esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient QC-LDPC Encoder for 5G New Radio
## 3 LDPC Encoding Algorithms  
Given a parity check matrix \(H\), the objective of LDPC encoding is to solve parity equations:  
\[HC^{T}=0^{T},\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(Z\)} & \multicolumn{8}{c}{_a_} & \\ \cline{2-10}  & **2** & **3** & **5** & **7** & **9** & **11** & **13** & **15** \\ \hline \multirow{5}{*}{\(j\)} & 0 & 2 & 3 & 5 & 7 & 9 & 11 & 13 & 15 \\  & 1 & 4 & 6 & 10 & 14 & 18 & 22 & 26 & 30 \\  & 2 & 8 & 12 & 20 & 28 & 36 & 44 & 52 & 60 \\  & 3 & 16 & 24 & 40 & 56 & 72 & 88 & 104 & 120 \\  & 4 & 32 & 48 & 80 & 112 & 144 & 176 & 208 & 240 \\  & 5 & 64 & 96 & 160 & 224 & 288 & 352 & & \\  & 6 & 128 & 192 & 320 & & & & & \\  & 7 & 256 & 384 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lifting size \(Z\) supported by standard 5G QC-LDPC codes.  
Figure 2: Shortening by zero padding and puncturing of standard 5G QC-LDPC codes.  
where \(C\) is the systematic codeword, which consists of the information bit vector \(S\) and parity code vector \(P\).  
This section presents a review on two generic encoding methodologies for the implementation of the LDPC encoder: the Gaussian elimination method and the RU method.
###### Abstract  
This study aims to evaluate the corrosion resistance of carbon steel, used for carabiners manufacturing, coated with three different types of phosphate layer. The phosphate layers have been obtained by phosphate conversion coating with three different types of phosphate solutions: zinc-based solution, zinc-iron-based phosphate solution, and manganese-based phosphate solution. Additionally, the test was performed on zinc phosphate samples impregnated with molybdenum bisulfate-based oil and zinc phosphate samples further coated with a layer of elastomer-based paint. Considering the areas where the carabiners are used (civil engineering, navigation, oil industry, rescue operations, etc.), the corrosive environments studied are rainwater, Black Sea water, and fire extinguishing solution. The structure of the deposited layers was studied by scanning electron microscopy, while the interface structure between the alloy and corrosive environment was analyzed by electrochemical impedance spectroscopy. According to this study, the corrosion resistance of zinc-based phosphate coated samples and zinc/iron-based phosphate coated samples is higher than that of the studied carbon steel samples, despite the corrosion environment. Also, the most aggressive corrosion environment was the fire extinguishing solution.  
corrosion resistance; carbon steel; zinc phosphate layer; manganese phosphate layer; electrochemical impedance spectroscopy
## 3 Discussion
### Impact of Environmental Factors on the Relationship between SIF and GPP  
Previous studies showed that the relationship between SIF and GPP is affected by environmental factors [28; 29]. Some studies have indicated that under drought or high-temperature conditions, vegetation photosynthesis is affected by climate, and SIF shows a significant decline, reflecting the high sensitivity of SIF to climate change. Therefore, climate change also affects the correlation between SIF and GPP [30; 31].  
The light energy absorbed by plants is mainly used for three parts: photochemical reactions, heat dissipation, and chlorophyll fluorescence [4]. Research has shown that vegetation SIF mainly responds to changes in PAR, rather than actual changes in photosynthetic efficiency, and the key driving factor for SIF and GPP is photosynthetic effective radiation [27]. This study also shows that PAR has a significant impact on both SIF and GPP. In addition, the radiant intensity of the sun will also cause the change of ground temperature, so Ta also has a significant impact on SIF and GPP.  
SWC is usually treated as an important indicator that reflects the variations of GPP when the vegetation is under drought stress. Previous research shows that plant photosynthesis efficiency decreases due to the reduction of transpiration rate of leaves when drought  
Figure 6: The influence of PAR on GPP on 8-day and monthly scales.  
occurs [32; 33]. However, the influence of SWC on SIF or GPP was not detected in this study. Probably, under the subtropical monsoon climate with abundant precipitation during the study period, no apparent drought occurred and the relative sufficient soil moisture content, the photosynthetic rate and chlorophyll fluorescence production of vegetation were not significantly affected by the variations of SWC. At the same time, there are still many deficiencies in this study, such as how SIF characterizes the changes in GPP under extreme drought and high temperature stress which occurs frequently. Further research is needed based on longer observation in the future.  
There are also some factors that can affect the SIF-GPP relationship, such as vegetation physiological status, chlorophyll content, canopy structure, water and dry matter [11; 24; 34; 35]. When the time resolution of data is reduced to a low resolution, i.e., the increase in temporal scale, the influence of these factors is reduced to some extent, and the linear correlation could be enhanced.
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 4 The Cache Placement Optimization Algorithm Based on Improved Tabu Search in Edge Computing
### Implementation of Cache Placement Algorithm Based on Improved Tabu Search
#### 4.2.2 The Cache Placement Algorithm Based on Improved Tabu Search  
Given the cache placement obtained through the replacement rate, the objective function, and the disciplinary strategy, an improved tabu-search-based cache placement algorithm is proposed. The specific solution steps are as follows:Initialization setting.  
Define the tabu array \(TabuList[1,2,3,\ldots,n]\), and the initial value of each element in the array is 1, which identifies the data block that can be swapped. Set the server random component function \(S*(h)\), and initialize the record count threshold \(h\), random discipline constants \(w\), and record count \(p_{t}=0\).  
Server selection.  
According to formulas (10), (18), and (20), the placement cost of placing a data block in a server can be expressed as \(E_{i}^{j}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}x_{ij}*\left(C_{i}^{j}-SC_ {i}^{j}-Pepc_{i}^{j}\right)\). Define \(E_{\max}\) and \(E_{\min}\) as the servers with the highest and lowest placement cost.  
\[E_{\max}=\max\left\{\begin{array}{l}E_{i}^{j}\Big{|}j\in m,i=1,2,\ldots,m \end{array}\right\} \tag{25}\]  
\[E_{\min}=\min\left\{\begin{array}{l}E_{i}^{j}\Big{|}j\in m,i=1,2,\ldots,m \end{array}\right\} \tag{26}\]  
Determine whether the number of records \(p_{t}\) reaches the record count threshold \(h\). If so, the disciplinary strategy is triggered and the minimum cost of an edge server \(E_{\min}\) is reselected according to the server random component function \(S*(h)\), i.e., \(S*(h)= S(h)+w\times h\).  
Data block swapping.  
Select the data block \(f_{k}\) that can be swapped on the server \(E_{\max}\), and set the tabu value \(tabuList[k]=0\). If all the data blocks on the server are marked as non-swapping, the data block allocation is completed and the algorithm ends; otherwise, define \(FE_{\min}\) as the task allocated to the server \(E_{\min}\) and calculate the data block swapping function \(MBT(k,l)\) in Formula (28). \(B_{kl}\) and \(B_{lk}\) denote the combined utility function in the non-swapping and swapping states.  
\[B_{ij}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}x_{ij}*\left(Value_{i}^{j}-C_ {i}^{j}-SC_{i}^{j}-Pepc_{i}^{j}\right) \tag{27}\]  
\[MBT(k,l)=\max\left\{\begin{array}{l}B_{kl}-B_{lk}|l\in FE_{\min},l=1,2,\ldots,m\end{array}\right\} \tag{28}\]  
If the data block swapping function \(MBT(k,l)>0\) exists, then swap the values \(A\leftrightarrow B\). This means that for the interchange of \(A\) and \(B\) values, the tabu value \(tabuList[l]=0\); otherwise, set the record count as \(p_{t}=p_{t}+1\).
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## Appendix A  
### Combination of ACO and the Model  
Firstly, the switches which are considered about the configuration of FTUs are numbered. The process of solving the model by ACO is a tour that ant colony starts from the initial switch, reaches each switch in turn, and stays at the last switch. Each ant gives a path combination for the optimization problem, that is, the decision vector \(X\) of Equation (6). Each ant has a state in each switch, which corresponds to the FTU configuration decision of the switch. The path that the ant chooses when it goes from the \(i\)-th switch to the \((i+1)\)-th switch determines the state of the ant in the \(i+1\) switch. According to the Equations (1)-(5) in reference [23], the path selection is based on the probabilistic state transition rule, which is related to the length of the path (i.e., the investment cost of the decision) and the pheromone concentration on the path. After the ant colony completes the tour, the path combination given by each ant is evaluated by Equations (7)-(12). It is worth noting that the penalty function generated by constraint Equations (13)-(14) should be added to the objective function during the evaluation process. Finally, the pheromone matrix is updated by concentration volatilization and concentration enhancement according to the evaluation results. This process is repeated until the end of iteration condition is reached, and the final solution is the best one in this iterative process.
## 3 Results
### Statistical Analysis
#### 3.2.1 Assessment of the Quality of the Dataset
There were studies (such as [35; 41; 63; 68; 69; 70]) that used EEG with less than five channels, but it should be noted that three of them detected gait disturbances, two diagnosed PD in combination with EMG, and [70] used one channel with emotional stimuli. Regarding the duration of the EEG recording, the mode was 5 min. Moreover, the tendency was to divide the signals in windows of uniform length, so that larger recordings provided larger datasets and allowed some studies to eliminate those EEG segments that had defects or could disturb the analysis. Finally, different types of EEG tests, such as resting state tests, stimulation of emotions, or motor activation tests, provide EEGs with different properties and therefore are not comparable. In the articles selectedfor this review, the resting state tests predominated, as they were considered in 22 articles, followed by Timed Up and Go tasks (present in 5 articles), and tests that require a physical response to a stimulus (Oddball and Visual Go/No-Go in 4 studies).
## 4 Discussion
### Material Selection for Optical Waveguide Refractive Index Sensor  
According to the previous description, the most commonly used materials for optical waveguide refractive index sensors are silicon-based materials, chalcogenide materials, lithium niobate materials, and polymer materials, as shown in Table 5.  
The sensitivity and _LOD_ of different material systems under three sensing structures of MRR, MZI, and MMI are compared. For MRR, devices made of silicon and Chalcogenide have high sensitivity and low _LOD_, which are ideal materials [37; 41; 64; 68]. For MZI, Si and polymer-based devices have similar _LOD_, and the former has higher sensitivity [59; 74; 111]. For MMI, Si-based devices have high sensitivity and _LOD_, polymer-based devices also have low _LOD_[5; 6; 130].  
It can be seen that different materials can be selected for each refractive index sensor structure, which increases the flexibility of device design and preparation. Among them, silicon and polymer materials have a wide application range and relatively excellent performance, which can be used as the preferred material.  
Figure 24: Schematic diagram of the structure of the tapered fiber optic surface plasmon resonance (SPR) sensor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Refs.** & **Structure** & **Material** & **Sensitivity (nm/RIU)** & **LOD (RIU)** \\ \hline
[37] & & Si & 1300 & \(10^{-4}\) \\
[41] & MRR & Chalcogenide & 471 & \(3.3\times 10^{-4}\) \\
[64] & & LNOI & 68 & \(10^{-2}\) \\
[68] & & Polymer & 130 & - \\ \hline
[74] & & Si & \textgreater{}1 \(\times\)\(10^{6}\) & \textless{}10^{-6}\) \\
[59; 111] & MZI & Polymer & 11,624 & \(10^{-6}\) \\ \hline
[5, 130] & MMI & Si & 1682 & \(10^{-4}\)-\(10^{-7}\) \\
[6, 130] & Polymer & - & 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing three material properties.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
AI  Artificial Intelligence  
AoI  Age of Information  
B5G  Beyond-Fifth-Generation  
BR-MP  Bernoulli-Rician Message Passing  
CoAP  Constrained Application Protocol  
CSI  Channel State Information  
CSS  Chirp-Spread Spectrum  
DL  Deep Learning  
DRL  Deep Reinforcement Learning  
DVB-RCS2  Digital Video Broadcasting--Return Channel via Satellite 2  
eDRX  extended Discontinuous Reception  
EM  Expectation-Maximization  
FDD  Frequency-division Duplex  
FL  Federated Learning  
GEO  Geostationary Earth Orbit  
GF-RA  Grant-Free Random Access  
GNSS  Global Navigation Satellite System  
HAPs  High-altitude platforms  
HARQ  Hybrid Automatic Repeat Request  
HSTN  Hybrid Satellite-Terrestrial Network  
IIoT  Industrial Internet of Things  
IMDs  IoT Mobile Devices  
IoAT  Internet of Agricultural Things  
IoBT  Internet of Battle Things  
IoMT  Internet of Maritime Things  
IoRT  Internet of Remote Things  
IoST  Internet of Satellite/Space Things  
IoT  Internet of Things  
IoUT  Internet of Underground Things  
IoUwT  Internet of Underwater Things  
IPF  Iterative Penalty Function  
LAPs  Low-altitude platforms  
LEO  Low Earth Orbit  
LPWAN  Low-Power Wide-Area Network  
LTE-M  Long-Term Evolution Machine Type Communication
## 1 Introduction
The multi-network management layer aims to (i) use the capabilities of machine learning and SDN to improve network and application performance, (ii) provide serial and parallel data transmission services across multiple heterogeneous networks, (iii) support the dynamic allocation of network interfaces, and (iv) adopt new machine learning-based link quality and Markov chain-based link lifetime estimation techniques to reduce communication and energy consumption costs. The resource management layer aims to (i) leverage regression analysis and reinforcement learning methods to allocate heterogeneous computing and network resources efficiently to application tasks and (ii) use parallel transmission techniques, dynamic interface allocation techniques, and network-level parameters to address diverse application requirements. This paper is organized as follows. Section 2 describes motivating smart city and 5G network applications. Section 3 describes the role of heterogeneous private edge cloud in a cloud and edge computing ecosystem. The key characteristics and challenges of a heterogeneous private edge cloud system are described in Section 4. Section 5 presents a detailed overview and comparison of existing middleware platforms for IoT systems and distributed computing systems. Section 6 presents the design of a new intelligent middleware platform for a heterogeneous private edge cloud system. Section 7 concludes the study.
###### Abstract
The voltage-power control technique proposed was combined with slime mould algorithm (SMA) for controlling purposes, which enhanced the carrying capacity and power quality for the photovoltaic power generation system in the grid. However, the disadvantage lies in relative complications in calculations. The literature [3] focused on the current controller of the multilevel neutral point clamped (NPC) inverter for photovoltaic power generation, where the hysteresis current controller is based on a space vector was proposed. In addition to transmitting the real power of the photovoltaic module array to a grid-connected system, the controller could also compensate for the harmonic current and reactive power at the same time; however, the control methods were extensively complicated. Based on the above, a simple and effective control method was proposed regarding the adjustment of real and reactive power for MPPT and smart inverter of the photovoltaic power generation system. Therefore, in this paper, a proportional-integral (P-I) controller commonly used in the industry is used to control the DC-link voltage and the real and reactive power of the smart inverter. Although its control performance is less robust than that of the intelligent controller, it can shorten the operation time of the controller and improve the response speed. The arrangement of the various section contents is as follows: Section 2 describes the maximum power point tracking algorithm for the photovoltaic system and the circuit design of the boost converter. Section 3 describes the smart inverter control architecture, including DC-link voltage control, output power control and voltage-power control. The simulation and actual test results of the three-phase photovoltaic smart inverter for three per-unit values of the main voltage were made in Section 4 to verify the effectiveness of the developed smart inverter. Finally, the conclusion of this paper describes the results of this work.
## 4 Bespoke Space System Design
### Detailed Constellation Design and Simulation
The orbit simulation results are summarised in Table 7 and visualised in Figures 5-7. For each region selected, the average time between viewings (the average revisit time across all discrete points in the region) is less than seven days and each discrete point in the regions would be seen an average of 11-22 times, depending on latitude. 100% of the northern region, 99% of the eastern region, and 97% of the southern region are observed with a maximum revisit time of less than 14 days, though it should be noted that cloud cover and lighting conditions would mean that only a portion of the passes would be useable. There are a small number of points with maximum revisit times in excess of 14 days shown as grey regions in Figures 5-7. These points of high maximum revisit are likely a result of inaccuracies in the calculation of the RGT orbit altitude and satellite distribution and could be addressed through precision orbit design. It should also be noted that maintaining the precise RGT orbit altitude and spacecraft distribution would necessitate active orbit maintenance using on-board propulsion. Figure 5: Maps of the north region of interest as described in Table 6 and indicated on Figure 2. The maps are oriented with north to the top and cover an area of approximately 57 km (N-S) \(\times\) 68 km (E-W). The colours overlaid on the maps indicate the (**a**) average and (**b**) maximum revisit times of each location in the region after 30 days. Coverage is calculated at approximately 5 km intervals and interpolated to provide the coverage estimates for the full region of interest (Map data: Google, TerraMetrics, www.google.com/maps [retrieved 16 March 2020]). Figure 6: Maps of the east region of interest as described in Table 6 and indicated on Figure 2. The maps are oriented with north to the top and cover an area of approximately 65 km (N-S) \(\times\) 77 km (E-W). The colours overlaid on the maps indicate the (**a**) average and (**b**) maximum revisit times of each location in the region after 30 days. Coverage is calculated at approximately 5 km intervals and interpolated to provide the coverage estimates for the full region of interest. (Map data: Google, TerraMetrics, www.google.com/maps [retrieved 16 March 2020]).
## 2 Assessment of Available RT Orbit and Clock Products and RT ZTDs
### Impact of IGS RTS Products on ZTD Estimates  
The impact of the IGS RTS products on PPP ZTD estimates was assessed by exploiting the GNSS4 SWEC Benchmark campaign with 400 GNSS stations in central Europe during the period of May-June 2013. The ZTD was calculated using the G-Nut/Tefnut software in the post-processing mode when supported with two precise products: (1) IGS final orbits and clocks, and (2) IGS RTS orbit and clock corrections. Two reference solutions were provided for the benchmark using different software and processing strategies [31]. GOP used the Bernese GNSS Software and the double-difference processing (DD) and GFZ used the EPOS software and the PPP method. Statistics from the comparison of both testing solutions with respect to both reference products are given in Table 2. Generally, the results indicate a good agreement, however, the impact of the IGS RTS products (IGS01) on ZTDs is clearly visible in two aspects: (a) a common systematic error of 2.4-2.8 mm, and (b) a lower precision of 13-17%. Interestingly, a better agreement in terms of SDEV is reached between 10-20% when using two PPP solutions (G-Nut/Tefnut vs. EPOS software) compared to the processing strategies (DD vs. PPP). The results also showed that input products and the processing strategy might result in a similar impact on the ZTD estimates, which can reach up to 20% in terms of accuracy. Finally, it should be noted that the PPP ZTD estimation used a stochastic model and an epoch-wise filtering method in the G-Nut/Tefnut software, while a deterministic model with the least-squares batch adjustment used in the EPOS software.  
Figure 5: Daily statistics SDEV (**top**) and RMSE (**bottom**) of real-time clocks.
## 2 Augmentation of Li-Fi Wireless DMT Transmission Capacity Through Sparse Coded Mask Optimization
### Sparse Coded Mask Modeling for DMT Signal
As shown in Equation (8), the loss function is calculated by comparing the reconstructed patches with the original patches using the mean squared error (MSE) loss, outputting the difference between them. The result of the calculated loss function indicates the degree of similarity between the reconstructed patches and the original patches. \[\mathcal{L}=\frac{1}{N}\sum\
olimits_{m=1}^{N}\left(\hat{P}_{m}-P_{m}\right)^{2} \tag{8}\]  
where \(\hat{P}_{m}\) is the reconstructed patch, \(P_{m}\) is the original patch. Algorithm 1 shows the detailed design and algorithm of the learning model. ```
1:Input Received DMT patches (both available and masked)
2:Step 1 Patch Embedding  Apply a 1-D convolutional layer to transform each patch into a feature space. Embed position information using positional encoding. 3:Step 2 Encoding  Feed the unmasked patches into a transformer encoder. Learn contextual relationships using multi-head self-attention (MHSA) layers. 4:Step 3 Masked Patch Reconstruction  Input the encoded features into a decoder network. Predict the missing patches based on surrounding patch information. 5:Step 4 Loss Calculation  Compute the MSE loss between the predicted and original DMT patches. 6:Step 5 Output the Reconstructed DMT Signal  Reassemble the patches and apply the inverse transformation to reconstruct the full DMT waveform. ```  
**Algorithm 1** Masked Autoencoder (MAE)-Based Sparse Coded Mask Modeling