esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Based Intelligent Monitoring System Applying RNN
## 1 Introduction
IoT technology is applied in various domains such as intelligent CCTV and intelligent robots, and users can connect to the server with a smartphone and manage real-time monitoring and immediate response [9; 10]. By applying technologies such as machine learning and deep learning, anomalies can be detected automatically and monitored intelligently [11]. In particular, an abnormal state may be monitored through the pattern recognition or image recognition of sensing data and context information obtained from various resources [12]. Many studies have been conducted on intelligent monitoring that detects abnormal patterns by applying machine learning or deep learning [13; 14]. Machine learning based on Recurrent Neural Networks (RNNs) is optimized for time series data or predictive maintenance and can mainly be used for stock and economic data [15; 16]. In biobanks, temperature sensor data are time series data, and RNN is useful as a predictive learning model. Here, we propose an IoT-based intelligent monitoring system that applies RNN to support the predictive maintenance of biobanks for the safe use of biomass. The proposed system can be used to measure not only the internal temperature of the cryogenic freezer, but also the temperature of each component (compressor, condenser, heat exchanger, evaporator, etc.) through which the refrigerant circulates. Then, the temperature change can be monitored in real time. To guarantee predictive maintenance we apply RNN deep learning techniques to the IoT-based monitoring of biobanks. The remainder of this paper is organized as follows. Section 2 explains the research background and related work on intelligent monitoring based on IoT. Section 3 presents the design of an IoT-based intelligent monitoring system and RNN model. Section 4 describes the experimental results. Finally, Section 5 presents a brief conclusion.
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 2 Methodology
### Radiator Size  
In a final step, an estimate of the radiator size that would enable a given thermal resistance was made. The radiator emissivity was set to 0.95 and the radiator was assumed to radiate to deep space, at a temperature of 3 K. The thermal resistance (\(R\)) was also calculated as a function of radiator temperature, according to [15]  
\[R=\frac{1}{\varepsilon\sigma A\Big{(}T_{rad}^{2}+T_{space}^{2}\Big{)}\big{(}T_{ rad}+T_{space}\big{)}} \tag{1}\]  
where \(\varepsilon\) is emissivity, \(\sigma\) is the Stefan-Boltzmann constant, \(A\) is the area of the radiator, \(T_{rad}\) is the radiator's temperature and \(T_{space}\) is the temperature of deep space--3 K.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results
### Simulation and Validation
#### 3.2.3 Simulation Results of PLUS Model  
In PLUS, land expansion is firstly extracted based on the 30 m resolution LU maps of 2015 and 2020. Then, the development potential of each LU type on each unit is predicted through the LEAS module, and the development potential map of each LU type is output. Finally, the LU in 2020 is simulated by setting various parameters through the CARS module, as shown in Figure 4b. The kappa coefficient is 0.759, a 0.032 improvement over the CLUE-S model, and the simulation result is better. This indicates that the PLUS model has good applicability in the study area and can be used for LU studies of future scenarios.
# An IP Multimedia Subsystem Services Proxy Gateway Based on a JAVA Dynamic Module System
## 3 System Architecture
### IMS Service Proxy Gateway  
In the IMS service proxy gateway, OSGi which plays the role of a middleware is used to integrate software and hardware services and effectuate the IMS service proxy gateway. As shown in Figure 3, the gateway includes: (1) OSGi framework; (2) functional modules (Bundles); and (3) configuration services. With Bundles carried in the OSGi framework via OSGi core services, all functional modules in which interdependency is created can be upgraded and unloaded conveniently. Moreover, the Bundles can be activated and configured flexibly by compiling the configuration service.  
The function/mechanism supported by the IMS service proxy gateway is available to a respective Bundle, for example, the IMS Bundle and the Real-time Transport Protocol (RTP) Bundle are used to manage protocols with respect to IMS services such that IMS services are effectuated by the IMS service proxy gateway. In addition to the above functional Bundles, other types of Bundles are also designed for basic operations.  
* _Core Bundle_: The Core Bundle provides a mechanism to manage events for interconnectivities between different types of software/hardware services and the OSGi framework via OSGi core services.
* _I/O Bundle_: The I/O Bundle takes charge of inputs/transfers of network services.
* _Model Bundle_: The Model Bundle is responsible for configuring parameters between all types of modules for the IMS service proxy gateway such that the flexible operation modes between objects via software configurations are available.
* _User Interface (UII) Bundle_: The UI Bundle provides a user interface through which the functional module, REST (Representational State Transfer) Bundle [19], for resources of the IMS service proxy gateway is accessed.
* _Action Bundle_: The Action Bundle is characteristic of additionally incorporating a distinct automated controllable context which is activated with the Action Bundle matching the configuration service.  
The IMS service proxy gateway has to support processing of IMS services or is a gateway to integrate peripheral software/hardware services in a field domain through OSGi in case of no capacity to process IMS services. Accordingly, the service modules such as IMS Bundle, RTP Bundle and Session Mobility Bundle should be incorporated in the IMS service proxy gateway, as shown in Figure 4. With the core protocol of SIP, the IMS Bundle is applicable to processing exchanged SIP signals specifically such that the true media transfer is handled by the RTP Bundle after completion of connectivity.  
Figure 3: Core architecture of the IMS service proxy gateway.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
###### Abstract
As shown in Figure 1, after introducing the VNDN architecture, we develop a thorough analysis on caching transient contents by also exploring related research conducted over the years, outside the NDN scope. Then, we present an in-depth overview about how VNDN can improve its delivery performance if freshness-awareness is introduced in the caching and replacement mechanism. A set of lesson learned and interesting research perspectives are also outlined. Figure 1: Organization of the paper (graphical view). A few existing survey papers about VNDN can be already found in the literature; however, none of them tackle our targets. In particular, the applicability of NDN in vehicular networks, coupled with a preliminary analysis of related work, was originally discussed in References [11, 12]; however, caching aspects were only briefly introduced. The work in Reference [13] surveys existing NDN forwarding strategies for vehicular ad hoc networks (VANETs), without focusing on the literature about caching. A comprehensive and systematic survey of VNDN-based communications is reported in Reference [14], where the authors cover different architectural aspects related to naming, routing, forwarding, caching, mobility, and security. However, their analysis does not consider the case of transient information. Similarly, the work in Reference [15] is specifically devoted to VNDN caching, but it does not elaborate on the benefits and challenges of caching transient contents. To the best of our knowledge, this paper represents the first effort to review and analyze transient content caching in VNDN. The rest of the paper is organized as follows. Section 2 introduces VNDN and the reference network scenario. Section 3 surveys existing works dealing with transient contents in traditional vehicular networks and identifies a set of lesson learned and inputs for the subsequent analysis in the NDN context, reported in Section 4. Section 5 surveys existing caching and replacement policies specifically designed for transient contents in VNDN, while Section 6 presents some future research perspectives. Finally, Section 7 concludes the paper.
## 5 Conclusions  
Based on the numerical results, we found that although the B-field in the central region of the inverter exceeds the reference level of the ICNIRP for general public exposure, when the distance from the inverter is increased, the B-field values sharply decrease. The maximum B-field over the space occupied by the driver's body and the E-field in the driver's trunk and CNS are all below the exposure limits defined by the ICNIRP for general public exposure. The electromagnetic environment generated by the inverter adopted in this study (12 kW, Semikron, Germany), at its rated state in the paper, is therefore safe and would not affect the PEV driver's health. Additionally, the results of this study could effectively supplement research regarding the electromagnetic environment of PEVs and provide references for the formulation of industry standards for electromagnetic exposure and the design of vehicles that are safe in terms of electromagnetic exposure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **B (\(\upmu\)T)** & **E (mV/m)** \\
**(Exposure Source)** & **(Head Area)** & **(CNS)** \\ \hline Reference [17] (equivalent source) & 0.040 & 0.20 \\ Reference [23] (wireless charging coil) & 0.003 & 0.30 \\ This paper (inverter) & 0.005 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the results of this study and those of other studies (with the relevant references listed).  
**Author Contributions:** Conceptualization, X.D. and M.L.; Methodology, X.D.; Investigation, X.D.; Writing--Original Draft, X.D. and Y.G.; Calculation, Y.G.; Data analysis, Y.G.; Funding Acquisition, X.D. and M.L.; Supervision, X.D. and M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Gansu Province, China (Grant No. 20 JR10 RA217, Grant No. 23 JRRA889) and 2023 Outstanding graduate student \"Innovation Star\" project in Gansu Province (2023 CXZX-552).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors would like to thank the technical team of the Key Laboratory of Opto-Electronic Technology and Intelligent Control of the Ministry of Education, Lanzhou Jiaotong University for their technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Impact of Ageing on Pea Protein Volatile Compounds and Correlation with Odor
## 2 Results and Discussion
### Odor Evolution  
The evolution of the odor during the two storage treatments was presented in Table 2. The evaluation of the odor showed that treatment B (30 \({}^{\circ}\)C--Dark) did not really affect the odor. The product developed a 'roasted' attribute. This new attribute could be attributed to the Maillard reaction [27]. Especially, the 'roasted' attribute could arise from the lysine, threonine, and leucine involvement in the Maillard reaction [28]. These amino acids and particularly lysine and leucine were well present in pea protein, as presented in the Appendix A, Table A1. Treatment A (20 \({}^{\circ}\)C--Light) led to an odor deterioration as early as three months, with an increase in the 'beany' and 'earthy' attributes, and later on with the development of 'rancid' and'sulfurons' off-notes. The increase of those attributes could be linked to lipid oxidation [16]. The 'rancid' attribute occurred from a strong increase of these reactions, leading to a quick deterioration of the product [29].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Months** & **0** & **3** & **6** & **9** & **12** \\ \hline \multicolumn{5}{c}{Treatment A—20 \({}^{\circ}\)C Light} \\ \hline L* & 82.9 \(\pm\) 0.2 d & 87.0 \(\pm\) 0.3 c & 88.2 \(\pm\) 0.5 b & 88.9 \(\pm\) 0.2 a & 88.8 \(\pm\) 0.2 ab \\ a* & 3.14 \(\pm\) 0.06 a & 2.3 \(\pm\) 0.2 b & 1.9 \(\pm\) 0.3 c & 1.37 \(\pm\) 0.03 d & 1.50 \(\pm\) 0.05 d \\ b* & 20.84 \(\pm\) 0.08 a & 11.1 \(\pm\) 0.4 b & 10.6 \(\pm\) 0.5 c & 10.4 \(\pm\) 0.1 c & 10.7 \(\pm\) 0.2 bc \\ \hline \multicolumn{5}{c}{Treatment B—309 \({}^{\circ}\)C Dark} \\ \hline L* & 83.1 \(\pm\) 0.2 a & 82.7 \(\pm\) 0.3 a,b & 82.1 \(\pm\) 0.1 c & 82.4 \(\pm\) 0.2 b,c & 82.5 \(\pm\) 0.3 b,c \\ a* & 3.21 \(\pm\) 0.06 c & 3.39 \(\pm\) 0.05 b & 3.62 \(\pm\) 0.06 a & 3.66 \(\pm\) 0.05 a & 3.62 \(\pm\) 0.06 a \\ b* & 21.21 \(\pm\) 0.08 a & 20.6 \(\pm\) 0.2 b & 20.8 \(\pm\) 0.2 b & 20.4 \(\pm\) 0.2 b & 18.8 \(\pm\) 0.3 c \\ \hline \multicolumn{5}{c}{\(\Delta\)E*} \\ \hline Treatment A & 10.6 & 11.6 & 12.2 & 11.8 \\ Treatment B & 0.8 & 1.2 & 1.2 & 2.5 \\ \hline \hline \end{tabular} a–d: Statistical groups for each parameter on each sample, during time. Different letters mean significant difference at the 0.05 level.  
\end{table}
Table 1: Color evolution during the ageing (\(n=5\) points in triplicates).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treatment A (20 \({}^{\circ}\)C—Light)** & **Treatment B (30 \({}^{\circ}\)C—Dark)** \\ \hline
0 & Light ‘beany’ & Light ‘beany’ \\ \hline
3 & Strong, ‘beany’, sharp, earthy & Light ‘beany’, roasted \\ \hline
6 & Strong, ‘beany’ +, sharp, earthy & Light ‘beany’, roasted \\ \hline
9 & ‘Beany’ +, sharp, earthy, rancid & Light ‘beany’, roasted, earthy \\ \hline
12 & ‘Beany’+, sharp, earthy, rancid & ‘Beany’, earthy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Odor evolution during the ageing (evaluation by three experts).
# The Size Effect of Shear Bands in Dense Sands--A Discrete Element Analysis
###### Abstract
It has been shown that the transition from an internally stable state to an unstable state can be revealed by the DEM [23]. In addition, the DEM can control the grain size easily [18]. In this study, DEM biaxial modeling is considered due to the representativeness of the plain strain condition (landslides, the failure of soils beneath shallow foundations, the failure of retaining structures, etc., are cases that can be considered as plane strain) and the fact that plane strain specimens are more likely to occur along a well-defined shear plane (as mentioned in the second paragraph). Although studies have shown some characteristics of shear bands, the influence of shear bands on different-sized specimens (namely, the size effect) is still worth paying attention to. The size effect has been well-recognized in concrete [24; 25] and, later, in rock [26; 27]. In both materials, the growth of fractures during brittle failures causes the strength to decrease when the specimen size increases. Yet, this is quite different with the generation of shear bands in sand; the latter tends to have a well-defined shear plane. The X-ray and optical microscopy investigation by [PERSON] and [PERSON] [28] indicated that the thickness of the shear band was 7-8 times the average particle size of Toyota sand and Ticino sand. Meanwhile, the biaxial DEM analysis by [PERSON] and [PERSON] [29] indicated that the width was about 10-14 times the average particle size. These findings indicate that the size of the shear band is not linked with the size of the specimen; therefore, some macroscopic characteristic differences might be caused due to specimen size. Focusing on how the size effect influences the macroscopic characteristics of sand, we implement a biaxial DEM study using different-sized specimens. The stress-strain curves, displacement development, and porosity inside/outside the shear band are investigated. It is believed that the difference in the proportion of the shear band mainly causes the size effect of the deformation characteristics. In addition, with the increase in isotropic confining stress, the type of shear band gradually changes from cross-type to single-type,and this might explain why the size effect becomes less obvious for higher confining stress levels. Our study provides valuable insights into understanding the behavior of granular materials.
###### Abstract  
In this article, the assessment of the radioactivity content in bovine meat from the Calabria region, Southern Italy, was performed. For this purpose, High Purity Germanium (HPGe) gamma spectrometry measurements were carried out in order to investigate any possible radioactive contamination by natural (\({}^{40}\)K) and anthropogenic (\({}^{37}\)Cs) radionuclides. Experimental mean values were found to be in the range from (78.9 \(\pm\) 10.5) Bq kg\({}^{-1}\) to (88.2 \(\pm\) 12.5) Bq kg\({}^{-1}\) for \({}^{40}\)K and lower than the minimum detectable activity (MDA) in all cases for \({}^{137}\)Cs, respectively. Moreover, any possible radiological health risk was also estimated, by calculating the total annual effective dose due to the ingestion of bovine meat by adult members of the population and by comparing it with the total natural radioactivity value (external + internal) for humans. Obtained values are in the range from 10.3 \(\upmu\)Sv y\({}^{-1}\) to 11.5 \(\upmu\)Sv y\({}^{-1}\), several orders of magnitude lower than the value of the total exposure to natural radioactivity for human beings, i.e., 2.4 mSv y\({}^{-1}\). It is worth noting that the used approach could be used, in principle, for the evaluation of the radiological risk due to the presence of radionuclides in a large variety of food samples of particular interest, and thus it can constitute a guideline for investigations focused on the monitoring of food quality.  
bovine meat; radioactivity; contamination; high purity germanium gamma spectrometry; radiological risk +
Footnote †: journal: _Scienze_
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Cosmic-Ray Muon and Its Interaction with Matter
#### 2.1.1 Energy and Angular Distribution at Sea Level  
[PERSON] [21] introduced a formula to describe the energy and angular distributions of muons flying to sea level. Considering muon decay and the curvature of the Earth, [PERSON] et al. [22] proposed a modified [PERSON]'s formula that expanded its applicability to low energies and all zenith angles:  
\[\frac{\mathrm{d}N_{\mu}}{\mathrm{d}E_{\mu}\mathrm{d}\Omega}=\frac{0.14}{ \mathrm{cm^{2}\ s\ sr\ GeV}}\Bigg{[}E_{\mu}+\frac{3.64\ \mathrm{GeV}}{(\mathrm{cos}\theta^{\star})^{1.29}}\Bigg{]}^{-2.7}\times \Bigg{[}\frac{1}{1+\frac{1.1E_{\mu}\mathrm{cos}\theta^{\star}}{115\ \mathrm{GeV}^{\star}}}+\frac{0.054}{1+\frac{1.1E_{\mu}\mathrm{cos}\theta^{ \star}}{850\ \mathrm{GeV}^{\star}}}\Bigg{]}, \tag{1}\]  
where \(N_{\mu}\) is the muon flux, \(E_{\mu}\) is the kinetic energy (hereinafter referred to as energy), \(\Omega\) is the solid angle, and \(\mathrm{cos}\theta^{\star}\) is defined as follows:  
\[\mathrm{cos}\theta^{\star}=\sqrt{\frac{(\mathrm{cos}\theta)^{2}+0.102573^{2}- 0.068287(\mathrm{cos}\theta)^{0.958633}+0.0407253(\mathrm{cos}\theta)^{0.8172 85}}{1+0.102573^{2}-0.068287+0.0407253}}, \tag{2}\]  
where \(\theta\) is the zenith angle. Figure 1 is the energy projection of Formula (2), and the minimum energy is taken as 1 GeV for simplicity without loss of generality. In the figure, the energy range of more than 100 GeV is what we studied, and its quantity accounts for 0.7%. Muons of this energy can easily pass through a thickness of matter of 150 m \(\times\) 2.8 g/cm\({}^{3}\) or more and are the primary sources of the signals in this study. We use an estimated value of 2.8 g/cm\({}^{3}\) as the reference density for mountain rock throughout this article. We do not consider the contributions of muons with energies of less than 100 GeV because the superficial part of the mountain too quickly blocks them from reaching the detector. The interest of this study is to image the deeper part.  
As pointed out by [PERSON] et al. [23], the angular and energy distributions vary little with the latitude and altitude of the muons that have small zenith angles and energies higher than 40 GeV, which is below the lower limit of 100 GeV of the studied energy range. In addition, for a large zenith angle, the muon is close to flying in the horizontal direction. Its trajectory length in the mountain is so long that the counting rate is limited, resulting in a minor contribution to the imaging. Furthermore, for mountain heights below 1 km, the altitude of the mountain top is typically a few kilometers, far less than the 30 km altitude where the muons are produced. Therefore, in this study, we assume that the muons, before entering into mountains, still follow the distribution represented by Formula (2), even though they are above sea level.  
In this study, we also ignore the possible deviation between the actual differential flux and the modified [PERSON]'s formula.
# Performance Qualification of Automatic System for Antineoplastic Preparation
## 2 Materials and Methods
### Compounding and Formulation System
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Fluorouracil**} \\ \hline  & \multicolumn{2}{c}{**DRUG**} & \multicolumn{2}{c}{**Diluent**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Bag Name**} & \multicolumn{2}{c}{**Theoretical**} \\  & & & & **(NaCl 0.9\%)** & & **(Drug + Diluent)** & & **Concentration** \\ \hline  & Fluorouracil 1 & 6000 mg & 120 mL & 180 mL & 300 mL & Run1\_1 & 20 mg/mL \\ RUN\({}^{\circ}\) 1 & Fluorouracil 2 & 6000 mg & 120 mL & 180 mL & 300 mL & Run1\_2 & 20 mg/mL \\ (High dosage) & Fluorouracil 3 & 6000 mg & 120 mL & 180 mL & 300 mL & Run1\_3 & 20 mg/mL \\  & Fluorouracil 4 & 6000 mg & 120 mL & 180 mL & 300 mL & Run1\_4 & 20 mg/mL \\  & Fluorouracil 5 & 6000 mg & 120 mL & 180 mL & 300 mL & Run1\_5 & 20 mg/mL \\ \hline  & Fluorouracil 1 & 600 mg & 12 mL & 138 mL & 150 mL & Run2\_1 & 4 mg/mL \\ RUN\({}^{\circ}\) 2 & Fluorouracil 2 & 600 mg & 12 mL & 138 mL & 150 mL & Run2\_2 & 4 mg/mL \\ (Low dosage) & Fluorouracil 3 & 600 mg & 12 mL & 138 mL & 150 mL & Run2\_3 & 4 mg/mL \\  & Fluorouracil 4 & 600 mg & 12 mL & 138 mL & 150 mL & Run2\_4 & 4 mg/mL \\  & Fluorouracil 5 & 600 mg & 12 mL & 138 mL & 150 mL & Run2\_5 & 4 mg/mL \\ \hline  & Fluorouracil 1 & 600 mg & 12 mL & 138 mL & 150 mL & Run3\_1 & 4 mg/mL \\ RUN\({}^{\circ}\) 3 & Fluorouracil 2 & 1800 mg & 36 mL & 264 mL & 300 mL & Run3\_2 & 6 mg/mL \\ (Random dosage) & Fluorouracil 3 & 2400 mg & 48 mL & 252 mL & 300 mL & Run3\_3 & 8 mg/mL \\  & Fluorouracil 4 & 3800 mg & 76 mL & 224 mL & 300 mL & Run3\_4 & 12.7 mg/mL \\  & Fluorouracil 5 & 4200 mg & 84 mL & 216 mL & 300 mL & Run3\_5 & 14 mg/mL \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recipe for preparation of infusion bags at different concentrations for the fluorouracil drug. In addition, for cyclophosphamideamide only, sterility and endotoxin tests were performed on the last dispensed bag of each cycle, according to European Pharmacopoeia methods [25; 26]. Cyclophosphamide was chosen as the worst case for sterility and endotoxin testing because it is the most complex drug to prepare, in addition to being a contaminant drug, as reported by many studies, often found on the surfaces of pharmaceutical compounding areas [20]. Furthermore, the last dispensed bag of each cycle was chosen as it represents the most stressed unit, i.e., the one most susceptible to possible contamination. The PHARMODUCT(r) dispensing module had to guarantee the dispensing of bags with a drug concentration, detected in the analyzed containers in each run, with a maximum error of \(\pm 10\%\) (% discrepancy between the set and prescribed amount of drug).