esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 5 Attitude and Velocity Control Commands
### Velocity Controller  
Given the Equation (3), we know the UAV is underactuated since \(\mathbf{u}_{1}\) contains only a thrust \(T_{x}^{b}\) along \(\mathbf{x}^{b}\). Hence, our approach introduces a velocity scalar and then utilizes the backstepping method to design a velocity controller to make the velocity scalar converge to a desired value \(\mathbf{v}_{d}\). The speed scalar of the UAV is defined as  
\[v_{UAV}=\sqrt{\big{(}\mathbf{v}_{UAV}^{b}\big{)}^{\mathrm{T}}\mathbf{v}_{UAV}^{b}} \tag{40}\]  
Taking the derivative of Equation (40), we obtain  
\[\dot{\mathbf{v}}_{UAV}=\frac{1}{\mathbf{v}_{UAV}}\Big{(}\mathbf{v}_{UAV}^{b}\Big{)}^{ \mathrm{T}}\dot{\mathbf{v}}_{UAV}^{b}=\frac{\Big{(}\mathbf{v}_{UAV}^{b}\Big{)}^{ \mathrm{T}}\dot{F}_{qg}^{b}\Big{(}\mathbf{v}_{UAV}^{b}\Big{)}}{m\mathbf{v}_{UAV}}+ \frac{\mathbf{v}_{UAV_{x}}^{b}T_{x}^{b}}{m\mathbf{v}_{UAV}} \tag{41}\]  
where we use the fact, \(\mathbf{v}_{1}^{\mathrm{T}}S(\mathbf{v}_{2})\mathbf{v}_{1}=0\). The desired velocity scalar is a constant \(v_{d}>0\), and the velocity error is defined as \(\hat{v}:=v_{UAV}-v_{d}\). Given Equation (41) and the introduced variables \(x_{1}=\int_{0}^{t}\hat{v}\ d\tau\) and \(x_{2}=\hat{v}\), we find  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=\frac{\big{(}\mathbf{v}_{UAV}^{b}\big{)}^{\mathrm{T}}\dot{F}_{qg}^{b} \big{(}\mathbf{v}_{UAV}^{b}\big{)}}{m\mathbf{v}_{UAV}}+\frac{\mathbf{v}_{UAV_{x}}^{b}T_{x} ^{b}}{m\mathbf{v}_{UAV}}\end{array}\right. \tag{42}\]  
We further introduce a Lyapunov function \(V_{1}=\frac{1}{2}x_{1}^{2}\), then take the derivative of it, giving \(\dot{V}_{1}=x_{1}x_{2}\). Another Lyapunov function is constructed as \(V_{2}=V_{1}+\frac{1}{2}z^{2}\), where \(z=x_{2}+k_{1}x_{1}\). Taking the derivative of \(V_{2}\) gives  
\[\dot{V}_{2}=\dot{V}_{1}+z\dot{z}=x_{1}z-k_{1}x_{1}^{2}+z\big{(}\dot{x}_{2}+k_{ 1}x_{2}\big{)}=x_{1}z-k_{1}x_{1}^{2}+z\left(\frac{\Big{(}\mathbf{v}_{UAV}^{b} \Big{)}^{\mathrm{T}}\dot{F}_{qg}^{b}\Big{(}\mathbf{v}_{UAV}^{b}\Big{)}}{m\mathbf{v}_{ UAV}}+\frac{\mathbf{v}_{UAV_{x}}^{b}T_{x}^{b}}{m\mathbf{v}_{UAV}}+k_{1}x_{2}\right) \tag{43}\]  
Above, a control input of the thrust is designed as  
\[T_{x}^{b}=\frac{m\mathbf{v}_{UAV}}{\mathbf{v}_{UAV_{x}}^{b}}\left(-\frac{\Big{(}\mathbf{v} _{UAV}^{b}\Big{)}^{\mathrm{T}}\dot{F}_{qg}^{b}\Big{(}\mathbf{v}_{UAV}^{b}\Big{)}} {m\mathbf{v}_{UAV}}-k_{1}x_{2}-x_{1}-k_{2}z\right) \tag{44}\]  
where parameters \(k_{1}\), \(k_{2}>0\). Substituting Equation (44) into Equation (43), we find  
\[\dot{V}_{2}=-k_{1}x_{1}^{2}-k_{2}z^{2}\leq-k_{1}x_{1}^{2}-k_{2}{x_{2}}^{2} \tag{45}\]  
According to the Lyapunov stability theory, when \(\left(x_{1},x_{2}\right)\rightarrow\left(0,0\right),\) the velocity error \(\hat{v}\) will go to zero.
## 4 Results
### Penetration Outcome
#### 4.2.1 GNSS Spoofing  
Whether the vehicle is in an active GNSS connection or not, the spoofing attack did not reflect a noticeable change in vehicle behaviour or metrics. In a disconnected state, the GNSS signal information remained the same according to the on-board monitor. Such a status is displayed through an orange symbol indicating that the vehicle is not receiving valid GNSS data. The main reasoning for such results is the dismissed access to the system logs and the limitation on the testing equipment or system knowledge. A lack of power in BladeRFx40, a safety device set up by the vehicle, or the angle of arrival of the signals to the GNSS antenna can be examples of such reasoning. On the same note, as the GNSS antenna is located on the roof of the vehicle, it is possible that the radio waves emitted by BladeRF were not received. Without access to the on-board computer logs or indications of the exact position of the vehicle, it is difficult to state the exact reason for the shuttle's inability to connect to our signals. These results were identical for all three positions and both vehicle configurations, totalling six tests.
###### Abstract
For accurate cephalometric analyses, we need to localize as many landmarks as possible, as only in this way can we diagnose discrepancies or patients' face disharmony, predict skull growth, or plan treatments. In this study, we will adapt the architecture of the state-of-the-art SCN network in order to detect 72 cephalometric landmarks on highly variable X-ray images. The aim is, on the one hand, to increase the capacity of the CNN (i.e., the ability to learn several different transformation functions), while maintaining approximately the same number of free parameters (degrees of freedom--DoF) as the basic SCN network has. The latter is achieved by expanding the local appearance and spatial configuration components of the SCN network, and not by a raw increase of filters' sizes and numbers of channels. Maintaining DoF while increasing network capacity is important, especially for a small learning set and limited computer resources, which is often the case in healthcare. This, in turn, means a better ability to train such an NN and prevent overfitting. The effectiveness of our proposed SCN-EXT method was confirmed experimentally by detecting 72 cephalometric landmarks on a challenging private database of 4695 cephalograms. The contribution of this research work is summarized in  
1. The development of a sophisticated landmark detection algorithm, where this algorithm is built on the state-of-the-art SpatialConfiguration-Net neural network. 2. Introduction of the most effective algorithm for the detection of 72 cephalometric landmarks on the lateral skull X-ray images. 3. The first study that assesses the effectiveness of the state-of-the-art cephalometric landmark detection algorithms on a large number of landmarks and on a large number of testing images. This article is structured as follows. A short overview of cephalometric landmarks' classification and employed evaluation databases is given in Section 2. A novel cephalometric landmark detection algorithm based on the SpatialConfiguration-Net architecture is described in detail in Section 3. Some considerations about the proposed method implementation and CNN training are clarified in Section 4. This section also introduces the evaluation metrics used in our experiments. Section 5 presents some of the results obtained on the public and private databases, followed by Section 6, which emphasizes certain aspects of our detection method. Section 7 concludes this paper briefly with some hints about future work.
## 4 Analysis of the Existing Datasets
### CUHK Avenue Dataset  
There are 21 test videos of anomalous events and 16 training videos of normal events in the Avenue dataset [80]. The Chinese University of Hong Kong (CUHK) is where the videos were shot, and they were released in 2013. A total of 47 abnormal events are included, including walking in the wrong direction, running, dancing, throwing objects, and similar anomalous actions. Clips recorded outdoors have a rate of 25 Frames Per Second (FPS) and a resolution of \(640\times 360\). The ground truth is available at both the pixel level and the frame level. Some examples of abnormal frames are shown in Figure 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Algorithms** & **Strengths** & **Weaknesses** \\ \hline Reconstruction-based & A compact representation of normal data can be learned, allowing subtle or complex anomalies to be detected. & They are generally less well-suited for real-time applications as they are more computationally expensive than statistics-based algorithms. A large amount of normal data for training is also required, which can be difficult to obtain in some scenarios. & They are not robust to noise, and false alarms may occur when the data are noisy or corrupted. \\ Prediction-based & Temporal dependencies in the data can be leveraged to detect anomalies, making them well-suited for time series data. & They are generally less well-suited for real-time applications as they are more computationally expensive than statistics-based algorithms. A large amount of normal data for training is also required, which can be difficult to obtain in some scenarios. & They are generally less well-suited for real-time applications as they are more computationally expensive than statistics-based algorithms. A large amount of normal data for training is also required, which can be difficult to obtain in some scenarios. & A large amount of normal data for training is also required, which can be difficult to obtain in some scenarios. \\ Prediction-based & Temporal dependencies in the data can be leveraged to detect anomalies, making them well-suited for time series data. & They do not possess robustness to noise, and false alarms can be produced when the data are noisy or corrupted. \\  & High-dimensional datasets can be handled, and global and local anomalies can be detected. & A anomalies involving spatial or temporal relationship changes may be difficult to detect. \\  & & They do not possess robustness to noise, and false alarms can be produced when the data are noisy or corrupted. \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Heterogeneous** & **Specific** \\ \hline UCSD & Subway \\ Avenue & UMN \\ UCF-Crime & \\ ShanghaiTech & \\ \hline \end{tabular}
\end{table}
Table 7: Category of video surveillance datasets.
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 1 Introduction
[30] highlighted the higher risks of offshore rigging and slinging operations compared to land-based work and proposed the development of training programs to improve worker safety. [PERSON] et al. [31] reviewed failure cases, preemptive replacements, and degradation phenomena across the mooring industry, emphasizing the importance of chain integrity management. These prior studies have contributed significantly to improving the safety of mooring chains by elucidating the effects of cyclic loading, corrosion, and residual stresses on fatigue life and structural integrity. However, research remains insufficient regarding the specific effects of non-standard connections--such as the direct attachment of chains to eyebolts without master links--and the impact of long-term use and aging frequently observed in real-world industrial environments. Non-standard connections are known to alter stress concentration patterns and deformation behaviors within the chain, thereby increasing the risk of fatigue failure. Yet, quantitative analyses of such effects are still rare. Similarly, experimental and analytical approaches to understanding the initiation and propagation of microcracks in aged chains are limited. Therefore, ensuring structural stability and durability under these conditions is an essential task for improving safety in the field. In this study, we analyze real-world chain failure cases to identify the root causes and mechanical behavior changes. Through failure testing, the durability of the chains is experimentally validated, and finite element analysis (FEA) is conducted to systematically assess structural changes under non-standard usage conditions, specifically when chains are directly connected to eyebolts without master links. FEA is a widely used tool that precisely predicts stress distribution and deformation characteristics, allowing identification of potential failure sites and areas of stress concentration within chain components. Using this approach, the complex load conditions and connection configurations commonly encountered in industrial settings are quantitatively evaluated for their effects on chain durability and failure potential. Based on the findings, the study aims to propose practical and specific guidelines for improving the safe use of chains in industrial environments, ultimately contributing to worker safety and accident prevention.
## 2 Background
### Graph Theory  
A simple network or graph, \(G\), is defined by the set of nodes, \(V\), the set of edges, \(E\), and the adjacency matrix, \(\mathbf{A}\). The adjacency matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\) is an undirected, symmetric and non-negative matrix that reflects the pair-wise affinity between nodes, where \(a_{ij}\in[0,1]\) for weighted networks and \(a_{ij}\in\{0,1\}\) for binary networks. A diagonal degree matrix, \(\mathbf{D}\), is defined from \(\mathbf{A}\), where \(d_{ii}=\sum_{j=1}^{n}a_{ij}\)[23; 24]. \(\mathbf{A}_{\mathbf{N}}=\mathbf{D}^{-0.5}\mathbf{A}\mathbf{D}^{-0.5}\) defines a normalized version of the adjacency matrix, which is a positive semi-definite matrix.  
An \(M\)-layer and \(n\)-vertex multiplex network, \(\mathcal{G}\), is utilized as a group of simple graphs, \(\{G^{(m)}\}\), where \(G^{(m)}=(V^{(m)},E^{(m)},\mathbf{A}^{(m)})\) with \(m\in\{1,\ldots,M\}\). \(\mathbf{A}^{(m)}\in\mathbb{R}^{n\times n}\) refers to the adjacency matrix of the \(m^{th}\) layer.
## 4 Statistical Model
### Fractional Factorial Design
#### 4.2.1 Fixed-Effects Model  
In the fixed-effects model, the levels of the factors are specific levels of interest to the researcher. We assume that these levels are fixed and are only interested in the effects at these particular levels. This model is applicable when assessing the impact of specific treatments or experimental conditions on the response variable.  
In this article, we establish the following model:  
Let \(Y_{ijkl}\) be the observation value at the \(i\) level of the LR, the \(j\) level of DR 1, the \(k\) level of DR 2, and the \(l\) level of convolution KS. All observation values \(Y_{ijkl}\) are mutually independent and follow a normal distribution with a constant variance \(\sigma^{2}\).  
We assume that the number of levels for each factor is \(r=5\), \(c=5\), \(t=5\), and \(u=2\), and each combination has the same number of replicate observations \(s>1\). Therefore, the total number of observations is \(n=r\times c\times t\times u\times s\).  
The formula for the fixed effects model is as follows:  
\[Y_{ijkl}=\mu+\alpha_{i}+\beta_{j}+\gamma_{k}+\delta_{l}+(\alpha\beta)_{ij}+... +(\gamma\delta)_{kl}+\epsilon_{ijkl} \tag{8}\]  
where \(\mu\) is the overall mean, \(\alpha_{i}\) is the main effect of the learning rate, \(\beta_{j}\) is the main effect of DR 1, \(\gamma_{k}\) is the main effect of DR 2, \(\delta_{l}\) is the main effect of convolution kernel size, and \(\epsilon_{ijkl}\) is the random error, which follows \(N(0,\sigma^{2})\). The rest are two-level interaction effects.  
Through permutation tests, we test each \((i,v)\):  
\[H_{vl}:\epsilon_{v}^{T}Z_{i}=0\quad\text{vs.}\quad K_{vl}:\epsilon_{v}^{T}Z_{ i}\
eq 0 \tag{9}\]  
where \(Z_{i}\) is the aligned data vector at level \(i\). We use \(\epsilon_{v}\) to represent the orthogonal vector associated with degree \(v\). Thus, \(e_{v}^{T}Z_{i}\) is the projection of the aligned data vector at level \(i\) to degree \(v\). The null hypothesis \(H_{vl}\) indicates no significant interaction effect \(i\) and degree \(v\).  
When conducting hypothesis tests, we typically use the F-test to evaluate whether the model's main and interaction effects are significant. The sum of squares for main effects (\(SS_{A}\), \(SS_{B}\)) and the sum of squares for interaction effects (\(SS_{AB}\)) measure the influence of different factors on the response variable.  
For example, for LR (\(A\)) and DR 1 (\(B\)), the formulas for calculating the sum of squares for the main effects and interaction effects are as follows:  
\[SS_{A}=s\sum_{i=1}^{r}(\bar{Y}_{i..}-\bar{Y})^{2} \tag{10}\]  
\[SS_{B}=s\sum_{j=1}^{c}(\bar{Y}_{j..}-\bar{Y})^{2} \tag{11}\]  
\[SS_{AB}=s\sum_{i=1}^{r}\sum_{j=1}^{c}(\bar{Y}_{ij..}-\bar{Y}_{i..}-\bar{Y}_{j.. }+\bar{Y})^{2} \tag{12}\]
## 3 Results and Discussion
### Earth Pressure Outside the Tunnel Segment Analysis  
Figure 9 shows the monitoring data of earth pressure arranged outside the tunnel segment. The damage to the pressure gauge outside the B1 segment leads to abnormal data that cannot be analyzed. According to the results of soil pressure changes, it can be found that the soil water pressure on each segment increases gradually with the development of the grouting time. The maximum earth pressures on segments L2, L1, B3, B5, B6, and B7 are 0.1638, 0.3179, 0.4124, 0.2678, 0.3503, and 0.2592 MPa, respectively. The earth pressure gauge shows no noticeable pressure change in the shield tail brush of the shield machine. The pressure increases sharply when the shield machine advances until the earth pressure gauge enters the shield tail brush. This is because, before the grounded segment pressure gauge enters the shield tail brush, it is in contact with the air in the shield machine, while the shield tail brush is a sealed chamber with a certain pressure. When it enters the shield tail brush, it is immediately under the pressure of the sealed chamber.  
Figure 8: Surface deformation: (**a**) section of DBC-L-101; (**b**) section of DBC-L-130.  
The pressure change outside the segment tends to be stable after grouting for about 1 h. Moreover, it can be seen from the 105-h monitoring data that the upper segment pressure has increased significantly while the lower segment pressure has increased slightly. Section 3.1 shows apparent geological changes near Ring 110, and the grouting pressure and amount have been adjusted. At this time, the shield tail is about 7 m away from the monitoring section, indicating that the change of grouting parameters within the 7 m range will affect the segment pressure within this range. Since the grouting entrance is located on the upper side of the section, the segment in the upper range will receive more strokes and be affected more significantly. After the earth pressure monitoring section comes out of the shield tail brush, the segment pressure will increase with the grouting pressure, and the pressure will become stable after five days. In addition, the earth pressure on the upper side of the lining structure is smaller than that on the lower side. This is because, in the whole grouting process, with the continuous increase in grouting volume, the shield tail pores are gradually filled, and the grouting body, as a part of the tunnel structure, will directly bear and transmit the pressure from the soil mass.
## 4 Bioavailability of Forskolin  
The administration form of forskolin depends on the tissue target, but it is a poorly water-soluble compound, which limits both its topical and its oral bioavailability. Despite this low bioavailability, forskolin has shown to be more potent than natural and synthetic analogs [97].  
Some studies have evaluated different forms of administration than suspension to improve ocular bioavailability when administered to eyes. In this respect, [PERSON] et al. (2009) tested several solubilization eye-compatible polymeric agents. Polyoxyethylene-polyoxypropylene block copolymer (Pluronic\({}^{\text{R}}\) F-127) increased 40 times the drug solubility in water (up to 120 mg/100 mL). It also prolonged the duration of the hypotensive effects of forskolin with respect to a 1.0% traditional suspension of this compound in rabbits presenting increased IOP [98]. More recently, a formulation based on forskolin nanocrystals stabilized by poloxamer 407 and Noveon AA-1 polycarbophil/poloxamer 407 gel was able to reduce IOP in rabbits around 31% and lasted for 12 h, better than the effect produced by traditional suspension (18%, up to 6 h) [99]. Proper vehicles, such as in situ gel forming systems, may also increase the contact time of this compound on the cornea [100], while nanoencapsulation within polymeric system provided sustained drug release and enhanced permeation profile with maximum depth penetration [101]. Other potential vehicles are through the formation of forskolin nanoemulsions [102] and cocrystals able to enhance the water solubility properties of forskolin [103].  
Concerning oral bioavailability, recent studies suggest that forskolin could be absorbed in all segments of the intestine with an effective permeability in the range of drugs with high intestinal permeability, but it was a saturable transport process mediated by P-glycoprotein. The authors estimated that, after oral administration in humans, the absorbed fraction of dissolved forskolin could be close to 100% [104]. Moreover, forskolin can bind to human serum albumin, which could play a role in the pharmacokinetics of this compound [105] and it could be the basis of nanoparticles [106].
## 2 Materials and Methods
### Proposed Algorithm Pipeline
Figure 4: Steps to get droplet edge points using the local gradient estimation method. (**a**) shows the key points representing the top of the droplet (S) and the contact point between the droplet and the base on the left side (E), the corresponding midpoint (M), and the edge pixel (in yellow). (**b–d**) show a repetition of the previous process, with edge pixels as key points. (**e**) shows the same previous process on the right side of the droplet. (**f**) shows the retrieved edge points and the active contour line. (**g**) shows the retrieved edge points on the original image. (**h**) shows the edge points along the boundaries of the droplet as well as the contact area between the droplet and the base. Figure 5: The edge points in the contact area between the droplet and the base on the left side of the droplet. (**a**) shows the edge points obtained from the gradient method and the active contour line. (**b**) shows the edge points on the original image. The pseudo code for the edge detection algorithm based on local gradient estimation is given below in Algorithm 1. ```
Input: IMAGE, an image of a copper specimen; Output: OUT, a set of edge points; Convert a grayscale image to a binary image based on threshold; Call activecontour with image, mask; Call edge with segmented image; Find LIND indices and values of nonzero elements on the edge line; Return the matrices r and c containing the equivalent row and column in the matrix LIND; Find S the point on top of the droplet; Find E the contact point between the droplet and the base on the left side; Compute M the midpoint between S and E; Calculate Grad the gradient along the bisector passing through M; OUT \(\leftarrow\) max(Grad); Repeat the previous process between each two successive edge points starting at (S) and ending with (E), Return OUT;
```  
**Algorithm 1**An edge detection algorithm based on local gradient estimation
## 3 Results
### Ionospheric Response to the Storm of 7-9 October 2012
Table 2 presents ionospheric response during the recovery phase for six intense geomagnetic storms at Darwin, Townsville, and Canberra stations. An increase and a decrease in _fo_F2 signify the positive and negative ionospheric storms, respectively. The negative ionospheric storms were more pronounced compared to positive ionospheric storms and were of longer duration as shown for examples in Figures 1-6 for 30 September to 3 October and 7-10 October storms. Similar long duration negative ionospheric storm was shown by 13-14 November geomagnetic storms at all three stations. The positive ionospheric storms occurred due to the 8-9 March storm and 14-15 July storm (except at Canberra) with a maximum \(\Delta\)_fo_F2% of about + 231% for the July storm at the Darwin station. The storm of 7-10 October produced a strong negative ionospheric storm at all three stations with a maximum \(\Delta\)_fo_F2% of \(-\)65.1% at Townsville. The storm of 23-24 April produced no effect at any of the three stations. A comparison of Tables 1 and 2 shows that during the main phase of the geomagnetic storms, the positive ionospheric storms occurred while during the recovery phase both positive and negative ionospheric storms occurred. The negative ionospheric storms, in general, were stronger and of longer durations (e.g., Figures 1-6) as compared to positive ionospheric storms.