esa-sceva/satcom-chunk-collection huggingface dataset:
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 5 ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
### Design and Implementation
* The mobile node starts scanning the beacon message on specific beacon channel to discover the new neighbors. * Once the discovery is done, the mobile node starts to transmit heartbeat message on the scanning channels of the new neighbors. * The new neighbors will forward the heartbeat message to the entire network, and the other nodes will update routing table when their route update timer expires. By means of the above mechanisms, the mesh network can maintain a connectable state adaptively without introducing additional traffic overhead. To this point, we give the solution to the first and fourth challenges. **Putting it All Together:** We introduce three main algorithms of ACE. Algorithm 1 gives a high-level abstraction of the entire algorithm, containing two main phases, route creation phase, and route maintenance phase. Algorithm 2 invoked by Algorithm 1 and Algorithm 3 shows the core logic of routing table generation and update process. Algorithm 3 is invoked by Algorithm 1 and loops with the following three parts: (1) scheduling packets reception or transmission, (2) updating routes, and (3) detecting topology change and then recovering. Combined with the above algorithms, we can conclude intuitively from the example depicted in Figure 3, ACE outperforms Bluetooth Mesh in end-to-end latency due to a direct channel mapping, and collisions issue can be alleviated for efficient traffic flows of ACE. ```
start_timer(initialize_timer);  publish heartbeat packet; whileinitialize_timer is not fireddo  listen(ADV_CHANNELS); ifpkt\(\leftarrow\)packet_RX() is not NULLthen  routing_table_pointer \(\leftarrow\)update_routing_table(pkt);  end while update_neighbor_table(routing_table_pointer);  end if listen(compute_channel(self.adv_addr)); routing_maintenance0;
```  
**Algorithm 1**ACE  
```
Input: packet Output: routing_table_pointer dst_addr, adv_addr \(\leftarrow\) get_addr(packet); hop \(\leftarrow\) compute_hop(packet, INIT_TTL); forentry in routing_tabledo ifdst_addr == entry_dstthen ifentry_hop_count \(>\)hopthen  update_routing_entry(dst_addr, adv_addr, hop);  end if break;  end if end if ifnodst_addr in routing_tablethen  add_routing_entry(dst_addr, adv_addr, hop);  end if
```  
**Algorithm 2**update_routing_table
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
###### Abstract  
Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.  
Keywords:sign language recognition (SLR); two-stream CNN; SignFi; CSI; attention mechanism +
Footnote †: journal: Medical and Computer Science
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 3 Underwater Target Polarization Detection System and Experimental Scheme  
### Setting Up and Testing of the Experimental Environment  
The pulsed ranging method is to transmit a laser beam to the measured target and then measures the round-trip time of the laser to achieve distance detection by receiving the echo signal from the target after diffuse reflection. Figure 2 shows the schematic diagram of pulsed laser ranging.  
The underwater laser polarization detection system includes a 532 nm Pulse Laser, a laser ranging module, a polarization energy detection module, and a seawater tank. The hardware connection diagram and the system experimental schematic diagram are shown in Figure 3. The experiment includes the measurement of weak laser energy, polarization characteristic, distance and location.  
Connect all the hardware devices as shown in Figure 3, start the computer software, turn on the hardware power switch, power on the control box, and wait for the device to connect. After connecting, the status bar shows the database connection and device connection. Set the computer parameters, laser frequency, ranging parameters and new test project name, and set the new test project as the current test project. Turn on the power supply of the laser and prepare to start the test. Through the corresponding buttons on the toolbar, the corresponding operation will be zero, zero, start the measurement, pause the measurement, stop the measurement operation, and  
Figure 3: Hardware connection diagram and system experiment schematic diagram.  
Figure 2: Schematic diagram of pulse ranging.  
the measurement, according to the actual situation of the test operation. Test data will be automatically written to the database and displayed. Data processing, polarization degree and polarization angle will be automatically calculated after the measurement; calculation generated terrain grid. Select the list data from the view, and the flat, three-dimensional view is displayed.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 5 Conclusions  
Downscaling methods in remote sensing provide a new observational approach to estimate high resolution vegetation productivity. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products based on low resolution (1000 m) FPAR and LAI data was developed. First, the time series of downscaled high resolution FPAR and LAI were obtained on the basis of STARFM and a regression tree approach. Then high resolution GPP and NPP were estimated by using the MuSyQ GPP/NPP algorithm. At last, downscaled GPP and NPP were validated by using ground-based data and high resolution data. The results of this paper indicated the applicability and reliability of the downscaling method in generating high resolution time series GPP and NPP.  
Generally, good consistency existed between the downscaled FPAR/LAI and inverted FPAR/LAI. The time series of D_FPAR_GLASS, D_FPAR_MODIS, MODIS FPAR and GLASS FPAR agreed well in cropland, orchard, vegetable field and wetland. Second, direct validation against the ground-based data and cross validation against the high resolution data showed that downscaled GPP achieved high accuracy. GPP from the downscaled data achieved a better precision than the estimated GLASS GPP and MODIS GPP. R\({}^{2}\) between the D_GPP_GLASS and ground-based GPP could be as high as 0.78, and RMSE was only 2.69 g C:m\({}^{-2}\):d\({}^{-1}\) And R\({}^{2}\) between D_GPP_MODIS and ground-based GPP could reach 0.84, and RMSE was only 2.20 g C:m\({}^{-2}\):d\({}^{-1}\). Third, the ground-based GPP, MODIS GPP, estimated GLASS GPP, D_GPP_MODIS and D_GPP_GLASS achieved good agreements. Temporal patterns of estimated GLASS NPP, D_NPP_MODIS and D_NPP_GLASS were also generally consistent. But estimated GLASS NPP in cropland was also relatively lower than D_NPP_MODIS and D_NPP_GLASS.  
[PERSON] and [PERSON] proposed the ideas; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] preprocessed and analyzed the data; [PERSON] and [PERSON] prepared the paper.  
This research was funded by the National Key R&D Program of China, grant number 2016 YFB0501502, and the National Natural Science Foundation of China, grant number 41471349, 41531174 and 61661136006001.  
**Acknowledgments:** This work was supported by the National Key R&D Program of China (2016 YFB0501502), the National Natural Science Foundation of China (41471349, 41531174 and 61661136006001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 4 Fixed-Point-like Method for TVL2 Problem (4)  
In this section, we propose a fixed-point-like method using CGLS for solving the new TVL2 problem (4) that can be obtained by modifying Algorithm 2. Notice that Algorithm 2 computes \(\hat{a}^{k+1}\) and \(a^{k+1}\) before the solution step of finding \(u^{k+1}\) (see lines 4 and 5 of Algorithm 2). However, the fixed-point-like method to be proposed in this section computes \(\hat{a}^{k+1}\) and \(a^{k+1}\) after the solution step of finding \(u^{k+1}\). Below, we describe how to develop the fixed-point-like method in detail. We first split line 4 of Algorithm 2 into  
\[\hat{a}^{k+1}=u^{k}+a^{k+\frac{1}{2}}, \tag{30}\]  
where \(a^{k+\frac{1}{2}}=a^{k}-\text{prox}_{\frac{1}{2}\|\cdot\|_{2}}\left(u^{k}+a^{k}\right)\). Replacing the _old_ value \(u^{k}\) of Equation (30) with the _new_ value \(u^{k+1}\), one obtains the following equation:  
\[\hat{a}^{k+1}=u^{k+1}+a^{k+\frac{1}{2}}. \tag{31}\]  
Then, the solution step (i.e., line 8 of Algorithm 2) is changed to  
\[A^{T}Au^{k+1}=A^{T}f-\alpha\gamma a^{k+1}-\lambda B^{T}b^{k+1}, \tag{32}\]  
where \(\hat{a}^{k+1}\) is computed using Equation (31) instead of using Equation (30). Substituting Equation (31) into Equation (32), one obtains  
\[(A^{T}A+\alpha\gamma I)u^{k+1}=A^{T}f-\alpha\gamma a^{k+\frac{1}{2}}-\lambda B ^{T}b^{k+1}. \tag{33}\]  
After finding \(u^{k+1}\) from Equation (33), we compute \(\hat{a}^{k+1}\) using Equation (31) and \(a^{k+1}=\kappa a^{k}+(1-\kappa)\hat{a}^{k+1}\). By incorporating the above ideas into Algorithm 2, we can obtain a fixed-point-like method, called Algorithm 4, for solving TVL2 problem (4).  
In addition, notice that the linear system in line 7 of Algorithm 3 is equivalent to solving the following least squares problem:  
\[\min_{u}\left\|\left(\begin{array}{c}f\\ -\frac{1}{\sqrt{\pi\gamma}}(\alpha\gamma a^{k+\frac{1}{2}}+\lambda B^{T}b^{k+1 })\\ \end{array}\right)-\left(\begin{array}{c}A\\ \sqrt{\pi\gamma}I\\ \end{array}\right)u\right\|_{2}^{2}. \tag{34}\]  
Hence, the linear system in line 7 of Algorithm 3 is solved using the CGLS instead of using the CG.  
```
1:Given : observed image \(f\), positive parameters \(\alpha,\ \beta,\ \gamma,\ \lambda\) and \(\kappa\in(0,1)\)
2:Initialization : \(a^{0}=0,b^{0}=0\) and \(u^{0}=f\)
3:for\(k=,0\) to maxitdo
4:\(a^{k+\frac{1}{2}}=a^{k}-\text{prox}_{\frac{1}{2}\|\cdot\|_{2}}\left(u^{k}+a^{k}\right)\)
5:\(\hat{b}^{k+1}=\left(\begin{array}{c}I-\text{prox}_{\frac{\alpha}{2}\varphi} \\ \end{array}\right)\left(Bu^{k}+b^{k}\right)\)
6:\(b^{k+1}=\kappa b^{k}+(1-\kappa)\hat{b}^{k+1}\)
7: Solve \((A^{T}A+\alpha\gamma I)u^{k+1}=A^{T}f-\alpha\gamma a^{k+\frac{1}{2}}-\lambda B ^{T}b^{k+1}\) for \(u^{k+1}\)
8:\(\hat{a}^{k+1}=u^{k+1}+a^{k+\frac{1}{2}}\)
9:\(a^{k+1}=\kappa a^{k}+(1-\kappa)\hat{a}^{k+1}\)
10:if\(\frac{\|u^{k+1}-u^{k}\|_{2}}{\|u^{k+1}\|_{2}}<tol\)then
11: Stop
12:endif
13:endfor
```  
**Algorithm 3** Fixed-point-like method for TVL2 problem (4)
## Materials and Methods
### Satellite Data  
PRI was derived from MODIS on-board the Terra satellite, and the data were processed using the MAIAC algorithm. MAIAC provides a daily 1-km gridded terrestrial bidirectional reflectance factor (BRF, also known as surface reflectance) for MODIS bands 1-12, which are derived from a time series as long as 16-day moving windows of MODIS measurements. MAIAC uses a new cloud screening technique and atmospheric correction procedure which are based on time-series and spatial analyses.  
A time series of surface reflectance was extracted for the 1-km grid that included the location of the flux tower. PRI was then calculated as the average of the nine closest pixels around the Missouri Ozark site, all within 2 km of the study site. All high-quality cloud-free observations and view zenith angles <45\({}^{\circ}\) were used. PRI from the MODIS satellite sensor (MODIS PRI, also known as the chlorophyll carotenoid index (CCI) [37], and here on named PRI) was calculated as:  
MODIS PRI (CCI) = (band 11 \(-\) band 1)/(band 11 \(+\) band 1)  
where band 11 (526-536 nm) is the detection band and band 1 (620-670 nm) is the reference band.  
PRI values were standardized as described by [49; 50] to ensure positive values more comparable to commonly used remotely sensed indicators such as the normalized difference vegetation index:  
\[\text{sPRI}=(1+\text{PRI})/2\]
## 2 Materials and Methods  
### Atacama Desert Solar Spectrum  
Spectral irradiance is a key parameter for estimating the performance of solar cells, since their response is spectrally dependent on incident radiation. The spectral distribution of solar radiation depends on the characteristics of the gas and aerosol mixture that make up the local atmosphere, and it also depends on the length of the path that is travelled by the light, which is related to the air mass. These local characteristics may largely vary over time and from one place to another, thereby giving the solar spectrum a different form [28; 29; 30].  
The reference standard spectrum G173 [31] was calculated according to the North American geographic and atmospheric conditions and a resulting air mass of 1.5 [32; 33]. These conditions may differ greatly from those found in other geographical regions of the world.  
In 2018, the mean solar spectrum of the Atacama Desert [34] was calculated from the spatio-temporal means of long-term databases for local atmospheric parameters. The databases contained satellite and model retrieval data from MODIS [35; 36] and AIRS [37], provided by Era Interim [38; 39] and Giovanni [40] for the Atacama Desert area located in Chilean territory and covering longitudes from 60 W to 85 W and latitudes from 10 S to 40 S. To replicate the methodology that was used to calculate the standard spectrum G173, we used SMARTS radiative atmospheric transfer code [41; 39] and the spatio-temporal means of the atmospheric parameters of the Atacama Desert, such as aerosol optical depth (AOD) at 550 nm corrected for land, precipitable water (PW), ozone total column (TOC), relative humidity (RH), albedo, and atmospheric temperature. The consulted databases cover 10 years from January 2006 to January 2016. To validate the methodology, several points of the estimated spectral irradiance were compared with ground spectral solar irradiance measurements. The results showed that the difference between the simulation and the measurements was within the uncertainty of the instrument, with a maximum relative error of 3%. Additionally, the 18\({}^{\circ}\) sloping surface facing the sun was chosen to represent the average latitude of the Atacama Desert between 13\({}^{\circ}\) S and 30\({}^{\circ}\) S. Finally, the mean air mass at noon was calculated for the same latitudes, resulting in 1.08 mean air mass (AM1.08). (For more details, see [34].)  
Thus, the Atacama Desert's mean solar spectra was obtained using a methodology similar to the referential standard spectrum [34].  
The main characteristic of the Atacama Desert average spectrum is its blue shift with respect to the standard G173 reference spectrum, as shown in Figure 1. The scarce presence of ozone and atmospheric aerosols leads to higher levels of irradiance in the short wavelength, the ultraviolet, and the visible spectral ranges, exceeding by 55% the ultraviolet irradiance of the standard reference spectrum [34].
## 3 Results and Discussion
### Dye Toxicity  
Dark toxicity of the ZnPc and ZnPcBCH\({}_{3}\) was assessed against A549 cells. The results, given in Figure 9, show that these dyes were nontoxic in the absence of light over 24 h (with \(p\) value > 0.05).  
Figure 8: A549 cell uptake of encapsulated ZnPcBCH\({}_{3}\) at different incubation times and nanoparticle dye levels. The boxed numbers on the graph indicate the specific amounts of encapsulated ZnPcBCH\({}_{3}\) added. Higher fluorescence intensity (616\({}_{\text{Ex}}\)/700\({}_{\text{Em}}\)) indicates higher cell uptake of the encapsulated ZnPcBCH\({}_{3}\).  
Figure 7: Subcellular distribution of ZnPC and ZnPcBCH\({}_{3}\) in A549 cells. After uptake the dyes were visualized using the Texas Red light cube for ZnPc, the Cy5 cube for ZnPc-BCH\({}_{3}\) and the DAPI cube for NucBlue.  
PDT cytotoxicity upon exposure to 650 \(\pm\) 50 nm light at 1.5 mW/cm\({}^{2}\) revealed no significant cell killing for cells that were treated with free dyes, based on results from the CTB assay. However, the cell viability was significantly reduced for ZnPcBCH\({}_{3}\) encapsulated in a nanoparticle (Np) (\(p\) value = 0.003), with respect to the PBS control, empty nanoparticles, and free ZnPc-BCH\({}_{3}\) (Figure 10). Note that the actual ZnPcBCH\({}_{3}\) concentration was 500 times lower for cells receiving the nanoparticle treatment. Although the cytotoxicity of the encapsulated dye was significantly higher than the free dye, the cytotoxicity could be further enhanced by increasing the intensity and/or duration of the light exposure. The average size of mammalian cells is \(\sim\)10-30 \(\upmu\)m and subcellular organelles also can be larger than the nanoparticles. For example, mitochondria may be \(\sim\)500 nm [28]. Thus, further design and development of the nanoparticle to include mitochondrial targeting tags [29; 30] in future work may improve the efficacy of the photosensitizer.  
Figure 10: Phototoxicity of the encapsulated ZnPcBCH\({}_{3}\) vs free dyes. The A549 cells were exposed to 80 ng of ZnPc or ZnPcBCH\({}_{3}\) free dye versus 0.158 ng of ZnPcBCH\({}_{3}\) encapsulated into the PEG-b-PLGA nanoparticles. The viability of the A549 cells after dye treatment and light exposure was measured using the CTB assay.  
Figure 9: Dark toxicity results following a 24-h treatment of A549 cells with 80 nM ZnPc or ZnPcBCH\({}_{3}\) and 2% DMSO (control) using the CTB assay.
## 1 Introduction  
Over the last few years, the adoption of power electronics has increased in a variety of applications: traction applications [1; 2; 3], mining equipment [4; 5; 6], and oil production and pumping stations [7]. This became possible thanks to the development of semiconductor technologies and high-power transistors [8; 9; 10]. To control high-power transistors, a special gate driver board is required. This board converts the logical control signal into the appropriate voltage values necessary to turn the resistor on and off. A gate driver can perform transistor protection functions using hardware and algorithmic approaches [11; 12; 13]. Application-specific integrated circuits are often used in driver solutions as well as field-programmable gate array (FPGA) technologies [14; 15]. The development of application-specific integrated circuits is a time-consuming and expensive procedure, which increases the cost of the driver. This paper describes the control and protection algorithms implemented in an intelligent IGBT driver based on a low-cost microcontroller from Texas Instruments (TMS320F28002x) [16]. The gate driver protects the IGBT from short-circuiting [17; 18], overcurrent, and overvoltage, monitors the power supply, and controls the switch on and switch off processes of the transistor. The use of the microcontrollermakes the gate driver design more flexible and allows for the accurate tuning of the protection thresholds. The developed driver has the same functions and communication protocol as ISP0635V2M1-17 from CT-Concept Technologie GmbH [19]. The development process was carried out with the aim of producing a concurrent device by applying more accessible electrical components and upgrading existing protection functions. The experimental results show that all the control and protection functions were implemented successfully, enabling the low-cost implementation of the intelligent functions.
# Modeling and Evaluation of Power-Aware Software Rejuvenation in Cloud Systems
## 2 Related Work
[22] only considered the energy saving aspect of cloud data centers; in contrast, we studied the simultaneous effect of power saving and rejuvenation on cloud data centers. There are also other related studies in the field of VMM rejuvenation that presented models and schemes using techniques other than analytical modeling. [PERSON] and [PERSON] proposed VMBeam, which enables lightweight software rejuvenation of virtualized systems using zero-copy migration [30]. The proposed VMBean has been implemented in Xen, an open source hypervisor. [PERSON] et al., proposed a neural network-based approach to approximate the non-linear relationship between resource usage statistics and the time to failure of VMMs caused by aging-related errors [31]. [PERSON] et al., investigated the software aging effects in the Eucalyptus framework, considering workloads composed of intensive requests for remote storage attachment and VM instantiations [32]. They also presented an approach that applies time series analysis to schedule rejuvenation to reduce the downtime by predicting the proper moment to perform the rejuvenation. In summary, the approaches that analyze the performance, availability, and power consumption of VMM rejuvenation tasks in cloud systems using analytical models are rare. In contrast to existing work, our presented scheme allows power-aware rejuvenation of VMMs using a two-threshold mechanism. The proposed model supports PM heterogeneity, which has not been considered in previously presented models. Moreover, the model considers several other aspects of real cloud systems that have not usually been considered in previously published reports. In addition, the number of PMs/VMMs and the number of VMs that can simultaneously be serviced on a PM/VMM are larger than those modeled in other approaches.