esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this work, a novel FSO communication link is proposed by combining OAM beams with the SAC-OCDMA system. Two distinct OAM beams (\(LG_{0,0}\), \(LG_{0,10}\)) are used, each carrying 30 Gbps of information data for three independent channels encoded with FRS code.  
As weather conditions impact the performance of the FSO communication systems, in this work, we consider the effect of different fog conditions.  
Additionally, as dust storms mainly occur in sand-filled areas, most researchers do not focus on studying their effect; however, in this work, we consider them and study the performance of our suggested model under their effects. Furthermore, we discuss the performance of our model using two real weather conditions of two different cities having different geographical locations: Alexandria city, which is surrounded by the sea, and Srinagar city, which contains mountains.  
The performance of our proposed system is simulated and investigated in terms of BER, eye diagrams, and ROP. The obtained results show successful transmission of 60 Gbps overall capacity with acceptable BER (less than \(2.7\times 10^{-3}\)) and wider eye opening that reveals successful receiving of transmitted information.  
Simulated results under diverse fog conditions show that the longest FSO range obtained under LF is 600 m, which decreases to 425 m and 340 m when the fog level becomes moderate and heavy, respectively. In addition, our results show that dust storm weather conditions have a high impact and cause high attenuation, which makes the propagation link very short.  
The maximum FSO links our proposed system achieves are 315 m under LD, 105 m under MD, and 40 m under HD. As Alexandria city has clear weather, it causes less attenuation and a more extended propagation range of 1400 m. While the cloudy weather of Srinagar leads to achieving an FSO link of 1000 m and its hilly area makes installing optical fiber cable difficult due to difficult terrain and mountains, we propose our FSO be implemented.  
According to this study, we recommend that our model be used in the fifth-generation networks and B5G/6G, implemented in areas with different geographical locations such as deserts, seas, mountains, and smart cities.  
Conceptualization, S.A.A.E.-M. and M.S.; methodology, S.A.A.E.-M. and M.S.; software, S.A.A.E.-M. and M.S.; validation, S.A.A.E.-M., M.S., A.C., H.Y.A. and M.Z.; formal analysis, S.A.A.E.-M., M.S., H.Y.A. and A.N.K.; investigation, S.A.A.E.-M., M.S. and H.Y.A.; resources, S.A.A.E.-M. and M.S.; writing--original draft preparation, S.A.A.E.-M. and M.S.; writing--review and editing, A.C. and H.Y.A.; visualization, A.C. and H.Y.A.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Failure Law and Combined Support Technology of
## 5 Field Test and Effect Analysis
### FLAG3D Numerical Calculation Verification  
#### 5.2.1 Numerical Simulation Model  
In order to verify the effectiveness of the combined support technology, a calculation model was established using the finite difference software FLAC3D 5.0 based on the geological conditions of the track roadway. The model size is 50 m \(\times\) 30 m \(\times\) 40 m along the X, Y, and Z directions. The roadway shape is a semi-circular arch section, 5.9 m wide and 4.35 m high, as shown in Figure 11. The vertical stress of 25 MPa is applied to the model's top according to the actual in situ stress measurement results. The lateral pressure coefficient is 1.0 [20]. The boundary conditions around and at the bottom of the model are fixed, and the upper boundary conditions are free.  
In order to reflect the softening characteristics of the post-peak strength, the strain-softening constitutive model is adopted in the simulation [6]. This simulation uses the Pile structural unit to simulate the anchor rod, the cable structural unit to simulate the anchor cable, and the liner structural unit to simulate the tray.  
Figure 11: Numerical model of the track roadway.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
###### Abstract  
Recent years have witnessed tremendous advances in clouding gaming. To alleviate the bandwidth pressure due to transmissions of high-quality cloud gaming videos, this paper optimized existing video codecs with deep learning networks to reduce the bitrate consumption of cloud gaming videos. Specifically, a camera motion-guided network, i.e., CMGNet, was proposed for the reference frame enhancement, leveraging the camera motion information of cloud gaming videos and the reconstructed frames in the reference frame list. The obtained high-quality reference frame was then added to the reference frame list to improve the compression efficiency. The decoder side performs the same operation to generate the reconstructed frames using the updated reference frame list. In the CMGNet, camera motions were used as guidance to estimate the frame motion and weight masks to achieve more accurate frame alignment and fusion, respectively. As a result, the quality of the reference frame was significantly enhanced and thus being more suitable as a prediction candidate for the target frame. Experimental results demonstrate the effectiveness of the proposed algorithm, which achieves 4.91% BD-rate reduction on average. Moreover, a cloud gaming video dataset with camera motion data was made available to promote research on game video compression.  
video coding optimization; inter prediction; cloud gaming videos; frame enhancement; deep learning Article  
Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement. _Appl. Sci._**2022**, _12_, 8504. [[https://doi.org/10.3390/app12178504](https://doi.org/10.3390/app12178504)]([https://doi.org/10.3390/app12178504](https://doi.org/10.3390/app12178504))
## 2 Background  
### The Foundation of Multi-Targetstacking Using RFS  
The MTT system is different from the single target tracking system. Here, the multi-targets space model shown in Figure 1 can be more intuitively understood than the MTT system. The RFS introduced in MTT is to describe the target state and measurement. Suppose that at time \(k\), \(x_{k}^{i}(i=1:n_{k})\) express the target states and belong to the state space \(E_{S}\), and \(z_{k}^{j}(j=1:m_{k})\) express the measurement and belong to the measurement space \(E_{o}\). Then, RFS of target state and measurement at time \(k\) can be described as in Equation (1).  
\[\begin{split}\mathbf{X}_{k}&=\left\{x_{k}^{i} \right\}_{i=1}^{n_{k}}\subset E_{S}\\ \mathbf{Z}_{k}&=\left\{z_{k}^{j}\right\}_{j=1}^{m_{ k}}\subset E_{o}\end{split} \tag{1}\]  
Given a RFS of multi-targets state \(\mathbf{X}_{k-1}\) at time \(k-1\) and considering the different dynamic motion of multi-targets, the RFS of multi-targets state \(\mathbf{X}_{k}\) at time \(k\) can be modeled using Equation (2). Here, we ignore the RFS of targets spawned at time \(k\) from a target with previous state at time \(k-1\)  
\[\mathbf{X}_{k}=S_{k|k-1}(\mathbf{X}_{k-1})\cup\Gamma_{k} \tag{2}\]  
where \(S_{k|k-1}(\mathbf{X}_{k-1})\) is the RFS of survive targets from time \(k-1\) to time \(k\) and \(\Gamma_{k}\) is the RFS of new born targets at time \(k\).  
Given a RFS of multi-targets state \(\mathbf{X}_{k}\) at time \(k\) and considering the uncertainty of detection and clutter, the multi-targets measurement \(\mathbf{Z}_{k}\) received at the sensor is formed by the target generated measurement and clutter as Equation (3) shown.  
\[\mathbf{Z}_{k}=K_{k}\cup E_{k}(\mathbf{X}_{k}) \tag{3}\]  
where \(E_{k}(\mathbf{X}_{k})\) express the measurement generated by the target and \(K_{k}\) express the RFS of clutter.  
Figure 1: The space model of multi-targets tracking.  
Based on Bayesian theory, the Bayesian estimation of a random finite set can be described as follows.  
\[\begin{array}{l}f_{k|k-1}(\mathbf{X}_{k}|\mathbf{Z}_{1:k-1})=\int f_{k|k-1}( \mathbf{X}_{k}|\mathbf{X}_{k-1})f_{k-1|k-1}(\mathbf{X}_{k}|\mathbf{Z}_{1:k-1}) \delta\mathbf{X}_{k-1}\\ f_{k|k}(\mathbf{X}_{k}|\mathbf{Z}_{1:k})=\frac{g_{k|k}(\mathbf{Z}_{k}|\mathbf{X }_{k})f_{k|k-1}(\mathbf{X}_{k}|\mathbf{Z}_{1:k-1})}{\int g_{k|k}(\mathbf{Z}_{k} |\mathbf{X}_{k})f_{k|k-1}(\mathbf{X}_{k}|\mathbf{Z}_{1:k-1})\delta\mathbf{X}_{ k}}\end{array} \tag{4}\]  
where \(f_{k|k-1}(\mathbf{X}_{k}|\mathbf{X}_{k-1})\) is the state transfer function of the state RFS and \(g_{k|k}(\mathbf{Z}_{k}|\mathbf{X}_{k})\) is the likelihood function of the measurement RFS. \(f_{k|k-1}(\mathbf{X}_{k}|\mathbf{Z}_{1:k-1})\) and \(f_{k|k}(\mathbf{X}_{k}|\mathbf{Z}_{1:k})\) are the functions of prediction probability density and posterior probability of multi-targets RFS, respectively. For the integrals that are usually difficult to solve, the practical application usually uses various suboptimal algorithms to approach the multi-targets Bayesian filtering, like the PHD filter and CPHD filter.
Sensors_**2014**, _14_, 5929-5951; doi:10.3390/s140405929  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Fast and Accurate Sparse Continuous Signal Reconstruction by Homotopy DCD with Non-Convex Regularization  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1,**  
1 Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei 230027, China; E-Mails: [EMAIL_ADDRESS] (T.W.); [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (X.Y.)  
2 China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-0551-6360-1303; Fax: +86-0551-6360-1326.  
_Received: 26 January 2014; in revised form: 10 March 2014 / Accepted: 24 March 2014 / Published: 26 March 2014_  
**Abstract:** In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, _etc._, have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and H\(l_{p}\)-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis.  
**Keywords:** compressed sensing; sparse continuous signal recovery; off-grid problem; low-complexity reconstruction; non-convex regularization
## 2 Previous Work
### Tracking Algorithms
To update the location information, the sink was allowed to choose from different access points while in motion, so that source nodes can be informed about the sink location at all times. Since energy efficiency is a critical issue in WSNs, several authors have addressed energy-efficient tracking solutions. [PERSON] _et al._ proposed in [PERSON] _et al._ (2014) a monitoring system for use in military applications, such as a surveillance system, that is able to operate for long periods of time. The system, designed from the standpoint of energy efficiency, was evaluated using a network equipped with 70 MICA2 modes with dual-axis-magnetometers. [PERSON] _et al._ later developed VigilNet He _et al._ (2015), a large-scale real-time WSN system that allows detecting, tracking and classifying targets within a reasonable time, while making efficient use of energy. VigilNet is a system designed for spontaneous military operations in remote areas, where events of interest happen infrequently and with a short duration, such as intruder related events. The VigilNet network infrastructure is based on multi-path diffusion tree rooted at bases. In [PERSON] _et al._ (2014), [PERSON] _et al._[PERSON] _et al._ (2014) established the relationship between the system parameters and attributes of surveillance, applied to both fixed and moving targets. The authors adopted the model of duty-cycle planning for individual, unsynchronized nodes, allowing nodes to periodically sleep and wake up. In [PERSON] _et al._ (2014), [PERSON] _et al._ introduced an EKF-based distributed adaptive multisensor scheduling scheme for energy efficiency, to improve tracking accuracy. The sensor scheduling problem is formulated as an optimization problem and solved by a sequential three-step heuristic algorithm. Our proposed tracking algorithm differs from the former ones for several reasons. Since we adopt binary detection sensors to achieve a low-cost solution, we propose a new tracking algorithm based on information aggregation that specifically targets this type of sensors. Moreover, since we want to support mobile sinks to model intruder pursuit by the security personnel, we required a robust and efficient algorithm that quickly adapts to topology changes. To achieve this goal, we devised an algorithm with low computational complexity. Additionally, the previously reviewed proposals are focused on the application layer and do not consider the routing protocol to be used. In contrast, the proposed tracking algorithm relies on our Mobile-sink Routing for Large Grids routing protocol, which provides high efficiency and very low routing overhead in scenarios with a large number of sensors and a mobile sink.
## 5 Discussion and Conclusions
### Contribution of the Work  
In this work, a stacking model developed based on GRU and LightGBM is proposed for the sales prediction of short-shelf-life products for e-commerce. Compared with the existing prediction methods, the new combination model has several contributions. Firstly, the GRU-LightGBM inherits both the ability of the GRU model to capture timing features accurately and the ability of LightGBM to solve multivariable problems; it performs well in the case applied in this paper. Secondly, GRU-LightGBM can forecast short-shelf-life product sales accurately and efficiently. The prediction result of the GRU-LightGBM model has the smallest _RMSE_, _MAE_ and _MAPE_ values, which means that this method has the best prediction performance among all the above methods. From the results, the GRU-LightGBM model's training speed is close to that of a single deep learning model; hence, it is acceptable. Finally, GRU-LightGBM delivers interpretability based on the features' importance, which may further assist sales strategy development.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm
### Mobile Node
#### 5.1.2 Energy Usage Function  
This function retrieves the current energy level of the mobile node at any time. Algorithm 4 describes this function.  
The function starts by calculating three parameters: the _remaining energy_, the _return_energy_, and the _double_return_energy_. The _remaining energy_ is defined as the sum of the _listening_ and _moving_ energy of the node (more info can be found in Section 6.3.2).  
The _return_energy_ is defined as the energy needed from the mobile node to return to its initial position (at the sink), whereas the _double_return_energy_ is defined as double the amount of the return_energy (to give enough time for the replacing node to arrive). The level of the remaining energy of the mobile node defines the energy threshold returned from this function. When the energy level equals the double_return_energy, the function returns _TH1_, which means that the first energy threshold is triggered. When the energy level drops to the return_energy, the function returns _TH2_, which means that the second energy threshold is triggered. Otherwise, the energy level of the mobile node is normal, and the function returns _OK_.
## 3 Results and Discussion  
### Protein Expression  
We combined the chrysotile-adhesive protein, DksA, with the DM-HTM platform to enhance the efficiency of asbestos detection, and demonstrated that selective detection of chrysotile is feasible. Expression and purification of DksA protein were conducted according to the method reported byKuroda _et al._[12], with the following modifications. The optimized condition for protein expression was obtained as proved in Figure 3(a), which indicates that the sample in the Lane 2 incubated for 3 h at 37 \({}^{\circ}\)C after 0.1 mM IPTG induction shows a greater protein yield. The protein was purified using Ni-NTA His-tag affinity chromatography and separated on a 12% SDS-PAGE gel.  
Figure 3: Purification of recombinant protein. (**a**) Result of IPTG induction test. Lane 1, before induction; Lane 2, 0.1 mM IPTG at 37 \({}^{\circ}\)C for 3 h; Lane 3, 0.5 mM IPTG at 37 \({}^{\circ}\)C for 3 h; Lane 4, protein marker (size = 24 kD); Lane 5, 0.1 mM IPTG at 18 \({}^{\circ}\)C for 13 h; and Lane 6, 0.5 mM IPTG at 18 \({}^{\circ}\)C for 13 h. (**b**) Results of protein purification. Lane 1 and Lane 3 are the first fractions of eluted protein through the purification column, and Lane 2 and Lane 4 are the third fractions. (**c**) Result of peptide mapping of the recombinant protein. (**d**) Verification of the properties of DksA adhered to chrysotile.  
Figure 3(b) shows an amount of DksA and its size having a purity more than 90%. After dialysis, the final purified protein was sent for N-terminal sequencing and the exact molecular weight was determined by MALDI-TOF, as shown in Figure 3(c). The size and the concentration of the protein were 20 kD and 2 \(\upmu\)g/\(\upmu\)L, respectively. We tested the protein against three types of asbestos--chrysotile, amosite and crocidolite--to check whether the purified protein bound only to chrysotile. The asbestos samples were incubated with the protein solution for 30 min and separated by boiling. After centrifugation, the supernatant was loaded onto a 12% SDS PAGE gel. Figure 3(d) shows the presence of DksA attached to the asbestos samples. Although the protein was slightly eluted from both amosite and crocidolite, it mostly bound to chrysotile, implying that the protein strongly and specifically attaches to the chrysotile fibers.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 2 Potentials of VLC System  
In the nearest decades, wireless connectivity has become one of the basic commodities in our daily life. The rapidly increasing demand for it has resulted in the ubiquitous deployment of wireless systems as well as a heavily congested wireless spectrum that has occurred as a result. In response, VLC has been gaining in popularity as a good complement and substitute for radio frequency due to the following motivations and inherent advantages.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## Appendix A Pathloss Calculation  
Assume that \(h_{BS}\) and \(h_{UT}\) denote the height of the RSU and vehicle, respectively, \(d_{2D}\) and \(d_{3D}\) denote the projected distance between the transmitter and receiver on the ground and the LOS distance in space, respectively, and \(f_{c}\) is the carrier frequency. The LOS pathloss is expressed as  
\[PL_{\text{RMA--LOS}}=\left\{\begin{array}{cc}PL_{1}&10\ \text{m}\leq d_{2D}\leq d_{\text{BP}}\\ PL_{2}&d_{\text{BP}}\leq d_{2D}\leq\ 10\ \text{km}\end{array}\right. \tag{1}\]  
\[PL_{1}=20\text{log}_{10}(40\pi d_{3\text{D}}f_{c}/3)+\min(0.03h^{1.72},10) \text{log}_{10}(d_{3\text{D}}) \tag{2}\]  
\[PL_{2}=PL_{1}(d_{\text{BP}})+40\text{log}_{10}(d_{3\text{D}}/d_{\text{BP}}) \tag{3}\]  
where \(d_{BP}=2\pi h_{BS}h_{UT}f_{c}/c(10\ \text{m}\leq h_{\text{BS}}\leq 150\ \text{m}, 1\ \text{m}\leq h_{\text{UT}}\leq 10\ \text{m})\), and NLOS pathloss is given by  
\[PL_{RMA-NLOS}=\max(\text{PL}_{\text{RMA--LOS}},\text{PL}^{\prime}_{\text{RMA-- NLOS}}) \tag{4}\]  
\[\begin{array}{c}PL^{\prime}_{\text{RMA--NLOS}}=161.04-7.1\text{log}_{10}(W)+ 7.5\text{log}_{10}(h)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-(24.3 7-3.7(h/h_{\text{BS}})^{2})\text{log}_{10}(h_{\text{BS}})\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+(43.42-3.1\text{ log}_{10}(h_{\text{BS}}))(\text{log}_{10}(d_{3\text{D}})-3)\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+20\text{log}_{10} (f_{c})-(3.2(\text{log}_{10}(11.75h_{\text{UT}}))^{2}-4.97)\end{array} \tag{5}\]  
where \(h\) and \(W\) denote the average height of the building and the average width of the street, respectively, and their values range are 5--50 m. The probability of LOS is defined as  
\[Pr_{LOS}=\left\{\begin{array}{cc}1&,\text{d}_{2\text{D--out}}\leq 10\ \text{m}\\ \exp\Bigl{(}-\frac{\text{d}_{2\text{D--out}}-10}{1000}\Bigr{)},\text{d}_{2 \text{D--out}}>10\ \text{m}\end{array}\right. \tag{6}\]
## 4 Case study Example--Beverage Production and Distribution Machine
### Description of the System  
Figure 1 presents a beverage production and distribution machine. To explain the decomposition process, we a modified version of a beverage production and distribution machine (previously shown in [35]) is used. The presented system works as follows. The machine starts in an idle state and pressing a button on the operator console (input signal x1) initiates the production process. Two valves (output signal y1 and y2) open, and two containers (1 st container and 2 nd container) are filled with liquid ingredients. Sensors x2 and x3 signal when the upper limit of each container has been reached, respectively.  
At that point, the liquid in two containers is warmed up (y4 and y5, respectively) and a cup is being loaded on the trolley (y3). The charged cart (x4) moves to the left edge (y10) until it reaches the leftmost position (x11), and the liquid in containers is warmed until it reaches the required temperature (x5 and x6). The output valves of the two containers are opened (y6 and y7), and the ready ingredients from both containers are mixed (y8) in the main (third) container. When both upper containers (the first and second one) are empty (x7 and x8), the beverage is ready for distribution (x9). Then, the output valve of the main container opens (y9), and the liquid is poured into the cup on the trolley. Once the main container is empty (x10) and thus the cup is full, the trolley moves to the right (y11), and after it reaches the appropriate position (x12), the cup is removed from the trolley, and the system returns to its initial state.  
Figure 2 shows a Petri net model of the discussed CPS. The Petri net-based specification contains twenty places and sixteen transitions. Moreover, there are twelve input signals associated with sensors (for instance liquid level sensor) and twelve output signals assigned to physical parts of the beverage machine. The association between transitions and input signals is presented in Table 1, while Table 2 exposes the relations between places and output signals. For example, sensor x1 (pressing of the start button) is assigned to the transition t1, while filling up of the first container is signalized by output y1 (associated with place p2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transition** & **Input** & **Condition** \\ \hline t1 & x1 & Pressed start button \\ t2 & x2 & First container filled \\ t3 & x3 & Second container filled \\ t4 & x4 & Cup is put on trolley \\ t5 & x5 & First ingredients ready \\ t6 & x6 & Second ingredients ready \\ t7 & x11 & Trolley at left edge \\ t8 & - & - \\ t9 & x7 & First container empty \\ \hline \hline \end{tabular}
\end{table}
Table 1: Association between transitions and inputs.  
Figure 1: Beverage production and distribution machine.  
Figure 2: A Petri net-based model of the beverage distribution and production machine.