esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 3 Results and Discussion  
### Engine Performance  
Figure 2 shows the brake-specific fuel consumption (BSFC). As shown in Figure 2, all test fuels have the highest BSFC at a low engine load of 40 Nm. Additionally, the BSFC shows a decreasing trend with the increase in engine load. The average BSFC of all tested fuels is, respectively, reduced by 15.74% and 18.82% at 60 Nm and 80 Nm compared with at 40 Nm. This can contribute to the increase in engine load, which improves the combustion in the cylinder (e.g., air flow intensity, temperature and pressure), thereby increasing the combustion efficiency. This is similar to the result reported by [15]. Compared with diesel, the BSFC of the blended fuels gradually increases with the increase in the percentage of ethanol at 40 Nm and 60 Nm. However, the BSFCs of the blended fuels are lower than that of diesel under the high load of 80 Nm. More specifically, at 40 Nm, the BSFCs of the diesel engine fueled with E5, E10 and E15 were, respectively, increased by 1.16%, 3.61% and 5.92% compared with that of E0; at 60 Nm, the BSFCs of the diesel engine fueled with E5, E10 and E15 were, respectively, increased by 1.94%, 3.77% and 4.80% compared with that of E0; at 80 Nm, the BSFCs of diesel engine fueled with E5, E10 and E15 were, respectively, reduced by 3.22%, 3.22% and 2.54% compared with that of E0. The reason for the increase in BSFCs caused by the addition of ethanol under low and medium loads may be related to the low heating value and the high latent heat of ethanol. Fuel with a lower calorific value needs to consume more fuel to obtain the same power output [16]. However, the opposite results under a high load can be attributed to the fact that the higher pressure and temperature in the cylinder under a high load counteract the negative effects of a low heating value and high latent heat generation of ethanol, which together with the high oxygen content promotes full combustion.
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 3 Methodology of RBFP  
### System Overview  
USVs use 2D LiDAR to scan the riverbank and collect point cloud data (PCD). The PCD, selected based on the LiDAR angle set by the experiment and the relative distance between the USV and the riverbank, is converted into the world coordinate system to generate the path for USV navigation. This iterative process enables high-precision autonomous navigation along the riverbank without the need for target points. To ensure the USV stays on the planned path, a PID controller is incorporated into the navigation process to promptly correct any deviations.  
The block diagram of the system framework is illustrated in Figure 1. For the algorithmic layer, we calculate the output yaw error setpoint \(\theta_{sp}\) and the velocity setpoint \(v_{sp}\) based on the input point cloud and USV position and yaw angle. The riverbank following planner takes as input the point cloud data (PCD), and current USV states, including position, and heading. The outputs of the planner are the heading setpoint \(\theta_{sp}\) and the velocity setpoint \(v_{sp}\). The process starts with the USV being initiated through mobile computing devices and receiving a start flag from the user interface. For the physical layer of the USV, we obtain real-time environmental information through sensor data, such as LiDAR and GPS, and establish connections through ROS. Our riverbank following planner model can be easily adapted to work with point cloud-based perception sensors and various USV configurations.
Unlike previous studies about the GNSS positioning errors caused by the influence of solar eclipses, our study is focused on a global scale. This allowed us to find other locations in the world that could be affected by a perturbation in the north pole and how that perturbation propagates to those potential locations.
# A Method to Reduce Non-Nominal Troposphere Error
## 4 VPL under Non-Nominal troposphere
### VPL Component to Bound the Zero-Mean Pseudorange Error  
The VPL component that arises from only the pseudorange error following a zero-mean Gaussian distribution is calculated as [15].  
\[VPL_{non}=\max(VPL_{H0},VPL_{H1}) \tag{2}\]  
where \(H0\) is the fault-free condition and \(H1\) is the single-receiver fault condition.  
\[VPL_{H0}=K_{ffmd}\sqrt{\sum_{i=1}^{N}S_{vert,i}^{2}\sigma_{i,H0}^{2}}+D_{v} \tag{3}\]  
\[VPL_{H1}=\max(|B_{j,vert}|+K_{md}\sqrt{\sum_{i=1}^{N}S_{vert,i}^{2}\sigma_{i,H 1}^{2}})+D_{v} \tag{4}\]  
\[B_{j,vert}=\sum_{i=1}^{N}S_{vert,i}B_{i,j} \tag{5}\]  
\[\sigma_{i,H0}^{2}=\sigma_{gnd,i}^{2}+\sigma_{trop,i}^{2}+\sigma_{tino,j}^{2}+ \sigma_{air,i}^{2} \tag{6}\]  
\[\sigma_{i,H1}^{2}=\frac{M_{i}}{U_{i}}\sigma_{gnd,i}^{2}+\sigma_{trop,i}^{2}+ \sigma_{tino,i}^{2}+\sigma_{air,i}^{2} \tag{7}\]  
where \(j\) is the ground subsystem reference receiver index, \(i\) is the satellite index, \(M_{i}\) is the number of reference receivers used to compute the pseudorange corrections for the \(i\)th satellite, \(U_{i}\) is the number of reference receivers used to compute the pseudorange corrections for the \(i\)th satellite, \(B_{i,j}\) is the B value for the \(i\)th satellite and \(j\)th reference receiver, \(K_{ffmd}\) is a multiplier determined by the probability of missed detection given that the ground subsystem is faulted, \(D_{v}\) is a parameter that depends on the active approach service type, \(S\) is the projection matrix that relates the range domain measurements to the position domain estimates, \(\sigma_{gnd,i}\) is the total (post-correction) fault-free noise term provided by the ground function for the \(i\)th satellite, \(\sigma_{air,i}\) is the standard deviation of the aircraft contribution to the corrected pseudorange error for the \(i\)th satellite, \(\sigma_{trop,i}\) is a term computed by the airborne equipment to cover the residual troposphere error for the \(i\)th satellite, and \(\sigma_{tono}\) is the residual ionospheric delay uncertainty for the \(i\)th satellite. The calculation model of \(B_{i,j}\) is as follows [16; 17; 18]:  
\[B_{i,j}=K_{B}\frac{\sigma_{gnd,i}}{\sqrt{M_{i}-1}} \tag{8}\]  
where \(K_{B}\) is a station-configurable parameter between 5 and 6; 5.6 is commonly chosen in this paper.  
For GBAS Approach Service Type C (GAST C), \(D_{v}\) is set to 0. For GAST D, \(D_{v}\) is related to real-time approach types and ionospheric conditions, which can be calculated as [19].  
\[T(D_{v})=K_{fdD}\times\sqrt{\sum_{i=1}^{N}S_{vert,i}^{2}\times\sigma_{D_{R}}^ {2}} \tag{9}\]  
\[\sigma_{D_{R}}=F_{pp}\times\sigma_{vig}\times 140\times v_{air} \tag{10}\]where \(K_{fdD}\) is equal to 5.5 according to a continuity risk of \(4\times 10^{-8}\), \(F_{pp}\) is the vertical-to-slant obliquity factor for a given satellite, \(\sigma_{vig}\) is the vertical ionospheric gradient, and \(v_{air}\) is the aircraft horizontal approach velocity, which is assumed to be 77 m/s for GBAS Approach Service Type C and 72 m/s for GAST D.
## 3 Design of Triple-Band Antenna
The trained SVR model was used to obtain geometric values of the MPATSAT (\(sL1\), \(sL2\), \(sW1\), \(sW2\), and \(r\)) for the desired resonant frequencies and desired minimum gains. Desired input resonance frequencies are chosen as \(f_{r1}\) = 2.4 GHz, \(f_{r2}\) = 4.7 GHz and \(f_{73}\) = 5.3 GHz bands for WLAN and 5G (n80) and expected minimum gain is \(g\) = 5 dB. Geometric values of MPATSAT were computed as \(sW1\) = 1.3 mm, \(sW2\) = 0.65 mm, \(sL1\) = 29.3 mm, \(sL2\) = 14.70 mm, and \(r\) = 4.9 mm using the trained SVR model. The design procedure is explained within a flow chart in Figure 2. \begin{table}
\begin{tabular}{c c} \hline
**Variables** & **Intervals and Number of Samples** \\ \hline slot Width 1 (\(sW1\)) & 0.5 mm \(\leq sW1\)\(\leq\) 10 mm \\ slot Width 2 (\(sW2\)) & 0.5 mm \(\leq sW2\)\(\leq\) 10 mm \\ slot Length 1 (\(sL1\)) & 10 mm \(\leq sL1\)\(\leq\) 30 mm \\ slot Length 2 (\(sL2\)) & 0.5 mm \(\leq sL2\)\(\leq\) 15 mm \\ radius of arc-truncation (\(r\)) & 0.5 mm \(\leq r\leq\) 5 mm \\ number of training samples & 1100 \\ number of testing samples & 149 \\ total number of samples & 1249 \\ \hline \end{tabular}
\end{table} 
Table 1: Sampling procedure for training and testing. \begin{table}
\begin{tabular}{c c} \hline
**SVR Parameters** & **Selected Kernel Function Type and Parameter Values** \\ \hline Kernel function & Mexican-hat wavelet (19) \\ \(C\) & \(2\times 10^{12}\) \\ \(\varepsilon\) & \(5\times 10^{-4}\) \\ \(a\) & 2.97 \\ \hline \end{tabular}
\end{table} 
Table 2: SVR model parameters.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods
### Monitoring of Non-Agriculturalization
#### 3.2.3 Pseudo-Change Removal  
The results of change detection inevitably contain some pseudo-changes. To extract the non-agriculturalization information needed, this study introduces the EVI [45] to eliminate pseudo-changes. The calculation of the vegetation index is shown in Equation (2). First, the binary difference map is vectorized to obtain the change patches, and the mean EVI values for each change patch are calculated for both the T1 and T2 periods. In theory, during the process of cropland transitioning to buildings, ponds, or pushed-fill soil, the vegetation index will change accordingly. Therefore, an empirical EVI threshold of 0.2 is applied to distinguish cropland from non-cropland. Change patches with an EVI greater than 0.2 in the T1 period and less than 0.2 in the T2 period are retained. This approach helps to eliminate pseudo-changes caused by phenological effects. The process is illustrated in Figure 5.  
\[EVI=\frac{2.5\times(NIR-RED)}{(NIR+6\times RED-7.5\times BLUE+1)} \tag{2}\]  
Figure 5: Removal of fake changes.  
Here, \(NIR\) represents the reflectance in the near-infrared band, \(RED\) represents the reflectance in the red band, and \(BLUE\) represents the reflectance in the blue band.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 8 Regulated Structural Alterations in LPS  
The LPS composition is highly heterogeneous and dynamically altered in response to various challenges like exposure to different stress conditions or changes in growth medium. Heterogeneity of LPS can be due to: modification of the lipid A part by the incorporation of phosphoethanolamine (P-EtN) and 4-amino-4-deoxy-l-arabinose (l-Ara4N) that mask negative charges, the addition or removal of acyl chains, changes in the inner core due to the incorporation of additional Kdo residue, rhamne (Rha), uronic acid and P-EtN and changes in number of phosphate residues, and truncation of the outer core [55]. All these structural changes are highly regulated, important for resistance to cationic antimicrobial peptides, for bacterial virulence in pathogenic bacteria and may have adaptive significance in specific environmental niches. Certain modifications in the LPS structure can also contribute to biofilm tolerance of antimicrobial compounds [56]. Lipid A of _E. coli_ under standard growth conditions is bisphosphorylated carbohydrate backbone disaccharide \(\beta\)-d-Glc\(p\)N4P-(l\(\rightarrow\)6)-\(\alpha\)-p-Glc\(p\)N1P, which is hexaacylated without any modifications. However, exposure to low pH, excess of Fe\({}^{3+}\), Zn\({}^{2+}\), Al\({}^{3+}\), change in divalent cations concentrations, challenge by antimicrobial peptides, treatment with the non-specific phosphatase inhibitor ammonium metavanadate (AMV), treatment with agents that disturb the OM symmetry like exposure to chelating agents like EDTA or genetic alterations that lead to the synthesis of tetraacylated derivatives can cause profound changes in the lipid A composition [7; 55; 57]. Some of these modifications are regulated at the transcriptional level, while some are subjected to a post-transcriptional control and certain modification occur at post-translational level [13].
## 3 Results
### Limitations of the Analytical Hierarchy Process (AHP)  
Despite its advantages, the analytical hierarchy process (AHP) also has several disadvantages, including:  
* **Subjectivity**: AHP heavily relies on subjective judgments and pairwise comparisons made by decision-makers. The accuracy and consistency of these judgments can vary, introducing potential bias and uncertainty into the decision-making process.
* **Complexity**: AHP can become complex when dealing with a large number of criteria and alternatives. The hierarchical structure and pairwise comparisons require significant effort and time from decision-makers, making it challenging to handle highly complex decision problems.
* **Sensitivity to Input**: small changes in the pairwise comparison judgments can lead to significant variations in the final results. AHP's sensitivity to input can make the decision outcomes vulnerable to individual biases or inconsistencies in judgment.
* **Lack of Quantifiability**: AHP relies on qualitative judgments and does not provide a quantitative measurement of the decision criteria or alternatives. This can limit the ability to conduct precise numerical analysis or statistical inference in the decision-making process.
* **Limited Representation of Interactions**: AHP assumes independence between the criteria and does not explicitly capture interactions or dependencies among them. This can oversimplify the decision problem and overlook the complex relationships that exist between the criteria.  
While AHP offers a structured and systematic approach to decision-making, it is important to consider these limitations and apply the method judiciously, taking into account the specific characteristics and requirements of the decision problem at hand.
## 3 5G Technology Enablers for V2X Use Cases
### Positioning Capabilities of 5G and Enablers
#### 3.3.1 Localization Algorithms and Evaluation Methods Concerning 5G  
As a radio-wave-based communication technology, 5G offers the possibility of using most positioning techniques and algorithms based on wave propagation. Ref. [43] gathers and compares six different possible positioning technologies. A survey on 5G positioning [50] also collects the basis of 5G-based localization and compares a few of the elementary methods. These methods are listed in Table 3. Choosing the right method depends on the specific application and is the designer's responsibility. They have to set that the desired accuracy and refreshment rate targets and make a choice based on those requirements.  
It is worth mentioning that, as 5G's main application is communication rather than precise positioning, the performance of these methods can be worse in some areas than radio technologies specialized for localization (e.g., UWB). Despite this, the technological advancements in 5G ensure that even the stricter demands regarding positioning are met, and the spread of 5G offers a great economic advantage over other radio technologies.
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Schemes** & **Adaptive?** & \begin{tabular}{c} **Frame** \\ **Aggregation** \\ **and Block Ack?** \\ \end{tabular} & \begin{tabular}{c} **Strict QoS** \\ **to Emergency** \\ **Traffic?** \\ \end{tabular} & \begin{tabular}{c} **Scalable?** \\ **Loads?** \\ \end{tabular} & \
\begin{tabular}{c} **High-Traffic** \\ **Loads?** \\ \end{tabular} \\ \hline BUMA [22] & yes & no & no & yes & yes \\ \hline O-BlockACK [30] & yes & yes & no & no & yes \\ \hline Adaptive A-MPDU [27] & yes & yes & no & no & yes \\ \hline TXOP-based frame concatenation and BlockAck (TFCB) [28] & no & yes & no & no & yes \\ \hline mA-MSDU [23] & yes & yes & no & no & no \\ \hline HTA [31] & yes & yes & no & yes & no \\ \hline ATA for EDCA [20] & yes & no & no & no & yes \\ \hline Two-level aggregation [24] & no & yes & no & no & no \\ \hline QAA-MPDU [26] & yes & no & yes & no & yes \\ \hline IEEE 802.11e block ACK [21] & no & yes & no & no & yes \\ \hline MU-MIMO adaptive algorithm [32] & yes & yes & no & no & no \\ \hline CA-TXOP scheme [33] & no & no & yes & yes & yes \\ \hline FAFA scheme [34] & yes & yes & no & yes & no \\ \hline A-MPDU aggregation scheme [35] & yes & yes & no & no & yes \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Our work (FASBA)** & yes & yes & yes & yes & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work on frame aggregation and block acknowledgement in WLANs.
# Degree of Conversion and Mechanical Properties of Modern Self-Adhesive Luting Agents
###### Abstract  
New self-adhesive resin composite luting agents have currently been developed, claiming improved properties. The study aimed to evaluate the composition, degree of conversion, and mechanical properties of Panavia SA Plus (PSP), Panavia SA Universal (PSU), SpeedCam Plus (SPC) and TheraCam Ca (THC), with the resin luting agent Panavia V5 (PV5) serving as a control. The structure of the materials was studied by FTIR spectroscopy and SEM/EDX spectrometry. Disk-shaped specimens were prepared from each material under dual- and self-curing modes (n = 5/mode and material). After a 3-week storage period (dark/37 \({}^{\circ}\)C/80%RH) the Martens hardness, indentation modulus, elastic index, and creep were determined by instrumented indentation testing (IIT), while the degree of conversion was assessed by FTIR spectroscopy. Statistical analysis was performed by 2-way ANOVA and post-hoc testing (\(\alpha\) = 0.05). All materials were based on aromatic monomers, except for SPC. Fillers with potentially bioactive Ca-glasses were identified in SPC and THC, which showed the highest P/Si ratio. The dual-curing mode demonstrated superior performance in all properties. Differences between materials within each curing mode were limited to SPC, THC (highest conversion) and PSA, PSU, SPC (highest elastic index) for dual-curing, and THC (lowest hardness and elastic index). The results confirmed a lower self-curing conversion in these materials, which may affect some of the mechanical properties tested.  
self-adhesive luting agents; composition; degree of cure; mechanical properties; X-ray microanalysis; infrared spectroscopy; instrumented indentation testing +
Footnote †: journal: Applied Sciences
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 4 Results
### Experiment Details  
For all datasets, the model has been trained with the training set for a total of 20 epochs. We used ResNet50 to initialize the model parameters. The initial learning rate was \(5\times 10^{-4}\), which decreased 10 times in 12 and 16 stages, respectively. During training, the weight attenuation and momentum were 0.0001 and 0.9, respectively, and the batch size was 1. The area anchored on the pyramid P3 to P7 was \(32^{2}\) to \(512^{2}\), respectively. In Rotation RetinaNet, the angle interval between the rotation anchors was 15 degrees. In our model, the original step size was 15 degrees, and the new step size was 5 degrees. As the goal of the auxiliary anchor was to achieve a better matching performance, the smaller the step size, the better the performance. In practice, a smaller value was no longer necessary when the step size solved most of the matching problems.
## 2 Field Testing
### Analysis of Soil Pressure Amplitude on the Pipe Top and Dynamic Strain Amplitude of the Pipeline
The axial and circumferential strain amplitudes along the pipe side and over the top are shown in Figures 7 and 8. It can be seen from the figure that the strain at the top of the pipe is mainly axial strain, while at the side of the pipe, circumferential strain is greater than axial strain. In addition, the strain values at the side of the pipe are all greater than those at the top of the pipe. This results from the vertical compression of the pipeline under the action of vertical pressure and the relative displacement occurring between the soil  
Figure 5: The vertical acceleration of the vibrating wheel in Condition 2. Figure 6: The amplitude values of soil pressure at different burial depths under Condition 2.  
above the pipeline and the soil at the pipeline side, which causes the soil arching effect. Therefore, part of the pressure above the pipeline was borne by the soil along the pipe side, the soil pressure thus increased, and the circumferential strain value at the pipeline side was greater than that of other parts. The strain above the pipe top was mainly axial. At the same time, the vertical compression deformation of the pipeline caused the pipeline to expand laterally, and the soil on the side of the pipe restricted this deformation, thus exerting a great force on the pipe. Therefore, the strain on the side of the pipe was greater than that of other parts. Due to the above reasons, the force on the pipe side was particularly complex, so its trend of change with the number of compactions was slightly different from the pipe top pressure. The trend of hoop strain of the pipe top was consistent with the pipe top pressure. For the axial strain of the pipe top, because the compact traveled in the direction of the length of the pipe, the axial strain was greatly affected by the adjacent parts, so its trend of change was also different from the pressure of the pipeline. Figure 7: Absolute values of the circumferential strain of (**a**) pipe side and (**b**) pipe roof.
# Towards a Real-Time Description of the Ionosphere:
## 2 IRI and IRTAM Models: A Brief Recall
### Iri
[41] with _M_(3000)F2 ionosonde data and is based on the anti-correlation between _lnm_F2 and the propagation factor _M_(3000)F2 [35], with _M_(3000)F2 values from CCIR [35] or URSI [31] mapping procedures (see [14] for further details). The second and the third ones have been recently developed, respectively, by [PERSON] et al. [42] with _lnm_F2 ionosonde data, and by [PERSON] et al. [43; 44] with _lnm_F2 ionosonde and COSMIC RO data, and they are both based on the spherical harmonic formalism.