esa-sceva/satcom-chunk-collection huggingface dataset:
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Simplified Channel Models for Code Design  
To facilitate the code design, we assume the following _surrogate_ channel model:  
* A fraction \(1-\alpha\), \(0\leq\alpha\leq 1\), of a (modulated) codeword is only affected by noise with power \(2\sigma_{n}^{2}\).
* A fraction \(\alpha\) of a (modulated) codeword is affected by noise and interference of constant power over the fraction.  
Thus, we make the assumption of a _block interference channel_[41], which is a widely-used model in the literature (see, e.g., [27] and the references therein). Clearly, this is a simplification, since a codeword in reality may experience various interference levels due to multiple packet collisions. Note also that for our asynchronous RA protocol, the interference is confined to the beginning, the end, or to the beginning and the end of a codeword. This observation is exploited for the code design. The surrogate channel model is exemplified in Figure 2. We will illustrate its validity in Section 4.
## 4 Experimental Results
### LSTM Model Performance Results
This suggests that these sounds have overlapping frequencies or similar acoustic patterns with other categories, which likely contributed to occasional misclassification. This observation is consistent with findings in the literature, where complex or less distinct sounds have been shown to challenge machine learning models due to their resemblance to other sound classes. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Air\_conditioner & 0.89 & 0.90 & 0.89 & 195 \\ Car\_horn & 0.88 & 0.80 & 0.84 & 91 \\ Children\_playing & 0.66 & 0.74 & 0.70 & 205 \\ Dog\_bark & 0.71 & 0.65 & 0.68 & 182 \\ Drilling & 0.87 & 0.83 & 0.85 & 202 \\ Engine\_idling & 0.94 & 0.88 & 0.90 & 216 \\ Gun\_shot & 0.83 & 0.75 & 0.79 & 87 \\ Jackhammer & 0.87 & 0.88 & 0.88 & 187 \\ Siren & 0.85 & 0.84 & 0.85 & 199 \\ Street music & 0.70 & 0.81 & 0.75 & 183 \\ Accuracy & 0.81 & 0.81 & 0.81 & 1747 \\ Macro avg & 0.82 & 0.81 & 0.81 & 1747 \\ Weighted avg & 0.82 & 0.81 & 0.81 & 1747 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance of LSTM model on UrbanSound8K dataset. In summary, the LSTM model's results were competitive compared to similar studies in urban sound recognition, indicating that the chosen architecture and preprocessing steps were effective. The balanced macro and weighted F1-scores highlight the model's ability to perform uniformly across diverse categories, contributing to the broader goal of balanced classification in urban sound environments. These results demonstrate the potential of the LSTM model within a fog computing architecture for efficient sound recognition in smart cities, though further testing with real-time data could provide additional insights into the system's practical applicability. Figure 5 illustrates the confusion matrix of LSTM model's performance on the Urban-Sound8K dataset, revealing that the model achieved high accuracy across most sound categories. Air conditioner, car horn, engine idling, and jackhammer sounds were generally classified correctly. However, some confusion was observed in categories such as children playing, dog bark, and street music. For instance, the children playing category was sometimes confused with dog bark and drilling sounds. Although the gunshot category was generally classified correctly, there were rare instances of confusion with children playing and jackhammer sounds. Overall, the LSTM model demonstrated effective performance in recognizing urban sounds, though confusion may occur between sounds with similar characteristics.
## 8 Conclusions  
This article outlines a detailed methodology that uses Ansible to automate the assessment of Kubernetes CNIs. This methodology provides a comprehensive and simple-to-use framework that facilitates the verification of CNIs for performance in Kubernetes environments. The intricacy of implementing and configuring CNIs, each possessing distinct criteria and protocols, necessitates automation to mitigate human mistakes, guarantee consistency, and improve overall assessment efficiency.  
A primary problem in manual performance evaluation is consistency, which can also be related to human mistakes. Manual evaluations are susceptible to variability owing to human variables, including divergent techniques or inadvertent errors. Conversely, automated systems adhere to established rules, guaranteeing outcome consistency and precision, which is the critical issue we addressed with this paper.  
A notable difficulty is customizability. Manual assessments become exceedingly problematic in Kubernetes environments, particularly when numerous configurations require testing. The time and money necessary for such comprehensive examinations rapidly become a constraint. Automation is the seamless way to manage extensive tests, markedly diminishing the time and workforce required--another issue our methodology addresses.  
Manual assessments face the challenge of reproducibility. Manual methods can be challenging to replicate accurately, resulting in variations in outcomes when identical tests are conducted repeatedly. Automated techniques guarantee consistent execution of each step across several runs, facilitating the reproduction of tests and the verification of results under different situations, a challenge we also addressed with our methodology.  
Lastly, automated evaluations possess a significant benefit in terms of time efficiency. Manual assessments of CNIs may last days or weeks, necessitating substantial manual input during the procedure. This paper's methodology demonstrated that a manual evaluation of a CNI required 60 h, in addition to 10 h for data collection and analysis, while the automated technique reduced the time to 40 h for five test runs across various configurations per CNI without any oversight.  
By automating the intricate and complex processes associated with CNI evaluations, teams may concentrate on more sophisticated issues, promoting innovation and improving the overall efficacy of their systems. Consequently, implementing an automated evaluation method guarantees that Kubernetes environments sustain optimal network performance across various infrastructures. Automating these operations substantially decreases the time needed for performance reviews, enabling teams to concentrate on more strategic endeavors, like having more time to design their future environments.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 5 LDPC Encoder Design for a Microcontroller Implementation
### Elementary Computations
All vectors computed as intermediate results and their sizes are summarized in Table 1. During the operation of the encoding algorithm, the system RAM can be dynamically interchanged for the storage of the intermediate vectors; for example, after step 2, vectors \(\mathbf{a}\) and \(\mathbf{c}\) are memorized, after step 3, vectors \(\mathbf{a}\), \(\mathbf{b}\), and \(\mathbf{c}\) are memorized, and so on, as can be easily read in Figure 2. It can be verified that the greatest memory consumption is after step 7, when the following vectors are memorized: \(\mathbf{a}\), \(\mathbf{g}\), \(\mathbf{p}_{1}\), and \(\mathbf{u}\). The memory utilization is quantified by experimental results in the next section.
## 3 Results and Discussion
### pH and Titratable Acid (TA) Analysis  
As shown in Figure 1B,C, after 9 h, TA content in the NMB sample decreased, while that in the NMBB continued to increase. The addition of BZ25 could increase acid content in the sample and decrease pH. The TA content of NMBB was 1.494 \(\pm\) 0.054 g/kg, and that of NMB was 0.696 \(\pm\) 0.01 g/kg. The TA content of NMBB was higher than that of NMB by 0.798 g/kg. From 21 h, titratable acid in NMBB was significantly higher than that in SFB. Results showed that the co-fermentation of two strains has a synergistic effect on acid production, which promoted the acid production of BZ25. [PERSON] et al. [33] found that more acids were produced by co-fermentation of _Lactobacillus_ and _Bifidobacterium_. The pH of NMB samples showed an upward trend, while the pH of SFB and NMB samples continued to decrease, indicating that BZ25 has a strong acid-producing capacity and changes the acidic environment of natto, thereby affecting the flavor of natto. [PERSON] et al. [34] found that the addition of _bifidobacteria_ would reduce the pH of the milk fermented by _lactobacillus plantarum_, which was consistent with the results from this study.
## 3 Results and Discussion
### Correlation Analysis between SSC and Total Content of Each Volatile Compound  
The SSC, the ester content and the hydrocarbon content of pear varieties showed highly significant positive correlation with each other, as seen in Figure 2. This indicated that the higher the SSC of pears, the higher the content of esters and hydrocarbons contained in their flesh. Ester content was highly significantly positively correlated with alcohol content. Alcohol content was significantly positively correlated with aldehyde content, and aldehyde content was highly significantly positively correlated with acid content. The content of other compounds showed a significant positive correlation with the content of esters and aldehydes and a highly significant positive correlation with the content of alcohols.  
Figure 1: Comparison of the types (**A**) and contents (**B**) of total volatile compounds in eight pear varieties.  
Figure 2: Correlation analysis between SSC and total content of each volatile substance. * indicates significance at \(p\) < 0.05, ** significance at \(p\) < 0.01. Color depth indicates Pearson correlation coefficient values.
## 4 Results and Discussions
### Synthesis
#### 4.4.1 Wasserstein-Based Operators  
A key result of this study is that when all other components of the algorithms are kept the same, switching from a sequence-based to a Wasserstein-based operator is beneficial. The only exception is the uniform crossover with \(F_{inert}\). We believe that this is related to the conservation of geometrical structure that is allowed by the Wasserstein  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Experiments** & **The Scientific Questions** & **Conclusions** \\ \hline Alternating vs. successive boundary and full domain mutations & Are there major differences between the three mutations in WBGEA? & Alternating Wasserstein mutation (Algorithm 2) yields the best results when coupled with equal weight crossover on all the functions. \\ \hline Handling of sets size in mutation: ternary vs. full size choice & How does the size of \(X_{rand}\) affect the performance of the algorithms? & The two schemes for choosing the size of \(X_{rand}\) do not make a significant difference. \\ \hline Wasserstein-based vs. reference evolutionary algorithm & How does the default Wasserstein-based evolutionary algorithm compare to a more classical evolutionary algorithm? & WBGEA, compared to Ref_alg, yields better results on wind-farm test functions and converges faster on \(F_{minDist}\). An adapted tuning helps to obtain similar results on \(F_{inert}\). \\ \hline Wasserstein vs. classical evolution operators & How do the different operators behave when composed together differently? & Wasserstein-based operators improve over sequence-based operators on all the test functions but \(F_{inert}\). \\ \hline Role of crossover & Are there noticeable differences between the random and the equal weight crossovers in WBGEA? Does the crossover play a major role in the performance of the algorithms? & WBGEA with random weight crossover (Equation (3)) improves over equal weight, but removing the crossover is an even better choice. \\ \hline Role of the boundary mutation & What happens if we diminish the chances of performing a boundary mutation in the absence of crossover? & High values of the boundary mutation probability (around 0.5) are adapted to the tested suite of functions. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of experimental questions and conclusions.  
barycenters underlying the studied evolutionary operators. Figures 2-4 illustrate this conservation property.
## 3 Results
### Case Study
For instance, low-pressure systems can trigger storms, while high-pressure systems are typically associated with heat waves; both significantly influence energy usage for heating or cooling. Wind speed plays a crucial role in energy generation in areas with integrated wind power and also affects perceived temperatures. Strong winds can enhance wind power production, reducing demand from traditional sources, whereas weaker winds might increase energy consumption from alternative sources. Furthermore, wind influences perceived temperature, further shaping the energy demand for temperature regulation. Finally, precipitable water measures the amount of water vapor in the atmosphere, influencing weather conditions such as cloud cover and storms, which in turn affect both solar energy availability and energy demand. By incorporating these data, the model can more effectively predict how weather disruptions--like rain, storms, or heavy cloud cover--will impact short-term energy consumption pattern [46; 47]. By integrating these meteorological variables into the LSTM neural network, the model can more accurately represent the external environmental conditions that drive energy demand, resulting in more reliable and dynamic forecasting capabilities, particularly in response to shifting weather patterns. The data used in this study were sourced from publicly available datasets, including the National Center for Energy Control (CENACE) [44] and the National Solar Radiation Database (NSRDB) [48].
###### Abstract  
Due to the continuous occurrence of water pollution problems, practical separation methods for oil-water mixtures have attracted more and more attention. To date, different kinds of materials have been developed with good hydrophobic properties and strong separation ability. Carbon aerogels, as a promising ideal adsorbent for dealing with oil-spill accidents, have received extensive attention. In this work, zeolitic imidazolate frameworks (ZIFs), nanoparticles, and carbon nanotubes (CNTs) in the three-dimensional (3D) interconnected network structure of cotton balls (CBs) were successfully prepared by a simple and scalable process. The as-prepared carbonized CBs with carbonized ZIF-8 and CNTs (CZIF-8/CNTs/CCBs) were characterized. The oil-water separation performance of the composite was also measured. The results show that the ZIF-8 clusters intercalated with abundant CNTs are fully loaded into the porous structure of the CBs after the in situ synthesis process. Additionally, ZIF-8/CNTs/CBs was carbonized in nitrogen, leading to the formation of CZIF-8/CNTs/CCBs. The prepared material possesses excellent hydrophobicity with a water contact angle of 152.7\({}^{\circ}\), showing good absorption capacities Q\({}_{1}\) in the range of 48 to 84 times its original weight for oil and organic liquids. In addition, CZIF-8/CNTs/CCBs exhibits good recyclability in the absorption-distillation test. In summary, this study proposes a novel and simple method for the preparation of a superhydrophobic material that could have wide application in the separation of oil-water mixtures.  
2022  
Fabrication of Carbon Aerogels Derived from Metal-Organic Frameworks/Carbon Nanotubes/Cotton Composites as an Efficient Sorbent for Sustainable Oil-Water Separation  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and A. School of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China; [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (W.Y.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-139-5599-7920
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
It is known that a road with bumps and potholes effect the ride comfort and vehicle stability thus to improve the ride performance, HOSMC based on STA is introduced. Designed controllers are tested by the execution of simulation using MATLAB/Simulink on quarter-vehicle suspension to verify the efficiency of active damping control algorithms in terms of wheel deflection, suspension deflection, and vehicle displacement. The organization of the paper is as follows: In Section 2, mathematical representation of active suspension quarter-car model is derived. Active control strategies: FOSMC, ISMC, HOSMC, LQR, PID are briefly proposed along with the stability analysis of FOSMC and ISMC in Section 3. The qualitative and quantitative analysis of the proposed controllers along with traditional suspension system are compared and deliberated in Section 4. Lastly, Section 5 executes the conclusion and future work of the paper.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments
### Ablation Experiments
#### 4.3.2 Comparison of Filters  
In this section, we compare the performance of three variants of the Kalman filter: the standard Kalman filter (KF), the Kalman filter with motion compensation (KF + MC), and the Kalman filter with both motion compensation and the proposed Observation-Aware Gain Scaling (KF + MC + OAGS). The standard KF and KF + MC were each evaluated under three different positioning update frequencies to assess the impact of observation delay on localization performance.  
The evaluation results are summarized in Table 4. The results show that as the localization observation frequency decreases (corresponding to increased observation delay from 1 s to 10 s), the standard KF suffers from significant degradation in localization accuracy. In contrast, KF with MC maintains better performance due to the integration of motion compensation. Figure 10 illustrates the trajectory segments of KF and KF + MC, highlighting the performance difference under a 10-s observation delay. Specifically, (a) shows the result of the standard KF, while (b) presents the corresponding trajectory obtained using KF + MC. The improvement is particularly evident in turning scenarios, where motion compensation effectively mitigates the impact of delayed observations.  
Figure 10: Comparison of KF and KF + MC. (**a**) The result of the standard Kalman Filter with a position observation delay of 10-second; (**b**) The corresponding result using the Kalman Filter with motion compensation.  
Figure 9: Matching results of different matching methods. The red circles emphasize the misalignments.  
Table 5 presents the comparison between KF + MC and KF + MC + OAGS. The results show that enabling OAGS further reduces the overall RMSE. This improvement is primarily due to the ability of OAGS to suppress the influence of incorrect matching results, which can otherwise degrade the fused localization accuracy, particularly in indoor environments where the referenced image is unavailable.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup
### Evaluation Setup  
The evaluation setup is the same as in previous studies [7; 20; 28]. The Spinet GSS9000 radio-frequency constellation simulator (RFCS) generates controlled scenarios at different \(C/N_{0}\) and signal dynamics levels. The simulator is configured to perform 20 min simulations of a specific scenario at eight different \(C/N_{0}\) levels (\(N_{C/N_{0}}=8\)). In contrast to previous studies, the selected \(C/N_{0}\) levels are [25; 29; 33; 37; 41; 45; 48; 52] dBHz. The simulation always starts at the highest \(C/N_{0}\) level of 52 dBHz to ensure successful signal acquisition. Next, the \(C/N_{0}\) level is reduced in 30 second intervals until reaching the desired \(C/N_{0}\) level. The tracking and system performance are measured considering the last ten minutes of the simulation, \(T_{\mathrm{sim}}=600\) s, in which the correct \(C/N_{0}\) level is assured.  
A static scenario and a dynamic scenario are selected to evaluate the adaptive tracking techniques. The static scenario represents stationary use-cases such as GNSS reference stations, and the dynamic scenario presents harsh vehicular conditions. Figure 13 shows the sky-plots of both scenarios. In the static scenario there are nine SVs during the entire simulation (see Figure 13a). However, SV G1 disappears behind the horizon after two minutes of the simulation. The dynamic scenario shows ten visible SVs (see Figure 13b). As in the static scenario, SV G1 disappears after two minutes of the simulation. Moreover, SV G30 rises above the horizon near the end of the simulation. Therefore, to avoid these transient SVs, SV G1 and SV G30 are not considered, and \(N_{\mathrm{sat}}^{\mathrm{total}}\) is set to eight for both scenarios.  
The same SVs as previous research are selected to evaluate the tracking performance [7]. The GPS L1 C/A signal of SV G4 is selected to evaluate the static tracking performance, and the GPS L1 C/A signal of SV G17 is assigned for the dynamic tracking performance. The maximum line-of-sight (LOS) signal dynamics for the dynamic simulated scenario is 8.7 g/s [7].
## 2 Precise Calculation of the Aluminum Extraction Flow Rate
### Unscented Kalman Filter
#### 2.2.2 Observation Prediction  
Predicting Observation Sigma Points: Use the observation model to predict all the sigma points [21]:  
\[\zeta_{i}=h(\chi_{i}^{\prime}) \tag{7}\]  
where \(h(x)\) is the observation function.  
The predicted observation mean and covariance are calculated:  
\[\mathbf{z}^{\prime}=\sum_{i=0}^{2n}W_{i}^{m}\xi_{i} \tag{8}\]  
\[\mathbf{S}=\sum_{i=0}^{2n}W_{i}^{e}(\zeta_{i}-\mathbf{z}^{\prime})(\zeta_{i}- \mathbf{z}^{\prime})^{T}+\mathbf{R} \tag{9}\]  
where \(\mathbf{R}\), the observation noise covariance matrix, determines the level of trust in the measurement data. A larger \(\mathbf{R}\) implies that there is significant measurement noise, leading the filter to trust predictions more and rely less on potentially noisy measurements; it thus fits for environments with heavy signal interference. A smaller \(\mathbf{R}\) indicates that the measurement data are more reliable, increasing dependency on observations and reducing the reliance of corrections on the model.
# A Hybrid Model for Freight Train Air Brake Condition Monitoring
## 2 Materials and Methods
### Main Components Implemented in the Model
#### 2.3.3. Weighing Valve  
The wagon weighing valve pressure (labelled with (3) in Figure 6) is considered an input parameter in the model. Starting from the full wagon configuration, the weight is distributed on the central and lateral bogie through the transfer loading function reported in Figure 9a. Two different transfer functions have been identified, given that the central bogie is usually more loaded than the lateral bogies. As shown in Figure 9b, the linear weighing valve transfer function is identified. These two transfer functions have been derived from the wagon technical specifications provided by Mercitalia Intermodal.