esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Sensor Approach to Follow _Campylobacter jejuni_ Development
## Materials and Methods
### GC-MS Analysis  
The Gas Chromatograph (GC) used during the analyses was a Shimadzu GC2010 PLUS (Kyoto, KYT, Japan), equipped with a Shimadzu single quadrupole Mass Spectrometer (MS) MS-QP2010 Ultra (Kyoto, KYT, Japan) and an autosampler HT280T (HTA s.r.l., Brescia, Italy). The GC-MS analysis was coupled with the Solid-Phase Micro Extraction (SPME) method in order to find the most characteristic VOCs for each sample.  
The fiber used for the adsorption of volatiles was a DVB/CAR/PDMS-50/30 \(\upmu\)m (Supelco Co., Bellefonte, PA, USA). The fiber was exposed to the headspace of the vials after heating and shaking the samples in the HT280T oven, thermostatically regulated at 40 \({}^{\circ}\)C for 15 min, with the aim of creating the headspace equilibrium. The length of the fiber in the headspace was kept constant. Desorption of volatiles took place in the injector of the GC-MS for 6 min at 240 \({}^{\circ}\)C.  
The gas chromatograph was operated in the direct mode throughout the run, with the mass spectrometer in electron ionization (EI) mode (70 eV). GC separation was performed on a MEGA-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). Ultrapure helium (99.99%) was used as the carrier gas, at the constant flow rate of 1.5 mL/min. The following GC oven temperature programming was applied. At the beginning, the column was held at 40 \({}^{\circ}\)C for 3.5 min, and then raised from 40 to 90 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min. Next, the temperature was raised from 90 \({}^{\circ}\)C to 220 \({}^{\circ}\)C, with a rate of 12 \({}^{\circ}\)C/min; finally, 220 \({}^{\circ}\)C was maintained for 7 min.  
The GC-MS interface was kept at 200 \({}^{\circ}\)C. The mass spectra were collected over the range of 40 to 500 \(m\)/z in the Total Ion Current (TIC) mode, with scan intervals at 0.3 s. The identification of the volatile compounds was carried out using the NIST11 and the FFNSC2 libraries of mass spectra.  
Four samples were analyzed: control and _C. jejuni_ at times T0 and T20.
Future Internet_**2014**, \(6\), 171-189; doi:10.3390/fi6010171  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Mobility Tolerant Firework Routing for Improving Reachability in MANETs**  
**[PERSON] 1,*, [PERSON] 2,3 and [PERSON] 3**  
\({}^{1}\) Central Research Laboratories, NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa 211-8666, Japan  
\({}^{2}\) Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), and Osaka University, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-44-431-7042.  
_Received: 8 January 2014; in revised form: 25 February 2014 / Accepted: 28 February 2014 / Published: 24 March 2014_  
**Abstract:** In this paper, we investigate our mobility-assisted and adaptive broadcast routing mechanism, called Mobility Tolerant Firework Routing (MTFR), which utilizes the concept of potentials for routing and improves node reachability, especially in situations with high mobility, by including a broadcast mechanism. We perform detailed evaluations by simulations in a mobile environment and demonstrate the advantages of MTFR over conventional potential-based routing. In particular, we show that MTFR produces better reachability in many aspects at the expense of a small additional transmission delay and intermediate traffic overhead, making MTFR a promising routing protocol and feasible for future mobile Internet infrastructures.  
**Keywords:** MANET; firework routing; potential based routing; future Internet; wireless connectivity
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 1 Introduction
* Implementation of deep learning algorithms to analyze movements and operation times, improving the allocation of mining equipment and correcting inefficiencies not considered by traditional systems. Figure 1: Cooccurrence of keywords with VOSviewer in recent research database. * Integration of virtual delimitation of risk zones and issuance of automatic alerts in real time when unwanted presence is identified, significantly reducing occupational accidents. * Application of data-augmented convolutional neural networks (CNN) to achieve 100% accuracy in the identification of key mining equipment during validation tests in real environments. The remainder of the paper is organized as follows. Section 2 develops a literature review. Section 3 describes and proposes the materials and methods. Section 4 presents the results. Section 5 presents the discussion of the research results. Section 6 presents the conclusions and some possible future studies.
# Partitioning Evapotranspirationiration in a Cotton Field under
###### Abstract  
Measuring evapotranspiration (_ET_) components in cotton fields under multiched drip irrigation is needed to improve water use efficiency and promote the development of water-saving agriculture. In this study, an Eddy Covariance (_EC_) system was used to observe the water-carbon fluxes of cotton fields under multiched drip irrigation in an arid region during two years (2021-2022). The Underlying Water Use Efficiency (_uWVIE_) method was used to partition the _ET_ into transpiration (_T_) and evaporation (_E_) in order to reveal the changing characteristics of _ET_ and its components in cotton fields under multiched drip irrigation and analyze the effects of environmental factors on each component. The results showed that the diurnal variation of _ET_ was the same as gross primary productivity (_GPP_), and their course of change showed a bimodal curve at budding, blooming, and boll stages. The relationship of T at different growth stages was the same as _ET_, which is blooming and boll stage > budding stage > boll opening stage > seedling stage. _ET_ and its components were mainly affected by temperature (_T\({}_{air}\)_) and net radiation (_R\({}_{n}\)_). This study can provide a theoretical and practical basis for the application of _uWVIE_ in cotton fields under multiched drip irrigation and a scientific basis for the rational allocation of water resources and the formulation of a scientific water-saving irrigation system for farmland in an arid region.  
evapotranspiration; underlying water use efficiency; water-carbon fluxes coupling; cotton fields under multiched drip irrigation; arid region
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 4 Evaluation Set Up
### AI-Based Positioning Methods  
In this paper, two kinds of AI-based positioning methods are considered for evaluation. The first one is the direct AI-based positioning as shown in Figure 4, in which the output of the AI model is device's coordinates, and the other one is the indirect AI-based positioning, in which the output of the AI model is the intermediate parameter for the positioning. For example, the output of the AI model could be the ToA, Angle of Arrival (AoA), or the LOS or NLOS identity. Then, derive the UE's coordinates based on the inference intermediate parameter by the traditional positioning. In this paper, we will use the AI model to predict ToA. Then, using the TDOA algorithm to calculate the coordinates as shown in the principle in Figure 5. In both AI-based positioning methods, the input of the AI model is the channel impulse response (CIR) of the received positioning reference signaling from 18 BSs.  
In the practical system, one possible deployment solution is an AI model trained and then deployed on the device side. The BSs transmit positioning reference signal (PRS) to the devices and the devices perform the measurements. For direct AI-based positioning, devices could input the obtained measurement result into the AI model to infer the device's coordinates as shown in Figure 4. For the indirect AI-based positioning, UE could first obtain the ToA by using the AI model and then obtain the final coordinates by using the inference ToA and TDOA as shown in Figure 5.
## 1 Introduction
The first step produces DMAP or IP by phosphorylating dimethylallyl alcohol, prenol or isoprenol; the second step produces DMAPP and IPP by phosphorylating DMAP or IP [3; 16; 17; 18]. The concise and robust nature of the ADH pathways significantly reduces the difficulties involved with the multiple gene engineering in C. _reinhardtii_, and decouples the DMAPP/IPP synthesis from the complex native metabolic regulation. This ensures that the carbon flux required for the terpenoid biosynthesis is not in competition with that required for cell growth [3; 16; 17; 18]. In this study, we introduced a nonnative C5 terpenoid precursor production pathway, the isopentenol utilization pathway (IUP) (an ADH pathway), into _C. reinhardtii_. By introducing the IUP into the chloroplast of _C. reinhardtii_, the transgenic algae showed a clear increase in IPP/DMAPP titers, and thus can be used as an ideal chassis for the high-value terpenoid production. Additionally, we further characterized the utility of the pathway by the sequential transformation with the isopentenyl diphosphate isomerase (IDI) and limonene synthase (LS) genes into the chloroplast of _C. reinhardtii_ to produce monoterenoid limonene (Figure 1). This study first tried to confirm that the IUP could be used as a two-step shortcut for IPP synthesis in the _C. reinhardtii_ chloroplast. This provides an effective approach to increase the production of limonene in _C. reinhardtii_ and might also be suitable for the biological production of other high-value terpenoid. Moreover, the isoprenol precursor needs to cross cell walls, cell membranes and chloroplast membranes to be used by the IUP, this cannot be demonstrated in _E. coli_ or cyanobacterium. Finally, we evaluated the culture parameters for the production of limonene from _C. reinhardtii_, including the capacity of a dodecane solvent overlay for the extraction of limonene. In engineered _C. reinhardtii_, hemiterpene diphosphate accumulation can be greatly improved by the IUP, and can robustly produce terpenoid-limonene by isoprenol feeding. What's more, the IUP engineering will facilitate the production of high-value terpenoids in _C. reinhardtii_.
## 3 Offshore Platform Overview and Finite Element Model
### Finite Element Model  
This paper assumes that all steel components (including piles, braces, and horizontal members) are modeled using linear elastic materials. This simplification is justified based on: (1) the research focus on global structural dynamic responses (displacement/internal force distributions) rather than local plastic failure mechanisms; (2) typhoon loads represent short-term events, with post-processing verification confirming maximum stresses remain below the yield strength of Q345 steel [24]; (3) soil nonlinearity (plastic deformation and gap effects) is independently characterized through P-y/T-z/Q-z curves. It should be noted that both the typhoon loads adopted in this study and the maximum pile--head forces derived from simulations are lower than those in [24]. Thus, it can be reasoned that the stresses in the present model never exceed the elastic limit defined in [24].  
This study employs the OpenSees software Version 3.7.1 to develop a high-fidelity finite element (FE) model of the offshore platform for dynamic analysis. As illustrated in Figure 4, the FE model comprises 456 nodes and 956 elements, strategically discretized to balance computational efficiency and resolution. Structural components are modeled using beam-column elements, and the nonlinear pile-soil interaction is simulated using the PySimple1, TzSimple1, and QzSimple1 material models within the OpenSees framework to capture axial, lateral, and tip resistance mechanisms. In this paper, the mesh size adopted in Ref. [25] is that the element length of the Tubular Joints is 1.5 D (D represents the outer diameter of the tubular component). Ref. [26] sets the time step as \(\Delta t=0.1T_{min}\), where \(T_{min}\) denotes the fundamental period.  
In terms of model loading and boundary condition setting, the combined effects of environmental loads and gravity loads are considered at the same time. The foundation model uses nonlinear soil springs to simulate pile-soil interaction and is based on the  
Figure 4: FE model of the Offshore platform.  
nonlinear Winkler foundation beam theory for modelling and analysis. The P-y cell is used to simulate the lateral response characteristics of the soil, while the T-z and Q-z cells are used to describe the lateral frictional resistance behavior of the soil and the end-bearing response of the soil in the near-field, respectively, so as to achieve an accurate simulation of the complex pile-soil interaction mechanism.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 5 Evaluation
### Optimal Algorithmic Strategy: OptTsNS  
Figure 6 explains the proposed algorithmic strategy OptTsNS and the main steps carried out are summarized below.  
* _Variations per service \((VarServ)\)_: A variation of a service is the result of the application of a specific discrete time shifting value to the \(t_{k,j}^{i}\) and/or the application of a specific quality degradation level to the \(p_{k,j}^{i}\) of a service \(S_{k,j}^{i}\). The analysis of all \(N\) services for each value of time shifting and for each quality degradation level produces a total number of variations \(AllVarServ\). Considering that, for simplicity, the time shifting forward and backward have the same value, this number is given by: \[AllVarServ=Q\times(2\times N\times max\{u_{k,j}^{i}\}+N)\] (26)
* _Combinations of services \((CombServ)\) and computation of cost functions_: In the algorithmic strategy, the set of \(N\) different variations of services \((VarServ)\) is named as a _combination of services_ (_CombServ_). Each _CombServ_ has specific characteristics and requires a certain power level \(P_{DCombServ}\). The algorithmic strategy evaluates the cost functions for each \(CombServ\). Regarding the \(AR\) metric, the algorithm prioritizes the execution of the highest priority services (with \(j=1\) the highest priority level). If the services have the same priority level, the algorithm selects the set of services that produce an optimization in the consumption of available power (maximization of energy use). The combinatorial analysis of all \((VarServ)\) delivers a total number of combinations of services \(AllCombServ\), which is given by: \[AllCombServ=Q\times(2\times max\{u_{k,j}^{i}\}+1)^{N}\] (27) The computation of combinations of services (variations) contributes largely to the growth of complexity of the problem. For instance, \(N=10\), \(max\{u_{k,j}^{i}\}=4\), and \(Q=3\) produce over 10 billion combinations. If a computer is able to process one _CombServ_ each millisecond, it would need over 2900 h to explore the complete search space.  
* _Sorting of combinations and selection of the best combination:_ In this step, a quicksort method is applied to all combinations, according to the descending value of \(Cost_{comb}^{i,j,k}\). Then, the best combination (which is the first in the sorted list) is chosen. Finally, the energy is allocated to the services that can be processed, and the performance metrics are computed.
## 3 Results
### Measuring Attention
#### 3.5.1 Machine Learning Methods
By comparison, the RF model, which excluded blink data, achieved up to 96% accuracy in classifying attention levels. These findings suggest that blink metrics alone are less reliable indicators of attention changes, whereas combining multiple eye features, as in the RF model, provides a more robust framework for analysing cognitive and attentional states. A geometric modelling approach was employed to track eye gaze movements relative to screen position, classifying users as attentive or inattentive based on whether their gaze fell within defined screen boundaries [44]. The model utilised Haar Cascade algorithms for eye frame extraction and centroid detection for pupil tracking. While achieving an accuracy of 76.66%, the system faced significant environmental limitations, such as sensitivity to light variations. As a result, threshold values required frequent adjustment to maintain accuracy. Despite its potential for monitoring attention, the system's effectiveness remains unverified, as no user testing was conducted to validate its ability to differentiate attention levels.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 4 Discussion
### Limitations of the Study
#### 4.5.1 Bias in Data Collection via Google Alerts  
Rockfall incidents often go unnoticed, and do not always make national news. Even diligent efforts to collect information on rockfalls can easily miss incidents reported only in local news. However, Google Alerts can scan a vast array of news sources and provide instant notifications, significantly enhancing the breadth and depth of rockfall incident data collection beyond what is possible for an individual. Despite these advantages, we acknowledge potential omissions and biases that may arise from relying solely on Google Alerts for data collection. Google Alerts may not capture all relevant rockfall incidents, leading to incomplete or biased datasets, which can affect the generalizability and accuracy of the model predictions.
## 4 Conclusions  
This work presents a fast and cost-effective detection approach of melamine based on surface enhanced Raman spectroscopy (SERS) using a novel hydrogen bonding-assisted supramolecular matrix and gold-coated magnetic nanoparticles. The detection utilizes Fe\({}_{3}\)O\({}_{4}\)/Au magnetic nanoparticles coated with 5-aminoorotic acid (AOA) as a SERS active substrate (Fe\({}_{3}\)O\({}_{4}\)/Au-AOA), and Rhodamine B (RhB) conjugated AOA as a Raman reporter (AOA-RhB). The formation of the [Fe\({}_{3}\)O\({}_{4}\)/Au-AOA\(\bullet\bullet\bullet\)melamine\(\bullet\bullet\)AAO-RhB] complex due to the strong multiple hydrogen bonding between AOA and melamine, followed by the laser excitation of the complex, produced strong SERS signals diagnostic for RhB. The logarithmic intensity of the characteristic peak at 1510 cm\({}^{-1}\) of RhB was found to be proportional to the concentration of melamine with a limit of detection of 1.0 \(\mu\)g/mL and a detection linearity range of 1.0-15.0 \(\mu\)g/mL in water, and a limit of detection of 2.5 \(\mu\)g/mL and a detection linearity range of 2.5-15.0 \(\mu\)g/mL in milk. As Fe\({}_{3}\)O\({}_{4}\) nanoparticle and AOA are tens of thousands of times less expensive than monoclonal antibodies used in traditional sandwich immunoassay, and the total analysis time of the assay was less than 1 h, the current method affords promise as a biosensor platform that can be adapted for portable and cost-effective detection applications.  
The authors wish to acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 31301483), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY17C200016), the Scientific Research Fund of Zhejiang Provincial Education Department (Grant No. Y201329221), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. The received funds will cover the costs to publish in open access.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the synthesis of gold magnetic nanoparticles; [PERSON] and [PERSON] acquired and analyzed the spectra data; [PERSON] contributed the project idea, reagents, and materials; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the manuscript submitted. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Methodology  
### Burnup Credit System for the Swiss Reactors-Repository (BUCSS-R)  
The BUCSS-R scheme applies BUC starting with the depletion calculation of a FA in the in-core burnup phase considering also shutdown cooling between cycles, followed by cooling during interim storage as well as cooling in the long term of final disposal. Based on detailed information about the irradiation conditions, this computational sequence allows for the determination of a realistic isotopic inventory in the FAs and its evolution over time. BUCSS-R is described in detail in [5]. Nevertheless, the elements of the methodology shall be briefly outlined here in order to provide the background for this study, see also Figure 2. Each step of the computational sequence is listed and outlined below, headed by the main code package(s) used for the current task:  
1. CASMO-SIMulate  
The first task is a realistic estimation of operation histories, including in-core depletion and shutdown cooling between cycles. The calculations are based on reference CASMO-SIMULATE core models, developed and validated for all Swiss reactors and almost all operated cycles within the PSI core management system (CMSYS) platform [14; 15; 16; 17].  
2. BOHR  
The detailed irradiation history can be extracted from SIMULATE3 results by means of an in-house code called BOHR [18]. State parameter values for every fuel assembly and axial elevation at numerous cycle instants are retrieved and corresponding CASMO5 [19] input files are produced.  
3. CASMO5  
New CASMO5 calculations are carried out based on BOHR output. Pin-wise and axially resolved isotopic compositions of the burnt fuel are determined. This step allows  
Figure 2: Schematic view of the Burnup Credit System for the Swiss Reactors–Repository (BUCSS-R) methodology and nuclear data perturbation tools (red frame) [4; 11].  
obtaining detailed spent nuclear fuel compositions, rather closely corresponding to the actual fuel assembly operating history.