esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 5 Experiment and Performance Verification
### Performance Comparison  
In Figure 6, we compare the average total rewards of four methods: EMC-PPO, EMC-PPO-NO, CLOUD, and Random. The reward value represents the weighted sum of cloud computing energy consumption and processing delay for each event. A higher reward value implies lower energy consumption and processing delay. The EMC-PPO algorithm achieved the highest reward in the final phase and had the highest reward value in the convergence phase. Although the initial reward value of the random offloading scheme was higher than that of the EMC-PPO algorithm, it decreased as the number of training rounds increased. The final results of the EMC-PPO offloading scheme were superior. The possibility of this situation arose because the random strategy lacks the ability to adapt to changes in environmental dynamics and cannot be optimized based on environmental states and historical experience. Since the bandwidth in this paper was set to simulate real-world conditions, it fluctuated randomly. In the early training phase of the EMC-PPO algorithm, due to the lack of sufficient experience, its performance may not have been as good as the random strategy. However, it could continuously adjust the strategy based on the reward signal, gradually finding the optimal offloading strategy, thereby reducing  
Figure 5: MEC-PPO convergence under different discounts.  
the total energy consumption and processing delay and ultimately achieving the highest reward value. Offloading all to the central cloud, although significantly reducing processing delay and energy consumption, increased the cost of transmission delay, resulting in poor overall performance. Without collaboration between edge clouds, tasks may have needed to be offloaded multiple times to the central cloud for processing, rather than being completed directly between edge clouds, thus increasing communication overhead and energy consumption.  
Figure 7 summarizes the reward situation under different numbers of edge computing nodes. As the number of edge devices increased, the speed at which the reward value improved accelerated. Although the rate of improvement varied, the final stable value tended to be consistent across all scenarios, stabilizing around \(-50\). The fluctuation was relatively large in the initial stage, but as training progressed, the volatility gradually decreased and eventually stabilized. This indicates that within a certain range, increasing the number of edge nodes could enhance the system's learning efficiency by providing more space for collaboration.  
Figure 6: Reward values and convergence graphs for different uninstallation strategies
## 2 Materials and Methods
### Assessment of Cell Viability  
The necrosis and apoptosis induction were studied by staining cell cultures with 1 uM of propidium iodide (PI) and 1 uM of Hoechst 33342 (HO342). Hoechst 33342 stains the chromatin of viable cells, but its fluorescence is 3-4 times higher in cells with apoptosis, which is explained by chromatin condensation. Propidium iodide stains the nuclei of necrotic cells, but does not penetrate into viable cells [20; 21]. PI and Hoechst 33342 fluorescence was recorded using an Axio Observer Z1 inverted fluorescent microscope supplied with Hamamatsu ORCA-Flash 2.8 high-speed monochrome CCD-camera. The illuminator Lambda DG-4 Plus (Sutter Instruments, Novato, CA, USA) was used as an excitation source. Probe fluorescence was excited and registered using Filter Set 01 with the excitation filter BP 365/12, beam splitter FT395, emission filter LP 397; Filter Set 20 with the excitation filter BP 546/12, beam splitter FT560, emission filter BP 575-640. Objective HCX PL APO 20.0 \(\times\) 0.70 IMM UV, refraction index 1.52 was used.
## 4 Experimental Results and Discussion  
In order to verify the reasonability of the proposed improved TSETMF algorithm, classical grayscale images in three different scenarios were used as samples in contrast experiments. Three filtering methods relevant to high-density denoising were used to compare with the proposed improved TSETMF, including the pixel-density-based trimmed median filter (PDBTMF) [58], modified decision-based median filter (MDBMF) [59], and adaptive dual-threshold median filter (ADTMF) [25]. Additionally, two assessment methods, including the peak signal-to-noise ratio and the structural similarity index measure (SSIM), were employed to carry out the experimental comparison analysis and discussion.
## 3 Results and Discussion
### Sulfonated Silica Nanofluids  
This section addresses the behaviour of sulfonated silica. Sulfonated silica NP were prepared by the method described earlier Section 2.2 and dispersed at 1 g/L concentration in DIW. The effluent concentration profile during the post flush for core saturated with 1 g/L surface modified (sulfonated) NP in DIW is shown in Figure 10a. For the sake of comparison, the effluent concentration profiles for cores saturated with 1 g/L unmodified NP and NP stabilized with MPTMS are also shown in Figure 10a. It may be observed that the behaviour of sulfonated NP was similar to silica NP. That is the majority of the NP were produced in the first 0.5 PV. Contrary to that with MPTMS (stabilizing fluid), the NP production continues for 1 PV. Based on mass balance it was calculated that 74.6% of the sulphonated NPs were adsorbed in the core. Pressure drop profile recorded with sulphonated NP is shown in Figure 10b. As may be observed that the entry resistance post application of sulfonated NP was lower than the initial DIW injection. This observation was consistent with the case of the post flushing for cores saturated with unmodified NP. As the NP were being produced, high variation frequency was observed in the pressure drop. Thereafter the pressure drop profile declined smoother and almost overlaps with the initial DIW injection. In summary, the application of sulfonated NP did not lead improvement of water injectivity.  
Figure 10: (**a**) Comparison of effluent concentration profiles of the different types of nanofluid (concentration of NP: 1 g/L) silica, sulfonated and silica with stabilizing fluid; (**b**) Pressure drop profiles for core treated with sulfonated NP.  
Figure 9: Absorbed MPTMS for cores saturated with nanofluid stabilized with MPTMS (Experiments: 4–8).  
Two important observations are made from the application of nanofluids and SEM imaging conducted: (1) The preferential adsorption of the silica NPs on quartz mineral, which to the best knowledge of the authors have not been reported previously and (2) The water injectivity improvement was observed upon the application of silica NPs only. That is, the two nanofluid stabilization methods tend to reduce the effectiveness of the surface modification by the NPs. These observations are further investigated and strengthened in latter sections.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## 1 Introduction
### Trends of Modern RF PA Research  
It is widely known that the RF PA is the most power-hungry component in radio transceivers and is also one of the most critical building blocks in radio front-end applications. Hence, research in this area will help drive overall 5G network costs down while achieving improved energy efficiency. A research study has been conducted in [9], which focused on investigating the development trend of RF PAs and describing the globalization, cooperation across affiliations, research cycle and architecture trends. Figure 2 presents an updated graph published in [9] adding traveling wave (TWA) and distributed PA to the overall number of published PA papers and the trend line picture.  
Various advanced PA architectures have been proposed throughout the years and demonstrated for increasing RF PA efficiency without losing linearity or even with improved linearity, including envelope elimination and restoration (EER), envelope tracking (ET), linear amplification using nonlinear components (LINC) and Doherty (DPA) [9]. Two more RF PA architectures that have a huge impact on modern RF PAs haven't been mentioned in [9] and are named TWA and distributed PA.  
Figure 1: 5G band licensing per geographical area [7].
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 3 Methods
### Iterative Filter Adaptive Module  
The IFA module [4] was added at the bottom of the U-net. IFA takes the defocused feature map extracted by the network as input and outputs the deblurred feature map. In the IFA module, I\({}_{\text{b}}\), which is the same as the main network input, was introduced to predict the deblurring filter. IFA consists of a filter encoder, a filter predictor, and an Iterative Adaptive Convolution layer (IAC). The filter encoder encodes I\({}_{\text{b}}\) as e\({}^{\text{F}}\in\) R\({}^{\text{h}\times\text{w}\times 256}\), the filter predictor predicts the deblurring filter map F\({}_{\text{deblur}}\in\) R\({}^{\text{h}\times\text{w}\times\text{c}}\)\({}_{\text{deblur}}\), where F\({}_{\text{deblur}}\) is c (2k + 1), c is 256, and k is 3, and finally, the IAC layer uses the prediction filter F\({}_{\text{deblur}}\) to transform the input feature e\({}_{\text{g}}\) to generate the deblurring feature e\({}_{\text{g}}\).  
The Iterative Aggregation and Convolution (IAC) layer operates on the input F\({}_{\text{deblur}}\), where each spatial location in F\({}_{\text{deblur}}\) is represented as an Nc (2k + 1)-dim vector, which corresponds to N sets of filters {F\({}_{1}\), F\({}_{2}\), \(\cdot\), \(\cdot\), F\({}_{\text{N}}\)}. The n-th filter set F\({}_{\text{n}}\) has two 1-dimensional filters, \(f_{1}^{n}\) and \(f_{2}^{n}\), whose size is k \(\times\) 1 and 1 \(\times\) k, respectively, and a bias vector \(b^{n}\). The IAC layer reshapes \(f_{1}^{n}\), \(f_{2}^{n}\), and \(b^{n}\) into a filter whose size is k \(\times\) k and the channel is c. Corresponding to each spatial location, a filter is generated with channel c. The IAC layer decomposes the vectors in each position F into filter and bias vectors and iteratively applies them to eB in a channel fashion to generate the output feature map. The process is shown in Figure 4.  
Figure 3: Application of wavelet transform in the encoding and decoding process.
## 3 Analysis of Kinematics
### Velocity Analysis  
If vector \(\mathbf{v}_{q}=\left[v_{q_{1}}\,v_{q_{2}}\,v_{q_{3}}\right]^{\mathrm{T}}\) is the velocity vector for the sliders on the three axes and vector \(\mathbf{v}=\left[v_{x}\,v_{y}\,v_{z}\right]^{\mathrm{T}}\) is the velocity vector for the moving platform, the Jacobian matrix \(\mathbf{J}_{p}\) between \(\mathbf{v}_{q}\) and \(\mathbf{v}\) is defined as [21; 22]:  
\[\mathbf{v}_{q}=\mathbf{J}_{p}\mathbf{v} \tag{16}\]  
Differentiating Equation (8) with time yields:  
\[\left\{\begin{array}{l}2q_{1}v_{q_{1}}-2v_{q_{1}}-2v_{q_{1}}x-2q_{1}v_{x}+2 rv_{x}+2 xv_{x}+2 vv_{y}+2 zv_{z}=0\\ 2q_{2}v_{q_{2}}-2 rv_{q_{2}}+v_{q_{2}}x+q_{2}v_{x}-rv_{x}-\sqrt{3}v_{q_{2}}y- \sqrt{3}q_{2}v_{y}+\sqrt{3}rv_{y}+2 xv_{x}+2 yv_{y}+2 zv_{z}=0\\ 2q_{3}v_{q_{3}}-2 rv_{q_{3}}+v_{q_{3}}x+q_{3}v_{x}-rv_{x}+\sqrt{3}v_{q_{3}}y+ \sqrt{3}q_{3}v_{y}-\sqrt{3}rv_{y}+2 xv_{x}+2 yv_{y}+2 zv_{z}=0\end{array}\right. \tag{17}\]  
Its matrix form is:  
\[\mathbf{D}\left[\begin{array}{c}v_{q_{1}}\\ v_{q_{2}}\\ v_{q_{3}}\end{array}\right]=\mathbf{H}\left[\begin{array}{c}v_{x}\\ v_{y}\\ v_{z}\end{array}\right] \tag{18}\]  
where  
\[\mathbf{D}=\left[\begin{array}{ccc}d_{11}&0&0\\ 0&d_{22}&0\\ 0&0&d_{33}\end{array}\right]=\left[\begin{array}{ccc}-2q_{1}+2r+2x&0&0\\ 0&-2q_{2}+2r-x+\sqrt{3}y&0\\ 0&0&-2q_{3}+2r-x-\sqrt{3}y\end{array}\right] \tag{19}\]  
Additionally,  
\[\mathbf{H}=\left[\begin{array}{ccc}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{array}\right]=\left[\begin{array}{ccc}-2q_{1}+2r+ 2x&2y&2z\\ q_{2}-r+2x&-\sqrt{3}q_{2}+\sqrt{3}r+2y&2z\\ q_{3}-r+2x&\sqrt{3}q_{2}-\sqrt{3}r+2y&2z\end{array}\right] \tag{20}\]  
From Equations (16) and (18), we have:  
\[\mathbf{J}_{p}=\mathbf{D}^{-1}\mathbf{H} \tag{21}\]  
where  
\[\mathbf{D}^{-1}=\left[\begin{array}{ccc}1/d_{11}&0&0\\ 0&1/d_{22}&0\\ 0&0&1/d_{33}\end{array}\right] \tag{22}\]  
The determinant of \(\mathbf{J}_{p}\) is calculated as:  
\[\left|\mathbf{J}_{p}\right|=\left|\mathbf{D}^{-1}\right|\mathbf{H}|=\frac{1} {d_{11}d_{22}d_{33}}\begin{array}{c|cc}-2q_{1}+2r+2x&2y&2z\\ 2q_{1}+q_{2}-3r&-\sqrt{3}q_{2}+\sqrt{3}r&0\\ 2q_{1}+q_{3}-3r&\sqrt{3}q_{3}-\sqrt{3}r&0\end{array}\right| \tag{23}\]  
Obviously, singularities occur for the following conditions:  
\[z=0 \tag{24}\]  
Contrastingly,  
\[q_{1}=q_{2}=q_{3}=r \tag{25}\]Equations (24) and (25) identify the singularities at which \(z=0\) or \(q_{1}=q_{2}=q_{3}=r\). There is no singularity at \(z=0\), because it implies that the moving platform is on the fixed base. Additionally, the initial length \(r\) of the moving platform is set to 60 mm so the singularity \(q_{1}=q_{2}=q_{3}=r\) possibly occurs when the three input actuated joint variables \(q_{1}\), \(q_{2}\) and \(q_{3}\) reach a distance of 60 mm at the same time. In our design, the length of the parallel chain of each arm for the PH-LDR is designed as 130 mm and the joints \(A_{i}\) are located at the center of the parallel chain of each arm, so the minimum value for the joint varibles \(q_{i}\) is 65 mm. Consequently, the singularity \(q_{1}=q_{2}=q_{3}=r\) certainly can be prevented for the operation of the PH-LDR.
## 2 Calculation Methods  
The structure optimization, electronic, and magnetic properties are calculated by utilizing the Vienna ab initio Simulation Package (VASP) based on density functional theory (DFT) within generalized gradient approximation (GGA) [29]. We make use of projector-augmental wave (PAW) [30] pseudopotential to deal with the electron-iron interaction, and the Mn (\(3d^{5}4s^{2}\)), Co (\(3d^{7}4s^{2}\)), Al (\(3s^{2}3p^{1}\)), and Ag (\(4d^{10}5s^{1}\)) are selected as valence-electron configurations. The mesh of special k-points in the Brillouin zone is set to 7 \(\times\) 7 \(\times\) 1 and the SCF convergence criterion of 10\({}^{-6}\) eV is applied. The cutoff energy is taken to be 500 eV. The optimized Ag and Mn\({}_{2}\)CoAl bulks are cleaved along the Miller indices (0 0 1) crystal direction, and we chose 13 Mn\({}_{2}\)CoAl layers as the electrode and nine Ag layers as the spacer layer to compose the Mn\({}_{2}\)CoAl/Ag/Mn\({}_{2}\)CoAl heterojunction. The in-plane  
Figure 1: **(a)** Schematic representation of Mn\({}_{2}\)CoAl bulk structure. The schematic density of states of \(n\) (E) as a function of energy E is shown for (b) a spin gapless semiconductor. The occupied states are indicated by filled areas. Arrows indicate the majority and minority states.  
lattice parameter is fixed to 4.1 A, which corresponds to \(1/\sqrt{2}\) of the experimental lattice constant of Mn\({}_{2}\)CoAl bulk (5.798 A [20]), and such a value is very close to the lattice constant of Ag. Therefore, the lattice mismatch between the Mn\({}_{2}\)CoAl electrode and Ag is very small. The spin-dependent transport properties calculations are based on a state-of-the-art technique where DFT is combined with the Keldysh nonequilibrium Green's function (NEGF) theory, as implemented in the Nanodcal package [31; 32].
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
###### Abstract  
Public live streaming web cameras are quite common now and widely used by drivers for qualitative analysis of traffic conditions. At the same time, they can be a valuable source of quantitative information on transport flows and speed for the development of urban traffic models. However, to obtain reliable data from raw video streams, it is necessary to preprocess them, considering the camera location and parameters without direct access to the camera. Here we suggest a procedure for estimating camera parameters, which allows us to determine pixel coordinates for a point cloud in the camera's view field and transform them into metric data. They are used with advanced moving object detection and tracking for measurements.  
depth map; radial distortion; perspective distortion; camera calibration; transport traffic statistics 20221036010390/math101936010390/202210360  
a pattern rotation can be useless for cameras on a wall or tower. In this case, we suggest the implementable camera calibration procedure, which uses only online data (global coordinates of some visible points, photos, and street view images).  
With known camera parameters, we construct the mapping between the ROI pixel coordinates and metric coordinates of points on the driving surface, which gives a way to estimate traffic flow parameters in standard units such as car/meter for traffic density and meter/second for car velocity with improved accuracy.
# S.O.V.O.R.A.: A Distributed Wireless Operating System
## 3 S.O.V.O.R.A. Wireless Distributed Systems
### Architecture  
The architecture used on the prototype is one of the microservices. The software architecture provides autonomy, isolation, and the definition of a task for each service or software module, so the set of microservices composes the overall software architecture for the Distributed Wireless Operating System. The microservices architecture is based on a simple concept: the creation of a system from the set of services, each one of them with its small, independent, isolated, scalable, and resilient to failure data. The decomposition of the systems into a discrete and isolated subsystem is reported in well-defined protocols.  
The key factor in microservices is the isolation, which is a prerequisite for resilience and elasticity. It requires asynchronous communication to decouple the limits of microservices in time to allow for concurrency and distribution. In addition, it requires Space that allows the services to move around the system (mobility).  
Figure 2 shows a set of microservices defined for the local and the OR agents to create the minimum components to deploy S.O.V.O.R.A. on a MANET: the client and multi-thread servers for wireless communication and message passing, the module to monitor resources, a monitor device, log information and command execution of some message from the orchestrator or the user.  
The primitives of S.O.V.O.R.A. are oriented to the distribution of resources over a MANET cluster to define the need for communication schemes. The distribution of tasks in the system makes it necessary to identify the elements called objects:  
* Nodo (Node): Represents the physical devices.
* GGlobal (Gyroup): Represents a gorup of physicial nodes on the same MANET.  
Figure 2: Local Agent-Orchestrator Model.  
* Group (Group): Represents a set of local agents.
* Local Agent (Agl): Represents an instance of local agent on the physical node. On each node can exist one or more instances of LA.
* Orchesrator (Orch): Represents a global controller the system. On each MANET cluster can exist one or more instances.  
Table 1 shows the commands that are available for users on the wireless operating system.
###### Abstract  
The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit's internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the 'Aiyuan 38' orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange's total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R\({}^{2}\), RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.  
orange; total soluble solid content; visible/near infrared spectrometer; electronic nose; data fusion
## 7 Synthesis of Two-Dimensional SnS
Square SnS nanosheets grown via physical vapour deposition (PVD) have also been prepared by [PERSON] et al., with square nanosheets of lateral sizes of 7 \(\upmu\)m and thicknesses of 12 nm produced. These nanosheets were prepared at 650 \({}^{\circ}\)C from 99% pure SnS and deposited onto mica. These nanosheets were then turned into photodetectors using a photolithographic-pattern-transfer (PPT) technique [130]. The ability to control the thicknesses and lateral size of SnS with PVD makes it an effective method for high quality SnS nanosheet synthesis--the ability to synthesise monolayer SnS is also a significant advantage. However, the morphology of the nanosheets appears limited, with most PVD producing square nanosheets or thinner, irregular, or circular shapes. [PERSON] et al. have developed a method for growing high quality, wafer scale monolayer SnS via the reaction of H\({}_{2}\)S gas with molten tin. Elemental tin was melted at 350 \({}^{\circ}\)C onto a clean glass slide in a glove box environment consisting of 50 ppm H\({}_{2}\)S in nitrogen. A heated glass slide was then used to expose the molten Sn surface, and finally a Si/SiO\({}_{2}\) (300 nm) substrate was touched onto the Sn droplet to exfoliate the monolayer SnS [108]. The SnS produced had a thickness of 0.7 nm, as determined by atomic force microscopy (AFM), with lateral sizes in the range of tens to hundreds of \(\upmu\)m with no cracks and a consistent thickness. The SnS was found to possess a carrier mobility of 35 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) with a piezoelectric coefficient of 26 pm V\({}^{-1}\). Piezoelectric nanogenerators were successfully formed from the monolayer SnS produced. This method appears to be the most effective for the synthesis of large area, monolayer SnS [108] (Figure 13). Lower dimensional SnS has also been produced with 0D quantum dots produced by several groups with applications in solar cells and photovoltaics [131; 132; 133; 134], degradation of organic dyes [136], and biomedical applications [137]. One-dimensional nanorods have also been produced by several groups [138; 139; 140; 141; 142], which have applications in hydrogen storage [143], lithium storage [144; 145], solar cells [146], and field emission applications [147]. Other SnS structures including nanopyramids [148], nanoflowers [149], and nanospheres [150; 151] have also been created by several groups. More detailed synthesis and property information of these materials is beyond the scope of this review.