esa-sceva/satcom-chunk-collection huggingface dataset:
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 4 Effective \"Green\" Technologies
### CRISPR-Based Engineering of Mosquitoes Refractive to Pathogens
#### 4.6.2 CRISPR/Cas13  
In 2015, the Class 2 candidate 2 (C2c2) of Cas proteins, which later became known as Cas13a, was identified in prokaryotes. Interestingly, unlike the well-known Cas9, Cas13 proteins are unique in that they specifically bind and cleave ssRNA using a guide crRNA of approximately 64-66 nucleotides. Notably, once activated, Cas13 becomes promiscuous and degrades RNA indiscriminately, and, therefore, can be lethal to the cell in which it is expressed [211; 212; 213; 214]. This feature makes Cas13 attractive from an applied perspective; the system could potentially be used to \"kill two birds with one stone\", i.e., suppress the replication of pathogens in the mosquito vector as an initial response to infection and, subsequently, target both host and pathogen RNAs indiscriminately. As a result, the system could be lethal to both the pathogen and the vector, or at least reduce the fitness of the vector. In principle, the transmission and dissemination of pathogens and vector population decline are theoretically possible.  
The 'proof of concept' application of CRISPR/Cas13 has been demonstrated for [PERSON]'s disease, an inherited disease that causes the degeneration of neurons in the brain [215], and in _Drosophila_[216] and mosquitoes [217; 218]. More recently, [PERSON] et al. [219] demonstrated the practical feasibility of CRISPR/Cas13 in _Aedes aegypti_. The research group developed an antiviral strategy called REAPER (vRNA Expression Activates Poisonous Effector Ribonuclease) that uses a sensor switch to activate the system in vivo. The system design allowed REAPER to remain dormant until the engineered mosquito acquired a blood meal. Soon thereafter, the activated expression of four gRNA led to the suppression of chikungunya virus replication, and the collateral effect, though not absolute, in which at least 35% of mosquitoes died post-viral infection.
## 5 Simulation Results
### Evaluating the Impact of the Radio Propagation Model
The results show that using a more realistic model tends to reduce protocol performance, allowing us to better understand the impact of buildings and obstacles along the road on car-to-car communications. Although the RAV model yields poorer performance results than TRG, it is in fact a more realistic radio propagation model, which should be considered in VANET simulations.
## 3 Mathematical Preliminaries
### Average Probability of No Subcarrier Collisions  
The SINR at the SU in this scenario will be given as \(S_{nc}=\lambda/\eta\), where \(\lambda\) is the received power and \(\eta\) is the AWGN (\(\mathcal{CN}(0,\eta)\)). The cumulative distribution function (CDF) [9,25] of the received power (\(\lambda\)) can be obtained as  
\[F_{\lambda}(x)=F_{\gamma p}\bigg{(}\frac{q}{P_{s}}\bigg{)}F_{\
u_{1}}(x)+F_{ \
u_{2}|\gamma_{ps}>\frac{q}{P_{s}}}\bigg{(}x|\gamma_{ps}>\frac{q}{P_{s}} \bigg{)}, \tag{15}\]  
where \(p_{s}\) is the power of SU-Tx, \(\gamma_{ps}\) is the channel power gain between PU and SU, \(\
u_{1}=\gamma P_{s}\) and \(\
u_{2}=\frac{qT}{\gamma_{ps}}\). The PDF of \(\
u_{1}\) and \(\
u_{2}\) can, therefore, be expressed as  
\[f_{\
u 1}(x)=\frac{e^{-\frac{x}{P_{s}}}}{P_{s}},\text{and}\ f_{\
u 2}(x)= \frac{q}{(x+q)^{2}}. \tag{16}\]  
On substituting Equation (16) into Equation (15), the final expression comes out as  
\[F_{\lambda}(x)=1-e^{-\frac{x}{P_{s}}}+\frac{x}{q+x}e^{-\frac{x+q}{P_{s}}}. \tag{17}\]  
From Equation (17), the PDF of \(\lambda\) can be found as  
\[f_{\lambda}(x)=\frac{dF_{\lambda}(x)}{dx}=\frac{e^{-\frac{x}{P_{s}}}}{P_{s}} \ \bigg{[}1-e^{-\frac{q}{P_{s}}}\ \bigg{(}\frac{x^{2}+qx-qP_{s}}{(q+x)^{2}}\bigg{)}\bigg{]}. \tag{18}\]  
Using the transformation of random variables and Equation (15), PDF of SNR (\(f_{s,nc}(x)=\eta f_{\lambda}(\eta x)\)), will be  
\[f_{s,nc}(x)=\frac{\eta e^{-\frac{\eta x}{P_{s}}}}{P_{s}}\bigg{[}1-e^{-\frac{q}{ P_{s}}}\frac{((\eta x)^{2}+q\eta x-qP_{s})}{(q+\eta x)^{2}}\bigg{]}. \tag{19}\]  
Therefore, the average BER will be given as  
\[\mathbb{E}[P_{b,nc}]=\int_{0}^{\infty}f(x)_{\text{AWGN}}\ f_{s,nc}(x)dx \tag{20}\]where \(f(x)_{\text{AWGN}}\) is the same as in the case of collision one, given in Equation (14). Finally, substituting Equation (13) and Equation (20) in Equation (8) will give the mean BER for the CRN. In the next section, we will look into the detailed analysis of these different BER (collision and no-collision), mean BER and SINR equations.
## 3 Results and Discussion
### Characterization of the Zinc-Peptide Fractions Interactions by FTIR  
The functional groups related to the chelate of zinc in the peptide fractions-zinc complexes were investigated by infrared spectroscopy [29]. There were no significant differences in the infrared spectra of the three peptide fractions or their zinc complexes, which indicated that these peptide fractions with different molecular weights have similar zinc-binding ligands in nature. The wavenumbers of the notable band shifts in the infrared spectra of these samples are listed in Table 3. There is a significant difference in the spectral band position and intensity between the peptide fractions and their peptide-zinc complexes. The characteristic absorption band of amide A is between 3300 cm\({}^{-1}\) and 3440 cm\({}^{-1}\) and is mainly related to N-H stretching [30]. The wavenumbers of the amide A band were 3316, 3388, and 3419 cm\({}^{-1}\) for the three peptide fractions and distinctly shifted to 3403, 3415, and 3426 cm\({}^{-1}\), respectively, for the three peptide-zinc complexes. This shift may be due to the electron-withdrawing effect of Zn\({}^{2+}\) and the nitrogen atoms can form coordination bonds with zinc ions by offering their electron pairs, the hydrogen bonds were replaced with Zn-N bonds [31; 32]. Compared with the other two peptide fractions, P3 exhibited less N-H stretching, which suggested that P3 had a weaker affinity for Zn\({}^{2+}\).  
The absorption band of the peptide fractions in the range of 1600-1700 cm\({}^{-1}\), characterized as the amide I band, was attributed to the C=O stretching vibrations coupled with the bending vibrations of N-H. There was no remarkable shift in this band for the peptide-zinc complexes, which indicated that the C=O of the peptide fractions was not intimately coordinated with Zn\({}^{2+}\). Amide II bands (1500-1600 cm\({}^{-1}\)), caused by N-H bending coupled with C-N stretching, shifted slightly with the different molecular weight of the peptide fractions [33]. The peak corresponding to the -COO\({}^{-}\) carboxylate group was different for any of the Zn\({}^{2+}\)-treated peptide fractions, which may result from the replacement of H with zinc The positions of the bands for amide III ([PERSON], 2007), caused by both N-H in-plane bending and C-N stretching vibrations, shifted slightly in the spectra of three chelates. The peak at 658 cm\({}^{-1}\), caused by the completely in-plane vibration of the O=C-N bond was shifted to 591 (P1-Zn), 570 (P2-Zn), and 597 cm\({}^{-1}\) (P3-Zn) when peptide fractions were bound to zinc, which was caused by the increasing electron cloud density of O=CH\({}_{2}\) and decreasing election cloud density of N-H, shifted [34; 35]. The FTIR results suggest that some functional peptide groups were more or less involved in Zn\({}^{2+}\) binding, the amino group was the primary Zn\({}^{2+}\) reaction site in the low molecular weight collagen peptide fraction.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 1 Introduction
In this work, we follow their definition of a simulation, stating that it is any computer program that imitates a real-world system or process. Being able to simulate an experiment instead of actually conducting it in the real world greatly reduces the required time, cost and other practical implications, such as possible health risks or consequences for the environment. However, since simulators are programmed to a specific task, they are not insensitive to bias. Moreover, for more complex simulators, the amount of time needed to run the simulations can become cumbersome. In order to overcome these drawbacks, the simulation itself can be modeled by a machine learning algorithm, which predicts the outcome of the simulator. Popular choices for these models are Gaussian processes [9], random forests [10] and neural networks [11]. In this sense, the emulator is a'model of a model'. The gain stems from the fact that a complex simulation is much more computationally expensive than a computationally cheap emulation. Over the past years, emulation has found its way in several domains. In [12], a Gaussian process was implemented to emulate a mechanical model of the left ventricle, which allowed for a more rapid discovery of the optimal parameter set for the design. The authors of [13] built an emulator to model the calibration of an engine. The spread of an infectious disease was modeled in [10]. For machine learning models that are probabilistic by nature, they serve as a statistical surrogate model. This allows for the quantification of uncertainty of their predictions, which plays an important role in decision making or risk assessment. For this reason, we focus on Gaussian processes in this work. By following the Bayesian paradigm, their predictions consist of both a mean and a variance, which is interpretable as a measure of uncertainty. A more detailed description is given in Section 2. The rest of this paper is structured as follows. In the next section, we explain how we generated the data and give some theoretical background on Gaussian processes and uncertainty sampling. Section 3 describes our results. In Section 4, we discuss these findings. Finally, the conclusions are provided.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Receiver-Based Data Forwarding
#### 2.4.1 Basic Idea  
Figure 1 illustrates the general receiver-based scheme in VANETs. It consists of mainly two features: forwarding zone and waiting time criteria. The forwarding zone, which is highlighted by the yellow region in Figure 1, is the eligibility criterion that every contender checks before forwarding the data packet. If the vehicle is not located within the forwarding zone, as in the case of vehicle B, it discards the data packet. Vehicles located in the forwarding zone contend for the contention right by setting the waiting time based on certain criteria. A vehicle in which the waiting time expires first wins the contention right and broadcasts the data packet further. Other contending vehicles, as in the case of vehicle C, upon hearing the redundant data packet reception discards the data packet.  
The purpose of forwarding zone criterion is to minimize the unwanted multiple path formation which occurs when two or more contending vehicles are not located in wireless range of each other [46]. The key to reduce the unnecessary multipath formation is to set the forwarding zone such that all vehicles in the zone are in wireless range of each other.  
Figure 1: Illustration of a receiver-based forwarding scheme in VANETs.
## 5 Experimental Validation
### Binocular Camera Ranging  
(1) Binocular parameter extraction  
We captured 45 checkerboard images using a stereo camera setup, with both the left and right camera images containing distinct and complete checkerboard patterns, as shown in Figure 14.  
Figure 14: Shooting checkerboard.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MOTA/\%** & **IDF1/\%** & **Frame Rate/Frame \(\times\) s\({}^{-1}\)** \\ \hline DeepSORT & 73.5 & 72.9 & 42.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results.  
The images captured by the stereo camera setup were segmented and imported into MATLAB's stereo camera calibrator. In the stereo camera calibrator, the square size of the checkerboard was set to 40 mm. The stereo camera calibrator automatically discarded any images with excessive errors from the left and right camera pairs and performed the stereo calibration. Stereo camera calibration outputs camera internal reference matrix, camera distortion coefficient, camera rotation matrix, camera translation matrix and other parameters, see Tables 6-9.  
where \(f\) represents the focal length (in pixels), \(b\) denotes the baseline length, \(d\) signifies the disparity, and \(c_{xr}\) and \(c_{xl}\) are the column coordinates of the principal points of the two cameras.  
As per the above equation, the depth accuracy of a pixel depends on the accuracy of the estimated disparity (d) at that pixel. Assuming a constant error in the disparity estimation, the depth accuracy deteriorates as the measured distance increases, which causes stereo cameras to be less suitable for measuring distant objects. To obtain reliable depth estimates for distant targets, the baseline distance of the cameras needs to be increased, but larger baseline distances result in smaller overlapping regions and greater content differences between left and right views, which, in turn, increases the difficulty of stereo matching. Cameras with larger focal lengths can be chosen, but larger focal lengths result in a smaller field of view, which makes it challenging to estimate the distance of objects close to the cameras. Therefore, for our experiment, human bodies were detected at distances ranging from 0.3 to 3 m.
## 4 The Novel Circularly Polarized Folded Transmitarray Antenna Design
### Simulated and Measured Results  
The simulated and measured results of NCPFTA are shown in Figure 16. The results show that NCPFTA maintains S11 bandwidth at range of 8.5-10.6 GHz. The 3-dB realized gain bandwidth is from 8.9 GHz to 10.5 GHz (relative bandwidth 16.5%), and the peak gain is 22.4 dBi with the aperture efficiency 23.9%. Moreover, the 3-dB AR of NCPFTA shows ultra-wideband at range of 8.6-10.9 GHz (relative bandwidth 23.6%), which obviously proves the effectiveness of the proposed design method.  
Figure 16: Simulated and measured results of NCPFTA (a) S11, realized gain and (b) AR.  
Figure 17 shows the simulated and measured radiation patterns of NCPFTA in phi = 0\({}^{\circ}\), phi = 45\({}^{\circ}\), phi = 90\({}^{\circ}\) and phi = 135\({}^{\circ}\). The amplitude of left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) differ greatly in the direction of main beam. Stable high gain pencil-shaped radiation patterns are observed at 8.7 GHz, 9.7 GHz and 10.1 GHz. The measured results are in good agreements with the simulated results.  
Figure 15: (a) The fabricated NCPFTA. (b) The measurement of NCPFTA in microwaves anechoic chamber. (c) The integrated reflectarray and planar feed with total size of 238 mm \(\times\) 238 mm \(\times\) 2.5 mm. (d) The bottom layer of transmitarray with size of 240 mm \(\times\) 240 mm. (e) The top layer of transmitarray with size of 240 mm \(\times\) 240 mm.  
Compared with the ideal directivity of the aperture, multiple factors lead to gain loss of the fabricated NCPFTA. A detailed gain loss analysis is summarized in Table 2. The overall gain loss mainly comes from transmitarray, including 1-bit phase quantization loss, taper loss, transmission and reflection loss of transmitarray. Other losses are composed of fabrication loss, assembly and measurement error.
# Improving RNN Performance by Modelling Informative Missingness with Combined Indicators
## 3 Results
### Padding  
Compared to the simplest strategy (minimal padding), padding the missing days in between measurements (partial padding) more than doubled the median training time per epoch (Table 5), while underperforming the unpadded approach (Table 6). Padding the missing days chronologically (complete padding) took even longer and resulted in inability to determine the test error, presumably due to the resulting large gaps in between observations and poor overlap between different individuals' dates of filling in the survey.  
To assess whether the padded approaches simply plateaued later than the minimal approach, testing accuracy was also assessed beyond the heuristically defined plateau at 2000 epochs. While a slight increase in accuracy after the additional training time was indeed observed, the minimal padding approach equally benefitted from the additional epochs.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & \multicolumn{2}{c}{**10-Fold CV Mean \(\pm\) Std**} & \multicolumn{1}{c}{**Test Performance**} \\ \hline \multirow{2}{*}{F\({}_{1}\)} & **Unscaled** & 0.623 \(\pm\) 0.078 & 0.618 \\  & **Standardized** & 0.780 \(\pm\) 0.039 & **0.781** \\  & **Normalized** & 0.578 \(\pm\) 0.064 & 0.576 \\ \hline \multirow{2}{*}{**AUROC**} & **Unscaled** & 0.613 \(\pm\) 0.084 & 0.601 \\  & **Standardized** & 0.751 \(\pm\) 0.040 & **0.740** \\  & **Normalized** & 0.474 \(\pm\) 0.061 & 0.465 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of scaling methods, using a lagged response variable and a missingness count. The highest test performance is shown in bold.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & \multicolumn{2}{c}{**10-Fold CV Mean \(\pm\) Std**} & \multicolumn{1}{c}{**Test Performance**} \\ \hline \multirow{2}{*}{F\({}_{1}\)} & **FCS** & 0.589 \(\pm\) 0.037 & 0.585 \\  & **FCS, LR** & 0.591 \(\pm\) 0.038 & 0.588 \\  & **I** & 0.679 \(\pm\) 0.064 & 0.676 \\  & **I, LR** & 0.701 \(\pm\) 0.050 & 0.696 \\  & **MC** & 0.728 \(\pm\) 0.037 & 0.723 \\  & **MC, LR** & 0.780 \(\pm\) 0.039 & **0.781** \\ \hline \multirow{2}{*}{**AUROC**} & **FCS** & 0.474 \(\pm\) 0.106 & 0.473 \\  & **FCS, LR** & 0.479 \(\pm\) 0.103 & 0.477 \\  & **I** & 0.649 \(\pm\) 0.069 & 0.645 \\  & **I, LR** & 0.667 \(\pm\) 0.081 & 0.648 \\  & **MC** & 0.728 \(\pm\) 0.079 & 0.717 \\  & **MC, LR** & 0.751 \(\pm\) 0.040 & **0.740** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different imputation strategies. Abbreviations: full conditional specification (FCS), lagged response (LR), indicators (I), missingness count (MC). Our method is the combination MC, LR. The highest test performance is shown in bold.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Minimal** & **Partial** & **Complete** \\ \hline
**Time per epoch (s)** & **35** & 81 & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Seconds per epoch for different padding strategies. The highest performance is shown in bold.
## 5 Discussion  
### Comparison with the Previous Approaches  
To further verify the accuracy of the proposed algorithm, a comparison is conducted with four commonly used spatial clustering algorithms to account for the variations in clustering effects across different algorithms and clusters. The joint DBI is utilized for this purpose, and the comparative results are presented in Figure 9. Notably, when the minimum number of applicants is set to 10, DBSCAN exhibits the highest DBI index. The results demonstrate that the MDBSCAN algorithm surpasses the HC and SC algorithms when the number of clusters exceeds 15 and outperforms the KM algorithm when the number of clusters exceeds 19, except for cases where the number of clusters is missing.  
Figure 8: Identification of development patterns of clusters. (**a**) Beijing; (**b**) Pearl River Delta; (**c**) Yangtze River Delta.  
The DBSCAN algorithm has the disadvantage of a long running time when dealing with large amounts of data, and this needs to be considered when adding computational variables. In relation to this matter, the running times of the MDBSCAN and DBSCAN algorithms are computed individually for varying proprietary data volumes and subsequently compared, as depicted in Figure 10. It is evident that for a data volume of 250,000, the run times of both algorithms are roughly equivalent. However, when the data volume reaches 500,000, there is a notable disparity of 264 s in the running time between the two algorithms. Given that MDBSCAN has 96 additional variables compared to DBSCAN in this specific scenario, the disparity in execution time is comparatively reduced. Furthermore, the investigation of clusters is inherently interconnected with the advancement of the entire industry, emphasizing the primacy of accuracy over computation time. Therefore, when comparing the results of the two metrics, the small amount of time added to the computation process based on the MDBSCAN algorithm is acceptable.  
Figure 10: Comparison of algorithm running time.  
Figure 9: Comparison of clustering effects of different algorithms.
The main problem with heterogeneous ad hoc networks is the diversity of their components. Each node has specific characteristics related to its nature, such as range, speed, routing protocol, etc. Ad hoc signifies without infrastructure, so depending on the node position in the data exchange route, it can play the role of a simple node, router, gateway, or translator between two nodes that do not speak the same language. Thus we can have one or more gateways/translators within the same network, which increases the sending time and creates a risk of losing the link due to the mobility of the nodes. As a result, data routing becomes increasingly complex, less secure, and less efficient.  
As the M3S team (Mobile Smart Security Systems team, in Smart Systems Laboratory, ENSIAS, UM5, Rabat, Morocco), we chose this novel research track by suggesting an original routing protocol which ensures reliable, secure, and optimal direct communication without intermediate nodes. It also respects the advantage of every node characteristic. As a matter of course, each member offers a new idea that affects one or more aspects of the proposed solution:  
* Multipath routing based on classification paths ensuring the exchange of data between source and destination as well as benefiting from the presence of several links [14].
* Reduction of traffic broadcast based on geographic broadcasting rules (GFR), which act on bandwidth, energy consumption, and packet loss [15].
* Security based on the location and insulation of black holes [16].  
In this paper, we present our multipath routing protocol that provides the same communication language to heterogeneous components of an ad hoc network by taking into account the characteristics of each of them (range, position, speed). This protocol has four main functions: node identification, path computation, path classification, and path selection. Based on several studies, we noticed that the OLSR protocol is the base protocol for the mobile ad hoc network \"MANET\", the vehicular ad hoc network \"VANET\", and the flying ad hoc network \"FANET\". Therefore, we chose a specific heterogeneous ad hoc network composed of nodes from these three environments using a dedicated version of the OLSR protocol entitled multipath heterogeneous ad hoc network OLSR \"MHAR-OLSR\". In the latter, we changed the form of the HELLO message and added the protocol's features. Under the NS-3 simulator, we tested the performance of our protocol by comparing it with the OLSR. Additionally, we designed the first three stages of the new version with the colored Petri net to understand their functional and dysfunctional behaviors.  
The rest of the paper is structured as follows: in the next section, an overview of the existing studies about routing within heterogeneous networks is undertaken. In Section 3, we present our new routing protocol concept, and we outline the developed model and sub-models of the new OLSR multi-path version. In Section 4, we propose a case study of the solution in Section 5, after we show the evaluation performances in Section 6, and finally in Section 7, we draw our conclusion and present directions for future work.