esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bacteriophages
### Characteristics of E. coli O157:H7 Phages  
Being a pathogenic strain, _E. coli_ phages can be targeted by various bacteriophages (phages) with different characteristics in terms of their biological, structural, and physicochemical characteristics. A typical phage particle structure consists of a three-dimensional shape made up of an icosahedral protein capsid with or without a tail and a filament [105]. Phage particles can range in size from 24 to 200 nm [106; 107], with the largest being the T4 phage, which infects _E. coli_ and is 200 nm long and 80-100 nm wide [108]. The physical properties of phages specific to _E. coli_ O157 can vary depending on their family. They do, however, often exhibit the conventional phage structure (Figure 2), with an icosahedral head (capsid) carrying the phage's genetic material, either DNA or RNA. The tail, which aids phage adherence to the bacterial surface and the transfer of genetic material into the host cell, varies throughout phage families and can be lengthy, flexible, or short. Certain _E. coli_ O157 bacteriophages have unique tail fibres or tailspikes that aid in targeted binding to receptors on the surface of _E. coli_ O157 cells.  
Figure 2: Diagrammatic representation of tailed phage ([[https://en.m.wikipedia.org/wiki/Bacteriophage](https://en.m.wikipedia.org/wiki/Bacteriophage)]([https://en.m.wikipedia.org/wiki/Bacteriophage](https://en.m.wikipedia.org/wiki/Bacteriophage)), accessed on 1 September 2023).  
The International Committee on Taxonomy of Viruses (ICTV) and the Bacterial and Archaeal Subcommittee (BAVS), primarily dedicated to the study of phages, play pivotal roles in classifying viruses and assigning taxonomic names to virus groups [109]. This classification process relies on evaluating several key characteristics of viruses, including the nature of the viral genome (single-stranded or double-stranded DNA or RNA), the composition of the capsid (the protein shell) and the presence or absence of an envelope, the range of hosts a virus can infect, its pathogenicity, and its genetic sequence similarities. However, in 2022, changes were made to the taxonomy of bacterial viruses, which involved the abolishment of the morphology-based families _Podoviridae_, _Stiboriidae_, and _Myoviridae_, as well as the order _Caudoviridae_, therefore establishing a binomial system of nomenclature for species [110]. All tailed bacterial and archaeal viruses with icosahedral capsids and a double-stranded DNA genome are now grouped under the class \"_Caudoviricetes_\". With 14 new families assigned to 4 orders, the process of grouping is still ongoing; hence, most taxa have remained \"unclassified\" at this level.  
The most common lytic phages, which includes the _E. coli_ O157 phage, associated with human pathogens and the gut microbiota belongs to the class _Caudoviricetes_[111], commonly known as \"tailed phages\", which contain double-stranded DNA genomes. They can belong to several families and orders, which can be confirmed via genetic similarities using online tools for phylogenetic similarities [112].
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 3 Case Studies
### Groundwater Exploration Cases
#### 3.2.2 Groundwater Survey in Huarong County with Geophysical Methods
The pseudo-3D resistivity results show that there is an obvious \"V\"-shaped low-resistivity anomaly here, and that the low-resistivity anomaly is thicker than 200 m. It is speculated that the low resistivity anomaly in the range of this measuring point is mainly the reaction of argillaceous slate. Since there is a resistivity interface in this measuring point, it is speculated that it is the contact surface of argillaceous slate and quartz sandstone formation, and that there are water-bearing fractures in this contact surface. In the later stage, ZK05, ZK01, and ZK02 verification holes are arranged near measuring points No. 120, 160, and 220, respectively (in Figure 11). The verification results show that the slate with high argillaceous content is mainly at the depth of 80 m. Also, when the slate is below 80 m, the sandy composition increases. There is an obvious crushing phenomenon at the contact surface, and the sandy stratum below the contact surface develops fractures. The later pumping test shows that the maximum water output of ZK01 is about 460 tons, that of ZK02 is about 170 tons, and that of ZK05 is about 100 tons. It is speculated that the change in water output is due to the F2 fault. Figure 11: Pseudo\(-\)3D resistivity in Huarong County, Nanshan Township. Figure 10: The pseudo\(-\)3D resistivity inversion results of the L2 survey line. The value of the median line in the figure is the base 10 logarithm of the resistivity (the red lines represent the fault. The pink lines in the segment layer represent the resistivity. The blue lines indicate the locations of boreholes). After the combined profiling method, resistivity sounding method, CSAMT, and the pseudo-3D resistivity method are carried out in the Huarong groundwater survey, it is found that there are relatively obvious fault indications in the survey area. Also, there are two structural damage areas, which are presumed to result from the intersection of the different directional faults, causing rock fragmentation, increased water content, and reduced resistivity. In the macroscopic parts where faults or broken zones pass through, the pseudo-3D resistivity results show that there is a steeper resistivity contact surface, which is presumed to be between argillaceous slate and sandy slate, and that fractures are often found on this contact surface. This provides a good condition for the collection of water and a good guide for the selection of verification holes. The resistivity value of the two lithological contact surfaces reflected by the electrical sounding results is about 400 \(\Omega\) m. The parameters are used to judge the position of the contact surface, and the contact surface can often form a relatively obvious broken layer. The best practice is to try to avoid areas where the resistivity is significantly lower or higher and the thickness is larger.
## 5 Conclusions  
Cell proliferation inhibition and apoptosis induction by CHSP in ovarian cancer cells were investigated in this study. The IC50 of inhibition on cell proliferation of OVCAR-3 cells was found to be \(10.33\pm 0.166\)\(\mathrm{\SIUnitSymbolMicro g}\)/mL. Flow cytometry results showed that the apoptosis rate was significantly increased after CHSP treatment. A 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL treatment of CHSP induced ovarian cancer cell apoptosis of 44.21%. Western blot analysis showed that CHSP induced apoptosis of ovarian cancer cells through a p53-dependent intrinsic pathway. In this research, the anti-proliferation of ovarian cancer cells by CHSP was also detected. Compared with control values, levels of VEGF excreted by OVCAR-3 cells were inhibited to 7.87% at 40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of CHSP. Consistent with our previous reports, CHSP inhibited VEGF secretion by regulating the HIF-1\(\mathrm{\SIUnitSymbolMicro g}\)-VEGF pathway. In addition, we also found that the inhibitory effect of CHSP on ovarian cancer was related to the up-regulation of PTEN and down-regulation of NF-kappa B.  
Conceptualization, Y.C.C.; methodology, J.L. and Y.C.C.; formal analysis, J.L. and Z.H.; resources, Y.C.C., I.M., S.W. and G.O.R.; writing--original draft preparation, Z.H.; writing--review and editing, Y.C.C., G.O.R. and A.B.; funding acquisition, Z.H. and Y.C.C.; All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by NIH grants P20 RR016477 from the National Center for Research Resources and P20 GM103434 from the National Institute for General Medical Sciences (NIGMS) awarded to the West Virginia IDeA Network of Biomedical Research Excellence. This research was funded by the key research and development program of Zhejiang Province (No. 2020C02040; 2020C02019), the cooperation projects between the People's Government of Zhejiang Province and the Chinese Academy of Forestry (2019 SY03), and the Public Projects of Zhejiang Province (LGC19C200008). This research was supported by Grant Number P20 GM104932 from NIGMS, a component of the National Institutes of Health (NIH) and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIGMS or NIH. This study was also supported by COBRE grant GM102488/RR032138, ARIA S10 grant RR020866, and FORTESSA S10 grant OD016165.  
We would like to acknowledge the technical help from [PERSON], Flow Cytometry Core at West Virginia University with the apoptosis analysis.  
The authors declare no conflict of interest.
## 4 Hardware-in-the-Loop Real-Time Simulation
### Comparison with the Extended Ackermann Steering  
Figure 8 shows the lateral deviation, hinge force, and width of the turning passageway under different control strategies. The test line is a circular curve with a radius of R50 m. The speed of the SRT is 5 m/s. The widely used extended Ackermann steering strategy was selected for comparison [24; 32]. It is easy to find that the control strategy proposed in this paper has higher path-tracking accuracy, lower hinge force, and smaller sweep width. Among them, the lateral deviation does not exceed 0.093 m, which is only 26% of the extended Ackermann steering. The maximum hinge force does not exceed 3368 N, which is 29% lower. The sweep width is 2.86 m, an improvement of 20.6%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Title** & & **Value** & **Unit** \\ \hline Time step & & 0.01 & & s \\ Predict horizon & & 10 & & - \\ Control horizon & & 10 & & - \\ \(Q_{Y}\) & \(diag(\left[10^{7}\right.\) \(10^{7}\) \(10^{7}\) \(10^{8}\) \(10^{8}\)\()\) & & - \\ \(Q_{F_{c}}\) & \(diag(\left[10^{-7}\right.\) \(10^{-7}\) \(10^{-6}\) \(10^{-6}\) \(10^{-6}\) \(10^{-6}\) \(10^{-6}\) \(10^{-6}\) \(10^{-6}\)\()\) & - \\ \(Q_{F_{h}}\) & \(diag(\left[2.5\times 10^{7}\) \(2.5\times 10^{7}\) \(2.5\times 10^{7}\)\()\) & & - \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the MPC algorithm.  
Figure 7: HIL real-time simulation platform.
## 3 Methodology
### Methodology and Autoencoder Formulation
#### 3.3.3 Autoencoder  
The proposed model consists of eight layers. Each comprises convolutional2D layers, MaxPooling layers, Batch Normalisation layers and more. Our autoencoder includes the encoder, the decoder and the bottleneck. The encoder consists of the first 4 big layers and the decoder of the rest 4. The immense layers in the network are the input and output layers located at the beginning and end of the network, respectively. The input of our model are the patches that we cropped in the preprocessing steps, so the input has (48,48,1) dimensions. As for the output, it has the exact dimensions as the input (definition of autoencoder). Also the second layer consists of 3 levels. The first level has a Convolution layer with a number of filters equals to 8 and a LeakyReLU layer, the second level has a convolutional layer which has 32 filters, a LeakyReLU and also a Batch Normalization layer,and the third level has a convolutional layer with 32 filters and parameter strides = (2,2) which act like a MaxPooling layer, a LeakyReLU and a Batch Normalisation layer. The compression our patches have so far is from their original to 24 \(\times\) 24. Then the second layer consists of 3 levels. The first level has a convolutional layer with a number of filters equal to 256 and a LeakyReLU layer, the second level has the previous two layers again, but with the addition of a Dropout layer, and finally, the third level is a MaxPooling layer with size (2,2), which means it compresses our patches to 12 \(\times\) 12.  
The third layer has two levels: a convolutional layer with 512 filters, a LeakyReLU layer, and a Dropout layer. Then, in the second level of our third layer, we use a MaxPooling layer for further compression, and now our patches have the most significant compression sized as 6 \(\times\) 6. Then, as we mentioned before, the decoding part of an autoencoder reconstructs the data and, more importantly, has the same layers as the encoder but mirrored. For example, the fourth layer has the same layers as the third, but we replace the MaxPooling layers with the Upsampling layers for the reconstruction. Also, it is important to mention that we replace all the Convolutional layers except the last one with the Conv2 DTranspose layer in the decoding part. The architecture of our proposed method is presented in following in Figure 5.  
Our database's final size, the patches' dimensions, the number of epochs our model will be trained, and the batch size will be chosen after experimentation. We chose to crop 200.000 patches randomly from the original fundus images since we did not see any further improvement of our model in the training phase. Then, the most efficient combination of the parameters above is: patch size = (48, 48, 1) (the third dimension is one because our patches are greyscaled), number of epochs = 4 and batch size = 8.
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
###### Abstract  
For energy conservation, nonlinear-optimal-control-law design for marine surface vessels has become a crucial ocean technology for the current ship industry. A well-controlled marine surface vessel with optimal properties must possess accurate tracking capability for accomplishing sailing missions. To achieve this design target, a closed-form nonlinear optimal control law for the trajectory- and waypoint-tracking problem of autonomous marine surface vessels (AUSVs) is presented in this investigation. The proposed approach, based on the optimal control concept, can be effectively applied to generate control commands on marine surface vessels operating in sailing scenarios where ocean environmental disturbances are random and unpredictable. In general, it is difficult to directly obtain a closed-form solution from this optimal tracking problem. Fortunately, by having the adequate choice of state-variable transformation, the nonlinear optimal tracking problem of autonomous marine surface vessels can be converted into a solvable nonlinear time-varying differential equation. The solved closed-form solution can also be acquired with an easy-to-implement control structure for energy-saving purposes.  
nonlinear control law; tracking design; closed-form solution; optimal performance index. 20202010 10.1686; doi:10.3390/app10051686
## 2 Experiments  
### Modeling Tools  
#### 2.1.1 The Chemistry Transport Model CHIMERE  
The CHIMERE off-line chemistry transport model [46; 48] was used to model the transport and transformation of trace species in the lower troposphere. Several international teams for research and environmental policy underpinning are using the model. It has been involved in several model inter-comparison initiatives [49; 50; 51; 52; 53; 54] and is now a part of the COPERNICUS services (Available online: [[http://www.copernicus.eu](http://www.copernicus.eu)]([http://www.copernicus.eu](http://www.copernicus.eu))). The model and further documentation can be found at [[http://www.lmd.polytechnique.fr/chimere/](http://www.lmd.polytechnique.fr/chimere/)]([http://www.lmd.polytechnique.fr/chimere/](http://www.lmd.polytechnique.fr/chimere/)). Simulations at very high resolution have been performed over domains encompassing the full European domain [29; 55] with resolutions up to 2 km thanks to refined emission inventories using adequate proxies for emission regridding. Besides the technical challenge, the authors found that model biases were significantly reduced, especially over urban areas. The high-resolution grid also allows reevaluating the contribution of individual city plumes to the European burden of pollution, providing new insights to target the appropriate geographical level of action when designing air pollution mitigation strategies. The CHIMERE 2017 version [46] has been used in this study. Several improvements to work at the hemispheric level on the parallelization, numeric schemes, deposition processes, mineral dust emissions (described in next section), chemistry and physics of the aerosols (condensation and coagulation processes) have been performed. Particularly, the notion of wet diameter and wet density was introduced in CHIMERE-2017. Particles are distributed between bins according to their dry diameter. The wet diameter of the particles is calculated as a function of humidity and the composition of the particle. To compute the wet density and wet diameter for each aerosol size bin, the amount of water in each bins is computed with the \"reverse mode\" of the thermodynamic module ISORROPIA [56] by using the composition of particles, assuming that only sulfate, nitrate, ammonium and sea salts have a high enough hygroscopicity to absorb a significant amount of water. The density of the aqueous phase of particles is computed according to composition following the method in [57].
## 2 Background and Core Technologies
### Mobile Agents  
A mobile agent is a software program or script that can autonomously move through and interact with computing environments. Unlike traditional programs that require continuous execution on a specific host, mobile agents can migrate from one system to another within a network to perform tasks. Mobile agents have key characteristics [18].  
Their autonomy allows them to make decisions and execute tasks without continuous supervision. In addition, communication amid computing nodes within a network allows for collaborative responses.  
* Adaptability: They are flexible, capable of operating in several backdrops and achieving a variety of missions;
* They communicate with other agents, services, or users to exchange information or coordinate activities;
* To prevent unauthorized access or tampering, secure communication and execution are incorporated.  
Mobile agents can be deployed across various domains including network operations, distributed computing, e-commerce, and resource allocation. These autonomous software entities possess the capability to navigate distributed environments independently, executing complex tasks while optimizing system efficiency. Their distributed nature enables efficient load balancing, reduced network traffic, and enhanced system flexibility through asynchronous execution and local processing capabilities [19; 20; 21]. Figure 2 shows the versatility of mobile agents in a distributed system, showcasing their generic functionality in various contexts like task execution, secure communication, and collaboration across nodes.  
Figure 1: Geographic Information System.
## 6 Challenges and Future Research Directions
### Challenges
#### 6.1.1 Power Splitting (PS) Efficiency  
One of the foremost challenges in implementing SWIPT is optimizing the power splitting (PS) technique. In practical scenarios, the efficiency of PS can be severely impacted by signal degradation due to fading and other wireless channel impairments. This can lead to substantial power loss, especially as the number of users in the system increases. As a result, maintaining a balance between harvested energy and information decoding rates becomes increasingly complex, particularly in dense networks with multiple users.  
Figure 13: SE variation against a number of BS antennas.
## 3 Performance Evaluation
#### Slope Analysis  
The high SNR slope is used to display the effective capacity of wireless communication, which can be expressed as  
\[S=\lim_{\rho\rightarrow\infty}\frac{C_{m}^{\infty}(\rho)}{\log(\rho)}, \tag{19}\]  
where \(C_{m}^{\infty}(\rho)\) is the asymptotic effective capacity in the high SNR region. To carry out more adequate research, the expression of the effective capacity of the \(m\) user is further derived from (8)  
\[C_{e}^{m}=-\frac{1}{\theta_{m}n}\ln\Bigl{\{}\mathrm{E}\Bigl{[}\varepsilon+(1- \varepsilon)(1+\gamma_{m})^{-\frac{\theta_{m}n}{\ln 2}}\varepsilon^{\theta_{m} \sqrt{n}\sqrt{\gamma_{m}}Q^{-1}(\varepsilon)}\Bigr{]}\Bigr{\}}. \tag{20}\]  
When \(\rho\rightarrow\infty\), \(\gamma_{m}\) and \(V_{m}\) can be expressed as follows:  
\[\gamma_{m}=\frac{\rho|h_{m}|^{2}a_{m}}{\rho|h_{m}|^{2}\bar{a}_{m}+1}\to \frac{a_{m}}{\bar{a}_{m}}, \tag{21}\]  
\[V_{m}\rightarrow\left[1-\left(1+\frac{a_{m}}{\bar{a}_{m}}\right)^{-2}\right] \left(\frac{1}{\ln 2}\right)^{2}. \tag{22}\]  
Hence, substituting (21) and (22) into (20), the asymptotic effective capacity of the \(m\)-th user is calculated as follows:  
\[\begin{split} C_{m,e}^{\infty}&=-\frac{1}{\theta_{m }n}\ln\Biggl{\{}\mathrm{E}\Biggl{[}\varepsilon+(1-\varepsilon)\Bigl{(}1+\frac{ a_{m}}{\bar{a}_{m}}\Bigr{)}^{-\frac{\theta_{m}n}{\ln 2}}\varepsilon^{\sqrt{\left[1-\left(1+ \frac{\theta_{m}}{\bar{a}_{m}}\right)^{-2}\right]\left(\frac{1}{\ln 2}\right)^{2} }\theta_{m}\sqrt{n}Q^{-1}(\varepsilon)}\Biggr{]}\Biggr{\}}\\ &=-\frac{1}{\theta_{m}n}\ln\Biggl{\{}\varepsilon+(1-\varepsilon )\Bigl{(}1+\frac{a_{m}}{\bar{a}_{m}}\Bigr{)}^{-\frac{\theta_{m}n}{\ln 2}} \varepsilon^{\theta_{m}\sqrt{\left[1-\left(1+\frac{\theta_{m}}{\bar{a}_{m}} \right)^{-2}\right]\left(\frac{1}{\ln 2}\right)^{2}}}\Biggr{\}}.\end{split} \tag{23}\]  
**Remark 1**.: _Upon substituting (23) into (19), the high SNR slope of the \(m\)-th user is zero._  
Similarly, the expression of the effective capacity of the \(M\) user is further derived from (7)  
\[C_{e}^{M}=-\frac{1}{\theta_{M}n}\ln\Biggl{\{}\mathrm{E}\Bigl{[}\varepsilon+(1- \varepsilon)(1+\gamma_{M})^{-\frac{\theta_{M}n}{\ln 2}}\varepsilon^{\theta_{M} \sqrt{n}\sqrt{\gamma_{M}}Q^{-1}(\varepsilon)}\Bigr{]}\Biggr{\}}. \tag{24}\]  
When \(\rho\rightarrow\infty\), \(V_{m}\) can be expressed as follows:  
\[V_{M}=\left[1-\left(1+\rho|h_{M}|^{2}a_{M}\right)^{-2}\right]\left(\frac{1}{ \ln 2}\right)^{2}\rightarrow\left(\frac{1}{\ln 2}\right)^{2}. \tag{25}\]  
Hence, substituting (3) and (25) into (24), the asymptotic effective capacity of the \(M\)-th user at a high SNR region is expressed by  
\[\begin{split} C_{M,e}^{\infty}&=-\frac{1}{\theta_{M }n}\ln\Biggl{\{}\mathrm{E}\Biggl{[}\varepsilon+(1-\varepsilon)\Bigl{(}\frac{ \frac{1}{\varepsilon}}{\frac{1}{\varepsilon}+|h_{M}|^{2}a_{M}}\Bigr{)}^{\frac {\theta_{M}n}{\ln 2}}\varepsilon^{\theta_{M}\sqrt{\gamma_{M}}}\Biggr{]}\Biggr{\}}\\ &=-\frac{1}{\theta_{M}n}\ln\{\varepsilon\}.\end{split} \tag{26}\]  
**Remark 2**.: _Upon substituting (26) into (19), the high SNR slope of the \(M\)-th user is zero._