esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Construction of Plasmids Containing Single (Cry1 Ac) Gene Constructs  
The presence of the Cry1 Ac-NOS gene, CLCuKoV-BuC1 and CLCuKoV-BuV1 promoter in the pCAMBIA-1301 were confirmed by PCR using Cry1 Ac internal primers and primers designed to amplify the specified orientation of the cloned inserts (Table 1) and by restriction digestion, as described. Ultimately, two plasmids were obtained successfully by cloning the Cry1 Ac-NOS gene cassette in the binary pCAMBIA1301 vector. The final gene constructs were referred to as CLCuKoV-BuC1-Ac consisting of 5\({}^{\prime}\)-CLCuKoV-BuC1-Cry1 Ac-NOS-3\({}^{\prime}\) and CLCuKoV-BuV1-Ac consisting of 5\({}^{\prime}\)-CLCuKoV-BuC1-Cry1 Ac-NOS-3\({}^{\prime}\) as illustrated in (Figure 1). Both single gene constructs were shown to be transferred into _A. tumefaciens_ GV1301 competent cells, used for _Agrobacteriumum_-mediated transient plant transformation, and stable transformation in cotton was obtained. The PK2-Ac consisting of a CaMV35S-Cry1 Ac-NOS cassette (2476 bp) harboring the CaMV35S promoter that is most commonly used for plant transformation, was also stably transformed into cotton embryos.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Constructs**} & **No. of** & **Survival \%** & **Survival \%** & **Plants in** & **Transformation** \\  & **Embryos** & **(3–4 Week)** & **(7–8 Week)** & **Soil** & **Efficiency (\%)** \\ \hline CLCuV-Ac & 3000 & 375 & 190 & 14 & 0.46 \\ \hline PK2-Ac & 2000 & 125 & 70 & 7 & 0.35 \\ \hline \end{tabular}
\end{table}
Table 3: Transformation efficiency of constructs in cotton variety MNH-786.  
Figure 1: **(A)** Diagram showing the genome organization of CLCuKoV-Bu. The five begomoviral ORFs are indicated by colored arrows. The viral plus strand encodes the V1 and V2 ORFs while the complementary strand encodes three ORFs, C1, C3 and C4. The viral coding regions are separated by a non-coding large intergenic region (LIR) of 455 nucleotides in size. (**B**) Diagram of the LIR shows the predicted regulatory motifs, cis-regulatory elements, transcription factor binding sites, and iteron elements involved in binding the replication-associated protein. (**C**) Map of the Cry1 Ac gene expression cassettes under control of the CLCuKoV-BuC1 and CLCuKoV-BuV1 promoters. The Cry1 Ac+ NOS was cloned at the restrict site Kpn1-BamH1 in the pCAMBIA1301 plasmid vector.
## 5 Kramers-Kronig Detection Scheme
### Related Impact on the KK Scheme
#### 5.2.2 Laser-Related Effects  
Additionally, it has been demonstrated that the KK implementation is sensitive to laser detuning from the center of the channel [122]. Moreover, it has been demonstrated experimentally that in spite of the SCOH direct detection nature, laser phase noise has a severe impact on the KK transmission performance, owing to its interactions with both fiber chromatic dispersion and a strong optical carrier. In that work, the system performance with distributed feedback and fiber lasers was compared at 11 dB CSPR. The presented results demonstrated a considerable performance gain for fiber laser employment. The gain was due to the low linewidth and frequency stability of the fiber laser. This resulted in a lower performance penalty by the interaction of dispersion and phase noise during propagation [123]. This implies that the employment of a low-quality laser with a high linewidth can bring about substantial degradation in the transmission performance. In [124], by considering the equalization-enhanced phase noise (EEPN) and phase-to-amplitude noise conversion, the impact of laser phase noise on direct detection transmission with the KK scheme was analyzed.
## 2 Direct \(\alpha\)-Halogenation of Aryl Ketones
### Nucleophilic Halogenation  
The nucleophilic halogenation approach to generate \(\alpha\)-halogenated ketones is much less attractive than its electrophilic counterpart, mainly due to the need for an intermediary step to install a leaving group on the carbon atom of the enolate. This in turn generates unnecessary waste. Nonetheless, in this section, we would like to mention a few reports to better clarify this approach.  
Figure 14: Alternative halogenation agents.  
A clear example of this strategy can be found in the report by [PERSON] and co-workers describing the solvent-free MgX\({}_{2}\) and HTIB-based halogenation (Scheme 49) [134]. The generality of this route was demonstrated by the bromination, chlorination and iodination of a set of aryl ketones under the same reaction conditions. Phenylethanolamine derivatives were brominated, chlorinated, and iodinated in good to excellent yields (82-91%, 80-90% and 65-75%, respectively). The procedure was thus very effective for both the bromination and chlorination; however, a slight decrease in efficiency was observed for the iodination. This route tolerated the presence of both electron-withdrawing (i.e., Br, NO\({}_{2}\)) and -donating groups (i.e., OMe) on the substrates. The reaction is performed in two steps; the first one using HTIB installs an OT leaving group on the \(\alpha\)-carbon atom, and the following step consists of Br\({}^{-}\) nucleophilic substitution on the same carbon to afford the corresponding \(\alpha\)-halogenated ketone.  
In an attempt to circumvent the need for stoichiometric amounts of a leaving group, [PERSON] and co-workers designed and reported a dioxidevanadium(V)-catalysed bromination (Scheme 50) [135]. Five ketones were brominated in good to excellent yields. Remarkably, the procedure tolerated acid-sensitive substituents (i.e., OH) on the aromatic ring. However, large amounts of H\({}_{2}\)O\({}_{2}\) as oxidant were needed. The authors gave a plausible mechanism, involving the initial formation of vanadium C-enolate via C-H activation of the ketone, followed by a Br\({}^{-}\) nucleophilic attack on the same carbon, which liberates the vanadium complex and generates the \(\alpha\)-bromoketone, thus closing the catalytic cycle. However, in our opinion, an electrophilic halogenation mechanism could not be excluded, since the protocol operates under harsh oxidative conditions; H\({}_{2}\)O\({}_{2}\) or even the vanadium catalyst can readily oxidise Br\({}^{-}\) into Br\({}_{2}\) (or HOBr after reaction with water), which would then be capable of reacting directly with the ketone.
## 3 Methodology
### LiDAR-Camera Processing Pipeline
#### 3.1.1 Transparent Sparse Depth Image Generation  
The sparse depth map is generated by converting the 3D LiDAR point cloud into a 2D depth image, with the depth of each point consisting of pixel color values. Our transparent Sparse Depth Image process creates a map image in which depth information is present in each pixel that corresponds to a LiDAR point, while other pixels remain transparent. The core idea is to convert LiDAR data into a transparent Sparse Depth Image enabling its seamless integration with the image captured by the camera. To project the LiDAR points into the 2D image plane, it is used the intrinsic and extrinsic parameters of both sensors in the calibration file. The intrinsic parameters have the camera's focal length and main point. The extrinsic parameters have the relative position and orientation between the LiDAR sensor and the camera. These parameters are utilized to compute and map the 3D points from the LiDAR into the 2D coordinate system of the camera.  
\[\begin{bmatrix}u\\ v\end{bmatrix}=\mathbf{R}_{\text{rect}}^{(0)}*\begin{bmatrix}\mathbf{R}\\ \mathbf{R}\end{bmatrix}*\begin{bmatrix}\mathbf{T}\end{bmatrix}*\begin{bmatrix}X &Y&Z&1\end{bmatrix}^{\top} \tag{1}\]  
Equation (1) shows the projection matrix between a 3D LiDAR point and its corresponding 2D pixel coordinate in the image. Here, (**u, v**) represent the pixel coordinates, while \(\mathbf{R}_{\text{rect}}\) is a rectification matrix. [**R, T**] represent the rotation matrix and translation vector that align the coordinate systems of the LiDAR and camera. Finally, [**x, y, z, i**] denote the coordinates of points in 3D LiDAR data [26].
# Elastic Reverse Time Migration for Weakly Illuminated Structure
## 2 Methodology
### Inner Product Imaging Condition with Filters  
Imaging condition can convert seismic data into structure images through the focus of the source and receiver wavefield. For the ERTM, the inner product imaging condition has an advantage in terms of definite physical meanings. However, applying such imaging condition inevitably results in migration artifacts because of backscatter. To suppress the unwanted seismic events in the migration profile, we add filters to the imaging conditions. The final images can be expressed as:  
\[\widetilde{I^{PP}}=\sum_{t=1}^{T}\left(F_{S_{0}}^{P}\widetilde{V_{S_{0}}^{P}} \right)\left(F_{R_{e}}^{P}\widetilde{V_{R_{e}}^{P}}\right) \tag{12}\]  
\[\widetilde{I^{PS}}=\sum_{t=1}^{T}\left(F_{S_{0}}^{P}\widetilde{V_{S_{0}}^{P}} \right)\cdot\left(F_{R_{e}}^{S}\widetilde{V_{R_{e}}^{S}}\right) \tag{13}\]  
where \(\widetilde{I^{PP}}\) and \(\widetilde{I^{PS}}\) are PP and PS images, the symbol \(\cdot\) denotes the inner product operation. \(\widetilde{\mathbf{V}}\) with the subscripts \(So\) and \(Re\) denotes the stained wavefields of seismic sources and receivers. The superscripts \(P\) and \(S\) represent P- and S-wave, respectively. \(T\) is the maximum time of seismic records. \(F_{So}^{P}\) is a filter with the mathematical form shown below:  
\[F_{So}^{P}=\left\{\begin{array}{cc}1,&\widetilde{\mathbf{D}_{So}^{P}}\mathbf{n}_{S_ {0}}^{P}>0\\ 0,&\widetilde{\mathbf{D}_{So}^{P}}\mathbf{n}_{S_{0}}^{P}<0\end{array}\right. \tag{14}\]  
where \(\widetilde{\mathbf{D}_{So}^{P}}=\left(\widetilde{d_{x}^{P}}\widetilde{d_{z}^{P}}\right)\) is the Poynting vector, \(\mathbf{n}_{S_{0}}^{P}\) is the direction vector. If the result of the inner product between \(\widetilde{\mathbf{D}_{So}^{P}}\) and \(\mathbf{n}_{S_{0}}^{P}\) is positive (negative), then the wavefield is retained (muted). Other filters have a similar expression. The Poynting vector can be calculated by:  
\[\left\{\begin{array}{c}\widetilde{d_{x}^{P}}=-\widetilde{I^{P}}\widetilde{v _{a}^{P}}\\ \widetilde{d_{z}^{S}}=-\left[\widetilde{\tau_{ab}}-\widetilde{\tau^{P}} \delta(a-b)\right]\widetilde{v_{b}^{S}}\end{array}\right. \tag{15}\]  
where \(a\) and \(b\) are the \(x\)- or \(z\)-component of the Poynting vectors, and \(\delta\) is the Dirac function. From formula (15), the Poynting vector is 0 if either particle-velocity and stress is 0. There is a position between the crest and the trough where the wavefield is 0, but the Poynting vector at that position should not be 0. To improve the stability of equation (15), the local least squares strategy [25] is applied.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Power Distribution
#### 2.2.2 Microgrid Types  
The diverse array of microgrid configurations, encompassing DC, AC, and hybrid systems within NMGs, significantly influences power flow analysis. In DC microgrids, the study involves the calculation of electrical power distribution primarily operating on direct current, allowing bidirectional power flow and integration of distributed energy resources, and energy storage systems. Voltage regulation is crucial for stability and efficiency in these systems. The absence of reactive power and the focus on controlling voltage levels distinguishes power flow analysis in DC microgrids from traditional AC power systems, presenting unique challenges and opportunities for innovative PFA strategies such as those proposed in [74; 75; 76].  
Power flow analysis in AC microgrids involves the assessment and calculation of the steady-state distribution of electrical power within the network. This analysis focuses on maintaining voltage and frequency parameters within acceptable limits, ensuring the stability and reliability of the microgrid. Traditional power flow methods, such as the NR algorithms, are commonly employed to solve the system's nonlinear equations and determine the flow of active and reactive power. Challenges in AC microgrid power flow arise from factors such as varying loads, intermittent renewable energy sources, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PF Techniques** & **Ref.** \\ \hline AC PF & [33; 34; 51; 53; 55; 58; 59; 60; 64] \\ Linear DistFlow & [31; 39; 40; 41; 63; 66] \\ NR & [8; 9; 10; 16; 20; 57] \\ BFS & [27; 23; 24; 23] \\ Kirchhoff’s law & [19; 25; 30; 56] \\ Gauss-Seidel & [26] \\ \hline \hline \end{tabular}
\end{table}
Table 2: PF techniques employed for configuration of NMGs.  
the presence of synchronous generators. The researchers proposed various power flow calculation methods, as seen in [77; 78; 79; 80; 81; 82], to enhance accuracy and efficiency, especially in AC microgrids  
Future power systems are envisioned as NMGs in which many hybrid microgrids, including AC and DC microgrids, are interconnected to exchange power in a controlled manner. Power flow analysis in hybrid microgrids involves the examination of electrical power distribution within a network that integrates both AC and DC components. Hybrid microgrids combine diverse energy sources and storage systems, presenting unique challenges in interoperability and coordinated control. Efficient power flow analysis in these systems requires sophisticated modeling techniques to address the complexities arising from the integration of different grid types. Researchers proposed various PFA methods, discussed in [83; 84; 85; 86], to analyze power flow within hybrid microgrids.
## 2 Methods and Hardware for Application of subTHz Instruments
### Receivers, Antennas, and the Atmosphere as Critical Factors of subTHz Systems
For example, the 3 mm transparency window is limited at the edges by two lines of oxygen absorption, mainly transparent to optical and longer radio waves. Moreover, the satellite data based on relatively wide beams (up to tens of kilometers) do not consider the natural ruggedness of mountainous surfaces. These data are suitable for the case of flat plateaus like Pamir and Tibet [34] and have no significance for rugged areas with very high surface RMC. In the analysis of the Suffa Plateau in the search for promising sites for the EHT network expansion [28; 35], this error was very well demonstrated. The integral altitude in [3] was averaged at 2000 m, which is significantly lower than the natural (2400 m) elevation of the RT-70 telescope being under construction now. The resulting analysis shows that the atmospheric absorption is higher than expected. At the same time, both theory and experiments show a significant decrease in atmospheric absorption with growing altitude. The satellite data [3] estimate the water contribution from the surface based on \(\sim\)1 square degree assessed as the integrated result of the peaks and ravines, which is like making the rugged surface flat. However, for rough surfaces, more detailed information is required. For example, at the Suffa site, the difference in altitudes inside the square degree was more than 700 m. For a reasonable selection of sites for subTHz instruments, long-term direct monitoring of the atmosphere at the base site, and direct measurements of the wavelengths at which the designed tools are supposed to operate, are required. There is a particular interest in the research performed on the Suffa Plateau and in the North Caucasus due to the projects to deploy two VLBI (very large base interferometer) antennas of the subTHz frequency range under the international Suffa project [11]. Within the Suffa project, as usually is the case, some space debris radar surveillance is to be completed along with astronomical observations. In addition, such large antennas, along with astronomical research, are generally used for distant space communications [4]. A typical profile of atmospheric absorption in the subTHz range is shown in Figure 4. Certain implementations of astronomy, communication, and radar applications in the subTHz range are available in atmospheric transparency windows only. Figure 4 is not presenting rainfall and snowfall effects. The reason is that the places chosen for the subTHz antenna are very dry without essential rains and thick clouds, and occasionally (a few percent of the time), when there is precipitation and thick clouds, no work is in progress. Figure 3: Block diagram and photo of MIAP-2, a sub-THz radiometric system for atmospheric measurements [31].
## 3 Results
### Perceived Soundscape Attributes and Emotional Responses
#### 3.3.3 Adjusting Human Emotional Responses with Soundscape Indicators  
The three types mapped in emotional coordinates directly present the relationship between soundscape perception attributes and human emotions. With the help of the correlation and cross effect analyses, they indicate how soundscape perception attributes can be adjusted to stimulate pleasure and arousal.  
Type A and C settings are more inclined to promote feelings of pleasure but facilitate relatively weak arousal of affect in environmental users. To further encourage people's happiness in type A settings, aesthetics, order and layering of the visual landscape and eventfulness and appropriateness of the aural landscape should be slightly improved. If  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Type A** & **Type B** & **Type C** \\ \hline \multirow{2}{*}{Pleasant} & Aesthetic, layering, order & Openness & Aesthetic, order \\  & Eventfulness, appropriateness & Calm, eventfulness, appropriateness & Vibrancy, eventfulness, appropriateness \\ \hline \multirow{2}{*}{Arousal} & Order & Layering & / \\  & Calm, appropriateness & Calm, appropriateness & Calm, appropriateness \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlational analysis between soundscape attributes and human emotions.  
Figure 5: Human emotional responses in different environmental types.  
people need to be aroused in this type of setting, visual order and calm and harmonious soundscapes should be greatly enhanced. Slight improvements on visual aesthetic and order, as well as to the environmental soundscape to make it more vibrant, eventful and harmonious, can help to further increase people's perceived pleasure in type C settings, while stronger interventions are required to provide users with a calm and harmonious acoustic environment for promoting arousal.  
Type B settings have the advantage of promoting affective arousal but have slightly weak effects on promoting happiness. Particular attention should be paid to introducing a layered visual landscape and calm and harmonious soundscape if people expect to be more aroused in this type of setting. These settings can also be endowed with the ability to facilitate happiness by improving their visual openness as well as aural calm, eventfulness and appropriateness.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Mission Planning System (MPS)  
MPS plays a vital role in the AIRDROP platform by processing missions initiated through CCWA or FRMA. It effectively utilizes information from the mission-relevant components to compute highly detailed flight plans, subject to approval by the CCWA-operator. Subsequently, the MPS verifies de-conflicted trajectories and transmits them to the ground control station (GCS). The three key subevices of MPS include routing for a set of locations, trajectory de-confliction, and autonomous landing. Notably, MPS prioritizes drone locations based on specific needs and available resources, effectively preventing conflicts in 4D trajectories (where each point represents UAV presence at a specific 3D location and time). Similarly, this de-confliction process applies to UGVs and USVs. Moreover, MPS ensures safety by enforcing geo-fencing restrictions. Furthermore, it empowers UAVs to perform precise landings on designated targets through the interpretation of visual cues using ML algorithms. Finally, MPS enables emergency autonomous flight and the removal of geo-fence restrictions when necessary.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
###### Abstract  
Single-pixel imaging (SPI) is a significant branch of computational imaging. Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. Nevertheless, multiple measurements and long reconstruction time constrain its application. The application of neural networks has significantly improved the quality of reconstruction, but there is still a huge space for improvement in performance. SAE and Unet have different advantages in the field of SPI. However, there is no method that combines the advantages of these two networks for SPI reconstruction. Therefore, we propose the EMNP-SPI method for SPI reconstruction using SAE and Unet networks. The SAE makes use of the measurement dimension information and uses the group inverse to obtain the decoding matrix to enhance its generalization. The Unet uses different size convolution kernels and attention mechanisms to enhance feature extraction capabilities. Simulations and experiments confirm that our proposed enhanced multi-network prior method can significantly improve the quality of image reconstruction at low measurement rates.  
Keywords:single-pixel imaging; multi-network prior; SAE; Unet +
Footnote †: journal: applied sciences