esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 L-PBF  
An SLM 250-HL L-PBF device from SLM Solutions Group AG Lubeck, Germany was used to manufacture the specimens. This L-PBF equipment allows a total build volume of 250 mm (X) \(\times\) 250 mm (Y) \(\times\) 280 mm (Z). A 400W Nd:YAG laser with a spot size of \(\sim\)80 \(\upmu\)m was used to fuse the powder particles. Nitrogen gas was employed as a protective atmosphere during manufacturing to avoid oxidation in the melted areas. The standard parameter set for the SS316L material available in the SLM 250-HL L-PBF system was chosen for the fabrication of specimens characterized and tested in this study. For successful fabrication of specimens, different areas were designated during the pre-processing step, and associated laser parameters were established, as shown in Table 2. The filling is the solid or core section of the specimen, which is bordered on the top and bottom by the up and down skins. The different support structure designs examined in the optimization are referred to as support. The L-PBF research has a well-established methodology for dividing into distinct manufacturing domains, as given by Table 2[34].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **Fe** & **Cr** & **Ni** & **Mo** & **Mn** & **Si** & **N** & **O** & **C** \\ \hline SS 316L & Balance & 16.9 & 11.1 & 2.4 & 1.2 & 0.4 & \(<\)0.1 & 0.056 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the as-received SS 316L powder material (wt. %).  
The additive manufacturing of a geometry such as an aortic stent requires a significant amount of support material because each strut (basic repeating unit of the stent including its inverted counterpart) of the stent needs to be fabricated with an associated support. As a result, in the optimization part of the present study, the simplified geometry of a single strut with an overhanging design was considered. A plate was positioned considerably above the strut for better handling of this specimen geometry. To link the support adhering to the bottom end of the strut, a build platform-like structure was built. Two distinct thicknesses for the specimen geometry, 0.5 mm and 1 mm, were considered for understanding the influence of size on support structure design optimization and support removal. Furthermore, as shown in Figure S1, specimen geometries were manufactured utilizing two distinct support structure widths for the line support, 0.5 mm and 1 mm. Support structure widths of 0.5 mm and 1 mm ensured maximum contact between the specimen geometry and the support in two and four locations, respectively. The Results section of the support structure design optimization provides additional and detailed information on this aspect of the study.
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 8 Results and Discussion
### Selection of an Oversampling Technique  
An initial analysis was conducted to determine the best synthetic oversampling technique among a few commonly used oversampling techniques, SMOTE, Borderline SMOTE, and ADASYN.  
Traditionally, a 70-30 training-testing split is performed on the data. However, in this case, since the occurrences of the minority class are minimal, there is no guarantee that samples from the minority class will be present in both the training data and the testing data without sample stratification by category. Hence, stratified sampling was used to split the data into a 70-30 training-testing ratio.  
From the evaluation metrics shown in Figure 2, it is evident that BSMOTE performs better than the other resampling techniques in terms of precision, F-score, and macro precision. SMOTE and ADASYN had better recall than BSMOTE, but the latter performed better overall.  
Hence, all future analyses in this work use BSMOTE as the primary oversampling technique, combined with random undersampling. Several researchers have confirmed that neither oversampling nor undersampling alone can successfully classify imbalanced datasets [12; 19], let alone identify rare attacks. Hence, this study looks at the effects of varying oversampling percentages.
### Description of BRDF
Considering potential environmental effects on the diffuse reflectors when radiometric calibration is not being conducted, both working and reference boards are stored in shaded areas where they are shielded from solar ultraviolet radiation when not in use. Figure 2: (a) On-board UVS calibration device. (b) Schematic diagram of incident light angles. The variations in \(\beta\) and \(\alpha\) are determined by satellite orbit and launch window calculations, as depicted in Figure 2b. Accurately measuring the BRDF distribution characteristics of the diffusers requires assessing the bi-directional reflectance distribution function of the diffuser under various sunlight incidence angles (\(\alpha\), \(\beta\)) (here \(\alpha\) and \(\beta\) are the angle between sunlight and the satellite's flight direction, where the satellite's flight direction is the direction normal to the ground of the L bracket) [8]. Therefore, adjusting the angle of the absorptive aerosol detector in the solar operation mode using a high-precision rotary table is necessary. The BRDF of the diffuser under different conditions is obtained using the aforementioned calculation method. The sun observation range of UVS is \(\alpha\in\left[-4^{\circ},+4^{\circ}\right]\), \(\beta\in\left[14.95^{\circ},37.95^{\circ}\right]\). During solar calibration, the sun enters diffuser 1 at a specific angle and is then scattered directly onto the first mirror (M1) of the UVS optical path. The scattering angle covers the entire \(112^{\circ}\) field of view of the UVS, allowing for complete solar calibration data to be obtained one time.
## Applications  
* Healthcare: Collaborative training using patient data from different hospitals.
* Finance: Banks combining transaction records to improve fraud detection models.
* Mobile Devices: Enhancing predictive text models using data from different smartphones.
## 5 Result and Discussions
### Evaluation of Trained Neural Network Model  
Machine leaning algorithms have wide application in almost all domains, many machine learning algorithms extensively use for data classification i.e., Naive Bayes, Support Vectors machines, Decision trees, Neural Networks, etc. Each of these algorithms are suitable for providing solution of different problem scenario. Like SVM have capability to handle larger data set values approximately 2500 Vectors dealing algorithm presented by researchers [31]. Although Support Vector Machines (SVMs) are very accurate modern classifiers that deliver up-to-mark performance in pattern recognition problems of real-world scenario and also preferred to use in data mining applications such as text categorization, hand-written character recognition, image classification and bioinformatics. Although they generate very accurate solutions but, they not preferred in online applications where classification is to be done on large no of vectors and it required in great speed [32]. We used Neural Network Model to predict Environment Type. NN already applied in medical domain for classification of images in order to diagnose disease. To measure Trained Neural Network performance, we use one standard error measure known as Mean Square Error which is simply mean of error squares. Term Error means difference in expected and actual output. Formula for MSE is given below in Equation (7).  
\[\text{MSE}=1/\
## 3 System Overview
### ROI Management
#### 3.2.3 ROI Adjustment
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# Investigations of Mixing Technique on the Rheological Properties of Self-Compacting Concrete
## 2 Experimental
### Samples Preparation  
Samples preparation was carried out in a single campaign. Particular attention was paid to maintaining a stable mixing environment to avoid unintentional discrepancy. An HJW-60 type single horizontal-axis forced mixer was used to prepare the SCC samples, each with a batch of 40 L and about 30 L was used for rheology measurements. Different mixing procedures and subsequently resultant rheological properties were studied. Five different charging sequences, from No. A to No. E as displayed below, were examined in order to understand their effect on the performance of fresh SCC:  
* No. A (ready-mixed dry materials method): All dry components (aggregates, cementitious materials, etc.) were firstly mixed for 30 s. Then SP, air-entraining agent, and water were added and mixed for another 3 min.
* No. B (SP subsequently added method): All dry components (aggregates, cementitious materials, etc.) and 3/4 of the water content were firstly mixed for 30 s. Then SP, air-entraining agent and the remaining water were added and mixed for another 150 s.
* No. C (Binding sand method): The sand and 3/4 of the water content were first mixed for 30 s. Then cementitious materials were added and mixed for another 30 s. Finally, SP, air-entraining agent and the remaining water were added, and mixed for another 150 s.
* No. D (Shell making with coarse and fine aggregate method): Aggregates and 3/4 of the water content were first mixed for 30 s. Next, cementitious materials were added and mixed for another 30 s. Finally, SP, air-entraining agent, and the remaining water were added and mixed for another 150 s.
* No. E (Cement paste wrapped sand method): Cementitious materials and 3/4 of the water content were first mixed for 30 s. Then, aggregates were added and mixed for another 30 s. Finally, SP, air-entraining agent, and the remaining water were added and mixed for another 120 s.  
In parallel, four different mixing time periods including 2 min, 3 min, 4 min, and 5 min were executed to capture the effect of mixing time on concrete performances. What's more, in order to study the effect of mixing speed on concrete performances, four variable mixing speeds were implemented, i.e., 15 r/min, 30 r/min, 45 r/min, and 60 r/min. Note that the ready mixed dry materials method (charging sequence No. A) was selected to study the effect of mixing time and mixing speed on the rheological properties of SCC.
## 3 Design and Fabrication of the Nanomanipulator
### Fabrication  
Standard MEMS micromachining fabrication techniques are usually required in order to obtain this kind of microscaled and nanoscaled electro-mechanical device [42]. In this work, we relied, in particular, on surface micromachining: according to this method, a complex system can be fabricated as a superimposition, on a chosen substrate, of various thin/thick films of different materials, whose geometries can be defined through lithography ad wet/dry etching techniques [43].  
Standard silicon-on-insulator stacks were not used. The fabrication procedure, with glass as the substrate, was carried out as follows:  
* The smallest geometric dimension of the system was the width of the comb-drive fingers (600 nm); in order to reach it, a sub-micrometric lithographic resolution is required: the geometries definition was carried out using Electron Beam Lithography (EBL) on an e-resist polymeric film [44].
* Highly versatile materials, suitable for low-cost and low-energy fabricated systems, such as hydrogenated amorphous silicon (a-Si:H) and Indium Tin Oxide (ITO), were investigated for the device mainframe: the main goal was to achieve a system that can be biologically compatible and, at the same time, monolithically integrable even on flexible and polymeric substrates [45; 46; 47; 48; 49; 50; 51]. Dry etching methods, such as sputter-etching and Reactive Ion Etching (RIE), for the structural layer patterning were adopted, using a metal as a masking layer, such as chromium or aluminum.
* An intermediate sacrificial layer between the substrate and the structural layer must be considered: its selective removal after completing the mainframe patterning phase assures the achievement of a structure partially suspended from the substrate. The wet etching procedure offers both isotropic and selective removal of a determined material; furthermore, \"etch holes: in the mainframe were specifically designed as under-etching promoters for the rigid suspended body of the system.  
Figure 4 shows 30\({}^{\circ}\) tilted SEM images (a) of a gripping device sample between the process of structure patterning and the releasing phase, with an enlargement (b) of the jaw-tips. The displayed device has n-doped a-Si:H (hydrogenated amorphous silicon), deposited by Plasma-Enhanced Chemical Vapor Deposition (PECVD), as the structural layer.
# Superior Control of Spacecraft Re-Entry Trajectory
## 2 Optimization Mathematical Model for Re-Entry Trajectory and Superior Control Algorithms
### Performance Indexes of Theoretical Model for Superior Control  
The concept of superior control refers to the process of optimizing performance indicators without using weighting coefficients, that is, without artificially imposing importance restrictions on each performance indicator, and optimizing all performance indicators equally.  
In the theory of superior control, the performance index matrix is \(J=\left[\begin{array}{cc}J_{1},J_{2},J_{3},.......,J_{n}\end{array}\right]\), and each performance index has its own constraints: \(J_{1 Min}\leq J_{1}\leq J_{1 Max}\); \(J_{2 Min}\leq J_{2 Max}\); \(J_{3 Min}\leq J_{3}\leq J_{3 Max}\);.......; \(J_{nMin}\leq J_{n}\leq J_{nMax}\). The performance indexes of more specific optimization control problems in spacecraft re-entry trajectory optimization can be expressed as follows:  
\[J_{min}=\left.\begin{array}{c}\begin{array}{c}P_{w}(t)\\ \Omega_{T}(t)\\ \tilde{W}_{P1}(t)\\ \Phi[\vec{x}(t_{f}),t_{f}]\\ \int_{t_{0}}^{t_{f}}\Theta[t,\vec{x},\Lambda,\Xi,\vec{U}(t),\vec{U}_{s},S_{X}] dt\\.......\end{array}\right\}, \tag{17}\]  
In the above equation, \(t_{V\Lambda}\) represents all detected information, including internal structural information of the spacecraft and external environmental information; \(U_{DU}\) refers to variables that spacecraft users can control and specify, including static and dynamic variables; \(U_{DP}\) is a variable that spacecraft designers can control; \(P_{w}(t)\) is the flight mission that the spacecraft needs to complete, including waypoints, mission points, and the impact of the mission on the spacecraft; \(\Omega_{T}(t)\) refers to the flight's restricted area that the spacecraft needs to avoid, specifically expressed as \(\Omega_{Treat}(t,x,h,z)\); \(\widetilde{W}_{PI}(t)\) represents the performance indexes of the spacecraft itself, including structural indexes and the index requirements of each subsystem, including weight, cost, volume, load, etc; \(\Phi[\vec{x}(t),t_{f}]\) represents terminal technical indexes, namely the terminal error; \(\int_{t_{0}}^{t_{f}}\Theta[\ldots\ldots\ldots]\) refers to the conceptual performance indexes that may be obtained during the flight process, where \(\vec{x}\) refers to the flight state of the spacecraft; \(\Lambda\) refers to the overall mathematical model of the spacecraft; \(\Xi\) refers to the mathematical model of the environment in which the spacecraft is located. The system motion equation is Equation (2), where \(x(t_{0})=x_{0}\), \(\mathbf{f}=f_{1},f_{2},\ldots\ldots,f_{k}\), that is, the state space is k-dimensional. In this paper, \(k=3\). The index function (1) can also be written in vector form:  
\[\frac{d\mathbf{z}}{dt}=\phi[\mathbf{x},\mathbf{u},t],\mathbf{z}(t_{0})=0, \tag{18}\]  
Combine the state equation and performance index equation, denoted as  
\[\mathbf{y}(t)=\mathbf{x}(t),\mathbf{z}(t),g=\{f,\phi\}, \tag{19}\]  
The entire system can be abbreviated as  
\[\frac{d\mathbf{y}}{dt}(t)=g[\mathbf{x},\mathbf{u},t],y(t_{0})=\{\mathbf{x}_{ 0},0\}, \tag{20}\]
## 2 CNN-LSTM Based Automatic Multi-Class Classification Network
### CNN-LSTM Network Structure
#### 2.2.1 Feature Extractor  
When the atomic numbers of charged particles are close, as shown in Figure 1, their waveforms often exhibit a high degree of similarity, which makes the particle identification task more difficult. To address this issue, we propose a feature extraction method based on a combined structure of CNN and LSTM. The CNN-LSTM based feature extractor effectively combines the ability of CNN to extract local features with the capacity of LSTM to capture long-term dependencies, thereby extracting more discriminative features from the waveforms of charged particles. Compared with other commonly used attention-based models for processing sequential data, such as the Transformer [16], CNN-LSTM is better at focusing on local features of the waveform while capturing long-range dependencies, whereas the Transformer primarily emphasizes global interactions. This difference can cause the Transformer to overlook subtle local variations between waveforms with similar atomic numbers, leading to a decrease in identification performance. Besides, the computational complexity of LSTM is \(O(n\cdot d)\), while the complexity of the Transformer is \(O(n^{2}\cdot d)\), where \(n\) is the sequence length and \(d\) is the dimension of the hidden state. Therefore, when the sequence length is large, the computational cost of LSTM is significantly lower than that of the Transformer, making it more suitable for a broader range of applications.  
The specific network architecture is shown in Figure 3a. The shape of the input data (i.e., particle's waveforms) is (Batch_size, 1, Sequence_length). After processing through two CNN layers, the output local feature shape becomes (B, 128, Sequence_length/2). Subsequently, the feature undergoes a shape transformation to match the input requirements of the LSTM. After being processed through two LSTM layers, the final hidden state is selected as the feature vector. Our feature extractor can filter out environmental noise from the input waveforms through CNN layers, simultaneously extracting more detailed and discriminative local features. Besides, it captures long-range dependencies through LSTM layers, thereby obtaining more generalized global features.
## 4 Actual Line Abnormality Verification  
Based on a certain line data, a vehicle-mounted dynamic test was conducted on the intelligent detection algorithm for pantograph slide defects.  
During the test, the pantograph slide condition-monitoring device was used to collect pantograph dynamic images, and the pantograph positioning and defect identification were analyzed based on on-site measured data to verify the timeliness and effectiveness of the algorithm.  
The line inspection obtained 350 pantograph pictures, 63 of which had structural abnormalities in the carbon slide. The on-site pantograph slide condition-monitoring device scans the camera with a sampling frequency of 10 kHz and generates 2048 image lines. The above-mentioned pantograph slide structural anomaly detection algorithm is used to determine structural anomalies in the picture. According to statistics, the average detection time of a single image during the detection process is 12.32 ms. The time-consumption curve of structural abnormality identification in 350 pantograph slides is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **YOLO v8** & **C2 FDarkNet-53** & **LeakyRelu** & **P/\%** & **R/\%** & **FPS** \\ \hline A & \(\surd\) & & & 90.89 & 87.63 & 34.3 \\ B & \(\surd\) & \(\surd\) & & 91.66 & 88.60 & 36 \\ C & \(\surd\) & & \(\surd\) & 92.42 & 89.71 & 35.3 \\ D & \(\surd\) & & & 93.43 & 89.99 & 34.3 \\ E & \(\surd\) & \(\surd\) & \(\surd\) & 96.64 & 95.72 & 38.5 \\ F & \(\surd\) & \(\surd\) & & 93.62 & 90.75 & 42.5 \\ G & \(\surd\) & & \(\surd\) & 94.98 & 93.72 & 41.1 \\ H & \(\surd\) & \(\surd\) & \(\surd\) & 95.73 & 94.49 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of ablation experimental indicators.  
Figure 9: Identification time-consumption curve.  
The test results show that the method in this paper has a recognition rate of pantograph slide abnormalities of 96.42% under normal exposure conditions, and the recognition rate of pantograph slide structural abnormalities can still reach more than 90% when the image quality is poor. The accuracy statistics are shown in Table 7.