esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Additionally, isolated Personal Healthcare Devices (PHDs) that collect valuable healthcare data, such as fitness bands, pedometers, heart rate sensors in smart watches, among others, require a functional architecture for data collection. Standards, such as M2M communications developed by the European Telecommunication Standards Institute [19] and its extension on the OneM2M standard [20] defined the main functionalities of this type of architecture. Computer paradigms, as cloud computing, data warehousing, big data and data mining, in addition to standards, such as OpenEHR and OneM2M, enable the definition of an architecture and a platform that meet the requirements listed above. Taking the aforementioned issues into consideration, this article introduces a platform that which: favors the creation of new IoMT applications; grants interoperability from the collection to the publication of data; integrates multiple communication protocols; provides functionalities for data warehouse and application of OLAP techniques; and provides tools for data mining through Hadoop. It is based on an extension of OpenEHR semantics for the IoMT domain, following an OneM2M communication architecture which integrates IoMT devices to an OpenEHR-based EHR. Moreover, a performance study regarding the communication protocols considered by the platform is provided. The main contributions of the platform include:Extension and integration of OpenEHR semantics to the IoMT domain for simplifying the collection and dissemination of data and enhancing IoMT interoperability;
* CTL programming tools for IoMT solutions and data from isolated devices;
* Simplified data inspection using OLAP features; and
* Simplified healthcare knowledge extraction through data mining and machine learning capabilities. A development framework is also provided for the integration of sensors and healthcare systems to the platform with little effort. It is extensible in terms of sensors and communication protocols. Furthermore, its abstraction layer can be extended to meeting the specific requirements of these components. The framework has already implemented the Message Queuing Telemetry Transport (MQTT), Constrained Application (CoAP) and Representational State Transfer over HTTP (REST HTTP) communication protocols and a performance analysis is provided. As for the structure of the article, it is organized as follows: Section 2 addresses related works and introduces the basics for the platform definition; Section 3 describes the proposed platform and evaluates its performance; finally, Section 4 provides the conclusions and proposes future studies.
# Landslide Prediction with Model Switching +
## 2 Related Work
In contrast, our work is focused on how landslides can be actually predicted accurately and early. Not only is the goal different, the methods or techniques used or proposed are also quite different. We employ a very popular mathematical estimation model for landslide prediction, as defined above (namely, the SHALSTAB model and the factor of safety). We use ANN models for the model switching, as well as for the predictions. We adapt ADASYN for resolving the data imbalance problem. We also propose novel methods for model retraining and prediction horizon tuning. Details are given in the next section.
# Development of Korean Indigenous Low-Temperature-Tolerant Yeast for Enhancing the Flavor of _Yakju_
## 3 Results and Discussion  
### Screening of Yakju Strains  
The results of the screening of strains are expressed in Tables S1 and S2. Through the first screening (Table S1), 79 strains with excellent low-temperature growth and sniffing test results were first selected. Based on the result of ITS region nucleotide sequencing, 22 _S. cerevisiae_ and 57 non-_Saccharomyces_ were identified. Among them, there were 40 _Wickerhamomyces anomulus_, 12 _Hansenispora vineae_, 3 _Torulaspora delbrueckii_, and 2 _Pichia kudriavzvevii_ strains. Then, the biosynthesis inhibitor resistance test and \(\beta\)-glucosidase enzyme activity experiment were conducted, and the results are shown in Supplementary Table S2 (Table S2). The \(\beta\)-glucosidase enzyme activity was higher than 1 U/mL in 11 strains, and in the case of _P. kudriavzvevii_ N373, 1.41 U/mL was the highest activity. In the case of resistance to FPA, most _H. vineae_ strains showed high resistance (11/12), and _P. kudriavzvevii_ also demonstrated the same result in both strains (2/2). For cerulenin resistance, colonies were identified in _W. anomulus_ (39/40), _T. delbreuckii_ (3/3), and _P. kudriavzvevii_ (2/2).  
As \(\beta\)-glucosidase hydrolyzes \(\beta\)-1,\(A\) bonds of nonvolatile glycosidic compounds to release volatile aromatic compounds [32; 33], strains with high \(\beta\)-glucosidase enzyme activity can produce a large amount of volatile aromatic compounds include 2-phenethyl alcohol. Cerulenin is an antibiotic that inhibits the biosynthesis of fatty acids. The higher the resistance to cerulenin, the more favorable it is to produce medium-chain fatty acid (MCFA) ethyl esters, especially ethyl caporate, a flavor representing traditional Korean liquor, which can be produced up to five times [34]. \(\rho\)-Fluoro-phenylalanine (FPA) is a biosynthesis inhibitor through feedback and exists in the form of fluoroalanine attached to phenylalanine. The stronger the resistance to this compound, the more it produces 2-phenylethanol and 2-phenylethyld acetate, which gives off a rose-like scent [29].  
Through the two-step screening, one _S. cerevisiae_ and three non-_Saccharomyces_ strains were finally selected, and one isolate from each strain was selected for the diversification of fragrance components, as shown in Table 1.  
_S. cerevisiae_ W153 exhibited the best cell growth at 15 \({}^{\circ}\)C, and _P. kudriavzvevii_ N373 was resistant to both cerulenin and FPA. _H. vineae_ G818 and _W. anomalous_ A159 strains exhibited resistance to FPA and cerulenin, respectively.
## 4 Experiments and Results
### Discussion of the GLRL and FGLRL  
In this paper, before performing non-linear mapping, a shrinking layer is added to compress the feature dimensions. After the mapping, an expanding layer is added to restore the original dimensions, which makes the network structure funnel-shaped. The main purpose is to reduce the number of parameters in the non-linear mapping layer and reduce the computational complexity. This section discusses the effects of the presence and absence of shrinking and expanding layers. We call the network without shrinking and expanding layers of GLRL [16]. Listed in Table 3 are the PSNR and SSIM of GLRL and FGLRL in Set5 and their running time. The reconstruction results of the two methods are compared in Figure 5. It can be seen from Figure 5 and Table 3 that the difference between the reconstruction effect with and without the compression and expansion layer is not large, but the running time with the compression and expansion layer is significantly lower. Without changing the depth of the convolutional network, the running time of the method in this paper is reduced by two to three times.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Bicubic** & **Aplus** & **SRCNN** & **VDSR** & **DRCN** & **FGLRL** \\ \hline Set5 & & 0.35 & 0.18 & 0.74 & 0.63 & 0.29 \\ Set14 & & 0.69 & 0.39 & 1.35 & 0.98 & 0.23 \\ Urban100 & & 0.54 & 0.33 & 0.89 & 0.73 & 0.43 \\ BSD100 & & 0.46 & 0.23 & 0.75 & 0.66 & 0.32 \\ \hline \hline \end{tabular} Comment: Red color indicates the best performance of all methods and blue color indicates the second-best performance of all methods.  
\end{table}
Table 2: Comparison of average running time of various algorithms.  
Figure 5: Qualitative comparison. The first image shows “baby” (Set5 with scale factor \(\times\)3). The second shows image “butterfly” (Set5 with scale factor \(\times\)3).
# Effects of Physical Exercise on Substance Use Disorder: A Comprehensive Review
## 4 Conclusions by Types of Physical Exercise Programs
### Aerobic Exercise  
Moderate-intensity aerobic exercise interventions stand out as the most effective and accessible interventions, showing consistent benefits in reducing depressive and anxiety symptoms, improving sleep quality, and enhancing overall physical fitness. Activities such as walking, running, or cycling are easy to implement in various contexts, including group settings, fostering social interaction and treatment adherence.  
Effective programs typically include a minimum frequency of three to five weekly sessions, lasting 60 min per session, for at least 12 weeks. This design ensures significant cardiovascular and psychosocial adaptations. Moreover, aerobic exercises have demonstrated a positive impact on craving, attributed to their ability to modulate neurobiological systems related to pleasure and reward. Despite their effectiveness, further studies are needed to analyze their specific impact on variables such as sleep quality.
## 3 Methodology  
### Flow Distance  
Due to the complexity of flow data involving both origin and destination sites, flow modelling is needed to accurately measure the distance between OD flows. In contrast to the distance between points or polygons, the Euclidean distance between two flows ij and i'j' (Figure 4) is measured as d(i)(i'j') (Equation (1)), in which the direction of flow is considered:  
\[\mathrm{d}_{\mathrm{(ij)}(i^{\prime}j^{\prime})}=\sqrt{\left(\mathrm{x_{i}-x_{ i^{\prime}}}\right)^{2}+\left(\mathrm{y_{i}-y_{i^{\prime}}}\right)^{2}+ \left(\mathrm{x_{j}-x_{j^{\prime}}}\right)^{2}+\left(\mathrm{y_{j}-y_{j^{ \prime}}}\right)^{2}} \tag{1}\]  
where \(\mathrm{L_{ij}}\) and \(\mathrm{L_{i^{\prime}j^{\prime}}}\) are the flow lengths. Using only the location information of the endpoint may sometimes be insufficient because the flow not only represents the interaction or movement between the two locations, but also the distance and direction in which the interaction or movement occurs [31]. Ref. [31] proposed an adjusted OD distance algorithm called Flow Dissimilarity (FDS) as follows (Equation (2)).  
\[\mathrm{FDS}_{\mathrm{(ij)}(i^{\prime}j^{\prime})}=\sqrt{\frac{\alpha[\left( \mathrm{x_{i}-x_{i^{\prime}}}\right)^{2}+\left(\mathrm{y_{i}-y_{i^{\prime}}} \right)^{2}]+\beta[\left(\mathrm{x_{j}-x_{j^{\prime}}}\right)^{2}+\left( \mathrm{y_{j}-y_{j^{\prime}}}\right)^{2}]}{\mathrm{L_{ij}}\mathrm{L_{i^{ \prime}j^{\prime}}}}} \tag{2}\]  
where \(\mathrm{FDS}_{\mathrm{(ij)}(i^{\prime}j^{\prime})}\) is the Flow Dissimilarity between the two flows; \(\mathrm{L_{ij}}\) and \(\mathrm{L_{i^{\prime}j^{\prime}}}\) are the flow lengths. FDS is a scalar distance. The coefficients \(\alpha\) and \(\beta\) are used to control the relative importance of the end points of the two OD flows (\(\alpha+\beta=2\), \(\alpha>0\), \(\beta>0\), default \(\alpha=\beta=1\)). In the case of transport flows, set \(\alpha=\beta=1\). Besides the endpoint position and flow length, another spatial element of the flow is its directionality. However, in the case of transport flows, the influence of the direction on the weight is not taken into consideration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{1}{c}{**Description**} \\ \hline Dependent variable & Traffic volume 1  & Transport flow between counties \\ \hline \multirow{3}{*}{Explanatory variables} & Tpop & Total population in a county \\ \cline{2-3}  & GDP & GDP in a county \\ \cline{2-3}  & Distance & Network distance between the center points of the counties \\ \hline \hline \end{tabular}
\end{table}
Table 1: Explanatory variables of spatial interaction modelling.  
Figure 3: Spatial distributions of pushing (Tpop) (**a**) and pulling force (GDP) (**b**) and the histogram of distance (**c**).
## 2 Materials and Methods
### Instrumentation  
The EPR spectra were detected as a first derivative of the absorption signal on a JEOL JES-FA 100 EPR, JEOL, Tokyo, Japan spectrometer equipped with a standard TE\({}_{011}\) cylindrical resonator operating in the X-band (9.3 GHz). The measurements were carried out at room temperature. The solid samples were placed in a quartz standard EPR tube of inner diameter 4 mm and positioned in the EPR cavity. Three independent measurements were used for every solid sample, including a procedure, where inserting-removing-inserting of the sample was performed in the cavity of the EPR spectrometer. The data were averaged, and in that way, the error of the measurement was determined to be 5%. Before and after each series of measurements under the same conditions, a reference sample, magnetic Mn diluted in MgO (an internal standard for the above-mentioned spectrometer), was analyzed in the same way as those used for the sample measurements, in order to normalize the signal intensity of the samples and to minimize the error resulting from any instability in the spectrometer.
## 2 The Basics  
For the sake of simplicity, we consider the electric field of the laser pulse as a linearly polarized plane wave, so we can write it in scalar form without space coordinates [12]  
\[\varepsilon_{\mathrm{R}}(t)=1/2I(t)^{0.5}\exp[ia\omega_{0}t-i\phi(t)]+c.c. \tag{1}\]  
where \(t\) is time in the reference frame of the pulse, \(\omega_{0}\) is the carrier angular frequency, \(I(t)\) and \(\phi(t)\) are the time-dependent intensity and phase, _c.c._ means complex conjugate. Except where otherwise stated, we use its complex form in this article for convenience: \(\varepsilon(t)=1/2I(t)^{0.5}\exp[ia\omega_{0}t-i\phi(t)]\). Here, both \(I(t)\) and \(\exp[i\phi(t)]\) usually vary much slower than the carrier term \(\exp(i\omega_{0}t)\). In laser pulse measurement, we usually ignore the carrier term and use electric field complex amplitude to describe the laser pulse:  
\[E(t)=I(t)^{0.5}\exp[-\mathrm{i}\phi(t)]. \tag{2}\]  
In the frequency domain, the spectrum of the electric field is the Fourier transform of \(\varepsilon(t)\): \(\varepsilon(\omega)=\int\varepsilon(t)\exp(-i\omega^{\prime}t)\mathrm{d}t=E( \omega^{\prime}-\omega_{0})\), which means that the spectrum structure of the pulse is decided by its amplitude. Separating \(E(\omega^{\prime}-\omega_{0})\) into its intensity and phase and let \(\omega=\omega^{\prime}-\omega_{0}\) which is the relative frequency around the carrier frequency, yields  
\[E(\omega)=S(\omega)^{0.5}\exp[-\mathrm{i}\varphi(\omega)]. \tag{3}\]  
Equations (2) and (3) are the equivalent descriptions for a laser pulse. In these two sets of parameters \(\{I(t),\phi(t)\}\) and \(\{S(\omega),\varphi(\omega)\}\), if we know one, another is easy to obtain by applying Fourier transform. Only \(S(\omega)\) can be measured by a spectrometer directly, so the restoring of \(E(t)\) or \(\varepsilon(t)\) is turned into the question of reconstructing \(\varphi(\omega)\). We can write a Taylor series for spectral phase \(\varphi(\omega)=\varphi_{0}\) + \(\varphi_{1}\omega+\varphi_{2}\omega^{2}/2+\varphi_{3}\omega^{3}/6+...\)[16]. The zeroth-order term is the absolute phase, the first-order term shifts the pulse in time without changing the shape, the second-order term induces a linear chirp, and the third-order term causes an asymmetric structure to the pulse [14]. The zero and first orders of the phase do not impact the shape of the pulse and can be ignored in measurement, what we take care of is the high-order terms. Correlation method measures or estimates \(I(t)\) in time-domain. A typical spectrogram method Frequency-Resolved Optical Gating (FROG) [11; 12], measures the information of the combine of \(I(t)\) and \(S(\omega)\) for \"piecing together\" \(\varphi(\omega)\). Spectral interferometry, e.g., Spectral Phase Interferometry for Direct Electric-field Reconstruction (SPIDER) [7; 8], measures the spectral phase \(\varphi(\omega)\) directly.
## 2 Data Transmission in IoT Networks
Each group of components is connected to other groups, and control devices can communicate with any device on the network. In such a data transmission model, MANET provides a definite advantage. Devices, such as sensors, can send data to a destination, transmitting it through other devices within the network. Figure 2 shows the data transmission from the sensor to the recipient in the DT model. The recipient can be a data warehouse, decision center, data processing center, cloud data analysis, etc. RRNS transmits data in the MANET network in parallel breaking the message into several shares. It improves the speed at which data are transmitted across communication channels since such shares are smaller than the original message. We use the term Weighted Number System (WNS) as a traditional positional decimal number system. Figure 3 shows the model of data transmission packets. The receiver collects shares of information and combines them. The application knows how many shares have arrived and how many shares should arrive. RRNS has self-correcting properties for recovering the message if one or several shares are lost or intentionally changed. If arrived shares are not enough, it waits a certain time, and the packet is requested again or ignored. As a result, we can have a network with increasing speed and reliability. Figure 1: The general model of data transmission. Figure 2: Distributed data transmission model.
## 3 Optimization Modeling for the Uniformity Design of Accuracy Lifetimes
### Solution of the Optimization Model  
The optimization design model, which is based on the principle of uniform accuracy lifetime, is often nonlinear and has some constraint conditions. The method of sequential quadratic programming (SQP) is one of the best algorithms to solve these kinds of problems [30]. SQP is adopted to solve this optimization model. First, in terms of the original nonlinear optimization model, the Lagrange function is constructed as an approximate solution. Then, the function is approximated to a quadratic function. Therefore, the original optimization model can be simplified as a quadratic programming problem. Finally, the optimal solution obtained by the quadratic programming problem is that of the original optimization model.  
However, the problem of the local optimal solution will appear due to the improper selection of initial values when using the SQP method. To solve this problem, a method combining uniform sampling technology with SQP is proposed in this paper. The solving process can be carried out according to the following steps:  
Step 1: employ uniform sampling technology to construct a large number of initial values of the vector \(\mathrm{X}_{i}\) (\(i=1,2,\ldots,\mathrm{M}\)), which is composed of the design variables uniformly distributed in the constrained interval.  
Step 2: use the SQP method to find the local optimal solution corresponding to the initial value of the \(i\)-th group.  
Step 3: search for the global optimal solution from a large number of local optimal solutions and obtain the global optimal solution, as well as the corresponding optimal design vector.
## 4 Conclusions  
According to the results of this study, CFP showed that it can be considered as a potential material for asphalt modification, due to the improvements induced on the physical properties of the pure asphalt such as softening temperature, penetration, and phase separation, which resulted in increasing its stability and stiffness. The rheological properties of the asphalt modified with CFP, exhibited its reinforced elastic nature, which was very close to that of the asphalt modified directly with the pure SBS elastomer. These results suggest that CFP is a promising material that can be applied to partially replace the non-renewable asphalt binder modifiers that are extensively used in the construction of road infrastructure, without sacrificing the good performance of commercial asphalts. The addition sequence of SBS and CFP shows that when the last one is added first to the asphalt, the physical, thermal and rheological properties of the asphalt are enhanced, because it favors the interaction between CFP (particularly the keratin functional groups) and the heavy fractions of asphalt (like asphaltene and resins), resulting in particle-asphalt interfacial effects and a change in volume produced by particle interaction. The overall results of this study lead to the conclusion that the use of CFP for preparing modified asphalt mixtures is convenient not only because of the enhanced functional performance of the asphalt, but also because it is an efficient way of using a natural waste of great scientific potential that has not been extensively exploited yet.  
Conceptualization, L.M.C.-S., B.A.S.-C., M.Y.C.-C., A.B.M.-C. and J.L.R.-A.; methodology, L.M.C.-S., B.A.S.-C. and J.L.R.-A.; formal analysis, A.B.M.-C., B.A.S.C., L.M.C.-S., C.E.R.-G. and J.L.R.-A.; investigation, B.A.S.-C., A.B.M.-C., M.Y.C.-C. and J.L.R.-A.; resources, M.Y.C.-C., C.E.R.-G., B.A.S.-C. and A.B.M.-C.; data curation, C.E.R.-G., B.A.S.-C., A.B.M.-C. and J.L.R.-A.; writing--original draft preparation, A.B.M.-C., C.E.R.-G., B.A.S.-C., M.Y.C.-C. and J.L.R.-A.; writing--review and editing, B.A.S.-C., C.E.R.-G., and J.L.R.-A.; visualization, M.Y.C.-C., A.B.M.-C. and B.A.S.-C.; supervision, J.L.R.-A.; project administration, C.E.R.-G., A.B.M.-C. and B.A.S.-C.; funding acquisition, B.A.S.-C. and C.E.R.-G.  
This research was funded by TECNOLOGICO NACIONAL DE MEXICO, grant number 6687.18-P.  
The authors wish to thank to Eng. [PERSON], [PERSON], [PERSON] and [PERSON] from TA instruments for facilities to carry out the rheological measurements. Also to Ergon Asfaltos and Dynasol Elastomeros S.A. de C.V., Altamira, Mexico, for provide the materials for this research work.  
The authors declare no conflict of interest.
## 4 Experiment
### Results
#### 4.5.2 TopK Results and Analysis  
As shown in Figures 7 and 8, the MDAR model is better than the CKAN and EMKR models with the best results in two indicators: Precision@K and Recall@K, which indicates that the AFM is used to extract user cross features. After the relation-fused multi-head graph attention network is used to process the head entity features and relationship features of the knowledge graph, the recommendation effect of the model is further improved. In terms of specific values, compared with the CKAN model, the MDAR model has the indicators of Precision@K increased by about 2.13%and Recall@K increased by about 1.24% in the best effect.  
On the two relatively sparse datasets, BookCrossing and Last.FM, it was proven that MDAR can handle sparse data well. In contrast to KGCN and MKR, MDAR shows a significant improvement in Precision@K and Recall@K, which demonstrates the importance and effectiveness of introducing multi-head attention mechanisms into relational modeling. MDAR also shows good performance compared to KGAT, due to the fact that MDAR uses a multi-headed attention mechanism to reconstruct the relationships in the knowledge graph, thus learning a richer representation for each entity and enhancing the user's representation of the item. In terms of specific values, the best effect of the MDAR model is higher than that of the CKAN model, the index of Precision@K is increased by 2.75%, and the best  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**MovieLens-20M**} & \multicolumn{2}{c}{**BookCrossing**} & \multicolumn{2}{c}{**Last.FM**} \\  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline CKE & 0.801 & 0.742 & 0.924 & 0.897 & 0.671 & 0.633 & 0.744 & 0.673 \\ DKN & 0.655 & 0.589 & 0.801 & 0.757 & 0.622 & 0.598 & 0.601 & 0.581 \\ KGCN & 0.901 & 0.824 & 0.974 & 0.922 & 0.677 & 0.622 & 0.801 & 0.729 \\ MKR & 0.914 & 0.840 & 0.962 & 0.907 & 0.734 & 0.704 & 0.791 & 0.742 \\ KGAT & - & 0.976 & 0.925 & 0.731 & 0.669 & 0.829 & 0.740 \\ CKAN & 0.887 & 0.811 & 0.976 & **0.929** & 0.739 & 0.665 & **0.842** & 0.745 \\ EMKR & 0.923 & 0.848 & - & 0.746 & 0.705 & 0.808 & 0.753 \\
**MDAR** & **0.926** & **0.854** & **0.977** & 0.919 & **0.753** & **0.717** & 0.810 & **0.755** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of AUC and ACC in CTR prediction.  
effect of the MDAR model is higher than that of the CKAN model; the index of Recall@K is increased by 2.58%.