esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.6 Amazon Forest AGB Map at 1 km Spatial Resolution  
[PERSON] et al. (2016) derived the biomass distribution of the Amazon rain forest at a 1 km spatial resolution by combining a canopy height map and an individual-based forest gap model. Comparison of biomass with field data showed a slight underestimation by 15%. Their study approach provides a foundation for large-scale analyses of heterogeneous forest structure, even in tropical regions.
# Non-Probabilistic Reliability Analysis of Slopes Based on Fuzzy Set Theory
## 1 Introduction
The non-probabilistic reliability method is a kind of convex-model-based-method [16] which can consider the uncertainty of parameters and evaluate the stability of the slope without the specific distribution characteristics of the parameters. A non-probabilistic reliability method based on an ellipsoidal model (a kind of convex model) not only can avoid the interval expansion problem but can also analyze all the points from the whole interval. Since the fuzzy geotechnical parameters are transferred into interval numbers by fuzzy cut set theory, it is more reasonable to use the non-probabilistic reliability method. This paper proposes a fuzzy-set-theory-based non-probabilistic reliability analysis method for the slope. Firstly, the fuzzy distribution characteristics of geotechnical parameters are described by the fuzzy affiliation function, and then the fuzzy numbers of geotechnical parameters are transformed into interval numbers using the \(\lambda\)-cut set method. Secondly, the slope performance function is constructed using the Bucher design and the response surface method. Thirdly, the non-probabilistic reliability index and failure degree of the slope at each cut set level are solved using the non-probabilistic reliability analysis method based on the ellipsoidal model. Finally, the overall failure degree of the slope is obtained by the weighted average method. The proposed method provides a reference basis for slope stability analysis considering the fuzziness of geotechnical parameters, and can be used to guide engineering practice. The novelty of this method lies in the application of an ellipsoidal-model-based non-probabilistic reliability method to handle the fuzzy uncertainty of geotechnical parameters in slope engineering, the use of the failure degree as well as non-probabilistic reliability index to evaluate the slope stability state, and providing the results not only for each fuzzy cut set but also for the overall slope.
## 7 CPS Architecture in GBG-RPL
### Workflow of Proposed Model (GBG-RPL)  
The step-by-step workflow of the proposed methodology is depicted in Figure 6. The figure depicts a fog server deployed at the fog layer, a root node at the device layer, and parent/child nodes at the device layer. A detailed description of each step is given below:  
1. When a member node departs the CPS network or a new node enters the network, the RPL DODAG reconstruction begins.
2. Parent nodes keep an eye on the trust parameters of their child nodes and neighbors. Dropped packets, energy consumption, latency from start to finish, and message size are all the parameters to keep an eye on. It is performed at the device layer and issometimes referred to as \"direct trust calculation\". Except for the sink node, all other nodes at the device layer have limited resources.
3. Each child node reports its trust parameters to its parent, which then shares them with the sink. Except for transmitting the parameters to the fog server, the sink node does nothing else. This means that the sink node is connecting the device layer and the fog layer. The update of trust parameters is sent from the device layer to the fog layer every 10 milliseconds (ms). At the same time, the trickle timer for considering a packet to be dropped/lost is set at 5 ms.
4. The fog server is deployed at the fog layer. It has high computational and storage capabilities and, therefore, is assigned many tasks or functionalities for execution. The first function is to maintain a global trust list, which is updated on the basis of direct and indirect trust calculations. Information regarding all member nodes resides in the global trust list.
5. The second functionality is to calculate the trust value for a node by applying the Gini logic. The resulting value of the Gini logic falls between 0 and 1, where 0 denotes a fully trusted node while 1 denotes a fully compromised node. Thus, the behavior of a node is declared either malicious or normal as per the trust value calculated.
6. Once the trust value has been calculated, the blockchain smart contract is implemented to update the ledger/database, which maintains the global trust list. The ledger is updated only when there is the addition of a new node or the trust status of the node changes.
7. After the update of the database, the BRGTL is disseminated from the fog layer to the device layer.
8. The sink node receives the updated BRGTL, which is then sent to the parent nodes so that the malicious node can be removed or a new node can be allowed to join the network.
9. The RPL DODAG is rebuilt in the event that a malicious node is removed from the CPS network, a new node joins the network, or a member node leaves the network.
10. After the reconstruction of the RPL DODAG, routine RPL network operations are resumed.  
Figure 5: A layered CPS architecture.
## 4 Insights into Bacterial-Algal Biomass in Different Operational Configurations
### Single-Batch Photobioreactor (SBP) Systems  
Studies conducted in single-batch reactors typically aim to investigate the interactions between microalgae and bacteria [17; 21; 101]. These works, as depicted in Table 2, have been conducted using both pure cultures [13; 106] and mixed cultures [17; 45].  
[PERSON] et al. (2018) [13] introduced Chlorella vulgaris into the treatment system and noted that the presence of oxidized forms of nitrogen facilitated the growth of microalgae. They hypothesized that during nitrogen assimilation, microalgae excrete H\({}^{+}\) to maintain intracellular pH, thus conserving energy for cell growth by preserving intracellular neutrality. Additionally, an increase in microbial diversity was observed, including the proliferation of nitrogen-fixing bacteria such as rhizobacteria [106]. Furthermore, studies exploring the application of photosynthetic oxygen have yielded promising results, with batch studies demonstrating over 90% removal of ammoniacal nitrogen and phosphate [17; 45; 99]. Another critical aspect addressed is biomass harvesting, a significant challenge for commercialization [17; 18; 44]. [PERSON] et al. (2016) [14] also observed a positive impact on the sedimentation of bacterial biomass.  
[PERSON] et al. (2012) [45] assessed sedimentability by measuring the total suspended solids (TSS) of non-sedimented biomass. They observed a reduction from 1.64 to 0.05 g/L in the algae-bacteria consortium, while the biomass with microalgae alone exhibited a TSS above 0.8 g/L. [PERSON] et al. (2016) [44] evaluated the sludge volume index (SVI), obtaining values of 76.8 g/L for systems with activated sludge only and 42.55 g/L for co-cultivation with _Chorella_ sp. [PERSON] et al. (2018) [13] assessed sedimentability through flocculation efficiency, obtaining indices of 88%, 42%, and 1.23% for the biomass of bacteria only, algae-bacteria co-cultivation, and systems with microalgae only, respectively.
## 2 Multiprecision Hardware
### Half-Precision  
In recent years, lower-precision multiplications accompanied by a higher-precision accumulation have proven useful in training deep neural networks [27]. Traditionally, a neural network has been trained using a single 32-bit precision data type for matrix multiplication and the convolution of vectors. However, it was recognised that half-precision hardware delivers a significant boost of speed while providing similar training quality. That motivated major hardware vendors to settle on mixed-precision hardware units to further enhance their performance [28]. Furthermore, a new floating-point format, BF16, has drawn attention as it is better suited to support deep learning tasks than the standardized FP16 variant due to the right balance between exponent and significant parts of a floating-point number [16]. The comparison of floating-point data types are given in Table 1 of Appendix A.
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 3 HDL Simulation Using DEVS Methodology
### RTL-DEVS Atomic Model  
Figure 2 explains the single RTL-DEVS atomic model. When digital logic consisting of 0 or 1 is transmitted to the model's event input X, the model stores the value of the corresponding signal in the register. The input event causes the DEVS model's 8 ext to be performed, resulting in internal transition behavior. Two main functions operate during the internal transition process: (1) A check of whether the digital signal currently contained in the register is the same as the value of the previous event input X, which was the condition for 8 ext. If a new event input of the existing input and the current register input are not the same, the model does not update the state of the state buffer connected to the output unit, so new values are not output. (2) For compatibility with Testbench, each model has an edge checker buffer. When the current state of the state buffer is reversed, it is processed as rising or falling.  
Figure 1: Execution environment using hierarchical scheduling.  
Figure 2: RTL-DEVS atomic model structure.  
Figure 3 explains an example of a 4-bit full adder in the RTL-DEVS model where a simple logic module is expressed as an atomic model. In previous research, we tested some models that could serve as basic modules to handle multiple inputs and outputs by expanding the internal registers of the atomic model into vector form to reduce simulation costs. There were no major problems with these simple logic gates or simple combinational logic circuits, but problems arose while developing the Verilog parser and performing larger-scale simulations to check compatibility. The wire connecting each atomic model is not implemented separately, but a message I/O function, commonly called a bag, is used in the DEVS engine to enable the transmission of electrical signals. The number of atomic models is increasing, and a zero-time transition, in which one atomic model has a time advance of 0, generates continuous input and output, resulting in too much message I/O at the same time, resulting in confusion in the message transfer interface of the DEVS engine and no longer ensuring proper operation.
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 3 First Approach: Single-Objective Evolutionary Algorithm
### Characteristics of the Algorithm  
Let \(D\) be a multi-label data set, \(k\) the desired number of disjoints subsets \(S_{1},\ldots,S_{k}\) of \(D\), and \(c_{1},\ldots,c_{k}\) the desired number of examples at each subset. Each individual is encoded as an integer vector of size \(|D|\), in which each gene represents the subset to which each example is assigned.  
Different possibilities can be used to generate new individuals by crossover and mutation. EvoSplit selects parents by ranking, recombination is performed using 1-point crossover, and a mutation is carried out by reassigning randomly 1% of the genes to a different subset. This process to generate new individuals would produce in most cases individuals that do not comply with the constraint of having \(c_{i}\) examples in subset \(S_{i}\), \(i=1\ldots k\). Therefore, a repairing process is applied to randomly reassign examples/genes to other subsets to fully comply with the constraint.  
This work considers two different fitness functions: a variant of the Label Distribution (\(LD\)) and the Label Pair Distribution (\(LPD\)), which were introduced in Section 2. The Label Distribution is appropriate for data sets in which a specific label can appear only once in an example. However, for data sets that might include in a particular example several instances of the same label, Equation (8) is not appropriate. This is the case, as it was shown earlier of well-known data sets in computer vision, as Microsoft COCO [8]. Therefore, the \(LD\) has being modified to consider also data sets with this characteristic.  
Let's consider \(\lambda_{i}^{S}\) and \(\lambda_{i}^{D}\)the number of appearances of label \(\lambda_{i}\) in subset \(S_{j}\) and data set \(D\) respectively, \(L^{S_{i}}\) and \(L^{D}\) the total number of labels in subset \(S_{j}\) and data set \(D\) respectively. The modified Label Distribution measure, which is used as fitness function in EvoSplit, is then calculated following Equation (11).  
\[LD^{\prime}=\frac{1}{q}\sum_{i=1}^{q}\left(\frac{1}{k}\sum_{j=1}^{k}\left| \frac{\lambda_{i}^{S_{j}}}{L^{S_{j}}-\lambda_{i}^{S_{j}}}-\frac{\lambda_{i}^{ D}}{L^{D}-\lambda_{i}^{D}}\right|\right) \tag{11}\]  
We could proceed similarly with the \(LPD\) measure. However, EvoSplit does not consider in its calculation the number of appearances of each label in each example, but only the co-occurrence of labels, as in the original \(LPD\) measure. In this case, a variant of the \(LPD\) would increase considerably the number of pair combinations and make difficult that different examples share the same pair.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 6 Simulation Results and Analysis
### Simulation Models  
It is assumed that each EN sends packets to the GW either directly or via intermediate hops using the LoRa protocol. The spreading factor (SF), bandwidth (BW), and coding rate \(R_{c}\) are fixed at SF = 7, BW = 125 kHz, and \(R_{c}=1\), respectively. The low data rate optimization is not enabled by default, while explicit header type (H = 0) and CRC are adopted. These parameter settings determine the simulation models adopted in the experiments, including those for packet transmission time, energy consumption, path loss, etc.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Transmitter System
#### 3.1.3 Modulation  
To implement the modulation, the integrated circuit CD-4066 BE was used, which is a quadruple bilateral switch with independent controls using a single channel controlled by the modulating signal.  
Figure 7 shows a comparison of the digital information signal (pink color) and the corresponding modulated signal (yellow color).  
Figure 5: Transmitter system.  
Figure 6: Carrier signal.
## 3 Experimental Validation
### Acquisition of Experimental Data
#### 3.2.2 Deployment of Sensors  
According to the structure and size characteristics of the experimental machine tool, a temperature sensor is arranged every 0.8 m in the column, and a total of 16 temperature sensors are arranged on the left and right sides. In addition, one sensor is arranged every 0.8 m in the vertical direction in the concrete foundation, and two temperature sensors are arranged in total (Figure 4). Sensor model: PT100 magnetic temperature sensor, measuring range: \(-\)20-100 \({}^{\circ}\)C, accuracy is 0.1 \({}^{\circ}\)C. If the change of temperature is in 0.01 \({}^{\circ}\)C level, the change of thermal error is relatively small, so, it the sensors that are used should be accurate enough.  
To measure the effects of ambient temperature changes caused by opening and closing the workshop door on the thermal error of the spindle, the machine was turned off for two days before the experiment to ensure no heat was generated by the machine. In order to realize the synchronous acquisition of thermal error data, the mandrel is installed in the experiment process, and the eddy current sensor is installed at the bottom of the mandrel, so as to collect the displacement data in Z direction in real time (Figure 5).  
Figure 4: Overview of the sensor deployment scheme.  
Figure 3: Working environment.
# A Study of Emergency Aircraft Control During Landing
## 3 Proposed Solution
### Longitudinal Control  
The control in the longitudinal channel (Figure 1) is accomplished by the regulation of flight path angle \(\gamma\). A signal present in the main control path is an integral of the flight path angle error and the anti-windup component preventing saturation of the control path. The gain vector K\({}_{\text{lon}}\) of the LQR controller converts the u, \(\alpha\), q and \(\theta\) measurements into a feedback signal, which is further subtracted from the main control path. The feed-forward gain F\({}_{\text{lon}}\) present in the system is responsible for bringing the main control path signal down to the feedback level. The resulting collective thrust lever position control is dynamically limited by the actual control conditions, i.e., the maximum negative and positive deflection of the thrust lever from the steady-state position.
## 3 Results
### Antioxidant Activity
It should also be noted that all the GID extracts from the studied samples had a statistically significantly higher RP value than the PBS extracts (Table 4). The potential of the samples to inhibit lipoxygenase and cyclooxygenase2 enzymes (proinflammatory enzymes) was evaluated in the present study as well (Table 5). The ethanolic extract from pasta fortified with 2.5 and 5% of dried Enoki mushrooms had higher LOX inhibition activity (EC\({}_{50}\) = 0.17 \(\pm\) 0.03 mg/mL) than the control (EC\({}_{50}\) = 0.52 \(\pm\) 0.13 mg/mL). The control PBS extract sample was characterized by no LOX inhibition, while the fortified pasta extracts had this activity (EC\({}_{50}\) from 0.83 \(\pm\) 0.27 mg/mL for E7.5 to 0.33 \(\pm\) 0.08 mg/mL for M10). In turn, the GID extracts showed higher LOX inhibition activity than the ethanol and PBS extracts. Additionally, the pasta fortified with Enoki (E7.5 and E10) and Maitake (M2.5 and M10) subjected to simulated digestion exhibited a statistically significantly higher LOX inhibitory ability than the control (\(p\) = 0.0035)--Table 5. Additionally, with the exception of the C and E10 samples, the difference in the LOX inhibitory ability between the PBS and GID extracts was statistically significant--see Table 5.  
The PBS extracts of the control and dried Enoki- and Maitake-fortified pasta samples exhibited no ability to inhibit COX-2 (Table 5), while the ethanol extracts did not differ statistically significantly in this parameter. Unfortunately, no GID-fortified pasta sample had statistically significantly higher COX-2 inhibition activity compared to the control. Additionally, no statistically different ability to inhibit COX-2 was found between the ethanol extracts and the samples subjected to simulated digestion (Table 5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**LOXI (EC\({}_{50}\) mg/mL)**} & \multicolumn{3}{c}{**COX2I (EC\({}_{50}\) mg/mL)**} \\ \cline{2-7}  & **ETOH** & **PBS** & **GID** & **ETOH** & **PBS** & **GID** \\ \hline C & 0.523 \(\pm\) 0.127 \({}^{\text{a}}\) & n.a. & 0.104 \(\pm\) 0.003 \({}^{\text{a}}\) & 0.245 \(\pm\) 0.055 \({}^{\text{a}}\) & n.a. & 0.221 \(\pm\) 0.027 \({}^{\text{ab}}\) \\ E2.5 & 0.169
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 2 Implementation of a Dual-Band RF Wireless Power Transfer (WPT) System  
In this paper, in order to avoid interference with other communications operating on the 2.4 and 5.8 GHz bands, 2.482 GHz (which is a frequency in the upper guard-band of the 2.4 GHz band) and 5.73 GHz (which is a frequency in the lower guard-band of the 5.8 GHz band), are determined as operating frequencies for the proposed dual-band WPT system. Additionally, radiating elements that generate RHCP (right-handed circular polarization)  
Figure 1: Wireless power transfer (WPT) scenarios according to use of the frequency and the transmission of the beam: (**a**) transmitting \(f_{1}\) wireless power to an Rx for \(f_{1}\), (**b**) transmitting \(f_{2}\) wireless power to an Rx for \(f_{2}\), (**c**) transmitting dual-band wireless power of \(f_{1}\) and \(f_{2}\) to an Rx for the dual band, and (**d**) transmitting \(f_{1}\) wireless power and \(f_{2}\) wireless power to an Rx for \(f_{1}\) and to another Rx for \(f_{2}\), respectively.  
are applied as array elements because using circular polarization can minimize polarization mismatch losses, ensuring polarization diversity in WPT system [21; 22; 23].
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
###### Abstract  
Induction-heating graphitization furnaces are widely used to produce high-purity graphite products due to their high heating rate, high-limit temperatures, safety, cleanliness, and precise control. However, the existing induction-heating systems based on copper coils have limited energy efficiency. This paper proposes a new induction-heating graphitization furnace based on graphene coils. Due to the excellent high-temperature resistance of the macroscopic graphene material, the coil can be placed closer to the graphite heater, which improves the electromagnetic efficiency; the coil itself does not need to pass cooling water, which reduces the heat loss of the furnace and ultimately results in a higher energy efficiency of the induction furnace. In this paper, a numerical model of the induction-heating process is established and verified, the temperature-field and electromagnetic-field distributions of the heating process are analyzed by using the model, and the energy balance calculations are performed for the original furnace and the new furnace. Through a comparison, it was found that the new furnace possesses an electromagnetic efficiency of 84.87% and a thermal efficiency of 20.82%, and it can reduce the energy consumption by 33.34%, compared with the original furnace. In addition, the influence of the coil parameters on the performance of the induction furnace is discussed. By changing the coil conductivity, the induction furnace can achieve an energy efficiency of 17.76%-18.11%. This study provides new ideas for the application of macroscopic graphene materials in high-temperature induction heating.  
graphene coil; graphitization furnace; induction heating; energy efficiency
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 5 Results  
In this section, we consider the results of the data transfer of captured mangrove forest images over the LoRa physical layer using different SFs and at different distances. In addition to the quality assessment metrics, we also consider packet loss and the effect of the surrounding environment on transmission.