esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 4 In-Situ Measurement-Based Fatigue Reliability Analysis
### Result and Discussion
The analysis has also been made in terms of hot spot stress, as shown in Figure 17a,b. In the OSD using CUs, the fatigue reliability of D1 reaches the upper limit after 43 years and then the lower limit after 74 years. A similar trend is shown in the reliability of D2 that the two limits are reduced to after 38 and 66 years, respectively. In the OSD using TEUs, the upper limit is reached in the details D1 and D2 after 109 and 97 years, respectively. However, the reliability of the two details stays well above the lower limit up to 120 years. The result in terms of hot spot also suggests an enhancement in fatigue life of RD joints by using TEUs. Meanwhile, it is worth noting that the reliability solved in hot spot stress is much lower than in nominal stress, indicating the conservativeness of the hot spot stress. Through considering the hot spot result with the lower limit, in the OSD using CUs, the design fatigue life can be predicted as 74 years for D1 and 66 years for D2. Under the same assumption, in the OSD using TEUs, the design fatigue life of D1 and that of D2 are well above 120 years. Besides, comparison can also be made based on the design fatigue life determined using the upper limit. As a result, the design fatigue life of D1 is 43 years in the OSD using CUs and 109 years in the OSD using TEUs, i.e., enhanced by about 153%. Similarly, for the detail D2, the design fatigue life is 38 years in the OSD using CUs while 97 years in the OSD using TEUs, i.e., about a 155% increment. Figure 16: Reliability in terms of nominal stress: (**a**) critical detail D1 and (**b**) critical detail D2.
## 2 Literature Review
### Heuristic and Metaheuristic Approaches  
Given the computational complexity of large-scale inspection planning problems, numerous researchers have turned to heuristic and metaheuristic methods. [PERSON] et al. [33], propose a bi-objective model that allocates inspection resources under strict capacity constraints in flexible manufacturing systems. Their approach minimizes overall costs while balancing inspection station workloads and employs a modified NSGA-II algorithm to efficiently search for near-optimal solutions. [PERSON] et al. also tackled a bi-objective inspection planning problem for serial MSMSs, integrating production and inspection costs with customer satisfaction criteria, and solved it using a metaheuristic approach. [PERSON] et al. [34] introduced a particle swarm optimization algorithm to plan inspections in serial production systems, accounting for fuzzy costs and inspection errors. In addition, [PERSON] et al. [35] proposed an intuitionistic fuzzy grey model that integrates fuzzy set theory and grey relational analysis to assist inspection scenario selection under expert opinion uncertainty. [PERSON] [36] developed a genetic algorithm considering queuing effects in multi-product, multi-stage production systems, while [PERSON] [37] combined evolutionary algorithms with simulation models to jointly optimize inspection locations and sampling parameters. These studies underscore the flexibility of heuristic and metaheuristic techniques in managing problem complexity and uncertainty. Nonetheless, they often provide approximate solutions without offering the optimality guarantees of exact methods like MILP.
## 2 Materials and LCA Methodology
### Life Cycle Inventory (LCI) Analysis  
The second phase of the LCA, referred to as life cycle inventory (LCI), involved the collection and quantification of all input and output flows that cross the system boundaries [26]. Table 2 presents the main inventory data for biogas production, detailing inputs from the technosphere and their associated material consumption, as well as outputs to both the technosphere and the environment. The anaerobic digestion (AD) plant operation utilizes various feedstocks, including manure (25.1 kg), waste maize silage (8.5 kg), fodder beet (8.5 kg), and cheese whey (8.5 kg), alongside an energy input of 4054.96 kJ. Additionally, construction materials, such as concrete (500.47 g), reinforced steel (18.1 g), and laminated timber (9.22 g), are required for the infrastructure. The outputs to the technosphere include biogas (7.18 Nm\({}^{3}\)) and digestate (42.82 kg), while avoided products, such as nitrogen (57.81 g), phosphorus (67.23 g), and potassium (179.8 g) fertilizers, are accounted  
Figure 4: LCA boundaries of H\({}_{2}\) production from (**a**) biogas reforming [44] and (**b**) agricultural residue gasification [45]. for. The environmental emissions include biogenic CO\({}_{2}\) (90.42 g), biogenic CH\({}_{4}\) (284.19 g), and NH\({}_{3}\) (40.45 g). In SimaPro, material flows were quantified based on Equation (8):  
\[\text{Total material flow (kg or MJ)}=\text{Activity Level (FU)}\times\text{Input/Output per FU} \tag{8}\]  
where  
Activity Level (Functional Unit, FU) = 1 kg H\({}_{2}\);  
Input/Output per FU = amount of material or energy required or produced per FU (from the inventory data). Table 3 provides the main inventory data for hydrogen (H\({}_{2}\)) production through biogas reforming, detailing inputs from the technosphere, construction materials, equipment, and purification processes. The process involves the use of biogas (7.18 Nm\({}^{3}\)), water (7.5 kg), air (19.1 kg), and electricity (1995.28 kJ) for feed conditioning and reforming. Construction materials, such as concrete (227.9 g) and steel (72.81 g), contribute to infrastructure, while steel, aluminum, and various alloys are utilized for equipment and purification. The output to the technosphere includes hydrogen (1.0 kg), with biogenic CO\({}_{2}\) emissions amounting to 13.43 kg. Similarly, Table 4 outlines the inventory data for hydrogen production from agricultural residue gasification, where rice husk (15.4 kg), diesel (2.5 \(\times\) 10\({}^{-3}\) kg), and water (0.248 kg) serve as primary inputs. Additional technosphere inputs include de-ionized water (0.209 kg) and zinc oxide (1.617 \(\times\) 10\({}^{-3}\) kg). The energy demand consists of electricity (0.19 kWh), with an avoided electricity production of 2.6 \(\times\) 10\({}^{-3}\) kWh. The process emits nitrogen oxide (1.76 g), carbon dioxide (biogenic) (1.78 kg), and water (0.15 kg) into the air, while emissions to the water include ash (1.63 kg) and hydrogen sulfide (58.5 g).
###### Abstract  
Saururus chinensis (SC), a traditional medicine, has been used for the treatment of edema, jaundice, gonorrhea, and several inflammatory diseases in China and Korea. Our previous studies reported the anti-osteoporotic activity of SC extract (SCE) in ovariectomized (OVX) rats but the mechanism of this effect was unclear. The aim of this study was to explore the anti-osteoporotic effect of SCE and elucidate the underlying mechanisms in ovariectomized rats using a metabolomics approach based on ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-Q-TOF MS) to analyze metabolic changes. Female Sprague-Dawley (SD) rats were divided into sham, OVX, and SCE treatment groups. Partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots separated OVX and sham groups fairly well. Further, 22 potential biomarkers were identified in the rat models of estrogen deficiency-induced osteoporosis, and SCE showed regulatory effects on three vital metabolic pathways associated with these biomarkers, namely, glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, and linoleic acid metabolism. The metabolomics approach reliably confirmed that SCE exerted its anti-osteoporotic effect by intervening with lipid metabolism, thus preventing osteoporosis. This study also showed the promising potential of this approach in an evaluation of natural medicine efficacy.  
Saururus chinensis; metabolomics; phytotherapy; osteoporosis; lipid metabolism
## 4 Conclusions
Furthermore, the relations between those peaks allow the system to obtain the incidence angles from each beacon on the QADA surface. From these angles, the position of the receiver is estimated, provided that the beacons' positions are known. The proposal has been characterized experimentally with a real setup installed in a room with a surface area of 55 m\({}^{2}\) and a height of 3.4 m. The beacons were installed in the ceiling in a square with a side of 1.2 m and the receiver on the floor (the effective testing area was \(3\times 3\) m\({}^{2}\), below the transmitters). Acquired experimental signals and their correlations with the different transmitted codes have been presented. Additionally, the estimation of the position of the receiver in that environment for seven particular points (P1 to P7), with P1 at the center of the emitters' projection on the floor and the other covering one of the octants of this projection, has been performed for one hundred trials, using an Optitrack system to determine the ground-truth. The obtained absolute average errors, considering all the points, are 7.1, 5.4, and 47.3 cm in the X, Y and Z axes, respectively. As for future works, it is intended to carry out a fine calibration of the intrinsic parameters of the QADA with its aperture in order to apply PnP algorithms and to estimate the complete 3D receiver pose. Conceptualization and methodology, E.A.-E., A.H. and J.U. ; software, hardware and data-curation, E.A.-E. and J.M.V. ; validation, E.A.-E. and S.L. ; formal analysis of coding, M.C.P.-R.; resources, J.U. and A.H.; writing--original draft preparation, E.A.-E., A.H. and J.M.V. ; writing-review and editing, J.U. ; visualization, E.A.-E.; supervision, J.M.V. and M.C.P.-R.; project administration, J.U. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry of Science, Innovation and Universities (MICROCEBUS project, ref. RTI2018-095168-BC51), the Comunidad de Madrid (CODEUS and PUILPOS projects, ref. CM/JIN/2019-043 and CM/JIN/2019-038), and the Junta de Comunidades de Castilla La-Mancha (FrailCheck project, ref. SBFLY/17/180501/000392). The authors declare no conflict of interest.
# A Division Algorithm in a Redundant Residue Number System Using Fractions
###### Abstract  
The residue number system (RNS) is widely used for data processing. However, division in the RNS is a rather complicated arithmetic operation, since it requires expensive and complex operators at each iteration, which requires a lot of hardware and time. In this paper, we propose a new modular division algorithm based on the Chinese remainder theorem (CRT) with fractional numbers, which allows using only one shift operation by one digit and subtraction in each iteration of the RNS division. The proposed approach makes it possible to replace such expensive operations as reverse conversion based on CRT, mixed radix conversion, and base extension by subtraction. Besides, we optimized the operation of determining the most significant bit of divider with a single shift operation of the modular divider. The proposed enhancements make the algorithm simpler and faster in comparison with currently known algorithms. The experimental simulation using Kintex-7 showed that the proposed method is up to 7.6 times faster than the CRT-based approach and is up to 10.1 times faster than the mixed radix conversion approach.  
Keywords:residue number system; redundant residue number system; modular division; fraction; algorithm +
Footnote †: journal: Applied Mathematics and Mathematical Modeling, Institute of Mathematics and Natural Sciences, North-Caucasus Federal University, 355000 Stavropol, Russia; [PERSON]ESS] (N.C.); [PERSON]DRESS] (M.B.); [PERSON]RESS] (M.D.); [PERSON]SS] (A.N.); [EMAIL_ADDRESS] (M.V.)
## 3 Results and Discussion
### Degradation of PAHs in Oily Sludge by P&A Bacterial Consortium  
The PAHs degradation study in this paper above were conducted under ideal conditions. However, there are many uncontrollable factors in the degradation process of PAHs in actual oily sludge, leading to the differences in degradation effect between them. Therefore, the oily sludge from oil polluted area in Shaanxi Province, China, was used to test the practical application possibility of P&A bacterial consortium in the test below (Figures 10 and 11).  
The P&A bacterial consortium was used to degrade oily sludge that did not only contain NAP, PHE, and PYR (0.2090 mg/g NAP, 3.1116 mg/g PHE, and 1.00595 mg/g PYR) but also other types of PAHs compunds. After 8 days, more than 80% of NAP, PHE, and PYR was degraded in triplicate parallel groups experiments (Figure 10). At the same time, it was found from chromatogram analysis of oily sludge that the P&A bacterial consortium had the ability to degrade Acenaphthylene, 1H-Phenalene, 4H-Cyclopenta[d,e,f]phenanthrene, Fluoranthene, Triphenylene, 9H-Cyclopenta[a]pyrene, and Benzo[k]fluoranthene in oily sludge (Figure 11).  
Figure 9: Effects of surfactants on the degradation efficiency of PAHs (\(\Diamond\): NAP degradation rate; \(\Delta\): PHE degradation rate; \(\Diamond\): PYR degradation rate; \(\Diamond\): Crude oil degradation rate).
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## 4 Simulation Analysis and Application
### Calculations of Three Safe Distances for Different Road Conditions and Slopes
Figure 10: Changes in the basic safe distance \(D_{2}\) with various \(V_{B}\) and \(V_{r}\) when \(i=-0.3\) and \(d=5\). Figure 9: Changes in the minimum safe distance \(\text{D}_{1}\) with various \(V_{B}\) and \(V_{r}\) when \(i=-0.3\) and \(d=5\). According to the implementation of Article 80 of the Road Traffic Safety Law of the People's Republic of China, when a car is being driven on an expressway at a speed of more than 100 km per hour, the driver shall keep a distance of more than 100 m from any vehicle that is in front and in the same lane. When the vehicle speed is lower than 100 km per hour, the distance between the vehicle and the vehicle in front in the same lane may be shortened appropriately, but the minimum distance shall not be less than 50 m.  
As can be seen in Figures 3-11 and Tables 2-10, the numerical simulation results meet the highway speed limit requirements stipulated in Article 80 of the Implementation Regulations of the Road Traffic Safety Law of the People's Republic of China. According to the detailed analysis in Figures 3-11 and Tables 2-10, under the same slope section, the minimum safe distance \(D_{1}\) is the minimum, the basic safe distance \(D_{2}\) is the second and the sufficient safe distance \(D_{3}\) is the maximum. Under the same driving conditions, such as the same speed \(V_{B}\) of car B, relative speed \(V_{r}\) and the same road adhesion, all kinds of safety distances required by the downhill section are the largest, followed by the corresponding flat section, and the smallest on the uphill section. The results of this study can provide a more specific reference value for the safe following distance at different vehicle speeds and provide a theoretical basis for the traffic management department to formulate relevant policies.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 3 Signal-Processing Techniques
### Wavelet Transform  
A wavelet transform (WT) effectively decomposes a signal into basis functions that are derived from a mother wavelet through processes of contraction, expansion, and translation. This method provides an opportunity for the in-depth analysis of the signal across different scales and positions, allowing for an enhanced understanding and interpretation of the data [55]. Wavelet compression is a data compression technique ideal for images, and sometimes for video and audio. Its goal is to minimize the file size, and it can be either lossless or lossy. Wavelet transforms effectively represent transients--like percussion sounds in audio or high-frequency components in images, such as stars in a night sky. This allows for a more efficient representation of these elements compared to other methods, like the discrete cosine transform.  
This technique is applied to accelerometer data processing, as in [56], where the device measures cardiac activity from the chest using an accelerometer.  
The paper [57] introduces a novel feature called wavelet-AR for recognizing human activities using triaxial acceleration signals. The authors employed wavelet transform techniques to break down the raw accelerometer data, deriving decomposed signals that effectively distinguish between various activities. Their findings demonstrate that wavelet-AR achieves significant differentiation among different types of human activities, presenting an innovative option for activity recognition.
# Triplet Loss Network for Unsupervised Domain Adaptation
## 3 Architecture and Methodology  
### Overview  
The following section describes the proposed model for UDA. We start by defining the notation that we use. The source domain images and labels are denoted as \(X^{s}=\left(x_{i}^{s},y_{i}^{s}\right)_{i=1}^{N}\) and the target domain images are \(X^{t}=\left(x_{i}^{t}\right)_{i=1}^{M}\); both \(x_{i}^{s}\) and \(x_{i}^{t}\) share the same dimensions, but different distributions. As our research focuses on closed-set domain adaptation, the target and source domain labels Y are exactly the same.  
Our model contains an encoder, a classifier, and a discriminator, as shown in Figure 2. The encoder and the classifier make up the final classification model, and the discriminator is used to train the encoder to generate domain-invariant features. Hence, our classification function \(f\) is the composition of two functions \(f=e\circ c\), where \(e:\mathcal{X}\longrightarrow\mathcal{Z}\) is the encoding function that maps the images into feature vectors and \(c:\mathcal{Z}\longrightarrow\mathcal{Y}\) categorizes the features for both domains. The discrimination function \(g\) is also the composition of two functions \(g=e\circ d\), where \(e\) is the same encoding function and \(d:\mathcal{Z}\longrightarrow\mathcal{A}\) is the binary classification function that discriminates the domain of the latent representation of the input image.  
In addition to the usual classification and discrimination loss, we introduced a separation loss that operates on the output of the encoder, similar to Linear Discriminant Analysis (LDA).
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## 4 Low-Rank Approximation
### Deflation Preconditioning  
The Krylov subspace has to be constructed for every collocation point \(\xi\). In order to simplify the notation, the dependence on \(\xi\) explicit is not made explicit. It is necessary to solve systems in terms of bending \(\mathbf{S}_{B}\) and membrane \(\mathbf{S}_{M}\) (membrane and shear summed together)  
\[(\mathbf{S}_{M}+t^{2}\mathbf{S}_{B})\,v=b, \tag{19}\]  
for every thickness \(t\). In order to avoid factorisation of \(\mathbf{S}\) at every \(\xi\), it would be natural to consider iterative methods. Unfortunately, shell problems are singularly perturbed and the parameter-independent preconditioning of such systems is an open problem. However, if one considers a _sequence of problems_, it is possible to transform the problem as follows: Let \(\mathbf{S}_{M}=\mathbf{L}\mathbf{L}^{T}\) (Cholesky decomposition); then,\[\mathbf{L}(\mathbf{I}+t^{2}\mathbf{L}^{-1}\mathbf{S}_{B}\mathbf{L}^{-T})\mathbf{L}^ {T}\,v=b, \tag{20}\]  
where the subspace defined by \(\mathbf{L}^{-1}\mathbf{S}_{B}\mathbf{L}^{-T}\) is invariant over all thicknesses \(t\). The inner systems \((\mathbf{I}+t^{2}\mathbf{L}^{-1}\mathbf{S}_{B}\mathbf{L}^{-T})\,\vartheta=b\), have their spectra bounded by \(1\) from below, making it sufficient to collect the largest eigenvectors into subspace \(W\), say. Once the subspace \(W\) has been constructed, the deflated conjugate gradient method can be applied [34; 35]. Here, collecting Lanczos vectors is not sufficient since the construction of the Krylov subspace leads to multiple right-hand sides. This can be taken still further by constructing the subspace only once, corresponding to the point \(\xi=(0,\ldots,0)\), and using this as an approximative preconditioner for every other collocation point, leading to only a small number of extra iteration steps.  
**Remark 1**.: _Since the system can be singularly perturbed, i.e., \(\mathbf{S}_{M}\) is not necessarily invertible, it is possible to use the stabilised version_  
\[\left((\mathbf{S}_{M}+\epsilon\mathbf{S}_{B})+(t^{2}-\epsilon)\mathbf{S}_{B} \right)v=b, \tag{21}\]  
_where \(\epsilon\in[0,t^{2}]\). The choice of optimal \(\epsilon\) is problem-dependent._  
**Remark 2**.: _This kind of preconditioner requires multiple sparse triangular solves. In high-level systems such as Matlab and Mathematica, the built-in solver routines typically have superior performance, and the benefits of the approach proposed here are difficult to realise [36]._
# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 2 Materials and Methods
### Equipment and Data Processing  
Heart rate data were collected using Garmin HRM-Pro heart rate bands (Garmin Ltd., Olathe, KS, USA), which transmitted real-time heart rate data via an ANT+ protocol to the WIMU PRO wearable inertial measurement devices (RealTrack Systems, Almeria, Spain). The WIMU PRO system combines various sensors in a lightweight 70 g unit of 81 \(\times\) 45 \(\times\) 16 mm and has been previously validated for heart rate registration during physical activity, showing excellent agreement (ICC \(>\) 0.96) with criterion measures [9]. The inertial devices were secured to the participants' upper backs using adjustable harnesses. The WIMU PRO devices recorded the heart rate data at a sampling frequency of 10 Hz, which were stored in the internal memory of the devices during the exercise sessions and subsequently transferred to a computer via USB connection for processing.  
Data processing was performed using SPROT software (Version 956, RealTrack Systems, Almeria, Spain) for initial data extraction and quality control, followed by custom algorithms developed in MATLAB (Version R2024a, MathWorks Inc., Natick, MA, USA). The raw heart rate data underwent preliminary processing to eliminate artifacts using a moving median filter with a window size of 5 samples, which effectively removed spurious readings while preserving the physiological dynamics of the heart rate signal. This approach has been recommended for pre-processing heart rate data in exercise settings [28]. Following artifact removal, the heart rate data were smoothed using a 7-point moving average filter to reduce random noise while retaining the characteristic patterns of cardiac response during high-intensity intervals and recovery periods.  
For the algorithm development, the processed heart rate data were segmented into individual HIIT cycles consisting of high-intensity work intervals and recovery periods. The segmentation was performed automatically using the timestamps recorded during the experimental protocol and verified through visual inspection of the data. Custom MATLAB functions were then developed to identify local maxima and minima in the heart rate signal, calculate the ascending and descending slopes, and determine intermediate averages between the extrema (see Section 2.5 for more details). The detection algorithm implemented a peak-finding function with adaptive thresholds based on the signal's local characteristics, which improved accuracy in identifying true physiological peaks while rejecting artifacts.