esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Topology Optimization Model Considering Fatigue Characteristics
### Sensitivity Analysis  
Consider that the objective function in this optimization is the mean compliance and the load on the bracket is the mechanical force with prescribed angular frequency. According to article [19], the sensitivity of the objective function \(f(\rho)\) with respect to the design variables \(\rho\), while the weighted coefficients and the sequence number of boundary conditions are neglected, is given by  
\[\frac{\partial f}{\partial\rho}=2\mathbf{F}_{i}^{T}\mathbf{U}_{i}(\frac{ \partial\mathbf{F}_{i}^{T}}{\partial\rho}\mathbf{U}_{i}+\mathbf{F}_{i}^{T} \frac{\partial\mathbf{U}_{i}}{\partial\rho}) \tag{5}\]  
The sensitivity of \(F_{i}^{T}\) with respect to design variables \(\rho\) will be zero if the load is design-independent. In this article, the mechanical force is design-independent. The sensitivity of displacement vector \(\mathbf{U}\) with respect to design variables \(\rho\) is given by  
\[(\mathbf{K}(\rho)-\omega^{2}\mathbf{M}(\rho))\frac{\partial\mathbf{U}_{i}}{ \partial\rho}+(\frac{\partial\mathbf{K}(\rho)}{\partial\rho}-\omega^{2}\frac {\partial\mathbf{M}(\rho)}{\partial\rho})\mathbf{U}_{i}=\frac{\partial \mathbf{F}_{i}}{\partial\rho} \tag{6}\]  
Instead of solving Equation (6), the adjoint method (see [20]) may be used to calculate the sensitivity of the objective function in a more efficient manner, which gives the following result  
\[\frac{\partial f}{\partial\rho}=-2\mathbf{F}_{i}^{T}\mathbf{U}_{i}\Big{[} \mathbf{U}_{i}^{T}(\frac{\partial\mathbf{K}(\rho)}{\partial\rho}+\omega^{2} \frac{\partial\mathbf{M}(\rho)}{\partial\rho})\mathbf{U}_{i}\Big{]} \tag{7}\]  
where the sensitivities of the stiffness and mass matrices can be directly obtained from the SIMP material model  
\[\frac{\partial\mathbf{M}(\rho)}{\partial\rho}=\sum_{md}^{l}\mathbf{M}_{0} \tag{8}\]  
\[\frac{\partial\mathbf{K}(\rho)}{\partial\rho}=\sum_{md}^{l}pp_{md}^{p-1} \mathbf{K}_{0} \tag{9}\]  
where \(\mathbf{K}_{0}\) is the element stiffness matrix, \(\mathbf{M}_{0}\) is the element mass matrix.  
In order to avoid topology optimization results falling into local minima, checkerboard, and numerical instabilities, a sensitivity filter or density filter techniques are often introduced into the calculation of topology optimization [21]. In this paper sensitivity filtering is used to achieve clear boundaries of the topology optimization result. To generate the weighted matrix of the filter, the matrix form of the discrete Laplace equation under the finite element method with the nodes as design variables is applied. In COMSOL, this method is characterized by high efficiency, easy implementation, good filtering performance, and little external filter parameters.
# Improving Multi-Label Learning by Correlation Embedding
## 4 Experiment  
### Comparing Algorithms  
In order to verify the performance of our proposed method, the paper selects five existing state-of-the-art multi-label classification approaches to compare with MLLCE, i.e., BR, JFSC, ML-LSS, MLL-FLSDR, and Glocal. The detailed information regarding the method of comparison and the linear and non-linear proposed in this paper are as follows:  
1. **BR**[21]: The basic idea of BR is to decompose a multi-label learning problem into a set of independent binary classification sub-problems. In this paper, linear regression is adopted as the base learner for each binary classification sub-problem, where the regularization parameter is searched in \(\{0.1,1,\dots,10\}\).
2. **JFSC**[15]: JFSC is a feature selection and multi-label classification algorithm by exploiting label correlation. The search scope for parameters \(\alpha\), \(\beta\) and \(\gamma\) are \(\{4^{-5},4^{-4}\dots 4^{5}\}\). Parameter \(\eta\) is searched in \(\{0.1,1,\dots,10\}\).
3. **ML-LSS**[60]: ML-LSS is proposed for multi-label learning by modeling local similarity. Parameter \(\lambda_{1},\lambda_{2}\) are tuned in \(\{2^{-5},2^{-4},\dots,2^{6}\}\).
4. **MLL-FLSDR**[20]: A multi-label learning method based on label embedding that is used to solve the problem of many labels and features, where the parameter \(\lambda_{1}\) is searched in \(\{10^{2},10^{3},\dots,10^{6}\}\), \(\lambda_{2}\), and \(\lambda_{3}\) and \(\lambda_{4}\) are searched in \(\{10^{-3},10^{-2},\dots,10^{1}\}\).
5. **Glocal**[61]: A multi-label learning approach that utilized the global and local label correlation. The parameter \(\lambda=1\) and the parameters \(\lambda_{1}\) to \(\lambda_{5}\) are tuned in \(\{10^{-5},10^{-4}\dots 10^{1}\}\), \(k\) is searched in \(\{0.1l,0.21\dots 0.6l\}\), where \(l\) is the number of labels in each data set. \(g\) is searched in \(\{5,10,15,20\}\).
6. **MLLCE and NL-MLLCE**:The two versions of our proposed method in this paper. Parameter \(\lambda_{1}\) and \(\lambda_{2}\) are tuned in \(\{10^{-6},10^{-4},\dots,10^{2}\}\). \(d_{1}=0.3d\) is the feature dimension in the low feature space, where \(d\) is the dimension of the original feature space.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 2 Neural Networks Training for SAR Imagery
### Data Preprocessing  
The filter types and their parameters depend on the original image parameters. With a higher Signal to Noise Ratio (SNR), the filtering should be weaker.  
For the used database, the proposed filtering was in the following order: Gaussian filtering and median filtering with 7 \(\times\) 7 pixels kernels. Such an approach limits single high-value outliers thanks to Gaussian blurring, and when the median filter is applied the speckle noise is reduced.  
It is worth mentioning that the median filter is computationally expensive. Thus, it can be omitted for faster operations or replaced with bilateral filters as proposed in [17].  
The filtering is shown in Figure 5. The goal was to supply the image without filtering to the neural network to learn, while the filtered image was used as the label (expected output). This way, neural networks should be able to cope with noise/speckle features while being more resistant to noises in the loss determination phase.
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 2 Materials and Methods
### Adsorption Kinetic Experiments  
Batch-type tests were carried out to determine the kinetics removal of arsenate and fluoride with Al-GSs, separately. For arsenate removal, 10 mL aliquots of a 2 mg/L Na\({}_{2}\)HAsO\({}_{4}\). 7H\({}_{2}\)O solution at pH 7 and 0.1 g of Al-GSs were placed in centrifuge tubes and shaken for a different duration at 25 \({}^{\circ}\)C. Then, the samples were centrifuged and filtered, and the supernatants were analyzed for total arsenic by atomic absorption spectrometry (AAS) by the hydride generation method. For fluoride removal, the same procedure was carried out, but using a 10 mg/L NaF solution at pH 7 and 0.1 g of Al-GSs. The concentration of fluoride ions in the solutions was determined with a selective electrode for fluoride (Hanna HI 98402. Hanna Instruments, Woonsocket, RI, USA). Total ionic strength adjustment buffer solution (TISAB II) was added to all fluoride standards and samples to control the pH and ionic strength. These methods of arsenic and fluoride measurement were used for aqueous solutions before and after adsorption processes to determine initial and final concentrations of these elements. The quantity of fluoride and arsenic adsorbed by Al-GSs was calculated from the initial concentration by the equation [24; 25]:  
\[q=\frac{V(C_{0}-C_{e})}{M} \tag{1}\]  
where,  
\(q\) = measured sorption per unit weight of solid, (mg/g).  
\(V\) = volume of solution, (L).  
\(C_{0}\) = initial concentration of fluoride or arsenic, (mg/L).  
\(C_{e}\) = equilibrium concentration of fluoride or arsenic, (mg/L).  
\(M\) = dry weight of biosorbent (g).  
All adsorption tests were carried out in triplicate to determine the reproducibility of the results, and we considered mean values to calculate adsorption parameters. Adsorption of fluoride or arsenic was not detected on the walls of centrifuge tubes when control experiments were performed.
## 4 DD Toposphere Delay and Ionosphere Delay Analysis
### DD Toposlere Delay  
Although the estimated DD troposphere delay in Equation (9) is in the zenith direction, it needs to be projected onto the station-satellite direction through a mapping function. In this section, we first conducted precise point-positioning ambiguity resolution (PPP-AR) at each station to derive precise slant troposphere delay [(23; 24)]. Then, the DD slant troposphere delay of ambiguity-fixed satellites is constructed by using between-satellite single-difference and between-receiver single-difference. Figure 2 plots the DD slant troposphere delay for six baselines, with an elevation angle mask of 15\({}^{\circ}\). It can be seen that the DD troposphere delay is negatively correlated with the elevation angle. When the elevation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Setting** \\ \hline Elevation mask & 15\({}^{\circ}\) \\ SNR 1  & 30 dB \\ PDOP 2  & 20 \\ Code precision 3  & 0.3 m \\ Phase precision 3  & 0.003 m \\ Ionosphere delay for SPP 4  & Klobuchar model \\ Troposphere delay for SPP & Saastamoinen model with VMF1 mapping function \\ DD ionosphere delay & Estimated as random walk parameter \\ DD troposphere delay & Estimated as random walk parameter \\ Fix elevation mask 5  & Estimated as random walk parameter \\ AR mode & 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data processing strategies.  
Figure 1: (**a**) Tree medium baselines in Hangzhou; (**b**) Tree medium baselines in Wuhan.  
angle is greater than 20\({}^{\circ}\), the DD troposphere delay is generally less than 15 cm, which is less than a quarter of the wide-lane wavelength. Taking the baseline HZDQ-HZLA as an example, the root mean square error (RMSE) of DD troposphere delay for all ambiguity-fixed satellites is calculated and their mean value is 6.2 cm. As a result, it can be disregarded when solving the wide-lane ambiguity in Equation (8).
## 4 Conclusions  
This paper proposes a three-dimensional space path planning method suitable for UAV atmospheric environment detection. The proposed method uses hexagonal prism units to decompose space, the Genetic Algorithm to generate optimal paths and B-spline curves to fit the path curve. The detection space can be segmented by combining energy constraints to achieve dense and uniformly distributed coverage sampling, while generating a full coverage path that meets the UAV flight constraints to access each segmented unit. Simulation experiments show that this method can produce excellent coverage paths under energy constraints. In the practical application scenario, the data collected every day are in line with the changing trend of the data of the national control station, and the numerical error is small. The gas distribution in the detection space can be reconstructed according to the data, which can display the specific distribution of pollutants in the local space and be used to track pollution sources and for the analysis of pollutant diffusion trends. The field test results show that the UAV loaded with this planning method can fully cover the target space and collect complete space environment data, which has certain practical application value and significance.  
The limitation of the algorithm is that when the study space is large, the sampling points of the sampling design will be reduced to meet the energy limitation, which will make the collected spatial data insufficient. In addition, to achieve optimal coverage density, a fairly accurate energy model is required to assess the endurance of the UAVs. In future work, considering that the entire study space is divided into subspaces of the same size, we plan to use multiple UAVs for collaborative measurement to increase the detection range and detection coverage density.  
Conceptualization, L.Y., S.F. and B.Y.; methodology, L.Y., S.F. and B.Y.; software, L.Y. and S.F.; validation, S.F. and Y.J.; data curation, L.Y. and B.Y.; writing--original draft preparation, L.Y.; writing--review and editing, S.F. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 42075129, the S&T Program of Hebei Province of China under Grant 19210404D and the S&T Program of Hebei Province of China under Grant 21351803D.  
Not applicable.  
The authors declare no conflict of interest.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 3 System Model and Problem Definition
### Global Path Planning Problem  
The objective of the GPP problem is to find a safe trajectory that begins at an initial position \(w_{0}\) and covers areas with a high potential of being a proper landing site. As the drone has limited energy and performs the trajectory in an urban area, the planned trajectory must comply with a time-of-flight constraint and avoid NFZ.  
For a given image footprint \(\mathcal{F}_{k}\), let the set of pairs of indices \(J_{k}\) be  
\[J_{k}=\{(m,n)\text{ s.t. }c_{mn}\subset\mathcal{F}_{k}\} \tag{2}\]  
Also, given \(0<\underline{p}\leq 1\), let \(\Gamma\left(\underline{p}\right)\) be the following set of pairs of indices:  
\[\Gamma(\underline{p})=\{(m,n)\text{ s.t. }p_{mn}\geq\underline{p}\} \tag{3}\]  
With this definition, we can treat \(\underline{p}\) as a threshold value that separates the cells of interest from the remaining cells.  
Definition 1: The set of waypoints \(W^{h}=\{w_{0},w_{1},...,w_{k}\}\), \(w_{i}\in\tau^{h}\) is said to be a \(\underline{p}\) -Coverage (note that this definition is different from the one in [29] for \(k\)-coverage--the latter ensures each cell is covered by at least \(k\) covers) at altitude \(h\) if for all \((m,n)\in\Gamma(\underline{p})\) there exists at least one \(w_{i}\) such that \(c_{mn}\subset\mathcal{F}_{i}\).  
Figure 2: Area grid decomposition where each cell is of appropriate size for drone landing.  
In general, a footprint contains numerous cells; therefore, \(\underline{p}\)-Coverage sets are far from unique, and two different ones may have a different number of elements.  
Definition 2: A trajectory passing through \(W^{h}\) is a continuous map \(T:[t_{0},t_{end}]\to\pi^{h}\) such that \(T(0)=w_{0}\) and there exists a set of time instants \(\{t_{i}\}_{i=\cdot,\cdot}\) such that \(T(t_{i})=w_{i},\ \forall w_{i}\in W^{h}\). The trajectory is said to be \(\underline{p}\)-covering if \(W^{h}\) is a \(\underline{p}\)-Coverage.  
In the following, \(T\) will simply be referred to as a trajectory, since \(W^{h}\) will be clear from the context. Note that the set of time instants in the definition is, in general, not ordered--namely, \(i<j\
ot\implies t_{i}<t_{j}\).  
Definition 3: A cell \(c_{mn}\) is said to be visited by a trajectory \(T\) if \(c_{mn}\subset\mathcal{F}_{k}\) for some \(k\).  
Definition 4: A trajectory is said to be safe if \(T(t)\in\pi^{h}_{f}\) for all \(t\in[t_{0},t_{end}]\).  
As a standard when introducing new planning algorithms, no dynamic constraints are placed in the definition of feasible trajectories.
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
###### Abstract  
Upcoming smart scenarios enabled by the Internet of Things (IoT) envision smart objects that provide services that can adapt to user behavior or be managed to achieve greater productivity. In such environments, smart things are inexpensive and, therefore, constrained devices. However, they are also critical components because of the importance of the information that they provide. Given this, strong security is a requirement, but not all security mechanisms in general and access control models in particular are feasible. In this paper, we present the feasibility assessment of an access control model that utilizes a hybrid architecture and a policy language that provides dynamic fine-grained policy enforcement in the sensors, which requires an efficient message exchange protocol called Hidra. This experimental performance assessment includes a prototype implementation, a performance evaluation model, the measurements and related discussions, which demonstrate the feasibility and adequacy of the analyzed access control model.  
access control model; fine-grained authorization; constrained device; expressive policy language; least privilege; message exchange protocol; policy codification; sensor; feasibility assessment; performance evaluation +
Footnote †: journal: Sensors
## 1 Introduction
One can code the result using RGB color, which is an additive color model in which red, green and blue light is added together in various ways to reproduce a broad array of colors. Ultrasound signals containing large layer scatterers are input into the R channel, and the Rayleigh scattering signals contributed by blood flow are input into the B channel to obtain the H-scan image of the biological tissue. Second, Doppler blood flow imaging was then performed with the multi-channel signals of the H-scan with the spatiotemporal singular value decomposition clutter filtering. Afterward, the background noise corresponding to the R channel was removed by the B channel Doppler signals to obtain high-quality blood flow imaging results. This method effectively improves the resolution and contrast of Doppler power imaging in small and low-speed flow vessels. To facilitate the presentation, the H-scan subtraction Doppler imaging method is abbreviated as H-Doppler in this paper. This rest of the paper is organized as follows, the theory of H-Scan ultrasound imaging is first introduced, and then, we exhibit the principle and working sequence of H-Doppler. The results section demonstrates blood flow imaging results in phantom and in vivo experiments with the proposed methods. Finally, the discussion and conclusion sections are given at the end of the paper.
## 3 Results and Discussion
### Property Profile
#### 3.2.2 Damping Capacity and Young's Modulus  
Damping capacity of the materials is presented in Figure 5 in terms of time and amplitude of the material and Table 2 with results from the resonance frequency damping analyzer (RFDA).  
The table above shows the results of frequency, damping capacity, loss rate, and Young's modulus of each composition. The damping capacity and Young's modulus are not observed to change in E21-1.5B4C magnesium alloy but increase significantly in E21-2.5B4C magnesium alloy. The amplitude of vibration against time for each composition can be seen in the Figure 5. The time taken for each composition to stop vibration is the qualitative measurement of the damping capacity. Among the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Composition** & **Frequency (Hz)** & **Loss Rate (\%)** & **Damping Capacity** & **Young’s Modulus (GPa)** \\ \hline E21 Alloy & 8760.58 & 6.2 & 0.000225 & 48.14 \\ E21-1.5B4C Nanocomposite & 8332.76 & 5.6 & 0.000213 & 47.18 \\ E21-2.5B4C Nanocomposite & 8361.67 & 10.8 & 0.000413 & 52.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the Resonance Frequency Damping Analyzer.  
Figure 5: Damping characteristics of (**a**) Pure Mg: (**b**) Elektron 21 (E21) alloy; (**c**) E21-1.5B4C nanocomposite; and (**d**) E21-2.5B4C nanocomposite given in terms of amplitude vs. time.  
three compositions, E21-2.5B4C takes the least time (\(\sim\)0.38 s) to stop the vibration, which is far less than the time required by pure Mg to stop the vibration (\(\sim\)0.8 s). Currently, most of the currently used metallic materials exhibit low-damping capacity and, hence, special energy absorbers and dampeners are necessary to be incorporated into the dynamic structures [24]. This is taken more seriously in the aerospace sector, where the vibrations in the aircraft during flight can be extremely high [25]. Therefore, a large damping capacity is desirable for materials used in structures where unwanted vibrations are induced during operation, such as machine tool bases or crankshafts; especially in the aerospace sector, to reduce the chance of a crack in the components of the aircraft. This makes E21-2.5 wt.% B4C a very suitable material, as it offers an excellent damping capacity.  
Young's Modulus can also be seen to increase to as high as 52 GPa in E21-2.5B4C. With a higher Young's modulus, the material is deemed more desirable in structural aerospace components in service for a longer duration, and works as a better replacement for other commercially used magnesium-based materials in applications requiring high stiffness.
# Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph
## 3 Hand Motion Capture System
### System Configuration and Synchronization  
The EMG capture system uses the Trigno wireless sensors shown in Figure 5a, and has 16 EMG channels and 48 accelerometer analog channels for motion capture. Each EMG sensor has a built-in trivial accelerometer, a guaranteed transmission range of 40 m and a rechargeable battery lasting a minimum of 7 h. It employs four silver bar contacts for detecting the EMG signal at the skin surface. The top of the sensor is shaped with an arrow to aid in the determination of this orientation. The arrow should be placed parallel to the muscle fibers underneath the sensor. In order to reduce the effects of crosstalk and redundancy, the sensor should also be placed in the center of the muscle belly away from tendons and the edge of the muscle. The signal resolution is 16 bits and a sampling rate of up to 4000 Hz.  
CyberGlove I shown in Figure 5b is a fully instrumented glove, which provides up to 22 high-accuracy joint-angle measurements. The CyberGlove system has three flexion sensors per finger, four abduction sensors, a palm-arch sensor, and sensors to measure flexion and abduction. Each sensor is extremely thin and flexible, being virtually undetectable in the lightweight elastic glove. It uses proprietary resistive bend-sensing technology to accurately transform hand and finger motions into real-time digital joint-angle data. VirtualHand(r) Studio software (CyberGlove Systems, San Jose, CA, USA) is used to convert the data into a graphical hand, which mirrors the subtle movements of the physical hand. The sensor resolution is 0.5 degrees, the repeatability is one degree, and the sampling rate is 150 Hz.  
The FingerTPS system utilizes highly sensitive capacitive-based pressure sensors to reliably quantify forces applied by the human hand, as shown in Figure 5c. It is the only practical and comfortable sensor solution that also connects wirelessly to your PC. By using powerful new Chameleon TVR Software (version 2012, PPS, Los Angeles, CA, USA), FingerTPS systems can be easily reconfigured and recalibrated for different uses of the hands on the fly. The data rate of the sensors is 40 Hz. The sensors have a scale range of 10-50 lbs and a sensitivity of 0.01 lbs. Video images can be captured and displayed in real-time, synchronized with tactile data.  
Figure 5: **(a)** Trigno wireless electromyography sensor; **(b)** FingerTPS; **(c)** CyberGlove I; **(d)** Multi-sensors for one hand.  
A high-speed digital signal processor (DSP) has been used to acquire, process and send raw synchronized information digitally to a PC for analysis. The CPU speed of the DSP is far greater than 10 MHz for fast and efficient data acquisition. We utilize USB whose maximum data transfer rate is 10 megabits per second to realize the interface connection between the DSP and the computer. The resolution is set to 16 bits, and the three devices are sampled simultaneously.