esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A stable and reliable connection between CNTs and metal electrodes is achieved using femtosecond laser irradiation. With this technique, the contact resistance between CNTs and electrodes can be greatly decreased. Furthermore, this technique does not depend on the specific kind of nanocomponent or metal electrodes. At the same time, by changing the shape of the laser spot, it is possible to irradiate regions of different sizes and shapes at a time, which can greatly improve the connection efficiency of the Carbon nanotube field effect transistor (CNTFET) device.  
Conceptualization, J.C. and J.Z.; methodology, J.C. and Y.C.; validation, Y.C. and H.M.; formal analysis, Y.C. and H.M.; investigation, Y.C.; resources, J.C.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., J.C. and J.Z.; supervision, J.C., J.Z. and X.W.; project administration, J.C.; funding acquisition, J.C.  
Figure 4: (a) I-V curve before and after laser irradiation, it can be clearly seen that the current increases, indicating that the contact resistance of SWCNTs and the electrodes can be greatly reduced after laser irradiation; (b) the resistance change of multiple samples before and after laser irradiation, resistance. There are different degrees of decline; (c) the change trend of resistance under different laser energy irradiation.  
**Funding:** This research was funded by the National Key Research and Development Program of China (2017 YFB1104900), National Natural Science Foundation of China (51875450, 51735010), Natural Science Basic Research Plan in Shaanxi Province of China (2017M5015), Young Elite Scientists Sponsorship program by CAST (2016 QNRC001), Open Research Fund of State Key Laboratory of Transient Optics and Photonics (SKLST201704) and Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (GZKF-2018016).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Numerical Results and Analysis
### The Computational Complexity Comparison  
To compare the computational complexity of CA-DFC and VL-DFC with that of OMD, the total number of calculating the value of the objective function in Equation (6) per K \(\times\) 1 information bit vector \(\mathbf{b}\) is considered here (listed in Table 2). As the calculation of this function is not involved in CD, SIC, DEC, MMSE, O-DFC, and MV-DFC, the computational complexity of them is negligible.  
In Table 2, we define \(n\) is the number of sub-optimal detectors used for fusion, and in this paper, \(n=3\). Besides, \(L\) is the number of undetermined bits in the voting vector, and \(K\) denotes the number of active users in this system. It is clear that the value of \(L\) has a relationship with \(K\), and in general, the greater \(K\) is (and the stronger the MAI is), the greater \(L\) is. Therefore, the ratio \(L/K\) is adopted and then the computational complexity of VL-DFC and OMD is depicted in Figure 4, conditioned on \(L/K=0.1\), \(0.3\), \(0.5\), respectively.  
From the simulation results in Figure 4, we can see that as the number of users increases, the more computational complexity will be saved by VL-DFC, particularly when \(L/K\) is smaller than \(0.3\). In fact, this demand is satisfied as Figure 5 shows, which gives the average \(L\)_versus_\(E_{b}/N_{0}\) curves conditioned on \(K=5\), \(10\), \(15\) and \(20\) in AWGN channel. From this figure, three conclusions are obtained as follows: First, as \(K\) increases, the average \(L\) also increases obviously, which is due to the enhancement of MAI. Second, all the situations (points in this figure) can achieve \(L/K<0.3\). The last is that, as the \(E_{b}/N_{0}\) is lower, the average \(L\) is greater, which means the solution space constructed in VL-DFC becomes larger for the chairman to make his global decision.  
\begin{table}
\begin{tabular}{c c} \hline System & DS-UWB \\ \hline Modulation Mode & BPSK \\ \hline Pseudo random sequences (PRS) & m sequences \\ \hline The length of PRS & 31 \\ \hline Communication channel & AWGN or IEEE 802.15.3a (CM2) \\ \hline The number of testing information symbols & \(10^{5}\) \\ \hline The width of UWB pulse & 0.7531 ns \\ \hline The pulse repetition period & \(\approx\)2 ns \\ \hline The number of active users & \(K=5\), \(10\), \(15\), \(20\) \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**MUD Algorithms** & **Calculation Number** \\ \hline CA-DFC & n \\ VL-DFC & \(2\mathrm{L}\) \\ OMD & \(2\mathrm{K}\) \\ \hline \end{tabular}
\end{table}
Table 2: The computational complexity comparison.
## 4 Conclusions  
The HEC-HMS model was applied for the assessment of climate change impacts in future streamflow by using ensemble climate projections from 10 GCMs under RCP 4.5, RCP 6.0, and RCP 8.5 scenarios for adaptive water allocation at Tanjung Karang Rice Irrigation Scheme Malaysia. The changes in major rice farming (dry and wet) season results are reported. The changes are the differences relative to the baseline period (1976-2005) for the three 30-year defined future period (2020s, 2050s, and 2080s). Temperature is projected to increase; however, rainfall would decrease in the dry season and slightly increase in the main season. Compared with baseline streamflow records, the seasonal streamflow is projected to decrease for the three scenarios under the three future periods. However, in all the future periods there will be a higher decrease during the 2080s in the dry season period under the worst-case scenario (RCP 8.5). The interquartile range, the median and average values of available irrigation supply at the scheme for future years would be smaller than that of past years. The average irrigation supply for historical (1976-2005) period is 15.97 m\({}^{3}\)/s, which would decrease by 12%, 18%, and 21% under RCP 4.5, 6.0, and 8.5, respectively.  
The irrigation supply is observed to fluctuate along the seasons with over-supply in some periods and under-supply in other periods with respect to the needed supply by the scheme. Therefore, the developed model could be integrated with adapted cropping patterns to enhance the water allocation through the proper supply of the desired needed water in the scheme, thereby avoiding over-supply in the period of low demand and under-supply in the period of high demand.  
Conceptualization, H.I. and M.R.K.; Data curation, M.R.K. and D.T.J.; Formal analysis, H.I. and M.R.K.; Funding acquisition, M.R.K.; Investigation, H.I.; Methodology, H.I. and M.R.K.; Project administration, M.R.K.; Resources, A.F.b.A. and D.T.J.; Software, H.I.; Supervision, M.R.K., A.F.A., and L.S.H.; Validation, H.I.; Writing--original draft, H.I.; Writing--review & editing, H.I., L.S.H., and M.R.K. All authors have read and agreed to the published version of the manuscript. This research was funded by UNIVERSITI PUTRA MALAYSIA, Putra Grant No: 9678600 and the MINISTRY OF HIGHER EDUCATION, FRGS Grant No: FRGS/1/2019/WAB01/UPM/02/37. The authors wish to acknowledge Department of Irrigation and Drainage, Malaysia and National Hydraulic Research Institute of Malaysia (NAHRIM) for providing hydro-meteorological data. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 1 Introduction
Hence it is a solution that is good for not only periodic data gathering, but also for reactive data gathering. The proactive building of the paths is similar to the LEACH, it is cluster based, and uses a two-level hierarchy. Hence, all the paths from nodes to the sink are just 2-hops long. Therefore, our protocol is based on LEACH in this aspect. By using a limited and small number of hops, we are reducing the average packet delays to the sink. This is important for emergency applications that need quick reaction. We focus on applications where events may happen at arbitrary times and where the networks need to react to those events very quickly. Such applications include chemical substance and gas detection and warning applications. Hence, we focus on reactive applications that can be served very quickly with proactive routing as in LEACH. Our protocol is designed for indoor environments in which there may be poisonous or detonating gases. We propose two protocols: R-EERP and S-EERP. In these protocols, cluster heads are selected randomly as in LEACH. In R-EERP, clustering mechanism is very similar to LEACH. In S-EERP there is a sequential cluster structure that reflects deployment of a sensor network sequentially in the rooms of the flats of a building. In S-EERP, cluster members do not change in the cluster change time. In addition, there are two threshold values, similar to the TEEN protocol in the literature, used for different goals. These are critical threshold and base threshold. Base threshold is the minimum value desired to be sensed and reported according to the application. The values below this threshold are not taken into consideration (_i.e._, are not reported; they are ignored at the sensor nodes). Critical threshold is a threshold value for the sensed attribute and reflects an emergency situation. Cluster heads try to send a value above the critical threshold to the sink without waiting (with as low delay as possible) in order to take the best emergency actions on the environment, in this way trying to avoid life losses. To be energy efficient and to extend the lifetime of a sensor network, our protocol has the following features:  
(i) The data between the critical and base thresholds are kept at the cluster heads to be sent to the base station. (ii) XOR operation is applied to the previous data when the data are received by cluster heads so as to decrease the number of the data packets that will be sent to the sink. Thus, duplicated data from different cluster members is sent to the sink once. The rest of the paper is organized as follows. In Section 2 we briefly describe some related work. In Section 3 we describe our proposed routing protocols in detail. In Section 4, we provide our simulation results performed to analyze and evaluate our protocols. In Section 5, we give our conclusions.
## 3 Results
### Analysis of the Musculoskeletal Forces  
Regarding the forces predicted via musculoskeletal modeling, the hip contact forces in the operated leg were larger in the Study Group throughout most of the gait cycle, with a maximum difference of 0.6 BW, although the differences were significant in limited portions of the gait cycle (Figure 1).  
Conversely, hip contact forces in the contralateral leg were larger in the Control Group, with a maximum difference of \(-0.6\) BW, although not statistically significant (Figure 1).  
However, from a qualitative analysis in the shape of the hip contact forces, the Study Group shows a more physiological pattern where a typical double-bump is present with a central absorption loading phase, in both the operated and contralateral leg. No remarkable differences were found in the hip adductor and flexor muscles.  
Focusing on the peaks of hip contact forces, no significant differences between the Study and the Control Group were found in the operated limb, while significant differences were present in the contralateral limb with higher force peaks in the Control Group (Figure 2).  
In addition, there was a moderate significant correlation between gait speed and contact force peaks in the hip in the Study Group (R = 0.44, \(p\) < 0.001) (Figure 3). The peak of hip contact force occurred always toward toe off.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Study**} & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Mann–**} \\  & **Group** & & **Group** & & **Whitney** \\  & & & & **U-Test** \\ \hline  & Median & IQR & Median & IQR & \(p\)-values \\ \hline
**Pelvic obliquity** & 2.7 & 4.1 & 2.3 & 1.2 & 0.658 \\
**max peak** & \(-2.5\) & 5.8 & \(-3.3\) & 3.4 & 0.999 \\
**Pelvic obliquity** & \(-2.5\) & 5.8 & \(-3.3\) & 3.4 & 0.999 \\
**min peak** & 32.4 & 5.2 & 34.6 & 16.1 & 0.999 \\
**Hip****abd/add** & 10.9 & 4.9 & 10.1 & 3 & 0.999 \\
**Hip****not** & 9 & 4.2 & 8.4 & 2.3 & 0.999 \\
**ROM** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of maximum peaks in pelvic and hip kinematics.  
Figure 1: Hip contact forces and muscle forces (normalized to body weight and represented as mean and standard deviation) of the operated leg (**A**) and contralateral leg (**B**) across the gait cycle, compared between the Study Group and the Control Group. The analysis is focused on the hip, including joint contact forces and major muscle forces grouped by function. The asterisk (*) indicates the maximum mean difference. Results from SPM t-tests are reported: when SPM t-values exceed the critical threshold (horizontal dotted lines), the differences between groups are significant, and when significant differences occurred for at least 4% of the gait cycle, the areas are reported in grey with the corresponding \(p\)-values.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### Authentication of Invariant Parts  
The SAODV protocol uses digital signatures to authenticate invariant parts of RREQ and RREP messages. The node that sends the routing message signs the invariant part of the routing message. Each node that receives the routing message verifies the signature in the message.
## 3 Proposed Method
### Novel Encoding of Generator States  
The traditional GA may encode the on/off states (\(u(t)\)) of each generator using binary bits (0 or 1), according to Equations (1)-(3). For example, the grey and white parts in a chromosome denote the on and off states, respectively, of a generator in one hour period over 24 h, as shown in Figure 1a.  
When the number of generators is large and traditional encoding is used, the length of a chromosome becomes very long. Consequently, this work proposes a pair-based encoding, in which the first and second slots (genes) in a pair represent the starting and ending hours in a period, respectively. Hence, each slot contains integer. Figure 1b shows the new encoding of the chromosome that corresponds to Figure 1a. With this new encoding, the length of a chromosome is much reduced and variable.  
Figure 1: (**a**) On (grey) and off (white) states of a generator over 24 h; and (**b**) new encoding.  
The crossover operation in this new encoding comprises three steps using a given crossover rate, as follows.  
Step A1: Identify the overlapping hours in which states of two selected chromosomes are on, as shown in Figure 2. The proto-offspring becomes (3,4)-(6,9)-(13,19).  
Step A2: Insert two on-periods arbitrarily. Figure 3a illustrates the insertion of two on-periods. The proto-offspring is (1,1)-(3,4)-(6,9)-(10,11)-(13,19).  
Step A3: Trim the redundant slots (genes). Figure 3b reveals that (6,9)-(10,11) can be trimmed to be (6,11). Thus, the final offspring becomes (1,1)-(3,4)-(6,11)-(13,19).  
Figure 3: (a) Insertion of two on-periods and resulting proto-offspring; and (b) trimming redundant slot.  
Figure 2: Identification of overlapping hours in which states of both chromosomes are on and resulting proto-offspring.
# Computing a Clique Tree with the Algorithm Maximal Label Search
## 2 Preliminaries
### Chordal Graphs and Clique Trees  
A graph is chordal (or triangulated) if it contains no chordless-induced cycle of length four or more. A graph is chordal if and only if all of its minimal separators are cliques [1]. It follows that for each moplex \(X\) of a chordal graph \(H\), \(X\) is simplicial and \(N[X]\) is a maximal clique of \(H\).  
A chordal graph is often represented by a clique tree:  
**Definition 1**.: _Let \(H=(V,E)\) be a connected chordal graph. A clique tree of \(H\) is a tree \(T=(V_{T},E_{T})\) such that \(V_{T}\) is the set of maximal cliques of \(H\) and for any vertex \(x\) of \(H\), the set of nodes of \(T\) containing \(x\) induces a subtree of \(T\)._  
**Characterization 1**.: _[_4_]_ _Let \(H\) be a connected chordal graph; let \(T\) be a clique tree of \(H\); and let \(S\) be a set of vertices of \(H\); then, \(S\) is a minimal separator of \(H\) if and only if there is an edge \(K_{1}K_{2}\) of \(T\) such that \(S=K_{1}\cap K_{2}\)._  
Every chordal graph has at least one clique tree, which can be computed in linear time with the nodes labeled by the maximal cliques and the edges labeled by the minimal separators [4].
## 3 Materials and Methods
### Synthetic Minority Over-Sampling Technique (SMOTE)  
In machine learning classification, imbalanced data refers to datasets where the number of instances belonging to different classes is uneven, leading to a potential bias in the classifier's performance. SMOTE, introduced by [PERSON] et al. [42], is an oversampling technique that aims to overcome the imbalance problem by generating synthetic examples for the minority class. The method works by randomly selecting a member from the minority class and identifying its K-Nearest Neighbors (k-NNs). From these neighbors, SMOTE creates new synthetic examples along the line segment connecting the minority class member and its neighbors [42].  
Generating synthetic examples involves selecting a minority class instance and calculating the feature-wise differences between it and its neighbors. SMOTE then randomly chooses a number between 0 and 1, multiplying the feature-wise differences by this value. The resulting values are added to the selected minority instance, producing new synthetic examples that represent the minority class but differ slightly in their feature values. Applying SMOTE makes class distribution more balanced, as artificial models are introduced to augment the minority class. This helps to alleviate the bias caused by imbalanced data and allows the classifier to learn from a more representative dataset [43]. The minority class member generation diagram based on SMOTE and k-NN algorithms is shown in Figure 4 below.
## 3 Results and Discussion
### Concentration of Chlorophyll A, Chlorophyll B, Chlorophyll A + B and Carotenoids in Stevia Leaves
Concentration of Various Elements (Calcium, Magnesium, Phosphorus and Potassium) and Nitrate Ions in Stevia Leaves  
Different irrigation practices did not have as significant of an influence on the concentration of calcium, magnesium, phosphorus and potassium (Table 5) as they had on the concentration of previously discussed compounds in Sections 3.3 and 3.4. The concentration of calcium in all the analysed samples was similar and it ranged from 886.11 to 936.11 mg\(\cdot\)100 g\({}^{-1}\) in July and decreased to 769.17-894.44 mg\(\cdot\)100 g\({}^{-1}\) in September. The concentration of calcium in the leaves of stevia grown in Poland was lower than total content of calcium in the leaves of stevia grown in warmer climates, in which it could range up to 1500 mg\(\cdot\)100 g\({}^{-1}\)([PERSON] et al., 2018; 2018). Magnesium content ranged from 159.44 to 173.33 mg\(\cdot\)100 g\({}^{-1}\) in July and in September, it was the lowest in sample 0 W (133.33 mg\(\cdot\)100 g\({}^{-1}\)) and the greatest in sample 2 W (170 mg\(\cdot\)100 g\({}^{-1}\)). In the study of [PERSON] et al. (2018), stevia plants grown in selected, more humid regions of Morocco were characterised with a greater concentration of magnesium than stevia plants grown in arid conditions. The concentration of phosphorus in samples NR and 1 W decreased from July to September, increased in the sample 0 W from July to September and remained at almost the same level in the sample 2 W. The total concentration of the phosphorus in all the samples gathered in July was statistically homogenous and it ranged from 128.72 (0 W) to 147.39 mg\(\cdot\)100 g\({}^{-1}\) (1 W). Similarly, the concentration of phosphorus was homogenous in all the samples from September and the results ranged from 116.54 (NR) to 143.78 mg\(\cdot\)100 g\({}^{-1}\) (0 W). In the study of [PERSON] et al. (2018), a similar concentration of phosphorus was determined in the stevia samples, albeit a significant increase in phosphorus content was noted between July and September. Potassium content decreased in all the samples from July to September by 10-30%; however, in each of the time periods during which the stevia leaves were analysed, potassium content remained at a similar level de
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 4 Results and Discussion
### Results
#### 4.2.2 Network Model Ablation Studies
With the non-stationary attention mechanism included, Designed_3 exhibited a performance increase of 3.4% over Designed_2 across the prediction steps of 8-64. This indicates that the non-stationary attention mechanism helps the model better adapt to the complex characteristics of changes in PEMFC RUL, thereby enhancing the robustness of the predictions. The introduction of the TILDE-Q loss function played a pivotal role in enhancing predictive performance for the RUL of PEMFC. This loss function, by incorporating a weighting term related to time series similarity, more comprehensively considers the correlations among data points within the series, thus aiding the model in more accurately capturing the trends in RUL changes. At prediction steps ranging from 8 to 64, the performance of Designed_4, which utilized the TILDE-Q loss function, showed a 5.23% improvement compared to Designed_3, which used MSE as its loss function. This indicates that the TILDE-Q loss function more effectively guided the model during the training process to learn information about the shape of the time series, increased sensitivity to changes in PEMFC RUL, and consequently significantly enhanced predictive performance.
## 2 Current Research of Cyber-Physical Power System (CPPS) and Co-Simulation Platforms
### Application Scenarios of Co-Simulation Platforms  
The co-simulation of a CPPS aims at verifying the effectiveness of the control algorithm and security analysis under a CPPS environment. From the perspective of services, its application includes demand response, network monitoring, system optimization, wide area measurement system (WAMS) security, and supervisory control and data acquisition (SCADA) security analysis. Three main application scenarios are discussed as follows.