esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 3 Soft Decision Decoding Algorithm Based on Cyclic Information Set
### The Proposed Soft Decision Decoding Algorithm
```  
**Algorithm 1** The soft decision decoding algorithm based on cyclic information set  
In the proposed soft decision decoding algorithm, the correlation discrepancy is used as a metric to generate the candidate sequence and select the optimal candidate codeword. First, \(I\) information sequences are generated using cyclic shifting, and for each information  
Figure 1: The diagram of the proposed soft decision decoding algorithm. Figure 2: The generation of the multiple cyclic information sets. sequence, the \(C\) nearest sequences in correlation discrepancy are selected as candidate information sequences. Then, the list of candidate codewords is established using the concise cyclic encoding method. Finally, the candidate codeword having the minimum correlation discrepancy with the received soft information sequence \(\mathbf{r}\) is selected as the decoding result. The size of the candidate codeword list is reduced from \(2^{k}\) to \(I\cdot C\) based on the minimum correlation discrepancy, which can effectively reduce the search range of the decoding results. Meanwhile, for each information sequence, it only needs to calculate the correlation discrepancy of the two \(k\)-dimensional sequences once, and calculate the \((n-k)\)-dimensional correlation discrepancy for \(C\) times. The proposed algorithm avoids the complex calculation and sorting of the correlation discrepancy between all \(2^{k}\) binary \(k\)-dimensional sequences compared with the ML decoding.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 4 Developed E-SSA-Based NOMA System Elements
### Current Deployment Status  
Eutelsat recently launched the IoT First service, dedicated to the IoT market, based on the HyperCube hub and SmartLNB terminals. The service, which is operated in the Ku band, is available in different regions of the world such as:  
* Europe, on Eutelsat 10A (see Figure 14);
* Africa sub-Sahara, on Eutelsat 7B;
* United States and Mexico (CONUS coverage), on Eutelsat 117 WA (see Figure 15);  
Figure 13: Detailed view of the RL GW.  
* North and South America (pan-America coverage), on Eutelsat 117 WA. Moreover, trials are ongoing in the Far East at the time of writing. The terminals are now at the third hardware generation. The latest generation terminals are smaller, lighter and overall cheaper than previous ones. Their architecture is based on the ST Cardiff3 chipset [21], which has been customized to both receive the DVB-S2 signal and to transmit the F-SIM waveform.  
Figure 14: A SmartLNB installed on a communication tower in Slovakia (EUTELSAT 10A coverage).  
Figure 15: A SmartLNB installed on an oil rig in the Gulf of Mexico (EUTELSAT 117 WA coverage).
## 2 Methodology
### Random Forest, Validation, and Test of Different Classification Scenarios  
The concluding phase of the data analysis involved the RF classification of pasture plots within the SuperDove images. The classification was executed using the attributes individually, such as reflectance, vegetation indices, SMA endmember fractions, and GLCM texture metrics, as well as in combination. These attributes were computed from the SuperDove image representing the most suitable period for discriminating the pasture degradation classes.  
Utilizing the Ranger package within the R software environment (v4.4.0), a unified model was constructed for all sites with an ntree parameter set to 1000 and an mtry parameter established as the square root of the total number of predictor variables. To circumvent spatial autocorrelation, a minimum distance criterion of three pixels was instituted for class sampling. The model incorporated a total of 3000 pixels, with 70% of the samples allocated for training and the remaining 30% for validation. Using the set of validation pixels, error matrices and the corresponding metrics, including overall classification accuracy (OA), Kappa, Precision (P), Recall (R), and F1-score (F1), were computed. The F1-score, a metric assessing the overall performance of a classification model by balancing Precision and Recall, takes into account false positives and false negatives, offering a comprehensive evaluation of the model's efficacy.  
Using the complete set of attributes, this study explored two distinct classification scenarios: a more challenging one consisting of five classes (NDP, LID, MID, SAD, and SBD), and a less complex yet still demanding scenario comprising three classes (NDP, SAD, and SBD). Examining these two scenarios is crucial due to their potential implications for funding strategies and policies in delineating priority areas for renewal or recovery.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
###### Abstract  
Orthogonal Chirp Division Multiplexing (OCDM) is a modulation scheme which outperforms the conventional Orthogonal Frequency Division Multiplexing (OFDM) under frequency selective channels by using chirp subcarriers. However, low complexity equalization algorithms for OCDM based systems under doubly selective channels have not been investigated yet. Moreover, in OCDM, the usage of different phase matrices in modulation will lead to extra storage overhead. In this paper, we investigate an OCDM based modulation scheme termed uniform phase-Orthogonal Chirp Division Multiplexing (UP-OCDM) for high-speed communication over doubly selective channels. With uniform phase matrices equipped, UP-OCDM can reduce the storage requirement of modulation. We also prove that like OCDM, the transform matrix of UP-OCDM is circulant. Based on the circulant transform matrix, we show that the channel matrices in UP-OCDM system over doubly selective channels have special structures that (1) the equivalent frequency-domain channel matrix can be approximated as a band matrix, and (2) the transform domain channel matrix in the framework of the basis expansion model (BEM) is a sum of the product of diagonal and circulant matrices. Based on these special channel structures, two low-complexity equalization algorithms are proposed for UP-OCDM in this paper. The equalization algorithms are based on block LDL\({}^{H}\) factorization and iterative matrix inversion, respectively. Numerical simulations are finally proposed to show the performance of UP-OCDM and the validity of the proposed low complexity equalization algorithms. It is shown that when the channel is doubly selective, UP-OCDM and OCDM have similar BER performance, and both of them outperform OFDM. Moreover, the proposed low complexity equalizers for UP-OCDM both show better BER performance than their OFDM counterparts.  
OCDM; doubly-selective channels; channel equalization; BEM; underwater acoustic communications +
Footnote †: journal: Sensors
## 5 Performance Evaluation
### Decision Model Results Analysis
#### 5.3.3 Performance According to Different SNRs  
To explore the reason for the throughput improvement, we plotted the PER performance of different methods at different SNRs, as shown in Figure 8. When the SNR is very low, the PERs of all methods are high. When the SNR is high, the PERs of the different methods are all 0 and, again, there is no difference.  
The \"junction\" of different encoding schemes, i.e., when the data rate needs to be switched, represents the point at which our method can confer an improvement. To verify the strategy of the proposed method to switch between the adjacent coding schemes, an additional AR4 JA code with a code rate of 60% is included in this paper. We have zoomed in on this region in the right half of the figure for ease of observation. The proposed method switches to the following encoding scheme earlier, using a more significant data rate to increase the total throughput. AMC is a trade-off between efficiency and accuracy, and our solution improves total throughput by learning historical information for accurate estimation.
## 2 Grounded Gyrator-C-Based Tunable Active Inductor
### Proposed Grounded Tunable Active Inductor
Figure 5:0  
It is evident that both \(\mathrm{L_{eq}}\) and Q are strongly dependent on the value of \(\mathrm{R_{f}}\). Specifically, the inductance increases proportionally with \(\mathrm{R_{f}}\). Simulation results indicate that the inductance varies from 51 nH to 80 nH over a frequency range of 1.32 GHz to 3.88 GHz. From the analysis of the corresponding plots, it is observed that the variation in the quality factor with frequency exhibits a parabolic profile within the inductive operating region. The quality factor increases with \(\mathrm{R_{f}}\) until it reaches a maximum value of 3942 at 0.987 GHz. Beyond this point, despite continued increases in \(\mathrm{R_{f}}\), the quality factor gradually decreases. This behavior can be explained by the increasing influence of resistive effects, particularly series parasitic resistance, which are minimal near the frequency where the quality factor is maximized but become more prominent outside that range. Additionally, increasing \(\mathrm{R_{f}}\) causes a downward shift in the resonance frequency. These findings validate the effectiveness of the proposed method, maintaining a quality factor greater than 250 across the frequency range 0.62 GHz to 1.97 GHz. The relationships between the principal circuit parameters considered in the parametric analysis and optimization process are summarized in Table 1[35]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**g\(\mathrm{m}\)** & \(\mathrm{\omega_{z}}\) & \(\mathrm{\omega_{0}}\) & \(\mathrm{\mathbf{R_{s}}}\) & \(\mathrm{\mathbf{L_{eq}}}\) & \(\mathrm{\mathbf{Q}}\) \\ \hline + & \(\downarrow\) & \(\uparrow\) & \(\downarrow\downarrow\) & \(\downarrow\downarrow\) & \(\uparrow\uparrow\) \\ \hline \(\mathrm{-}\) & \(\uparrow\) & \(\downarrow\) & \(\uparrow\uparrow\) & \(\uparrow\uparrow\) & \(\downarrow\downarrow\) \\ \hline \(\mathrm{\mathbf{R_{f}}}\) & \(\mathrm{\omega_{z}}\) & \(\mathrm{\omega_{0}}\) & \(\mathrm{\mathbf{R_{s}}}\) & \(\mathrm{\mathbf{L_{eq}}}\) & \(\mathrm{\mathbf{Q}}\) \\ \hline + & \(\downarrow\downarrow\) & \(\uparrow\) & \(\downarrow\) & \(\downarrow\) & \(\uparrow\downarrow\) \\ \(\mathrm{-}\) & \(\uparrow\uparrow\) & \(\downarrow\) & \(\uparrow\) & \(\uparrow\) & \(\downarrow\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimization summary of tunable active inductor performance based on transconductance and feedback resistance variation. Figure 6: Effect of feedback resistor \(\mathrm{R_{f}}\) on (**a**) equivalent inductance and (**b**) the quality factor.
## 3 Results and Discussion
### Morphological Characterization by SEM  
The morphological examination by scanning electron microscopy (SEM) has shown the shape of GEF-pure was altered after its conversion into solid dispersions (Figure 7). The SEM imaging were done for GEF-pure, GEF-TPGS SDs (F1), the physical mixture of GEF, TPGS and HP \(\beta\)-CD and GEF-TPGS complexed with HP \(\beta\)-CD (F3). The SEM images of pure drug appeared as thin and elongated crystal aggregates as shown in Figure 7a, while GEF-TPGS SDs (F1) only exhibited one component (Figure 7b) [51]. The SEM image of pure HP \(\beta\)-CD (Figure 7c) appeared as almost hollow and spherical in shape with numerous visible cavities of different sizes on the surface. A similar structural morphology of HP \(\beta\)-CD was also reported previously [52]. The physical mixture and the inclusion complex had a typical shape with a fluffy structure, which was morphologically different from the pure HP \(\beta\)-CD.  
Figure 5: Molecular structure of GEF [N-(3-Chloro-4-fluorophenyl)-7-methoxy-6-(3-morpholinoprooxy) quinazolin-4-amine].  
Figure 6: The proton NMR of GEF pure (**a**); the binary complex or GEF–TPGS SDs as F1 (**b**); physical mixture (PM) of GEF, TPGS and HP \(\beta\)-CD (**c**) and the complex of GEF–TPGS SDs with HP \(\beta\)-CD as F3 (**d**).  
Figure 7: SEM images of pure GEF pure (**a**); GEF–TPGS SDs (**b**); HP \(\beta\)-CD pure (**c**); physical mixture of GEF–TPGS and HP \(\beta\)-CD (**d**) as well as the inclusion complex of GEF–TPGS SDs and HP \(\beta\)-CD as F3 (**e**).  
There was an obvious difference between the morphology of GEF-TPGS SDs from the GEF pure. The distinguished amorphous structure of smaller dimensions as compared to the GEF-pure has replaced the shapes and patterns of crystalline GEF. The addition of HP \(\beta\)-CD to GEF-TPGS SDs (i.e., F3) showed that further morphological and crystalline changes offer compelling proof that most of the drug molecules were converted to amorphous form (Figure 7d). The surfaces of GEF-TPGS SDs complexed with HP \(\beta\)-CD (F3) were irregular, spongy and composed of homogeneously distributed amorphous aggregates (Figure 7e). It is beneficial and interesting that the amorphous substances dissolve rapidly as compared to its crystalline counterpart because of its increased internal energy and molecular mobility [53]. The findings of the SEM imaging support the results of dissolution study. The solid dispersion prepared by solvent evaporation followed by freeze-drying of the products illustrated the highest solubility and dissolution due to the more amorphous nature of the drug.
## 2 Design of the Generator
### Operation of the Marx Generator  
The operation of the generator can be divided into the following two phases: (1) the input of the pulse parameters and (2) the pulse generation. During the first phase, the microcontroller A6 (see Figure 1) monitors the input and updates the parameters (the module number, charging voltage--up to 3.5 kV--number of pulses--from 1 to 99 or continuous regime--and the pulse repetition period from 1 s) on the LCD screen A7. Entering and exiting this phase is performed by pressing the \"OK\" button in block A8. Once the main parameters are set, the capacitors can be charged by pressing the \"Charge\" button in group A9. From this moment on, the system enters the pulse generation phase.  
In the pulse generation phase, the microcontroller A6 commands the high voltage power supply A2 via the optical driver A4 to start charging the capacitor connection in block A3. During this process, the voltage of the capacitors is monitored in real-time on the LCD A7. This phase is also indicated by the flashing of the red photodiode until the capacitors are charged to the set voltage. Once the capacitors are charged, pressing the \"Pulse\" button initiates the discharge of the capacitors via the load.  
During this time, the microcontroller sends a command to the control unit of the thyristor drivers and generates optical pulses that are sent to the thyristor drivers. The thyristor drivers form the trigger pulse for the thyristors and open them. The thyristors connect the charged capacitors in series and discharge them via the load. After completion, the system returns to pulse generation and waits for the next command from the user.
## 3 System Test and Application
### Technical Standards of Measuring Equipment
#### 3.1.2 Professional Vibration Accelerometer  
* 6 Integrated high-precision acceleration sensor and gyroscope;
* 7 The acceleration measurement range is \(\pm\)16 g, and the accuracy is 0.0001 g;
* 8 The data output frequency is 100 Hz; the interface adopts serial TTL communication, and the baud rate is 115,200.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 5 Simulation Results and Analysis
### Vehicle Following Scenario  
In driving scenario 1, the vehicle in the nearby lane did not perform cut-in behavior. In the FSM, the host vehicle was in a following the preceding vehicle state at that time. The host vehicle used the longitudinal displacement and speed of the preceding vehicle as a reference to follow the vehicle stably, while also meeting the constraints of safety and driving comfort. The longitudinal velocity of the host vehicle, the relative distance from the preceding vehicle, acceleration of the host vehicle, and jerk of the host vehicle are shown in Figure 21.  
In Figure 21, the host vehicle can use the MPC-ACC control system to stably follow the preceding vehicle. Since there was no the cut-in vehicle, the host vehicle adopted the corresponding speed after recognizing the presence of the preceding vehicle to maintain its speed consistent with that of the vehicle and to maintain a safe relative distance. In Figure 21a, the initial speed of the vehicle was 17 m/s. After the presence of the leading vehicle was detected at time 0, the host vehicle began to decelerate, and gradually coincided with the speed of the preceding vehicle. The relative distance between this host vehicle and the preceding vehicle, as shown in Figure 21b, gradually decreased from the initial 35 m to 24 m, which met the conditions of driving safety. When the vehicle was close to the preceding vehicle, the acceleration always changed at \(\pm\)1.5 and jerk at \(\pm\)1, as shown in Figure 21c,d and Table 1. Because the acceleration and jerk change range is small, driving comfort was guaranteed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Maximum** & **Minimum** & **Average** \\ \hline Acceleration (m/s\({}^{2}\)) & 0.9702 & \(-\)0.8606 & \(-\)0.0331 \\ Jerk (m/s\({}^{3}\)) & 0.4651 & \(-\)0.6451 & \(-\)0.0000163 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the acceleration and jerk of the host vehicle under driving scenario 1.  
Figure 21: The simulation results of vehicle following scenarios: (**a**) the velocity of the host vehicle and the preceding vehicle; (**b**) the relative distance between the host vehicle and the preceding vehicle; (**c**) the acceleration of the host vehicle; and (**d**) the jerk of the host vehicle.
# Towards Design and Development of Security Assessment Framework for Internet of Medical Things
## 4 Results
Table 17 shows how the scores of dist+i and dist-i, followed by Gap Degree and Satisfaction Degree values, were estimated to prioritize the various alternatives. To weight the different alternative solutions, Satisfaction Degree figures were taken into account. The security assessment of IoMT is presented in this study. A diagrammatic illustration of the research results from this study is shown in Figure 3. The results show that satisfaction levels for A1, A2, A3, A4, A5, and A6 are estimated to be 0.62585971, 0.64446598, 0.44458799, 0.52112  
of the scenario puts healthcare organizations, as well as the communities they represent, in more jeopardy now than ever before. The IoMT platform's utmost goal is to collect and communicate health information such as ECGs, weight, pulse rate, insulin levels, etc. Such information may be disclosed to an authorized person, who could be a doctor, an involved health organization, an insurance company, or perhaps an external consultant, irrespective of time, place, or tool. However, the tale is not as straightforward since IoMT is vulnerable to a variety of evolving cyberattacks. Since an attacker exploits the fact that this industry is worth billions of dollars, new ransomware attacks are developed and initiated on IoMT devices every day. The main objective of this study was to design and develop a security assessment framework for IoMT devices. The security assessment of different IoMT devices is presented in this research study. The intensity and difficulty of characterizing security profile information is a significant challenge for the suggested framework. This seems to be due to the extensive pool of evaluation characteristics provided by the framework. The researchers hope that this work inspires technology solution providers to collaborate in order to compete straightforwardly as well as meet the needs of clients. Furthermore, the evaluation attributes may be difficult to comprehend, particularly for inexperienced users, including patients and healthcare workers, who frequently lack software and operational understanding. We are confident that this work will motivate them to understand possible security concerns as well as mitigation strategies. We predict that the proposed framework will be utilized only when incorporating, improving, or designing IoMT services. As a result, we strongly recommend IoMT participants to engage their time as well as dedication in the process because it will then enable them to explore and avoid serious security implications.
## 2 Fundamentals and State of the Art
### Morphing Alternatives  
Morphing is not the only approach to investigate concept performance in the digital phase. Topology optimization generates new geometries on a mesh level depending on predefined load cases. Morphing techniques however, change an existing design into a new shape to investigate the influence on specific criteria.  
FE models can consist of beam-elements. Especially for thin beam-like structures, this is an easy way of modeling geometries parametrically. Furthermore, [4] use beam-modeling to perform cross-section optimization [4]. Moreover, [14; 19] use beams to investigate body modifications. In addition, [9] use beam modeling for twist-beam axles, as these concepts cannot be calculated without FE calculation [9]. As many components do not consist of thin beam-like structures, these approaches are not applicable onto complex shapes in general.
## 2 Mechanism of Microbial-Induced Calcite Precipitation (MICP)
### Influencing Factors of MICP Reaction
#### 2.3.3 Nutrient Solution Concentration  
[PERSON] et al. [61] found that changing the nutrient solution can affect the nucleation rate and the size of CaCO\({}_{3}\) crystals. Meanwhile, the effect of nutrient solution on different environments was also different. [PERSON] [62] added urea to the culture medium to increase the precipitation rate of CaCO\({}_{3}\), which can overcome the inhibition of solidification under low temperature environments. The adding urea does not only increase the urease activity, but also will cause the soil to become alkaline, thus inhibiting the growth of microorganisms [63]. The high urea content causes the uneven distribution of overall precipitation crystals, resulting in the low strength of soil. [PERSON] [64] found that the concentration of the nutrient solution has an important impact on the shear strength of solidified muddy soil. With the increasing concentration of the nutrient solution, the internal friction angle first increases and then decreases, and the optimal concentration of the corresponding optimal nutrient solution is 1.60 mol/L. [PERSON] et al. [65] found that the bio-cementation effect will reach a peak value with the change in nutrient concentration, according to which the most appropriate nutrient concentration can be obtained. In addition, cells will shrink due to water loss under a high salt environment, thus affecting the physiological and biochemical reaction process of microorganisms. Therefore, the concentration of nutrient solution is very important in the study of microbial solidification of soil. [PERSON] et al. [66] found that  
Figure 2: Changes of urease activity influenced by (**a**) Temperature (**b**) PH value.  
the activity of bacterial urease decreased significantly, almost linearly, with the increase in calcium ion concentration, indicating that a high concentration of calcium ions have an evident inhibition on urease under this condition.  
In conclusion, the output of CaCO\({}_{3}\) is positively related to the concentration of nutrient solution within a certain range, but high concentration inhibits the microbial induced CaCO\({}_{3}\) generation. At low concentration, the microbial induced CaCO\({}_{3}\) is smaller and more evenly distributed in the soil.