esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Kinematic Analysis of the Hexapod Robot
### Direct Kinematics  
In this subsection, the direct kinematics of the hexapod robot are analyzed. Figure 17 shows the free-body diagram of the leg joints and the closed-loop trajectory whose origin corresponds to the system \(o_{5}\). The equations for the direct kinematics are as follows.  
The vector describing the kinematic chain \(o_{0}\prec o_{1}\prec o_{2}\prec o_{3}\prec o_{4}\prec o_{5}\) is \(\mathbf{r}_{0,5}\). Therefore, the formula is:  
\[\mathbf{r}_{i,j}=\mathbf{r}_{i,j-1}+\mathbf{R}_{i,j-1}\mathbf{r}_{j-1,j}. \tag{21}\]  
The position vectors and rotation matrices for different points in the hexapod leg and the corresponding rotation matrices are summarized in Table 3.  
In this case, the variable \(q_{1}\) represents a rotation around the \(z\)-axis in the system \(o_{1}\), while \(q_{2}\) and \(q_{3}\) correspond to rotations around the \(y\)-axis in the systems \(o_{3}\) and \(o_{4}\), respectively. The variable \(q_{4}\), however, is not a joint variable but it is used to represent the different legs of the robot. The values of \(q_{4}\) are multiples of \(360^{\circ}/6\), starting from \(0^{\circ}\), with each value denoting a specific leg. This approach enables the analysis of a single leg while accounting for the variations represented by the different values of \(q_{4}\). Additionally, \(q_{5}\) is used to adjust the orientation of the trajectory by rotating it around the z-axis in the system \(o_{6}\).  
Figure 16: HexV3\(-\) Mythbots Articular Dimensions (mm).  
Figure 17: Free body diagram of the leg joints and closed-loop trajectory representation.  
In addition to the primary kinematic chain, there is a secondary kinematic chain formed by the systems \(o_{0}\), \(o_{6}\), and the point \(p\) on the trajectory. Here, \(\mathbf{r}_{6,p}\) represents the position vector of the point \(p\) in the system \(o_{6}\), and \(\mathbf{R}_{1,p}\) is the rotation matrix that defines the orientation of the point \(p\) relative to the system \(o_{1}\). This rotation is determined by the angle \((q_{5}-q_{4}+\frac{\pi}{2})\), which accounts for the orientation difference between \(q_{5}\) and \(q_{4}\). The position vector \(\mathbf{r}_{0,6}\) describes the location of the system \(o_{6}\) relative to the base system \(o_{0}\). This vector is defined with respect to the geometric constraints of the robot, as it represents the portion of the trajectory that each leg must follow. Similar to the parameters \(a\) and \(b\) of the trajectory, \(\mathbf{r}_{0,6}\) must ensure that the trajectory exists within a safe operating zone for all rotations, avoiding collisions during the robot's movement. This is crucial for maintaining a valid trajectory throughout the robot's operation.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 1 Introduction  
This work presents an innovative process by which the health status of difficult-to-access structures (e.g., bridges) can be determined, utilizing a wireless sensor package for data acquisition, deployed by an Unmanned Aircraft System (UAS) (commonly known as a drone), which provides data for Dynamic Signature Response (DSR) techniques for damage detection. This sensor technology is a compact and low-cost sensor package with an onboard power supply that allows wireless acceleration data collection, eliminating the need for wired connection to the power grid. The sensor package is equipped with an electropermanent magnet that facilitates the rapid installation to various points on the underside of a metal structure or any structure outfitted with metal plates. The sensor package is deployed and retrieved using a drone designed to operate under structures and deliver the packages safely to regions that may not otherwise be readily accessible (such as the underside of bridges that cross rivers or busy highways). The data collected by the sensor package is then analyzed with damage detection algorithms that process the vibration data and extract the DSR, which depends only on the structural condition. Based on the correlation between data collected at a prior, known, and healthy, state and the current state, Level 1 damage detection (the presence of damage in the structure) can be determined [1].  
This paper is organized as follows: Section 2 provides the relevant background on structural health assessment using drones and vibration-based sensing algorithms. In Section 3, we present the development of the novel sensor package, the UAS for sensor deployment,and the data processing algorithm for the structural vibration data collected. The experimental validation of the proposed system along with experimental results are presented and discussed in Section 4. Finally, in Section 5, we conclude the paper and discuss future work.
## 5 Flight Verification and Data Analysis  
### The Composition of Thermal Management System of a Certain Module  
Starting from 2021, CSS has been launched following the plan, and three modules have been built on orbit so far. The payload thermal management system based on this paper has been started and tested. A large number of on-orbit data are obtained, and the on-orbit verification of the control and heat transfer performance is carried out. Taking one of the modules as an example, the comprehensive performance data are analyzed. Based on the system bus structure and the basic scheme of the thermal control system inside the rack, the fluid network of thermal control system in this module is shown in Figure 17:  
There are a total of seven payload racks in this module; each rack is connected to the TCB in parallel. Among them, four racks are equipped with gas-liquid mixed heat transfer terminals; the air-cooled thermal control resources are reserved inside the rack. The other three racks adopt the form of liquid cooling. In addition, there are three load equipment parts connected in series to the fluid loop.
###### Abstract  
Extrovascular lung water (EVLW) is a basic symptom of congestive heart failure and other conditions. Computed tomography (CT) is standard method used to assess EVLW, but it requires ionizing radiation and radiology facilities. Lung ultrasound reverberation artifacts called B-lines have been used to assess EVLW. However, analysis of B-line artifacts depends on expert interpretation and is subjective. Lung ultrasound surface wave elastography (LUSWE) was developed to measure lung surface wave speed. This pilot study aimed at measureing lung surface wave speed due to lung water in an ex vivo swine lung model. The surface wave speeds of a fresh ex vivo swine lung were measured at 100 Hz, 200 Hz, 300 Hz, and 400 Hz. An amount of water was then filled into the lung through its trachea. Ultrasound imaging was used to guide the water filling until significant changes were visible on the imaging. The lung surface wave speeds were measured again. It was found that the lung surface wave speed increases with frequency and decreases with water volume. These findings are confirmed by experimental results on an additional ex vivo swine lung sample.  
Keywords:lung ultrasound surface wave elastography (LUSWE); lung water; surface wave speed; ex vivo swine lung +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### The Task Kinematics  
Consider two independent variables to define the task space of the antenna: let azimuth be \(\alpha\in[-\pi,\pi]\subseteq\mathbb{S}\) (measured from the North) and the elevation be \(\beta\in[0,\pi/2]\subset\mathbb{S}\) (measured upwards from the observer horizontal plane), which are complementary to the platform kinematics:  
\[y\triangleq\left(\alpha\quad\beta\right)^{T}\in\mathcal{TS}\subset\mathbb{S} ^{2}.\]  
Therefore, the task space kinematics (TSK) relates the task variables to theoperational space variables, called the antenna's kinematics:  
Antenna's FK: \[\mathcal{CS}\mapsto\mathcal{OS}\mapsto\mathcal{TS}: y=f(R(q),d(q))\] Antenna's IK: \[\mathcal{CS}\leftarrow\underbrace{\mathcal{OS}\leftarrow\mathcal{ TS}}_{\text{TSK}}: q=f^{-1}(R(y),d(y))\]  
To produce the remaining TSK functions, let \(\Sigma_{u}\) be placed at some point in the antenna and let the world frame be \(\Sigma_{w}\), upon which the attitude of the desired tracking objects is defined; see Figure 5. The world frame can be placed such that the \(x_{w}\) axis points northwards, the \(z_{w}\) points upwards, and the \(y_{w}\) is defined so that the frame \(\Sigma_{w}\) fulfills the right-hand rule. Then, there exists an angle \(\alpha_{0}\) to measure the installed orientation of the platform structure _w.r.t._ to the North direction at the settling point. In addition, although the antenna's frame \(\Sigma_{a}\) is placed on an arbitrary point in the antenna (design engineer choice), its orientation is not constrained; therefore, it can be chosen parallel to the platform \(\Sigma_{p}\) to produce an identity as a rotation matrix: \(R_{p}^{a}=I\).  
In this way, there exists three homogeneous transformations, living in \(SE(3)\):  
\[\Sigma_{w} \Leftrightarrow\Sigma_{0}: H_{w}^{0}=\begin{bmatrix}R_{w}^{0}(\alpha_{0})&d_{0/w}^{(w)}\\ 0&1\end{bmatrix} \tag{8a}\] \[\Sigma_{0} \Leftrightarrow\Sigma_{p}: H_{0}^{p}(q)=\begin{bmatrix}R(q)&d(q)\\ 0&1\end{bmatrix}\] (8b) \[\Sigma_{p} \Leftrightarrow\Sigma_{a}: H_{p}^{a}=\begin{bmatrix}I_{3}&d_{a/p}^{(p)}\\ 0&1\end{bmatrix} \tag{8c}\]such that the antenna frame kinematics, _w.r.t._ the world frame \(\Sigma_{w}\), are obtained by the following product in \(SE(3)\)[18]:  
\[H_{w}^{a}(q)=H_{w}^{0}\;H_{0}^{p}(q)\;H_{p}^{a}=\begin{bmatrix}R_{w}^{a}(q)&d_{a }^{(w)}(q)\\ 0&1\end{bmatrix}\]  
from which, the attitude and position become  
\[R_{w}^{a}(q) =R_{w}^{0}\;R(q)\in SO(3) \tag{9a}\] \[d_{a}(q) =d_{0/w}^{(w)}+R_{w}^{0}\Big{(}d(q)+R(q)d_{a/p}^{(p)}\Big{)}\in \mathbb{R}^{3} \tag{9b}\]
## 4 Results
### Influence of Urea Crystallization Risk under Structural Factors
#### 4.2.2 Influence of the Nozzle Number on Crystallization Risk  
The variation of maximum urea film thickness with time under different nozzle numbers is shown in Figure 23. The maximum urea film thickness gradually increases with the extension of spray time under all operating conditions, and the growth rate increases after 100 ms ASOI time. At a time of 200 ms ASOI, the maximum urea film thickness corresponding to different nozzle numbers is between 150 and 400 \(\upmu\)m. After the 40 ms ASOI moment, the maximum urea film thickness corresponding to the 3-hole injector consistently exceeded that of other nozzle numbers. Within the range of 40\(\sim\)130 ms ASOI, the maximum urea film thickness of the 4, 5, and 6-hole injectors is basically the same. When the ASOI exceeds 130 ms, the maximum urea film thickness corresponding to the 4-hole injector begins to exceed that of the 5 and 6-hole injectors. The maximum urea film thickness corresponding to the 5- and 6-hole injectors always maintains a small gap. Overall, the nozzle number has a profound effect on the maximum urea film thickness.  
Figure 21: Variations of maximum urea film thickness with time under different rotating-stream-ray angles.  
Figure 22: Variation of urea and UWS film thickness with rotating-stream-ray angles.  
Extract urea and UWS film mass accumulation data under different nozzle numbers at 200 ms ASOI time, and compare the urea and UWS film mass accumulation under different nozzle numbers in Figure 24. The more spray holes the injector has, the more urea film mass accumulation it corresponds to. The reason is that increasing the number of orifices will increase the wall collision point of the UWS spray beam, which is conducive to the transformation of UWS droplets into urea films after they hit the wall. Similar to the situation of the maximum urea film thickness, the impact of increasing the nozzle number on the accumulation of urea film mass will gradually weaken as the nozzle number increases. For example, increasing the nozzle number from 3 to 4 increases the accumulation of urea film mass by 2%, while increasing the nozzle number from 5 to 6 increases the accumulation of urea film mass by less than 1%. Based on the impact of the nozzle number on the maximum urea film thickness and the accumulation of urea film mass, it can be concluded that increasing the nozzle number appropriately is beneficial for reducing the crystallization risk while ensuring that the atomization quality does not decrease.
## 2 Methods
### Data Analysis  
We conducted two types of data analysis: (1) overall findings and (2) specific findings. Each type is described below.  
1. Analysis of overall findings: The data trends from the 802 articles were visualized using CiteSpace software. The procedure for data analysis is described below. First, we keyed the full records in plain-text format into CiteSpace software. Next, we set the parameters, including time slicing, year span (from 2018 to 2023), and years per slice  
Figure 1: Flow chart of the literature review.  
(1 year) for country and keyword analyses. We then employed the CiteSpace software and obtained the networks and data, obtaining the countries and keywords. We also obtained the data of grants used to cluster the keywords and countries. Finally, we conducted keyword co-occurrence analysis and presented the visualization results. We set a g-index of 20 (k = 20) to ensure the selected literature demonstrated significant citation impact while controlling for node numbers to exclude irrelevant articles. A higher g-index indicates greater influence of the literature within the citation network. However, a higher g-index increases the number of nodes, indicating more irrelevant literature [38]. We excluded articles with low citations, minor influence, and limited clustering relevance, allowing for a focused analysis of works that make substantial contributions to the field. The number of articles selected after screening of the most relevant articles and keywords was 277.
2. Analysis of specific findings: These 277 articles were then assessed by two reviewers to identify the articles with the most relevant themes along with supplementary content from Google Scholar. The articles were reviewed by two independent reviewers who are not co-authors of this manuscript to avoid any potential bias. The reviewers are experts in automotive technology studies. At this stage, each result was reviewed, and the search results were refined by retaining articles related to driver behavior and emotion regulation, cross-cultural adaptation, and sensory input in driver emotion modulation. The criteria for excluding articles included the following: relevance to the research questions and objectives; methodological rigor and quality of the data; and a high impact factor of the journals to ensure credibility. Each full text was evaluated based on the exclusion criteria shown in Figure 1. It was ensured that the selected literature not only had a high impact in terms of citations, but was also closely related to our research topic. The remaining articles after peer review were categorized as follows: (1) driver emotion and behavior (13 articles), driver emotion modulation based on unisensory and multisensory inputs (34 articles), and cross-cultural impact on drivers' emotions (27 articles).
## 3 Methodology
### Foreground-Background Segmentation
If \(W(p)>\theta\), then the point is considered dynamic and is removed from the static background reconstruction; otherwise, it is retained as part of the static background. To update the dynamic list \(L\), we remove elements whenever a new frame arrives based on \(y_{ci}\). If \(y_{ci}<T_{y}\), then the corresponding element is removed from the list. Therefore, when a new frame is input, if a LiDAR point is both non-ground and falls within any mask in the list \(L=\{C_{i}\}_{i=N}^{M}\), these points are considered part of the dynamic foreground and are not included in the static background reconstruction. The method for determining whether a LiDAR point is a ground point follows the approach outlined in [13]. When processing dynamic foreground regions, our method assumes that only the ground surface \(G\) needs to be generated within these areas. However, completely ignoring dynamic objects may lead to gaps in the ground surface reconstruction. To address this, we first calculate the average ground height \(z_{G}\) along the \(z\)-axis within a radius \(r\) around the composite mask \(C_{i}\). The average height is computed as  
\[z_{G}=\frac{1}{|P_{G}|}\sum_{p\in\mathcal{P}_{G}}z_{p}, \tag{7}\]  
where \(P_{G}\) is the set of ground points within radius \(r\) around \(C_{i}\) and \(z_{p}\) is the \(z\)-coordinate of each point in \(P_{G}\). To ensure continuity of the ground surface, we generate artificial LiDAR points \(\hat{P_{G}}=\{\hat{p_{1}},\hat{p_{2}},\dots,\hat{p_{n}}\}\) at the estimated height \(z_{G}\). These points \(\hat{p_{j}}\) satisfy \[\hat{p}_{j}=(x_{j},y_{j},z_{G}),\quad j=1,2,\ldots,n, \tag{8}\]  
where \((x_{j},y_{j})\) are randomly sampled within \(C_{i}\). These artificially generated points \(\hat{p}_{j}\) are then incorporated into the reconstruction process. For each point \(p\) in the composite mask \(C_{i}\), we calculate its uncertainty; if a point is classified inconsistently between the LiDAR segmentation and the visual segmentation (i.e., marked as dynamic by one method and static by the other), then it is labeled as uncertain. To represent the degree of uncertainty, we introduce an uncertainty weight \(w_{\text{unc}}(p)\) ranging from 0 to 1:  
\[w_{\text{unc}}(p)= \begin{cases}1,&\text{if consistent in $D_{i}$ and $V_{i}$}\\ \alpha,&\text{if inconsistent in $D_{i}$ and $V_{i}$}\end{cases}  \tag{9}\]  
where \(\alpha\) is a parameter between 0 and 1 that represents the degree of uncertainty, which can be adjusted as needed. In this study, we set \(\alpha=0.9\).
## 3 Materials and Methods
### User Evaluation
#### 3.2.3 Procedures  
To investigate and explore the design opportunities presented by audio and non-visual AR interfaces, our study engaged participants in a unique evaluation process. The primary aim was to understand how users interact with and perceive AR environments through audio cues as non-visual interactions, focusing on the exploration of interface designs rather than the comparison of their efficacies. Given this focus, the evaluation was carefully designed to ensure that participants relied solely on auditory feedback without visual cues from the AR visualization.  
Upon arrival, all participants were briefed on the study's objectives, with an emphasis on our interest in non-visual experiences within AR. Each participant signed a consent form acknowledging the voluntary nature of their participation and the confidentiality of their responses.  
The evaluation sessions were conducted in a controlled environment specifically tailored to minimize visual distractions and highlight auditory interactions. Participants were comfortably seated in a quiet, dimly lit room to prevent visual influences on their experience of the AR interfaces.  
In a departure from the original design, where participants were divided into two distinct groups, each participant in the revised study design experienced both versions of the AR interface in a randomized order to mitigate order effects:  
In one session, participants interact with an interface that simulates TAI in AR, relying solely on audio feedback. Wearing blindfolds to eliminate visual input, participants explore the interface's functionalities, such as identifying objects or navigating through a virtual space using touch gestures on a device, with the AR system providing corresponding audio feedback.  
In another session, participants explore an AR interface that facilitates NAI without visual cues. Also wearing blindfolds, participants are introduced to tasks that require the performance of physical gestures in the air, with the system delivering audio feedback based on their actions. This session assesses the intuitiveness and immersion of interactions with AR content through movement and sound without visual confirmation.  
Following their interaction with both interface versions, participants completed the SUS and UES to provide quantitative data on usability and overall user experience. Additionally, in-depth interviews were conducted to gather qualitative feedback on their experience, focusing on the perceived effectiveness of audio feedback, challenges encountered during the tasks, and suggestions for enhancing the non-visual AR experience.  
The combination of quantitative and qualitative data allows for a comprehensive analysis of participants' experiences with audio and non-visual AR interfaces. This revised approach facilitates an in-depth exploration of design opportunities within non-visual AR, aiming to uncover innovative ways to enhance accessibility and user engagement through auditory and tactile interactions.
# The Exploitation of Lysosomes in Cancer Therapy with Graphene-Based Nanomaterials
###### Abstract  
Graphene-based nanomaterials (GNMs), including graphene, graphene oxide, reduced graphene oxide, and graphene quantum dots, may have direct anticancer activity or be used as nanocarriers for antitumor drugs. GNMs usually enter tumor cells by endocytosis and can accumulate in lysosomes. This accumulation prevents drugs bound to GNMs from reaching their targets, suppressing their anticancer effects. A number of chemical modifications are made to GNMs to facilitate the separation of anticancer drugs from GNMs at low lysosomal pH and to enable the lysosomal escape of drugs. Lysosomal escape may be associated with oxidative stress, permeabilization of the unstable membrane of cancer cell lysosomes, release of lysosomal enzymes into the cytoplasm, and cell death. GNMs can prevent or stimulate tumor cell death by inducing protective autophagy or suppressing autolysosomal degradation, respectively. Furthermore, because GNMs prevent bound fluorescent agents from emitting light, their separation in lysosomes may enable tumor cell identification and therapy monitoring. In this review, we explain how the characteristics of the lysosomal microenvironment and the unique features of tumor cell lysosomes can be exploited for GNM-based cancer therapy.  
graphene-based nanomaterials; graphene-based drug delivery systems; lysosomes; cancer; endosomal/lysosomal escape; lysosomal cell death