esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Testing of a Measurement Device for High-Speed Bearing Evaluation
## 3 Results
### Measurement of Acoustic Emission  
Measurement of acoustic emission is a technique used to capture, analyze, and interpret sounds generated by objects or systems. This method is based on the idea that materials, structures, or devices emit sounds as a result of internal processes or interaction with the environment. Measurement of acoustic emission is often used for diagnosing, monitoring, and assessing the condition of materials, structures, and mechanical devices.  
To investigate the acoustic emission of rolling bearings, measurements under the same conditions were conducted and at the same time intervals as the measurements of high-frequency vibrations. Two tables of values were created, one for reaching 110,000 rpm (Table 4) and the other for 5 min of operation at 11  
Based on the measured values, graphical representations were created (Figures 15 and 16) for a better illustration of changes in the acoustic emission of the tested bearings in both time intervals. These data are crucial for assessing the overall impact of re-lubrication with nano-particle-based lubricants on acoustic emission and its potential effects on the operational characteristics of rolling bearings. With this data, it is possible to evaluate whether re-lubrication with nano-particle-based lubricants is suitable and effective in achieving the desired results in the field of rolling bearing acoustic emission.  
On the selected bearings from Group A and B, specifically bearing L027.6 and L093, the FFT method was used (Figures 17 and 18) to analyze the frequency spectrum of acoustic emission. The goal is to determine and compare the impact of adding nano-particle-based additives to the lubricant on this spectrum. Once again, the test is conducted for both cases, that is, for reaching the desired speed of 110,000 RPM and after 5 min of operation at the same speed.  
Fourier analysis was conducted on selected bearings from Group A and Group B to provide a more comprehensive evaluation of the impact of nano-particle additives compared to lubrication with the original lubricant.  
Figure 16: Graphical representation of acoustic emissions of bearings at 110,000 rpm and after 5 min at 110,000 rpm—Group B.  
Figure 15: Graphical representation of acoustic emissions of bearings at 110,000 rpm and after 5 min at 110,000 rpm—Group A.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **L088** & **L091** & **L093** & **L094** & **L098** & **L099** & **L100** & **L338** & **Avg.** \\ \hline
110 k rpm (0 min) & 102.8 & 102.4 & 109.7 & 110.1 & 109.7 & 103.9 & 111.6 & 105.9 & 103.99 \\
110 k rpm (5 min) & 101.4 & 102.9 & 110.1 & 109.2 & 108.2 & 103.9 & 110.8 & 106.2 & 106.21 \\ \hline \end{tabular}
\end{table}
Table 5: AE—Group B.  
The determination of the frequency spectra of selected bearingss can provide crucial information about the impact of lubrication with modified lubricant. A decrease in significant amplitudes in the resonance frequency ranges can be expected. This should be verified by applying the lubricant and conducting repeated tests on the selected bearings.
## 3 Results and Discussion
### Physicochemical Properties Evaluation of Biodiesel Yield and NS Oil  
Evaluation of physicochemical properties is essential to examine the practicality of fuels (niger seed oil, NS-oil-derived biodiesel yield) to use in diesel engines as per ASTM D6751-15C. The average of three values corresponding to each property was recorded to conduct the analysis [45]. Specific gravity is one of the paramount physicochemical properties that could influence the fuel injection system of a combustion engine. The specific gravity of NS oil is reduced from 0.93 to 0.88 (after biodiesel conversion viz. transesterification process). Transesterification of biodiesel yield resulted in a specific gravity within the acceptable ranges (0.87 to 0.89) as per biodiesel standard. Viscosity corresponds to fuel samples (NS oil, NS-oil-derived biodiesel yield) that affect the diesel engine fuel injection system. The higher viscosity of (7.4 mm\({}^{2}\)/s) NS oil could result in incomplete combustion due to poor atomization and the deposit of carbon on the injector. After transesterification of NS oil, the kinematic viscosity of biodiesel yield was found to be reduced to 3.9 mm\({}^{2}\)/s (falling under the permissible standard limit: 1.9 to 6.0). The calorific value is of paramount importance that defines the energy content in the samples. The determined calorific values for NS oil and biodiesel yield were found to be equal to 38,560 kJ/kg and 40,400 kJ/kg, respectively. The 35,000 kJ/kg is the minimum standard calorific value to be tested in a diesel engine. The acid value of NS oil reduces from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & \begin{tabular}{c} **Algorithm Specific** \\ **Parameters** \\ \end{tabular} & **Fitness Value (Yield\%)** & \begin{tabular}{c} **Computation Time** \\ **(seconds)** \\ \end{tabular} & **Transesterification Condition** \\ \hline GWO & Convergence constant = 0.5 & 94.15\% & 1.66 & \begin{tabular}{c} MO = 9.32; CC = 1.5 wt.\%; \\ RT = 65 \({}^{\circ}\)C; RT = 60 s \\ \end{tabular} \\  & Randomness, \(\alpha\) = 0.9; & & & \\ FA & Absorption coefficient, \(\gamma\) = 1; & 94.15\% & 15.06 & \
\begin{tabular}{c} MO = 9.32; CC = 1.5 wt.\%; \\ RT = 65 \({}^{\circ}\)C; RT = 60 s \\ \end{tabular} \\  & Randomness reduction, \(\beta\) = 0.6 & & & \\ BB-BC & Exploration factor = 14 & 93.07\% & 0.8 &
\begin{tabular}{c} MO = 9.02; CC = 1.47 wt.\%; \\ RT = 63.8 \({}^{\circ}\)C; RT = 86.1 s \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization of transesterification parameters using three algorithms.  
14 mg KOH/g to 0.36 mg KOH/g. The acid value obtained for biodiesel yield resulted in acceptable test limits for biodiesel equal to 0.5 mg KOH/g. The copper strip corrosion, cloud point, pour point, flash point, iodine value, and cetane number are found to be equal to 1, 14 \({}^{\circ}\)C, 9 \({}^{\circ}\)C, 0.01% \(w\)/\(w\), and 65.50, respectively, and are within the permissible limit corresponding to the biodiesel standard.
## 3 Rheological Models
### Fractal Derivative Generalized Maxwell Model  
A generic constitutive equation for viscoelastic materials based on fractional derivative orders is expressed in the following form:  
\[\sum_{n=0}^{N}a_{n}\frac{d^{x_{n}}\sigma(t)}{dt^{x_{n}}}=\sum_{m=0}^{M}b_{m} \frac{d^{\beta_{m}}\varepsilon(t)}{dt^{\beta_{m}}} \tag{8}\]  
Figure 2: Models: (a) Generalized Maxwell, (b) Fractal derivative.  
where \(\alpha_{n}\) and \(\beta_{m}\) are the fractional derivative orders included between 0 and 1, \(N\)=\(M\) and \(b_{0}\)=0 for the considered case of Maxwell model. Turning to frequency domain, and assuming that \(\alpha=\beta\), Equation (8) becomes:  
\[E^{*}(i\omega)=E_{0}+\sum_{k=1}^{N}\frac{(i\omega)^{\alpha_{k}}E_{k}\eta_{k}}{E_ {k}+(i\omega)^{\alpha_{k}}\eta_{k}} \tag{9}\]  
In analogy with the GM model, (9) can be equivalently expressed in the pole-zero formulations as in (10):  
\[E^{*}(i\omega)=E_{0}\,\prod_{k=1}^{N}\frac{1+\left(\frac{i\omega}{\omega_{x,k }}\right)^{\alpha_{k}}}{1+\left(\frac{i\omega}{\omega_{p,k}}\right)^{\alpha_{ k}}} \tag{10}\]
## 2 Material and Methods
### GPP Data  
Given that the inversion of GPP is based on different methods, we chose two different GPP products for analysis with tree rings to make the results more reliable. GLASS GPP  
Figure 1: Elevation map of Ningxia Hui Autonomous Region.  
data from Earth System Science Data ([[https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)]([https://doi.org/10.6084/m9.figshare.8942336.v3](https://doi.org/10.6084/m9.figshare.8942336.v3)), accessed on 1 June 2022) with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days from 1982-2018 were downloaded. This was based on MODIS remote sensing data and meteorological data from flux sites, using a Bayesian multi-algorithm integration approach, integrating the following eight light energy utilization models that are currently widely used internationally: CASA, CFix, CFlux, EC-LUE, MODIS, VPM, VPRM, and Two-leaf [14].  
EC-LUE GPP comes from the National Basic Condition Platform for Science and Technology; the National Earth System Science Data Sharing Service Platform ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 2 June 2022). This product with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days from 1982 to 2018 is an EC-LUE model modified by [PERSON] et al., which integrates the effects of atmospheric CO\({}_{2}\) concentration, solar radiation, and atmospheric vapor pressure deficit (VPD) based on the original EC-LUE model driven solely by four variables: normalized difference vegetation index (NDVI), photosynthetic effective radiation (PAR), temperature, and the Bowen ratio of sensible and latent heat fluxes. The modified EC-LUE model can effectively simulate the spatial, seasonal, and interannual changes of global GPP [24; 25].  
The downloaded GPP data were processed in ArcMap using the crop and value extraction to point tools; the extracted GPP data were also detrended in R with package \"stat\" to obtain the GPP data corresponding to the tree-ring sampling sites. In total, for each GPP product, 46 8-day GPP values per year were calculated, and the time span was from 1982 to 2018 (see Figures S1 and S2). In addition, considering the asynchronism between tree photosynthesis and trunk growth, we also calculated the cumulative GPP values at different time scales (1-92 8-day, a total of 92 scales) to reveal the relationships between radial growth and canopy growth [26]. For example, when calculating the three cumulative 8-day scales of the 21 st 8-day GPP, we added the 21 st 8-day and two 8-days from 19-20. Finally, we obtained the 1-92 scales of the 1 st to 46 th 8-days at a 2-year scale (from the previous year to current growth year), which counted for 4232 time series (92 \(\times\) 46).
## 3 Proposed M\({}^{2}\)PC Based-ANN for the 2L-VSI  
### Motivation of Using ANN in the Proposed System  
The artificial neural network (ANN) can be used to model complicated systems in order to save time and effort while analyzing difficult mathematical relationships. It can map nonlinear relation between the input and output using a historical data [29]. The neuron is the fundamental unit of the neural network, as shown in Figure 3, and ANN attempts to imitate the human brain. During the training phase, the neural network attempts to map the relationship between the input and output data by changing the weighting and biasing factors, which are usually initialized randomly, by optimizing the employed cost function. By taking advantage of the flexibility of M\({}^{2}\)PC at training phase, this work proposes a radial bias function (RBF) ANN-based controller for a three-phase 2L-VSI with an output \(LC\) filter targeting UPS applications. The goal is getting lower THD and good performance in the output voltage at different types of loads. The proposed controller undergoes two main steps: (i) we use it to evaluate the single objective cost function, as an expert or a teacher for generating the data required for training, validation, and testing phases in an off-line mode, where the proposed neural network applies standard supervised learning under the full-state observation of the system; (ii) when the off-line training is performed, the trained RBF-ANN can successfully calculate the duty-cycle of the two active vectors, which will be applied in the next sampling interval, and then control the output voltage of the inverter as illustrated in Figure 4. We study a performance comparison between the proposed ANN-M\({}^{2}\)PC based approach and the conventional MPC, under various operating conditions.
_Applied_  
_sciences_  
_Article_  
**Modal Analysis and Rotor-Dynamics of an Interior**  
**Permanent Magnet Synchronous Motor:**  
**An Experimental and Theoretical Study**  
**[PERSON] \({}^{\star}\)\({}^{\copyright}\) and [PERSON]**  
Laboratory of Electrical Machines, Department of Mechatronics, Faculty of Electrical Engineering,  
University of Ljubljana, Trzaska 25, SI1000 Ljubljana, Slovenia; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
Received: 1 August 2020; Accepted: 20 August 2020; Published: 25 August 2020  
**Featured Application: One of the major objectives of this study was to comprehensively summarize the key parameters related to the mechanical vibrations response phenomena, which are important when designing rotating electrical machines, based on a case study of an interior permanent magnet (IPM) motor. To this end, the IPM prototype was built and its components examined. The presented findings have important potential application in prediction and prevention of premature failure of electrical machine components, which may occur due to the presence of excessive mechanical vibrations of both stator and rotor.**  
**Abstract: This paper investigates mechanical vibrations of an interior permanent magnet (IPM) synchronous electrical motor designed for a wide range of speeds by virtue of the modal and rotordynamic theory. Mechanical vibrations of the case study IPM motor components were detected and analyzed via numerical, analytical and experimental investigation. First, a finite element-based model of the stator assembly including windings was set up and validated with experimental and analytical results. Second, the influence of the presence of the motor housing on the natural frequencies of the stator and windings was investigated by virtue of numerical modal analysis. The experimental and numerical modal analyses were further carried out on the IPM rotor configuration. The results show that the natural frequencies of the IPM rotor increase due to the presence of the magnets. Finally, detailed numerical rotordynamic analysis was performed in order to investigate the most critical speeds of the IPM rotor with bearings. Based on the obtained results, the key parameters related to mechanical vibrations response phenomena, which are important when designing electrical motors with interior permanent magnets, are provided. The main findings reported here can be used for experimental and theoretical mechanical vibration analysis of other types of rotating electrical machines.**  
**Keywords: electrical machines; IPM motor; machine design; mechanical vibrations; gyroscopic effect; critical speed; numerical modeling; finite element method**
## 3 Mathematical Formulation
### Preliminaries  
Table 1 provides an overview of the primary notations used in this paper. Within a transit schedule, there are \(L_{n}\) bus lines, \(R_{n}\) trips on different bus lines, and \(B_{n}\) buses serving all trips. A trip \(i\) is a service designed on line \(S_{i}=l\), starting from departure stop \(O_{S_{i}}\) at time \(T_{i}\), following the entire sequence of stops along line \(S_{i}\), and arriving at the destination, \(D_{S_{i}}\), with a total travel time of \(t_{i}\). And, the timetable is formed by assembling all trips and providing comprehensive information on both the timing and route details of transit services. According to the timetable, each bus is scheduled to serve multiple trips sequentially per day; for example, \((i\to j\rightarrow\ldots)\) for bus \(b\). After completing the prior trip \(i\), bus \(b\) engages in deadheading, which is the movement of an empty bus to help manage vehicle resources, to reach the subsequent trip service \(j\). Therefore, two consecutive trips within any bus schedule adhere to trip compatibility, as outlined below:  
Trip compatibility: Trips \(i,j\) are compatible if the following conditions are satisfied:  
* The arrival time of trip \(i\) is earlier than the departure time of trip \(j\);
* There exists adequate time for a bus to deadhead from trip \(i\) to trip \(j\);
* The idle time of the bus is guaranteed.  
Let \(\varphi(i,j)=1\) denote that the trip pair \((i,j)\) is compatible, and 0 otherwise. \(\textbf{tra}(D_{S_{i}},O_{S_{j}})\) calculates the travel time from \(D_{S_{i}}\) to \(O_{S_{j}}\). In summary, trip compatibility can be described as follows:  
\[\varphi(i,j)=1\iff T_{i}+t_{i}+\textbf{tra}(D_{S_{i}},O_{S_{j}})+t_{min}^{idle }\leq T_{j} \tag{1}\]  
Sometimes, bus \(b\) experiences a breakdown during trip \(i\), impacting the subsequent schedule \((j\rightarrow\ldots)\). To mitigate the impacts of disruptions, rescheduling is applied to all trips and buses, ensuring both service capability and regularity.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Average Energy Consumption Analysis
#### 4.5.2 Mobile EDs Scenario  
Figure 13 shows the average energy consumption of the proposed schemes compared with typical ADR and ADR+. Overall, the energy consumptions of the proposed G-ADR and EMA-ADR (with and without I-SFA) are lower because of the higher \(PSR\). Generally, the energy consumption of all schemes shows an increasing trend as the number of EDs increases owing to multiple retransmission with high SF and TP [32]. When packets are transmitted with higher SFs, it causes a high interference due to the high ToA. Because higher SFs are highly susceptible to interference, they can negatively affect the energy consumption [37; 38; 39; 40; 41].
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications
### Osteosarcoma  
Osteosarcoma is the most common primary bone tumor among children and teenagers [54] and the third most common cancer in adolescence. Even though the combination of surgery and chemotherapy is improving the survival rate, many still suffer from metastases with the lung as the most common site. Since highly invasive biopsies are used for osteosarcoma diagnosis, new approaches for early diagnosis are needed for successful treatments and a good prognosis.  
As far as MALDI-exosome applications are concerned, [PERSON] and co-workers recently conducted a proteomic analysis of plasma exosomes from osteosarcoma patients in two distinct works devoted to rapid detection and metastasis evaluation, respectively. In both the applications, the vesicles were isolated from the plasma samples using ultracentrifugation and subsequently analyzed by MALDI-TOF. In one study [55], data arising from surface-enhanced Raman scattering and MALDI were synergically used to obtain high precision discrimination between osteosarcoma patients and healthy subjects. In the other work [56], MALDI and multivariate statistical analyses allowed for the differentiation between plasma exosomes from osteosarcoma patients with lung metastasis, osteosarcoma patients without lung metastasis, and healthy individuals. Furthermore, seven proteins were proposed as potential biomarkers of osteosarcoma lung metastasis using machine learning methods and LC-MS/MS analysis. Both methods demonstrate great potential for the clinical diagnosis of osteosarcoma.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 2 Core Principles of SLAM: Front-End and Back-End
### SLAM Front-End  
The front-end module of a SLAM system processes sensor data, typically from cameras or LiDARs, to extract features and estimate the robot's pose. The two main approaches for front-end visual SLAM (VSLAM) are feature-based and direct SLAM.
# Aerodynamic Benefits by Optimizing Cycling Posture
## 5 Discussion
### Comparing the Positions  
As expected, on changing the cyclist's posture, significant differences were observed. The aerodynamic drag coefficients (CdA) varied by more than a factor of 2, ranging from 0.150 (TT) to 0.328 (HH). Within a position, the CdA tends to increase slightly at crosswind yaw angles of 5-10\({}^{\circ}\) and decrease at higher yaw angles compared to a straight head wind. These findings were observed for the HH, HD and DB positions. While the HH and HD positions show poor performance in crosswinds, the DB position remains stable across yaw angles. The TT position, however, benefits from crosswinds and takes advantage of the sail effect.  
The fastest bike split was calculated for the TT position (weighted CdA model) with a predicted time of 04:07:11, close to the IM Hawaii bike course record. The slowest bike split, on the other hand, was calculated for the HH position (weighted CdA model) with a predicted time of 05:06:06, and therefore, 20% slower than the TT position. These substantial time savings by roughly 60 min over the course of 180 km were found only by posture optimization. Within the two different bike split models, notable time differences of \(\pm\)2.5% (5-8 min) were observed. The average CdA model generally predicts slower bike splits; however, it appears to be inaccurate over the whole course. The weighted CdA model, on the other hand, considers wind probability for each segment more accurately. Thus, wind yaw and the use of an appropriate model must be considered for bike split predictions.
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
###### Abstract  
This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG components are used. The aim is to enhance context relevance, answer faithfulness, and answer relevance. The results indicate that model one (OpenAI ADA and OpenAI GPT-4) achieved the highest performance, showing robust accuracy and relevance in response. Model three (MiniLM Embedder and OpenAI GPT-4) scored significantly lower, indicating the importance of high-quality components. The evaluation also revealed that well-structured reports result in better RAG performance than less coherent reports. Qualitative questions received higher scores than the quantitative ones, demonstrating the RAG's proficiency in handling descriptive data. In conclusion, a tailored RAG can aid investors in providing accurate and contextually relevant information from financial reports, thereby enhancing decision making.  
retrieval-augmented generation; large language models (LLMs); financial reports; question and answering with LLM +
Footnote †: journal: _Applied Sciences_  
approach was first introduced by [PERSON] et al. [4] and [PERSON] et al. [5]. RAG was devised to extend the capabilities of LLMs beyond conventional training data. By integrating a specialized body of knowledge, RAG enables LLMs to provide more accurate responses to user queries. In essence, RAG comprises two distinct phases: retrieval and generation. During the retrieval phase, defined sources (e.g., indexed documents) are analyzed to extract relevant information that is aligned with the user's prompt or question. The retrieved information is then seamlessly integrated with the user's prompt and forwarded to the language model. In the generative phase, the LLM leverages the augmented prompt and internal understanding to craft a tailored response that addresses the user's query effectively [6; 7].  
RAG is gaining momentum for its potential to improve LLM-generated responses by grounding models on the said external sources, thereby reducing issues such as inconsistency and hallucination [8; 9].