esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
###### Abstract  
In cache-enabled device-to-device (D2D) -aided cellular networks, the technique of caching contents in the cooperative crossing between base stations (BSs) and devices can significantly reduce core traffic and enhance network capacity. In this paper, we propose a scheme that establishes device availability, which indicates whether a cache-enabled device can handle the transmission of the desired content within the required sending time, called the delay, while achieving optimal probabilistic caching. We also investigate the impact of transmission device availability on the effectiveness of a scenario of cooperative crossing cache placement, where content delivery traffic can be offloaded from the local cache, a D2D transmitter's cache via a D2D link, or else directly from a BS via a cellular link, in order to maximize the offloading probability. Further, we derive the cooperation content offloading strategy while considering successful content transmission by D2D transmitters or BSs to guarantee the delay, even though reducing the delay is not the focus of this study. Finally, the proposed problem is formulated. Owing to the non-convexity of the optimization problem, it can be rewritten as a minimization of the difference between the convex functions; thus, it can be solved by difference of convex (DC) programming using a low-complexity algorithm. Simulation results show that the proposed cache placement scheme improves the offloading probability by 13.5% and 23% compared to Most Popular Content (MPC) scheme, in which both BSs and devices cache the most popular content and Coop. BS/D2D caching scheme, in which each BS tier and user tier applies cooperative content caching separately.  
c 2018 8 1578 10.3390/app8091578
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 3 Dark Spot Detection Using Segnet  
### Introduction to Segnet  
Segnet is a deep convolution neural network with a sound performance of image semantic segmentation [17]. The basic framework of Segnet is an encoder and a decoder. The most important components of Segnet include a convolution layer, pooling layer, up-sampling layer, and softmax layer, see Figure 3. The encoder consists of the convolution layer, batch normalization layer, and rectified linear unit (ReLU), and its structure is similar to the visual geometry group (VGG)-16 network [22]. The convolution layer is the main component of the encoder, and each output pixel is only linked to the local area of the next input layer, thus forming a local receptive field [23]. The decoder consists of a transposed convolutional layer and an up-sampling layer, and its structure is symmetrical to that of the encoder. The convolution layer corresponds to the transposed convolution layer and the max pooling layer corresponds to the up-sampling layer [24]. At the end of the decoder, the category of each pixel is output through a softmax layer.  
The training process of Segnet can be summarized as follows:  
1. Each sample in the training set and its corresponding label are input into the Segnet in sequence.
2. The cross loss entropy is used as the objective function of the training model, and its value is calculated by weighted average for all pixels in each training sample [17].
3. Through the back propagation algorithm, the weights are updated according to the minimum error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Boundary Complexity - Boundary Strength** & **No.1** & **No.2** & **No.3** & **No.4** & **No.5** \\ \hline Low - Low & - & - & - & - & 190 \\ Low - Moderate & - & - & - & - & 50 \\ Low - High & 320 & 50 & 420 & - & 30 \\ Moderate - Low & - & - & - & - & 130 \\ Moderate - Moderate & - & 140 & -  
The information in Steps (1) and Steps (2) is forward propagating, and the outputs are obtained by convolution of inputs and weights. Step (3) is the backward propagation process. According to the results of Step (2), the weights are passed to the previous layers through the backward propagation algorithm, and the weights are updated.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.5 Control Packet Overhead  
For the implicit sharing of routing information and estimating the link quality, sensor nodes must make the most use of overhearing the control packets broadcasted. However, the broadcasting of these control packets may be responsible for a large portion of energy consumption. Efficient overhearing of the control packets demands the timely wakeup of sensor nodes in ADC. However, none of the ORs have explicitly addressed the issues so far. We found that ORW [11] used continuous probing in order to estimate a link quality, which on the other hand consumes energy. In LORA [16], after consuming a quantity of energy, a node is supposed to broadcast a control packet so as to help other nodes calculatethe routing metric again. It is going to be interesting to study the relation between minimizing the control packets and operating ADC.
## Materials, Methodology and Methods  
### Materials  
Silica sand was used as natural aggregate and Portland cement CEM II/B-L 32.5 N (from Secil manufacturer, Portugal) was used as a binder. Three wastes were also incorporated in the mortars: sanitary ware powder (SWP), sanitary ware fines (SWF) and forest biomass ashes (FBA). SWP replaced sand at 20%, SWF replaced sand at 100% and FBA replaced CEM II at 15%, all by volume.  
The sanitary ware was provided by a Portuguese producer of sanitary ware pieces. These ceramics include toilets, washbasins and bidet pieces. The sanitary ware waste was provided in sizes up to 10 cm. Therefore, the waste had to go through a treatment process before its application in mortars. The wastes were crushed in a jaw crusher and a roll mill to reduce their size. Only particles lower than 149 \(\upmu\)m were used as powder waste (SWP). The SWF particles were lower than 2 mm.  
The forest biomass bottom ashes were provided by a Portuguese power generation station that produces energy by burning forest residues. The wastes already came as ashes and did not go through a treatment process before their application in mortars.  
Seven mortars were analysed in this research: a reference mortar (without wastes), three mortars with one waste only (SWP, SWF or FBA waste), two mortars with a combination of two wastes, SWF+FBA (83% of wastes incorporated) and SWP+FBA (19% of wastes incorporated) and a mortar with all wastes SWF+SWP+FBA (83% of wastes incorporated). All mortars had a volumetric ratio of binder to aggregates of 1:4.
## 3 Enabling the Enhancements of Language Features and Domain Knowledge
### Dealing with an Unordered Dependency Library Set
#### 3.3.2 Processing Output Dependency Library Set  
In order to process output dependency libraries, EMGSCC first considers the unordered set characteristic. The \"order does not matter\" problem is also suitable for the output result. In this case, each dependency library \(recd_{i}\) in the output set \(RecD=(recd_{1},recd_{2},recd_{3}\ldots recd_{n_{out}})\) can be regarded as a label category \(label_{i}\). Considering the possible mutual exclusion, dependency, inclusion and other relationships between categories, the problem of finding multiple ADLs from candidates can be regarded as a multi-label classification task.  
Let \(n_{D}\) denote the number of dependency libraries in the dataset. EMGSCC will finally output an \(n_{D}\)-dimensional vector \(probs=(prob^{\prime}_{1},\ldots,prob^{\prime}_{n_{D}})\), where \(prob^{\prime}_{i}\) represents the probability of the library numbered \(i\). Driven by the binary relevance method [21], we decompose the multi-label classification task into independent binary classification problems. To be specific, we construct \(n_{D}\) binary classifiers to output the ADLs. In order to better make the generated dependency library as close as possible to the target result set, on the basis of cross-entropy loss, the optimization goal of EMGSCC is to minimize the cross-entropy of \(n_{D}\) binary classifiers:  
\[L_{\theta}=\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{n_{D}}-(prob_{ij}\text{log}( prob^{\prime}_{ij})+(1-prob_{ij})\text{log}(1-prob^{\prime}_{ij})) \tag{7}\]  
where \(n\) is the test set size. \(prob_{ij}\) represents whether the target set of the \(i\)-th test sample contains the library numbered \(j\); if it contains this library, it assigns this value to 1, and otherwise, it assigns it to 0. \(prob^{\prime}_{ij}\) represents the probability that the library numbered \(j\) may be imported by the test project for the \(i\)-th test sample. \(\theta\) is the learned weights parameter.
## 3 Materials and Methods
### GNSS Continuous Monitoring Data and GPS Benchmark Surveying
Precise orbits from CODE analysis centre ([[ftp://ftp.unibe.ch/aiub/CODE](ftp://ftp.unibe.ch/aiub/CODE)]([ftp://ftp.unibe.ch/aiub/CODE](ftp://ftp.unibe.ch/aiub/CODE))) were used in the analysis, and a set of Earth orientation parameters was calculated. Crustal deformation caused by changing mass due to ocean tides and the atmosphere was taken into consideration, introducing auxiliary files. For the ocean loading effect, tide loading corrections were based on the FES2004 model ([[http://holt.oso.chalmers.se/loading](http://holt.oso.chalmers.se/loading)]([http://holt.oso.chalmers.se/loading](http://holt.oso.chalmers.se/loading))). Atmospheric tidal loading coefficients were used from a global grid based on the [PERSON] (2003) model [57]. For troposphere modelling, the Vienna Mapping Function ([[http://ggosatm.hg.tuwien.ac.at](http://ggosatm.hg.tuwien.ac.at)]([http://ggosatm.hg.tuwien.ac.at](http://ggosatm.hg.tuwien.ac.at))) as well as the Neill mapping function were used. Ambiguities were solved using several resolution strategies based on the baseline length and the occupation time: (i) the wide-lane and narrow-lane techniques for the medium baselines (<10-200 km), where initially the L5 linear combination is processed (wide-lane) and introduced as known (fixed) in the subsequent run (narrow-lane) where the L3 linear combination is processed; (ii) the SIGMA strategy for the very short baselines (in between the two Methana stations, where both L1 and L2 data were available and used); and finally, (iii) the quasi-ionosphere-free (QIF) strategy for the long baselines (<1000-2000 km) were the main adopted ambiguity resolution strategies. The long occupational time of the two GPS benchmark stations in Methana, during all measurement periods, resulted into low uncertainties (at a 95% confidence level) with overall errors of about 2.4-3.1 mm and 2.5-5.1 mm for the horizontal and vertical coordinates, respectively. The processing performed on daily sessions allowed for the extraction of a set of coordinates for each 24-h long period. The final set of coordinates was the mean value of all the sessions, and the errors led to quantification of the deviation of the daily coordinate solutions from the mean value.
## 2 Case Presentation
A Confida (Medtronic, Minneapolis, MN, USA) stiff wire with a preshaped loop was inserted through the pigtail to the apex of the right ventricle (Figure 3) to ensure stability for transcatheter valve crossing from the right atrium to the right ventricle. Figure 2: Cardiac computed tomography angiography measurements of the pre-existing tricuspid bioprosthesis. The 7F sheath and the pigtail were removed, and a 16F Edwards eSheath Introducer was inserted. The Edwards Delivery System with the Sapien 3 29 mm valve (Edwards Lifesciences, Irvine, CA, USA) was passed through the introducer. Under fluoroscopic (C-arm perpendicular to the bioprosthetic valve annulus) and TEE guidance, the system was advanced to the annulus of the degenerated bioprosthetic valve that served for accurate alignment and positioning of the transcatheter valve. Fine adjustment was carried with the balloon catheter (pulling and rotating to optimal centering of the valve). Prior to valve deployment, rapid pacing was initiated using the Confida guide wire as a cathode while the anode was placed in the subcutaneous tissue of the right inguinal region. The Sapien 3 valve was then deployed by expanding the balloon to the nominal pressure value with excellent results (Figure 4). The delivery system, the introducer sheath and the guide wire were removed after confirming the position and the competence of the TTVIV by TEE (Figure 5). After extracting all materials, the two purse strings were ligatured followed by meticulous hemostasis. The right lung was reinflated using  
Figure 4: Fluoroscopic image showing the normofunctional Edwards Sapien 3 valve-in-valve after deployment. Figure 3: Fluoroscopic image showing the mechanical mitral and aortic prostheses, the tricuspid bioprosthesis, prior pacing leads and the stiff wire. the Valsalva maneuver and a drainage catheter was inserted in the right costodiaphragmatic recess before closing the thoracotomy site. Postoperative evolution was uneventful, and the patient was discharged after two weeks with NYHA class II heart failure compared to IV at admission. The patient was re-evaluated at one month and three months after the intervention. TTE revealed normofunctional aortic, mitral and tricuspid valves, bi-atrial dilatation, and a residual systolic dysfunction of the right ventricle. The patient maintained NYHA class II heart failure, permanent atrial fibrillation, and cardiac cirrhosis. Bilateral pleural effusion persisted at one month (8 cm on the right and 9 cm on the left) and markedly regressed at three months (2 cm on the right and absent on the left) after the intervention. Ascitic fluid persisted at one month and was absent at three months.
## 3 Results and Discussion
### Water Activity  
Water activity (a\({}_{\mathrm{w}}\)) measures how much water is available for microorganisms to grow, influencing food products' quality and shelf life [55]. Thus, meat products with water activity above 0.95 need refrigerated storage at 5 \({}^{\circ}\)C [56]. No significant effect of turmeric on the a\({}_{\mathrm{w}}\) in the meatloaves was noted. However, a significant reduction in the a\({}_{\mathrm{w}}\) occurred in the control and T1 after seven days (from 0.981 to 0.978 and 0.976, respectively) (\(p<0.05\)), but then remained stable onwards. Similarly to other authors [6; 57], the storage time did not affect the a\({}_{\mathrm{w}}\) in T3. Their values ranged from 0.975 to 0.978 (\(p>0.05\)) (data not shown). According to [PERSON] et al. [58], the pH values of meat products range from 0.990 to 0.918, and pork meatloaf usually scores at about 0.973, which is in line with our results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \multicolumn{4}{c}{**Storage Time (Days)**} \\ \hline  & **0** & **7** & **14** & **21** \\ \hline \multicolumn{5}{c}{TBARS [mg MDA/kg of products]} \\ \hline C & \({}^{\mathrm{c}}\) 0.97 \(\pm\) 0.03 \({}^{\mathrm{C}}\) & \({}^{\mathrm{c}}\) 0.97 \(\pm\) 0.02 \({}^{\mathrm{C}}\) & \({}^{\mathrm{b}}\) 0.83 \(\pm\) 0.02 \({}^{\mathrm{B}}\) & \({}^{\mathrm{
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 2 Methodology
### Performance Assessment of Estimated Reflectance and Shadow Fraction  
The assessment of error using relative reflectance was evaluated by root mean square error (RMSE) of the reflectance for the two seasons obtained from Sentinel-2 and CSRS, respectively, as shown in Equation (4). Based on the results of step 2, images for the two seasons were selected.  
To evaluate the shadow fraction estimated by CSRS, we compared it with one calculated from the voxel model based on the point cloud data obtained by UAV-SfM. The shadow fraction was calculated under solar geometry conditions at 8:00, 10:00, 12:00, 14:00 and 16:00 on UAV observation date (30 September 2018). The corresponding solar geometry is listed in Table 3. In addition, we calculated the RMSE (Equation (5)) to evaluate the performance of the simulated shadow fraction from the virtual forest.  
\[RMSE_{reflectance}=\sqrt{\left(\frac{\rho_{satellite}(season_{1})}{\rho_{satellite }(season_{2})}-\frac{\rho_{simulation}(season_{1})}{\rho_{simulation}(season_{2})} \right)^{2}} \tag{4}\]  
where \(RMSE_{reflectance}\) is the \(RMSE\) of relative reflectance between Sentinel-2 and simulation; \(\rho_{satellite}\)(season \(i\)) is average reflectance from Sentinel-2 at season \(i\); \(\rho_{simulation}\)(season \(i\)) is the simulated reflectance at season \(i\).  
\[MAE_{shadow}=\sqrt{\frac{1}{5}\sum_{i=1}^{5}(Shadow_{3 dmodel}(i)-Shadow_{ simulation}(i))^{2}} \tag{5}\]  
where \(RMSE_{shadow}\) is the \(RMSE\) between shadow fraction calculated from the 3D model obtained from UAV-SfM and simulation.
# Laser Technology in Photonic Applications for Space
## 2 Laser Devices
### Semiconductor Lasers
#### 2.1.2 Vertical-Cavity Surface-Emitting Laser (VCSEL)  
The VCSEL differs from the EEL in its manufacturing process but, more characteristically, in its beam emission. The output laser beam of a VCSEL is emitted perpendicular to the top surface. Due to the differences in the manufacturing and packaging processes, the devices have to be independently qualified for the missions. Moreover, VCSELs require less electrical power due to their low threshold. Already in early studies, VCSELs were identified as good candidatesfor space applications. [PERSON] et al. (2014) reported that, although several factors must be taken into account to optimise VCSEL lasers for space applications, they display better radiation resistance than their EEL counterparts. [PERSON] et al. (2015), described in detail the possibility to use VCSELs as space multi-processors, as well as the issues they may exhibit when exposed to radiations. The publication focused on the implementation of devices for satellite missions in different orbits. Moreover, [PERSON] et al. (2016), demonstrated the functionality of VCSELs in harsh environmental conditions, such as low vacuum. The aim of this last work was to demonstrate that these devices could be used in space missions, such as JUpiter ICy Moon Explorer (JUICE) 2022, where the devices must be operative for about 17 years and support severe amounts of radiation. Similarly, [PERSON] et al. (2017), presented in their study of 1550 nm VCSEL devices the effects of temperature on different parameters of the optical device performances, such as optical output power, threshold current, and relative noise intensity.
_applied sciences_  
_Article_  
**Compound Boronizing and Its Kinetics Analysis for H13 Steel with Rare Earth CeO\({}_{2}\) and Cr\({}_{2}\)O\({}_{3}\)**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1,2,*, [PERSON] 1, [PERSON] 1,3,4,* and [PERSON] 1,3**  
1 Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430073, China; [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (A.G.)  
2 Department of Special Technology, Poluzunov Altai State Technical University, 656038 Barmaul, Russia  
3 Department of Mechanical Engineering, East-Siberian State University of Technology and Management, 670013 Utan-Ude, Russia  
4 Zhejiang Xinclang Sanonga Bearing Co., Ltd., Shaoxing 312500, China  
*Correspondence: [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (B.L.); Tel.: +86-27-59367841 (Y.Z)  
**Abstract**: The compound boronizing of H13 steel sample was carried out by adding rare earth CeO\({}_{2}\), and the effects of main parameters of compound boronizing on the properties of the boronizing layer were investigated. The boronizing kinetics were also analyzed. Under the conditions of boronizing temperatures of 850 \({}^{\circ}\)C, 900 \({}^{\circ}\)C, 950 \({}^{\circ}\)C, and 1000 \({}^{\circ}\)C, and boronizing times of 2 h, 3 h, 4 h, and 5 h, the boronizing experiment was carried out by adding CeO\({}_{2}\) with mass fraction of 2%, 4% and 6%, Cr\({}_{2}\)O\({}_{3}\) with a mass fraction of 1%, 2%, 3%, and 4%. The results show that boronizing H13 steel with rare earth CeO\({}_{2}\) is conducive to the diffusion of boron atoms and can significantly improve the thickness and microhardness of boronizing layer. Boronizing kinetic analysis shows that the diffusion activation energy of boron atom decreases significantly when the rare earth content is 4%, which indicates that the content of rare earth CeO\({}_{2}\) with 4% is an ideal selection for considering improving the utilization rate of rare earth materials, and the boronizing kinetic analysis can provide a quantitative basis for determining the content of rare earth metals. Therefore, experiments and analysis show that for H13 steel, when the boronizing temperature is 950 \({}^{\circ}\)C, time is 4 h and rare earth content is 4%, the thickness of the boronizing layer can reach 71 \(\upmu\)m and the microhardness at the depth of 70 \(\upmu\)m from the surface layer can reach 1546.32 HV. Moreover, on this basis, when Cr\({}_{2}\)O\({}_{3}\) with a rare earth content of 2% is added, the microstructure and wear resistance of the boronizing layer are significantly improved.  
**Keywords**: compound boronization; activation energy; boronizing kinetics; rare earth CeO\({}_{2}\); H13 steel
## 3 The Proposed Approach
### Adjustment of the Reliability Model
#### 3.1.3 Maximum Likelihood Estimation Method  
The estimation of the parameters \(\lambda\), \(\beta\), and \(\theta\) was performed to solve a problem of maximizing the likelihood function, defined according to Equation (7):  
\[\begin{split} L(\lambda,\beta,\theta)&=\prod\limits_ {k=1}^{m}\left\{\prod\limits_{\Pi}^{c_{k}}\left[\prod\limits_{i=1}^{n_{ij}} \frac{1}{\lambda^{\beta}}\cdot\beta\left(t_{k,j,i}-a(s\ _{k,j-1})T_{k,j-1}\right)^{\beta-1}\right]\right.\\ &\qquad\left.\cdot EXP\left[-\sum\limits_{j=1}^{c_{k}}(\frac{1}{ \lambda^{\beta}}\cdot(T_{k,j}-a(s_{k,j-1})T_{k,j-1})^{\beta}-\frac{1}{\lambda^ {\beta}}\right.\right.\\ &\qquad\left.\left.\left.\left(1-a(s\ _{k,j-1})\right)T_{k,j-1}\right)^{\beta}\right)\right] \right\}\end{split} \tag{7}\]  
which is defined considering \(m\) repairable systems, indicated by \(k=1,\ldots,m\) and subject to \(c_{k}\) cycles of preventive maintenance performed at times \(T_{k,j}\), with \(k=1,\ldots,m\) and \(j=1,\ldots,\)\(c_{k}\). Each of the PM actions is associated with an intervention level, which is known and denoted by \(s_{k,j}\). In each maintenance cycle \(j\), \(n_{k,j}\) failures are observed for the \(k\)th system, which occurs at times \(t_{k,j,j}\). In Equations (4) and (5) the values of \(T_{k,0}\) and \(s_{k,0}\) are equal to zero and coincide with the beginning of systems observation.
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.3.1 Behavior Tree-Based Detection Framework  
[PERSON] et al. [32] propose detection at both the client-side and server-side levels. Host-side detection involves identifying botnets based on self-concealing, dubious network traffic, and unreliable provenance. Server-side detection focuses on identifying suspicious communication with social media platforms by analyzing the content of transmitted messages or posts. [PERSON] et al. [27] present a behavior-tree-based detection framework for identifying social bots by monitoring host activity. This framework analyzes host behavior using a behavior tree constructed from real-world social bot samples. The similarity between suspicious behavior trees and a template library is calculated to determine the presence of social bots.