esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Design of the Fear Pair and Flow Characteristics of Circular-Arc Gear Pumps
## 3 Results
### Correlation Analysis  
By processing all the above simulation data, the correlation coefficients between them can be obtained, and the correlation coefficients are shown in Table 3.  
Figure 11: Effect of pressure angle on flow characteristics: (**a**) flow rates at different pressure angle; (**b**) standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Number of Teeth**} & **Module** & **Pressure Angle** \\ \hline \multirow{2}{*}{Flow rate} & correlation coefficient & 0.550 * & 0.778 ** & \(-\)0.007 \\ \cline{2-5}  & significance & 0.027 & 3.840 \(\times\) 10\({}^{-4}\) & 0.979 \\ \hline \multirow{2}{*}{Standard deviation} & correlation coefficient & 0.166 & 0.737 ** & \(-\)0.251 \\ \cline{2-5}  & significance & 0.538 & 0.001 & 0.349 \\ \hline \end{tabular}
\end{table}
Table 3: Spearman correlation coefficient.  
Figure 10: Effect of the module on the flow characteristics: (**a**) flow rates at different module; (**b**) standard deviation.  
The correlation coefficient represents the direction and degree of influence. If the value is greater than zero, the two parameters are positively correlated; if it is less than zero, the two parameters are negatively correlated. The closer the absolute value of the value is to 1, the more significant the correlation. A value less than 0.05 indicates a significant correlation, whereas a value less than 0.01 indicates an extremely significant correlation [26]. According to the table, the correlation coefficient between the number of teeth, module, and flow rate is greater than zero, indicating a positive correlation. The correlation coefficient between the pressure angle and flow rate is negative. The significance of the number of teeth is at the 0.05 level, indicating a significant impact on the flow rate. The module is at the 0.01 level, indicating an extremely significant impact on the flow rate. The significance of the pressure angle is greater than 0.05, and its effect on the flow rate is not significant. According to the correlation coefficient between the profile parameters and standard deviation, the module has an extremely significant effect on flow pulsation. The influence decreases in the order of module, pressure angle, and number of teeth.  
From the correlation analysis, it can be concluded that the module has the greatest impact on the flow rate, followed by the tooth number, whereas the pressure angle has no significant effect on the flow rate. The impact of the module on the flow pulsation is most significant, whereas the influence of the pressure angle is greater than that of the tooth number. Therefore, when gear pairs with the same flow rate are designed, adopting a small module can reduce the flow pulsation.
## 4 Performance Evaluation
### Transfer Learning Tasks
#### 4.2.1 UNSW-NB15 to CIC-IDS2017 Transfer Learning
As the performance metrics are defined as  
\[\text{Recall} =\frac{TP}{TP+FN}, \tag{16}\] \[\text{Precision} =\frac{TP}{TP+FP},\] (17) \[\text{F1} =2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}, \tag{18}\]  
we can conclude that the method works well for attack detection, i.e., detecting the true positive data samples, while there is a relatively high possibility to mis-detect normal data samples as attack instances (the false positive rate). The reason why the false positive rate is high might be that the dataset is extremely unbalanced, that is to say, the communication sessions in those datasets comprise only a minor portion of attack instances. To resolve this issue, we may try to up-sample the attack data instances to reduce the imbalance. In addition, we can try to increase the weight of attack instances during the training stage. During tuning of the hyper-parameters, we compared the results of using several variants of ReLU activation functions, i.e., Leaky ReLU, Parametric ReLU, and ELU, to determine if the model is influenced by \"dead neuron\" due to negative input. The results are show in Figure 2.  
Though Leaky ReLU, Parametric ReLU, and ELU introduce some mechanism to eliminate zero gradients for negative input, from the results, we can see that ReLU does not degrade the model's performance. Hence, in this experiment, it is safe to use ReLU as an activation function. However, for a wider-range application of the learning model, using those modified ReLU variants usually achieves better stability in terms of the model's performance. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**\#** & **Source** & **Target** & **Description** \\ \hline
1 & Normal, Fuzzers &
\begin{tabular}{l} BENIGN, Web Attack, Brute Force, FTP- \\ Patator, SSH-Patator \\ \end{tabular} & accessing target system via brute-force manner \\ \hline
2 & Normal, DoS &
\begin{tabular}{l} BENIGN, DoS \{Hulk, GoldenEye, \\ slowloris, Slowhttptest\} \\ \end{tabular} & denial of service attack \\ \hline
3 & Normal, Reconn, Analysis & BENIGN, PortScan & retrieve information about target system \\ \hline
4 & Normal, Generic &
\begin{tabular}{l} BENIGN, Bot, Web Attack XSS, Web Attack SQL Injection, Infiltration \\ \end{tabular} & other attack types cannot be categorized \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heterogeneous transfer learning data configuration for UNSW-NB15 to CIC-IDS2017. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TaskID** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 0.987 & 0.465 & 0.938 & 0.622 \\
2 & 0.983 & 0.457 & 0.969 & 0.621 \\
3 & 0.985 & 0.431 & 0.984 & 0.599 \\
4 & 0.998 & 0.981 & 0.994 & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics for transfer learning from UNSW-NB15 to CIC-IDS2017.
## 4 Discussion
### Field Trials
[40] state that capillary forces are mainly responsible for increasing cohesion forces in sandy soils, and water adsorption around the soil particles can be neglected when the soil is relatively wet. Only a few studies have conducted field experiments simulating wind erosion on exposed mine soils treated with biodegradable reagents. Hence, only limited field trial data on dust suppressants exist to compare the study results. [PERSON] et al. [41] have already stated a lack of studies evaluating the durability and reliability of biopolymers for geotechnical applications (i.e., dust control, soil stabilization, erosion control) under field conditions. For example, [PERSON] et al. [42] tested two biocompatible polymers, polyethylene glycol and poloxamer, to suppress wind-blown dust emissions from a bauxite tailings substrate. Their experiments showed that polyethylene glycol and poloxamer solutions could reduce PM\({}_{10}\) emissions by up to 44% and 66%, respectively, compared to water-treated samples. [PERSON] [8] demonstrated similar performances for calcium lignocellulosic and tree resin on bedding substrate used for unpaved roads in a calcareous quarry. Conducted wind tunnel experiments indicated a 63% and 53% reduction in PM\({}_{10}\) emissions from lignosulfonate and tree resin solutions, respectively, compared to untreated samples [8]. However, only limited conclusions can be drawn due to partially substantial distinctions in experimental design, substrate type, climatic conditions, and other factors. Regardless, the chosen food processing by-products displayed a distinct ability to suppress PM emissions in the field (Figure 6).
## 5 Experiment and Results
### Transmitting the Blinking LED Video to A Remote Receiver  
The transmission of the blinking LED video to a remote receiver is done via the internet. To ensure a safe and secured transmission, we used a light version of the Chaotic key stream cipher executed during real-time video communication proposed by [PERSON] et al. [7]. It embeds cryptography in real-time video streaming using a symmetric cryptographic algorithm (DES). With the same key on both sides, the video is correctly displayed on the receiver's side with a reasonable bit rate. The received video is properly displayed on the receiver's display.
# Robust Cross-Validation of Predictive Models Used in Credit Default Risk
## 7 Results and Discussion
### Sample Size Impact
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Validator**} & \multicolumn{2}{c}{**Lending Club—N = 500**} & \multicolumn{2}{c}{**Lending Club—N = 1000**} & \multicolumn{2}{c}{**Lending Club—N = 1500**} \\ \cline{2-7}  & **Error** & **Rank** & **Error** & **Rank** & **Error** & **Rank** \\ \hline CV:DOB-WOE/10/50 & 0.3337 & 1 & 0.2514 & 1 & 0.2233 & 2 \\ HO:MCCV/0.1/1000 & 0.3422 & 2 & 0.2587 & 4 & 0.2301 & 4 \\ CV:DOB-SCV/10/50 & 0.3424 & 2 & 0.2506 & 1 & 0.2213 & 1 \\ CV:DB-SCV1/10/50 & 0.3462 & 2 & 0.2544 & 2 & 0.2285 & 3 \\ CV:SCV/10/50 & 0.3466 & 2 & 0.2564 & 3 & 0.2300 & 4 \\ HO:FMCCV/0/1000 & 0.3468 & 2 & 0.2557 & 2 & 0.2301 & 4 \\ CV:DB-WOE/2/10/50 & 0.3473 & 2 & 0.2569 & 3 & 0.2298 & 4 \\ CV:RSCV/10/50 & 0.3481 & 3 & 0.2570 & 3 & 0.2295 & 4 \\ HO:FSMCCV/0/1000 & 0.3483 & 3 & 0.2563 & 3 & 0.2309 & 4 \\ CV:kfold/10/50 & 0.3490 & 3 & 0.2569 & 3 & 0.2309 & 5 \\ HO:SMCCV/0.1/1000 & 0.3494 & 3 & 0.2601 & 5 & 0.2308 & 4 \\ CV:RndCV/0/50 & 0.3497 & 3 & 0.2574 & 3 & 0.2313 & 5 \\ CV:DB-SCV2/10/50 & 0.3500 & 3 & 0.2592 & 4 & 0.2292 & 3 \\ CV:FNkfold/0/50 & 0.3503 & 3 & 0.2570 & 3 & 0.2301 & 4 \\ CV:FUkfold/0/50 & 0.3521 & 3 & 0.2582 & 3 & 0.2302 & 4 \\ CV:LOOCV & 0.3605 & 4 & 0.2713 & 6 & 0.2496 & 6 \\ CV:MS-SCV/10/50 & 0.4173 & 5 & 0.3498 & 7 & 0.3127 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact of the sample size on the selection error and ranking of cross-validation methods with \(k=10\) or non-predefined k-fold parameter for the Lending Club 2009 credit dataset. Selection error is given by the normalized Kendall Tau averaged over 150 random samples of sizes \(N=500\), 1000, and 1500.
## 2 Four New Signal-Based Approaches to Be Compared
### Data-Based Classification Approach (Approach D)  
The central idea of this approach is based on the assumption that algorithms should easily, rapidly, and reliably determine suitable features, like statistical or other features or other types of features to describe a data set, or allow the distinction between data sets. On this basis, it is assumed that data sets and data set elements are first classified by human experts into a small and strongly limited number of states (3-4), which can be distinguished for practical purposes (training). In the next step, algorithms are used for automated analysis to determine the best classification parameters that distinguish the given sets of classes. In this contribution, automated calculation of statistical characteristics of the analyzed data sets yields parameters and similar models. The sum of these structured parameters and their distribution is defined as a fuzzy-based model. Using suitable internal algorithms, any contradictions and ambiguities in the fuzzy-based model can be minimized, and a revised description to describe the different states of the system is used.  
This classification approach is used as an example directly serving as a supervised classification approach. Other methods such as SVM, Artificial Neural Networks, or k-nearest-neighbor can also be used [32; 33; 34].  
For the results firstly published in [14], 34 statistical features were used as descriptive variables. According to previous published results [14], the trained fuzzy model was generated based on five membership functions according to five characteristics. These features were used to distinguish the five states considered are described as follows (description and colored notation).  
* Regular behavior 1: Stable and error-free operation: Green.
* Regular behavior 2: Stable with small changes: Blue.
* Regular behavior 3: Stable operation with changes: Cyan.
* Abnormal behavior 1: Acceptable changes in the surface condition due to minor surface condition changes: Yellow.
* Abnormal behavior 2: Significant changes in the surface condition due to major changes in the surface condition, ranging up to the destruction of the surface with partial loss of functionality: Red.
## 3 Results
### Between-Group Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{**Standard Differences (U16 vs. U14; 95\% Cl)**} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **\(p\) Value** \\ \end{tabular} }} \\ \hline Body Fat (\%) & \(-\)0.22 \(\pm\) 0.24 & 0.069 \\ ISRT 1 min recovery (bpm) & 0.41 \(\pm\) 0.88 & 0.320 \\ NHEes (N) & 0.10 \(\pm\) 0.30 & 0.519 \\ CMJ (cm) & 0.01 \(\pm\) 0.79 & 0.982 \\ SLJ (cm) & 0.37 \(\pm\) 0.72 & 0.293 \\ Sprint 10 m (s) & 0.18 \(\pm\) 0.45 & 0.420 \\ Sprint 30 m (s) & \(-\)0.24 \(\pm\) 0.30 & 0.112 \\ Modified \(t\)-test (s) & \(-\)0.65 \(\pm\) 0.38 & 0.002 \\ Pull Ups & \(-\)0.04 \(\pm\) 0.42 & 0.829 \\ Push Ups & \(-\)0.62 \(\pm\) 0.53 & 0.026 \\ \hline \end{tabular}
\end{table} 
Table 5: Between-group differences. Differences in the measured external load parameters across the different days of the microcycle can be seen in Figure 2. The U16 group covered a greater total distance on MD (+18.8%, \(p\) < 0.05; large effect) and MD-2 (+15.6%, \(p\) < 0.05; large effect) than the U14 group, with no statistical differences for the other days of the week. The U16's number of accelerations and decelerations were also higher on MD (+20.6%, \(p\) < 0.05; large effect) and MD+2 (+9.4%, \(p\) < 0.05; moderate effect) in comparison with U16. The U16 team players covered more HSR on each day of the week (\(p\) < 0.05), except on MD-1 where there was no difference. On MD+2, the U16 group covered more HSR distance compared to the U14 group (+92.9%, \(p\) < 0.05, large effect size), on MD-4 (+100%, \(p\) < 0.05, large effect size), on MD-2 (+49.1%, \(p\) < 0.05, large effect size), and on MD (+42,3%, \(p\) < 0.05, very large effect). The total training duration for the U14 group was 2033 min, while for the U16 group, it was 1811 min. The total match duration for the U14 group was 579 min, while for the U16 group, it was 335 min. The total duration of activities (training + matches), total distance covered, and number of accelerations and decelerations during the intervention period were higher for U14 (\(p\) < 0.05) in comparison with U16 (see Figure 3). However, the total distance covered at high speed was higher in the U16 group (\(p\) < 0.05). Figure 2: Differences in the external load parameters (effect size (ES \(\pm\) 95%CI)) across the different days within the microcycle.
## 4 Metro Track Geometry Defect Identification Model and Its Optimization Method
### MTGDI-CNN Based on DARTS
#### 4.2.2 Cell Architecture  
Because directly searching for a space composed of connections between all operation layers is a challenging task, considering the presence of several identical architectures in neural networks, in the study of NAS, the entire network can be regarded as a stack of several cells with the same architecture, and only the cell architecture is targeted during the architecture search to mitigate the difficulty of the NAS problem [38]. The EA-based or RL-based methods, even in the face of the reduced search space of the cell architecture, still require a significant period of time. Accordingly, DARTS was employed in this study to search the cell architecture for solving the NAS problem in the MTGDI-CNN development.  
The TGD is reflected as the amplitude variations of the CVD In an input sample. After processing via a feature extraction operation such as convolution, these variations are transformed into multiple sets of numerical vectors or matrices, which are called feature maps [46] and represent the vibration features of the TGD. As illustrated in Figure 7, a cell is a directed acyclic graph comprising several feature maps as nodes and feature extraction operations as edges [38]. Three fixed nodes and several intermediate nodes in each cell can be observed. Two of the fixed nodes serve as the input nodes, receiving the input of the previous cell, and a single node serves as the output. Each intermediate node is connected to the other two intermediate or input nodes, and all intermediate nodes are connected to the output node through concatenation. Additionally, the cells are classified into normal and reduced cells [47]. Moreover, the output feature map of the normal cell possesses the same size and channel number as those of the input. The output feature map of the reduction cell is half the size and twice the number of channels of the input. Such a cell architecture can not only extract the features of different lengths of TGDs but also deal with the relationship between different features.
## 1 Introduction
Its unique characteristics ensure effective patient therapy while minimizing the risk of complications associated with implantation. In biomedical engineering, orthopedic implants are also manufactured using traditional methods from titanium bars, such as the Ti6 Al4V alloy. The production process for these implants involves precision machining, which allows the implant to be shaped and sized according to the engineering design specifications. After the initial machining, the implant's surface undergoes additional finishing processes, such as grinding and polishing, and sometimes also specialized techniques to enhance biological and mechanical properties, such as sandblasting or anodizing. Therefore, the main aim of this research was to compare the mechanical and physicochemical properties of the Ti6 Al4V alloy in its state after plastic deformation and after the application of 3D printing technology, considering additional processes such as heat treatment and steam sterilization. This study also aimed to assess whether modern additive technologies could effectively replace traditional orthopedic implant manufacturing techniques, as well as to identify differences in mechanical and physicochemical properties, which could further improve the quality and functionality of implants in medical applications. The conclusions from this study of Ti6 Al4V after additive manufacturing could lead to new design guidelines for implants, considering the optimization of manufacturing processes and the improvement in their mechanical and physicochemical properties, which is crucial for the effectiveness of orthopedic therapies.
## 1 Introduction  
During space exploration, thermally uncontrolled microelectronic assemblies have to be exposed to extreme temperature environments with large temperature variations, such as under the condition of Moon (\(-180\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C), Mars (\(-140\)\({}^{\circ}\)C to \(20\)\({}^{\circ}\)C) and Giant Planets (\(-140\)\({}^{\circ}\)C to \(380\)\({}^{\circ}\)C) [1]. Solder joints provide mechanical support and electrical connection in microelectronic assemblies, the performance and quality of solder joints are vital to the overall functioning of microelectronic assemblies [2; 3].  
The formation and growth of intermetallic compounds (IMCs) at interface between the solder and the substrate are inevitable during soldering and processing [4; 5]. The evolution and growth of interfacial IMCs significantly affect the mechanical properties and reliability of solder joints due to the intrinsically brittle nature of IMCs [6; 7; 8]. In our previous study [9], we found that the fast growth of interfacial IMCs in Sn-3 Ag-0.5 Cu solder joints was responsible for the early formation of cracks at the Sn-3 Ag-0.5 Cu solder/IMC layer interface under extreme temperature thermal shock from \(-196\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C, and the growth of interfacial IMCs and crack formation at the IMC layer/solder interface led to the reduction in pull strength of Sn-3 Ag-0.5 Cu solder joints. Sn-Pb solder alloys are commonly used in deep space application until now [10]. Thus, it is essential to investigate the evolution and growth behavior of interfacial IMCs in Sn-Pb solder joints and their effect on the mechanical properties of Sn-Pb solder joints under extreme temperature environment. However, the correlating research is very limited.  
In this study, extreme temperature thermal shock test from \(-196\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C was conducted on quad flat package (QFP) assemblies to investigate the evolution and growth behavior of interfacial IMCs in Sn-37 Pb solder joints under extreme temperature environment. Pull test was adopted to evaluate the pull strength and fracture behavior of Sn-37 Pb solder joints. Based on these results, the effect of interfacial IMC growth on the pull strength and fracture behavior of Sn-37 Pb solder joints was discussed.
## 5 The AltiKa payload  
The AltiKa payload includes :  
- a Ka-band altimeter (35.5-36 GHz)  
- a dual-frequency radiometer (23.8/36.8 GHz),  
- a DORIS receiver,  
- a Laser Retro-reflector Array (LRA).  
The main instrument of the payload is the Ka-band radar altimeter. This altimeter is derived from the Poseidon altimeter and operates at 35.75 GHz. A dual frequency microwave radiometer (23.8 and36.8 GHz) is embedded in the altimeter. A detailed description of the altimeter / radiometer instrument is given in section 6.  
To achieve adequate orbitography performances, it is proposed to embark a DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) receiver [28]. The receiver that is envisaged for an AltiKa mission is the most modern and compact one which is developed for the Jason-2 mission. DORIS is a dual-frequency system (400 MHz and 2 GHz) based on network of emitting ground beacons spread all over the world. The DORIS on-board package is composed of:  
- a dual-frequency antenna (omnidirectional antenna located on the nadir face of the satellite),  
- a BDR which is composed of two DORIS chains in cold redundancy accommodated inside the same electronic box. Each DORIS chain includes a MVR (Mesure de Vitesse radiale) unit achieving beacon' signals acquisition and processing, navigation, Doppler measurements storage and formatting, electrical satellite interfaces management functions, and a USO (Ultra-Stable Oscillator) delivering a very stable 10 MHz reference which is also used by the altimeter.  
A laser retroreflector array (LRA) will also be implemented on the payload, in order to calibrate the precise orbit determination system and the altimeter system several times throughout the mission. The LRA is a passive system used to locate the satellite with laser shots from ground stations with an accuracy of a few centimetres.  
Mass and power budgets (see below) are such that the payload can be accommodated either on a micro-satellite, either as a passenger on opportunity missions.  
Figure 1: Instrument block diagram.