esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Compliant Behaviour of Automated Vehicles in a Mixed Traffic Environment
## 3 Implementation of Automated Driving Functions in Microscopic Simulation
### Non-Compliant Trajectories
#### 3.2.1 Computation of Non-Compliant Trajectories  
Figure 6 shows schematically the approach to determining if a planned lane-change trajectory is compliant with the norm. Although this concept is valid for lane-change trajectories as well as for maintain-lane trajectories, the approach in this paper is demonstrated for a lane-change maneuver. First, the trajectory of the lead vehicle affected by the planned maneuver has to be predicted. In Figure 6, the predicted trajectory \(\mathrm{s_{lead}(t)}\) of the lead vehicle on the target lane is highlighted in blue. For the prediction of \(\mathrm{s_{lead}(t)}\), the velocity of the lead vehicle is assumed to be constant during the lane-change maneuver of the ego vehicle. The planned lane-change trajectory of the ego vehicle \(\mathrm{s_{ego}(t)}\), highlighted in green in Figure 6, is calculated by the ego vehicle's trajectory-planning module. Then, \(\Delta\mathrm{s_{t}}\), the difference between the distances traveled by both vehicles in each time step, is calculated. \(\Delta\mathrm{s_{t}}\) represents a vector containing the distance between the ego and the lead vehicle at each time step.  
\[\Delta\mathrm{s_{t}}=\mathrm{s_{lead}(t)-s_{ego}(t)} \tag{15}\]  
All entries of the vector \(\Delta\mathrm{s_{t}}\) below the safe distance are summarized to yield a time period referred to as time below \(\tau_{min}\) in seconds, which describes how long the planned trajectory would be below the safe distance. If the time below \(\tau_{min}\) is between zero and a maximum allowed time \(\mathrm{T_{max,nc}}\), the trajectory is a non-compliant trajectory. Otherwise, it is an ordinary trajectory.
## 2 Algorithm for Tracking Target Trajectories
Key metrics include the following:  
- \(P_{search\_failure}(t)\)--the failure probability of the object's searching over time;  
- \(P_{capture\_failure}(t)\)--the failure probability of the object's capturing over time;  
- \(P_{tracking\_failure}(t)\)--the failure probability of the object's tracking over specified time;  
- \(P_{transfer\_failure}(t)\)--the failure probability of the object's transferring for servicing to the control and execution system over time;  
- \(t_{search\_failure}\)--the search failure time;  
- \(t_{capture\_failure}\)--the capture failure time;  
- \(t_{tracking\_failure}\)--the tracking failure time;  
- \(t_{transfer\_failure}\)--the transfer failure time to the control and execution system;  
- The spatial zones in which the failure of operations \(Z_{failure}\) is ensured;  
- The spatial zones and radar suppression ranges where the failure of the problems' solution for search, capture, tracking, and transfer of an object for servicing \(Z_{failure\_search}\), \(Z_{failure\_capture}\), \(Z_{failure\_ resist}\), and \(Z_{failure\_forward}\) is ensured with specified probability and time parameters. These indicators can be used in the mathematical formalization process as components of a general indicator, such as radar interaction time with a tracked object. Ultimately, these metrics help in analyzing the efficiency of radar suppression under various interference conditions during the initial stages of mission execution. Having established the operational framework, the next section delves into the mathematical foundation underpinning radar performance under interference conditions.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 3 Formulation
### Problem Formulation
#### 3.3.1 Data Gathering while Flying  
In the majority of the flight time, the UAV gathers data while it is flying. During this time, \(0>v\geq v_{max}\), the UAV collects data from the ground nodes. The flight time of the UAV is determined as \(F_{(t)}=(y_{n}-x_{n}/v)\). The sensors' transmission distance might change slightly when the UAV is flying; therefore, the UAV's height and speed should be adapted to achieve better network performances. The LOS ground-to-air channel model between the UAV and sensors with path loss exponent \(\propto\geq 2\) was adopted. In this model, the prompt data rate at time \(t\) is expressed as  
\[R(t)=\frac{1}{2}W\text{log}2\left(1+\frac{\beta}{\left(v(t)^{2}+H^{2}\right)^{ \frac{\beta}{2}}}\times E_{trans}\right), \tag{10}\]  
where \(t\in\left[0,f_{(t)}\right]\); \(W\) is the bandwidth, \(\beta\) is the reference signal-to-noise ratio at the reference distance of 1 m; \(H\) is the height of the UAV; \(E_{trans}\) is the transmission energy, which satisfies the total energy constraint  
\[\int_{i=0}^{F_{(t)}}E_{trans(i)}dt\leq E_{T}. \tag{11}\]
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 4 Simulation Settings
### Simulation and Performance Analysis  
#### 4.2.1 Simulation Scene  
According to the Iridium constellation structure in Figure 2, the LEO mega constellation orbit number, number of satellites in each orbital plane, orbital height, satellites on the same orbital plane and links between adjacent orbital planes, and other parameters are configured while maintaining dynamic ISL updating. The orbital coordinates of the Iridium constellation in Table 3 are used as the initial position of the LEO mega constellation.  
The coordinates of the two ground stations on Earth are (31\({}^{\circ}\) N, 3\({}^{\circ}\) E) and (148\({}^{\circ}\) N, 99\({}^{\circ}\) E), and are denoted by GS0 and GS1, respectively, as shown in Figure 13. GS0 is the requester of interest, and GS1 is the data responder, i.e., producer. Communication between the ground stations is established via the LEO mega constellation ISL with multiple hops. For example, the #13 satellite above GS0 transmits the interest request to GS1 through the #23 satellite, the #44 satellite, the #54 satellite, and the #55 satellite ISL, as shown in Figure 14.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 3 Design of the IDRF System
### Real-Time Phase
Because the real-time vehicle characteristics may differ from those in their historical datasets, \(v_{carry}\) may have no \(v_{relay}\) as its neighbor, or the calculated link connection time between \(v_{relay}\) and \(v_{carry}\) may be insufficient to transmit the packet at that time, causing packet transmission failure. In this case, the decision made by DQN is meaningless for the real-time vehicle and hence requires the DQN routing decision and exception handling mechanism to be used (Figure 25). 1. If the packet carried by \(v_{carry}\) needs to be transmitted to the destination, go to step 2. Otherwise, the DQN routing decision flow ends. 2. The DQN executed in the SDN CN first determines which relay node \(v_{relay}\) is chosen to forward the data. 3. This information is initially sent from the SDN CN to the RSU, which then forwards it to \(v_{carry}\), thereby carrying the packet. 4. When the \(v_{carry}\) carrying the packet searches its neighbor table and discovers the presence of \(v_{relay}\), it forwards the packet to \(v_{relay}\) and the process moves to step 5. If \(v_{carry}\) cannot find \(v_{relay}\) in its neighbor table, the process moves to step 10. 5. If the packet transmission from \(v_{carry}\) to \(v_{relay}\) is successful, go to step 6. Otherwise, go to step 7. 6. \(v_{carry}\) sends back a success packet to the RSU, notifying them that the transmission has been successful. Go to step 8. 7. If the packet transmission to \(v_{relay}\) fails, \(v_{carry}\) sends back a failure packet to the RSU, notifying them that the packet transmission has failed. 8. Subsequently, the RSU informs the SDN CN that \(v_{relay}\) is unable to complete the packet transmission. \[d=\sqrt{{D_{xij}}^{2}+{D_{yij}}^{2}}\leq r \tag{7}\]2 The DQN forwarding path considers real-time vehicles with insufficient data and vehicles with sufficient historical data.
###### Abstract  
Bisphenol A (BPA), which is widely used for manufacturing polycarbonate plastics and epoxy resins, has been banned from use in plastic baby bottles because of concerns regarding endocrine disruption. Substances with similar chemical structures have been used as BPA alternatives; however, limited information is available on their toxic effects. In the present study, we reviewed the endocrine disrupting potential in the gonad and thyroid endocrine system in zebrafish after exposure to BPA and its alternatives (i.e., bisphenol AF, bisphenol C, bisphenol F, bisphenol S, bisphenol SIP, and bisphenol Z). Most BPA alternatives disturbed the endocrine system by altering the levels of genes and hormones involved in reproduction, development, and growth in zebrafish. Changes in gene expression related to steroidogenesis and sex hormone production were more prevalent in males than in females. Vitellogenin, an egg yolk precursor produced in females, was also detected in males, confirming that it could induce estrogenicity. Exposure to bisphenols in the parental generation induced a decrease in the hatchability associated with offspring generation. In zebrafish exposed to bisphenols, significant decreases in thyroxine concentrations and increases in thyroid-stimulating hormone concentrations were commonly observed. Alternative compounds used to replace a chemical of concern are believed to be less toxic than the original compound; however, several BPA alternatives appear to have similar or greater effects on the endocrine system in zebrafish. Since endocrine systems interact with each other, further studies are needed to assess the primary target of BPA alternatives among the endocrine axes.  
bisphenol A alternatives; endocrine disruption; reproduction; thyroid; zebrafish +
Footnote †: journal: Applied Sciences
## 1 Introduction
In a previous work, we introduced the adaptive backoff algorithm (ABA) [11] (see also [12]) as an improvement to the standard backoff mechanism--known as the binary exponent backoff (BEB)--in the 802.15.4 MAC protocol. ABA aimed to adapt the size of the contention window used in BEB to the value of the probability of collision. Since the probability of collision is directly impacted by the size of the WSN, ABA manages to shorten or extend the contention window dynamically based on how big or small the network is. This way, ABA responds to the level of activity happening over the wireless medium. In our evaluation of the ABA's performance, we simulated networks of sizes up to 350 nodes while imposing an upper bound of 256 on the contention window size. We observed that the overall performance degrades as the network size increases beyond 50 nodes. This paper introduces an improved version of ABA, and we name it the improved-ABA (I-ABA). The main objective of I-ABA is to maintain a promising performance in the network compared to the standard MAC, while increasing the size of the network to accommodate hundreds of sensor nodes in a WBAN; this is referred to as large-scale WBAN in this paper. Supporting large-scale WBANs is realistic and needed in many situations. The IEEE 802.15.6 standard itself supports a WBAN of sizes up to 256 nodes (we stress again, as mentioned earlier in this section, that the focus of this work is solely the IEEE 802.15.4 standard as it is still within the scope of a huge body of works on WBANs) [13]. The main contributions of this work are outlined as follows:  
1. We introduce I-ABA, a new, improved IEEE 802.15.4 MAC protocol for large-scale WBANs. 2. The improvements in I-ABA rely on a new mathematical definition of the contention window that is not computationally complex and poses no overhead on sensor nodes to compute. 3. We use the Markov chain model developed in [11] to derive mathematical expressions for essential performance metrics that include channel utilization, channel idle time, channel collision time, delay, reliability, average energy consumption, and average energy wasted in collisions. We evaluate the performance of I-ABA using user-defined tools (programmed in Java SE 21 and C) and conduct a comparative study with the standard BEB and ABA. The rest of the paper is organized as follows. Section 2 provides a brief description of the binary exponent backoff (BEB) algorithm that is incorporated in the MAC protocol of the 802.15.4 standard. Section 3 reviews recent proposals on improving the performance of the 802.15.4 MAC protocol. Section 4 introduces the new I-ABA algorithm along with a mathematical model to evaluate its performance. Section 5 studies I-ABA's performance and verifies the accuracy of the mathematical model developed in Section 4. Finally, Section 6 concludes the paper.
# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 3 Results
### Ablation Studies
#### 3.3.3 The Impact of Adjusting Depth Curve Estimate Networks  
We validated the effect of the depth curve estimation network structure on our method by comparing the enhancement effect with and without dropout layers. The image-enhancement effects of adding each of a dropout layer after all convolutional layers, the fifth convolutional layer, and the last convolutional layer were verified, and finally, we chose to add the dropout layer after the fifth convolutional layer and the last convolutional layer and compared the enhancement effect with our full-model approach. As can be seen from the comparison results in Figure 6 and Table 4, the adjusted network could reduce the color shift and image noise to some extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) \\ \hline w/o Noise Fusion & 18.725 & 0.764 & 0.253 \\ w/o \(L_{noise}\) & 18.573 & 0.759 & 0.261 \\ w/o Adjust Network & 18.209 & 0.753 & 0.267 \\ Our full model & **19.457** & **0.786** & **0.235** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison results of the ablation studies by module on the LOL dataset.  
Figure 6: Enhancement results of our method for low-light images of a mine and indoor squares.
## 4 Active Load Alleviation Performance
### Time Simulation Results  
The manoeuvres analysed with a static aeroelastic analysis were reproduced also using a dynamic simulation. A step deflection of the elevator is used to get the target load factor of 2.5g. The time history of the command is shown in Figure 20 and includes both a negative and positive command, to have both pull-up and pull-down manoeuvres. The dynamics of the elevator actuation system is considered by introducing a 10 Hz bandwidth and a saturation for the deflection rate. The slow winglet response prevents it to reach the saturation limits, and the deflection is not identical in the two sections due to the different sizes that lead to different aerodynamic hinge moments and then to different actuator responses. The larger surface of the outboard winglet section, in particular, leads to a lower rotation.  
The time history of wing internal loads shows that the MLA system is able to reduce structural loads even if the deflection values predicted in the static aeroelastic analysis are not reached in dynamic analyses. This is shown in Figure 21 where the bending moment both at wing root and at wing tip are shown. In the time histories the time has been non-dimensionalised considering as a reference the period of the first wing structural mode.
## 3 Results and Discussion
### Dried Juice and Pomace
#### 3.3.6 Color of Dried Juice and Pomace  
The \(L^{\star}\) parameter values ranged from 22.62 to 23.88 for the beetroot powders (Table 4). Variants DASDJC0 and DASDJC1 differed from each other, but DASDJC2 was similar to the control sample (DASDJC0), which was not subjected to PEF pretreatment. The \(a^{\star}\) parameter values ranged from 34.48 to 42.00, while the \(b^{\star}\) parameter values ranged from 7.62 to 11.52. It was noted that PEF treatment significantly increased the \(a^{\star}\) and \(b^{\star}\) color parameters, which translated to a higher share of red and yellow pigments in the powders that were treated with more pulses. This supports the idea that PEF pretreatment led to more efficient extraction of betalains responsible for color as a consequence of electroporation.  
The freeze-dried beetroot pomace (FDP) was characterized by \(L^{\star}\) parameter values ranging from 42.26 to 42.92 (Table 4). The color parameter \(a^{\star}\) ranged from 26.70 to 27.16, with the highest value for variant FDP1 and the lowest for FDP0. Variant FDP2 was similar to the others regarding the share of red color. As for the \(b^{\star}\) parameter, there was no difference between the control sample (FDP0) and the variant treated with the highest amount of pulses (FDP2). However, the samples treated with the lowest amount of pulses (FDP1) were characterized by a larger share of blue color than the FDP0 and FDP2 variants. This study did not confirm the previous research on freeze-dried beetroots pretreated with a PEF conducted by [PERSON] et al. [52], who observed that the samples treated with a PEF were darker in color than the untreated ones, while in our study, no differences were observed. Moreover, they noted more reddish freeze-dried beetroots which were pretreated with a PEF prior to drying.
## 1 Introduction
Finally, we are frequently presented with labeled data for small calculations that are costly in practice. As a result, it is vital to recognize this imbalance and provide a transferable solution for predicting the properties of large molecules using a model that has been trained on smaller molecules. To solve these problems, this research proposes a one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) for predicting the intramolecular coupling constants of molecules and for increasing the properties of molecules of possible liquid medications/drugs and other generic molecules, by directly adding their quantum interactions. In the feature extraction step, it also extracts molecular property features based on sequence data characteristics. Unlike previous feature extraction approaches, CNN can extract data features with minimum user effort or professional knowledge. Finally, a property may be predicted using the whole interaction description from all levels (for example, the additional energy required to fragment a molecule into separate atoms). Extensive testing was performed on both balanced and unbalanced molecular datasets, with the experimental results confirming the usefulness of our suggested technique. Furthermore, 1D-CNN-GRU demonstrated a greater capability for generalizability and portability. The suggested model can naturally transmit molecular interaction information at each level. The contributions of this work are outlined below:  
1. This study proposed a one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) for improving the properties of chemical compounds in possible liquid drugs/medications. 2. A comparison of the performance of molecular property systems based on a recurrent neural network, long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1 DCNN), and the proposed one-dimensional convolutional gated recurrent unit neural network (1D-CNN-GRU) was conducted. 3. A 1D-CNN-GRU optimization algorithm that can accurately predict the molecular properties of molecules is presented. 4. The efficacy of the suggested model was assessed using various performance criteria.
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 2 Principle
### Steps to Determine the Rotation Center of the Rotating Optical Measurement System  
The procedure for determining the rotation center of the rotating optical measurement system is divided into three steps. First, we collect images when the camera rotates from different positions (_i_) and calibrate the internal parameters and external parameters (rotation matrix \(R\) and translation vector _T_) of the camera by [PERSON]'s method. Then we can get the coordinates of the optical centers in the unified world coordinate system. Second, with the help of the auxiliary coordinate system, we calculate the rotation center by the LSC fitting method from the obtained world coordinates of the optical centers. At last, the final rotation center is obtained through a weighted algorithm. The flow chart of the procedure is shown in Figure 3.
## 2 Results
### Reinforcement of the MVA Pathway  
Considering the high accumulation of MVA in the production of (\(-\))-\(\alpha\)-bisabolol, we reinforced the whole MVA pathway through the expression of an additional copy of lower MVA pathway genes. A newly generated plasmid, pSSN12 Didi-MrBBS-IspA, contains the lower MVA pathway genes (_mvaK1_, _mvaK2_, _mvaD_, _idi_), _MrBBS_, and _ispA_ (Figure 4A). The MvaK1 of pSSN12 Didi-MrBBS-IspA plasmid was adopted from _Streptococcus pneumoniae (S. pneumoniae)_, which has a 2.6-fold faster turnover number (\(k_{cat}\)) than that of _Mm_MvaK1 [17]. In the absence of IPTG, _E. coli_ DH5\(\alpha\) harboring both pTSN-Bisa-Mm and pSSN12 Didi-MrBBS-IspA plasmids produced 1.2 g/L of (\(-\))-\(\alpha\)-bisabolol and 988 mg/L of MVA (Figure 4B,C), which are 2.2-fold higher and 1.4-fold lower than those of the _E. coli_ DH5\(\alpha\) containing the pTSN-Bisa-Mm plasmid alone, respectively. Because the metabolic flux was changed by the introduction of additional lower MVA pathway genes, we probed the effect of IPTG concentrations on the (\(-\))-\(\alpha\)-bisabolol production in the _E. coli_ DH5\(\alpha\) containing both pTSN-Bisa-Mm and pSSN12 Didi-MrBBS-IspA plasmids. Unlike the results from the _E. coli_ DH5\(\alpha\) harboring the pTSN-Bisa-Mm alone (Figure 3A), production of both (\(-\))-\(\alpha\)-bisabolol and MVA decreased as the IPTG concentrations increased (Figure 4B,C), indicating that enzymes for (\(-\))-\(\alpha\)-bisabolol biosynthesis were sufficiently expressed in the absence of IPTG to increase the (\(-\))-\(\alpha\)-bisabolol production when the lower MVA pathway was reinforced.  
Figure 3: Effect of IPTG on (\(-\))-\(\alpha\)-bisabolol production. The concentrations of (\(-\))-\(\alpha\)-bisabolol and MVA produced by the _E. coli_ DH5\(\alpha\) harboring pTSN-Bisa-Mm, a high-copy plasmid (**A**) or pSEVA231-Bisa-Mm, a medium-copy plasmid (**B**). Cells were grown in TB medium containing 10 g/L glycerol and 20% (v/v) of _n_-dodecane in the presence of different IPTG concentrations (0, 0.025 and 0.1 mM) at 30 \({}^{\circ}\)C for 72 h. The error bars represent the standard deviation of the concentrations of (\(-\))-\(\alpha\)-bisabolol and MVA from three biological replicates.