esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The results of implementing a smart grid demand scheme using the power line communication technique underscore its potential to revolutionize the energy landscape. By enabling real-time communication, efficient load management, and consumer engagement, PLC-driven smart grids offer a pathway towards a more sustainable, responsive, and resilient electricity distribution network. At the forefront of these results is the marked improvement in grid efficiency and stability. The real-time communication enabled by PLC empowers utilities to closely monitor demand fluctuations and swiftly respond with load adjustments. This dynamic load management mitigates the risk of grid congestion during peak hours, reducing the likelihood of power outages and enhancing the reliability of the electricity supply.  
The consumer-centric aspect of the PLC-based demand scheme has demonstrated a transformative impact on consumer behavior and engagement. By providing consumers with immediate access to their energy consumption data, the scheme fosters a sense of ownership over energy usage patterns. As a consequence, consumers are more inclined to adopt energy-efficient practices and make strategic decisions about their electricity consumption. The implementation of dynamic pricing further incentivizes consumers to shift high-energy activities to off-peak hours, not only reducing their utility bills but also contributing to a smoother demand curve for the grid. Furthermore, the two-way communication facilitated by PLC has opened avenues for demand response programs that were previously challenging to execute effectively. Consumers can now actively participate in demand-side management, responding to signals from the grid to reduce consumption during periods of high demand. This collaborative approach not only alleviates strain on the grid but also promotes a sense of environmental responsibility among consumers. Moreover, in Table 5, a comparative analysis can be found.  
Figure 14: A comparison of processing time comparison between the PLC and ZigBee techniques for power transmission to a smart grid.  
Furthermore, the results from the implementation of a smart grid demand scheme using the power line communication technique underscore its transformative potential. This integration has led to improved grid efficiency, heightened consumer engagement, and a tangible shift towards energy-conscious behavior. As smart grids continue to evolve, the PLC-driven demand scheme stands out as a pivotal advancement in achieving a more resilient, sustainable, and consumer-empowered energy landscape.
## 1 Introduction
Therefore, by using MODIS NPP data, meteorological data such as temperature and precipitation, and the soil microbial respiration model, this paper estimates the vegetation carbon sink in the Taihang Mountain area from 2000 to 2022 through the pixel-by-pixel calculation method with the help of remote sensing technology, aiming to deeply analyse the dynamic characteristics and driving mechanism of its spatio-temporal evolution. The findings of this study offer valuable insights that can facilitate the acceleration of the transition towards carbon neutrality and the establishment of an ecological civilisation within the Taihang Mountain region. Moreover, this study's results can serve as a paradigm reference for similar initiatives elsewhere.
## Materials and Methods
### Detection of PUEAs Using the DE Based Localization Algorithm  
Figure 1 shows the proposed system design, which consists of a CRN with a PU transmitter and TV sinks, a CRBS, randomly deployed SUs, and a PUEA. The radius of CRN ranges from 30 km to 100 km. The main components of the system are PUs, SUs, and PUEAs. The PU is considered to be fixed and located outside the network at a distance of 30 km to 100 km. The PU is licensed and has a high priority for transmitting using the band. SUs are not licensed and use the PU only when it is free. The PUEA does not interfere with the PU, but it destroys the spectrum and damages the spectrum sensing process of the system; in this way, the spectrum remains unused by the SUs when there is no transmission from the PUs. The PUEA can capture the PU signal, imitate it, and recommunicate its very peculiar sign, having attributes similar to the PU sign, to cheat the SUs.  
The PUEA can be positioned outside or inside the CR network. Based on predefined information stored in BS, the attacker can be identified. The attacker's position is measured and matched with the PU position stored in BS. If the location matches, then the sender is the PU, otherwise the PUEA. Here, TDOA finds the location by calculating the distance differences between the PU transmitter and the TV sinks. This distance difference is calculated from the time of arrival signal data using Equation (8).  
The TDOA obtains this difference in arrival of the signals by using the cross-correlation technique at two or more nodes. To locate the transmitter, the TDOA uses two hyperbolic curves. These curves intersect at a point that is the location of the transmitter. This location is further compared with the PU location in terms of the PUEA. To refine this location identification process, the DE is used. The DE reduces the possible error in the location identification process by optimizing the search space iteratively, as shown in Figure 2. This helps to attain the high fitness value for the required optimization.  
The main aim of the proposed algorithm is to detect the PUEA correctly and with maximum accuracy. Three main steps need to be followed for the proposed technique:  
* Characteristics of the PU and the SU signals match with the received signals.  
By using the DE algorithm, the TDOA measurements for an appropriate distance need to be used.
* Measured location is compared with the PU transmitter's location to distinguish between the PU and the PUEA.
## 1 Introduction
proposed a field-circuit coupling finite-element method established by considering the slotting effect and its interaction with stator winding generated by the magnetic field harmonic [16]. In order to increase power density, an efficient cooling method is necessary to break the thermal constraint. Cooling methods including liquid, forced-air, and spray-oil cooling were studied and compared [17]. In order to closely cool a rotor, a forced air-water cooling structure with air channels set in stator slots was proposed to further cool the rotor [18]. In recent years, rotor configurations with different sleeves were studied. A rotor with different PM configurations has not been studied, especially considering multiphysical field analysis. In this paper, quantitative comparative analysis was conducted for a 19 kW, 160 krpm HSPMSM with four different rotor configurations. This paper is organized as follows. In Section 2, the specification of HSPMSMs with different rotor configurations is presented. General design concepts are described in terms of construction and fabrication. In Section 3, multidisciplinary analysis and optimization based on the finite-element method (FEM) are performed. The torque capability, efficiency, and mechanical strength of HSPMSM with different rotors are investigated. Rotor nature frequency ensures safe operation. On the basis of FEM, the nature frequency of permanent-magnet rotors is predicted. Section 4 shows the power loss of these machines. Furthermore, thermal analysis is performed in Section 5. Ultimately, the weight average method was adopted to compare and discuss the final performance of these four machines in Section 6, and conclusions are then summarized.
## 2 Materials and Methods
### Satellite Data Processing
Then the NASA Reverb ECHO website provided all 546 NBAR (MCD43A4) images corresponding to the start of the MODIS data production through the end of the Landsat 5 data production. The BRDF describes the scattering of light from one direction into another ([PERSON], 2016). BRDF effects are not a concern when developing a surface reflectance product for Landsat imagery since it is acquired within less than 7.5 degrees of nadir ([PERSON], 2016). Due to no data being collected by MODIS from Day 166 to Day 184 in 2001 ([PERSON], 2016), the MODIS scene that corresponds to this timeframe was omitted, leaving us with 545 MODIS scenes to use. The Landsat scene from Day 175 of 2001, which paired with this MODIS scene, was also removed from analysis leaving 53 Landsat images available for analyses based on the informal screening process, 81 images based on a 95% threshold, and 92 images based on a 90% threshold (Figure 3). MODIS data were also prepared for STARFM using Python scripts. The scripts first extracted the Red and NIR bands and reprojected the images to the same coordinate system as Landsat. To help with reducing the file size before further processing, these data were subset to include a buffer around the final extent. The buffer allowed us to resample MODIS data to the same 30 m resolution as Landsat in a shorter amount of processing time while still preserving the ability to apply a final mask of the same extent of Landsat. If the final extent was applied before resampling, the edge of the image would include additional areas of no data due to the clip line bisecting a larger cell. Then the scripts shifted the cells to align perfectly with Landsat data. Last, all images were converted to a 16-bit signed generic binary format (Figure 2). The step of shifting cells was simplified for the MODIS data used in the statistical selection methods by shifting the final clip file instead. Figure 3: The temporal distribution of the Landsat scenes used in the base pairing.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 4 Proposed Architecture
### Intelligent Orchestration of Virtual Resources on 5G Edge Infrastructures  
The problem addressed in this work focuses on the intelligent orchestration of virtual resources (VMs or containers) in 5G edge infrastructures. As depicted in Figure 3, the scenario under consideration involves multiple bare-metal clusters or servers located in different 5G edge locations. These edge clusters are managed by a centralized instance of the OpenNebula cloud manager. From the perspective of OpenNebula, these clusters form a uniform physical resource pool, where virtual resources can be dynamically deployed on an on-demand basis.  
Intelligent orchestration of this infrastructure entails finding an optimal allocation of virtual resources to the available physical servers within the edge infrastructure, while satisfying specific criteria. Various optimization criteria can be considered, such as minimizing the number of servers in use by consolidating virtual resources onto the fewest possible servers to reduce energy consumption, balancing the load of different servers to prevent overloading and CPU contention, or optimizing latency by selecting the closest edge server based on specific proximity criteria. In addition, the orchestration system can also deal with different constraints, including a limit on the maximum number of hosts used, or a limit on the number of violations of the proximity criteria.  
To address these challenges, the ONEDeg5G module incorporates various prediction and optimization mechanisms. Prediction mechanisms leverage historical data to forecast  
Figure 3: Distributed edge infrastructure.  
the load of different virtual resources for the upcoming allocation period. This load can be quantified in terms of virtual resource consumption, such as CPU, memory, and/or bandwidth usage. Meanwhile, optimization techniques utilize the aforementioned predictions to determine the optimal allocation of virtual resources to physical servers based on specific optimization criteria (e.g., minimizing the number of servers, achieving server load balancing, or optimizing latency) and constraints.
# On the Optimisation of Practical Wireless Indoor and Outdoor Microcells Subject to QoS Constraints
## 1 Introduction
Specifically, we consider various propagation modes of both indoor and outdoor environments with different antenna types, including single-input single-output (SISO), multiple-input single-output (MISO) and multiple-input multiple-output (MIMO). The proposed method can therefore be used for planning and optimisation of the WIOMs to reduce the waste of resources in terms of cost and power consumption. The rest of this paper is organised as follows: Section 2 summarises related works on network planning and optimisation with various radio frequency (RF) simulation tools as a motivation for our work. Section 3 introduces the system model of WIOMs along with the optimisation problem of cell planning. Section 4 presents the proposed BSCP algorithm. Simulation results are illustrated in Section 5 to validate the effectiveness of the proposed algorithm. Finally, Section 6 concludes this paper with suggestion for future works.
## 1 Introduction  
Probiotics are defined as live microorganisms, which, when administered in adequate amounts, provide a health benefit to the host [1]. The majority of commercially available probiotic strains are _Lactobacillus_ spp. and _Bifidobacterium_ spp. [2]. In addition, several _Bacillus_ species have probiotic potential. [PERSON] [3] has reported that the use of _Bacillus_ species as probiotic dietary supplements has been rapidly expanding as the number of studies of _Bacillus_ species on probiotic effects has increased. It is demonstrated that _Bacillus_ exerts beneficial probiotic effects, including the secretion of antimicrobials [4], stimulation of the immune system [5], and overall enhancement of gut microflora [6]. _Bacillus_ spp. have the advantage of being able to survive in foods that require harsh processing conditions such as high temperature and high pressure [7]. _Bacillus_ spp. used as probiotictics in humans include _Bacillus coagulans_, _Bacillus subtilis_, _Bacillus licheniformis_, _Bacillus cereus_, _Bacillus cereus_ var. _toyoi_, _Bacillus__auto_ (_subtilis_), _Bacillus__causi_, _Bacillus__pumilus_, _Bacillus__amyloliquefacians_, and _Bacillus__pofermicicus_[3, 8].  
L-asparaginase (E.C. 3.5.1.1) is an amidohydrolase enzyme that hydrolyzes L-asparaginase to L-asparagine acid and ammonia [9]. In the pharmaceutical industry, L-asparaginase is also used for the treatment of acute lymphocytic leukemia (ALL), Hodgkin's disease, acute myeloid leukemia, chronic lymphocytic leukemia, lymphomasarcoma, reticulosarcoma, and melanosarcoma [10]. The anticancer mechanism of L-asparaginase inhibits tumor cell growth by causing the depletion of extracellular L-asparagine. Tumor cells grow depending on extracellular asparagine because they do not produce intracellular asparagine or have a reduced activity of asparagine synthetase [11, 12]. However, L-asparaginase, which is used to treat cancer, has various side effects such as fever, chills, vomiting, weight loss,pancreatitis due to hepatocellular dysfunction, and death during treatment of ALL [13]. This is due to its intrinsic toxicity associated with the presence of L-glutamine activity. L-glutamine is important for nitrogen transport in the blood, and prolonged depletion of amino acids during L-asparaginase treatment causes severe biochemical disorders in the body [14; 15].  
L-asparaginase enzymes have been already found in several bacteria, _Bacillus_ species being one of the most abundant ones due to its resistance to extreme environments. [PERSON] et al. (2020) [16] have reported that the production L-glutamine-free L-asparaginase activity makes _B. subtilis_ superior to the commercially available asparaginases that show glutamate activity causing diverse severe health problems to patients during anti-cancer therapy.  
Thus, this study aimed to isolate _Bacillus_ strains possessing L-asparaginase activity and free of L-glutamine activity from Korean fermented soybean foods and to assess the probiotic characteristics of _Bacillus_ strains.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 5 Limitations  
Our research team faced a lot of limitations due to the time it takes to train the data. Therefore, we were not able to run enough training sessions because we were limited on computers and we needed to conduct other research to improve the accuracy. Therefore,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Criterion_ & **Improved YOLOv3 [26]** & **Improved YOLOv4 [27]** & **Improved YOLOv4 BVI** & **Proposed Method** \\ \hline
**Scene Independence** & standard & robust & standard & robust \\ \hline
**Object Independence** & standard & robust & robust & standard \\ \hline
**Robust to Noise** & powerless & robust & standard & robust \\ \hline
**Robust to Color** & standard & standard & powerless & robust \\ \hline
**Small Fire Detection** & robust & standard & robust & powerless \\ \hline
**Multiple Fire Identification** & standard & powerless & powerless & robust \\ \hline
**Processing Time** & powerless & standard & robust & robust \\ \hline \hline \end{tabular}
\end{table}
Table 3: Employing many characteristics to evaluate the effectiveness of fire detection.  
some of the images came out as having a low confidence level. However, those images were outputting small fire images (Figure 6). Furthermore, YOLOv5 would mistakenly recognize red shirts or red blinking lights as fires. We are creating a sizable fire image dataset utilizing data augmentation methodologies that includes fire and non-fire photos for model training and testing in order to efficiently identify the target data and address the mentioned difficulties.  
The Internet of Things (IoT) has evolved into a free interchange of useful information between various real-world devices. Several technological issues must be overcome in order to improve fire detection and warning accuracy, which can be separated into five major issues: security and privacy, storage and cloud computing, energy, communication, and compatibility and standardization [46].
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 2 Related Work
The multi-hop mechanism enables communication while avoiding obstacles. The approach is interesting. A centralized LoRaWAN network with a wide coverage range is proposed. The devices used are cost-effective and have low power consumption. The main advantage is the change of the LoRaWAN coverage area when moving the central station. [PERSON] et al. [16] presented a reconfigurable architecture to support multimedia services in rescue operations such as radio communication or video surveillance. The services are provided by an unmanned aerial vehicle (UAV). This vehicle implements a standard digital mobile radio (DMR) hotspot and a video streaming server on an unmanned aerial vehicle (UAV). To achieve fast and flexible deployment of the planned services, they proposed virtualization using Kubernetes. In addition, they tested performance by measuring the deployment time and recovery time after disconnection. This work is complementary to our system, due to the fact that UAV nodes and our network can use the same frequencies. The main problem of the system using UAVs is the operating time, while our network is designed with low-power devices to extend the operating time. The mentioned works present a practical approach based on monitoring networks or parameters periodically using LoRaWAN technology. They also present the possibility of offering wide coverage ranges and extending them using multi-hop mechanisms. On the other hand, works related to emergencies propose the use of ad hoc networks for communications or the deployment of new infrastructures. Regarding our work, it is considered to be an intersection between the two points of view. We opted to work with low-power wide-area networks (LPWANs [17]) due to their low power and +20 dB gain with respect to the standard cellular system. LPWAN networks such as Sigfox, LTE-M and NB-IoT can provide extensive wireless connectivity between the IoT devices in the network. These technologies are complex and usually require high economic and energy costs compared with LoRaWAN [18]. This work is innovative due to using LoRaWAN for human-to-human emergency communications. In addition, the proposed system offers mobility despite being a centralized network.
## 3 Results and Discussion
### Other Indicators of Wastewater Treatment
These observations are consistent with the results obtained by [PERSON] and [PERSON] (2009) [24], who proved that volatile fatty acids can be sorbed on alkaline materials. Numerous authors emphasize that during volatile sorption of fatty acids by the alkaline sorbent, the pH value of the solution increases ([PERSON] and [PERSON], 2009). An example of research on the removal of ammonium ions from wastewater by clinoptilolite in the presence of organic compounds is the research conducted by [PERSON] and [PERSON] (2003) [25]. These authors found that in the presence of organic compounds, the efficiency of ammonium ion removal was higher ([PERSON] and [PERSON], 2003) [25]. The results presented in Figure 2 show that when the concentration of clinoptilolite was lower (50 g/L--sample 5Z), the concentration of volatile fatty acids decreased to a level of 62 mg CH\({}_{3}\)COOH/L, while at a concentration of 100 g/L (sample 10Z), the final concentration of volatile fatty acids was 69 mg CH\({}_{3}\)COOH/L. In the control wastewater (K) on the last day of the process, the concentration of volatile fatty acids remained at a level of 112 mg CH\({}_{3}\)COOH/L. A macroscopic view of the structure of sewage sludge formed during the sewage treatment process in dynamic conditions is shown in Figure 3.  
The photographs show that the microalgae were effectively immobilized on polysaccharide beads with good biomass-binding properties, which allowed them to be easily separated from the treated wastewater. In turn, clinoptilolite and microalgae constituted a homogeneous suspension. Micrographs of sewage sludge after the assimilation of N-NH\({}_{4}\)+ from wastewater carried out in dynamic conditions are shown in Figure 4.  
Figure 3: Photographs of sludge after the sorption processes. The sewage sludges are uniform and consist of sorbent residues. The locations of microalgae in clinoptilolite are shown as green, round-shaped particles. In the structure of sludge containing immobilized microalgae, a much higher cell density is visible compared to sludge with microalgae in the form of a suspension. These results are in agreement with the literature that states that the immobilizing of algae results in the obtaining of a several times greater biomass than the suspended system [26, 27]. Therefore microalgae immobilized in alginate are proposed for removal of contaminants and easier biomass harvesting [28]. In the control sample, dark clusters of impurities in the form of flocs are noticeable.
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 2 Materials and Methods
### Method
Use HemiView 2.1 SR5 software to analyze and process the photos taken. Extract the canopy openness data from the VisSky column in the values worksheet from the processed data. As shown in Figure 3, the three pictures reflect the different forest environments with different degrees of density. They are environments with low, medium, and high-canopy openness from left to right. Figure 3: The hemispherical photo above represents observation points. Figure 2: Sketch map of observation points and surrounding trees. The canopy openness of the 57 test points is shown in Figure 4 and the test points were classified according to different environmental openness. Low openness was defined as forest canopy occupying less than 30% of the sky, medium openness was defined as forest canopy occupying 30% to 50% of the sky, and medium-high openness was defined as forest canopy occupying 50% to 70% of the sky. Finally, high openness was defined as forest canopy occupying more than 70% of the sky. Fifty-seven test points were recorded in January 2022.