MaxPacket.  
In order to justify the choice of keeping MPRs nodes to optimize network broadcasts, we now evaluate the total number of TCs sent (cumulating generated TCs and forwarded TCs) during network lifetime in two cases:  
* with EOLSR where TCs are forwarded by MPRs. This corresponds to the curve denoted MPR in Figure 4;
* with another solution where TCs are forwarded by EMPRs, solution denoted EMPR in Figure 4.  
We observe a significant improvement when MPRs, instead of EMPRs, are in charge of optimizing broadcasts. As the selection of EMPRs must take energy into account to maximize network lifetime, two types of multipoint relays are used in EOLSR: MPRs to optimize network broadcasts and EMPRs to build energy efficient routes. We can conclude from this performance evaluation that the simulation results validate our design choices made with EOLSR.  
Figure 4: Number of TCs forwarded in the network.  
Figure 3: Comparison of the amount of data delivered with EOLSR, MinEnergy and MaxPacket.