esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
## 3 Framework of Proposed ETLHCM  
In this section, the issues of recent work over three layer clustering routing in WSN are highlighted and the motivation for the proposed mechanism in this paper is explained.  
Recently in \"an improved three-layer low-energy adaptive clustering hierarchy for wireless sensor networks\" [8] paper, a new Hybrid Hierarchical Clustering Approach (HHCA) is proposed for three layer clustering routing, to improve the network lifetime of WSN. According to HHCA, all deployed sensor nodes in the first step transmit their location information along with their energy status to the sink node. By applying centralized Fuzzy C-Means (FCM) clustering approach [9], every sensor node determines wether it is selected by sink as GH. In case a sensor node is selected as GH, it broadcasts a control packet announcing its selection. If the sensor node is not selected as GH, it joins the grid that is selected to it by sink via FCM approach. The second step in HHCA is that all sensor nodes exchange control packets with each other and distributively select lower layer heads which are also called cluster heads in the same way as proposed in LEACH, with the addition of taking residual energy of nodes into consideration too, giving priority to sensor nodes with higher residual energy during CH selection process. Sensor nodes that are selected as CHs broadcast control packet announce their assigned role, while other sensor nodes send join requests to nearest CH. Both the first and second steps in HHCA protocol are repeated after every round. Finally, all sensor nodes that are not selected as CH and GH transmit their data packets to CHs, and CHs in turn forward these data packets to GHs and they then forward to the sink.  
It is noticed that the operations which are categorized as wasteful in Section 1 are taking place in large amounts after every round in the aforementioned protocol. The repeated exchange of control packets between sensor nodes in HHCA and other routing techniques is best for lower layer heads selection and energy balancing purpose, which results in waste of significant amounts of network energy. As stated in [27], the high power consuming operation in WSN is communication, i.e., sending and receiving packets, takes about two thirds of the total power consumption of a wireless module.  
To save this excessive amount of energy that is wasted by exchanging control packets between sensor nodes, ETLHCM mechanism is proposed in this paper to limit the exchange of control packets after every round and in the mean time keeps energy consumption between sensor nodes balanced.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Wi-Fi 7
#### 10.2.2 Higher-Order Modulation  
The modulation is expected to be 4096-QAM, which carries 12 bits per modulation symbol. This improves the throughput of Wi-Fi 7 by 20% compared to 1024-QAM in Wi-Fi 6. It provides a high transmission speed of 30 Gb/s per access point. This modulation is only practical in combination with beamforming.
_Article_  
**Simultaneous Determination of Pyrethroid, Organophosphate and Carbamate Metabolites**  
**in Human Urine by Gas Chromatography-Mass Spectrometry (GCMS)**  
**[PERSON] 1**, **[PERSON] 1, [PERSON] 2 and [PERSON] 2,*(c)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Received: 1 February 2019; Accepted: 25 February 2019; Published: 1 March 2019  
**Abstract:** We have developed a rapid, sensitive, and reliable method for simultaneous determination of the urinary metabolites of common insecticides in a single analytical run using gas chromatography-mass spectrometry (GCMS). Thirteen metabolites, one originating from carbamate, six from organophosphates, and seven from pyrethroids, were selected for method validation. Samples at different concentrations (0.5-15 \(\upmu\)g/L) were prepared by mixing working solutions containing the analytes with blank urine. After acid hydrolysis for 45 min at 90 \({}^{\circ}\)C, samples were processed with liquid-liquid extraction and derivatization by _N-tert_-butyldimethylsilyl-_N_-methyltrifluoroacetamide (MTBSTFA) before analysis on GCMS. The limits of detection for all thirteen analytes were below 0.1 \(\upmu\)g/L. The recovery rates, evaluated at two concentrations (1, 10 \(\upmu\)g/L), were found to be 90.48%, on average. The precision of multiple analyses at three different concentrations (0.5, 5, 15 \(\upmu\)g/L) within one day or between 10 days was evaluated, and the resultant relative standard deviations were 8.1% or under. We also applied this method to analyze genuine urine samples collected from 30 human subjects, and successfully detected all the metabolites, with detection frequencies more than 50% for pyrethroid metabolites. In summary, this method is not only as good as others in performance, but is advantageous in terms of cost effectiveness and multiplicity of analytes.  
**Keywords:** carbamate; GCMS; liquid-liquid extraction; metabolites; organophosphate; pyrethroid; urine
## 5 Conclusions  
Our research proposes a set of optimal image quality enhancement strategies after conducting multiple sets of experiments through different combinations of image processing procedures such as noise reduction, sharpening, partial image color area homogenization, and color channel adjustment. After testing two public remote sensing scene image datasets on scene image classification task, it was shown that the image quality enhancement strategy proposed in this study can effectively improve the generalization performance of the deep learning model after training.  
This study uses the locally interpretable XRAI technology to analyze the significant image location difference before and after the image quality enhancement strategy of the original misclassified case. It also verifies the automated image quality enhancement strategy proposed in this study, which can assist in training the deep learning model to find the location of the image feature texture of the correct category, allowing the accuracy of the model to be further improved.  
Additionally, our objective is to develop a supervised deep learning model with high generalization performance and well-designed model architecture, thereby improving the data quality of the image dataset, which is an equally important factor.  
In future work, we will apply region-based processing [4] to eliminate background information and obtain faster convergence for model training and inference.  
Conceptualization, M.-H.T. and S.-C.H.; acquisition of data, M.-H.T. and S.-C.H.; analysis and interpretation of data, S.-C.H. and M.-H.T.; drafting of the manuscript, M.-H.T., S.-C.H. and H.-C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, grant numbers MOST 109-2121-M-040-001 and MOST 110-2121-M-040-001.  
Not applicable.  
Data Availability StatementData available on request.  
All authors declare that there are no potential financial or non-financial conflict of interest.
## 3 Classification
### Communication Technologies  
As Table 1 shows, various communication technologies have been used to design V2P systems. Some characteristics of the V2P systems largely depend upon the choice of the underlying communication technology. Examples of such characteristics are the range of communication, the choice of V2P device, the availability of infrastructure etc. In this section, we briefly discuss each communication technology and its characteristics.
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 2 Related Studies
### Optimization of Maintenance Scheduling to Minimize the Impact on Circulation  
Equally important are optimization techniques that incorporate time windows for railway maintenance, since many models do not consider train circulation. For this reason, [PERSON] et al. [36] used a simulated annealing solution as a basis for their algorithm, which produced results when using random block placements. The constraints were related to the initial time of a track block and the most recent finishing time of a track block, considering crew working hours. Moreover, [PERSON] and [PERSON] [37] emphasized the significance of railway services and the maintenance of the railway network, as they needed to be considered together in their planning. They presented a mixed-integer programming model for solving railway circulation and network maintenance. Their objective was to find a long-term plan that optimally schedules train-free windows for a specific volume of spatial and temporal aggregation for network capacity. [PERSON] et al. [38] addressed the problem of regular scheduling to optimize train routing decisions, temporally correlated with short-term maintenance tasks in railway networks. They used a MILP model, incorporating stochastic variables, constraints, and objectives related to the flow of train circulation and track maintenance. Two objectiveswere set: one focused on minimizing deviations from a scheduled timetable, and the other on maximizing the consolidation of maintenance tasks. In long-distance railway networks, maintenance and train routing scheduling are highly complex, especially in single-track networks. Building upon this issue, [PERSON] et al. [39] investigated the Problem Space Search (PSS) through metaheuristic approaches, rapidly creating many alternative train routes and a timetable incorporating track maintenance. The approach of [PERSON] et al. [40] was very close to this, using a MIP model to optimize train schedules and preventive maintenance. The model allowed train rerouting or cancellation to achieve the best possible traffic flow, considering a fixed set of scheduled maintenance activities. An overview of the most relevant reviewed studies is provided in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 3 Results
### Global Gravity Recovery Solutions
#### 3.1.2 Covariance Matrices  
The illustration of the covariance matrices of the gravity solutions by different scenarios and cases can be employed as an indicator for the associated error levels, very much influenced by the geometry of the orbits and their sampling behaviors.  
Figures 16 and 17 depict the covariance matrices of 6-day solutions of the scenario (a) inline and Pendulum formations for \(\Omega=116^{\circ}\), up to \(L_{max}=32\), \(L_{max}=64\), \(L_{max}=\) CNR and \(L_{max}=\) MCNR solutions. The illustrations imply that for the inline formation when the maximum spherical harmonic degree and order goes above the CNR rule for 6-day (\(L_{max}=64\) and \(L_{max}=\) MCNR), the level of error shown by the arcs and off-diagonal elements (due to the spatial aliasing) gets significant. For the Pendulum formation, however, those error patterns are not as strong as the inline formation.  
Inline (GRACE-like) Formation
## 3 Proposed Maturity Framework
### Maturity Levels  
The maturity model has its maturity levels. This means that each of the requirements and subrequirements has its own maturity levels. The maturity levels are 6 scores, from 0 to 5, with 0  
Figure 5: Data Security and Protection Toolkit (DSPT) Mapping.  
Figure 6: Merging of different requirements into the proposed HCYMAF.  
being the lowest and 5 being the highest. Each of these maturity levels has a meaning, it represents a staged path for an organisation's performance and process improvement efforts based on predefined sets of practice areas. Each maturity level also builds on the previous maturity levels by adding new requirements. An example of such a scale is shown in Figure 7 below. A brief description of each level is presented:  
* Level 0: Incomplete; Ad hoc and unknown. Work may or may not get completed.
* Level 1: Initial; Unpredictable and reactive. Work gets completed but is often delayed and over budget.
* Level 2: Managed; Projects are planned, performed, measured, and controlled.
* Level 3: Defined; the organisation is proactive, rather than reactive. There are organisation-wide standards that provide guidance across projects, programs, and portfolios.
* Level 4: Quantitatively Managed; the organisation is data-driven with quantitative performance improvement objectives that are predictable and align to meet the needs of internal and external stakeholders.
* Level 5: Optimising; the organisation is focused on continuous improvement and is built to pivot and respond to opportunity and change.  
In terms of evaluation of the performance of an organisation against an individual requirement, the maturity notes should be read one at a time in ascending order (from 0 to 5). If all notes are fulfilled, then the next level should be read and examined. In order to assign a certain score, all of the lower levels must be completely fulfilled first. It should also be noted that some subrequirements have a Not Applicable (N/A) option, this is because not all subrequirements are applicable to every organisation.
## 2 Materials and Methods
### Data Analysis  
A quadratic equation was used to fit the response function of GEP or NEE to soil temperature [25]:  
\[\mathrm{F}=\mathrm{a}\times\mathrm{T}^{2}+\mathrm{b}\times\mathrm{T}+\mathrm{c} \tag{3}\]  
Figure 2: Monthly precipitation (columns) and mean air temperature (solid line) during the experimental period (July 2013 to September 2015) in a semiarid grassland of the northwest China.  
where F is GEP or NEE; T is soil temperature at a depth of 0-10 cm; and a, b, and c are parameters. The values of a and b indicate the quadratic and linear slopes of the response function.  
The optimal temperature (T\({}_{0}\)) at which NEE and GPP were the smallest and greatest, respectively, was calculated from the parameters of the temperature-response curve Equation (3) as follows:  
\[\mathrm{T}_{0}=-\frac{\mathrm{b}}{2\mathrm{a}} \tag{4}\]  
An exponential equation was used to fit the response function of Re or Rs to soil temperature [17; 18]:  
\[\mathrm{F}=\mathrm{a}\times\mathrm{e}^{\mathrm{b}\times\mathrm{T}} \tag{5}\]  
where F is Re or Rs, and a and b are the parameters. The value of b indicates the slope of the response function.  
The temperature sensitivity (Q\({}_{10}\)) of the respiration rate (Re or Rs), a metric that describes the proportional increase in the respiration rate for a 10 \({}^{\circ}\)C increase in temperature, was calculated from the slope (b) of the temperature-response curve by Equation (5) as follows:  
\[\mathrm{Q}_{10}=\mathrm{e}^{10\times\mathrm{b}} \tag{6}\]  
A linear equation was used to fit the response function of CO\({}_{2}\) flux component to soil moisture [26; 27; 28]:  
\[\mathrm{F}=\mathrm{a}+\mathrm{b}\times\mathrm{M} \tag{7}\]  
where F is the CO\({}_{2}\) flux, M is soil moisture at a depth of 0-10 cm, and a and b are parameters. The value of b indicates the slope of the response function.  
A repeated measure of ANOVA was conducted to compare the CO\({}_{2}\) flux components among the precipitation treatments, stages of the season (early, middle, or late), and interactions between the precipitation treatments and stages of the season through 2013 to 2015. Linear and nonlinear regression analyses were conducted to fit the equations. We tested normal distribution (Shapiro-Wilk test) and homogeneity of variances (Levene test) for each metric prior to statistical analysis. All data were analysed with JMP 10.0 (SAS Institute, Cary, NC, USA). In this study, although there are lots of scatters in soil moisture, nearly all the fittings of CO\({}_{2}\) flux to soil moisture were significant at \(p<0.05\). Therefore, our fittings reflected the general response pattern of CO\({}_{2}\) flux to soil moisture. However, we recommended that such measurements should be intensified to avoid scatters.
## 4 Simultaneous Utilization of Multiple RANs within a 3 GPP Framework  
The effective utilization of radio resources has become a significant focal point in academic research and global standardization efforts, increasingly incorporating machine learning algorithms [(63; 64; 65)]. With the emergence of 5G and beyond systems, a multitude of RANs have become accessible, expanding their coverage even to non-terrestrial regions, thanks to the addition of satellite components such as mobile RANs integrated into 5G and beyond. Furthermore, UAVs are poised to play a pivotal role as a mobile RAN, facilitating expanded coverage in the forthcoming 6G standardization. Traditionally, cellular networks like LTE connect to just one access network at a time [(14)] or utilize unlicensed spectrum bands as part of 3 GPP-based radio access resources for offloading wireless data traffic [(66; 67)], alongside a range of radio access interface-level solutions [(68)]. However, the simultaneous connection and utilization of multiple access networks now empowers the distribution of data traffic [(69; 70)]. This section explores transport layer-level solutions for efficiently distributing data traffic simultaneously across two RANs for several applications in the context of 3 GPP standardization, as outlined in Table 1.
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
###### Abstract  
Distribution grids are complex networks containing multiple pieces of equipment. These components are interconnected, and each of them is described by various attributes. A knowledge graph is an interesting data format that represents pieces of information as nodes and relations between the pieces as edges. In this paper, we describe the proposed vocabulary used to build a distribution system knowledge graph. We identify the concepts used in such graphs and a set of relations to represent links between concepts. Both provide a semantically rich representation of a system. Additionally, we offer a few illustrative examples of how a distributed system knowledge graph can be utilized to gain more insight into the operations of the grid. We show a simplified analysis of how outages can influence customers based on their locations and how adding DERs can influence/change it. These demonstrative use cases show that the graph-based representation of a distribution grid allows for integrating information of different types and how such a repository can be efficiently utilized. Based on the experiments with distribution system knowledge graphs presented in this article, we postulate that graph-based representation enables a novel way of storing information about power grids and facilitates interactive methods for their visualization and analysis.  
knowledge graph; grid analysis; electrical path; distribution grid +
Footnote †: This paper is an extended version of conference paper “GridKG: Knowledge Graph Representation of Distribution Grid Data” published in the 2020 IEEE Electric Power and Energy Conference (EPEC), Edmonton, AB, Canada, 9-10 November 2020.
## 6 CFD Analysis of Drying as a Mechanically Aided Process
### CFD Modeling of Particle-Fluid Interactions
Notable applications include optimizing fluidized bed dryers for dairy powders, coffee, and spices; improving airflow and droplet dispersion in spray drying of emulsions and encapsulates; minimizing energy use and particle breakage in pneumatic conveying lines; and enhancing powder rehydration through controlled porosity development in agglomerated particles. These efforts contribute to more energy-efficient, uniform, and scalable processes [120]. Despite these advances, CFD modeling of particle-fluid interactions remains computationally intensive, especially in simulations using CFD-DEM coupling or large-scale Lagrangian tracking. Additional challenges include the limited availability of accurate thermophysical properties for food materials (e.g., thermal conductivity, cohesion parameters) and the difficulty of model validation due to the optical opacity and heterogeneity of food systems [41]. Future developments are expected to include hybrid modeling approaches that integrate CFD with machine learning algorithms or PBM to better capture agglomeration, droplet size evolution, and breakup. Emerging experimental techniques such as Particle Tracking Velocimetry (PTV), X-ray tomography, and MRI will support more robust model validation. Furthermore, the integration of CFD with real-time sensor data into digital twin frameworks may enable predictive and adaptive control of particle-fluid processes in next-generation food manufacturing environments [121]. CFD-based modeling of particle-fluid interactions thus plays a vital role in advancing the understanding and optimization of drying, mixing, and transport operations in food engineering. Its continued evolution--alongside developments in materials characterization, computing, and automation--will facilitate the design of efficient, high-performance, and quality-focused food processes [122].
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
### Spoofing  
Spoofing of an RF-based receiver refers to a deliberate and intentional transmission of fake or false radio signals to fool a system receiver into providing incorrect information [13]. Unlike jamming, the main intention behind a spoofing attack is to secretly force a GNSS receiver to track down the fake GNSS signals with the main objective of providing or, at least, inducing an incorrect navigation solution. ADS-B spoofing attacks are intended to create a false representation of a fake aircraft by transmitting fake ADS-B messages (Figure 2b). Since there is no authentication or other protection measures, it is easy to record real ADS-B messages using an ADS-B receiver and rebroadcast them later in a specific area or at a different time. Such an attack can be easily carried out using inexpensive and simple technological means such as Software-Defined Radio (SDR) and the GNURadio application and using widely available manuals on the Internet [4]. This type of attack is characterized by the presence of a fake aircraft flying in a collision path or by flooding a particular ATC airspace with fake aircraft [11].