esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Design
### Semi-Physical Simulation of the Space Environment  
When the semi-physical simulation is conducted on the ground, the temperature and pressure will influence the measurement results because of Rayleigh scattering, refractive index fluctuation, and optical element deformation [53; 54]. To avoid these influences, a vacuum and cold-black space environment must be simulated in the semi-physical simulation test.  
An oil-free vacuum tank with a heat sink, as shown in Figure 11, is adopted to simulate the cosmic vacuum and cold-black environment. The inner wall of the vacuum tank is blackened, with the heat sink surrounding the outside. The heat sink is filled with liquid nitrogen to obtain a uniform low temperature, which is used to simulate the cold-black space environment. The main technical parameters of the vacuum tank are listed in Table 6, which at a highly technical level can be achieved in the laboratory [55; 56].  
Figure 10: Technical solution for the semi-physical simulation test for the interferometry of inter-satellite ranging.  
Figure 9: Schematic diagram and block diagram of active suspension vibration isolation.  
For the inter-satellite laser interference, simulation is required of the cosmic vacuum and cold-black environment where the two spacecrafts are located; therefore, the dual vacuum tank system shown in Figure 12 is used for the simulation. The dual vacuum tank system is composed of two vacuum tanks with a heat sink and a vacuum pipeline, the length of which is 10 m and which connect the two tanks. The inner diameter of the pipeline is 1 m, so as to ensure enough room to complete the laser acquisition reciprocally when establishing the inter-satellite laser link. An optical table is designed for each vacuum tank to carry the spacecraft simulation platform and the laser interference payload. According to Section 3.2, only ground vibration noise above 0.7 Hz can be isolated by the precision active vibration isolation platform. In order to minimize the impact of ground vibration noise on the laser interferometry, the optical table is also subjected to strict vibration isolation treatment. A two-stage vibration isolation method for the independent foundation plus active vibration isolation is adopted to ensure the vibration acceleration is attenuated to \(10^{-5}\) g within the frequency range of 0.1-100 Hz.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 3 Path Planning Problem
### Objectives
#### 3.1.1 Path Length  
The line segment formed by any two points in the space can be calculated based on the Euclidean distance. Thus, for two consecutive rotation points (\(p_{i}=(x_{i},y_{i})\), \(p_{i+1}=(x_{i+1},y_{i+1})\)), the line segment of the points can be computed. Then, the path length can be given by summing all the ordered line segments. The mathematical expression is presented in Equation (1):  
\[\left\{\begin{array}{l}dis(p_{i},p_{i+1})=\sqrt{(x_{i}-x_{i+1})^{2}+(y_{i}-y_ {i+1})^{2}}\\ Length(p)=\sum\limits_{i=0}^{n}dis(p_{i},p_{i+1}).\end{array}\right. \tag{1}\]
# Simple Thalidomide Analogs in Melanoma: Synthesis and Biological Activity
## 3 Results and Discussion
### Biological Results
#### 3.2.3 Docking Studies  
To first evaluate the binding poses and the calculated affinities between our more active compounds (**2d**, **2i** and **2k**) and tubulin and in order to screen the molecules for the best lead candidate, we performed molecular docking simulations using, as a target, the three-dimensional structure of tubulin in complex with its well-known ligand vinblastin, a vinca alkaloid that inhibits the formation of microtubules within cells [26]. Affinities of the three compounds to the protein were calculated using Autodock according to the expression \(K_{\text{i}}=\exp\) (deltaG/(R*T)). As discussed in our previous works [46; 47; 48], to identify the most promising compound, we adopted a strategy based on the clusterization of the results from our simulation runs, together with the visual inspection of the ligand-protein binding mode. All our compounds share the same protein binding site with vinblastine, at the interface between the \(\beta\)- and \(\alpha\)-tubulin subunits (Figure 6) as determined by X-ray crystallography [26]. All the tested compounds seem to be good ligands of the protein. They are almost superimposed over each other and share most of the interactions with the protein amino acids. Particularly, molecule **2d** is positioned within the vinblastine binding site of tubulin, forming hydrogen bonds with protein residues Asp \(\beta\)179 and Tyr \(\beta\)224. This binding mode is stabilized by hydrophobic interactions with residues Tyr \(\beta\)210, Phe \(\beta\)214, Val \(\beta\)217, Pro \(\beta\)222, Ile \(\alpha\)332, and Phe \(\alpha\)351. Ligands **2k** and **2i** share the same interactions described above with **2d**, but form an additional hydrophobic interaction with Val \(\alpha\)328. The slightly different activity of **2i** in comparison with **2k** might be due to the hydrophobic environment surrounding the CH\({}_{3}\) group of **2k**, unlike the more hydrophilic chlorine atom present in ortho position of the phenoxy portion of **2i**.  
Figure 5: PARP-1 cleavage. A2058 melanoma were treated with compound **2k** (used at its IC\({}_{50}\) value) for 48 and 72 h and then the total protein extracts were analyzed by Western blot. PARP-1 native form (116-kDa) and the cleaved form (89 kDa) were visualized. Time points 0 h, 48 h and 72 h for protein extraction. GAPDH was used for loading normalization. Blots are representative of three independent experiments.
## 4 Experiments
### Experimental Design  
The experiment selects the navigation data of the ASV in the port. The algorithm performance test experiment is divided into two groups, namely the single-frame experiment and the continuous-frame experiment. In the single-frame experiment, 10 pairs of data are selected from the data of the whole navigation stage, and each pair of data is two adjacent frames. The average registration time and root mean square error (_RMSE_) of ten algorithms on 10 pairs of data are calculated. The purpose of the single-frame algorithm is to test the registration accuracy of each algorithm and select appropriate parameters for each algorithm. The continuous-frame experiment is designed to continuously register each frame data on the entire navigation data set and obtain the final result. In this experiment, the performance of each registration algorithm is evaluated by calculating the absolute trajectory error (ATE) and relative pose error (APE) between the true pose value and the registration result. The whole process of the experiment is shown in Figure 1. All methods are implemented depending on the robot operating system (ROS) and the point cloud library (PCL).
# Numerical Evaluation of Sample Gathering Solutions for Mobile Robots
## 6 Conclusions  
This paper details three methods for planning a team of robots such that multiple samples (items) from the environment are collected and deposited into a storage location. The environment is represented by a weighted graph, and the robots have limited amounts of energy for performing movements on this graph. The goal is to plan the agents such that the samples are collected in minimum time, under the assumption that each robot can carry at most one item at a time. The first solution is given by a MILP formulation that can be too complex to solve when there are many samples or many robots in the team. The second solution provides a QP relaxation that represents in some cases a good trade-off between the time for finding movement plans and the cost difference from the optimal one. The third solution is an IH algorithm that yields plans even when the QP fails due to low amounts of available energy for robots.  
Based on the results reported in the previous subsection, the recommendations for a user that solves Problem 1 are the following: MILP (6) or IH algorithm are to be used when the robots have small amounts of energy in comparison with the energy required for moving to sample locations. Otherwise, MILP (4) and QP (8) may fail in providing any solution for such restrictive scenarios.  
The usage of QP or IH solutions generally yields an acceptable loss in total time for accomplishing the mission whenever the MILP optimization becomes computationally intractable on a decently powerful computer. We emphasize that the above recommendations resulted from an intensive campaign of numerical simulations and they could not be drawn by only inspecting the formal solutions. Extensive simulations yielded quantitative comparison criteria. Based on this information a decision scheme is given for indicating the proper method to be used in a specific problem instance. Besides suggestions from Figure 6, we recall that MILP and QP solutions involve existing optimization tools.  
A real-time experiment was performed for illustrating a sample gathering solution. Although the collision avoidance problem is not treated in this paper, in the mentioned experiment the possible collisions are avoided by pausing the motion of one robot. In future work we intend to embed formal tools inspired by resource allocation techniques for collision and deadlock avoidance, while considering the effects of acceleration and deceleration of the mobile robots and restricted energy.  
The authors contributed equally to this work, each of them being involved in all research aspects.  
This research was partially supported by a grant of Romanian Ministry of Research and Innovation: CNCS-UEFISCDI project PN-III-P1-1.1-TE-2016-0737.  
Figure 6: Decision diagram for choosing an appropriate solution for Problem 1 when a fast computational scenario is considered.
## 4 Discussion  
The analysis of the results obtained shows that the parameter most sensitive to changes in the tracing speed, regardless of the nature of the measured surface, is the root mean square slope of the roughness profile RAq. Its values increase with increasing tracing speed, reaching a change of more than 300% in relation to the reference speed of \(v\) = 0.1 mm/s in extreme cases.  
It should also be noted that the parameter whose values decrease with increasing tracing speed in most cases is the mean spacing of the profile elements RSm.  
The largest changes in the values of the roughness profile parameters in response to changes in the tracing speed were observed for the surface of the standard FL 0.2 surface (the surface after flat lapping) and G 0.4 surface (the surface after grinding). These are surfaces with a random profile, characterized by large values of slope angle of the profile peaks and valleys, small distances between profile elements, and at the same time, small values of the height of the grooves. Figure 16 shows the nature of the changes in the individual parameters of the roughness profile as a function of the tracing speed for standard G 0.4.  
The smallest changes in the roughness profile parameters in response to changes in the tracing speed were observed for the surface of standard HM 3.2 (the surface after horizontal milling  
The comparison of profile fragments measured at tracing speeds of \(v\) = 0.25, 0.50, 1.0, and 2.0 mm/s with the profile measured at the reference value of a tracing speed of \(v\) = 0.10 mm/s for standard G 0.4 (the surface after grinding) is shown in Figure 18.  
Figure 16: Changes in individual roughness profile parameters as a function of tracing speed for standard G 0.4.  
Figure 17: Changes in roughness profile parameters as a function of the tracing speed for standard HM 3.2 (the surface after horizontal milling).  
The red color shows the profile obtained for the reference tracing speed of \(v=0.10\) mm/s.  
Figure 19 shows a fragment of the standard profile HM 3.2 (surface after horizontal milling) measured at different tracing speeds.  
Figure 18: Comparison of fragments of standard G 0.4 measured at the following values of the tracing speed: (**a**) \(v=0.1\) and 0.25 mm/s, (**b**) \(v=0.1\) and 0.50 mm/s, (**c**) \(v=0.1\) and 1.0 mm/s, (**d**) \(v=0.1\) and 2.0 mm/s. Red line: the profile obtained with the use of the reference speed, black line: the profile obtained with the use of analyzed speed.  
Figure 19: Comparison of a fragment of the measured HM 3.2 standard profile for the following values of the tracing speed: (**a**) \(v\) = 0.1 mm/s, (**b**) \(v\) = 0.25 mm/s, (**c**) \(v\) = 0.5 mm/s, (**d**) \(v\) = 1.0 mm/s, (**e**) \(v\) = 2.0 mm/s.
## 3 Numerical Results
### Probabilistic Analysis of the Reference Case  
This section evaluates probabilistically the tunnel liners by following the procedure proposed in this paper. A flow chart of the procedure is given in Figure 4. It starts by preparing a computational model and defining the input uncertainties. The analytical model introduced in [17] is suggested for a preliminary study of deep tunnels due to its high computational efficiency which is an attractive feature for probabilistic analyses. It is followed by estimating the \(Pf\) of the double liners by using the SS which allows having a first idea of the tunnel's reliability. Then, the samples generated in the first subset of the performed SS are further explored to quantify the variation such as a confidence interval of the Qols (i.e., volumetric strain and equivalent stress). Lastly, the importance of each stochastic parameter is quantified via a Sobol sensitivity analysis so that all the parameters can be ranked according to their importance. The advantage of the procedure lies in the fact that it can provide many valuable results for understanding and evaluating the tunnel reliability which is favorable for a rational decision.  
The parameters of the viscoelastic rock behaviors and the initial stresses are modelled by Lognormal-distributed random variables as shown in Table 2. Their mean values are taken from Table 1 and a coefficient of variation (CoV = 15%) is assumed due to the lack of relevant information in the literature. Other parameters in Table 1 are kept constant. Although the anisotropic ratio \(\lambda>1\) can be found in some cases as mentioned above, in the following reliability analysis, it is supposed that this parameter \(\lambda\) is bounded at the upper value 1 to be convenient with the usually observed cases.  
Figure 4: Flow chart of the probabilistic analysis procedure introduced in this paper (example of two random variables).  
Figure 3: Evolution of volumetric strains in the outer flexible liner (**a**) and of equivalent stress in the concrete liner (**b**) at the springline and at the roof as function of the anisotropic coefficient of initial stress state.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 4 Experimental Evaluation and Results
### Performance Evaluation
Obviously, the Pipelined structure has the fastest real-time performance. As for the reason why the cycles of the processor proposed was smaller than the Pipelined structure, we considered that in order to realize the AXI4 interface in Xilinx IP, some handshake signals were added, which also required additional cycles. When the proposed processor works in a four-channel mode, it will achieve 128k-point FFT calculation with almost the same cycles in Table 8 at the cost of nearly four times the hardware resources. In practical applications, we need to make a trade-off between real-time performance and hardware resource consumption.
## 2 Related Work
### Hierarchy-Based Protocol  
In a hierarchy-based protocol, after dividing the entire network into several groups called clusters, a cluster head (CH) representing an individual cluster is selected, and then the CH transmits data on behalf of the cluster. The CH collects data and receives data collected by general sensor nodes in the cluster, and then performs data aggregation and transmits the data to the BS. Therefore, CH may have problems in terms of energy consumption compared to normal sensor nodes, and resolution requires the ability to change the CH by taking into account the selection of CH, the duration of the CH role, etc., as well as the derivation of the required number of clusters across the entire network and the scope of clusters. Typical examples of hierarchy-based protocols are Low-Energy Adaptive Clustering Hierarchy (LEACH) [7] and Energy-Efficient Uneven Clustering (EEUC) [8].  
LEACH is the beginning of a hierarchy-based protocol for WSN, and clusters the entire network to create groups and then selects the CH for each cluster. At this time, the CH manages all sensor nodes in the cluster, and the data transmitted by the sensor nodes are integrated and transmitted to the BS. Therefore, CH consumes more energy than a general sensor node. To address this, a separate CH is selected for each round. When selecting a CH in LEACH, all sensor nodes in the cluster are eligible to be equally selected as CH, which causes all sensor nodes to consume energy evenly. However, a limitation of LEACH is that it does not reflect energy consumption according to the physical location of the sensor node when selecting CH.  
Based on the distance between the BS and the sensor nodes, EEUC configures a cluster with a smaller range of clusters located closer to the BS than other clusters. This is because the CH in the cluster close to the BS performs a relay function of data transmitted from the other CH to the BS, so energy consumption is large. However, as shown in Figure 4, EEUC operates smoothly when sensor nodes close to the BS are evenly distributed. There is a disadvantage in this approach in that the energy of the sensor nodes adjacent to the BS is rapidly consumed.
## 1 Introduction
### Mobile Telepresence Robots  
Mobile telepresence robots can help people socially connect with each other when they are distant. People use these robots to socially connect in entertainment, medical, military, research, and education contexts [27]. Telepresence robots allow people from different locations to be virtually present, like in video conferencing, by allowing users to communicate with family members and friends through a more physical and emotional interaction than with video or phone calls [1; 25; 28]. Middle and older adults who were isolated from social connections requested access to technology like mobile telepresence robots to help them feel more connected to loved ones [29; 30]. By helping people socially connect in this deeper interaction, these robots can help people decrease feelings of loneliness and depression, and improve health and social connection [5; 6; 7; 8].  
However, research on mobile telepresence robots primarily for social connection is limited. Some studies examined these robots for social connection in eldercare facilities [25; 31]. Residents in eldercare facilities who used the robots felt that the robots helped them connect with their families and decreased feelings of loneliness [7]. Two prior studies examined mobile telepresence robots in homes of independent-living older adults primarily for social connection [25]. In a two-day study, older adults received daily calls via the robot from the research team and up to two additional daily calls from a family member or friend trained to use the robot [5]. Having someone check in on them through the robot reduced loneliness and increased the perceived presence of the caller [5]. In a twelve-month case study, when two participants could use a robot in their homes whenever they liked, they showed continued use of and interest in the robot for social connection [6]. More research should examine the long-term (\(\geq\)six months) use of robots in older adults' homes because use and acceptance can change over time [9]; see Section 1.4.  
Some drawbacks to telepresence robots include technical difficulties and privacy. Some features (e.g., audibility) can diminish due to poor internet [7; 28; 32]. Older adults also had privacy concerns (e.g., that a pilot or attacker could find sensitive data like bank information; [33]). See [10] for our discussion of technical and privacy concerns in this study. Other difficulties of the robots relate to general factors in technology acceptance; see Section 1.3.
## 3 Results  
### Analysis of Model Training Results  
In this section, three loss functions, namely box_loss, cls_loss, and dfl_loss, are introduced to evaluate the accuracy of the model in predicting bounding boxes. These loss functions guide the training and optimization of the model, and lower values indicate a higher prediction accuracy. As shown in Figure 11, at the beginning of the training, both the training and validation loss functions decrease rapidly, but they exhibit oscillations before reaching 100 iterations. After 100 iterations, the loss function values stabilize. In particular, the YOLOv8n-WSE-pest model shows decreases of 7.46%, 22.79%, and 28.45% in box_loss, cls_loss, and dfl_loss values, respectively, compared to the original YOLOv8n network in the training set. In the validation set, the box_loss, cls_loss, and dfl_loss values decrease by 6.39%, 26.70%, and 36.15%, respectively, compared to the original network.  
In this study, a comparison was conducted between the YOLOv8n-WSE-pest model and the original YOLOv8n model to assess the localization and prediction performances of the improved model. The comparison results, as shown in Figure 12, indicate that the improved model achieved a 3.16% increase in the precision, a 5.65% improvement in the recall, and a 4.43% improvement in the F1 score.  
Figure 11: Model loss function: (**a**–**c**) represent the trend graphs of box_loss, cls_loss, and dfl_loss for the YOLOv8n-WSE-pest model, respectively; (**d**–**f**) represent the trend graphs of box_loss, cls_loss, and dfl_loss for the original YOLOv8n model, respectively; (**a**,**d**) compare the box_loss between the validation set and the training set; (**b**,**e**) represent the cls_loss comparison between the validation set and the training set; (**c**,**f**) depict the dfl_loss comparison between the validation set and the training set.  
Figure 12: Comparison between YOLOv8n-WSE-pest vs. original YOLOv8n. Note: This graphical representation showcases a comparative assessment of the foundational YOLOv8 model against a tailored variant, YOLOv8-WSE-pest, which is specifically adapted for pest recognition tasks. The depicted analysis spans three pivotal performance metrics—precision, recall, and F1 score—across a spectrum of confidence threshold values. Each distinct curve within the illustration corresponds to a separate pest category, while the overarching blue line signifies the aggregated average performance across all pest types considered. Progressing along the horizontal axis, which denotes the incremental confidence thresholds, the vertical axis records the respective scores of the outlined evaluative criteria. This meticulous comparison serves to highlight the augmented effectiveness of the YOLOv8-WSE-pest model in consistently identifying and classifying diverse pest species under a broad range of confidence levels, thereby affirming its advancement in specialized detection capabilities.