esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis of Perfluoroethers  
Synthesis of 3,6-dioxaperfluoro-5-methylnonane (polyether I). The trimer of hexafluoropropene oxide (III) was stirred with SbF\({}_{5}\) (1%-1.5% (\(w\)/\(w\))) for \(\sim\)1 h in order to remove the traces of diglyme. After that acyl fluoride III was decanted from resinous precipitate. The mixture of 20 g (40 mmol) III and 3 g SbF\({}_{5}\) was stirred for several hours while the reaction mixture was gradually heated up to 70-75 \({}^{\circ}\)C, and gas evolution (CO) was observed. After the decarbonylation was over (the reaction was controlled by NMR-19F), the reaction mixture was poured into crushed ice, washed sequentially with 10% aq. HCl, water and 30% aq. K\({}_{2}\)CO\({}_{3}\), dried over MgSO\({}_{4}\) and distilled to give 15 g (80%) III, b.p. 93-94 \({}^{\circ}\)C (lit.: 93-94 \({}^{\circ}\)C).  
Synthesis of polyether II. The mixture of 20 g (20 mmol) hexamer of hexafluoropropene oxide (IV), 3.5 mL H\({}_{2}\)O and 15 mL hexane was stirred vigorously for 1 h until exothermic reaction ceased, lower layer was separated, acidified with 3 mL of conc. H\({}_{2}\)SO\({}_{4}\), distilled under reduced pressure and then redistilled to give 17.5 g (88%) 4,7,10,13,16-pentaoxaperfluoro-5,8,11,14,17-pentamethyloctadecanoic acid (V), b.p. 147-148 \({}^{\circ}\)C/1 Torr.  
A mixture of acid V (14 g, 14 mmol), KOH (0.122 g, 2.2 mmol), 29 ml McNN, and 1 mL H\({}_{2}\)O was electrolyzed at a Pt/10% Ir anode (15 cm\({}^{2}\)) using a stainless steel cathode (2 cm\({}^{2}\)) in a one-compartment cell at 30-35 \({}^{\circ}\)C (current 0.8 A, current density 51.6 mA cm\({}^{-2}\), initial electrolyzer voltage 24 V, theoretical charge for consumption of 11.8 mmol of acid 0.32 A-h). After passing 0.37 A-h, the final voltage was 40 V. Then, the electrolyte was washed with water and aq. K\({}_{2}\)CO\({}_{3}\) solution, the organic layer was separated and distilled to give 9.2 g (yield 82% by substance and 71% by current) of 4,7,10,13,16,19,22,25-octaoxaperfluoro-5,8,11,14,17,18,21,24-octamethyloctacosane II (diastereomers mixture), b.p. 161-164 \({}^{\circ}\)C/1 Torr.  
Found %: C 21.70; F 69.53. C\({}_{28}\)F\({}_{58}\)O\({}_{8}\). Calc. %: C 21.46; F 70.37. The spectrum 19F-NMR of II (the spectrum was recorded using a Bruker AVANCE-300 spectrometer at 282 MHz.  
Chemical shifts are given in ppm vs. CFCl\({}_{3}\). Downfield shifts are positive):  
[CF\({}_{3}\)CF\({}_{2}\)CF\({}_{2}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OCF(CF\({}_{3}\))]\({}_{2}\)  
CF\({}_{3}\)CF\({}_{2}\)CF\({}_{2}\), 84.69 (6F, s); CF\({}_{3}\)CF\({}_{2}\)CF\({}_{2}\), 132.63 (4F, s);  
[CF\({}_{3}\)CF\({}_{2}\)CF\({}_{2}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OCF(CF\({}_{3}\))CF\({}_{2}\)OCF(CF\({}_{3}\))]\({}_{2}\), 80-85.1 (a group of signals (40 F); CF(CF\({}_{3}\))CF(CF\({}_{3}\)), 143.26 (2F, br.m); OCF(CF\({}_{3}\))CF\({}_{2}\)O, 147.26 (6F, br.m).  
The acyl fluorides III-IV were supplied by \"P&M-Invest\" Ltd., Moscow, Russia.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 3 Model Construction
### Adaptive Fireworks Algorithm (AFWA)  
Fireworks algorithm is a new search algorithm that simulates the explosion process of fireworks to conduct multi-point simultaneous explosion search [34]. Adaptive fireworks algorithm (AFWA) optimizes the algorithm by calculating the adaptive explosion range. AFWA has distributed parallelism and good adaptability [38], which is suitable for multi-energy coupling scenarios. We applied it to the optimization of kernel function width parameters and penalty parameters of LSSVM. In multi-energy scenarios, AFWA can obtain more stable and accurate calculation results compared with other algorithms [39].
## 2 Materials and Methods
### Spectroradiometric Data and Processing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name and Abbreviation of SRIs** & **Equation** & **References** \\ \hline \multicolumn{3}{c}{Vegetation-SRIs} \\ Blue normalized difference vegetation index (BNDVI-P) & \((\text{R}_{1245}-\text{R}_{415})/(\text{R}_{1245}+\text{R}_{415})\) & [62] \\ Blue normalized difference vegetation index (BNDVI-M) & \((\text{R}_{1640}-\text{R}_{415})/(\text{R}_{1640}+\text{R}_{415})\) & This study \\ Green normalized difference vegetation index (GNDVI-P) & \((\text{R}_{1245}-\text{R}_{550})/(\text{R}_{1245}+\text{R}_{550})\) & [62] \\ Green normalized difference vegetation index (GNDVI-M) & \((\text{R}_{1640}-\text{R}_{550})/(\text{R}_{1640}+\text{R}_{550})\) & This study \\ Red normalized difference vegetation index (RNDVI-P) & \((\text{R}_{1245}-\text{R}_{680})/(\text{R}_{1245}+\text{R}_{680})\) & [62] \\ Red normalized difference vegetation index (RNDVI-M) & \((\text{R}_{1640}-\text{R}_{680})/(\text{R}_{1640}+\text{R}_{680})\) & This study \\ Red-edge normalized difference vegetation index (RENDVI-P) & \((\text{R}_{1100}-\text{R}_{715})/(\text{R}_{1100}+\text{R}_{715})\) & [62] \\ \hline \multicolumn{3}{c}{Water-SRIs} \\ Water index (WI-M) & \((\text{R}_{640}/\text{R}_{1640})\) & This study \\ Normalized water index -1 (NWI-1-P) & \((\text{R}_{970}-\text{R}_{850})/(\text{R}_{970}+\text{R}_{850})\) & [63] \\ Normalized difference water index (NDWI-P) & \((\text{R}_{660}-\text{R}_{1640})/(\text{R}_{860}+\text{R}_{1640})\) & [64] \\ Normalized difference moisture index (NDMI-P) & \((\text{R}_{2200}-\text{R}_{1100})/(\text{R}_{2200}+\text{R}_{1100})\) & [65] \\ Normalized difference moisture index (NDWI-M1) & \((\text{R}_{669}-\text{R}_{1300})/(\text{R}_{669}+\text{R}_{1300})\) & This study \\ Normalized difference water index (NDWI-M2) & \((\text{R}_{737}-\text{R}_{1360})/(\text{R}_{737}+\text{R}_{1360})\) & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The full name, abbreviation, and equation of published (P) and modified (M) spectral reflectance indices (SRIs) used in this study.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 3 Study of the Receiver Criticalities and Workflow for the Upgrading Feasibility Study
### Mitigation of the Self-Produced RFIs  
Besides the RFIs from the surrounding environment, unfortunately, there are a number of self-produced RFIs. In Section 3.2.1, the mitigation of RFIs generated by the electronic boards for the remote control of the receiver [32] is addressed. The mitigation of unwanted signals generated by the feeding process of the active microwave components (i.e., microwave switches) is discussed in Section 3.2.2.
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
## 3 Results
### Navigation Performance  
#### 3.3.1 GEO Navigation Performance  
The above analysis showed that, with sidelobe signals included, good GNSS satellite geometry could be provided for the GEO spacecraft. In this section, the position of the spacecraft was derived by single-epoch least square estimator. The spacecraft positions used for measurement simulation were deemed as true positions. The differences between single-epoch least-square solution and the true positions were calculated; RMSs of all the differences are listed in Table 9. We only show the differences of BDS-3 SPP here in Figure 16. For the dual-GNSS processing mode, i.e., BDS-3/GPS combination, the 3D position error decreased by 18.6% in contrast to BDS-3 only processing. For GREC combination, the SPP accuracy increased by 21.5% compared with the dual-GNSS processing. Obviously, the improvement of navigation performance resulted from the improved signal geometry. For each epoch for each satellite, code post-fit residuals were computed by the difference between the actual code measurements and the estimated distance between the satellite and the receiver; the related errors were also removed. The RMSs of code post-fit residuals were 3.75 m, 5.08 m, 4.82 m, and 5.1 m for BDS-3, GPS, BDS-3/GPS, and GREC combination, respectively. In total, 42.4% of the received signals were higher than 30 dB-Hz, and the pseudorange measurement noises were comparable to the results in [37], proving that our observation simulation routine was reliable.
## 3 Simulation Results and Analysis
### Write Delay and Stability Comparison  
The stability and delay characteristics during the write operation for the cells can be observed in Figures 7 and 8. Write delay or write access time (TWA) is measured from the time the word line and write word line reach 50% of VDD to the moment Q and QB are flipped [5]. Cases such as WE-QUATRO, QUCCE12T, and RHPD14T utilize both pairs of access transistors simultaneously to help flip the states of internal nodes, thus commonly yielding a shorter TWA. RSP14T and RHD12T only involve one pair of transistors in the write operation, resulting in a relatively long TWA. RSLP16T, while having the largest  
Figure 6: Read delay among comparison cells.  
number of transistors internally, uses PMOS for one pair among the two pairs of access transistors for SEMNU recovery, thereby not significantly aiding in reducing write delay. Consequently, the cell structure proposed in this paper demonstrates a TWA that is slightly slower than average.  
The ability of an SRAM cell to switch states during write operations is evaluated based on write capability. Typically, the write static noise margin (WSNM) is used to assess write capability; however, recent studies have claimed that the word line write trip voltage (WWTV) is a more reliable method to evaluate the write capability of SRAM cells [18; 21]. To measure WWTV, the write operation is simulated, and the voltage difference between VDD and the word line at the point of the storage node inversion is determined. In the case of the WWTV, the write operation is simulated, and the voltage difference between VDD and the word line at the point of the storage node inversion is determined.  
of RSLP16T, although the internal nodes have low CR, the final write time for the entire node is long; however, the storage nodes, as stated earlier, have a large CR (4.0), leading to fast inversions and relatively high write stability.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 5 Analysis
### Application of Machine Learning to LiDAR Data
#### 5.3.1 Measured Distance from LiDAR Sensors to Tie/Ballast Surfaces  
First, for simplicity, we used the measured distances from LiDAR sensors to tie/ballast surfaces for training the machine learning models for both Logistic Regression and Decision Tree algorithms. Figure 12 shows a sample of distance LiDAR measurements from the mainline track testing. Distinct patterns for ties (orange-shaded) and ballasts (unshaded) are evident throughout the data, which were explored by the models.  
The models were assessed across various window sizes, ranging from 5 to 55 data points. This range was determined to be sufficient after several trial-and-error iterations. Larger window sizes would lead to higher accuracies but require more data points. Therefore, selecting the smallest window size to achieve the necessary accuracy is advantageous. Figure 13 shows the performance of ML models using Decision Tree/Logistic Regression over different window sizes when trained on the measured distances from LiDAR sensors to tie/ballast surfaces. As shown in Figure 13, the Decision Tree algorithm exhibits higher accuracies than Logistic Regression. Additionally, the accuracy for both algorithms increases with the increase in window size. Both algorithms, however, reach a plateau at higher window sizes.  
The analysis shows that the tie/ballast differentiation achieved by both algorithms can be negatively influenced by the condition of ties (e.g., elevated, sunk, tilted, or cracked)  
Figure 12: A sample of measured distances from LiDAR sensors to tie/ballast surfaces used for training the ML models for tie/ballast differentiation. Ties are marked with orange shading, and unshaded areas represent ballasts. (Plots are shifted by 3 cm for clarity).  
Figure 13: Accuracy of models from Logistic Regression and Decision Tree algorithms for various window sizes trained on the left LiDAR’s distance measurements (distance from LiDAR sensor to tie/ballast surfaces).  
or factors such as their position, elevation, and alignment. To remedy this issue, we evaluated other approaches, such as using the difference between the left and right LiDAR measurements to gauge the two measurements against each other and possibly make the ML models less sensitive to tie conditions.  
A model using the Decision Tree algorithm, trained on the measured distances from the LiDAR sensors to tie/ballast surfaces with a window size of 40, was applied to an unseen (unlabeled) section of the data. In Figure 14, the blue line represents the model's predictions, with 1 indicating a tie and 0 indicating ballast. At a specific location in this dataset, two ties with cracks are present, as shown in Figure 14. The model struggles to distinguish between ties and ballasts in these cracked sections.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 2 Related Works
### The Matrix Encoding  
The Matrix encoding is used to embed the authentication codes into bitmaps of smooth blocks. The limitation of Matrix encoding is that only 3 bits can be embedded at a time, and our method requires 6 bits to be embedded, thus a block has to be embedded 2 times. Matrix encoding \((\beta,\ k)\) is an efficient embedding method [30] based on Hamming code, which is a linear error correction code proposed by [PERSON] [31]. \((\beta,\ k)\) represents the \(k\) secret bits \(s\) embedded into the vector \(V=\{V_{i}\}_{i=0}^{\beta-1}\) of length \(\beta\). Matrix encoding \((\beta,\ k)\) is embedded based on a parity matrix \(H\). To embed \(s\) into \(V\), \(p=((H\times V^{\mathrm{T}})\mathrm{mod}\ 2)\bigoplus s^{\mathrm{T}}\) is calculated, where \(\mathrm{T}\), \(\bigoplus\) and \(\mathrm{mod}\) represent transpose, exclusive-or and modulo-2 operations, respectively. Note that \(p\) is column vector of length \(k\). Let \((p)_{10}\) be the decimal value of \(p\) and initialize \(V_{\mathrm{M}}=V\) as the marked vector of \(V\). If \((p)_{10}=0\), then no bit is required to be flipped in \(V_{\mathrm{M}}\). Otherwise, flip the \(((p)_{10}-1)\)-th bit of \(V_{\mathrm{M}}\). The secret bits can be extracted directly by calculating \(s=(H\times V_{\mathrm{M}})^{\mathrm{T}}\mathrm{mod}\ 2\).  
Next, an example of \((\beta,\ k)=(7,\ 3)\) is taken to introduce the embedding and extraction procedures of matrix encoding. Following equation shows the \(H\) of (7, 3):  
\[H=\begin{bmatrix}0&0&0&1&1&1&1\\ 0&1&1&0&0&1&1\\ 1&0&1&0&1&0&1\end{bmatrix}. \tag{2}\]  
Let \(V=[1,\ 1,\ 1,\ 1,\ 1,\ 0,\ 0]\) be the original vector used to embed the secret bits \(s=[1,0,0]\). Calculate \(p=((H\times V^{\mathrm{T}})\mathrm{mod}\ 2)\bigoplus s^{\mathrm{T}}\) to get \(p=[1,\ 0,\ 1]^{\mathrm{T}}\). Convert \(p\) to its decimal value, and \((p)_{10}=5\). Then, flip the \(5-1=4\)-th bit of the initialized marked vector \(V_{\mathrm{M}}\), and \(V_{\mathrm{M}}=[1,\ 1,\ 1,\ 1,\ 0,\ 0,\ 0]\). The embedded secret bits can be extracted by \(s=(H\times V_{\mathrm{M}})^{\mathrm{T}}\mathrm{mod}\ 2=[1,\ 0,\ 0]\).  
Figure 1: The examples of APPM embedding method. (**a**) The schematic diagram of the APPM; (**b**) An example of \(R_{64}\).
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis
### Parameter Setting and Evaluation Standard  
#### 3.2.1 Subband Number  
The primary purpose of the filter bank analysis is to decompose SSVEP into subband components to extract independent information from harmonics more efficiently. This approach separates the SSVEP into equal-bandwidth subbands and assesses the accuracy by varying the number of subbands. The number of subbands is set to 3\(\sim\)7, and all other variables are strictly controlled. The time duration is 1.5 s with three harmonics. The accuracy is shown in Figure 6.  
We obtained the greatest accuracy, 87.9%, based on five subbands. When the number of subbands is greater than 5, the accuracy will be reduced to a certain extent. Thus, the number of subbands should be based on 5. When the number of subbands is 5,  
Figure 6: Accuracy of different subband numbers.  
the decomposition of subbands at 8 Hz is shown in Figure 7, where the time-frequency diagram of six blocks of the single channel (Pz) is drawn.
## 4 Experimental Results and Analyses  
### Ablation Experiments  
In order to verify the improved performance of the model, ablation experiments were performed on combinations of various components, which were then tested on the tibia dataset. The average precision (AP) metric was utilized to evaluate the detection performance of tibia-containing facets of the model. mAP@[0.5, 0.95] represents the average AP achieved across various IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05. The results of the ablation experiments are shown in Table 2.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 2 System Model
### SCMA System Description  
We consider the downlink SCMA system with \(J\) users spreading over \(K\) orthogonal resources served by a base station (BS). The overloading factor is defined as \(\lambda=J/K\). Each user transmits its signal over \(d_{v}^{\prime}\) orthogonal resources, while each orthogonal resource is occupied by \(d_{c}^{\prime}\) users. This structure can be represented by a sparse binary \(K\times J\) indicator matrix \(\mathbf{F}^{\prime}\), where the columns and rows denote the users and orthogonal resources, respectively. The element in the \(k\)-th row and the \(j\)-th column of the indicator matrix \(\mathbf{F}^{\prime}\) is denoted as \(f_{k,j}^{\prime}\), whose value is \(f_{k,j}^{\prime}=1\) when the \(k\)-th resource node (RN) and the \(j\)-th variable node (VN) are connected; otherwise, \(f_{k,j}^{\prime}=0\). In this work, the typical \(4\times 6\) and \(5\times 10\) indicator matrices are taken into consideration, given by  
\[\mathbf{F}_{4\times 6}^{\prime}=\left[\begin{array}{cccccc}1&1&1&0&0&0\\ 1&0&0&1&1&0\\ 0&1&0&1&0&1\\ 0&0&1&0&1&1\\ \end{array}\right], \tag{1}\]  
and  
\[\mathbf{F}_{5\times 10}^{\prime}=\left[\begin{array}{cccccccccc}1&1&1&1&0&0&0&0&0 \\ 1&0&0&0&1&1&1&0&0\\ 0&1&0&0&1&0&0&1&1&0\\ 0&0&1&0&0&1&0&1&1\\ \end{array}\right], \tag{2}\]  
where the loading factors are \(\lambda=150\%\) and \(\lambda=200\%\), respectively. The column weights of both \(\mathbf{F}_{4\times 6}^{\prime}\) and \(\mathbf{F}_{5\times 10}^{\prime}\) are \(d_{v}^{\prime}=2\), while the row weights of \(\mathbf{F}_{4\times 6}^{\prime}\) and \(\mathbf{F}_{5\times 10}^{\prime}\) are \(d_{c}^{\prime}=3\) and \(d_{c}^{\prime}=4\), respectively. The corresponding factor graph of the indicator matrix \(\mathbf{F}_{4\times 6}\) is depicted in Figure 1.  
Based on the SCMA principle, each user is equipped with a unique complex codebook \(\mathbf{C}^{\prime}\), where \(M\) sparse codewords with \(K\)-dimensions are involved. This sparsity reduces the interference from other users and enables the message passing algorithm (MPA) to carry out detection.
## 2 Background
### MRAM Technology  
Magnetoresistive random-access memory (MRAM) is an emerging technology that utilizes a magnetic tunnel junction (MTJ) configuration and was initially proposed by [PERSON] in 1975 [22]. In this configuration, two ferromagnetic films are separated by a thin insulating material. One of the films is fixed in a specific magnetic orientation, while the other is free to change during programming cycles.  
If the insulating material is thin enough, electrons can tunnel through it, effectively creating electrical resistance. This resistance is dependent on the thickness of the insulating material and may be behaviorally modeled as a resistor.  
MRAM cells utilize this configuration to store information. The magnetization orientation in the ferromagnetic films is changed to encode data. The electrical resistance exhibited by the MRAM cell determines whether it stores a 0 or a 1. If the magnetic orientations of both films are aligned (parallel configuration), the MRAM cell exhibits lower resistance, a state of 0. Conversely, if the films have different magnetic orientations (anti-parallel configuration), the cell exhibits higher resistance for a 1.  
The tunneling magnetoresistance ratio (TMR) refers to the relative change between the high and low resistance states in MRAM. A higher TMR ratio enhances the read capabilities of MRAM, making it easier to differentiate between the high and low states. In MRAM circuitry, CMOS transistors are employed, introducing series resistance typically in the k\(\Omega\) range. This series resistance alters the overall relative resistance of MRAM cells. It must be considered when utilizing MRAM as memory or when using its resistance for purposes such as PUF responses. Several studies, including those by [PERSON] et al. (2016) [23] and [PERSON] et al. (2016) [17], discuss the influence of magnetoresistive effects and considerations in MRAM.  
MRAM technology can be classified as toggle, spin-transfer torque (STT), and spin-orbit torque (SOT) MRAM. These classifications are based on the method of writing to memory [23]. These MRAM cells are shown in Figure 1.
## 3 Establishment of the Scale Factor Model
### Monte Carlo Verification of Scale Factors under Orthogonal Basis Functions  
In the conditions mentioned earlier, a simulation with 50 sets of different velocities \(v\) with perturbations was conducted. These velocities were then incorporated into the modified orthogonal basis function variables for testing. In the \(\beta\)-OBF relationship, we identified the variable \(w\) corresponding to two different receiver operating characteristic (ROC) curves. These results are illustrated in Figure 11.  
Figure 10: v\(-\)Em energy detection curve.  
Figure 9: Velocity perturbation curve.  
Among these, the conventional orthogonal basis functions exhibit significant variability in the independent variables. However, the modified orthogonal basis decomposition, characterized by the independent variable \(\beta-w\), demonstrates greater stability. Following this assertion, the distribution of the aforementioned detection curves was simulated by generating 1000 sets of random data. The Monte Carlo method was employed to validate and assess the independent variables \(w\) and \(\beta-w\) within the orthogonal basis functions. The model's generalization capability was estimated using random sampling. The \(\beta-w\) detection curve exhibits superior generalization performance. The evaluation curve is depicted in Figure 12.
## 5 Test Results
### Tracking Performance Test  
In the signal tracking performance test, we generate a set of intermediate frequency data whose C/N0 decreases from 21 to 1 dB-Hz with a speed of 2 dB per 40 s. The traditional tracking method without assisted information is realized for comparison, where the total integration time is set to 400 ms, including a 20 ms coherent integration time and 20 non-coherent integrations. The tracking results are shown in Figures 15 and 16. Figure 15 shows the estimated Doppler with and without the LEO assistance. Moreover, Figure 16 shows the estimated Doppler errors relative to the reference value of the Doppler frequency. When the estimated Doppler error exceeds the threshold of 5 Hz, it is considered to lose lock. We can see that the proposed tracking method can track up to 320 s, corresponding to C/N0 at 5 dB-Hz. It is consistent with the performance analysis in Figure 13. For the traditional method, it can be seen the estimated Doppler error exceeds 5 Hz at 250 s (9 dB-Hz), resulting in a loss of lock. Such a high-sensitivity performance in the standalone receiver is mainly from the DA-based FLL structure mentioned above. Thus, the tracking sensitivity of LEO-assisted method is improved by about 4 dB compared with the unassisted method.  
Figure 14: Acquisition performance comparison between LEO-assisted and unassisted method.