esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 4 Comparison of Selected AI Models
### Results on the CARLA Platform
This comparative analysis reveals that models like ReasonNet and LAV on CARLA Leaderboard 1.0 demonstrate a high overall performance and route completion efficiency, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Driving Score** & **Route Completion** & **Infraction Penalty** & \multirow{2}{*}{**Year**} & **Ref.** \\ \cline{1-1} \cline{5-6}  & **Score** & **Complete** & & & \\ \hline ReasonNet & 79.95 & 89.89 & 0.89 & 2022 & [155] \\ InterFuser & 76.18 & 88.23 & 0.84 & 2022 & [156] \\ TGCP & 75.14 & 85.63 & 0.87 & 2022 & [157] \\ LAV & 61.84 & 94.46 & 0.64 & 2022 & [158] \\ TransFuser & 61.18 & 86.70 & 0.71 & 2022 & [159] \\ TransFuser & 55.04 & 89.65 & 0.63 & 2022 & [159] \\ (Reproduced) & 47.91 & 65.73 & 0.77 & 2023 & [157] \\ Latent TransFuser & 45.20 & 66.31 & 0.72 & 2022 & [159] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of leading deep neural models on the CARLA Leaderboard 1.0 task. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Driving Score** & **Route Completion** & **Infraction Score** & \multirow{2}{*}{**Year**} & **Ref.** \\ \cline{1-1} \cline{5-6}  & **Score** & **Completion** & & & \\ \hline CarLLaVA & 6.87 & 18.08 & 0.42 & 2024 & [160] \\ CarLLaVA (Map Track) & 6.25 & 18.89 & 0.39 & 2024 & [160] \\ TF++ (Map Track) & 5.56 & 11.82 & 0.47 & 2024 & [161] \\ TF++ & 5.18 & 11.34 & 0.48 & 2024 & [161] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of leading deep neural models on the CARLA Leaderboard 2.0 task. respectively. For CARLA Leaderboard 2.0, CarLLaVA and its Map Track variation stand out for their advanced route completion and low infraction rates. Advances such as the Interpretable Sensor Fusion Transformer, TCP framework, and initiatives addressing hidden biases highlight the ongoing efforts to enhance the robustness and reliability of autonomous driving systems. These models and methodologies collectively contribute to the progress in achieving more efficient and safer autonomous driving solutions.
## 7 Concluding Remarks  
Since the first description of the _phl_ BCG by [PERSON] and [PERSON] in 1999, great progress has been made in our understanding of the DAPG biosynthesis pathway. The two core enzymes of this pathway, PhlD and the MAPG ATase have been extensively characterized and the crystal structure of the later has been recently established [23]. These two enzymes have generated a growing interest in their unusual catalytic properties. PhlD has been used to produce high amounts of phloroglucinol in _E. coli_ strains carrying plasmid-localized _phlD_[143; 144; 145]. Regarding MAPG ATase, this enzyme is a new biocatalytic tool, which enables C-C bond formation without the need for CoA-activated substrates [23; 28]. Despite these advances, several questions remain unanswered regarding the biosynthesis of phloroglucinol derivatives in _Pseudomonas_ spp. For example, the identity of the acyl donor leading to the production of MAPG remains unknown. In addition, several authors reported the isolation of various dimers, such as DAPG-DAPG or MAPG-MAPG dimers [4]. Little is known about how they are produced and the role they play in the rhizosphere.  
Secondly, we have seen in this review that the biosynthesis of DAPG is regulated by the Gac/Rsm signal transduction pathway and by two TetR pathway-specific regulators, PhLF and PhlH. PhLF and PhlH act, to some extent, as sensors of DAPG and MAPG concentration, thereby allowing DAPG-producing _Pseudomonas_ spp. to control the amounts of DAPG being produced. Even more interesting is the fact that these two regulators can be influenced by exogenous metabolites, such as fusaric acid [15; 75], or by two plant flavonoids, apigenin and phloretin [78]. This suggests that PhLF and PhlH could act as sensors of the rhizosphere environment. Finally, DAPG-producing _Pseudomonas_ spp. are efficient biocontrol agents, capable of protecting the plant root system from numerous soil-borne plant diseases. They are present as long-lasting indigenous communities in several agroecosystems, including in fields under wheat and barley monocultures. The fact that _Gaeumannomyces triti_ did not become less sensitive to DAPG after several decades of wheat monoculture [87] is very promising for the extensive use of DAPG-producing _Pseudomonas_ spp. as biocontrol agents. This is probably linked to the mode of action of DAPG, which does not target a specific protein.  
This research received no external funding.  
We are grateful to [PERSON] for proofreading the manuscript and for his advice during the conception of the different figures.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
The destruction of cultural heritage caused by dams represents a major issue especially in an age of climate change and narrowly focused development policies. To counteract this phenomenon, archaeologists and cultural heritage experts have relied upon rescue archaeology practices generally limited to fieldwork methodologies, while remote sensing of satellite imagery remains under-considered. To bridge this gap, we build on a multidisciplinary collaboration exploring the potential of Synthetic Aperture Radar (SAR) and open access multispectral satellite imagery, for quantifying the archaeological evidence located within a prospective reservoir area before dam construction. Based on previous research by [PERSON] (2020) claiming the necessity for ad hoc protocols to document and monitor the impact of dams on cultural heritage, we selected two complementary situations: the planned dam of Halabiyeh in Syria and the under construction Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. These case studies were analyzed with state-of-the-art methodologies to develop a feasible workflow that may contribute to fostering the use of satellite imagery in operational contexts such as those represented by these particular cases, and be replicated by archaeologists in other areas. The workflow is designed to be integrated to ground-truthing methodologies into two dedicated protocols named Pre-Construction Archaeological Risk Assessment (PCARA) and Pre-Flooding Rescue Archaeological Program (PFRAP) which could eventually become a standard procedure for rescue archaeology in dams areas.  
cultural heritage; archaeology; dams; risk assessment; satellite imagery; multispectral; SAR; COSMO-SkyMed; Sentinel-2
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 2 Materials & Methods
### Rectification of the Socket  
The first step in modelling the socket was to incorporate the prosthetic liner. Therefore, a frequently used silicone liner was modelled by extruding the outer surface of the residual limb to a thickness of 6 mm. In practice, the silicone liner is rolled up like a sock and takes the form of the residual limb [25]. Following the modelling of the liner, the socket was reshaped according to the guidelines of two common rectification approaches. The first was the standard PTB method, which relieves pressure on sensitive areas, and stress areas that can withstand higher loads without causing discomfort or tissue damage. The second approach used was TSB, where the contact pressure is distributed as evenly as possible across the limb [2]. During the rectification process, the trim line of the socket was determined, and the edge was rounded to remove sharp edges that could damage the tissue. Once the suitable shape was achieved, the surface was extruded evenly to 3 mm, which is the approximate thickness of the definitive composite socket. Figure 3 shows a heat map depicting the socket's indented regions compared to the liner's outer surface. In the PTB, the patellar tendon region is the most indented, while in the TSB, it is the posterior region.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Design Parameter** & **Value** \\ \hline Tibia length & 150 mm \\ Fibula & cut 10 mm above the tibial end \\ Mid-high circumference & 533 mm \\ Mid-patella circumference & 368 mm \\ Calf circumference & 392 mm \\ Knee bent angle & 4 deg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main design parameters used for the modelling of the generic residual limb.  
Figure 2: Transparent view of the bulk soft tissue with marked circumferences **(a)**; a 3D view of the generic transtibial limb **(b)**.  
The final step was to design the distal part of the socket relative to the position of the leg skeleton to enable the attachment of the standard prosthetic adapter. Moreover, the distal part was reshaped to reduce the stress concentration and, thus, distribute the stresses more uniformly over the socket (Figure 4a). Rectification was performed in the prosthetic software Rodin 4D Neo (RODIN SAS, Merignac, France) with the assistance of Prosthetists from the University Rehabilitation Institute in Slovenia (URI SoCa). The Prosthetists reshaped the sockets in a virtual environment according to the PTB and TSB rectification methods for transtibial prostheses. The geometry of the final virtual model consists of bones, bulk soft tissue, silicone liner and PTB or TSB socket. In Figure 4b, the socket overlaps with the silicone liner due to the rectification process. The overlap is resolved in the first step of the simulation using the interference fit and represents the donning of the prosthesis.  
Figure 3: Heat map of rectified sockets. The red and blue colours represent concave and convex regions, respectively.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 5 Improving a Bin-Picking Workcell through Human-Robot Collaboration
### Human-Robot Collaboration in Bin-Picking Failures
#### 5.5.1 Failures Because of Constraints in Robot Motion  
Figure 7 represents a typical case of failure due to constraints in robot motion. The circled nut in Figure 7a is correctly recognized, but because it is close to the edge of the bin, it cannot be picked up, as can be seen in Figure 7b. In fact, picking it would mean colliding with the bin faces, and tilting the robot end effector cannot solve the problem. In this case, after 3D data acquisition and object recognition, the system is able to recognize that the part cannot be picked and the robot does not try to move above the grasping point and pick the part. Instead, it seeks the help of the human operator by turning the gripper LED strip light red. Once the operator notices that the robot is looking for help, he or she manually picks the part from the bin (Figure 7c) and places it where it needs to be placed. When this is done, the operator informs the robot that the fault has been cleared. This is done by simply touching the robot: the robot recognizes the contact and restores bin-picking task execution. Alternatively, the operator can push the button placed on the robot arm, but this typically requires a greater amount of time.
## 2 Vertical Acceleration and Turbulence Severity Estimation
### Grid Generation on Wing-Tail Combination  
As shown in Figure 1, the origin point is located on the leading edge of the wing root and a spatial coordinate system is established. A grid with certain number of rows and columns is assigned. The wing and horizontal tail are divided by dozens of rectangular vortex rings. Please refer to Appendix A for grid arrangement and further geometric parameters computation. The wing on one side is divided by \(N_{RW}\) rows chord-wise and \(N_{CW}\) columns span-wise, and the horizontal tail on one side is divided by \(N_{RT}\) chord-wise and \(N_{CT}\) span-wise. Since the circulation distribution changes greatly at the wingtip, wing root and leading edge, the neighboring lattices can be redesigned according to the semicircle division method [28]. In addition, the geometric boundary of the control surface is close to the boundary of each vortex ring since elevators and spoilers can be deflected by pilot's manipulation or automatic flight control system.  
As shown in Figure 2a, rectangular vortex rings are arranged on each lattice panel. The leading edge of the vortex ring is located at the 1/4 chord of the grid, the collocation point is located at the midpoint of the 3/4 chord of the grid and the trailing edge is located at the 1/4 chord of the grid in the next row. The trailing edge of the vortex in the last row coincides with the grid boundary. In order to approximate the mean camber surface, the vortex filaments of Section \(AE\) and \(BD\) are further divided by Section \(AF\) and \(FE\), \(BC\) and \(CD\), respectively.  
In Figure 2b, according to Biot-Savart law, the velocity induced by a vortex filament at arbitrary point \(P\) in space is [16]  
\[\mathbf{V}=\frac{\Gamma}{4\pi}\frac{r_{1}\times r_{2}}{\left|r_{1}\times r_{2} \right|^{2}}[r_{0}\cdot(\frac{r_{1}}{r_{2}}-\frac{r_{2}}{r_{1}})]=\frac{\Gamma }{4\pi}\frac{(r_{1}+r_{2})(r_{1}\times r_{2})}{r_{1}r_{2}(r_{1}r_{2}+r_{1} \cdot r_{2})} \tag{1}\]  
Figure 1: Vortex rings on mean camber surface of wing–tail combination.  
Figure 2: (**a**) Vortex ring and (**b**) induced velocity.  
In Equation (1), \(\mathbf{V}\) is the induced velocity, \(\Gamma\) is the strength of the vortex filament, \(\mathbf{r}_{0}\) is the vector of the vortex filament. \(\mathbf{r}_{1}\) and \(\mathbf{r}_{2}\) are the position vectors of both ends to an arbitrary point in space. Based on Equation (1), a coefficient vector \(\mathbf{k}\) of induced velocity is defined as  
\[\mathbf{k}=\frac{1}{4\pi}\frac{(r_{1}+r_{2})(\mathbf{r}_{1}\times\mathbf{r}_{2})}{r_{1}r_{ 2}(r_{1}r_{2}+\mathbf{r}_{1}\cdot\mathbf{r}_{2})} \tag{2}\]  
Except for the trailing edge, the induced velocity of a vortex ring to arbitrary point in space can be expressed as the vector sum of the induced velocity by six vortex filaments:  
\[\mathbf{V}=\Gamma(\mathbf{k}_{AB}+\mathbf{k}_{BC}+\mathbf{k}_{CD}+\mathbf{k}_{DE}+\mathbf{k}_{EF}+\mathbf{k} _{FA})=\Gamma\mathbf{K} \tag{3}\]
## 3 Results and Discussion
### Molecular Dynamics of SARS-CoV-2 S Protein  
Emodin might also prevent the interaction between SARS-CoV-2 S protein and ACE2 on the basis of the conserved mechanism of ACE2 recognition [19]. In addition, emodin has emerged as a possible re-purposable candidate for drug combinations targeting SARS-CoV-2 [13], but more details of its antiviral action are urgently required to provide a more informed assessment of its potential. In this framework, this study provides a preliminarily assessment of the possible functional conservation of emodin. The two S protein ortholog portions under analysis shared nearly 75% of primary sequence identity with a full conservation in terms of 3D organization (Figure 6A,B), further pointing to the plausible conserved activity of emodin. The surface pocket that is potentially able to host emodin was also found at the contact surface of the SARS-CoV-2 S protein-ACE2 complex (Figure S1, Supplementary Materials). Therefore, the inhibitory potential of the SARS-CoV-2 S protein-ACE2 complex formation was investigated by checking the capability of emodin to persist at the protein-protein contact surface of SARS-CoV-2 S over the time. Emodin was docked within  
Figure 5: Molecular dynamic of SARS-CoV-1 S protein–ACE2 complex in the unbound state or bound to emodin. (**A**) RMSD plot of complex C\(\alpha\). (**B**) RMSD plot of S protein C\(\alpha\). (**C**) Plot of complex’s radius of gyration. (**D**) Plot of S protein’s radius of gyration. (**E**) Hydrogen bonds plot.  
the surface pocket and the best scored pose (65.4 \(\pm\) 0.2 GOLDScore units) was used as an input for the molecular dynamic simulations. Emodin, while complexed with the SARS-CoV-2 S protein, showed a more stable geometry of interaction with lower RMSD values compared to the complex with SARS-CoV-1 S protein (Figure 6). In addition, the analysis of emodin trajectories at the SARS-CoV-2 S protein surface compared to those at SARS-CoV-1 S protein surface, showed a lower spread over the protein surface and a more marked permanence nearest the focal contact point enriched of interaction hotspots, which were also confirmed for the SARS-CoV-2 S protein-ACE2 complex (Table S3 and Figure S1, Supplementary Materials). Taken together, these results provide evidence for the possible inhibition activities of emodin towards complex formation between SARS-CoV-2 S protein and ACE2.
## 3 Proposed Scheme  
In this section, we present the detailed discussion of our proposed scheme. Our scheme is compared with the AHH-VBF that uses the holding time \(HT_{p}^{i}\) of node \(i\) to forward packet \(p\) towards the Sink \(D\). The \(HT_{p}^{i}\) suppresses extra copies of \(p\) by selecting the potential forwarder \(i\) using its projection distance from the center of the virtual cylinder or pipeline, distance towards \(D\), and distance from the node \(S\) (a node from which \(i\) received a copy of \(p\)). The terms virtual pipeline or virtual cylinder are interchangeably used in the context of this paper. AHH-VBF adaptively adjusts the transmission power and radius of the virtual cylinder to its maximum distance mobile neighbor. In contrast to AHH-VBF, the proposed scheme estimates \(HT_{p}^{i}\) based on the normalized residual energy scaled distance from \(S\), expanded distance from the virtual cylinder's centerline, and distance towards \(D\). The resultant \(HT_{p}^{i}\) prioritize the nodes that have large residual energy, near the center of the virtual cylinder, and least distant to \(D\). In addition to that, it also increases the difference between holding times of all nodes in the potential forwarding zone to suppress more copies of \(p\). In result, the packet collision at next hops can be avoided and network energy can be conserved to maximize the network lifetime.
## 5 Data Collected and Statistical Analysis
### Compressive Strength  
An analysis of 300 compressive strength point values for cement mortar, gathered from the published literature (Table 5), reveals that the compressive strength ranges from 3 MPa to 65 MPa after 28 days of curing. The mean value is 33 MPa, with a standard deviation of 17.23 MPa and a coefficient of variation (COV) of 43%. Various distribution  
Figure 8: Lime content % vs. flow % of modified cement-lime mortar.  
Figure 9: Normal distribution of compressive strength for (**A**) cement mortar and (**B**) modified cement-lime mortar.  
tests were conducted to assess the compressive strength of the cement mortar. Figure 9A demonstrates that the Anderson-Darling statistic (AD) and \(p\)-value, which are used for hypothesis testing, indicate that the Weibull frequency distribution is a good match for the compressive strength of cement mortar.  
Modified cement-time mortar: A dataset including compressive strength values of lime-reinforced cement mortars was compiled from several literature sources (Table 5). The compressive strength varied between 6.7 MPa and 65 MPa, with an average of 37.5 MPa. The standard deviation was 21 MPa, and the coefficient of variation (COV) was 49.7%. By using Anderson-Darling (AD) statistics and \(p\)-values for hypothesis testing, it was determined that the compressive strength data adhere to a Weibull distribution, as seen in Figure 9B.  
A multiple regression analysis was conducted to examine the impact of lime on the compressive strength of cement mortar. Figure 10 demonstrates that the compressive strength was influenced by independent factors such as water-cement ratio, curing time, and lime via nonlinear interactions. In Equation (4), the value \(\mathrm{d}=-0.28\) represents the nonlinear model and indicates that lime has a more significant impact on diminishing the compressive strength of cement mortar compared to the water-cement ratio and setting time.  
\[Comp.str.=8.56\times\left(\frac{t^{0.27}}{w/c^{1.26}}\right)-0.28\times\left( \frac{t^{0.17}\times L^{1.04}}{w/c^{0.027}}\right) \tag{4}\]  
with R\({}^{2}\) = 0.82.
# Failure of Rock Slope with Heterogeneous Locked Patches: Insights from Numerical Modelling
###### Abstract  
Rock slope stability is commonly dominated by locked patches along a potential slip surface. How naturally heterogeneous locked patches of different properties affect the rock slope stability remains enigmatic. Here, we simulate a rock slope with two locked patches subjected to shear loading through a self-developed software, rock failure process analysis (RFPA). In the finite element method (FEM)-based code, the inherent heterogeneity of rock is quantified by the classic Weibull distribution, and the constitutive relationship of the meso-scale element is formulated by the statistical damage theory. The effects of mechanical and geometrical properties of the locked patches on the stability of the simulated rock slope are systematically studied. We find that the rock homogeneity modulates the failure mode of the rock slope. As the homogeneity degree is elevated, the failure of the locked patch transits from the locked patch itself to both the interfaces between the locked patched and the slide body and the bedrock, and then to the bedrock. The analysis of variance shows that length and strength of locked patch affect most shear strength and the peak shear displacement of the rock slope. Most of the rock slopes exhibit similar failure modes where the macroscopic cracks mainly concentrate on the interfaces between the locked patch and the bedrock and the slide body, respectively, and the acoustic events become intensive after one of the locked patches is damaged. The locked patches are failed sequentially, and the sequence is apparently affected by their relative positions. The numerically reproduced failure mode of the rock slope with locked patches of different geometrical and mechanical properties are consistent with the laboratory observations. We also propose a simple spring-slider model to elucidate the failure process of the rock slope with locked patches.  
rock slope; locked patch; shear strength; mechanical model +
Footnote †: journal: Applied Sciences