esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Bluetooth, BLE, and ANT  
Various technologies used for wireless data exchange, such as Bluetooth, BLE, and ANT, can also serve as positioning techniques. Bluetooth, a local wireless communications standard operating at UHF wavelengths (2.4 gigahertz (GHz)), is commonly used to exchange data and connect devices (e.g., wireless headsets, keyboards, controllers, etc.) [5; 26; 113], but can also provide localization and proximity awareness to users. For example, Bluetooth has been successfully incorporated into hazard proximity detection and real-time alert systems to improve safety on construction sites [113]. As described in the UWB section above, positioning is based on distances from known locations of anchor devices and access points, calculated from RSSI measurements and radio propagation models [114; 115; 116]. Bluetooth is relatively inexpensive and easy to use, but high energy requirements can quickly drain the associated device's battery [5; 113]. Alternatively, BLE increases battery life by exchanging small volumes of data interspersed with periods of little to no energy consumption [26]. BLE can accurately position within approximately 2-5 m indoors [117], but operates at shorter ranges (up to 50 m) [26] compared to Bluetooth (up to 100 m for version 4.x, and up to 200 m for version 5.0) (Table 1) [118]. ANT, another local communication standard, uses deep-sleep modes to reduce power consumption but is also limited to shorter ranges (up to 30 m) [119; 120; 121].  
In natural resources, Bluetooth is currently used primarily for transmitting raw data to mobile devices, such as from fitness monitors on forestry workers [122] (Figure 2, panel E), or from environmental sensors like the Kestrel(tm) weather meter [123].
# Probabilistic Modeling of Dust-Induced FSO Attenuation for 5G/6G Backhaul in Arid Regions
## 3 Results and Discussions
The Johnson SB distribution offers a superior visual fit, capturing the sharp peak near the origin as well as the extended heavy tail of the empirical data. In contrast, the negative exponential distribution underestimates the peak and over-smooths the tail, indicating limited flexibility in adapting to skewed and heavy-tailed attenuation data. Quantitatively, although both models yield comparable RMSE and MAE values, the \(R^{2}\) for the Johnson SB distribution reaches 0.95, significantly outperforming the negative exponential model's \(R^{2}=0.82\). These findings reinforce that the Johnson SB distribution is a more accurate and adaptable choice for modeling FSO attenuation under complex dust conditions across the full visibility spectrum.
## 3 Results and Discussion
### Cybersecurity and Privacy in Energy Management Systems
#### 3.5.3 Multi-Layered Security Frameworks and Standards  
An effective cybersecurity strategy for microgrids requires the deployment of multi-layered security frameworks that address vulnerabilities across all network levels, from physical infrastructure to application-level controls [15; 31]. These frameworks typically include device authentication, network segmentation, intrusion detection systems, and encryption protocols.  
Standardization organizations have published cybersecurity guidelines specifically designed for energy infrastructures, including the IEC 62443 series and the NIST Cybersecurity Framework [23; 32]. However, the implementation of these standards varies across regions and system designs, highlighting the need for greater regulatory consistency and enforcement.
## 3 Results and Discussion
### Baseline Case Study of the Solar ORC System Performance
#### 3.1.2 Net Power Output  
Since the net power output of the ORC is not affected by the addition of a regenerator, it generally follows the same trend as the cycle efficiency of the ORC without the regenerator, the solar input is drastically reduced with the addition of regenerator for these mixtures. The influence of mass fraction depends on the components being used in the mixture. Figure 7 presents the net power output for various mass fractions of WF mixtures. It can be seen that mixtures with heptane and hexane as components have a low net work output even though they are being operated at relatively higher temperatures.  
The optimal net work output of ORC system with mixtures can be found in Table 6. The range of optimal net work output is from 2.88 to 9.07 kW, where the highest net work output is obtained from R245 fa/R152a (0.9/0.1) mixture i.e., 9.07 kW.  
Figure 6: Cycle efficiency vs. mass fraction of all mixtures with regenerator.  
Figure 7: Net work output vs. mass fraction for all mixtures.
## 3 Results and Discussion  
### SBP-Encapsulated Bacterial Culture Characterization over Time  
Prior to examination of _R. zopfii_'s ability to biodegrade EE2, we explored the viability and adaptation of the encapsulated culture for long-term survival in the confined environment provided by the SBP capsule. _R. zopfii_ is a member of the _Actinobacteria_ phylum. _Rbloodoccus_ spp. are defined as Gram-positive to Gram-variable, aerobic, and nonmotile. Their cell morphology varies during different stages of their growth cycle. These bacteria grow in filaments and exhibit extensive branching and hyphal growth before fragmenting into an irregular rod-shape or coccoid units. Some can be found as cocci that later turn into short rods, while others continue transforming into long filamentous rods or start branching out.  
_R. zopfii_ cells were cultivated in LB medium (30 \({}^{\circ}\)C, 150 rpm), and created suspended colonies of 1-2 mm diameter after a few days of incubation. Microscopic observation of the grown culture was conducted, presenting a Gram-positive filament growth state and the presence of Gram-positive cocci that are assumed to be released from the mother filaments (data not shown).  
Figure S1 shows a microscopic observation of the encapsulated _R. zopfii_. The encapsulated culture exhibited an adaptation to the confined SBP environment. Eleven days after the activation point, where the SBP-encapsulated culture was incubated in MSM medium enriched with EE2, it presented large filamentous colonies (50-1000 \(\upmu\)m), which are similar to the structure of colonies that were observed in the suspended state of the culture. Moreover, it seems that the culture has three growth states in the internal medium of the capsule: Filamentous colonies with extensive branching and hyphal growth; single irregular rod-shaped filament units; and single Gram-positive cocci cells. The encapsulated _R. zopfii_ culture formed a floc particles structure and an advanced stage of fragmentation into coccoid elements was also observed. The inner suspension of the SBP capsule contained a mean of \(2.88\times 10^{8}\) CFU/mL _R. zopfii_ at the end of the capsules activation stage.  
A similar procedure was employed for the encapsulated _P. putida_ F1 strain. This bacterial strain was also resistant to the freeze-drying procedure and culture assimilation was observed within the SBP capsules, with bacterial counts of at least \(3.38\times 10^{8}\) CFU/mL. This strain was studied previously for its ability to degrade phenolic compounds as an encapsulated culture and was described in detail in [PERSON] et al. (2018).
## 5 Results Analysis
### Meta-Atom Array Configurations
#### 5.2.1 \(\times\) 2 Array Analysis  
Figure 10 shows the effective medium parameter amplitudes at the different rotations of the 1 \(\times\) 2 array geometry. The amplitudes of the permittivity, permeability and refractive index are plotted in the same Figure for every rotation. Figure 10a shows permittivity from 4.64-7.70 GHz (bandwidth of 3.06 GHz) and 8.36-10.36 GHz (bandwidth of 2 GHz), permeability from 7.89-14 GHz (bandwidth of 6.11 GHz) and a refractive index from 4.50-7.47 GHz, 7.80-10.11 GHz and 12.62-14 GHz at zero-degree rotation. Figure 10b exhibits permittivity from 4-7.19 GHz (bandwidth of 3.19 GHz) and 7.55-10.02 GHz (bandwidth of 2.47 GHz), permeability from 7.60-14 GHz (bandwidth of 6.40 GHz) and a refractive index from 3.86-6.86 GHz (bandwidth of 3 GHz) and 7.56-9.45 GHz (bandwidth of 1.89 GHz) at 90-degree rotation. Furthermore, in Figure 10c at 180-degree rotation, the unit cell displays permittivity from 3.34-3.54 GHz (bandwidth of 0.20 GHz), 4.58-7.72 GHz (bandwidth of 3.14 GHz) and 8.42-10.41 GHz (bandwidth of 1.99 GHz); permeability from 7.90-14 GHz (bandwidth of 6.10 GHz); and a refractive index from 4.42-7.48 GHz, 7.83-10.11 GHz and 12.54-14 GHz. Additionally, permittivity of 4.01-7.19 GHz (bandwidth of 3.18 GHz) and 7.55-10.02 GHz (bandwidth of 2.47 GHz); permeability from 7.55-14 GHz (bandwidth of 6.45 GHz); and a refractive index from 3.87-6.84 GHz, 7.59-9.45 GHz and 11-11.25 GHz are shown in Figure 10d for 270-degree rotation of the proposed meta-atom unit cell. At 9.20 GHz, the permittivity, permeability and refractive index curves display negative peaks. As a result, the array structure appearances as double-negative meta-atoms are shown in Table 3.  
Figure 10: Amplitude of the permittivity, permeability and refractive index at: (**a**) 0\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 180\({}^{\circ}\), (**d**) 270\({}^{\circ}\) rotation of the 1 \(\times\) 2 array structure.
## 2 Background Description
### Basic Concepts  
A directed graph \(G=\{Q,\varepsilon,W\}\) consists of a set of nodes \(Q=\{q_{1},q_{2},\ldots,q_{n}\}\), a set of edges \(\varepsilon\subseteq\big{\{}\big{(}q_{i},q_{j}\big{)}:q_{i},q_{j}\in Q\big{\}}\), and a weighted adjacency matrix \(W=\big{[}w_{ij}\big{]}\in\mathbb{R}^{N\times N}\) with nonnegative elements \(w_{ij}\). An edge of \(G\) is denoted by \(e_{ij}=\big{(}q_{i},q_{j}\big{)}\). Then, if and only if \(e_{ij}\in\varepsilon\) and \(w_{ii}=0\) for all \(i\in\{1,\ldots,N\}\), the \(w_{ij}>0\). The set of neighbors of node \(q_{i}\) is denoted by \(N_{i}=\big{\{}q_{j}\in Q:(q_{i},q_{j})\in\varepsilon\big{\}}\). The in-degree of node \(q_{i}\) is defined as \(\deg_{in}(q_{i})=\sum_{j=1}^{N}w_{ij}\). The degree matrix of \(G\) is denoted by \(D=\text{diag}\big{\{}\deg_{in}(q_{i}),i=1,2,\ldots N\big{\}}\). The Laplacian matrix of \(G\) is d \(L=D-W\).  
Further information about graph theory can be obtained from [41].  
For a given \(\Psi\in\mathbb{C}\), \(\text{Re}(\Psi)\) and \(\text{Im}(\Psi)\) represent the real and the imaginary part of \(\Psi\), respectively. The superscript H denotes the Hermitian adjoint of matrices.  
The following lemma is useful for multi-AUV systems.  
**Lemma 1**.: _Define \(L\in\mathbb{R}^{N\times N}\) is the Laplacian matrix of a directed graph \(G\)[42]:_  
1. \(L\) _has at least one zero eigenvalue, and_ \(1_{N}\) _is the associated eigenvector; that is,_ \(L1_{N}=0\)_;_
2. _If_ \(G\) _has a spanning tree, then_ \(0\) _is a simple eigenvalue of_ \(L\)_, and all the other_ \(N-1\) _eigenvalues have positive real parts;_ \(1_{N}\) _means a column vector of size_ \(N\) _with 1 as its elements, and_ \(I_{n}\) _means an identity matrix with_ \(n\) _dimensions;_ \(0\) _means zero matrices of an appropriate size, with zero vectors and zero number as special cases._
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 3 Results
### The Observed Grassland Degradation Points  
The observed grassland degradation points were mainly distributed in the northeastern and central parts of the TP (Figure 8a). Among them, Guoluo Tibetan Autonomous Prefecture has the largest number of observed degradation points, accounting for 44.35% of all observed degradation points. The number of degraded points in Haibei Tibetan Autonomous Prefecture and Yushu Tibetan Autonomous Prefecture accounted for 12.50% and 8.47%, respectively. Combining the data of the grassland degradation at the prefecture level on the TP, it was found that the grassland in Guoluo Tibetan Autonomous prefecture was seriously degraded, which was also proved by observed points. In addition, the southern part of the TP, where grassland degradation still needs to be monitored more intensively, such as Changdu City, Lasa City and Linzhi City.  
Figure 7: Degradation of grassland on the TP displayed by multiple indexes: (**a**) spatial distribution of multiple indexes of grassland quality change at the prefecture level; (**b**) the percentage of degraded grassland in TP prefecture-level cities.  
The consistency between the six vegetation indexes and the observed grassland degradation points varied greatly (4.66-31.54%) (Figure 8b). The consistency between the six vegetation indexes and the observed grassland degradation points is SIF (31.54%) > GPP (17.56%) > LAI (17.20%) > NDVI (10.75%) > NPP (4.66%) > EVI (3.23%). The consistency between the superimposed six vegetation indexes and the observed grassland degradation points of the TP is 56.63%.
## 6 Precise Control in Activating and Deactivating Rotor Unbalances  
The timing and magnitude of the unbalance mass must be adjusted to optimize the propulsion generation. To achieve precise control of the unbalance mass, advanced control systems can be used. These systems monitor the rotational position of the rotor and activate electromechanical actuators to release or adjust the unbalance mass at the desired time and magnitude [19]. Electrically controllable actuators [20] are fixed on the rotor, which applies the piezoceramic length expansion effect (PCT) or the magnetostrictive length expansion effect. The local direction of the length expansion effect must be aligned with the local radial direction of the rotor, causing controlled mass displacement effects towards the rotor periphery and thus causing controlled unbalance (Figure 5).  
The characteristic feature of applying PCT is that the energy requirement for the permanent modulation of the piezoelectric unbalance generator is low because the generator can work highly powerless due to the high PCT internal resistance. Furthermore, the piezoelectric effect allows for rapid changes in unbalance. Possible PCT length changes are in the order of a few 100 \(\upmu\)m, with mechanical response times of only \(10^{-4}\) s or less. Furthermore, the characteristic feature of the piezoelectric unbalance generator is that rotations with frequencies of 10 kHz and higher become technically possible. Therefore, we conclude that by applying rotors equipped with current bearing technology, the unbalance control could be realized up to rotor speeds of about 1000 rps, and even higher speeds are possible for magnetically levitated rotors. Note that during accelerated flight, only unbalance-induced inertial forces act on the rotor bearing, as the elastic support forces are eliminated when the rotor fixation is released. As a result, the load on the bearings due to unbalance is significantly smaller during flight compared to the operation of a tethered rotor. This is expected to have a favorable effect on the service life, especially for roller bearings.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 2 System Model and Problem Formulation
### Potential User Locations  
Each user is equipped with a single isotropic antenna. The black dots in Figure 2 illustrate the users' possible outdoor locations. These locations are initially generated in a way that they are one meter apart from each other and uniformly cover the region. The points that are encapsulated by buildings are then excluded from the set of all considered user locations in the region, which is denoted as \(\mathcal{R}\).  
Figure 2: Example of an area to be covered by a D-mMIMO deployment. The dashed line indicates the border of the coverage region. The black dots are the potential user locations that need to be covered by the signal, hence a discretization of the continuous area into a grid. The D-mMIMO system has multiple APs. The black dots next to buildings also indicate the potential locations for deploying the APs.
## 1 Introduction
These studies underscore the ongoing efforts to improve the performance and capabilities of optical power limiters using liquid crystal technologies. Recent studies have demonstrated that oligothiophene-doped liquid crystals can function as low-threshold optical limiters with the added benefit of deformability, allowing for their application in a wide range of shapes and configurations, including those required for CCD protection [41]. The application of an electric field to these LC-based optical limiters significantly decreases their optical limiting threshold, enhancing their protective capabilities, which is crucial for adapting the optical limiter to different sensitivity levels of CCDs. In this paper, we present a method that utilizes dye-doped liquid crystals in a twisted nematic cell configuration to develop an efficient optical power limiter for the protection of charged-coupled devices (CCDs) against damage and dazzling. This approach capitalizes on the intrinsic optical properties of nematic liquid crystals, which are enhanced by selective dye doping, to modulate light transmission without requiring external electric fields, as is customary in conventional systems. The primary mechanism of our optical limiter is based on the optical properties facilitated by the unique alignment of nematic molecules within a twisted configuration. We take advantage of the high reorientational response of NLC molecules combined with strongly enhanced thermal effect. To enhance the thermal response of NLC molecules, we dope a host NLC with dye having a strong absorption at \(\lambda\) = 532 nm. Moreover, by incorporating absorbing plastic sheet polarizers, we harness the thermal nonlinearity mechanisms and reduce the activation threshold of the limiter, enabling protection against both high (approximately 1 W) and low (less than 200 mW) light intensities.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods
### Energy Consumption on the Control Plane  
As SDN's main feature, the separation of control from forwarding packets brought the ability to increase the applications that a network can run, but this increases the energy consumption on control-plane and the complexity to measure the network's overall consumption.  
Most energy-related studies on networks focus on measuring link usage, which on SDN accounts only for the data-plane. As data centers networks grow in size, complexity and redundancy, the control structure, the SDN controllers, now also must be taken into account for energy consumption measuring.
## 1 Introduction  
LiCoO\({}_{2}\), introduced by [PERSON] [1], was the first layered transition metal oxide used in commercial Li-ion batteries and has since become the most common cathode material for rechargeable batteries [2]. However, cobalt is very expensive and has been subsequently replaced partially by other metals to decrease the manufacturing costs of the cathode materials [3]. A common type of mixed metal oxide, NCM333, was first introduced by [PERSON] et al. [4] in 2001 and has an equal mix of nickel, cobalt, and manganese. Cathode materials with a high nickel content (LiNi\({}_{x}\)Co\({}_{y}\)Me\({}_{1}\)-x\({}_{y}\)O\({}_{2}\). x \(\geq\) 0.8-1.0) have attracted much interest as lithium storage materials for rechargeable lithium batteries. These layered oxide materials typically have high energy densities (>200 mAh/g) but suffer from low thermal stability and capacity fading during cycling. Recently, the focus has been on high nickel-containing, mixed-metal oxides, tailored especially for the demand of electric vehicles [3; 5].  
Pure LiNiO\({}_{2}\) is an interesting candidate for cathode material in Li-ion batteries, because most of its high theoretical capacity of 274 mAh/g is utilizable at a reasonable voltage range between 2.6 and 4.2 V and the material is low cost. For these reasons, it has been under study for over 15 years [6; 7; 8]. However, the electrochemical and structural stability results noted by earlier studies lacked promise, driving interest towards LiCoO\({}_{2}\) and mixed LiNi\({}_{x}\)Co\({}_{y}\)Mn\({}_{x}\)O\({}_{2}\) materials [9]. Recent published articles about the synthesis conditions of LiNiO\({}_{2}\) include [PERSON] et al.'s [10] structural study, which showed a low lithiation temperature of 650-700 \({}^{\circ}\)C would annihilate most defects in the LiNiO\({}_{2}\)'s crystal structure. [PERSON] et al. [11] went further, showing that an electrolyte dopant can stabilize LiNiO\({}_{2}\) cells.  
The present study focuses on the development of a continuous and simple process for producing high quality LiNiO\({}_{2}\). This work will provide direction for future work on preparing and testing different dopants, coatings, and other subjects relating to the performance of LiNiO\({}_{2}\) cathode material.
## 3 ANN Drag Model and Training Strategy
### The Construction of ANN
#### 3.1.1 The Learning Algorithm of ANN Drag Model  
Since the gradient descent with momentum (GDM) algorithm and the Levenberg-Marquardt (LM) algorithm are the commonly used learning algorithms in ANN [45], we decided to compare their performance on the convergence of ANN. Two termination rules, \"rule A\", which gives the maximum number of iterations, and \"rule B\", which is the given error tolerance, are adopted simultaneously. To reduce the impact of the random initialization of weights, we conducted three independent experiments and recorded the mathematical expectation of the Spearman correlation coefficient [57] between the outputs and the expected outputs in Table 3. It can be found that LM has more accurate prediction results in terms of convergence and applicability compared to the GDM algorithm. However, GDM has advantages of computational efficiency in simulations. Considering the principle of precision priority in designing the ANN, the LM algorithm will be adopted as the learning algorithm of ANN.  
Figure 2: ANN structure of polydisperse drag model.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Network Coverage Optimization Model Development
#### 2.4.2 Model Evaluation Indicators  
In the network coverage optimization model, the area to be monitored can be considered as consisting of countless grid points (x\({}_{i}\),y\({}_{i}\)). The coverage ratio (CR) represents the ratio of the total number of grid points within the node's sensing range to the total number of grid points within the area to be monitored, calculated as Equation (6). A larger CR indicates a larger percentage of the area monitored by the wireless sensor network.  
\[CR=\frac{\sum_{i=1}^{n}{(x_{i},y_{i})}}{\sum_{i=1}^{n}{(x_{i},y_{i})}}\times 100 \% \tag{6}\]  
where (x\({}_{i}\),y\({}_{i}\)) is the grid point coordinates, n is the total number of grid points within the sensing range of the node, and m is the total number of grid points in the area to be monitored.  
The overlap rate (OR) indicates the ratio of the total number of grid points in the overlapping area of the node's sensing range to the total number of grid points in the area to be monitored, calculated as Equation (7). A smaller OR indicates a more even distribution of nodes, less redundancy, and fewer wasted nodes.  
\[OR=\frac{\sum_{i=1}^{k}(x_{i},y_{i})}{\sum_{i=1}^{n}{(x_{i},y_{i})}}\times 100\% \tag{7}\]  
where (x\({}_{i}\),y\({}_{i}\)) is the grid point coordinates, k is the total number of grid points within the overlapping area of the node sensing range, and m is the total number of grid points within the area to be monitored.