esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Comparision of GS-Based Algorithm at the Transceiver  
According to the above analysis, no iteration exists in the GS-based CD pre-compensation scheme at the transmitter. That is to say, the computational complexity is lower than that at the receiver. However, FFE at the receiver is needed to compensate the residual ISI. The number of taps depend on the extent of bandwidth limitation. As for the GS-based CD post-compensation scheme at the receiver, FFE is not indispensable. It reduced the computational complexity to some extent. However, more iterations are needed to guarantee the system performance. Furthermore, the GS-based CD post-compensation algorithm is sensitive to ISI. The ISI induced by band limitation inhibited the effect of the GS-based CD post-compensation algorithm. Therefore, the pre-distortion operation is necessary, which increases the system complexity.  
The latency of the whole DSP is mainly accumulated by real multiplications (RMs). Therefore, we computed the number of RMs required in different algorithm to access the real-time processing feasibility. The computational complexity of the pre- and post-compensation algorithm per symbol is shown in Table 1. For the pre-compensation algorithm, it required 11 RMs per symbol in the GS algorithm and 250 RMs in FFE. However, the post-compensation algorithm needed 23 RMs per GS operation and 80 iterations at  
Figure 10: BER versus ROP for different algorithms for 28 GBaud PAM-4 under 40 km SSMF transmission.  
Figure 9: BER versus the number of iterations for GS-based CD post-compensation algorithm without and with 7-symbol LUT.  
least. The computational complexity of the post-compensation algorithm is much higher than the pre-compensation algorithm.  
However, in the GS-based CD pre-compensation algorithm, the algorithm parameter is determined by the channel, which needs to be known in advance. Therefore, it is only applicable to a specific fiber length. In the GS-based CD post-compensation algorithm, the CD estimation function is adaptive and can be suitable for systems of any fiber length. Generally speaking, both schemes have their advantages and disadvantages. The choice depends on the specific situation.
## 6 Sensitivity Analysis
### Indifference Curve to Determine the Subcontracting Participation  
Based on the results discussed in the last section, it was observed that subcontracting was an effective countermeasure to improve the level of demand satisfaction and minimize costs. The key idea of the proposed model was to optimize in-house production and compensate with the use of the subcontractor if needed when the deterioration process reduced the production capacity of the production unit. In this section, we present further analysis results that served to properly select the participation of subcontracting based on two key characteristics, namely, the cost of subcontracting \(C_{sub}\) and its availability \(D_{sub}\). The optimization procedure described in the previous section was used to determine under which conditions it was more advisable to use subcontracting than in-house production. The approach consisted of considering the cost of subcontracting \(C_{sub}\) as an independent variable that was optimized with the rest of the control parameters (\(Z,A_{sub},A_{o}\)) of Policy-A. In addition, the total cost was defined as the dependent variable of the optimization. In this way, different values of the subcontractor's availability were analyzed to define until what amount of cost \(C_{sub}\) it was still profitable to use subcontracting. By analyzing different subcontracting costs and availabilities, the inference curve of Figure 6 was obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**Control Parameters**} & \multirow{2}{*}{**Total Cost**} & \multirow{2}{*}{**Observed Difference**} \\ \cline{2-2} \cline{4-6}  & \(Z^{*}\) & \(k^{*}\) & \(A_{o}^{*}\) & & \(\Delta\)**-Cost (\%)** \\ \hline _Policy-A_ & 31.5 & 0.6707 & 150.32 & 34.33 & — \\ _Policy-B_ & 43.26 & - & 189.93 & 50.59 & +32.14 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Policy Comparison.  
Figure 6: Indifference curve to determine the subcontractor participation.  
The interpretation of Figure 6 is in terms of two main zones. The first zone (see indicator 1 in Figure 6) is where the system recommended using a combination of subcontracting and in-house production to reduce the total incurred cost. Furthermore, zone 2 (see indicator 2 in Figure 6) is where in-house production was the most cost-effective and profitable option. For example, when the subcontractor had availability of \(D_{sub}=91\%\), the manager could manage to pay a cost of \(C_{sub}=16\) to the subcontractor. Otherwise, there was no economic incentive in negotiating with the subcontractor. However, the company could pay more to the subcontractor, for instance, a cost of \(C_{sub}=17.84\), only if the subcontractor guaranteed a higher availability of \(D_{sub}=99\%\). In this sense, it could be stated that if the availability of the subcontractor was high, a higher cost could be paid for its services.
## 3 Physical Layer Design
### Two Stage PA
#### 3.3.2 60 GHz Receiver  
Here, we discuss a 60 GHz receiver proposed in [68] that can demodulate the signal received over inter-chip wireless interconnect transmitted from the above the transmitter. The Figure 5b shows  
Figure 10: BB signal (**Top**) and transmitter output (**Bottom**) for a pseudo-random sequence.  
Figure 9: S parameter of the PA.  
the components of the receiver that consists of a high gain Low Noise Amplifier (LNA) and an OOK demodulator to demodulate the amplified received signal. The LNA consists of a two-stage CS configuration as both the Common Gate (CG) and Resistive feedback-based topologies suffer from Noise Figure degradation due to the occurrence of noisy resistances in the signal path. Moreover, cascade structures, which are used commonly in low-frequency design for their high gains, are not suitable for the high frequency application. This is because the parasitic capacitances in the cascode transistors become dominant at higher frequencies, which reduces the inter stage impedance and hence, overall gain. The output of the LNA is then matched to the input of the demodulator. Since this information is modulated with a high frequency carrier signal, the OOK demodulator will exhibit a Low Pass Filter characteristic, removing the carrier wave and recovering the baseband digital signal. The proposed OOK demodulator consists of a source degenerated Envelope Detector (ED) at the input stage and a two-stage Baseband (BB) amplifier at the final output. The received waveform in Figure 11 shows that the receiver is capable of achieving a data rate of 17 Gbps with 12 mVpp at the input. The total DC power consumption of the OOK receiver is 6.1 mW, which results in a bit energy-efficiency of 0.36 pJ/bit [68] at a data rate of 17 Gbps. The LNA of the receiver has a Noise Figure of 2.8 dB making the noise floor \(-\)68.7 dB at 300 K for a bandwidth of 17 GHz Therefore, together with the transmitter the transceiver consume a total of 0.59 pJ/bit operating at 17 Gbps.  
Figure 11: LNA input of the receiver (**Top**) and BB receiver output (**Bottom**) for the same sequence.
## 2 State-of-Art Review
### The Effect of Varying RFA Incorporation Ratio on Concrete Performance
#### 2.2.2 Bulk Fresh Density  
Generally, the density of RFA concrete is lower than that of the conventional concrete and their density decrease with increasing incorporation of RFA due to the presence of lower density residual-cement mortars attached to aggregate particles [73]. Figure 5 shows the influence of increasing incorporation of RFA on the bulk density of concrete mixes in the fresh state, sourced from these  
Figure 4: Relationship between RFA concrete and slump for similar w/c ratio.  
studies [54; 64; 70; 71; 77]. In order to avoid sacrificial pseudo-replication error [78], for each study, the data of high- and low-strength concrete mixes are separated then the global trends for each concrete are drawn. The fresh-bulk density of low-strength concrete mixes were lower than that of high-strength concrete mixes, and the fresh density of both concrete types decreased when replacing fine NA with RFA. The results show that the rate of reduction in the fresh density by increasing incorporation of RFA was similar for high- and normal-strength concrete.  
[PERSON] et al. [77] and [PERSON] [71] obtained similar results and both investigated three concrete mix families. The first family was normal-strength concrete without admixtures, the second family was high-strength concrete without air entrainment, and the third family was high-strength concrete with air entrainment. The fresh density of the three families decreased between 1% and 5% as the replacement ratio of fine NA with RA increased. The reduction range due to incorporating RFA did not change for high- and low-strength concrete. Moreover, the use of SP increased the fresh density of concrete by about 3% as a consequence of lowering the w/c ratio and increased the compactness of concrete as a result, while the use of air enter admixture offset the gain in density obtained by using the SP. [PERSON] et al. [64] obtained unit weight values of fresh concrete mixes. The values changed between 2350 kg/m\({}^{3}\) and 2510 kg/m\({}^{3}\). The unit-weight value decreased as the RFA content in the concrete increased. The reason behind this was that the specific gravity of RFA was lower than that of fine NA.
# Offloading of Atomic Tasks in Satellite Networks:
## 5 Numerical Experiments  
### Time Complexity  
We divide the offloading of satellite atomic tasks into three parts: the maximum stream transmission calculation, the optimization of atomic task offloading, and the optimization of task cache. This paper designs corresponding optimization solutions for these three problems. As shown in the Algorithm 1, the algorithm mainly uses the Ford-Fulkerson algorithm to calculate the maximum flow value and path of the network. The algorithm's time complexity is \(O(Ef)\), where \(E\) is the edges, and \(f\) is the maximum flow in the graph. Each expansion path can be found within the time of \(O(E)\) and increase the traffic by at least the entire amount of 1, with an upper limit of \(f\). According to the time range and the calculation of each satellite, the algorithm includes period traversal and satellite nested traversal. The total time complexity of the algorithm is \(O(TEVf)\). The atomic task offloading optimization Algorithm 2 mainly uses the Lagrangian expansion and Karush-Kuhn-Tucker (KKT) conditions to solve the optimization problem. The algorithm also considers the traversal of satellites in discrete time and the traversal of tasks on each satellite, so the algorithm time complexity is \(O(TV)\). The task cache optimization algorithm mainly involves constructing and searching the node tree. The algorithm's time complexity in discrete time is \(O(T(V+VE))\). In summary, the algorithm time complexity of the model \(\mathit{OCMF}\) proposed in this paper is \(O(TV(Ef+1+E+1))\), which can be simplified to \(O(TEVf)\). In the whole process of computing task offloading algorithm, \(f\) is constant, so the time complexity of \(\mathit{OMD}\) algorithm can be expressed as \(O(TEV)\).  
In space information networks, the location of satellites changes continuously with time, and the visible time window between satellites also changes dynamically. The communication cost between low-orbiting satellites is different from that between high-orbiting satellites, which can be found in the Formulas (2) and (3). The link resources between satellites are valuable, and most satellites have weak storage and computational capabilities. Therefore, if an algorithm has excellent time complexity, it can save a lot of waiting time, optimize the mission execution efficiency in general, reduce the mission waiting time, and improve resource utilization. Research on task offloading is increasingly inclined to use deep reinforcement learning methods. However, the learning time of deep reinforcement learning methods is not a problem.  
learning methods is long and cannot meet the low latency requirements for task offloading in time-varying satellite networks.
## 4 Simulation Results
ICM-CS has slight BER improvement compared with IAM-CS but obvious improvement in MSE performance. CoSaMP algorithm obtains the best BER and MSE performances. In Figure 6a, ARCoSaMP gives performance that are about 0.2 dB better than SAMP method, when BER \(=10^{-2}\). CoSaMP can obtain about 1.2 dB gain compared with LS when BER of \(10^{-2}\) is considered. In Figure 6b, the OMP, CoSaMP and ARCoSaMP, the three curves, are very close. ARCoSaMP and OMP provide similar MSE performance, and CoSaMP is less well performing than ARCoSaMP. The simulation times of ICM-SAMP and ICM-ARCoSaMP are less than IAM-SAMP and IAM-ARCoSaMP. ICM-ARCoSaMPstill needs less time than ICM-SAMP. Figure 4: Probability of exact recovery versus the number of measurements. Figure 5: Bit error rate (BER) and mean square error (MSE) performance comparisons based IAM preamble structure: (**a**) BER and (**b**) MSE. Figure 6: BER and MSE performance comparison based ICM preamble structure: (**a**) BER and (**b**) MSE. As shown in the simulation results, it can be verified that the CS-based channel estimation approach can provide more effective performance than conventional LS methods in FBMC/OQAM systems. The proposed ARCoSaMP based channel estimation method can achieve similar performance than CoSaMP without a prior sparse knowledge of the channel and has better channel estimation performance than SAMP with lesser time complexity.
## 3 Simulation-Based System Architecture
### Requests Queue Scheduler
#### 3.4.4 Prioritised Classification of Requests and Their Dispatching Policies

Table 2: Summary table for the various subsets of requests that are considered by the scheduler and the Outer Dispatcher. anticipate the vehicle's availability. The vehicle relocation strategy should be triggered before the last-leg dispatch, so \(\theta^{\prime\prime}=40\) min and of \(\theta^{\prime\prime\prime}=5\) min have been assumed to avoid client requests being left without an available vehicle (e.g., during high-demand peaks). In addition, the system prioritiises them over new requests.
## 3 Filters for Feature Extraction: LBP, HOG, and Saliency
### Local Binary Pattern (LBP)
#### 3.1.1 Definition and Calculation  
1. Pixel Comparison: For each central pixel \(I(x_{c},y_{c})\) in the image, its intensity value is compared with those of its \(p\) neighbors. The neighbors are arranged in a circle with radius \(r\), and each neighbor \(I(x_{i},y_{i})\) is compared with the central pixel using the following binary formula:  
\[LBP_{p,r}=\sum_{i=0}^{p-1}s(I(x_{i},y_{i})-I(x_{c},y_{c}))2^{i}\]  
where \(I(x_{c},y_{c})\) is the intensity of the central pixel, \(I(x_{i},y_{i})\) are the intensity values of the neighboring pixels, and \(s(x)\) is a thresholding function, which returns 1 if \(x\geq 0\) and 0 if \(x<0\), defined as  
\[s(x)=\begin{cases}1&\text{if }x\geq 0\\ 0&\text{if }x<0\end{cases}\]  
The value of \(LBP_{p,r}\) is a binary number converted to decimal, representing the local texture pattern in the neighborhood of the central pixel.  
2. LBP Pattern Calculation: For each pixel in the image, an \(LBP_{p,r}\) value is computed. This process generates a matrix of \(LBP\) values, where each value represents a specific binary pattern based on the comparison between the central pixel and its neighbors.  
3. LBP Pattern Histogram: After calculating the LBP for each pixel in the image, a histogram is built to count how many times each pattern occurs in the image. This histogram is used to create a feature vector that represents the global texture of the image.  
The histogram \(H(k)\) of the image, which has \(N\) possible patterns, is calculated as the frequency of each pattern \(k\) in the image:  
\[H(k)=\sum_{i=1}^{M}\sum_{j=1}^{N}\mathbb{I}(LBP_{p,r}(I(i,j))=k)\]where \(M\) is the number of rows in the image, \(N\) is the number of columns in the image, \(LBP_{p,r}(I(i,j))\) is the LBP calculated for the pixel \(I(i,j)\), and \(k\) is the pattern value, which can take values in the range \([0,N-1]\).  
The feature vector \(F\) is  
\[F=[f_{1},f_{2},...,f_{N}]\]  
where \(f_{i}\) is the number of times pattern \(i\) appears in the image.
###### Abstract  
To investigate the spatial and temporal variability of air quality (CO, NO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\)) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 \(\times\) 800 m monitoring domain in the center of the largest megacity (Seoul) in South Korea. The sensor network was operated in summer and winter. The daily variation in air pollutant concentrations revealed a similar trend, with discernible concentration differences among monitoring sub-sites and a government-operated air quality monitoring station. These differences in pollutant levels (except PM\({}_{2.5}\)) among the sub-sites were pronounced in the daytime with high volumes of traffic. The coefficient of divergence and Pearson correlation coefficient showed that spatial and temporal variability was more significant in summer than winter. Ozone displayed the greatest spatial variability, with little temporal variability among the sub-sites and a negative correlation with NO\({}_{2}\), implying that ozone concentrations were primarily determined by vehicular NO\({}_{\chi}\) emissions due to NO titration effects under the urban canopy. The PM\({}_{2.5}\) concentration displayed homogeneous spatial and temporal distributions over the entire monitoring period, implying that PM\({}_{2.5}\) monitoring with at least a 1 \(\times\) 1 km resolution is sufficient to examine the spatial and temporal heterogeneity in urban areas.  
low-cost sensors; dense air quality monitoring; intra-community air quality; air quality sensor network; spatial heterogeneity +
Footnote †: journal: Medical Imaging
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
###### Abstract  
Spaceborne synthetic aperture radar (SAR) plays an important role in many fields of national defense and the national economy, and the Fast Fourier Transform (FFT) processor is an important part of the spaceborne real-time SAR imaging system. How to meet the increasing demand for ultra-large-scale data processing and to reduce the scale of the hardware platform while ensuring real-time processing is a major problem for real-time processing of on-orbit SAR. To solve this problem, in this study, we propose a 128k-point fixed-point FFT processor based on Field-Programmable Gate Array (FPGA) with a four-channel Single-path Delay Feedback (SDF) structure. First, we combine the radix-\(2^{3}\) and mixed-radix algorithms to propose a four-channel processor structure, to achieve high efficiency hardware resources and high real-time performance. Secondly, we adopt the SDF structure combined with the radix-\(2^{3}\) algorithm to achieve efficient use of storage resources. Third, we propose a word length adjustment strategy to ensure the accuracy of calculations. The experimental results show that the relative error between the processor and the MATLAB calculation result is maintained at about \(10^{-4}\), which has good calculation accuracy.  
spaceborne SAR; FFT; FPGA +
Footnote †: journal: 2021  
10
## 3 Results and Discussion  
### Compressive Strength  
The 28-day compressive strengths of various wollastonite-reinforced hardened cement pastes are plotted in Figure 3 along with the control sample. In line with ASTM C109, this mechanical property is achieved by locating the maximum stress before failure [57]. As can be seen in Figure 3, the compressive strength increases significantly with increasing wollastonite fiber content for up to 10% fiber content beyond which the strength starts dropping. About a 35% increase in compressive strength is observed for 10% wollastonite dosage. Such strength enhancement can be explained by the fact that stiffer wollastonite fibers can carry significantly higher stress without breaking than the hardened cement paste matrix which ultimately reduces stress on the matrix and prevents it from cracking at smaller loads thereby increasing the strength capacity of the composite. Beyond, 10% wollastonite content, any further reduction in cement content translates into strength loss when compared to the best-performing wollastonite-fiber-reinforced cementitious composite. At 15% wollastonite dosage, the compressive strength is still higher than the control sample. The error bars in Figure 3 (and all the figures in this paper) range across all replicate samples. [PERSON]'s HSD (honestly significant difference) test on the compressive strength data reveals a p value of less than 0.00001 which suggests that the compressive strength results for mixtures with varying wollastonite fiber dosage are statistically different.  
Figure 3: Compressive strength of the wollastonite-fiber-reinforced hardened cement pastes under quasi-static loading condition.
## 2 Materials and Methods
### Physical and Mechanical Characterization of MBCs
#### 2.3.3 Compressive Resistance at 10% Deformation  
The behavior during compression is important to define the appropriate fields of application of MBCs; for instance, whether they can bear some loads (in flooring) and whether they are compressed too much under normal use, which might harm the insulation behavior.  
Since in the preliminary studies of compressive behavior of MBCs [11] no fracture was observed, the behavior under compression is characterized by the compressive stress corresponding to 10% of longitudinal strain, i.e., compressive stress at 10% relative deformation, performed according to EN 826:2013 [50]. In order to create smooth and parallel loading surfaces, a preliminary treatment by a thin layer of cement paste was applied and the testing was performed after 48 h. Three samples of approximate size \(50\times 50\times 50\) mm were used for each of the bio-composites.  
The compressive stress at 10% relative deformation was calculated as follows:  
\[\sigma_{10}=\frac{F_{10}}{a\cdot b} \tag{5}\]  
where \(\sigma_{10}\) is the compressive stress at 10% deformation, in MPa; \(F_{10}\) is the load at which the test specimen reaches the required longitudinal deformation of 10%, in N and \(a\), \(b\) are the width and length of the samples, in mm.