esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The high damping alloy materials, by virtue of the excellent mechanical properties and damping performance generated by the material's properties and internal friction mechanism, can be employed directly in the reduction of structural vibrations. On the basis of summarising the current research progress and application status of existing damping alloy materials, the damping characteristics of Fe-12 Cr-3 Al ferromagnetic high damping alloy material were tested and applied to the vibration reduction of an aero-engine lubricating oil tank. Subsequent to this, vibration characteristics simulation analysis and tests were conducted. The results show that the amplitude reduction rate of mounting structures utilising the Fe-12 Cr-3 Al ferromagnetic high damping alloy is 30% in comparison to Q235 carbon steel. Additionally, the vibration transmission rate can be diminished by 40%. The utilisation of the Fe-12 Cr-3 Al ferromagnetic high damping alloy in the aero-engine mounting structure has been observed to result in a discernible reduction in vibration.  
high damping alloy; vibration reduction; damping test; aero-engine; mounting structure +
Footnote †: journal: applied sciences
# Student Dropout Prediction for University with High Precision and Recall
## 3 Data Characteristics
### Measurements of Existing Methods  
In this paper, we are interested in devising a method that produces high precision and recall. Since the existing works used different data sets and features, comparing the performance of existing works is challenging. To understand existing works' precision and recall performance, we used our data to measure the metrics. The results of running the methods used in the existing works are illustrated in Figure 2. The methods used in the experiment are TomekLinks [12], RandomUnderSampler [13], EditNearestNeighbours [14], SMOTE [15], BorderlineSMOTE [16], ADASYN [17], SMOTEENN [18], and SMOTETomek [19]. We used 10-fold cross-validation. We chose ensemble, logistic regression, artificial neural network, and gradient boosting methods to compare with the method (SDP) proposed in this paper because, in general, they showed high precision and high recall rates in the previous works.  
As shown in Figure 2, the unprocessed methods showed high precision, whereas the imbalanced data processing models showed high recall. In addition, gradient boosting and ensemble methods showed relatively high precision and recall rates. Logistic regression did not show good results on our data.  
We have to emphasize that we need to have high precision and recall to correctly identify the students and all the students who are about to drop out of the university.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature Class** & **Feature Name** & **Type** \\ \hline Academic Data & Academic Status & Real \\  & Number of Rewards and Penalty & Real \\  & Registration Fee Installments Count & Real \\  & Number of Volunteer Activities & Real \\  & Department & Categorical \\  & Number of Leave of Absence & Real \\  & Word Count of Counsel Report & Real \\  & Seasonal Semester Courses Count & Real \\  & Collage & Categorical \\ \hline Academic Records & Grade Ranking & Real \\  & Grade Average & Real \\  & Number of Majors & Real \\  & Completed Credits & Real \\  & Number of Scholarships Received & Real \\  & Completed Credit of Seasonal Semester & Real \\  & Courses & Real \\  & Total Amount of Scholarships & Real \\ \hline Personal Information & Disability & Binary \\  & Residence Postal Code & Categorical \\  & Gender & Binary \\  & Counseling Count & Real \\ \hline Facility Use History & Library Overdue Count & Real \\  & Library Loan Count & Real \\  & Dormitory Use Count & Real \\  & Dormitory Penalty Count & Real \\  & Number of Facility Rentals & Real \\ \hline Website Use History & Univ. Main Site Login Count & Real \\ \hline \end{tabular}
\end{table}
Table 6: Feature Set Used in the Paper.
## 2 Materials and Methods
### Literature Review and Analysis on HRC Modeling Methods
#### 2.1.1 Business Process Modeling Notation (BPMN)  
BPMN was originally developed as a standard notation for business users to bridge the gap between business process design and process implementation. A Business Process Model contains a network of graphical objects representing activities or work and the flow controls representing the order of performance [31]. WhileBPMN is typically used for business processes, its capabilities can be extended to the field of robotics. More specifically, it can be used to model the flow, tasks, decisions, and interactions that occur in HRC processes.  
One approach found that there was a lack of consideration for the modeling of collaborative tasks and therefore attempted to completely model processes such as assembly workflows, interactions, and decisions made by machines and humans. It supports variability modeling, making it attractive for customized manufacturing scenarios. A BPMN-based workflow designer was implemented to allow the user to model processes intuitively [32].  
BPMN was also used for the development of a risk analysis software tool to support changes in adaptive collaborative robotic scenarios [33]. The work presents a tool that can automatically identify any changes that have been made to a particular application's components or processes, providing safety experts with a tool to monitor manufacturing processes. The results showed that this technology provided better usability and decreased errors when compared with conventional methods. BPMN was used as a basis for development, with certain modifications to make it appropriate for collaborative scenarios.  
Another paper tackles the task of warehouse material handling using an automated guide vehicle system [34]. The Manufacturing Process Management System (MPMS) is used to control the process. The aim is to achieve a system that performs all tasks automatically, without requiring major changes for various processes. The MPMS was designed using BPMN and Camunda as a platform, allowing for the BPMN model to be executed and controlled in real time.  
Concerning the lack of efficiency arising from static task allocation, one paper focused on the use of adaptive task sharing in manufacturing and assembly [35]. A model was developed for experimental purposes, which included task sharing worker assistance software based on BPMN. The results indicated that adaptive task sharing reduced the productivity gap in automated assembly.
## 3 Results and Discussion
### Influence of Studied Saponins on Cell Viability
#### 3.1.2 Cytotoxicityicity of Saponins in NCTC Cells  
The results of the MTT assayfor the standard saponins in NCTC cells are presented in Figure 2.  
The values indicated that the compound \(\alpha\)-hederin presented a high degree of biocompatibility in NCTC cells in the concentration range of 2-10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (99-82% cell viability) at 24 h. It was slightly cytotoxic at 25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (62% cell viability) and severely cytotoxic throughout the range of concentrations 50-400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, when the cell viability decreased to 0.4%.  
Hederagenin was biocompatible in NCTC cells from 2 to 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, had moderate cytotoxicity in the range of 100-300 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, with values of 73-56% cell viability, and was cytotoxic at 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, with only 49% cell viability at 24 h.  
Hederacoside C was biocompatible at concentrations ranging between 2-300 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, with high cell proliferation (105-88% viability) at 24 h, compared to the control culture, and non cytotoxic (79% viability) at 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL.  
Figure 1: Saponins structures.  
Comparing the cytotoxicity assay results of the three studied saponins in NCTC cells, it was concluded that \(\alpha\)-hederin was biocompatible only up to10 \(\upmu\)g/mL, while in the concentration range of 25-400\(\upmu\)g/mL, it was cytotoxic; hederagenin had moderate cytotoxicity between 100-300\(\upmu\)g/mL concentrations, and increased cytotoxicity at 400\(\upmu\)g/mL, while hederacoside C was biocompatible in the concentration range of 2-400\(\upmu\)g/mL.  
Figure 2: \(Cont.\)
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 2 Case Study  
The research was based on an underground mine that extracts potash using an irregular room and pillar method, at a depth between 700 and 1000 m. The layout of the mine involves two main levels with a U-shaped ventilation circuit. The principal ventilation circuit is placed on the lower level, while the higher level contains the workings faces, with several splits of airflow from the level below, as well as the auxiliary ventilation system. The main ventilation system consists of a single exhausting fan. Extraction is achieved via three shifts lasting eight hours each, working continuously throughout the day.  
The different exploitation panels include an extension of the lower level and, subsequently, an extension of the same panel above. Therefore, it is important to know, in advance, the temperature evolution along the principal circuit, to determine the environmental conditions at the entrance of the panel and to be able to analyze different scenarios.
## 2 Materials and Methods
### Satellite Data and Image Processing
#### 2.4.2 Preparation of Map for Carrot Crop Monitoring  
A best-fit-model-generated simulated LCC layers were utilized for the preparation of maps for crop health monitoring by converting the LCC values to leaf nitrogen content. An empirical model was generated by performing the linear regression analysis between field-measured SPAD readings and the laboratory-analyzed leaf nitrogen content. Subsequently, the generated maps of carrot fields were used for fertilizer management and to identify the growth stage of carrots.
## 4 Data Collection, Aggregation and Dissemination Challenges in WSNs
### AI Based Solutions to Data Collection, Aggregation and Dissemination Challenges in WSNs
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**AI Paradigms** & **Algorithm** & **Ref.** & **Optimization Criteria** & **Simulation/ Real-Deployment** & **Centralized/ Distributed** & **Mobility** & **Performance Parameters** \\ \hline Swarm Intelligence & DAACA & [44] & Minimizing energy consumption & Java Simulation & Distributed & Static & Average energy cost, Energy difference, WSN lifetime, One hop transmission success ratio and time complexity. \\ \hline Swarm Intelligence & ACOPSO & [55] & Improving inter-cluster data aggregation & MATLAB simulation & Distributed & Static & Stability period, Network lifetime, Average remaining energy, and Throughput. \\ \hline Swarm Intelligence & ABC & [47] & Optimizing energy consumption & Random network deployment & Distributed & Static & Average path length \\ \hline Swarm Intelligence & AMDT & [46] & Reducing minimum-power multiresolution data dissemination problem & Globosim simulator with PARSEC compiler & Distributed & Static & Energy cost for path finding, and Total power consumption. \\ \hline Swarm Intelligence & ALOC & [54] & Improving lifetime and throughput of a network & MATLAB simulation & Distributed & Static (MS) & Dead nodes, Alive nodes, Residual energy, Throughput, Lifetime, Number of individuals nodes, Number of CHs, and DC tour length. \\ \hline Swarm Intelligence & HFSO-ADC & [51] & Improving quality of service & Real network deployment & Distributed & Mobile & Network lifetime,Data packets delivered to BS, Energy consumption, and Alive nodes. \\ \hline Swarm Intelligence & SDMA-PSO & [56] & Improving efficiency and reduces buffer overflow & \(NS2\) simulator & Distributed & Static (MS) & Delay, Throughput, Average energy consumed, and Network lifetime. \\ \hline Swarm Intelligence & CC-MRT, CC-TRT, CC-WMRT & [66] & Improving network lifetime & Simulation & Distributed & Static & Transmissions count, leftover energy, energy usage variance, and WSN lifetime \\ \hline Swarm Intelligence & DFWA & [77] & Maximizing the amount of data collected & Simulation & Distributed & mobile & Cell numbers and Sojourn time. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of AI based solutions to WSN DC, aggregation and dissemination issues surveyed in Section 4.
## 4 Discussion
### Performance of Calibrated Models Using Only Sentinel-2 Bands: DNN vs. PLS and RF  
When using only Sentinel-2 bands, calibrated DNN models outperformed PLS and RF models for all three iterations. These results are consistent with previous studies that found that DNN models are more suitable for predicting SOC due to their ability to capture complex relationships through multiple hidden layers and neurons [61; 105; 107]. In addition, DNN models can model complex data. For example, [PERSON] et al. [105] found that a DNN model improved the prediction of SOC at the national scale, even when using a dataset with high spatial uncertainty (i.e., multiple scales of variation, different sampling sources, and different acquisition dates). Moreover, DNN models in the present study were less sensitive to high-leverage samples (Figure 8) than PLS and RF models were. The low performance of the PLS regression was expected since PLS models are sensitive to highly leveraged samples, which are generally considered as outliers [114]. Although RF models showed RMSEv of 1.73-1.85 g\(\cdot\)kg\({}^{-1}\) and fair RPIQ values (RPIQ = 1.41-1.53), validation performance was very poor (RMSEp = 3.51 g\(\cdot\)kg\({}^{-1}\) (\(\pm\)0.45) and RPIQ = 0.90 (\(\pm\)0.11)). Thus, several studies have found that RF predicted better than other machine-learning techniques, but others have found that the performance of RF classifiers was sensitive to training samples and data dimensions [115]. Compared to the study of [PERSON] et al. [35], our DNN models (R\({}^{2}\) = 0.18-0.65 and RPIQ = 1.21-1.46) outperformed their PLS, SVM, CR, and ensemble models developed at the field scale (R\({}^{2}\) = 0.11-0.27 and RPIQ = 1.22-1.31). Our models yielded RMSEP of 2.33 g\(\cdot\)kg\({}^{-1}\) (\(\pm\)0.1), which is better than those obtained by [PERSON] et al. (2023) [116], RMSE = 3.5 g\(\cdot\)kg\({}^{-1}\) (\(\pm\)0.3), for SOC content ranging from 7.2 to 14.2 g\(\cdot\)kg\({}^{-1}\) and [PERSON] et al. (2021) [38], and RMSE = 3.02-5.86 g\(\cdot\)kg\({}^{-1}\) for SOC content ranging from 7.04 to 31.9 g\(\cdot\)kg\({}^{-1}\), in cross validation.
## Materials and Methods
### Studies of CaMn\({}_{2}\)O\({}_{4}\) Electrode on FTO as OER Photocatalysts  
Working electrodes were placed within a fully-enclosed quartz cell (\(5\times 5\times 5\) cm) inside a closed cabinet that comprised a Faraday cage. A Pt mesh (\(1\times 2\) cm) was used as the counter electrode. A BASi Ag/AgCl aqueous salt bridge (KCl, 3 M) was used as a reference electrode. The cell was filled with a 0.2 M Na\({}_{2}\)SO\({}_{4}\) aqueous solution electrolyte, adjusted to pH 12 by adding NaOH. Prior to the experiment, nitrogen gas was bubbled in the electrolyte for 30 min. Linear sweep voltammetry (LSV) and chronoamperograms (CA) were performed using an EDAQ466 potentiostat (Biologic, Seyssinet-Pariset, ARA, France). Where applicable, a SoLux daylight MR16 halogen light bulb (12 V, 50 W, 24\({}^{\circ}\) ca. 0.25 sun intensity) with a stable output range of 275-750 nm was used to illuminate the sample. The distance between light and working electrode was 10 cm. A Thorlabs visible-light bandpass filter (315-710 nm) was placed 1.5 cm in front of the light source to remove infra-red wavelengths.
# Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes
### Fault Diagnosis  
For the high-speed train duct failure, 25 sets of measured duct pressure data are taken as an example for diagnosis. Test1 to Test5 are the normal test data of the air duct, Test6 to Test10 are the air duct blocking fault test data, Test11 to Test15 are the compressor fault test data, Test16 to Test20 are the air duct leakage fault test data, and Test21 to Test25 are the unknown fault test. The data and the diagnosis results are shown in Table 7.  
It can be seen from Table 7 that all test data fault diagnosis results are correct, and the norm of the same fault type is stable and maintained within the same order of magnitude. The difference between different fault type norms is large, and the data difference is as high as one to two orders of magnitude compared with the same fault type norm. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Function Approximation**} \\ \cline{2-3}  & **Approximation Method** & **Approximation Method** \\ \hline Normal State & 3 rd Order Fourier & \(U(x)=9.172-0.3338\times\cos(x\times 0.0305)+0.4737\times\sin(x\times 0.0305)-\) \\  & 0.2006 \(\times\cos(2\times x\times 0.0305)-0.013\times\sin(2\times x\times 0.0305)-\) \\  & 0.0122 \(\times\cos(3\times x\times 0.0305)-0.0718\times\sin(3\times x\times 0.0305)\) \\ Air Brake Pipe Leakage Fault State & 1 st Order Fourier & \(U(x)=9.149-0.5561\times(\cos(x\times 0.0359)+0.3462\times\sin(x\times 0.0359)\) \\  & \(U(x)=9.2-0.6427\times\cos(x\times 0.0439)+0.0169\times\sin(x\times 0.0439)\) \\ Air Brake Blocking Fault State & 3 rd Order Fourier & \(+0.0368\times\cos(2\times x\times 0.0439)+0.0275\times\sin(2\times x\times 0.04 39)\) \\  & \(-0.0204\times\cos(3\times x\times 0.0439)-0.0701\times\sin(3\times x\times 0.04 39)\) \\ Compressor Fault State & 2 ndOrder Fourier & \(U(x)=9.17-0.5635\times\cos(x\times 0.0318)+0.18\times\sin(x\times 0.0318)-\) \\  & 0.0791 \(\times\cos(2\times x\times 0.0318)-0.1252\times\sin(2\times x\times 0.0318)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal equations in function approximation.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Empirical-Model-Based Predictive Modeling
#### 2.1.3 Log-Ray Path Loss Model  
The log-ray path loss model is an approach developed to model path loss in vehicle-to-vehicle (V2V) communication channels [43]. This model incorporates the path loss exponent derived from measurements to overcome the limitations of traditional log-distance and two-ray path loss models. Mathematically, this is expressed as  
\[P_{\text{log-ray}}(d)=\frac{|E_{\text{log-ray}}|^{2}G_{r}\lambda^{2}}{480\pi^ {2}} \tag{4}\]  
where \(E_{\text{log-ray}}\) represents the total electric field at the Rx and is given by  
\[\begin{split} E_{\text{log-ray}}(d,t)=E_{0}\biggl{(}\frac{d_{0}} {d^{\prime}}\biggr{)}^{n/2}\cos\biggl{(}\omega_{c}\biggl{(}t-\frac{d^{\prime} }{c}\biggr{)}\biggr{)}\\ +\Gamma E_{0}\biggl{(}\frac{d_{0}}{d^{\prime\prime}}\biggr{)}^{n /2}\cos\biggl{(}\omega_{c}\biggl{(}t-\frac{d^{\prime\prime}}{c}\biggr{)}\biggr{)} \end{split} \tag{5}\]  
where the path loss exponent, which varies from one environment to another, is denoted by \(n\), and the other parameters are explained in detail in Section 2.1.2.
## 1 Introduction
### Objectives and Contributions of This Paper  
This paper primarily aims to identify the fundamental design considerations in UAV-assisted WSN/IoT networks for ensuring the information reaches its destination within the least tolerable time. The following research questions are formulated to address the research aim and objectives of this study:  
* What are the pertinent architectural design considerations identified from the previous works on AoI minimization in UAV-assisted data collection for WSNs/IoT?
* What are the concerning issues related to the identified fundamental design aspects of AoI minimization in the literature on UAV-assisted WSNs/IoT?
* What are some potential study areas of AoI-aware UAV-assisted WSN/IoT network architectures?  
Given the above, this paper presents three main contributions:  
* A comprehensive study of previous work on age-aware UAV-assisted WSNs/IoT for data gathering applications, focusing on the technical design aspects. This includes the classification of the studied works into three major categories and 17 subcategories, presenting illustrations on some of the common architectures, and identifying limitations and significant results in these works.
* A discussion on pertinent issues related to the identified design aspects, namely energy management, flight trajectory, and UAV/SN scheduling.
* A discussion of potential research directions in this field towards motivating further research and problem-solving in this area.
## 5 Ablation Study and Discussion  
We evaluate the effectiveness of our proposed HMA Conv-LSTM model. First, we explored the effect of the choice of model hyperparameters on performance. Second, we evaluated the effectiveness and contribution of each module of the model through ablation experiments. Then, we analyzed the confusion matrix obtained by testing the model on some of these datasets. Finally, we visualized the feature weights in SFS when recognizing some actions to improve the interpretability of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Opportunity** & **PAMAP2** & **USC-HAD** & **Skoda** \\ \hline SVM [53] & - & 0.71 & - & 0.82 \\ RF [54] & - & 0.74 & - & 0.83 \\ CNN [55] & 0.59 & 0.82 & 0.41 & 0.85 \\ \hline LSTM [56] & 0.63 & 0.75 & 0.38 & 0.89 \\ b-LSTM [18] & 0.68 & 0.84 & 0.39 & 0.91 \\ DeepConvLSTM [21] & 0.67 & 0.75 & 0.38 & 0.91 \\ DeepConvLSTM + Attention [27] & 0.71 & 0.88 & - & 0.91 \\ LSTM + Continuous Attention [57] & - & 0.90 & - & 0.94 \\ \hline ConvAE [48] & **0.72** & 0.80 & 0.46 & 0.79 \\ SADDeepSense [58] & 0.66 & 0.66 & 0.49 & 0.90 \\ AttnSense [25] & 0.66 & 0.89 & 0.49 & 0.93 \\ Self-Attention * [26] & 0.63 & 0.84 & 0.51 & 0.87 \\ \hline
**HMA Conv-LSTM** & **0.68** & **0.91** & **0.53** & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Macro F1-score of different methods on the benchmark set.
## 1 Introduction
### Novelty and Contributions  
Novelty yields a flexible crypto processor design with efficient control functionalities to obtain the area-efficient implementation of PM operation. Towards our contribution, we have proposed an area-efficient elliptic-curve PM-based hardware crypto processor architecture over \(GF(2^{233})\), where we implemented a bit-serial finite field modular multiplier to keep hardware resources lower. For further area optimizations, we computed the square and modular inversion operations using the implemented bit-serial multiplier; this approach results in lower hardware resource utilization with throughput or performance overhead. Moreover, keeping low-area PM design, users can load different curve parameters to infer data memory of the proposed crypto processor design and secret key into the corresponding buffer, enabling flexibility. Finally, we implemented a finite-state-machine (FSM) based controller for providing efficient control functionalities.