esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experimental Simulation and Analysis
### Algorithm Performance Analysis  
#### 4.3.1 Comparison of Matrix Condition Numbers  
In the solution process of a linear system, the size of the condition number can be used to measure the stability of the matrix under numerical operations. The larger the condition number, the more unstable the matrix \(A\) becomes during numerical computation; that is, a small change in the input data may lead to a significant change in the output result. For a given matrix \(A\), the condition number is defined as follows:  
\[\kappa(A)=\left\|A\right\|\cdot\left\|A^{-1}\right\| \tag{20}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Matrix Dimension** & **Non-Zero Elements** \\ \hline
1 & 1565 & 78,425 \\
2 & 26,742 & 1,362,615 \\
3 & 475,750 & 24,262,002 \\
4 & 3,843,405 & 196,012,424 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model and matrix information.  
where \(|\mid A\mid|\) is some norm of matrix \(A\) (usually Manhattan, Euclidean, or maximal), and \(|\mid A^{-1}\mid|\) is the corresponding norm of the inverse matrix of \(A\). The condition number \(\kappa(A)\) can be any positive real number, including infinity.  
The condition numbers of the matrix \(A\) in its initial state were compared with those after preconditioning using ILU and G-Pre, respectively. The results are shown in Table 2. In the initial case, the condition numbers of the different matrices were very large, leading to instability during numerical computation. However, after preconditioning, the condition numbers of the matrices were reduced to some extent. Specifically, the G-Pre preconditioning algorithm significantly outperformed ILU factorization in optimizing the condition numbers. For several matrices in this test, the G-Pre preconditioning algorithm improved the condition number by an average factor of 2.6 compared to that of ILU factorization. Lower condition numbers also imply higher stability in the solving iteration process for G-Pre, as will be demonstrated in the convergence performance comparison during the matrix solving process.
## 3 Results and Discussion
### Study on the Performance of Uranyl Sensor  
#### 3.4.1 Evaluation of Selectivity of \(\rm{MIIP}/Fe_{3}O_{4}@SiO_{2}/MCPE\) Sensor  
The electrodes were incubated in solutions embodying the mixtures of \(\rm{UO}_{2}{}^{2+}\) (\(1.5\times 10^{-7}\) mol L\({}^{-1}\)) and various concentrations of some potentially interfering cations, followed by electrochemical analysis to investigate the selectivity of \(\rm{UO}_{2}{}^{2+}\) imprinted polymers in \(\rm{MIIP}/Fe_{3}O_{4}@SiO_{2}/MCPE\) (Figure 6B). Then, the solutions were a mixture of one metal ion and uranyl ion. It can be seen from Figure 6A that when the \(\rm{Cu}^{2+}/\rm{UO}_{2}{}^{2+}\) ratio is 125:1, the influence of the presence of copper on the voltammetry signal of uranyl ions is negligible. However, \(\rm{Cu}^{2+}\) ions with a molar volume of more than 150 times can significantly affect the voltamter signal of \(\rm{UO}_{2}{}^{2+}\), indicating that these ions competed with each other when the concentration of interference ions is too high, which will occupy the selective holes in the IIP and affect the selective binding of \(\rm{UO}_{2}{}^{2+}\) on the upper cavity of the \(\rm{MIIP}/Fe_{3}O_{4}@SiO_{2}/MCPE\). It is worth mentioning that the weakly adsorbed substance (the most common interference ion) has been removed from the electrode surface when the electrode is washed. Therefore, this washing process can also significantly reduce the interference effect to enhance the selectivity of the sensor. Within 95% confidence interval, the interference level of potential interference ions of uranyl ion of 1.5 \(\times\) 10\({}^{-7}\) mol L\({}^{-1}\) are presented as following (the data is expressed as interference ion/\(\rm{UO}_{2}{}^{2+}\) ratio): \(\rm{Pb}^{2+}\), \(\rm{Zn}^{2+}\), \(\rm{Hg}^{2+}\), \(\rm{Cd}^{2+}\) (\(>\)150), \(\rm{Cu}^{2+}\), \(\rm{Ag}^{+}\), \(\rm{Mn}^{2+}\), \(\rm{Fe}^{2+}\) (\(>\)125), \(\rm{Cr}^{3+}\), \(\rm{Co}^{2+}\), \(\rm{Ni}^{2+}\) (\(>\)175), \(\rm{NO}_{3}\)-, \(\rm{Cl}\) (\(>\)150), \(\rm{PO}_{4}{}^{3-}\), \(\rm{CO}_{3}{}^{2-}\) (\(>\)200) and \(\rm{SO}_{4}{}^{2-}\) (\(>\)150). Obviously, these cation and anion sensors based on IIP exert no significant effect on the testing of \(\rm{UO}_{2}{}^{2+}\) ions by the IIP sensor.  
Figure 5: The _vollammograms_ of the \(\rm{MIIP}/Fe_{3}O_{4}@SiO_{2}/MCPE\) electrode when detecting of \(\rm{UO}_{2}{}^{2+}\) (**A**,**B**) the effect of pH (a: pH = 3, b: pH = 4, c: pH = 5, d: pH = 5.5, e: pH = 6, f: pH = 7, g: pH = 8, h: pH=9); (**C**,**D**) the effect of enrichment time (a: 4 min, b: 8 min, c: 12 min, d:16 min, e: 20 min, f: 24 min, g: 28 min).
## 4 Results
### Wireless Data Stream Error Ratio Evaluation  
This section presents a wireless data stream error ratio evaluation. The target sample patient's aggregate sensor data signal was monitored up to a maximum length of 8 h. Data transmission status was measured at several set time intervals--first at 10 s, then at 100 s, 10 min, 1 h, 4 h, and finally, at 8 h. Eight hours was set because it is the most commonly known average suggested sleep time for adults.  
According to theoretical calculations, the number of data transmission samples per second should be 250. This theoretical calculation was compared with the actual transmission sample calculation to assess the actual error rate. The measurement results are presented in Table 5. The results showed the highest error rate of 18.24% at 10 s elapsed time. However, the error rate greatly decreased as time passed. Eventually, the error rate decreased to a minimum of 0.1% when 8 h had fully elapsed.  
Figure 10: (**a**) Accelerometer signals; (**b**) gyroscope signals; (**c**) temperature signals; and (**d**) SpO\({}_{2}\) related signals.
## 6 Results and Discussions
### Assessing the Coefficient of Determination  
Apart from assessment of forecasting accuracy, the coefficient of determination is evaluated to reflect the strength and validation of the models. To check the appropriateness of our proposed model, the values for the coefficient of determination are computed and compared with those for other methods. For this purpose, the transformer tank demand over a period of one year, the average sales of Danfoss District Heating, and the data on plastic bag demand are used as before. Table 7 and Figure 12a-c exhibit the coefficient of determination.  
It is to be noted that the coefficient of determination computed by our proposed Coll-MHW approach is compared with those yielded by the CMHW for Case-1 and Case-2 and by the Coll.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **Method Used Earlier** & \(R^{2}\) **Using Earlier Method** & \(R^{2}\) **for Proposed Coll-MHW Approach** & **Improvement** \\ \hline
1 & CMHW & 0.3614 & 0.9999 & 63.85\% \\
2 & CMHW & 0.9101 & 0.9798 & 6.97\% \\
3 & Coll. Adj. & 0.9180 & 0.9903 & 7.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Improvement in the coefficient of determination.  
Figure 11: Actual and forecasted values for (**a**) Case-1 (**b**) Case-2 (**c**) Case-3.  
Adj. for Case-3. Regrettably, it could not be assessed for the ARIMA and IMHW methods due to the absence of the required data. However, as delineated in Table 7 and in Figure 12a-c, the coefficient of determination (\(R^{2}\)) values validate the superior performance of our proposed Coll-MHW approach.
## 2 Materials and Methods
### Binary Semantic Segmentation  
#### 2.1.1 Data  
Anatomical preparations were scanned using a commercial CBCT device, the See Factor CT3 (Imaginalis, Florence, Italy). The Feldkamp, Davis, and Kress (FDK) algorithm was employed to reconstruct the scans [24]. The volumetric data include \(512\times 512\times 512\) pixels and boast an isotropic resolution of 0.2 mm. Scans were performed under varying acquisition parameters (kV and mA).  
To train and evaluate the proposed deep learning models, an in-house annotated dataset of CBCT scans was created. A total of fourteen CBCT scans, acquired with different parameters, were considered. These scans focus on extremities, where bone segmentation is particularly challenging due to the high number of adjacent bones and the similar gray levels between spongy tissues and soft tissues.  
To generate ground truth labels, the scans were masked using 3D Slicer (version 5.6), an open-source software developed by the Surgical Planning Laboratory at Brigham. Initially, a custom threshold was applied to each scan to isolate cancellous tissues. Subsequently, manual segmentation refinements were performed using the 3D Brush tool in 3D Slicer to ensure accuracy.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 4 Experiment Setup
### Simulation Setup  
In the simulation scenario, we used the identification model that yielded optimal results on the test dataset. We constructed a MATLAB-based simulation test environment for the evaluation scenario. The parameters considered in the simulation settings are presented in Table 4.  
The target network is a wireless time-triggered network with a time slot length of \(T_{\text{slot}}\), based on the IEEE 802.11 ac [34]. Three STAs sequentially obtain repetitive opportunities to transmit uplink data frames to the AP. In each slot, the data rate of the STA is determined based on the MCS selected by the algorithm. The payload size of each packet is set to 500 bytes, and packets are transmitted continuously during the given \(T_{\text{slot}}\), with a minimum IFS interval between transmissions. If there is not enough time to complete the packet transmission, the remaining data are sent in the next cycle. In the scenario, it is assumed that MCS feedback control frames and ACK frames are not lost, and to minimize transmission errors, these control frames are always transmitted at the lowest data rate. If the transmitted data packet has a bit error rate (BER) of \(10^{-5}\) or higher, the packet is considered lost.  
\begin{table}
\begin{tabular}{c c c} \hline
**Signal Type** & **Parameter** & **Values** \\ \hline \multirow{6}{*}{WLAN [34]} & Standards & IEEE 802.11a, b, n, ac, ax \\  & Modulation Schemes & BPSK, QPSK, 16-QAM, \\  & & 64-QAM, 256-QAM \\  & IEEE 802.11 & Transmission Rate & Determined by MCS \\  & Center Frequency & 2.452 GHz \\  & Bandwidth & 20 MHz \\  & Transmission Power & \(\leq\)20 dBm \\ \hline \multirow{6}{*}{ZigBee [36]} & Modulation Schemes & QQPSK \\  & Transmission Rate & 250 Kbps \\  & IEEE 802.15.4 & Center Frequency & 2.445, 2.455, 2.46 GHz \\  & Bandwidth & 5 MHz \\  & Transmission Power & \(\leq\)0 dBm \\ \hline \multirow{6}{*}{Microwave oven [37]} & Center Frequency & 2.45 GHz \\  & AC Frequency & 60 Hz \\  & Transmission Power & \(\leq\)60 dBm \\ \hline \multirow{6}{*}{Bluetooth [35]} & Modulation Schemes & GFSK, DQPSK, 8 DPSK \\  & Transmission Rate & Determined by Tx Mode \\  & IEEE 802.15.1 & 1, 2, 3 Mbps \\ \cline{1-1}  & Center Frequency & 18 channels, 2.443–2.461 GHz \\ \cline{1-1}  & Bandwidth & 1 MHz \\ \cline{1-1}  & Transmission Power & \(\leq\)20 dBm \\ \hline \end{tabular}
\end{table}
Table 3: Parameter options for waveform generation.  
The CTI devices are located 3 m away from the receiver, the AP, with transmission power of 0 dBm, 60 dBm, and 20 dBm for ZigBee, MWO, and Bluetooth, respectively. ZigBee transmits data with a 120-byte payload at 30 ms intervals [40], MWO operates with on/off cycles at 60 Hz [37], and Bluetooth uses adaptive frequency hopping at 1600 hops per second [35].
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 3 Design of Framework
### Feature Extraction and Fusion
#### 3.2.1 Spatial and Temporal Feature Extraction
Therefore, to alleviate the issue of excessive parameter count, VGG16 eliminates the batch normalization (BN) layers after the convolutional layers. Additionally, the parameters are shared between different layers, which reduces the number of parameters in the network, makes the model more compact and efficient, and helps prevent the problem of overfitting. As for the fully connected layers used for deep feature extraction, they are configured the same as in AlexNet. VGG16 gradually reduces the size of the feature map by stacking convolutional and pooling layers several times, thus producing a smooth feature representation. This representation is useful for subsequent classification tasks to better distinguish between the different classes of traffic images. The output of the VGG16 network channel is also 1000 256-dimensional feature vectors. \[F_{\text{VGG16}}=\{F_{1},F_{2},\dots,F_{1000}\} \tag{3}\]  
AlexNet and VGG16 are used in parallel and complement each other to extract sufficient spatial traffic features from images for classification. In addition to single packet features, the temporal features between the previous and subsequent packets significantly contribute to the classification of traffic. Due to the lack of memory function in CNN networks, they cannot capture the temporal features contained in the contextual relationships between packets and flows in traffic data. RNNs perform exceptionally well in handling these features. Long short-term memory (LSTM ) [47] is a variation of RNN that has a time-sequential structure. It retains the advantage of RNN in capturing contextually correlated information while effectively overcoming the gradient vanishing and exploding problems. LSTM can retain the information of time sequences through internal memory units and gating mechanisms, its output at a certain time is related not only to the input of the current time but also to the input and state of the previous time. In this study, we used LSTM structures to extract these time-dimensional features and combine them with space-dimensional features extracted by AlexNet and VGG16 as metric features to better process traffic data. The temporal features extracted by the LSTM are also 1000 256-dimensional vectors, which are defined as follows:  
\[F_{LSTM}=\{F_{1},F_{2},\dots,F_{1000}\} \tag{4}\]
# RPKI Defense Capability Simulation Method Based on Container Virtualization
## 6 Experimental Verification and Analysis
### Comparative Analysis with Other Simulation Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation Method** & \begin{tabular}{c} **Single Physical Server** \\ **Simulation Scale** \\ \end{tabular} & \begin{tabular}{c} **Support Complete** \\ **Deployment of RPKI** \\ \end{tabular} & \
\begin{tabular}{c} **Deployment Speed** \\ \end{tabular} &
\begin{tabular}{c} **Deployment** \\ **Difficulty** \\ \end{tabular} \\ \hline This article & Approximately 350 nodes & Yes & Fast & Medium \\ GNS3 [24] & Approximately 25 nodes & No & Slow & Hard \\ NS3 [34] & Approximately 1000 nodes & No & Fast & Hard \\ SEED [25] & Approximately 70 nodes & No & Fast & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of various simulation methods. The discrete event network simulation method based on NS3 can achieve a large-scale network simulation on a single server, but its performance in simulation authenticity is poor. The simulation of the RPKI is insufficient and its implementation is difficult. Both the method proposed in [16] and the method presented in this paper are based on Docker containers. However, the approach in [16] cannot achieve the deployment of simulated networks across physical hosts, failing to fully leverage the advantages of distributed deployment. Consequently, its deployment scale is far inferior to that of the method proposed in this paper. Considering the comprehensive comparison above, the cloud-based BGP simulation network attack and defense technology proposed in this paper exhibits a good balance in terms of the single physical node scale, comprehensive RPKI support, deployment speed, and difficulty. This approach provides a solid foundation for in-depth research on the relationship between the security of the BGP networks and the RPKI defense mechanisms. Additionally, it offers an effective and feasible solution for the application of simulation technology in the context of the RPKI.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Experimental Setup
#### 5.1.2 System Setup  
In order to simulate the situation of the warehouse scenario and realize the localization of goods with passive RFID tags placed on the shelves, the experimental layout is designed as shown in Figure 6. The containers are distributed on three five-level shelves. Some tags are attached to the containers, and some tags are attached to the shelves as reference tags. The antenna is fixed on the guide rail and moves along the straight track parallel to the shelves with a whole rail length of 2 m.
## 5 Empirical Analysis and Results
### Assessment of Measurement Model
#### 5.2.4 Discriminant Validity  
Discriminant validity refers to the extent to which a particular latent variable is different from the other latent variables [64]. It was ascertained using AVE as suggested by [65], accomplished by comparing the squared correlation of the paired constructs with the AVE of each construct.  
Discriminant validity for reflective constructs occurs when the loadings of the items of a construct is an order of magnitude above the loadings for other constructs (loadings higher by 0.1) and the square root of the AVE for each construct is much higher than the correlations among pairs of constructs, above 0.5 [65]. Following [63] criterion, discriminant validity was determined by comparing the indicator loadings with other reflective indicators in the cross-loading table. To achieve adequate discriminant validity, the square root of the AVE should be greater than the correlations among latent constructs. As shown in Table 3, the AVE values are acceptable, ranging between 0.515 and 0.989. The correlations among the first-order variables were compared with the square root of the AVE (values in bold face).
## 3 Results and Discussion
### Photoautotrophic Medium Optimization Using Box-Behnken Design  
Thereafter, a Box-Behnken design was performed for the photoautotrophic cultures based on the results obtained during the optimization of heterotrophic growth. The optimum nutrient concentration was diluted 10 times to fit the photoautotrophic growth requirements. Different nutrient concentrations were evaluated using half to double the amount of each class of nutrients to find the best proportion for this strain's photoautotrophic growth. \"Other Macro\" corresponds to the macronutrients NaH\({}_{2}\)PO\({}_{4}\)\(\cdot\)H\({}_{2}\)O + K\({}_{2}\)HPO\({}_{4}\), CaCl\({}_{2}\)\(\cdot\)2H\({}_{2}\)O, and MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O and \"Micro\" corresponds to the micronutrients FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, ZnSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, Cl\({}_{2}\)Co-6H\({}_{2}\)O, Na\({}_{2}\)MoO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O, MnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O, NiCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O, and H\({}_{3}\)BO\({}_{3}\). The design layout and the respective specific growth rate, experimental, and predicted results are described in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Run Order**} & **[Ureal]** & **[Other Macro]** & **[Micro]** & **\(\mu\) Experimental** & **\(\mu\) Predicted** \\  & **Times** & **Times** & **Times** & **d\({}^{-1}\)** & **d\({}^{-1}\)** \\ \hline
1 & 1.25 & 0.50 & 2.00 & 0.30 & 0.30 \\ \hline
2 & 2.00 & 0.50 & 1.25 & 0.34 & 0.34 \\ \hline
3 & 0.50 & 1.25 & 2.00 & 0.29 & 0.28 \\ \hline
4 & 1.25 & 1.25 & 1.25 & 0.31 & 0.31 \\ \hline
5 & 1.25 & 0.50 & 0.50 & 0.35 & 0.34 \\ \hline
6 & 1.25 & 1.25 & 1.25 & 0.31 & 0.31 \\ \hline
7 & 1.25 & 2.00 & 2.00 & 0.29 & 0.29 \\ \hline
8 & 0.50 & 1.25 & 0.50 & 0.27 & 0.27 \\ \hline
9 & 0.50 & 0.50 & 1.25 & 0.28 & 0.29 \\ \hline
10 & 1.25 & 1.25 & 1.25 & 0.32 & 0.31 \\ \hline \end{tabular}
\end{table}
Table 5: Experimental layout of the Box–Behnken design and the results of the experimental and predicted specific growth rates (\(\mu\)) of the photoautotrophic cultures.  
Additionally, the interactions between urea, other macronutrients, and the micronutrients and the specific growth rates were revealed by response surface plots (Figure 2).  
According to the results, the model predicted the highest specific growth rate using two times the concentration of urea, and half the concentration of other macronutrients and micronutrients.  
Herewith, the final composition of the photoautotrophic optimized medium was set as 12 mM of (NH2)2 CO; 0.50 mM of NaH2 PO4.H2O + K2 HPO4; 0.15 mM of CaCl2.2H2O; 0.275 mM of MgSO4.7H2O; 0.015 mM of FeSO4.7H2O; 0.075 mM of ZnSO4.7H2O; 0.001 mM of Cl2 CO-6H2O; 0.0025 mM of Na2 MoO4.2H2O; 0.0425 mM of MnSO4.H2O; 0.00025 mM of CuSO4.5H2O; 0.05 mM of H3 BO3; 0.0005 mM of NiCl2.6H2O, and 1 dose of photoautotrophic vitamins mix (Thiamine-HCl (350 mg\(\cdot\)L\({}^{-1}\)), d-Biotin (50 mg\(\cdot\)L\({}^{-1}\)), cyanocobalamin (B12) (30 mg\(\cdot\)L\({}^{-1}\)). Dose: 0.5 mL\(\cdot\)L\({}^{-1}\)).
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 3 Results and Discussion
### Upscaling of PHA and Carotenoid Co-Production in a Fermenter  
Although _C. incator_ KCTC 2649 exhibited the highest PHA production in shake flask culture, _H. mediterranei_ DSM 1411 was selected for batch cultivation in the fermenter owing to its ability to produce P (3 HB-_co_-3 HV) copolymers. In fact, P (3 HB-_co_-3 HV) copolymers with a high 3 HV molar ratio are usually required for the processing of bioplastic materials with high mechanical and thermal properties. Moreover, along with P (3 HB-_co_-3 HV) copolymer accumulation, _H. mediterranei_ DSM 1411 was able to produce carotenoids as high-value products, which may reduce the overall cost of PHA production.  
Batch fermentation of _H. mediterranei_ DSM 1411 was performed in a 1 L working volume fermenter under specific conditions of temperature, pH, aeration, and agitation. Figure 6 shows the cell growth profile, carotenoid production, PHA accumulation, and substrate utilization by _H. mediterranei_ DSM 1411 during 144 h of cultivation in the fermenter. PHA production began during the exponential growth phase and continuously increased as cell biomass increased, reaching a maximum level of 5.50 \(\pm\) 0.21 g/L (58.4% of DCW) at the beginning of the stationary phase with a carotenoid co-production of 2.48 \(\pm\) 0.18 mg/L. In fact, during the stationary phase, cell division ends and cells start the process of polymer accumulation induced by the excess of carbon source and nitrogen-limiting conditions [34]. The DCW and PHA levels recorded in this study were respectively 148% and 150% higher than those obtained by [PERSON] et al. [7] when the same strain was cultivated in the presence of 25% (\(w/w\)) of _Ulwa_ sp. hydrolysate. This difference in terms of PHA production can be attributed to the difference in carbohydrate composition between the two algal biomasses as well as to the presence of toxic fermentation inhibitors in _Ulwa_ sp. biomass hydrolysate (1.3 mg of 5-hydroxymethylfurural per gram of dried macroalgal biomass) that may affect cell growth and thus PHA accumulation. Although a higher carotenoid concentration of 11.7 mg/L was reported by [PERSON] et al. [10] when upscaling the bioconversion of HCl DCB hydrolysate to a 3 L working volume fermenter by _Paracoccus_ sp. LL1, PHA content accumulated by _H. mediterranei_ in this work was 1.5-fold higher. Our results demonstrate the high potential of _H. mediterranei_ in converting DCB-hydrolysate-derived sugars into P (3 HB-_co_-3 HV) without any precursor supplementation. These findings are of great importance, as the cultivated strain was able to convert reducing sugars from an inexpensive substrate into a P (3 HB-_co_-3 HV) copolymer with higher functionality compared with a PHB homopolymer.