esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioremediation of Crude Oil by _Corynebacterium stationis_ CsPe-1: A Preliminary In Vitro Study
## 3 Results and Discussion
Furthermore, the genus _Corynebacterium_ has demonstrated a remarkable capacity for hydrocarbon degradation, as evidenced by studies on _Corynebacterium_ sp. strain GS5-66 [60; 61]. Conversely, the propensity of _C. stationi_s to degrade medium- and long-chain hydrocarbons (F2 and F3) has been observed in _Corynebacterium variable_ HRJ4, a salt-tolerant strain capable of degrading n-alkanes with chains ranging from 16 to 28 carbons [61]. The lowest percentage of reduction occurred in fraction 1, at both low and high concentrations of initial inoculum, reaching values of 47.03% and 65.07%, respectively. This outcome can be attributed, potentially, to the toxicity of short-chain n-alkanes due to their capacity to disrupt bacterial membranes. These findings are significant because they demonstrate the potential for experimental optimization of the hydrocarbon degradation process using this strain [36]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Control** & **10\% Inoculum** & **15\% Inoculum** & **t (\(p\)-Value)** \\ \hline BOD\({}_{5}\) Initial (mg O\({}_{2}\).L\({}^{-1}\)) & 160.0 & 15.8 & 52.2 & \(-\)4.06 (0.01) \\ BOD\({}_{5}\) Final (mg O\({}_{2}\).L\({}^{-1}\)) & 160.5 & 44.1 & 52.9 & \(-\)11.16 (0.00) \\ \hline COD Initial (mg O\({}_{2}\).L\({}^{-1}\)) & 20,400.0 & 18,200.0 & 26,800.0 & - \\ COD Final (mg O\({}_{2}\).L\({}^{-1}\)) & 21,600.0 & 26,800.0 & 28,200.0 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the variation of the concentration of oils and fats in oil degradation systems inoculated with a strain of _C. stationi_s CsPe-1. Figure 1: (**a**) Concentrations and (**b**) percentage reduction of the petroleum and TPH fractions analyzed by gas chromatography.
## 6 Analysis of the eLPRT Protocol
### The ACK Bitmap Mechanism  
The data packet in the evaluation scenario has a length of 46 bytes, so the corresponding transmission time (\(T_{data}\)) is 1,472 \(\upmu\)s. The length of the ACK frame is 11 bytes, which corresponds to a transmission time (\(T_{ack}\)) of 352 \(\upmu\)s. Since the interval between the data packet and the ACK (\(t_{ack}\)) is 192 \(\upmu\)s, the overhead introduced by the ACK (\(O_{ack}\)) is 37%, according to Equation (2). The ACK bitmap mechanism of the eLPRT protocol eliminates this overhead through the replacement of the ACK frame by a single ACK bit in the following beacon, which results in an increase in the network throughput in the same proportion, as well as a decrease in the node energy consumption:  
\[O_{ack}=\frac{T_{ack}+t_{ack}}{T_{data}} \tag{2}\]  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline \multicolumn{3}{|c|}{\(T_{SF}=61.44\) ms} & \multicolumn{3}{|c|}{\(T_{SF}=122.88\) ms} & \multicolumn{3}{|c|}{\(T_{SF}=100\) ms} \\ \hline \multirow{3}{*}{**Scheduled**} & \multicolumn{2}{|c|}{**Number of**} & \multicolumn{2}{|c|}{**Number of**} & \multicolumn{2}{|c|}{**Number of**} & \multicolumn{2}{|c|}{**Number of**} \\  & \multicolumn{2}{|c|}{**samples**} & **Scheduled** & \multicolumn{2}{|c|}{**samples**} & **Scheduled** & \multicolumn{2}{|c|}{**samples**} \\ \cline{2-9}  & \(f_{s}=10\) & \(f_{s}=30\) & **time** & \(f_{s}=10\) & \(f_{s}=30\) & **time** & \(f_{s}=10\) & \(f_{s}=30\) \\  & \(\mathbf{Hz}\) & \(\mathbf{Hz}\) & & \(\mathbf{Hz}\) & & \(\mathbf{Hz}\) & & \(\mathbf{Hz}\) \\ \hline
0 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 \\
## 3 Gray Value Compression  
To increase the compression ratio of the EEDC, a first possible solution is the gray value compression. A randomly selected portion of the grey values is selected as an example of the data to be compressed and is shown in Figure 3. This data stream is generated by the binary triangular encoding method described above.  
\[\left[...\ 164\ 108\ 204\ 220\ 204\ 156\ 140\ 140\ 92\ 76\ 140\ 180\ 100\ 148\ 132\ 76\ 60\right.\] \[\left.\ 84\ 180\ 172\ 180\ 156\ 140\ 188\ 172\ 156\ 60\ 140\ 84\ 92\ 124\ 116\ 116\ 116\] \[\left.124\ 164\ 132\ 140\ 156\ 172\ 124\ 116\ 140\ 196\ 156\ 60\ 60\ 116\ 140\ 124\ 116\...\right]\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PDE Method** & **AAE** & **MSE** \\ \hline Homogeneous diffusion & 14.98 & 366.94 \\ Biharmonic smoothing & 13.16 & 373.52 \\ Triharmonic smoothing & 16.25 & 491.15 \\ AMLE & 14.91 & 376.99 \\ Charbonnier diffusion & 18.66 & 572.03 \\ Edge-enhancing diffusion & 12.52 & 353.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average absolute error (AAE) and mean square error (MSE) for the sparse data interpolation of the image _horse_.  
Figure 3: Random part of the gray value stream acquired using the EEDC.  
When looking at the data stream in Figure 3, the regularity, repetition, and redundancy stand out, i.e., all the phenomena that compression methods use to increase the compression ratio. The compression methods described and used in this article are: Huffman coding, arithmetic coding, range coding, and LZ coding family.
## 11 Optimal Design Strategies for Earthquake-Resistant Composite Buildings
### Optimal Design Strategies for Earthquake-Resistant Composite Buildings: Key Findings  
Recent investigations have indicated that optimized composite systems frequently outperform traditional single-material structures in both cost-effectiveness and seismic resistance. The strategic arrangement of steel and concrete, in conjunction with innovative connection designs, can significantly enhance a building's ability to dissipate energy during earthquakes. Additionally, research has underscored the importance of considering the entire life cycle of the composite structure, including construction, maintenance, and potential retrofitting costs, in the optimization process. These findings highlight the potential for composite structures to offer superior seismic performance while achieving cost savings, making them an attractive option for earthquake-prone areas.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 2 Literature Review
### UAV-Assisted Methods  
Unmanned aerial vehicles (UAVs) have emerged as powerful tools for enhancing radio source localization due to their high mobility, flexible deployment, and ability to secure line-of-sight (LoS) conditions. Unlike fixed sensors, UAVs can dynamically adjust their positions, making them particularly suitable for dynamic or obstructed environments such as urban canyons and NLoS scenarios.Recent research has explored various UAVassisted approaches to improve localization accuracy and efficiency. [PERSON] et al. [5] investigated multi-source localization using UAVs in NLoS environments, demonstrating that UAVs can mitigate the impact of signal shadowing by adjusting their altitude and observation angle. [PERSON] et al. [6] proposed a robust cooperative localization method using multiple UAVs based on AOA, focusing on optimal sensor placement to maximize estimation performance. [PERSON] et al. [7] discussed machine learning-assisted handover and positioning strategies for cellular-connected drones, contributing to adaptive resource control in mobile sensing environments. These studies collectively show that UAVs can not only improve spatial coverage but also enhance the quality of signal information collected for localization. However, most existing works either use UAVs in a static hovering mode or treat flight trajectories as predefined. As UAVs are subject to constraints such as battery life and flight safety, there is a growing need for methods that combine localization with UAV path planning. In this study, we utilize UAVs not only as sensing agents but also as optimization targets. Our method incorporates trajectory design tailored to the propagation environment and integrates signal features such as RSSI, AOA, and EOA into a fingerprint-based framework.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
###### Abstract  
With the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AV), recent years have seen an increasing evolution of onboard sensors and communication interfaces capable of interacting with available infrastructures, including satellite constellations, road structures, modern and heterogeneous network systems (e.g., 5G and beyond) and even adjacent vehicles. Consequently, it is essential to develop architectures that cover data fusion (multi-sensor approach), communication, power management, and system monitoring to ensure accurate and reliable perception in several navigation scenarios. Motivated by the EMERGE project, this paper describes the definition and implementation of an On Board Unit (OBU) dedicated to the navigation process. The OBU is equipped with the Xsens MIi-630 AHRS inertial sensor, a multi-constellation/multi-frequency Global Navigation Satellite System (GNSS) receiver with the u-blox ZED-F9P module and communication interfaces that afford access to the PointPerfect augmentation service. Experimental results show that GNSS, with corrections provided by augmentation, affords centimere accuracy, with a Time To First Fix (TTFF) of about 30 s. During the on-road tests, we also collect: the output of fusion with inertial sensor data, monitoring information that assess correct operation of the module, and the OBU power consumption, that remains under 5 W even in high-power operating mode.  
on board unit; navigation system architecture; sensor fusion; loosely coupled algorithm; EMERGE project; IMU; GNSS; PointPerfect Correction Service +
Footnote †: journal: Applied  
on board unit; navigation system architecture; sensor fusion; loosely coupled algorithm; EMERGE project; IMU; GNSS; PointPerfect Correction Service
# Extending Battery Lifetime by Avoiding High SOC
###### Abstract  
The impact of ageing when using various State of Charge (SOC) levels for an electrified vehicle is investigated in this article. An extensive test series is conducted on Li-ion cells, based on graphite and NMC/LMO electrode materials. Lifetime cycling tests are conducted during a period of three years in various 10% SOC intervals, during which the degradation as function of number of cycles is established. An empirical battery model is designed from the degradation trajectories of the test result. An electric vehicle model is used to derive the load profiles for the ageing model. The result showed that, when only considering ageing from different types of driving in small Depth of Discharges (DODs), using a reduced charge level of 50% SOC increased the lifetime expectancy of the vehicle battery by 44-130%. When accounting for the calendar ageing as well, this proved to be a large part of the total ageing. By keeping the battery at 15% SOC during parking and limiting the time at high SOC, the contribution from the calendar ageing could be substantially reduced.  
electric vehicle; Plugin Hybrid electric vehicle; Li-ion battery; modelling; measurements 1
## 3 Experiments and Discussions
### Comparisons in Computation Complexity  
The main steps of the HVD method include decomposing each S2 into components by Hilbert vibration decomposition and SPWVD operation for each component to extract timing information for aortic and pulmonary components. The main step of the STFT method is performing a Short Time Fourier Transform to each S2 and extracting timing information from the time-frequency representation. Meanwhile, the main steps of the proposed method are averaging and subtracting. So, the HVD method has the greatest complexity, the STFT method is medium and the proposed method has the least complexity. The CPU time to calculate the respiratory split and the RMS error of the difference between the scatters and the fitted values for the 12 subjects are listed in Table 3 for complexity comparison and accuracy comparison. A personal computer is used to run the three methods where the CPU (Intel Core 2 Quad CPU Q8300, Intel, Kuala Lumpur, Malaysia) has two cores and the frequency is 2.5 GHz. It is found from Table 3 that the proposed method has much less complexity than those of the HVD method and the STFT method. The CPU time of the HVD method is hundreds of times longer than that of the proposed method, and that of the STFT method is over ten times longer than that the proposed method. So, the proposed method has a great attractive advantage in computation complexity. The RMS error of the proposed method is the lowest for each subject compared with the other two methods.
# Research Progress on Router Devices for the OAM Optical Communication
## 3 OAM Channel Switching
They employed a 16-channel wavelength division multiplexing (WDM) grid, which enabled the switch to receive 160 optical Gaussian data inputs and route each input signal to a distinct output port, all of which take advantage of 10 OAMs. The foundation of this optical switch is built on an integrated OAM multiplexer and a tiny OAM demultiplexer. When verified experimentally, a 19.2 Tb/s total enabled throughput is made possible by each port's 30 GHz bandwidth. The switching time is observed to be less than 1 \(\upmu\)s. Since the OAM demux is passive, the OAM switch power consumption is 1.35 mW/Gb/s, which may be entirely attributed to the thermal tuning of the OAM emitters. This is advantageous compared to single-layer switches that cascade elementary blocks, such as 2 \(\times\) 2, in order to achieve enormous port counts that increase in proportion to the square of the number of ports. The switch is compatible with standard telecom devices because it accepts input and output signals with Gaussian phase profiles that pass through optical fibers and waveguides. At present, most OAM routers that can realize channel-switching functions switch the data information transmitted by different channels by controlling the change in the OAM beam mode value. In the past, the OAM switcher could switch data between two OAM beams, but with the continuous development of OAM multiplexing technology, the OAM switcher can now switch data between multiple composite beams [125; 126; 127]. With the development of OAM-RD, the flexible control of the OAM mode carried by multiplexed beams and the practicability of OAM communication links have been greatly improved, which lays a foundation for the research of OAM-RD. Table 3 summarizes the works reported to date for the channel-switching routers in OAM-based optical communication.
# Smart Cities Big Data Algorithms for Sensors Location
## 2 Process to Locate and Relocate Sensors
Although the sensor placement points are initially selected for a better control of the areas, it does not necessarily mean that they are located in the focus of the phenomenon, especially if there are not enough sensors, or if they are dispersed. By recursively identifying the hot-zones, that is to say, applying or reapplying Phase 4 of the process, midpoints between two sensors can be inferred--this represents the proposed fixed locations to place a new sensor. With newly located sensors, we can get new and different matrices with a reduced scope of hot-zones. In Figure 3, an example of dispersed sensors in a geo-referenced region is observed. Two of the four sensors were identified as hot-zones: Sensor 2 (in red) and Sensor 3 (in magenta) in contrast with green sensors. In Figure 3, the scope of the hot-zone is reduced, approaching sensor 3, but it could go to the other way around, approaching sensor 2. The scope of the hot-zone will be reduced based on the sensors' data and using the analogy of a binary search, that is, taking one of two paths. This process will generate another dynamic map of the sensor. In the next section, the process is applied for a specific situation. Figure 3: A recursive search of midpoints within hot-zones.
## 5 Case Study and Simulation Results
### Converter Is Connected to an Ideal Grid (Zero Impedance)  
In this case, the grid Thevenin impedance is considered negligible (\(z_{s}=0\)). The results of the simulation are presented in Figure 8, indicating that both the DC-link voltage and the reactive power smoothly track their reference values. Also, the changes of \(P_{dc}\) and \(Q^{ref}\) have a negligible effect on each other. This confirms that the coupling between the active and reactive power channels is effectively removed. The minor coupling observed in the simulation results of Figure 8 is due to the time delay and observer operation in the control loop.  
Figure 7: Frequency characteristics of the closed loop system.
## 4 Discussion
### Control Strategy and Mechanism in 3D Tracking Movements
In contrast, the minor component in a higher frequency range (named by the F2 component) of the tracking movement encodes mostly the positions of small step-wise movements with intermittent FB control. In this study, the 3D tracking movement for higher target speeds and _ROT(0)_ showed lower frequent components in two temporal parameters (\(\Delta\theta\) and \(\Delta\omega\)). Therefore, we will quantitatively analyze the control strategy and mechanism of 3D tracking movements in the F1 and F2 components based on the predictive control and intermittent FB control in our future work.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 3 Design of Framework  
This paper suggests a framework that combines a data preprocessing algorithm, a feature extraction and fusion algorithm, unknown traffic recognition, and an incremental learning algorithm to achieve the identification of unknown types of traffic in a closed set. Figure 1 depicts the model architecture suggested in this paper.
###### Abstract  
Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the 'C1' and 'C2' RT packets, and the average throughput of RT is 10.4% better when compared with NRT.  
wireless sensor networks (WSNs); congestion; priority-based data delivery; trust; K-Harmonic Means (KHM); Enhanced Gravitational Search Algorithm (EGSA) 202525864103390/s25030864
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results
#### 5.3.4 Energy Balance Evaluation  
This set of experiments is carried out for the proposed ERCP approach evaluation in terms of energy balance, EIF. The proposed ERCP approach is compared to HMBCR [10], ECRP [11], and EPSO-CEO [24] for both homogeneous and heterogeneous networks. The EIF was determined during the running time to obtain the network's balance efficiency. The average traffic rate is set to three packets per second. Figures 7 and 8 show the EIF variation with respect to the simulation time for networks of both a homogeneous and heterogeneous nature, respectively. From the figures, it is clear that with the increase in simulation time, the EIF increases. Indeed, the sink node neighbors are highly used more than the others. Undoubtedly, it is negatively affecting the energy variance across the network. It reveals the reason why EIF is augmented with more running time.  
However, based on the results in Figures 7 and 8, the proposed ERCP algorithm produced a lower EIF compared to the others. This means that the sensor nodes' energy in the entire network using the proposed algorithm is closer to the average energy than that of the others. That is to say that the obtained EIF is evidence that the proposed ERCP algorithm can efficiently balance the energy usage compared to the other. In the case of HUMBCR, ECRP, and EPSO-CEO algorithms, the data packets have to be relayed via the nodes with the highest residual energy to balance energy consumption. According to the resulting EIF, it is evident that relying on residual energy is inadequate to achieve an appropriate energy balance. Therefore, this further validates the benefits of the proposed nodes' energy metric to obtain a better energy balance level. This explains why the proposed ERCP  
Figure 5: Influence of increasing average traffic rate on average end-to-end delay for homogenous networks.  
Figure 6: The influence of increasing average traffic rate on end-to-end delay for heterogeneous networks.  
algorithm balances network energy utilization more efficiently than the HUMBCR, ECRP, and EPSO-CEO algorithms.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 6 Results and Discussion
Moreover, we modeled the imperfect CSI case as \(\hat{g_{1}}=g_{1}+e\) and \(\hat{g_{2}}=g_{2}+e\), where \(e\) denotes the the channel estimation error with distribution \(\mathcal{CN}\sim(0,\sigma_{err}^{2}),\sigma_{err}^{2}\) denoting the power of the channel estimation error. It is shown that system performances get worse when the power of channel estimation error increases for both linear and non-linear EH models. Figure 10: \(P_{s}\) and \(P_{r}\) of the WP DH AF relaying system (**a**) \(d_{sd}=2\), (**b**) \(\eta=\eta_{s}=\eta_{r}=0.9\). Figure 5.0 Throughput performance of the WP DH AF relaying system versus \(P_{T}\) (\(R=1\) bits/s/Hz, \(d_{sd}=1\)).