esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FAME Analysis and Biodiesel Quality Assessment  
The fatty acid profiles derived from the microalgal biomass cultivated under the various conditions were analyzed to assess the impact of these conditions on lipid metabolism. The analysis revealed significant variations in the fatty acid composition, indicating the activation of differential biosynthetic pathways in response to environmental stress or nutrient availability. Among the saturated fatty acids (SFAs), palmitic acid (C16:0) was prominent, with percentages varying across the different cultivation conditions (Table 2). In all the cultivation conditions analyzed, oleic acid (C18:1) was the only monounsaturated fatty acid (MUFA) identified. Polyunsaturated fatty acids (PUFAs) showed a consistently high presence. Notably, \(\alpha\)-linolenic acid (C18:3) was a significant component, highlighting its substantial contribution to the total fatty acid content.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **BG-11** & \
\begin{tabular}{c} **BG-11 +** \\ **25\% DSW** \\ \end{tabular} &
\begin{tabular}{c} **BG-11 +** \\ **50\% DSW** \\ \end{tabular} & **100\% DSW** \\ \hline Proximate analysis (wt\%) & & & & \\ Moisture & 4.59 & 4.36 & 5.76 & 4.58 \\ Volatile matter & 83.66 & 83.96 & 84.21 & 86.52 \\ Ash & 11.81 & 11.82 & 10.03 & 8.89 \\ \hline Ultimate analysis (wt\%) & & & & \\ Carbon (C) & 45.4 & 44.9 & 45.4 & 45.3 \\ Hydrogen (H) & 6.51 & 6.64 & 6.73 & 6.44 \\ Oxygen (O) & 28.4 & 27.5 & 27.0 & 28.4 \\ Nitrogen (N) & 5.24 & 6.10 & 6.55 & 6.12 \\ Sulfur (S) & 0.49 & 0.56 & 0.57 & 0.37 \\ CV 1 (MJ kg\({}^{-1}\)) & 20.9 & 21.0 & 21.4 & 20.74 \\ \hline \hline \end{tabular} \({}^{\dagger}\)Calorific value.  
\end{table}
Table 1: Proximate and ultimate analyses of _H. rubescens_ KNUA214 biomass over an 8-day period.  
Figure 4: (**a**) Astaxanthin, (**b**) lutein, (**c**) zeaxanthin, (**d**) canthaxanthin, and (**e**) beta-carotene contents of _H. rubescens_ KNUA214 cultivated under different concentrations of DSW on 4 and 8 days. Statistical significance between groups is denoted as follows: * \(p<0.05\), *** \(p<0.001\).  
In all the cultivation conditions, parameters such as the CFPP, \(\
u\), and \(\rho\) met EN14214 (European) [45] and ASTM D6751 (American) [46] standard (Table 3). However, due to the high content of PUFAs, parameters such as the IV and CN did not meet the standards. As the DSW proportion that was mixed with the control BG-11 medium increased, the IV increased and the CN decreased. At 100% DSW, the values were observed to return to levels similar to those seen with the BG-11 control.
## 5 Conclusions  
The proposed scheme provided a comprehensive analysis of the results obtained from individual-based and hybrid-based approaches of feature-extraction using Wavelets, VGG-19 Net, ResNet-50, and IncetioNet-V3. The features were examined with two classifiers, namely MLP and SVM. The scheme applies the benefits of deep convolutional neural networks with low cost of recognition. The feature-vector size was successfully reduced by 84.37% with respect to the character image. The scheme managed to score a maximum of 98.73% recognition accuracy with mean character recognition time of 10.65 ms. Table 17 summarizes some excellent work accomplished earlier in the field of handwritten Hindi characters recognition; it includes state-of-the-art models related to the feature-based classification. Here, it is reasonable to mention that no standard dataset of handwritten Hindi script is available in the public domain, restricting a comparison of related models on a common platform [3]. However, one can gain some idea about the performance of the proposed work by comparing it to existing methods. The proposed model was experimented with the fresh approach of feature-extraction; for the very first time, the features received from pre-trained DCNN models were combined with the handcrafted features obtained from BDWT. The outcomes are promising, as the proposed scheme gained comparable results with respect to the mentioned benchmarking models, even with the smallest size of the feature-vector. The model achieved percentage reduction in feature-vector size in the range of 15.78 to 97.12, with respect to the given benchmarking models (refer to Table 17). The classification cost of a machine learning algorithm is directly dependent on the size of the feature-vector. In this regard, the proposed model was successful in reducing the classification cost. The features were so effective that
## 3 Results and Discussions
### Results for Ring-Only Configurations--Design Change Evaluation  
The results for different analyzed ring-only configurations are shown in Table 8, with the results exceeding the tensile strength allowable criteria or having high displacements highlighted in bold. For baseline ring, the displacements for the 3D-printed PLA-CF EF configuration are almost 100 times higher than those of the metallic EF. The stresses of the 3D-printed PLA-CF EF configuration are also exceeding the allowable values of the studied parts; thus, preloads of 1000 N or even 500 N cannot be applied on the current baseline configuration using 3D-printed PLA-CF. The displacements for the 3D-printed PLA-CF EF configuration are almost 20 times higher than those of the baseline metallic ring, and thus five times lower with the new design. Moreover, the stress values obtained have decreased, but remain too high for the new design to be acceptable. The displacements for the 3D-printed PLA-CF EF configuration with a full ring are only seven times higher than the baseline metallic configuration. A significant reduction in stress values was also observed. The displacements for the 3D-printed PLA-CF EF with a new full ring configuration are about 4.8 times higher than the baseline metallic configuration. The stress has continued to decrease with the use of the new design with up to 84% at 1000 N pre-tension compared to the initial baseline configuration, but the maximum stress is located in an area where  
Figure 14: Metallic EF—Wire normal stress—scenario with fibrous callus 700 N of axial force and 500 N pre –tension. the transverse net section is in the minimum, topological optimization being required to further improve the ring design. A true scale deformed plot showing the PLA-CF ring VM stress is shown in Figure 15 for the studied configurations under 500 N of wire pre-tension, providing safety factors above 1.5 for full ring designs. For 500 N wire pre-tension, the ring stress levels remain below the allowable limit for PLA + CF. However, at 1000 N wire pre-tension, the strength criterion is not met, indicating a need for design modifications. To address this, alternative designs incorporating an additional ring with only two wires per ring should be considered. As demonstrated in [45] for Iizarov fixators, configurations with two rings exhibit better mechanical behavior and higher structural strength. The results presented in Table 9 correspond to the new full ring configuration with only two wires per ring. In the first case, offset wire 1 was removed, while, in the second case, wire 2 was suppressed to evaluate the structural response under modified conditions. For the new ring design with only wire 2 and wire 3, although both displacements and wire stresses are significantly reduced, the maximum VM stress in the ring decreases by only 5%, remaining above the PLA-CF ultimate strength. This is primarily due to both
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 5 Evaluation
On the other hand, the MOS of all services increases in Scenario 3 as the user density decreases (Figure 6c). In contrast, the opposite effect is observed when propagation losses grow (Figure 6d). To conclude, Figure 6e shows that the MOS of the predominant services in regions with lower user density is larger despite higher propagation losses. However, the QoE perceived by RTV users is similar to that perceived in Scenario 2, as most RTV users have similar conditions in these two scenarios, as can be seen in Figure 3.  
Figure 5: Weight of each metric of the estimation model for each service and scenario. (**a**) Weights in Scenario 1. (**b**) Weights in Scenario 2. (**c**) Weights in Scenario 3. (**d**) Weights in Scenario 4. (**e**) Weights in Scenario 5. Figure 6: MOS obtained by each solution for each service in every scenario. (**a**) MOS in Scenario 1. (**b**) MOS in Scenario 2. (**c**) MOS in Scenario 3. (**d**) MOS in Scenario 4. (**e**) MOS in Scenario 5. To thoroughly analyze the improvement in each service and scenario, Figure 7 represents the percentage of gain of the proposed methodology compared to the 3 GPP solution. As can be noted, WB almost always obtains the lowest gain because these users are the least demanding and already perceive a good QoE when using MR-DC, so the MOS is not much affected by the proposed node selection. However, FD users demand more throughput. Therefore, the higher QoE gain indicates that this throughput improvement is more valued by FD users. On the other hand, the MOS of video users is lower than that of FD and WB users in all scenarios (Figure 6), as they demand higher throughput as well as greater availability of radio resources that does not lead to video stalls. In this sense, the MOS of VS users is even lower due to the higher video quality. Hence, the MOS gain obtained by proper node management is more noticeable for this service, especially in scenarios 2 and 5 where there are regions with a large number of VS users strugggling to access network resources. To conclude, it is also worth noting that the MOS improvement is more prominent in Scenario 2 (Figure 7b), where different regions of the network usually have a high traffic flow of the same service, making it particularly fruitful to deploy the proposed approach in this type of regions. Therefore, the results demonstrate that the QoE perceived by users of a mobile network can be optimized by an alternative node management in MR-DC-enabled scenarios. To maximize the QoE, the methodology must adapt to the characteristics of the area surrounding the users as well as the service used. In addition, network load and quality metrics must be considered. Figure 7: _Cont._
###### Abstract
High slip rates, the presence of locking zones, and seismic gaps on the northern segment of the Sumatran Fault, which includes the Aceh and Seulimeum segments, are of serious concern to researchers and continue to be studied by various related scientific fields, including the geophysics. One of the geophysical methods that can be used to study fault structures that are generally relatively deep is magnetotellurics. Magnetotellurics (MT) is a passive geophysical exploration method based on electromagnetic induction and it uses the natural variations in the Earth's magnetic and electric fields as a source. These variations are caused by the interaction of solar wind with the magnetosphere and ionosphere, as well as by thunderstorm activity in equatorial regions [15; 16]. Alternating currents that are induced in the rocks provide information about the Earth's structure. The direction and strength of these currents depend on the electrical conductivity distribution [17]. Analyzing the relationships between the values of respective electromagnetic field components recorded at the surface allows us to determine the subsurface electrical conductivity [18]. The MT method has been widely used for the study of fault investigation [19; 20; 21; 22]. Previous studies about the Sumatran Fault using the MT method have been performed by characterizing a model of 2D MT inversion, using an algorithm developed by [PERSON] and [PERSON] [23]. The results show that there is a contrast resistivity which characterizes the structure of the Sumatran Fault. The shallow part just beneath the contrast resistivity is characterized as the locked zone at a depth of 15 km in the form of the resistive zone, while at a depth of 20 km, it is characterized as a melting zone [24]. Other studies about the Sumatran Fault have been performed using 2D gravity data modeling along with the Aceh and Seulimeum fault segments in the NW-SE direction, which showed that at the distance of 25-50 km, Aceh segment faults had been identified, characterized by two different vertical blocks densities. In addition, at a distance of 50-75 km, the Seulimeum Fault Segment has been identified, which is shown by two vertical blocks of relatively high and low densities [25]. The other study about the electrical resistivity distribution of the Seulimeum Fault Segment System was also performed using the geoelectrical method [26]. The study revealed that the Seulimeum Fault System consists of a host and graben system. The depth of the Seulimeum Segment increases from 20-50 m in the southern part to 50-200 m in the northern part as it approaches the Andaman Sea. A conductive zone characterizes the graben, while the host is characterized by a resistive zone [26]. In this study, a three-dimensional (3D) MT data inversion was used to investigate and confirm more realistic information about the resistivity structure of the Aceh Segment Fault and the Seulimeum Segment Fault to better understand the fault characteristics associating hazards with the fault, considering that these segments have the potential for seismic activity in the form of earthquakes with magnitude \(M\geq 7.0\)[14; 27].
## 2 Materials and Methods
### Control Samples
#### 2.1.4 Flow Cytometry Data Processing and Analysis
Regarding Panel B, FSC-A vs SSC-A and doublet exclusion tests were used in a similar way as for Panel A. CD90 was used with FSC-A to separate CD90-FSC-A+ hematopoietic cells from CD90+FSC-A+ non-hematopoietic populations [68; 71; 73; 75]. CD31 was used to confirm the population of hematopoietic cells and to separate non-hematopoietic cells into endothelial cells and fibroblasts [68; 71; 73]. His48 was used to separate His48+CD31+ neutrophils from His48-CD31+ macrophages by using a His48 vs CD31 gate [77; 78]. CD45 and CD163 were used to confirm populations of neutrophils and macrophages, respectively [68; 71; 72; 73; 74; 75]. Multiple plots and parameters were used to decide the threshold limit in the generation of gates and to verify gate positions (see Figure S1 for illustration in Supplementary Materials). Contour and density plots (Figure S1A (iii) and (iv)), which show expression levels and relative density of data, are commonly used in placing the gates [86; 93]. Backgating was used to verify the gate positions (Figure S1B). Backgating not only helped to analyse cells identified in a gate on dot plots with different parameters but also showed the final gated population within the population of its ancestors. The same gating strategy was applied across all eight time points within the same panel to compute the percentage of each cell type in the total cell population.
## 2 Methods
### Data Acquisition  
The detector's time-gating capability allows for the segmentation of the target scene. Specifically, a gate width of 50 ns is employed to capture all incoming light that arrives within this temporal delay. With its rapid rise time, objects can be clearly distinguished as they enter and exit the captured signal, depending on the positioning of the gate. In other words, by strategically delaying the gate, returning light from certain objects can be detected within the imaging gate, while light from other objects outside of this range is effectively rejected. This significantly enhances the system's signal-to-noise ratio (SNR).  
To obtain 3D information about a target, it is necessary to temporally scan the gate across the object and accurately measure the precise time at which the object enters the gate. This measured time can then be converted into distance. Furthermore, it should be noted that the fixed repetition rate of the laser used in this study was significantly lower [28], at 10 KHz, resulting in a pulse separation of 30,000 m and a maximum stand-off distance free from range ambiguity of 15,000 m. As such, any potential range ambiguity can be considered negligible for this experiment.
## 5 End-to-End Performance Estimation  
In this section, we show how to estimate three key end-to-end performances in mmWave MWNs: _end-to-end hop count_, _outage probability_, and _transmit energy consumption_, based on the hop distance distributions derived in the previous section. To calculate the transmit energy consumption, we follow the model used in [13], where the per-hop transmit (radiation) energy consumption is proportional to \(D^{\alpha}\), where \(D\) is the hop distance and \(\alpha\) is the path-loss exponent. We only consider the transmit energy, assuming that the radiation energy to transmit a packet is the most dominant factor in energy consumption of the network as in [13; 52]. In fact, the transmission energy consumption is only part of the whole device consumption. Based on the studies on baseband and radio-frequency (RF) circuitries to obtain comprehensive energy consumption models of wireless transceivers, the energy consumption due to the baseband processing circuitry is generally significantly smaller compared to the energy consumption due to RF circuitry [53]. Furthermore, the total transmit energy for the end-to-end communication is the sum of the transmitted energy consumed by the source and all the relays to forward the packets.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 5 Experiments for LoRa MAC
### Impact of Payload Length on LoRa  
The first experiment's purpose is to examine how the payload length affects LoRa MAC performance. To this end, this subsection configures the parameters as shown in Table 6 (Exp.A). In the experiment, we fix four parameters. Each node sends a data packet every 15 s. The LBT sensing threshold is set to \(-130\) dBm, the median value of the range between \(-124\) dBm (as a sensitivity value at SF 7) and \(-137\) dBm (at SF 12), as reported in the previous section. The experiment uses a fixed 4 s back-off algorithm to reduce the possibility of collision. Upon detecting a radio channel that is busy or has collisions, a node chooses a random back-off time within 0\(\sim\)4 s, during which it waits (i.e., it backs off) before trying to sense the channel condition again. The node can keep sensing the channel condition and hop over multiple channels unlimitedly until it finds an idle channel to send data.  
The experiment varies two parameters. The payload length is increased from 4 bytes to 242 bytes. It is noted that the SF value at a node determines its maximum payload length according to the regional parameters in LoRa. In Korea, the maximum length for SF 7-10 is 242 bytes; for SF 11 and SF 12, the maximum lengths are 151 bytes and 65 bytes, respectively. This implies that although we set the payload length to 100 bytes in the experiment, nodes with SF 12 are still set to 65 bytes. The experiment increases the number of nodes from 200 to 3000 and measures the PDR.  
Figure 13(a) illustrates the experimental results. As the packet length increases, the PDR decreases. This phenomenon becomes more prominent as the number of nodes increases. A PDR of 90% is achieved only when the length is less than 10 bytes and there are 200 nodes. To investigate the details, we split the results based on SF values for each number of nodes, as shown in Figure 13(b). For the SF 12 case, the performance values saturate at around 65 bytes, which is consistent with the maximum length value for that SF setting. With 200 nodes and 242 bytes of payload, the average value remains around 80%. PDR performance for SF 10-12, however, drops below the average to as low as 35%. This implies that these SF values may not be adequate for use in a real-world testbed. The graphs also show that performance at higher SF nodes degrades dramatically as the number of nodes increases. At SF 12, for instance, a long ToA and short data period together increase the possibility of collision. Moreover, nodes with SF 12 are often far from each other, which leads to a failure in channel detection, even with the LBT scheme.  
Figure 13: \(Cont\).
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 2 Basic Rotation Machinery Faults
### Misalignment and Imbalance Fault  
Misalignment occurs when a shaft is not located perfectly at the center of the rotation. There are two common types of misalignments: vertical misalignment and horizontal misalignment. Vertical misalignment occurs when two shafts are misaligned in the vertical plane, while horizontal misalignment occurs in the horizontal plane. While, imbalance occurs when uneven weight is distributed around a rotating shaft's centerline. Both misalignment and imbalance can cause several issues where a high vibration level can occur. Such vibration leads to increased noise and heat levels leading to premature failure of the bearings.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 2 Space Exploration through Different Methods/Vehicles
### Space Exploration through UAVs  
For private use (e.g., UAV filming of holidays), commercial use (e.g., UAV delivery systems) [12], and space mission applications, the use of UAVs has grown significantly (e.g., The Mars Helicopter for 2021). This is due to the benefits of low-speed forward flight, VTOL, and hovering [13]. In addition, the use of UAVs to study other planets or moons has been one of the critical goals of space agencies over the past decade. The use of UAVs for planetary exploration may have many advantages, particularly that a UAV can map a wider area than a rover at a resolution far more significant than that provided by current satellites or orbiters [14]. The overall details of the Venus, Mars, Titan, and Earths' moon's atmospheric conditions, characteristics, and configurations of the UAV flights are described below.
## 3 Overview of Layered Backhaul (LayBack) Network Architecture  
The LayBack network architecture [12] categorizes the backhaul network elements, such as switches, gateways, and core networks, into layers that are broader than the traditional access networks, aggregation networks, and data center networks. The LayBack architecture envisions to homogenize the multitude of networking technologies, such as cable, cellular, and traditional Ethernet through a unifying SDN orchestrator.