esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Variable-Step-Size Adaptive Filter Algorithm with a Momentum Term
## 2 Algorithm Principle
### Principle of Variable-Step LMS Algorithm  
By using the Lagrange multiplier method to find the extreme value under the constraint, the following equations are derived:  
\[\Delta w(n+1)=w(n+1)-w(n) \tag{3}\]  
\[d(n)=w^{T}(n+1)X(N) \tag{4}\]  
where Equation (3) is the formula for the incremental weight, and Equation (4) is the expected value (the constraint). Therefore, the cost function obtained using the Lagrange multiplier algorithm is as follows:  
\[J(n)=\|\Delta w(n+1)\|^{2}+\lambda\Big{[}d(n)-w(n+1)^{T}X(N)\Big{]} \tag{5}\]  
where \(\lambda\) is the Lagrange multiplier coefficient. If we take the partial derivative of \(J(n)\) about \(w(n+1)\) and obtain 0, then we have the following:  
\[\frac{\partial J(n)}{\partial w(n+1)}=2[w(n+1)-w(n)]-\lambda X(N)\longrightarrow =0\]  
\[w(n+1)=w(n)+\frac{1}{2}\lambda X(N) \tag{6}\]  
Based on the above formula, there is the following expression:  
\[\begin{array}{ll}e(n)&=d(n)-y(n)=w^{T}(n+1)X(N)-w^{T}(n)X(N)\\ &=\frac{1}{2}\lambda\|X(N)\|^{2}\end{array} \tag{7}\]  
The Lagrange coefficient \(\lambda\) can be obtained as follows:  
\[\lambda=\frac{2e(n)}{\|X(N)\|^{2}} \tag{8}\]  
The new normalized weight update algorithm obtained from the above formula is as follows:  
\[w(n+1)=w(n)+\frac{\mu}{\gamma+\|X(N)\|^{2}}e(n)X(N) \tag{9}\]  
where \(\mu\) is the iterative step size. To prevent the divergence of the algorithm when \(x(n)\) is small, a parameter \(\gamma\) (\(0<\gamma<1\)) is introduced. It is evident that the iterative step size of the algorithm remains constant. The logistic function is a widely used nonlinear mapping S-shaped function in deep learning. Its general form is as follows:  
\[f(x)=\frac{1}{1+e^{x}} \tag{10}\]Therefore, the functional model with the step size varying with the error is as follows:  
\[\mu(n)=\frac{1}{1+\exp(-|e(n)|)}-0.5 \tag{11}\]  
Considering Equations (9)-(11) comprehensively, the improved model of the step size varying with the error is obtained as follows:  
\[\mu(n)1=\frac{\alpha\Big{[}1-\exp(-\beta|e(n)|^{2})\Big{]}}{\gamma+\left\|X(N )\right\|^{2}} \tag{12}\]
## 3 Results and Discussion
### Modifications of the Topology Optimization Algorithm
The original algorithm became 20 times slower than the modified algorithm at the case shown in Figure 7c, which can be explained by the matrix inversion used in the Wei's optimization. At the same time, both algorithms produce similar values of the objective function, differing in the third significant digit, depending on the optimization case. Small modifications of the algorithm listed in Appendix A were used to produce models shown in Figure 7. These modifications are listed in Appendix B. As in the case of direct slicing, more accurate optimization takes more time and requires more RAM installed. Both tasks, slicing and optimization, lead to the solution of the trade-off between precision and time. Although calculations on the mentioned laptop provide valid data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **Modified Algorithm, s** & **[PERSON]’s Algorithm, s** \\ \hline
1 & 36.8 & 80.8 \\ \hline
2 & 35.2 & 77.3 \\ \hline
3 & 36.1 & 81.7 \\ \hline
4 & 35.9 & 75.8 \\ \hline
5 & 35.2 & 76.6 \\ \hline
6 & 35.9 & 77.3 \\ \hline
7 & 35.4 & 76.2 \\ \hline
8 & 35.5 & 76.4 \\ \hline
9 & 35.9 & 76.1 \\ \hline
10 & 36.3 & 76.8 \\ \hline
**Average time, s** & **35.8** & **77.5** \\ \hline
**Number of iterations** & **213** & **179** \\ \hline
**Objective function** & \(\mathbf{1.71\times 10^{5}}\) & \(\mathbf{1.71\times 10^{5}}\) \\ \hline
**Volume** & **0.45** & **0.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of algorithms comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Objective Function**} \\ \cline{2-5}  & **Modified Algorithm, s** & **Wei’s Algorithm, s** & **Modified Algorithm** & **Wei’s Algorithm** \\ \hline Figure 10b & 21.9 & 51.9 & \(1.06\times 10^{6}\) & \(1.06\times 10^{6}\) \\ \hline Figure 10c & 22.2 & 448.6 & \(6.00\times 10^{5}\) & \(6.02\times 10^{5}\) \\ \hline Figure 10d & 165.2 & 14902.2 & \(1.47\times 10^{10}\) & \(1.59\times 10^{10}\) \\ \hline Figure 10e & 24.4 & 437.1 & \(2.48\times 10^{7}\) & \(2.48\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Efficiency of algorithms for four cases.
## 4 Experimental Results  
To predict drivable areas in off-road environments in real time, we benchmarked the performance of our framework using RUGD. A lightweight deep learning prediction model was trained based on GPT-4o, allowing the system to predict drivable areas in real time. During the benchmarking process, recommended parameter values were used to optimize the performance of each model. We compared the performance of the original model implemented in PyTorch to that of an optimized model using the TensorRT(TRT) library [50]. The evaluation was conducted using the RUGD dataset. Furthermore, the proposed approach was validated through experiments in real off-road environments, where accurate predictions of drivable areas under various terrain conditions demonstrated the robustness and reliability of the model. These experimental results contribute significantly to enhancing the reliability of autonomous driving systems.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 3 Max-SINR Joint Precoder-Decoder Design
### Distributed Max-SINR Precoder-Decoder Design
It could be shared between the subsystems to reduce the calculation time, by using the same memory or optical fibers.
## 3 Routing algorithms based on sensor position
### Ant algorithm-based intelligent routing
First, how to realize data aggregation is difficult because position-based routing algorithms are flat routing and there are not clusters to aggregate data. In sensor networks with dense deployed nodes, one phenomenon can be found by many nodes near the phenomenon. If each sensor detecting the same phenomenon sends the data alone, it is unnecessary and consumes too much energy. So, data aggregation is important to extend the application areas of position-based routing algorithms. Second, how to improve the reliability of position-based routing is another problem. Most position-based routing algorithms are stateless. It is hard to judge that the next-hop has forwarded the data correctly or not. Considering that the sensor is low cost and tends to fail, the problem is more serious. Otherwise, position-based routing algorithms with the characteristics of QoS, security or multipath are necessary to special applications. These can also be regarded as the future directions and are discussed in detail in section 4.
and jolchannamu (_Q. serrata_, JOL). These oaks also are part of broad-leafed Korean forests and were selected as representative oak species of Korea.  
The conventional extraction methods for analyzing volatile food substances are vacuum simultaneous steam distillation solvent extraction (V-SED) [11; 12], direct solvent extraction (DSE) [13; 14], solvent-assisted flavor evaporation (SAFE) [15], and steam distillation under reduced pressure (DRP-LLE) [16; 17]. However, these methods need large amounts of extraction solvents, have low recovery, and can be used only with low-fat samples. To offset these disadvantages, headspace extraction methods such as dynamic headspace (DHS) [18], headspace solid-phase microextraction (HS-SPME) [19], and headspace stir-bar sorptive extraction (HS-SBSE) [20; 21] are widely used. Headspace extraction methods do not need organic solvents and they can be applied with even small amounts of samples. Moreover, their selectivity and extraction efficiency can be improved by using a quick sorbent that does not take long to extract [22; 23; 24; 25].  
Recently, a new technology--SPME Arrow--has been developed that combines the benefits of both SPME and HS-SBSE. SPME Arrow protects the fiber with an outer metal tube, making the fiber more stable and extending its lifetime. As a result, the limitations of mechanical failure associated with SPME can be overcome [26]. In addition, SPME Arrow comprises fibers of greater diameter and length than the SPME fibers [27; 28; 29], providing a larger amount of the adsorbent phase, affording up to 10 times higher sensitivity [30; 31]. In addition, similar to SPME, SPME Arrow can be fully automated. Therefore, SPME Arrow is more advantageous for analyzing volatile substances in various types of foods [32; 33].  
The present study aimed to find a suitable sorbent for Soju by analyzing the volatile substances present in different Korean distilled spirits using SPME Arrow. Five SPME Arrow fibers were analyzed for optimization. Finding a suitable adsorbent and using the results obtained, the study aims to analyze changes in the volatile profiles of Soju when aged in oak barrels made of six species of carbonated oak. Traditional Korean liquors such as Soju have not been sufficiently studied, in contrast to the distilled liquors of other countries that have been investigated extensively. This study will help improve the analysis of such Korean liquors.
## 3 Results and Discussion
### Multivariate Analysis (PCA)  
To present the variation in physico-chemical characteristics among carrot samples, a principal component analysis (PCA) was applied (Figure 3). It was found that the first two principal components (PC 1 and PC 2) accounted for 80.8% of the variation in the original data. The PCA analysis showed the grouping of heat-treated carrot samples, while the raw carrot was clearly distinguished from each other. The raw carrot was generally characterized by a lighter color with a higher proportion of both yellow and red tones, as well as the highest content of 13-_cis_-\(\beta\)-carotene, and the presence vanillic acid and rutin. Steamed in a combi oven and sous-vide-cooked carrots were distinguished by their higher content of ferulic acids, but the content of some phenolic compounds (chlorogenic and _p_-coumaric acids) and carotenoids (\(\beta\)-carotene, lutein, and 15-_cis_-\(\beta\)-carotene) as well as antioxidant capacity (determined by both assays) in these samples showed high similarity to those in boiled carrots. In contrast, carrot samples prepared by steaming in a combi oven and sous-vide were distinguished by the content of m-coumaric acid and 9-_cis_-\(\beta\)-carotene.  
Figure 3: PCA plot defined by the first two principal components (PC 1 and PC 2), illustrating the relationships among the analyzed carrot samples and the identified similarity groups (**a**), as well as the relationships among the analyzed physical and chemical properties (**b**).
## 4 Evaluation Study
### Quantitative Findings  
#### 4.4.1 Overall Experience of Collaboration in IVR  
To understand participants' experience during IVR-based collaboration, we conducted a descriptive analysis. As shown in Table 5, participants were satisfied with the IVR collaboration experience, which can be seen from the high evaluation of all dimensions (all Mean \(>\) 3), especially in spatial presence (Mean = 4.22, SD = 0.66) and social experience (Mean = 4.11, SD = 0.66).
## 5 Conclusions  
We have observed in the results that when all nodes in a WSN are using a fixed transmission power, the system exhibits either a good connectivity or good power savings, but not both at the same time. Thereby in the first part of the results analysis we have observed that using the higher transmission power available means having the poorest power saving, but still we have nearly a perfect connectivity. On the other hand, if we choose to use a medium transmission power, we get the better power saving of all the experiments, but with a very poor connectivity. From the same observations we can conclude that experiment _e05_ offers the best trade-off between energy saving and communications reliability. Also experiments _e06_ and _e07_ offer good results. These three experiments have in common that they use a high value for the \(k_{CR}\) parameter, that is related to the variation rate of the transmission power. In future works we shall explore for the definition of a performance index for the estimation of the better trade-off of energy and communications reliability.  
The fuzzy control based self-adaptive system allows the nodes in the network to achieve a balance between those two targets. It can provide a good enough power saving while keeping a high reliability of the communications. And this is specially useful when the network topology is random, and/or prone to changes, like networks with mobile nodes. Anyway the parameters with the best and the worst results in the experiments described in this paper should be tested in longer experiments.  
Figure 13: \(J_{c}\) for current transmission error rate per round per experiment comparison.  
Another open issue that worth exploring is to study how the network react to unexpected changes, like new nodes in the neighborhood, or known nodes disappearing for whatever reason. It is expected that the self-adaptive system reacts properly reaching an steady state after a while. And this is also an important issue: how long does it take to the network to reach an steady or equilibrium state?  
In our experiments, we have used a fixed interval at each node for monitoring the state of the network. Of course this has influence on the observation on the current dynamic behavior of the network, and on how long it takes for the transient period. However, it can also be updated adaptively. For instance, the interval can be lower when the network is in the transient period, and higher when it has reached a steady state.  
A deeper knowledge about what is the best solution will require different studies related to the dynamic behavior of networks following also cyber-physical systems approaches. The rationality of this proposal is that the dynamic behavior of the network is influenced by the topology, the physical environment, the computational load of the nodes and their processing capacity, among others.
## 4 Investigation of the Influence of Major Parameters on the Pipeline Damping Performance
### Effect of Filling Rate on Damping Performance of the Pipeline System  
The six-cavity particle impact damper is filled with steel balls with a particle size of 1 mm and a filling rate of 20, 40, 60, 80, 90, and 100% respectively. This paper judges the size of the filling rate by weighing the mass. The specific method is to fill the cavity with particles, weigh the filled particles with an electronic scale and consider that the filling rate corresponding to the weight at this time is 100%, then multiply this weight by the filling rate selected in the test, and add a certain mass of particles corresponding to the filling rate to the cavity. The RMS values of responses under different filling rates are shown in Figure 11.  
Figure 11 indicates that the vibration reduction effect of the particle impact damper increases and then decreases with the increase in the filling rate, and the best damping effect of the particle impact damper is achieved when the filling rate reaches 90%, and there exists an optimal filling rate for the particle impact damper. When the particle filling rate is small, the damping effect is not obvious due to the small proportion of particles in the  
Figure 11: Influence of different filling rates on vibration reduction effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Material** & \begin{tabular}{c} **Coefficient of** \\ **Restitution** \\ \end{tabular} & \
\begin{tabular}{c} **Mass of 20\% Filling** \\ **Rate/g** \\ \end{tabular} &
\begin{tabular}{c} **Mass of 80\% Filling** \\ **Rate/g** \\ \end{tabular} \\ \hline Steel & 0.56 & 15.4 & 61.6 \\ Aluminum & 0.75 & 5.1 & 20.9 \\ Glass & 0.94 & 4.9 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of different particle materials.  
cavity, in which the number of particle-particle and particle-cavity collisions is limited. Thus, the energy dissipation through collision and friction is lower. With the increase in filling rate, the momentum exchange due to the collision and the particle-particle and particle-container wall friction are more sufficient. When the particle filling rate is large, the space for particle movement is restricted, the free movement stroke of the particles becomes smaller and the ability to dissipate energy by collision and friction is reduced. Therefore, the particle impact must have an optimum filling rate, damper size, particle size and other factors related to the particle impact damper. In practice, a good damping effect can be achieved when the particle filling rate is controlled within the range of 70-90%.
## 3 Results and Discussion
### Syntheses by the Conventional Hydrothermal Method
In samples ZACF-4 and ZACF-5, both the residues were submitted to sonochemical treatment. However, in the sample ZACF-5, the addition of sodium aluminate was proposed after magnetic stirring for 120 min under reflux in the oil bath at 80 \({}^{\circ}\)C and remained for another 30 min under the same conditions of temperature and stirring under the reflux. The result of this modification in the synthesis process resulted in a crystallinity of more than 50% in a synthesis time of 2 h. This finding indicates that the 80 \({}^{\circ}\)C treatments at reflux and sonochemical treatment favor the dissolution of the residue when the sodium aluminate is not present. Figure 4 and Table 3 present these results;  
Figure 4: (a) X-ray diffractogram of zeolites ZACF-4 and ZACF-5, where q—quartz, p—zeolite P, s—sodalite, x—zeolite X, a—zeolite LTA; (b) SEM Images of ZACF-4 and ZSCF-5 obtained at 2 h and 4 h.  
the presence of more intense peaks is observed, identifying the zeolitic material of the LTA type. There is also a decrease in the peak of the quartz and mullite. The scanning electron microscopy analysis shows the morphology of cubic crystals characteristic of LTA-type zeolite in the ZACF-5 sample. In the ZACF-4 image, a stick-like structure is observed, indicating the presence of quartz, while agglomerated crystals of spherical shape possibly indicate the sodalite phase. In the ZACF-5 and ZACF-6 samples, the aluminate was added after the initial stirring under reflux at 80 \({}^{\circ}\)C in an oil bath for 120 min; however, for the ZACF-6 sample, the residue was not ultrasonicated. The sonochemical treatment allowed the formation of more intense peaks that are characteristic of LTA-type zeolite, indicating a greater formation of the material and consequently, crystallinity (Figure 5). The percentage of crystallinity in the ZACF-6 sample did not reach 15%.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### BackCom for Logistics  
BackCom, due to its popularity and cost-effectiveness, has been widely utilized in logistics [201]. BackCom assists in reading information of heavy logistics items even without picking them up. Figure 44 shows BackCom tags used in logistics. These passive tags are used for the online tracking and effective management of different items. The emerging BackCom technique in logistics is based on three-dimensional orientation tracking [202]. This technique considers an array of tags, mounted on the surface of an object, to determine three-dimensional information available online [10]. Consequently, BackCom is boosting the logistics industry.
# Parameter Study for the Ice Adhesion Centrifuge Test
## 1 Introduction
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ice Formation Type** & **Main Conclusions on Surface Property Dependencies** & **Ref.** \\ \hline Impact ice 10 m/s & Low ice adhesion was measured on superhydrophobic low contact angle & [9] \\  & hysteresis coatings. & \\ \hline Impact ice (rotor testing) & Ice adhesion strength increases with increasing roughness. & [7; 10] \\ \hline Impact ice 25 m/s & Low contact angle hysteresis and low surface roughness enable low ice adhesion. & [4] \\ \hline Static ice & Contact angle hysteresis is a crucial parameter for the determination of icephobicity, whereas hydrophobicity (water contact angle) is not linked to icephobicity & [11] \\ \hline Impact ice (velocity not indicated) & Increasing coating thickness (0.1 to 2 mm) of elastomeric materials leads to decreasing ice adhesion strength. & [12] \\ \hline Impact ice 95 m/s & Optimum in surface free energy for the lowest ice adhesion values. & \\  & A further decrease in the surface free energy is accompanied with increased roughness, leading to increased ice adhesion strength. & [13] \\ \hline Impact ice 10 m/s & Decreasing receding contact angle data correlate with increasing ice adhesion strength for superhydrophobic surfaces (assessed during the degradation process). & [14] \\ \hline Impact ice 25 m/s & Hydrophobicity is beneficial for low ice adhesion in many cases but has a better relationship with contact angle hysteresis. Higher roughness can increase ice adhesion but can also be beneficial (for slippery liquid-infused porous surfaces: SLIPS approach). & [15] \\ \hline Impact ice 95 m/s & Coating-specific correlations of ice adhesion strength vs. surface properties during coating degradation. No general rules for correlating parameters are identifiable. & [16] \\ \hline Static ice & Ice adhesion strength decreased with surface roughness. For surfaces with similar roughness, low surface free energy favored a reduction in ice adhesion. Materials with different wettability showed a similar ice adhesion strength. & [17] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies on the surface properties and their influence on ice adhesion strength, assessed in centrifuge test designs. and good to poor water wettability to assess the effects of the surface properties on the ice adhesion test results. With this study, we aimed to provide data for the effects of ice types on the results of the ice adhesion centrifuge test. We also investigated a relevant range of surface properties to clarify the impact of the roughness and surface wettability on the measured ice adhesion strength. With these topics, we offer an improved understanding of the needs for ice adhesion test procedures that can be used for necessary harmonization and standardization activities.
###### Abstract  
The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R\({}^{2}\) of 78.32%, whereas the random forest regression model had an R\({}^{2}\) of 63.7%. The final model for predicting the remaining useful life had an R\({}^{2}\) of 99.6%.  
reliability; PIMS; random forest regression; remaining useful life; CRISP-DM