esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Equalization in the form of the least mean square algorithm [28] and simple adaptive median filter algorithm (AMF) is applied for the 16 QAM modulation format, while a constant modulus algorithm [29] is utilized for QPSK modulation for the OFDM subcarriers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature Review** &
\begin{tabular}{c} **Approach/Methods** \\ **Discussed** \\ \end{tabular} & **Pros** & **Cons** \\ \hline \multirow{4}{*}{[8]} & Used parallel & & \\  & electro-optic & & \\  & modulation by using & & \\  & parallel electro-optic & & \\  & phase and & & \\  & intensity modulators & & \\ \hline \multirow{4}{*}{[9]} & Mode-locked lasers & & \\  & for mm-waves & & \\  & generation combined & & & \\  & with & mm-wave generation & & \\  & MZM interferometer & & \\ \hline \multirow{4}{*}{[10,12]} & A dispersion & & \\  & compensation and & & \\  & dispersion shifted & & \\  & fiber is used & & \\ \hline \multirow{4}{*}{[11]} & Use field & & \\  & programming gate & & \\  & arrays (FPGAs) to & & \\  & restore the & & \\  & transmitted signal & & \\ \hline \multirow{4}{*}{[13]} & 25 GHz mm-wave & & \\  & over SSMF system & & \\  & using OFDM which & & \\  & uses a dual & & \\  & wavelength source & & \\ \hline \multirow{4}{*}{[14]} & SFDR amplification & Simple structure to & \\  & strategy based on & & \\  & single side band (SSB) & & \\  & phase modulation & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods discussed in the literature review to compensate for the dispersion in converged mm-wave OFDM A-RoF systems. The paper is organized as follows. In Section 2, a complete converged 5G mm-wave A-RoF multicarrier OFDM digital communication system is explained. Section 3 provides the details of the different equalization algorithms used. Subsequently, the simulated setup and the error vector magnitude calculation are described in Sections 4 and 5, respectively; finally, Sections 6 and 7 present the results and conclusions.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Behavioural Verification
#### 5.2.1 Advert Association  
Honest users utilize adverts as consumers and are therefore likely to not simply view an advert but to also engage with it by clicking or making a purchase. The act of engaging with an advert can therefore be considered as a typical behaviour of honest users but it also has to be noted that not all honest users engage with adverts in the same rate, and some users do not engage at all. In order to therefore avoid false positives, the system regards the engagement of adverts as an indicator of honesty but the lack of engagement is **not** treated as suspicion of dishonesty. To compensate for users who do not engage adverts, our system exploits other forms of honest behaviour as explained in the following sections.  
In Section 5.1.1, we illustrated the available types of Ad-Reports and called attention to the fact that a _RoA_ is harder to forge than an _RoC_ which is in turn harder to forge than an _RoV_ as they contain tokens which are acquired by accessing the Advertiser's website. The _RoCs_ and _RoAs_ can therefore serve as indicators of honesty as they signify that the user took the time to visit the Advertiser's website. The remaining _RoVs_ may not verifiable but they can be validated-by-association since the _ARC_ follows the same architecture as a blockchain as shown in Figure 6.  
A limitation to this approach lies in the fact that _RoCs_ and _RoAs_ are designed to be used by Advertisers who support the Cost-Per-Click (CPC) and Cost-Per-Action (CPA) pricing models. This may limit the number of _RoCs_ and _RoAs_ as it excludes all the Advertisers who only support Cost-Per-Impression (CPM). To overcome this shortcoming, our system utilizes the different types of Ad-Reports (_RoV_, _RoC_ and _RoA_) not based on the Advertiser's pricing model but in accordance to the user's engagement with the advert. Regard a simple example where an Advertiser supports the CPM model which means that a simple _RoV_ would normally suffice. For the same application however, we can also use a _RoC_ or a _RoA_ when the user interacts with the advert by clicking or by making a purchase. The commission is still going to be awarded based on the viewing but the use of a more secure Ad-Report will validate the authenticity of the claim as _RoVs_ can be forged more easily than _RoCs_ and _RoAs_.
## 3 Results  
### Models Performance and Driving Factors of ET Prediction  
The statistical result of the two machine learning models for predicting half-hour ET at different sites is provided in Table 5. The _GRI_ of the XGBoost models was smaller than that of the RF models. In addition, the XGBoost models showed higher \(R^{2}\) and lower _RMSE_ and _MAE_ as compared to the RF models, with _RMSE_ ranging 0.0187-0.0314 mm hr\({}^{-1}\), \(R^{2}\) ranging 0.8671-0.9377, and _MAE_ ranging 0.0110-0.0169 mm hr\({}^{-1}\) for XGBoost, _RMSE_ ranging 0.0207-0.0325 mm hr\({}^{-1}\), \(R^{2}\) ranging 0.8583-0.9240, and _MAE_ ranging 0.0121-0.0198 mm hr\({}^{-1}\) for RF, respectively. At the same time, the _MBE_s of the two models were almost close to zero.  
The scatter plots of ET observed values and the values predicted by the two machine learning models are shown in Figure 2. Figure 2 shows that many points in the two models were located above and below the 1:1 line. The correlation between the observed and predicted values of the XGBoost models was slightly better than those of the RF models. The \(R^{2}\) values of the XGBoost and RF model reached the maximum at IT-Cas sites, which were 0.9338 and 0.9240, respectively.  
It is of great significance to analyze the sensitivity of rice paddy ET to input factors for further understanding the impact of global climate change on rice paddy ET. The XGBoost model has the ability to evaluate the importance of predictors. Figure 3 shows the importance of different factors at different sites to ET through the XGBoost model (a higher value of gain implies greater importance). It can be seen from Figure 3 that SW is the most important factor for ET prediction at each site; the mean gain value is 0.6872. It is quite normal, after all, that solar radiation provides the energy needed for evapotranspiration. Except for SW, the gain values of other factors were less than 0.3. After averaging the gain of each site factor, the order was SW (0.6872), VPD (0.0989), TA (0.0611), NDVI (0.0470), SWC (0.0402), WS (0.0385), PA (0.0177), and DeltaTA (0.0150).
## 3 System Model
### Intra-Cluster Communication Session
The base station responds with an ACK packet if all data packets are received correctly as illustrated by direct transmission frame in Figure 6, else a NACK is sent, and the CH retransmits the incorrectly received packet to the BS. The direct transmission process is outlined in Algorithm 4. #### 3.4.2 Cooperative Transmission

When CH sends RTS to the BS to show their request to send data, the RCHs hear the RTS packet; they calculate CQI for the link between relays and CH. Also, when the BS sends the CTS to CH, the RCHs hear the CTS, they calculate the RCH-BS link CQI which they send to the CH through a RCTS. The CH uses this CTS and RCTS information and distances to decide which transmission phase to follow. If the channel condition is bad between CH and BS or if channel is fair but the distance

Figure 6: Cluster head direct transmission session. between CH and BS is huge, an RCH is selected to relay the data. The transmission distance has an effect on the energy consumption by first order radio model [2]. The CH then transmits a relay selection update (RSU) packet to indicate the relay node selected, the other RCH then ignores data transmission from the CH. The CH then sends data to the selected RCH; the RCH then relays the data using facilitation technique to the BS. The RCH then keeps the data until an ACK is received from the BS as illustrated in Figure 7 and outlined in Algorithm 5. If a NACK is received, Selective Repeat Automatic Repeat Request error control [16] is used to retransmit the packets. ```
CHs calculate relay selection cost, Determine CH with minimum cost, IfCH's cost=minimumthen  elect as RCH node, Else  Remain as CH, Endif CH check buffer for packets Ifpackets availablethen  Send RTS to BS and RCHs hear it, BS calculates CQI from CH, RCHs calculate CQI for CH to RCH link, BS sends CTS to CH and broadcast it to RCHs, RCHs calculate CQI for RCH to BS link, RCHs send RCTS to CH, CH determines channel conditions based on CQIs, If(CH to BS channel condition is fair and distance is greater than threshold) or (CH to BS channel condition is bad)then  Select RCH with highest CQI, CH sends a relay selection update to possible relays, Non selected RCHs retire from communication, CH transmits data to selected RCH, RCH relays data to BS using facilitation technique, BS sends ACK to CH and RCH hears it and communication ends, Endif Else  Continue with intra-cluster communication Endif
```

**Algorithm 5** Cooperative Transmission

**E

## 4 Analytical Model

The symbols and acronyms for the parameters used in the analysis of the proposed protocol are defined in Table 1.

### CH Relay Selecion

The relay selection in this communication framework is based on the channel quality of the wireless transmission channel. The statistical time varying nature of the wireless channel between communicating node pairs is modeled as a Rayleigh fading process. The received instantaneous signal to noise ratio is a random variable that is exponentially distributed and has a probability distribution function given by Equation (1) [17]:
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### Vitis AI Deploys CNN
#### 3.4.2 YOLOv4-MobileNetv3 Model Compilation and Deployment  
The quantized network model needs to be compiled and mapped into a highly optimized DPU instruction stream before the DPU core can be scheduled on the FPGA to achieve hardware acceleration. The compiler in the Vitis AI suite can perform the compilation operation of the quantized network, which includes an optimizer, parser, and code generator. After the Vitis AI Library compiler (VAL_C) analyzes the topology of the optimized and quantized input model, VAL_C will build an internal calculation graph as an intermediate representation (IR), and build a control flow and data flow based on this. Then, it compiles and optimizes operations based on the calculation graph. Finally, the code generator will map the optimized calculation graph to the DPU instruction stream.
## 2 Materials and Methods
### Experimental Design and Culture Conditions  
A semi-continuous experiment was conducted using a set of two reactors: an absorption column and a photobioreactor (PBR) (Figure 1). Three different CO\({}_{2}\) supply rates, 0.20, 0.39, and 0.59 gC L-PBR\({}^{-1}\) d\({}^{-1}\) (Runs 1-3; Table 1), were tested based on a previous _A. platensis_ CO\({}_{2}\) fixation rate of 0.39 gC L\({}^{-1}\) d\({}^{-1}\) into account [17]. CO\({}_{2}\) was injected by the headspace replacement instead of bubbling for controlling CO\({}_{2}\) supply rates and prolongation of gas-liquid equilibration time.  
The absorption column was a 1-L glass medium bottle with an active volume of 0.4 L and a gas-liquid interfacial area of approximately 73 cm\({}^{2}\). For the start-up of the experiment, the autoclaved column was filled with a 0.4-L filter-sterilized medium. The headspace of the absorption column was replaced with different concentrations of CO\({}_{2}\)/N\({}_{2}\) mixture for each CO\({}_{2}\) supply rates (Table 1). Nitrogen gas of the volume same as that of headspace was injected to all the gasbags so that the headspace pressure remained at the atmospheric pressure (\(1.013\times 10^{5}\) Pa) even after all CO\({}_{2}\) was absorbed. The absorption columns were placed in an incubator at 25 \({}^{\circ}\)C under dark, and the absorbent was stirred continuously with magnetic stirre  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{2}{c}{**CO\({}_{2}\) Supply Rate**} & \multicolumn{2}{c}{**Headspace**} & \multicolumn{2}{c}{**Headspace CO\({}_{2}\)**} & \multicolumn{2}{c}{**CO\({}_{2}\) in Gasbag**} & \multicolumn{2}{c}{**N\({}_{2}\) in Gasbag a**} \\ \cline{2-7}  & **(gC L-PBR\({}^{-1}\) d\({}^{-1}\))** & **(gC L-Abs\({}^{-1}\) d\({}^{-1}\))** & **(L)** & **(\%)** & **(L)** & **(L)** \\ \hline
1 & 0.20 & 0.49 & 0.73 & 55 & 0 & 0.40 \\
2 & 0.39 & 0.98 & 0.72 & 100 & 0.07 & 0.74 \\
3 & 0.59 & 1.5 & 0.74 & 100 & 0.45 & 0.75 \\ \hline \hline \end{tabular}  
* Nitrogen was prepared to maintain the headspace at the atmospheric level. PBR: Photobioreactor. Abs: CO\({}_{2}\) absorption column.  
\end{table}
Table 1: Gas supply configuration for CO\({}_{2}\) absorption column.  
Figure 1: Experimental set-up of the semi-continuous two-phase CO\({}_{2}\) recovery process. After 23.5 hours of CO\({}_{2}\) absorption and algal incubation period, the medium was partially recirculated between the two reactors. After recirculation, the headspace of the absorption column was replaced with CO\({}_{2}\) containing gas (Table 1) and a newly filled gas bag was attached.  
The PBR was a glass column reactor with an active volume of 1 L and an inner diameter of 106 mm. The PBR was semi-open with 0.2-\(\upmu\)m air-filter (Aervent-50, Millipore, USA) attached on top to aseptically alleviate pressure build-up due to oxygen production. The PBR was incubated at 35 \({}^{\circ}\)C with 24-hour continuous light with the surface photosynthetic photon flux density (PPFD) of 300 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\).
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 4 Ant Colony Optimization Algorithm Based on Load Balancing  
### Ant Colony Optimization Algorithm Analysis  
[PERSON] first proposed an ant colony optimization algorithm in 1991 to achieve global optimization by simulating ant foraging behavior [18]. Suppose that \(P_{ij}^{k}(t)\) is the transition probability value of ant \(k\) from resource node \(i\) to resource node \(j\) at time \(t\), using this value to select the next task to schedule. It is expressed by Equation (1).  
\[P_{ij}^{k}(t)=\left\{\begin{array}{ll}\left[\tau_{ij}(t)\right]^{\alpha}* \left[\eta_{ij}(t)\right]^{\beta}\bigg{/}\sum\limits_{i=1}^{n}\left[\tau_{ij} (t)\right]^{\alpha}*\left[\eta_{ij}(t)\right]^{\beta},\ \ j\in A\\ \\ 0,\ \ \ \text{Others}\end{array}\right. \tag{1}\]  
In Equation (1), \(\tau_{ij}(t)\) is the pheromone value for transferring resource node \(i\) to resource node \(j\) at time \(t\); \(\eta_{ij}(t)\) is the heuristic function for transferring resource node \(i\) to resource node \(j\) at time \(t\); \(\alpha\) is the information heuristic factor, which represents the relative importance of scheduling order; \(\beta\) is the expect heuristic factor, which represents the importance of heuristic information in ant selection scheduling sequence. The larger the value of \(\beta\), the more likely the ants are to choose the scheduling sequence autonomously. Set \(A\) as the task that ant \(k\) can choose to perform in the next step.  
According to Equation (1), the two key factors affecting the selection of resource nodes are \(\tau_{ij}(t)\) and \(\eta_{ij}(t)\). The improvement of these two factors is the difficulty and emphasis of the algorithm research. The ACTS-LB algorithm proposed in this paper is based on the standard ant colony algorithm to make corresponding improvements to \(\tau_{ij}(t)\), and to update pheromone rules according to the node resource hardware performance and load balancing value.
## 2 Coherent Burst-Mode Receivers for PDM-QPSK and PDM-16 QAM Modulated Payloads
### Digital Coherent Burst-Mode Receiver DSP Stages  
Figure 3 shows a diagram of the digital signal processing (DSP) blocks used in the proposed DCBMRX [17]. First, a packet detector detects when an incoming optical packet arrives to the receiver and triggers the execution of the subsequent receiver stages [36]. Note that, in this work, this packet detector was not implemented and perfect packet detection was emulated with knowledge of the pattern timing from the transmitter. After the packet detector, a front-end correction and resampling stage corrects for channel skew and resamples the captured signal to four samples-per-symbol or the Nyquist rate of two samples-per-symbol. Then, the signal is normalized and the carrier frequency offset estimated and compensated by means of a fast Fourier transform (FFT). After the frequency offset has been removed, samples are aligned in a clock recovery stage using the Lee algorithm for two samples-per-symbol signals [37] or the Oerder algorithm for four samples-per-symbol signals [38]. Note that Nyquist shaped signals with small roll-off factor may require custom algorithms for clock recovery [39]. Next, the SOP angles \(\theta\) and \(\varphi\) are estimated using the proposed algorithm and its values used to initialize the CMA equalizer in case of receiving a PDM-QPSK modulation or a radially directed equalizer (RDE) for PDM-16 QAM modulation. After equalization, the carrier phase is estimated with the Viterbi and Viterbi algorithm [40] or a set-partitioned Viterbi and Viterbi [41] for QPSK and 16 QAM modulations, respectively. Finally, bit error rate (BER) is measured by error counting.  
Figure 2: (**a**) Eye diagram for any of the tributary components of a PDM-QPSK signal, sampled at two samples-per-symbol; (**b**) format of the optical packet header. Each number represents the transmitted constellation symbol on each symbol period; (**c**) QPSK constellation diagram showing the constellation symbol mapping; and (**d**) example of the lens-shaped object for PDM-16 QAM and a polarization rotation.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 3 In-Orbit Results
### Satellite Status
#### 3.1.2 Battery Voltage  
The battery voltage of the CubeSat also needs to be considered for the attitude analysis, as there is no power generation when the battery is fully charged. Two lithium-ion 18650 cells were used for the power system of the SNUGLITE CubeSat. The maximum battery voltage was set to 8300 mV. Battery voltage variation from the launch date to the end of June 2019 is described in Figure 6. From March 2019, ground stations have observed a decrease in the battery voltage to under 7900 mV. This decrease has been attributed to the operation of the battery heater.  
Figure 6 depicts several full-charge cases, which decreases the number of valid epochs for the attitude analysis because power generation is essential to compute the reference sun-pointing vector, as per Equation (19).
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 5 PHAs Biorefinetics  
The United Nations identified numerous crops worldwide that can be used as bioenergy feedstocks. Many of these crops have the potential for coproducing PHAs. In the crop-based synthesis approach of PHAs, a metabolically engineered system is established in one crop and, at the same time, generic technology is created which can be transferred to a different biomass crop. In this way, additional crops will be harvested and commercialized in different regions based on need [129; 130]. The sugarcane industry was the first PHAs producing biorefinery. This industry faced many challenges, which were clearly relevant to dedicated bioenergy crops where the residual bagasse is combusted to produce steam and power for the process while excess power is returned to the power grid [80].  
The major advantage of sugarcane is that 42% of the total dry weight of the stalk can be extracted to fermentable sugar. However, another bioenergy crop, switchgrass, holds potential for multigene expression systems to coproduce PHAs. Moreover, switchgrass holds the advantage over sugarcane as it can be grown in less moist environments and can be processed year-round, leading to overall improved capital efficiency. Moreover, switchgrass is a high yield crop, meaning it can be harvested on land set aside for marginal use of other crops, needs few chemical supplies, can potentially reduce the runoff of topsoil, pesticide, and fertilizers, and fixes CO2 in its roots systems [81] (Figure 9).  
In summary, the solvent extraction process and residual dry biomass of switchgrass are used to recover PHAs and to produce bioenergy in cogeneration (cogeneration offers efficient generation of both steam and electricity while the excess steam can be used in other biorefinery processes) [82]. Unlike fully integrated switchgrass-based PHAs biorefinery, PHAs biorefinery can be as simple as the installation of a recovery system near an existing power plant which provides a reliable economical supply of dry and easily handled feedstock. Over time, additional capital, such as bioproduct capacity, biofuel, and cellulose hydrolysis, can be added to this emerging integrated PHA biorefinery [132; 133]. In addition, biomass residues and fresh biomass could be converted to biofuel via thermochemical processes. According to the literature, the greenhouse gas emissions from switchgrass-based cellulosic C2H5 OH is 94% less than that of gasoline [84]. Hence, PHAs are value-added coproducts obtained from the large scale production of biofuels and bioenergy from plants. Therefore, the synthesis of these biopolymers from biomass crops can considerably improve the economics of biomass biorefineries to produce liquid biofuels and bioenergy [134; 135; 133].