esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link Level Capacity Expressions
### Spatial Multiplexing (SM)  
When SM with zero forcing is employed, the MIMO channel is effectively decoupled into \(M\) parallel streams, for which the capacity is given by [34]:  
\[C_{\text{SM}}=\sum_{k=1}^{M}E[\log_{2}(1+\gamma_{k})] \tag{8}\]where \(\gamma_{k}\) is the conditional post-processing signal-to-noise ratio (SNR) for the \(k\)-th stream. For the zero-forcing (ZF) receiver, \(\gamma_{k}\) is:  
\[\gamma_{k}=\frac{\gamma_{0}}{M}\frac{1}{[\mathbf{H}^{H}\mathbf{H}^{-1}]_{k,k}} \tag{9}\]  
where \([\mathbf{A}]_{m,n}\) is an element in the \(m\)-th row and \(n\)-th column of matrix \(\mathbf{A}\) and \(\gamma_{0}=E_{s}/N_{0}\), where \(E_{s}=E\{\parallel s\parallel_{2}^{2}\}\). Assuming no receive correlation, \(\gamma_{k}\) has the probability density function [35]:  
\[f(\gamma_{k})=\frac{M\left|\mathbf{R}_{t}^{kk}\right|}{\gamma_{0}\left| \mathbf{R}_{t}\right|}\frac{\exp\left(-\frac{M\gamma_{k}\left|\mathbf{R}_{t} ^{kk}\right|}{\gamma_{0}\left|\mathbf{R}_{t}\right|}\right)}{(N-M)!}\left( \frac{M\gamma_{k}\left|\mathbf{R}_{t}^{kk}\right|}{\gamma_{0}\left|\mathbf{R} _{t}\right|}\right)^{N-M} \tag{10}\]  
where \(\left|\mathbf{A}\right|\) is the determinant of matrix \(\mathbf{A}\) and \(\mathbf{R}_{t}^{kk}\) corresponds to \(\mathbf{R}_{t}\) with the \(k\)-th row and column removed. Then, the link level capacity can be calculated in a closed form as:  
\[C_{\text{SM}}=\sum_{k=1}^{M}\frac{\exp\left(M\left|\mathbf{R}_{t}^{kk}\right|/ \gamma_{0}\left|\mathbf{R}_{t}\right|\right)}{\ln 2}\times\sum_{m=1}^{N-M+1} \frac{\Gamma\left(m-N+M-1,M\left|\mathbf{R}_{t}^{kk}\right|/\gamma_{0}\left| \mathbf{R}_{t}\right|\right)}{\left(M\left|\mathbf{R}_{t}^{kk}\right|/\gamma_ {0}\left|\mathbf{R}_{t}\right|\right)^{m-N+M-1}} \tag{11}\]  
where \(\Gamma(\cdot,\cdot)\) is the incomplete gamma function. An upper bound of \(C_{\text{SM}}\) can be written concisely [38] as:  
\[C_{\text{SM}}\leq\sum_{k=1}^{M}\log_{2}\Bigg{(}1+\frac{\left(N-M+1\right) \left|\mathbf{R}_{t}\right|\gamma_{0}}{M\left|\mathbf{R}_{t}^{kk}\right|} \Bigg{)}. \tag{12}\]
## 2 Materials and Methods
### Homogeneous Assay with DNA aptamer  
The single-stranded DNA aptamers for lysozyme, namely Lysoapt 42 (5'-AAGATATACATACGAATTCACAGGCTAAAAGAGTGCAGAGTTACTTAG-3'), and for thrombin, namely TBA 29 (5'-AAGATATACAGTCCGGTAGGGCAGTTGGGTGACT-3'), containing the Rep recognition sequence, were used in the homogeneous assay [19; 20]. Human \(\alpha\)-thrombin was purchased from Haematologic Technologies. Equal amounts (1 uM each) of VtpRNH and DNA aptamer were mixed in 50 uL of PBS containing 0.5 mM of MgCl\({}_{2}\) followed by incubation at 37 \({}^{\circ}\)C. After incubation for 30 min, 10 uL of solutions containing varying concentrations of target molecules was added to the mixture of VtpRNH and DNA aptamer followed by incubation at room temperature for 1 h. For the thrombin-binding DNA aptamer-VtpRNH conjugate, samples containing bovine serum albumin (BSA) (instead of thrombin) were evaluated as a negative control. Samples were mixed with the same volume of Nano-Glo Luciferase Assay Reagent. Emission spectra (350-650 nm) were acquired 10 sec after addition of the reagent using a FP6500 spectrofluorophotometer. Emission spectra were normalized to the peak emission of NanoLuc at 450 nm, which was set to an intensity of 1.00. The change in BRET ratio (\(\Delta\)BRET) was calculated as follows:  
\[\Delta\textbf{BRET}=\{\text{(emission ${}_{\text{at 528 nm}}$)/(emission ${}_{\text{at 450 nm}}$)}\}-\text{BRET basal ratio}\]  
The BRET basal ratio is defined as (emission ${}_{\text{at 528 nm}}$)/(emission ${}_{\text{at 450 nm}}$) at 0 uM of the target molecule.
# Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps
## 4 Conclusions  
In conclusion, with a recoverable component of NBTI stress-recovery, a new noise model is formulated based on a fitted CEE map with soft clustering of GMM. The proposed model offers the following unique features: (1) The model is based on the occupancy of defects in the capture-emission energy map. Therefore, it allows modeling of the noise under both stationary as well as cyclo-stationary excitation conditions. (2) The GMM with soft clustering enlarges the CEE window and performs independent estimations of two distinct components of CEE map, enabling the accurate calculation of noise characteristics. (3) New theoretical expressions can capture the noise behavior in terms of activation energies and provide the identification of noise sources and the reliable prediction of \(1/f\) noise from NBTI.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1050640).  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The author declares no conflict of interest.
## 5 Simulation Experiment and Analysis
### Scenario Three  
In this scenario, 100 nodes of a wireless sensor network were randomly distributed in a square area of 100 m \(\times\) 100 m. The proposed algorithm, the TANARP algorithm, the ACORC algorithm and the QOS-PSO algorithm are compared under the same experimental conditions. In this experiment, a fixed number of packets are sent to the sink node using a fixed data source node. The source node and sink node are located at both ends of the network. In this simulation network, including the normal nodes and malicious nodes, the number of malicious nodes accounted for fifteen percent of the total number of network nodes. We set up a number of nodes with different residual energy, which is randomly distributed between the data source and destination. These nodes are randomly deployed in the network for many times. We choose the most representative of the 10 simulation instances for comparison. Each time, the proposed algorithm, TANARP, ACORC and QOS-PSO are used to select the transmission path to send 1000 data packets from the data source to the sink node. According to the number of data packets received by the sink node, the performance of the protocols can be compared.  
Figure 11 compares the final numbers of data packets successfully sent from the source node to the receiving node in 10 simulation instances. It can be seen from the graph that for most of the 10 instances, the MPASR algorithm resulted in the largest number of successfully sent data packets, compared with relatively small numbers for the other three algorithms. However, in the seventh simulation, the proposed algorithm yielded results similar to those of ACORC, and the results for both algorithms were inferior to those of the QOS-PSO and TANARP algorithms. The main reason for this phenomenon is that the proposed algorithm comprehensively considers the node reputation, residual energy and time delay in the pheromone calculation process, with the node reputation having the highest weight, and consequently, the algorithm will give priority to the node with the highest reputation value during data packet transmission. Therefore, it is possible that the node with the highest reputation value will  
Figure 10: Comparison of packet loss rates for different numbers of nodes.  
be chosen as the next-hop node even if its remaining energy is very low and its time delay is relatively long. The other two algorithms are more likely to pass data packets to malicious nodes with high remaining energies and short delays. As a result, in the seventh simulation, the numbers of packets received when these algorithms were used were much higher than that achieved with the algorithm proposed in this paper. However, this is merely an isolated phenomenon. On average, the number of packets received using the proposed algorithm is higher, and it can be seen from the results that this algorithm demonstrates good performance and offers improved security of data transmission.
## 3 Measurement Results  
Figure 15 shows a micrograph of the fabricated active phase shifter, implemented using a 0.18 \(\upmu\)m SiGe BiCMOS process, with TSV (through-silicon via) used as the ground via. The overall chip size is 800 \(\upmu\)m \(\times\) 500 \(\upmu\)m, with the core area measuring 610 \(\upmu\)m \(\times\) 270 \(\upmu\)m. For chip testing, a GSG probe was used, and calibration was performed using the SOLT method. The power consumption of the whole circuit is 19.5 mW under a power supply of 2.5 V.  
Figure 14: Phase shifting accuracy RMS value. Figure 15: Die micrograph of the fabricated active phase shifter. Figure 16 shows the phase shifting accuracy RMS value and gain fluctuation at room temperature (25 \({}^{\circ}\)C). Figure 17 presents the measured 64-QAM constellation diagram at room temperature. The active phase shifter designed in this paper achieves an RMS phase accuracy of less than 2\({}^{\circ}\) across the bandwidth range, with gain fluctuation ranging from \(-1.2\) dB to \(0.1\) dB, demonstrating excellent phase shifting performance. To evaluate the three-temperature performance of the active phase shifter, tests were conducted on the same chip, using the same phase control codes, at \(-40\)\({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 85 \({}^{\circ}\)C. Figure 18 shows the phase accuracy RMS values and gain fluctuations at the three temperatures, where red represents low temperature, blue represents room temperature, and pink represents high temperature. The test results indicate that, at both room temperature and low temperature, the RMS phase error remains below 2\({}^{\circ}\) across the frequency band, while at high temperature, the maximum phase error is slightly worse at 3.3\({}^{\circ}\). The gain fluctuation across the three temperatures ranges from \(-1.7\) dB to \(0.7\) dB. To verify the inter-chip consistency, three chips were tested at room temperature, and the results for phase accuracy RMS and gain fluctuation are compared in Figure 19. The trends of the three chips are consistent, with the maximum RMS phase error being 0.3\({}^{\circ}\) and the gain fluctuation curves being tightly grouped. This demonstrates that the chip exhibits good inter-chip consistency. Table 2 presents a performance comparison between the proposed phase shifter and other reported designs. Compared to existing studies, the prototype developed in this work demonstrates clear advantages in terms of area and power consumption, showcasing overall competitive performance. Figure 16: (a) RMS phase error and (b) gain fluctuation results at room temperature. Figure 17: Measured 64-QAM constellation diagram at room temperature. Figure 19: Inter-chip consistency test for **(a)** phase accuracy and **(b)** gain fluctuation.
## 3 Results
### Membrane Stabilization Test
#### 3.8.1 Heat-Induced Hemolysis  
Ethanol extracts of _P. emblica_ and _A. indica_ were found to have protective effects against heat-induced hemolysis at all concentrations. At the site of inflammation, the mechanism behind the protection from heat-induced hemolysis most probably involves the inhibition of the lysosomal content release within the neutrophils. The inhibition of heat-induced hemolysis was found to increase significantly with an increase in the concentration. The extracts showed the maximum inhibition 54.44 \(\pm\) 0.31% and 50.21 \(\pm\) 0.63% at 600 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (Figure 7) (Table 2). The standard drug aspirin was used as a standard, and it showed significant protection (73.64 \(\pm\) 0.117%) at a concentration of 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (data are not shown in the graph).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Concentration** \\ **(\(\mathrm{\SIUnitSymbolMicro g}\)/mL)** \\ \end{tabular} } & \multicolumn{2}{c}{_P. emblica_} & \multicolumn{2}{c}{_A. indica_} \\ \cline{2-5}  & **Percentage Protection** & **S. E. M.** & **Percentage Protection** & **S. E. M.** \\ \hline
100 & 9.13 & 0.1 & 7.21 & 0.003 \\ \hline
200 & 15.91 & 1 & 11.68 & 0.07 \\ \hline
300 & 24.62 & 1.321 & 18.47 & 0.256 \\ \hline
400 & 30.73 & 0.038 & 25.47 & 1.18 \\ \hline
500 & 45.56 & 1.028 & 44.16 & 0.76 \\ \hline
600 & 54.441 & 0.631 & 50.217 & 0.313 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage protection from heat-induced hemolysis.  
Figure 7: Percentage protection from heat-induced hemolysis. The blue columns (from 1–6) at the x-axis denotes concentration 100–600 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of _P. emblica_ extract. The yellow columns (from 1–6) at the x-axis denotes the concentration of 100–600 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of _A. indica_ extract. Sample 7 denotes samples having aspirin with a concentration of 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The results are presented as means \(\pm\) SEM (\(n=3\)). The statistical differences are denoted with asterisk (*) for _P. emblica_, indicating significance at \(p<0.05\) in comparison with the reference sample, and a hashtag (\(\mathrm{\SIUnitSymbolMicro g}\)), signifying \(p<0.05\) for _A. indica_ in comparison with 7.2. Protection from hyposaline-induced hemolysis the reference sample.  
The ethanol extract of _P. emplica_ and _A. indica_ exhibited significant protection ability against hyposaline-induced hemolysis at various concentrations. The osmotic loss has been reported to be caused by hemolysis induced by hypotonicity. Both extracts inhibited the hemolysis mediated by hypotonicity and protected from concentration variable osmotic loss. The data show that these extracts have the potential to stabilize the RBC membrane and are effective membrane stabilizers even at higher concentrations (600 \(\upmu\)g/mL) (Figure 8) (Table 3). Diclofenac sodium showed maximum protection of 69.34 \(\pm\) 0.27% at 200 \(\upmu\)g/mL (data are not shown in the graph).
## 3 Results and Discussion
### Total Pigment Content  
The residual blood is an important factor in meat quality. Blood remaining in the muscle decreases shelf life and causes undesirable discoloration and microbiological deterioration. The amount of residual blood is in positive correlation to residual levels of myoglobin and hemoglobin as the two main pigments of meat [47]. Therefore, residual blood in the muscle was characterized by total pigment content. As Table 2 shows, the bleeding efficiency was similar for both stunning methods, thus there was no effect of the stunning method on the amount of removable blood. Also, no significant difference was found in residual blood with the use of electrical or CO\({}_{2}\) stunning in the case of pork m. longissimus lumborum [19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Quality Attribute**} & \multicolumn{5}{c}{**Stunning Method**} \\ \cline{2-6}  & \(\mathbf{CO_{2}}\) & \(\mathbf{SEM}\) & \(\mathbf{Electrical}\) & \(\mathbf{SEM}\) & \(\boldsymbol{p}\)-\(\mathbf{Value}\) \\ \hline WHC \({}^{1}\), cm\({}^{2}\)/g meat & 2.26 & 0.17 & 1.72 & 0.15 & 0.042 \\ Total pigments, ppm & 14.96 & 1.70 & 14.63 & 1.65 & 0.892 \\ Cooling loss, \% & 3.01 & 0.12 & 3.06 & 0.15 & 0.740 \\ Cooking loss, \% & 9.53 & 0.22 & 6.41 & 0.21 & 0.000 \\ \hline \end{tabular} \({}^{1}\) WHC: Water holding capacity.  
\end{table}
Table 2: Effect of CO\({}_{2}\) and electrical stunning on water holding capacity, total pigment content, cooling loss, and cooking loss in pork m. gluteus medius 24 h after slaughter.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work
### Related Work
#### 2.2.1 Statistical Rule-Based Methods  
Rule-based models typically make use of statistical models generated from the content and data collected during the encoding loop. For example, [PERSON] et al. and [PERSON] et al. [9] applied a Bayesian decision rule to early prune the recursive CU evaluation process. Data accumulated during the encoding process such as RD costs, prediction residuals, distribution of RD cost in neighbouring blocks, and CU structures in neighbouring blocks are often treated as important features when determining the early termination of the current CU evaluation. For example, RD cost of the current CU is compared with a predetermined threshold to early terminate the encoding process of a CU in [17]. However, fixed thresholds and less flexible decision trees of these algorithms make the encoding decisions less content-adaptive. Most probable CU depth is determined using the spatial correlation among neighbouring CUs in [18; 19]. A similar correlation analysis is carried out between Base and Enhancement Layers of Scalable HEVC (SHVC) to achieve fast intra-coding in [20]. Statistical properties of neighbouring PUs are considered to avoid evaluation of all 35 intra-prediction modes in [21]. In the meantime, [PERSON] et al. [22] introduce a combined use of statistical properties and Hadamard cost in Rough Mode Decision (RMD) to select both CU size and prediction mode for the PU. In addition, RMD costs are also utilised in [23] to make subsequent CU split decisions. Following a similar approach by [PERSON] et al. [17], the CU is split to the next depth level straight away without processing the current level if the RMD cost exceeds a predefined threshold. However, diversity of content and large numbers of prediction modes and CU size combinations make the estimation of accurate CU sizes a compelling challenge with a small number of features and rigid thresholds. Further to that, unnecessary evaluation of the current CU depth level, rigid less flexible decision trees, and limited encoding time reduction achieved are identified as common drawbacks in these approaches. The use of CU information from the parent CU is often seen in methods that focus on improving the complexity  
Figure 2: (a) Sample Partitioning structure of a typical CTU and (b) the corresponding quadtree structure.  
reduction of inter-prediction. For example, [PERSON] et al. [24] propose a complexity reduction algorithm for Versatile Video Coding (VVC), by adaptively determining the search range for CUs and the reference frame directions, based on the parent CU information. Furthermore, a complexity reduction algorithm is proposed in [25], where fractional pixel motion estimation of sub-PUs are skipped based on the motion estimation result of the parent CU. However, these methods utilise the features extracted from the inter-picture prediction process. Therefore, these methods cannot be applied in the context of intra-picture prediction complexity reduction.
### Lorenz Waveforms for Communication Systems  
Chaotic Lorenz waveforms have been proposed for use in communication systems for a variety of applications. One application is to provide a secure and robust means of transmitting information [27, 28]. The chaotic nature of the waveform can make it difficult to intercept or decode by unwanted parties, For some cases, this quality enhances the security and privacy of communication schemes. Another use of chaotic Lorenz waveforms in communication systems is to provide signals for spread spectrum techniques [27] that lead to efficient spectral usage that supports multi-user applications [4]. These schemes are often achieved by modulating signals with a chaotic waveform such that the resulting transmitted waveform's spectrum is spread over a wide bandwidth. These schemes have further been suggest to improve the resistance of the communication system to interference and jamming.  
In a landmark finding, chaotic waveforms were shown to synchronize [29]. This property enables various receiver schemes in communication systems [10, 27]. By transmitting reference waveforms that synchronize a receiver to the waveform, accurate timing and phase alignment of transmitted signals can be achieved. This technique can be used in a variety of communication systems, including radar [11, 30] and sonar [30], as well as wireless networks [31, 32]. These attributes provide several benefits to communication systems including the aforementioned security, resistance to interference and jamming enhancements.  
Although, these systems have the ability to synchronize signals with high accuracy. The use of chaotic waveforms often requires sophisticated signal processing techniques and can be more computationally intensive than traditional waveforms. This drawback must be taken into account in the design and implementation of potential communication systems. We note that the reduction of these computational taxes is largely solved by employing solvable chaotic systems [2] that are important extensions of the simplifications of the chaotic Lorenz system put forth by [PERSON] [21].
## 4 Performance Analysis for Variable-Gain Relaying  
### Outage Probability  
The outage probability can be defined as the probability that the instantaneous SNR falls below a threshold, \(\gamma_{th}\). The outage probability expression of the considered communication system can be expressed by replacing \(\gamma\) with \(\gamma_{th}\) in the CDF in (23), which is given by:  
\[P_{out}^{V}(\gamma_{th})=F_{\gamma\bar{\gamma}}(\gamma_{th}) \tag{34}\]
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Passenger Flow Prediction  
Passenger flow prediction involves forecasting the number of passengers expected to use a particular transportation service or facility over a specified period. It is significant for transportation as it aids in optimizing the allocation of resources, such as scheduling of transit services, staffing, and facility management. Accurate predictions enhance operational efficiency, reduce waiting times, improve service quality, and improve capacity management. This results in increased passenger satisfaction and a more reliable and efficient transportation system. Several traffic prediction techniques estimate passenger flow at different traffic nodes, such as bus stops, subway stations, and airports.  
[PERSON] et al. [91] studied the application of the RNN and LSTM in passenger traffic forecasting and demonstrated their performance in experiments. The advantage is that the RNN and LSTM show superior performance compared to other models, but there may be challenges in handling long time series data. [PERSON] et al. [92] explored the application of LSTM for passenger flow prediction on railroad networks. They find that LSTM slightly outperforms ARIMA and other statistical models in terms of Mean Absolute Error (MAE) at railroad stations but performs poorly in train line prediction. [PERSON] et al. [93] proposed an LSTM-based model for predicting passenger traffic at subway stations. This model can comprehensively consider multiple factors to enhance prediction accuracy, but it requires high data diversity. [PERSON] [94] demonstrated the advantages of the Genetic Algorithm-optimized LSTM (GA-LSTM) in terms of passenger traffic forecasting accuracy. The benefit is its superior accuracy compared to non-optimized RNNs, but the drawback is that the parameter optimization process may be complex. [PERSON] et al. [95] used the DCRNN model to perform excellently in bus passenger traffic forecasting, achieving a 5% improvement in accuracy compared to traditional RNNs. [PERSON] [96] conducted comparative studies and found that the CNN-LSTM outperformed pure LSTMs in subway station passenger flow prediction despite their higher complexity and computational requirements. [PERSON] et al. [97] successfully predicted the passenger flow of attractions through a GCN-RNN model using passenger flow data from neighboring bus and metro stations. The model's strength lies in its utility, but its dependence on region-specific traffic data limits it. [PERSON] et al. [98] introduced a novel Residual RNN Channel Spatiotemporal Graph Convolution Network (RRC-STGCN) for predicting passenger traffic at railway stations. The model outperforms several baseline models, but its implementation is relatively complex, requiring a substantial amount of data for training.  
RNNs and their variants are increasingly used in passenger traffic prediction, and they show significant advantages in processing time series data. However, these models usually require large amounts of data for training and may exhibit different strengths and weaknesses in various application scenarios.