esa-sceva/satcom-chunk-collection huggingface dataset:
As a result, polymers are an appealing choice for the development of innovative radiation protection materials because they are inexpensive, chemically stable, lightweight, and possess excellent mechanical features. In addition, polymers are able to considerably reduce the effects of ionizing radiation by utilizing metal structures with both high atomic weights and densities as composites [11; 12; 13]. Epoxy resin is a form of reactive prepolymer and a polymer that contains epoxide groups in both its monomer and its polymer chains. These resins can either react with one another in the presence of catalysts or with a large number of other co-reactants. Epoxy resin is extensively used in the industrial sector for a wide range of projects. For example, on one hand, epoxy is the most common type of resin utilized in the electronic industry for overmolding integrated circuits, hybrid circuits, and transistors. On the other hand, the fabrication of advanced radiation shielding materials may be achieved in an efficient manner by incorporating heavy metal oxides, such as Bi\({}_{2}\)O\({}_{3}\), into the epoxy matrix [14; 15; 16; 17]. As a result of the wide variety of uses that may be found for heavy metal oxides, there has been a lot of interest in these compounds in both the academic and commercial sectors. The most obvious feature of Bi\({}_{2}\)O\({}_{3}\) is its high density, and thus a relatively high-density polymer composite is expected when high concentrations of Bi\({}_{2}\)O\({}_{3}\) are used. In addition, bismuth (Bi), which is currently under investigation as a possible candidate material for lead (Pb) replacement in radiation protection, is starting to play a useful role in the creation of next-generation shielding components with much more favorable characteristics [18; 19; 20]. In view of the significance of incorporating Bi\({}_{2}\)O\({}_{3}\) into epoxy resin as a filler, the primary emphasis of this research was on the radiation-shielding applications of epoxy matrices, which are inexpensive and simple to manufacture. In order to provide a theoretical and simulation-based contribution to the ongoing research on radiation protection using epoxy, Bi\({}_{2}\)O\({}_{3}\) particles were introduced into epoxy resin in various mass fractions. Using Monte Carlo simulations, the radiation shielding capabilities of the prepared composites were calculated, and a comprehensive study was carried out by matching the findings of the simulation with the XCOM software results.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 5 Twice Zero-Padding of Two-Dimensional Discrete Data
### The Second Zero-Paddingof 16 \(\times\) 8 Two-Dimensional Discrete Data  
According to Figure 9, it is not difficult to find that the two-dimensional discrete data after one smoothing are relatively sharp in local areas. At this time, they can be smoothed again to achieve the purpose of data smoothing.  
On the basis of one zero-padding, the new angular spectrum can be obtained by zero-padding again, as shown in Figure 10.  
The angular spectrum after two zero-paddings is transformed by IDFT to obtain two-dimensional discrete data after twice smoothing, as shown in Figure 11.  
Figure 10: Generated angular spectrum after second zero-padding.  
Figure 9: Generated two-dimensional discrete data after first smoothing.  
A comprehensive comparison of Figures 6, 9 and 11 shows that the value of the discrete data after smoothing is unchanged. The data density increases, but there is some sharpness. After twice zero-padding, the value of discrete data is unchanged, the data density increases again, and the curve is relatively smoother.  
The two-dimensional discrete data smoothing theory put forth in this paper is verified, as shown by the aforementioned numerical results. After the preceding zero-padding process, it is possible to verify that the method improves the density of the discrete data without changing the value size. In addition, the multiple computation feature of the smoothing theory for two-dimensional discrete data allows for the adjustment of data density to the proper level of computation.
## 2 Concepts, Forms, and Classification of Non-Conventional Lanes
### Main Forms of Non-Conventional Lanes
#### 2.2.6 Other Non-Conventional Forms  
Besides the main forms mentioned above, there are various other non-conventional intersection designs, including U-turns, continuous flow intersections, parallel flow intersections, etc. These designs have their own characteristics and are applicable to different traffic conditions.  
[PERSON] and [PERSON] [37] compared seven non-conventional intersection designs, including Quadrant Roadway Intersection, Median U-turn, Superstreet Median, Bowtie, Jughandle, Split Intersection, and Continuous Flow Intersection.  
[PERSON] et al. [38] evaluated the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design, which, compared to the traditional U-turn design, could reduce travel time by 29.15%, delay by 66.70%, and the number of stops by 100%.  
[PERSON] et al. [39; 40] studied the critical parameters and delay emission characteristics of median U-turns, proposing three critical left-turn ratio boundary curves. [PERSON] and [PERSON] [41] compared the performance of Crossover Displaced Left-Turn (XDL) and Upstream Signalized Crossover (USC) intersections, finding that XDL significantly outperformed USC in high-volume scenarios. [PERSON] et al. [42] analyzed the effects of U-turns on the saturation flow rates of signalized intersections, establishing a regression model between U-turn proportion and saturation flow rate. [PERSON] [43] proposed a method to increase signalized intersection capacity through non-conventional use of special width approach lanes (SWAL), which consists of two narrow approach lanes that can be dynamically utilized by either two passenger cars or one heavy vehicle depending on traffic composition, effectively enhancing the capacity of space-constrained intersections.  
[PERSON] [44] constructed a robust optimization model for geometric design and signal control coordination of tandem intersections, which, by considering fluctuations in traffic demand, saturation flow rate, and operational speed, achieved reductions in delay standard deviation and maximum values by 48% and 23%, respectively, while maintaining basically the same average delay level, significantly improving system stability. [PERSON] et al. [45] proposed a combined optimization method for lane control and signal timing of parallel flow intersections, with the four-direction configuration scheme able to increase capacity by up to 70.51%. [PERSON] et al. [46] developed planning-stage models for analyzing continuous flow intersections. [PERSON] et al. [47] proposed a continuous-flow-intersection-lite design, which performed better than conventional designs in 90% of the studied cycles. [PERSON] et al. [48] studied an innovative design for left-turn bicycles at continuous flow intersections, eliminating conflicts between left-turn bicycles and through vehicles.
## 2 Materials and Methods
### Photodynamic Treatment Protocol  
The mice were anaest with ketamine (100 mg/kg, i.p.) and xylazine (10 mg/kg, i.p.), and fixed in a stereotactic frame. The PDT was performed 30 min after intravenous injection of PS (5-ALA, ALASENS, Niopik Inc., Moscow, Russian Federation, 20 mg/kg); AlPcS, ZnPc and the galactosilated zinc phthalocyanine (GalZnPc) were synthesized according to procedures described in our previous papers [18; 19; 26; 27] with modifications depending on coordinating metal. Photodynamic treatment protocol is presented on Scheme 1.  
The epicranium was exposed by a parieto-occipital midline skin incision. With the use of a microsurgical technique, the periosteum was pushed back, and biparetal parasagittal groove-shaped trephination (\(2\times 2\) mm) was performed with a microdrill (Craniotome Drill, Aesculap Inc., Pennsylvania, USA) accompanied by a continuous irrigation with saline to prevent heating of the tissue. Special care was taken to avoid a penetration of the dura mater.  
A preliminary quantitative evaluation of the photosensitizer appearance in brain tissue was made using fluorescence detection in the red and near-infrared region (600-1000 nm). Excitation was used in the Soret-band of the sensitizers, and the light source applied was LED with maximum of emission at 400 nm and full width half maximum - FWHM = 20 nm, 25 mW output power (LED405, Polironik Ltd., Moscow, Russia). Fiberoptic probe 6 + 1 was used for delivery of excitation and emission light from the brain tissues. Microspectrometer USB4000 (OceanOptics Inc., Dunedin, USA) was used for detection of the exogenous fluorescence signals.  
For irradiation at 635 nm, we used a high-power continuous-wave red LED (XPeBRD-L1-0000-00901, CREE, Inc.) emitting up to 1 W output light power with maximum at 635 nm, with a possibility to control the emitted-light power density in the range of 20-200 mW/cm\({}^{2}\) at a step of 10 mW/cm\({}^{2}\), as measured on the tip surface of an 8-mm diameter solid light-guide. The spectral width of the emitted light at full width at half maximum (FWHM) of intensity was 30 nm. For irradiation at 670 nm we used a laser diode (RLT67300T, [PERSON], Vienna, Austria) with 300 mW maximum output power.  
The murine brains sensitized with phthalocyanine compounds were also irradiated with a constant power density of 40 mW/cm\({}^{2}\) and exposure times of 62.5 s, 125 s, and 250 s to achieve a light dose of 2.5 J/cm\({}^{2}\), 5 J/cm\({}^{2}\) and 10 J/cm\({}^{2}\), respectively, for all types of photosensitive drugs applied.  
Figure 1: Illustration of the time points for phthalocyanine injection and laser irradiation in relation to photodynamic treatment-mediated opening of the BBB and the brain collection.  
The heating of the brain tissue caused by exposure to light was monitored by using a thermocouple data logger (Pico Technology, USB TC-08, Cambridgeshire, UK).  
No significant temperature increase was observed during the light irradiation of the brain tissues. The temperature rise did not exceed 2 \({}^{\circ}\)C above the mouse body level of 28 \({}^{\circ}\)C for the irradiation doses chosen.
## 2 Method
### SPAC Encoder  
A spatial pyramid atrous convolution encoder (SPAC Encoder) is proposed to obtain the spatial features of objects of various sizes in remote sensing images and avoid the disappearance of small objects due to the loss of information in the convolution process. Based on the spatial pyramid principle, SPAC Encoder extracts and fuses feature information from feature maps by using atrous convolution with different dilation rates. This module is simple and has fewer convolutional layers. It aims to extract relatively low-order pixel-level features from original images and retain the semantic information as much as possible.  
The structure of SPAC Encoder proposed in this paper is shown in Figure 4. The processing of input image by SPAC Encoder mainly includes three parts. In the first part, the original image is processed with the atrous convolution of \(d=2\) (dilation rate). The expression is as follows:  
\[y_{1}=f_{d=2}(x),x\in R^{C\times H_{0}\times W_{0}} \tag{6}\]  
where \(f_{d=2}\) is atrous convolution of \(d=2\). In the second part, the feature maps in the first part of the output are respectively processed with three different expansion rates of the atrous convolution, and then using the global average pooling operation, a unified output size is output, resulting in three of the same size feature maps. The expression is as follows:  
\[y_{2k}=f_{d=2\times k}(y_{1}),k=1,2,3 \tag{7}\]  
where \(y_{2k}\in R^{C\times H_{2k}\times W_{2k}}\) is output of atrous convolution with \(d=2\times k\) in the part. In the third part, the cat function is first used to join the three feature maps in the second part of the output, and then \(1\times 1\) convolution is used to adjust the channel number of feature maps. The expression is as follows:  
\[y_{3}=g(y_{21}\oplus y_{22}\oplus y_{23}) \tag{8}\]  
where \(\oplus\) is concatenation with channel, \(g\) is \(1\times 1\) convolution to adjust the number of channels, and \(y_{3}\in R^{C\times\frac{H_{0}}{4}\times\frac{W_{0}}{4}}\) is final output of SPAC Encoder. To sum up, a feature map with the same output size as the 4-times downsampled feature map extracted by SFA Encoder is finally obtained by SPAC Encoder, which is involved in the subsequent fusion. In addition, in the SPAC Encoder, each convolutional layer is normalized by BN [25] and activated by GeLU [26].
## 2 Materials and Methods
### Assessment of Antioxidant Activity  
Antioxidant activity was assessed using several indicators, such as 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (ABTS) [18]. The DPPH free radical scavenging activity of various NPs--chemically synthesized Au NPs (Che-AuNPs; product reference: 741965), chemically synthesized Ag NPs (Che-AgNPs; CAS number 7440-22-4), bio-capped Ag NPs, bio-capped Au NPs, and bio-capped Au-Ag alloy NPs--was evaluated as follows: 2 mL of NP solution or MQLE at different concentrations was mixed with 0.5 mL of 0.25 mM DPPH solution in ethanol and incubated in the dark for 30 min. Absorbance was measured at 517 nm by using an UV-Vis spectrophotometer. The percentage of scavenging activity was plotted against concentration to derive half-maximal inhibitory concentration (IC\({}_{50}\)) values [19].  
The ABTS free radical scavenging assay was performed following a previously described method [20]. ABTS solution (7 mM) was mixed with an equal volume of potassium persulfate (2.45 mM) and allowed to react in the dark for 16 h. The resultant ABTS\({}^{\prime}\bullet\) solution was diluted until its absorbance reached 0.7 at 734 nm. Then, 20 \(\upmu\)L of NP solution or MQLE at various concentrations was mixed with 180 \(\upmu\)L of the ABTS\({}^{+}\bullet\) solution and incubated for 2 h. Absorbance was measured at 734 nm by using a microplate reader. The percentage of ABTS\({}^{\prime}\bullet\) scavenging along with IC\({}_{50}\) values was calculated. All measurements were performed in triplicate.
## 4 Coverage Path Planning
### Coverage Path Planning for Special Terrain
#### 4.2.1 Area Decomposition  
The traditional region decomposition method involves dividing the region and then covering each sub-region in order, which may result in the unnecessary duplication of paths and an increase in the elevation difference in the paths in special terrain with large undulations. To minimize the energy consumption of the operation, this paper proposes a special terrain-oriented region decomposition method that employs the backtracking technique to prioritize the coverage of special terrain regions encountered by the robot during its operation. The robot then returns to the point where it exited the simple terrain region to continue completing the coverage. This approach effectively avoids generating too many repeated paths during the transition to the next region after traversing a certain region. The specific operation process is illustrated in Figure 7. Firstly, during the map construction stage, areas with flat or minimally undulating terrain are classified as simple terrain areas, while areas with complex terrain and significant undulations, such as slopes and pits, are classified as special terrain areas. During the traversal process, the simple terrain coverage method is utilized to cover the simple terrain. For instance, the robot detects the presence of special terrain area rasters in the surrounding rasters of the current point, records the current point, marks the location of this special terrain area raster, and then enters the special terrain area to cover it. After completing the coverage of the special terrain area, the robot returns to the recorded point to continue covering the original simple terrain area until the next special terrain area is encountered.
# Graph Dilated Network with Rejection Mechanism
## 3 Preliminaries  
We begin with summarizing the common GNN models and, along the way, introduce our notations. We represent a graph \(G\) as \((V,E)\), where \(V\) denotes the node set and \(E\) is the edge set. Let \(A\) be the adjacency matrix of \(G\). The \(k\)-hop neighborhood \(N_{k}(v)\) of node \(v\) is the set of all nodes reaching to node \(v\) in \(k\) steps exactly. As default, \(N(v)\) is the 1-hop neighborhood of node \(v\). Each node \(v\in V\) is associated with a feature vector \(X_{v}\) and a label \(y_{v}\).  
Most GNNs aim to learn a node representation mapping function by using the graph structure and node features. Current GNNs follow a \"message propagation\" scheme, where at each iteration, the node can aggregate the information (i.e., \"messages\") from its neighbors. Therefore, after \(k\) iterations of propagation, the node representation can capture \(k\)-hop structure information. Formally, at the \(k\)-th layer, a GNN can be expressed as  
\[m_{v}^{(k)}=AGGREGATE\left(\{h_{u}^{(k-1)}:u\in N(v)\}\right) \tag{1}\]  
\[h_{v}^{(k)}=COMBINE\left(m_{v}^{(k)},h_{v}^{(k-1)}\right) \tag{2}\]  
where \(h_{v}^{(k)}\) is the representation (i.e., \"message\") of node \(v\) at the \(k\)-th layer, which captures the \(k\) hop structure information, and \(m_{v}^{(k)}\) can be taken as the integrated message representation from neighbors. The initialization message \(h_{v}^{0}=X_{v}\). _AGGREGATE_ is a message propagation function which aggregates information from neighbors, and _COMBINE_ is a function that combines the information from different hops. An illustration is shown in Figure 1. All the information from the neighbors (i.e., nodes 2-6) of the center node 1 is merged by AGGREGATE. Then, _COMBINE_ combines the information from the neighbors and the center node 1.  
Figure 1: The illustrations of the _AGGREGATE_ and _COMBINE_ functions. To learn the representation of node 1, the _AGGREGATE_ function aggregates the neighbor information of node 1. Then, the _COMBINE_ function combines the aggregated information and the information of node 1 itself.  
Different models have different propagation functions. For example, Graph Convolutional Network (GCN) [17] proposes a degree-normalized algorithm to aggregate neighbors' messages. The propagation rule (i.e., Equation (1)) of GCN can be presented as  
\[h_{v}^{(k)}=\sigma\left(W^{(k)}\sum_{u\in\{v\}\cup N(v)}\frac{h_{u}^{(k-1)}}{ \sqrt{|N(u)||N(v)|}}\right) \tag{3}\]  
where \(W^{(k)}\) is the weight matrix. _AGGREGATE_ and _COMBINE_ are fused into one function (i.e., \(u\in N(v)\) in Equation (1) and \(u\in\{v\}\cup N(v)\) in Equation (3)), and \(\sigma\) is a nonlinear function.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 6 Performance Analysis and Computational Complexity
### Performance Analysis
#### 6.1.3 Reasonable Buffer Threshold  
The transmission action with sufficiently large buffer threshold is shown in Figure 9. In order to use the optimal transmission power of Equation (36), which is the optimal solution of (34), the buffer threshold should be large enough. In this paper, the minimum buffer threshold required for obtaining the best solution of (34) is called the reasonable buffer threshold \(B_{r}=2B_{1}\). And \(B_{1}\) will be derived below.  
Figure 8: A linearly varying channel state series.  
Constraint (35) can be derived as  
\[\begin{split}\int_{t_{1}}^{t_{5}}R_{t}\ dt&=\int_{t_{1} }^{t_{5}}\rho log_{2}(1+P_{t}c_{t})dt=\rho\int_{t_{1}}^{t_{5}}log_{2}(\mu c_{t} )dt\\ &=\rho T_{s}log_{2}(\mu)+\rho\int_{t_{1}}^{t_{5}}D_{2}h_{t}dt\\ &=\rho T_{s}log_{2}(\mu)+\rho D_{2}(2h_{m}-k_{h}T_{s})T_{s}/2\\ &=N_{t_{0}}^{t_{5}}\end{split}, \tag{37}\]  
where \(T_{s}=t_{5}-t_{1}\) is the length of transmission time during the time of \(t_{0}\) to \(t_{5}\).  
Assume that the transmission rate becomes positive from \(t_{1}\), so \(P_{t_{1}}=\mu-\frac{1}{c_{t_{1}}}=0\), and  
\[log_{2}\mu=-log_{2}c_{t_{1}}=-D_{2}h_{t_{1}}=k_{h}D_{2}T_{s}-D_{2}h_{m}. \tag{38}\]  
Combining (37) and (38), \(t_{1}\) can be calculated as  
\[t_{1}=t_{5}-T_{s}, \tag{39}\] \[T_{s}^{2}=\frac{2\lambda(t_{5}-t_{0})}{k_{h}\rho D_{2}}. \tag{40}\]  
And \(\mu\) is calculated by (38) and (40).  
If the buffer threshold \(B_{c}\) is long enough, the transmission action is shown in Figure 9. The transmission rate will gradually increase from zero and eventually exceed the data arrival rate. The buffer length will rise to a maximum at \(t_{3}\); so, at \(t_{3}\), the transmission rate is equal to the arrival rate,  
\[R_{t_{3}}=\rho log_{2}(\mu c_{t_{3}})=\rho(log_{2}\mu+log_{2}c_{t_{3}})=\rho( log_{2}\mu+D_{2}(h_{0}+k_{h}t_{3}))=\lambda. \tag{41}\]  
Figure 9: Transmission action when buffer threshold is long enough (\(B_{c}\geq 2B_{1}\)), for case 1.  
\(t_{3}\) can be calculated as  
\[t_{3}=\frac{1}{k_{h}}\bigg{(}\frac{1}{D_{2}}(\lambda/\rho-log_{2}(\mu))-h_{0} \bigg{)}. \tag{42}\]  
From \(t_{0}\) to \(t_{3}\), the transmission amount of data is  
\[TN(t_{1},t_{3})=\int_{t_{1}}^{t_{3}}R_{t}dt\ =\rho\int_{t_{1}}^{t_{3}}log_{2}( \mu c_{t})dt=\rho(t_{3}-t_{1})log_{2}(\mu)+\rho D_{2}(h_{3}+h_{1})(t_{3}-t_{1} )/2. \tag{43}\]  
From \(t_{0}\) to \(t_{3}\), the amount of arrival data is \((t_{3}-t_{0})\lambda\), so the increment of buffer length is  
\[B_{1}=(t_{3}-t_{0})\lambda-TN(t_{1},t_{3}). \tag{44}\]  
In order to use the optimal transmit power of Equation (36) from \(t_{0}\) to \(t_{5}\), the minimum buffer threshold required, i.e., the reasonable buffer threshold is \(B_{r}=2B_{1}\). Then, the transmission performance will be derived in three cases, which are divided according to the buffer threshold.
# Research on Reliability Assessment of Mechanical Equipment Based on the Performance-Feature Model
## 4 Case Study
### Distribution and Verification of Pseudo Failure Characteristics  
Gaussian and Logarithmic distribution, as the alternatives, are chosen to calculate the parameters of distribution depending on the different models between the feature value and the tool wear.  
Gaussian distribution can be expressed by Equation (3) and used in model A.  
Logarithmic distribution can be expressed by Equation (13) and used in model B.  
\[f_{Logarithm}(x)=a\log_{10}x+b \tag{13}\]  
Figure 3: Time-dependent trend chart for Gn of vibration signals in the milling process.  
Tables 2 and 3 show the parameter \(a\), \(b\), and \(c\) calculated for these samples' distribution, and relevancy (RE) is shown in Tables 2 and 3. As these tables show, the distribution fitting of model A and model B are highly correlated.  
Assuming that the failure threshold of the cutting tool wear was 0.16 mm, according to the degradation model, we calculated the PFC of each failure threshold value. The results are shown in Table 4. From the chart, it can be seen that the PFCs obtained from Gaussian distribution were larger than those obtained from Logarithmic distribution.  
Failure characteristics of cutting tools are assumed to be an exponential distribution, in which maximum likelihood estimation is calculated, and the goodness of fit test is done based on the Kolmogorov-Smirnov (KS) test.  
Nine groups of samples were arranged to obtain their empirical distribution function and calculated \(\delta_{i}\). The results are shown in Tables 5 and 6.  
According to the critical value table, the critical value \(d_{n,\alpha}\) was equal to 0.51332 at the 0.05 significance level, and \(D_{n}\) can be obtained in Tables 5 and 6. Since \(D_{n}<d_{n,\alpha}\), both model A and model B followed the hypothesis of exponential distribution.