esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Hardware Failure of Information System and Its Causes
#### 4.1.4 Other Hardware Faults  
For the operation of information system communication technology, the problems in hardware are also reflected in many small hardware aspects. For example, a circuit board is a common basic element, and the causes of its failure are also diverse. For example, the short circuit problem of circuit board, or the humidity of its own environment, may affect the operation effect of the circuit board. This will also form a more obvious threat, leading to the formation of circuit board defects, and ultimately affect the orderly operation of the entire computer network communication technology. In addition, for the laying and operation of some basic cable structures, if there is any damage, it will also lead to the damage of the working fluency of the whole system, and the threat brought by it is also relatively prominent.
## 3 Results
### Geochemistry and Diffuse Reflectance Spectra (DRS) and Hm/(Hm + Gt) Ratio Characteristics  
Figure 4 shows that the value range for the Fe\({}_{2}\)O\({}_{3}\) concentration in the CQS loess section was 6.04% \(\sim\) 6.60%, with an average of 6.25%. CIA did not change much in the whole section; it ranged from 0.81 to 0.83 with an average of 0.82. The Fe\({}_{2}\)O\({}_{3}\) and CIA of the weak paleosol layer (L1S1) were basically the same as those of the loess layer (L1L2), indicating that the Shengshan Island area was weakly pedagogenic in the later stages and had underdeveloped paleosol horizons [35]. The formation of hematite (Hm) and goethite (Gt) indicated that some dry and wet climates prevailed at that time [36; 37; 38]. The Hm/Gt ratio in the CQS loess section was 0.37\(\sim\)0.47, with an average of 0.42 (Figure 4c). The Hm/(Hm + Gt) ratio in the CQS loess section was 0.27\(\sim\)0.34, with an average of 0.29 (Figure 4d). The curves for  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Depth** & **K** & **Th** & **U** & **DOS** & **De** & **OSL Age** \\  & **(cm)** & **(\%)** & **(ppm)** & **(ppm)** & **(Gy/Ka)** & **(Gy)** & **(ka)** \\ \hline CQS-1 & 50 & \(1.69\pm 0.04\) & \(14.44\pm 0.80\) & \(2.71\pm 0.40\) & \(3.29\pm 0.25\) & \(130.5\pm 5.1\) & \(39.66\pm 3.4\) \\ CQS-2 & 80 & \(1.68\pm 0.04\) & \(14.43\pm 0.80\) & \(2.38\pm 0.30\) & \(3.16\pm 0.23\) & \(154.4\pm 9.0\) & \(48.9\pm 4.6\) \\ CQS-3 & 130 & \(1.69\pm 0.04\) & \(13.65\pm 0.80\) & \(2.31\pm 0.30\) & \(3.05\pm 0.23\) & \(177.3\pm 9.4\) & \(58.0\pm 5.3\) \\ CQS-4 & 160 & \(1.72\pm 0.04\) & \(14.14\pm 0.80\) & \(2.62\pm 0.40\) & \(3.17\pm 0.24\) & \(202.5\pm 9.9\) & \(63.9\pm 5.8\) \\ CQS-5 & 200 & \(1.80\pm 0.04\) & \(14.14\pm 0.80\) & \(2.67\pm 0.40\) & \(3.19\pm 0.24\) & \(216.4\pm 11.0\) & \(67.8\pm 6.2\) \\ CQS-6 & 230 & \(1.82\pm 0.04\) & \(15.46\pm 0.80\) & \(2.77\pm 0.40\) & \(3.22\pm 0.24\) & \(244.3\pm 15.8\) & \(75.9\pm 7.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for OSL ages.  
Figure 3: Variations in (**a**) TOC, (**b**) \(\delta^{13}\)C\({}_{\rm{org}}\) composition, (**c**) C\({}_{4}\) abundance and (**d**) \(\chi_{\rm{fd}}\) of the CQS section.  
Hm/Gt and Hm/(Hm + Gt) of the L1S1 layer from the CQS loess section were higher than that of the L1L2 layer. The curves for Hm/Gt and Hm/(Hm + Gt) of the CQS loess section were similar to those for the \(\delta^{13}\)C\({}_{\text{org}}\) composition, TOC concentration, C\({}_{4}\) abundance and \(\chi_{\text{fd}}\) (Figures 3a-d and 4c,d).
# Strain Relaxation Effect on the Peak Wavelength of Blue InGaN/GaN Multi-Quantum Well Micro-LEDs
## 2 Experiments  
### Epitaxial Growth and InGaN/GaN Micro-LED Arrays Fabrication  
The InGaN/GaN LED wafer was grown on a 4-inch c-plane sapphire substrate by metal-organic chemical vapor deposition. The epitaxy layer is illustrated in Figure 1: a 3 \(\upmu\)m undoped GaN buffer layer; a 1.5 \(\upmu\)m n-type GaN layer; twelve periods 3 nm MQWs separated by 12 nm GaN barrier layers; and a 0.2 \(\upmu\)m p-type GaN top layer.  
In order to study strain relaxation of InGaN/GaN MQWs, the top p-GaN layer thickness was reduced to 100 nm by inductively coupled plasma (ICP) etching. Then, we divided a 4-inch wafer into 18 areas of the same size with area sizes of 12.24 mm \(\times\) 19.38 mm. We etched an equal number of micro-pillars of different sizes in each area and the etching period was 30 \(\upmu\)m (row: 12,240 \(\upmu\)m /30 \(\upmu\)m = 408, column: 19,380 \(\upmu\)m/30 \(\upmu\)m = 646). The diameters of the etched micro-pillars ranged from 3 \(\upmu\)m to 20 \(\upmu\)m. (as shown in Figure 2)  
Figure 1: Schematic of GaN blue LED epitaxial wafer layers.
## 6 Proposed Scheme
### Malicious User Tracking  
After the RSU and a vehicle completing the mutual authentication and sharing a session key, they are able to encrypt traffic information through session key \(SK\) for information transmission. Once RSU detects malicious behavior of the vehicle, it computes the \(MP=PID_{v}^{*}\bigoplus h(PSID_{r}\|t_{4})\), \(M_{3}=h\left(MB,PID_{v}^{*},PSID_{r},t_{4}\right)\) and sends \(\{ID_{r},MP,M_{3},t_{4},MB\}\) to TA, where \(MB\) means the message of malicious behavior. While receiving a message from the RSU, TA checks the freshness of \(t_{4}\) and calculates \(PSID_{r}=h(ID_{r}\|K)\), \(PID_{v}^{*}=MP\bigoplus h(PSID_{r}\|t_{4})\), and verifies whether \(M_{3}=h\left(MB,PID_{v}^{*},PSID_{r},t_{4}\right)\) is right or not. If yes, TA computes \((ID_{v}\|y_{v}\|t_{v})=D_{K}\left[PID_{v}^{*}\right]\), and knows the identity of the malicious vehicle.
## 3 Experiments And Discussion  
To evaluate the performance of our proposed scheme, we designed four testing experiments oriented toward testing the reachability, manipulability, and dexterity during common surgical tasks. We compared it with the use of conventional forceps used in endoscopic endonasal surgery. The following four tasks were performed by the participants using both the conventional surgical instruments and the proposed cooperative interface: (1) a reachability task to test the ability of the robot to reach anatomic areas of interest in endonasal surgery such as pituitary tumors in the stellar area, (2) a pick-and-place task to determine if the users can manipulate objects and translate them dexterously in a constrained environment, (3) a block-in-the-hole task to add the need of orientation adjustments in object manipulation, and (4) a needle stitching task to evaluate the performance in a common surgical task in endonasal surgery. All subjects gave their informed consent for inclusion before they participated in the study. The study was approved by the Ethical Research Committee of Nagoya University.
## 2 Materials and Methods
### Study Variables and Data Collection
The EQ-5D-5L is a questionnaire for the assessment of patients' quality of life, consisting of a descriptive system (EQ-5D) and a visual analog scale (EQ-VAS). The first part, the EQ-5D, comprises five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression), with each one having five levels (no problems, slight problems, moderate problems, severe problems and extreme problems). The score for each item ranges from 1 to 5, in which 1 represents \"no problems\" and 5 represents \"extreme problems\", whereas the \"index value\" ranges from 0 to 1 [20]. The second part, the EQ-VAS, measures health outcomes according tothe patient's own judgment on a vertical VAS, where the endpoints range from \"The best health you can imagine\" to \"the worst health you can imagine\", with scores ranging from 0 to 100. The T25-FW is the most commonly used standalone measure of physical activity and is based on a timed 25-foot walk, measuring the time needed to complete the task. The patient is instructed to walk as fast and as safely as possible across a clearly marked, linear 25-foot course, with the task immediately administered again by having the patient walk the same distance while counting backward from 100. The T25-FW score is the average (in seconds) of the two successive trials [21]. Finally, the MoCA is a brief screening tool for mild cognitive impairment that assesses different cognitive domains (executive function; visuospatial capacity; memory; attention and concentration; working memory; language) and both temporal and spatial orientation), and its score varies between zero and 30 [22].
# Effect of CeO2@GNPs on the Corrosion Properties of 2024 Alloy
## 3 Results
### Mechanical Properties  
Table 2 displays the tensile properties and microhardness of the matrix and fabricated nanocomposites with different types of GNPs. The strength and microhardness of the fabricated nanocomposites showed an increasing trend when two kinds of GNPs were added to the matrix, respectively. It was noticeable that the CeO\({}_{2}\)@GNPs/2024 Al nanocomposite exhibited a larger increase in the yield strength (YS) as well as ultimate tensile strength (UTS) than the GNPs/2024 Al composite. The YS and UTS of the composite fabricated with 0.5 wt% CeO\({}_{2}\)@GNPs were 184.1 and 271.8 MPa, increasing by 21.1% and 24.7% as compared to the matrix, respectively. The carbon-based reinforcement with coated particles was reported to impede the movement of dislocation in the composite during the deformation process [19]. Meanwhile, these reinforcing effects could also be attributed to the load transfer mechanism, which mainly referred to the load transfer from the matrix to the GNPs during the deformation process [20]. The theoretical values for increasing the YS of the nanocomposites due to the load transfer effect could be calculated via the Kelly-Tyson formula.  
\[\Delta\sigma_{LT}=\frac{f_{V}}{2}\sigma_{m} \tag{1}\]  
where is the volume fraction of the GNPs. denotes the YS of the matrix. Typically, the increment of YS obtained through the theoretical calculation mentioned above is much larger than the actual increment value. The key to the reduction in disparity is the optimization of the interface wettability between the GNPs and the matrix as well as its dispersion in the matrix [20; 21]. In this study, the existence of CeO\({}_{2}\) nanoparticles on the surfaces of the GNPs was beneficial for dispersing the GNPs uniformly and the interfacial wettability. Thus, it led to a further improvement in the mechanical properties of the composites.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 4 Proposed Fuzzy Logic Dijkstra-based Routing Algorithm
### Cluster Head Selection Phase  
After the network field is organized in the form of clusters, the nodes participate in the CH selection phase within each cluster. The objectives of the CH selection problem are as follows:  
Organization of sensor nodes into several clusters, where each cluster has its own CH such that:  
1. The node selected must have highest residual energy, i.e., \(E_{i}\geq\ E_{std}\) as compared to other CM nodes of the particular cluster to enhance network lifetime.
2. The CHs are well-distributed in the network to minimize energy consumption as it is directly proportional to the distance, i.e., \(d_{i-C}\leq\ avg(d_{n-C})\) (not concentrated in one area of the network) and serve their job for a long time period.  
The transmission cost of some CMs may increase if the CH node is selected solely based on residual energy. It is also possible that the CH selected is located further away from some member nodes while being near to others. As a result, large amounts of communication energy were used by a member node to send its data to the selected CH, while the CH of another cluster was nearby. Our proposed scheme takes both the node's residual energy and communication cost within the cluster into account for selecting a node to be a CH. During this phase, a standard weight function: \(C_{std\_w}\) is calculated for each cluster using Equation (11) [17]:  
\[C_{s\ std_w}=\alpha\cdot E_{std}+\beta\cdot avg(d_{n-C}) \tag{11}\]  
where \(E_{std}\) is the standard energy of a node that can participate in the CH selection process described in Section 3.2, \(avg(d_{n-C})\) represents the average distance of all the CMs to the selected cluster center in the previous round, and \(\alpha\) and \(\beta\) are algorithmic parameters ranging from (0,1), and these parameters should be balanced such that \(\alpha+\beta=1\).  
Once the standard weight of each cluster is known, each node calculates its own weight according to Equation (12), and the node with weight closest to the standard weight of the corresponding cluster is selected as the CH for that round.  
\[C_{node\_w}=\alpha\cdot E_{i}+\beta\cdot d_{i-C} \tag{12}\]where \(E_{i}\) is the energy of \(i\)th node and \(d_{i-C}\) represents the distance of the \(i\)th node to the selected cluster center. The CH selection process is presented in Algorithm 2.  
```
1:Begin
2:For(\(i=1\): number of cluster centers) do
3:\(C_{std\_w}=\alpha\cdot E_{std}+\beta\cdot avg(d_{n-C})\)
4:For(\(j=1\): number of cluster members) do
5:\(C_{node\_w}=\alpha\cdot E_{i}+\beta\cdot d_{i-C}\)
6:end
7:end
8:If (weight of node \(\sim=\) standard weight of the cluster) then
9: select node as the CH for the corresponding cluster
10:else
11: check other CM nodes with closest weight to \(C_{std\_w}\)
12:end
13:End
```  
**Algorithm 2** CH selection.
## 3 Prediction Results  
In the process of cross-validation, the original data set is randomly divided into 10 equal-sized subsets using the 10-fold cross-validation method. Among the 10 subsets, one subset is reserved as the verification subset of the test model, and the remaining 9 subsets are used for the training model. Then, this cross-validation process is repeated 10 times, and each of the 10 subsets is only used once as validation data. The Figures 3-6 show the prediction result and error in one of the cross-validations. It can be seen that the FOS values calculated by ANN and GBDT are very close to the actual values and the absolute error is less than 0.05, while the test set results are quite different from the actual values in the other six models. In particular, the median and outliers of SVM and kNN errors are significantly higher than other models, which perform poorly in the training set. In the test set, the median GBDT error is the smallest. Meanwhile, the predicted values of FOS obtained by all ensemble learning models are relatively close to the real values, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameters** \\ \hline \multirow{6}{*}{Random Forest} & N\_estimators = 10 \\  & Max\_depth = 6 \\  & Min\_samples\_leaf = 1 \\  & Min\_samples\_split = 2 \\  & Criterion = entropy\({}^{\prime}\) \\ \hline \multirow{6}{*}{Decision Tree} & Criterion = ‘gini’ \\  & Max\_depth = 5 \\  & Ccp\_alpha = 0.0 \\  & Min\_samples\_leaf = 1 \\  & Random\_state = 111 \\ \hline \multirow{6}{*}{SVM} & Kernel = ‘poly’ \\  & Degree = 2 \\  & C = 100 \\  & Epsilon = 0.1 \\ \hline \multirow{6}{*}{GBDT} & n\_Estimators = 500 \\  & Max\_Depth = 4 \\  & Min\_Samples\_Split = 2 \\  & Learning\_Rate = 0.01 \\  & Loss = 'Ls’ \\ \hline KNN & n\_Neighbors = 3 \\ \hline \multirow{6}{*}{AdaBoost} & Max\_depth = 2 \\  & Min\_samples\_split = 20 \\  & Min\_samples\_leaf = 5 \\  & Algorithm = ‘SAMME’ \\  & N\_estimators = 200 \\  & Learning\_rate = 0.8 \\ \hline \multirow{2}{*}{Bagging} & Max\_samples = 0.5 \\  & Max\_features = 0.5 \\ \hline \multirow{6}{*}{ANN} & Activation = ’relu’ \\  & Neurons = (256, 128, 64, 32) \\ \cline{1-1}  & Optimization\_method = rmsprop \\ \cline{1-1}  & Loss\_function = ’mse’ \\ \cline{1-1}  & Metrics = ’mae’ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal parameter of the eight machine learning algorithms.  
the errors are generally distributed around 0.15. It should be emphasized that although the calculation result after a certain segmentation of the data set cannot fully objectively reflect the accuracy of the model, it can present a basic judgment in different prediction models.  
Figure 3: Results of the eight machine learning methods on training set.  
Figure 4: Results of the eight machine learning methods on test set.
## 4 Experiments
### Experiment Settings
#### 4.1.3 Datasets  
We conducted experiments on two public datasets: Foursquare-NYC and Four-square-TKY. The Foursquare-NYC dataset was gathered in New York City between April 2012 and February 2013, and the Foursquare-TKY dataset was collected during the same timeframe in Tokyo. For each dataset, we sorted the check-in records by time and divided them into training, validation, and test datasets in an 8:1:1 ratio. Basic dataset statistics are shown in Table 1. The stability and reliability of these datasets have been extensively validated and used as benchmark data in several studies. These datasets also provide a reasonable exploration and validation of the effectiveness of the AST-PG model in the next-POI recommendation task. Moreover, it can be of great value in terms of rationally evaluating and comparing different recommender system approaches.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 3 Problem Statement and Methodology
### Methodology Overview  
The purpose of this research work is not developing the fusion process itself, but designing an algorithm for fusion tree generation. We propose a non-uniform FTG (NU-FTG) algorithm to generate the fusion tree for the two-tier fusion process. In the NU-FTG, the score of a radar is in proportion to the number of local tracks of the radar and its neighbors. Radars with higher scores get more chances to become local fusion nodes.  
Numerous nodes are required to see the impact of the different fusion trees on the performance of the fusion process. Therefore, the NU-FTG is evaluated in the OPNET (Optimized Network Engineering Tool), network simulator. The NU-FTG algorithm, and the fusion process, are implemented on the node model of the OPNET. In the case of NU-FTG, all operations of the algorithm are implemented on the process model of the _traffic_engine_ application. On the other hand, the fusion process is implemented as a time-delay model. The time-delay model is the sum of \(T_{alg}\), \(T_{seg}\), and \(T_{upt}\) defined in Equations (2)-(4), respectively.  
The performance, defined as the number of process tracks in a fusion cycle, of the NU-FTG was compared to the performance of the FTG [23], and the HEED [16]. The HEED is a clustering method for wireless sensor networks. The HEED is able to generate a fusion tree which fulfills the requirements that are presented in Section 3.1. However, the HEED uses residual energy which is not available in the dynamic multi-radar system. In this paper, we assume that the residual energy of every node is the same.
# Influence of Blade Wrap Angle on the Hydrodynamic Radial Force of Single Blade Centrifugal Pump
## 2 Pump Model and Simulation Method
### Validation  
The experiment of a single blade centrifugal pump with 360\({}^{\circ}\) blade wrap angle was conducted [22]. Pump performance experimental setup and distribution of pressure sensors are shown in Figures 3 and 4. The experimental device consists of the test pump, connecting pipes, valves, electromagnetic flowmeter and other components. Four pressure taps were located in the spiral volute wall. Pressure taps were instrumented with fast response pressure sensors (Figure 4), which provided absolute pressure values with an uncertainty of less than 0.1% according to the manufacturer's data.  
Figure 3: Experimental device: (1) valve in the outlet, (2) flowmeter, (3) valve in the inlet, (4) pressure transducer in the outlet, (5) pressure transducer in the inlet, (6) pump.  
Figure 2: Mesh of different impellers.  
The pump performance curves of single blade centrifugal pump with 360\({}^{\circ}\) blade wrap angle were acquired by turn off or turn on the valve in the outlet. Figure 5 presents the results of CFD and experiment. The predicted power of CFD is hydraulic power, whereas the result of the experiment is total power. In order to compare with CFD results, based on the empirical coefficients of different power levels in the laboratory, the measured total efficiency is transformed into hydraulic efficiency. The experimental heads are smaller than numerical results, and it is less than 1 m. It can be observed that the CFD results are in good agreement with the experimental results.  
The pressure distribution of model 1 is presented in Figure 6[22]. The CFD predicted pressures were similar to those obtained by experiment. The time history of the pressure in the numerical simulation approximately overlapped with the test results. The qualitative agreement of the pressure obtained by CFD numerical calculation and experiment was extremely high; and the CFD results show a reasonable agreement with the test data. Thus, numerical calculation results can be considered reliable.  
Figure 4: Pressure sensors distribution.  
Figure 5: Pump performance.
## 4 Results and Analysis  
### Verification of the Numerical Simulation Approach  
The flow-induced noise obtained by experiment and simulation is compared to verify the accuracy of the approach. Figure 5a-c depicts the Acoustic Pressure Level (APL) distribution characteristics in broadband range at several rotational speeds. It is revealed that with the increase of frequency, the fluctuating downward trend of APL obtained by simulation and experiment is similar at various rotational speeds.  
Table 4 further compares the deviation of flow-induced noise between simulation and experiment in detail. It was found that the deviation between experiment and numerical simulation is small, the average deviations of APL at characteristic frequencies and Total Acoustic Pressure Level (TAPL) at various rotational speeds are about 3.68% and 1.07%, respectively. The analysis results of Figure 5 and Table 4 manifest the effectiveness of the approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rotational Speed** **(rpm)** & **Flow-Induced Noise** & **Experiment (dB)** & **Simulation (dB)** & **Deviation (\%)** \\ \hline \multirow{3}{*}{2100} & APL (\(f_{b}\)) & 150.55 & 142.66 & \(-\)5.24\% \\  & APL (\(2f_{b}\)) & 120.56 & 117.67 & \(-\)2.40\% \\  & TAPL & 171.18 & 169.22 & \(-\)1.15\% \\ \hline \multirow{3}{*}{2500} & APL (\(f_{b}\)) & 150 & 160.35 & 6.90\% \\  & APL (\(2f_{b}\)) & 131.13 & 126.24 & \(-\)3.73\% \\ \cline{1-1}  & TAPL & 174.48 & 172.07 & \(-\)1.38\% \\ \hline \multirow{3}{*}{2900} & APL (\(f_{b}\)) & 152 & 156.33 & 2.85\% \\  & APL (\(2f_{b}\)) & 136.41 & 135.13 & \(-\)0.94\% \\ \cline{1-1}  & TAPL & 181.82 & 180.57 & \(-\)0.69\% \\ \hline \hline \end{tabular} Note: \(f_{b}\) represents the blade-passing frequency.  
\end{table}
Table 4: The deviation of flow-induced noise obtained by experiment and simulation.  
Figure 4: Test loop diagram.  
The Distribution and Evolution Characteristics of Acoustic Field in Time Domain at Rated Operation Condition  
In this section, the distribution and evolution characteristics of acoustic field in time domain inside the pump at 2900 rpm are studied. The study time span is one period of blade passing through  
Figure 5: The Acoustic Pressure Level (APL) distribution characteristics obtained by experiment and simulation.  
the solute tongue, which includes 30 time steps of flow field. The middle span surface of volute is chosen as the study surface.