esa-sceva/satcom-chunk-collection huggingface dataset:
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology
### Estimating the Number of Wavelet Bases and the Pre-Selected Range for \(p_{ij}^{3}\)
A plot of the corresponding output signal representing this trend is shown in Figure 6. The predicted output (red) shows a very close similarity with the expected result (blue), meaning high accuracy output, high/fine resolution data prediction, and a wider range of composite frequency. Trend 2: High SDE--Low slope. This shows a non-continuous linear trend between the number of wavelet bases \"N\" and the pre-selected range for \(p_{ij}^{3}\) \"S_range\" (highlighted red), limited to values below 5. A plot of the corresponding output signal representing this trend is shown in Figure 6. The predicted output signal (red) represents a low pass filter response with a very high wavelength. Trend 3: Low SDE--Relatively low slope. This shows mostly an inverse relationship between the number of wavelet bases \"N\" and the pre-selected range for \(p_{ij}^{3}\) \"S_range\", which is traced with a white marker; that is, as N increases, the expected value for S_range reduces. A plot of the corresponding output signal representing this trend is shown in Figure 6. The predicted output (red) shows similarity with the expected result (blue) but with better curvature and low pass filtering characteristics. In total, the signal with the lowest slope and SDE is represented at N equals 4 and an S_range of 7. A plot of the corresponding output signal with these parameters is shown in Figure 6. The predicted output signal is representative of a strong low pass filter with a high curvature. Therefore, considering the obtained trends and their respective predicted output signal, a more appropriate number of wavelet bases \"N\" and the range for \"S_range\" could be selected to fit the desired output, especially when dealing with noisy signals or frequency-based output prediction. That is, quick analysis can be performed on a few pieces of data to select the best \"N\" and \"S_range\" that produces the desired output, as applied in the next section, where noisy data are considered in EMT demodulation. Figure 5: The plot of the slope around the peaks across the data, which is a measure of the curvature, and the standard deviation of error (SDE) across the signal to determine the possible prediction trends with respect to the number of wavelet bases and the S-range. The green, red, and white lines represent the observed trends 1, 2, and 3, respectively. Figure 6: The plot of the representative trends observed from the chosen range of numbers of wavelet bases and S-range. The blue line represents the expected result, while the red lines are the prediction results. (**a**) represents the signal with the lowest slope and SDE, (**b**) represents the signal with Low SDE and High slope, (**c**) represents the signal with High SDE and Low slope, and (**d**) represents the signal with Low SDE and Relatively low slope.
## 2 System Model and Proposed Scheme
### Proposal: Blind Adaptive Array with Subcarrier Transmission Power Assignment
#### 2.2.2 Power Inversion (PI)  
PI has a property to invert the power ratio of the received signals by suppressing the signal having larger power. This leads to PI being able to effectively work when the power of the desired signal is smaller than that of the interference signal, i.e., SIR \(<\) 0 dB. The PI weight, \(\textbf{W}_{\text{PI}}(n)\), is calculated by autocorrelation matrix of the received signal, \(\textbf{R}_{xx}(n)\). They are defined as  
\[\textbf{W}_{\text{PI}}(n)=\textbf{R}_{xx}^{-1}(n)\textbf{C}, \tag{8}\]  
\[\begin{array}{llll}\textbf{R}_{xx}(n)&=&\text{E}\left[\textbf{X}(n,i) \textbf{X}^{H}(n,i)\right]\\ &=&\frac{1}{S}\sum_{i=1}^{S}\left[\textbf{X}(n,i)\textbf{X}^{H}(n,i)\right], \\ \textbf{C}=\left[\begin{array}{cccc}1&0&\cdots&0\end{array}\right]^{T},\end{array} \tag{9}\]  
where **C** denotes a constraint vector.  
As described, PI simply inverts the power ratio of incoming signals. Its interference suppression performance is limited in smaller absolute SIR value. \(\textbf{W}_{\text{PI}}(n)\) can be used as the initial weight of CMA, \(\textbf{W}_{\text{CMA}}^{(0)}(n)\), in Equation (5). It can enhance interference suppression performance of PI even when the power difference between desired and interference signals is small. Once the desired signal is captured by PI, residual interference signal can be further suppressed in the iteration process of CMA [19]. This approach can be classified as one variant of beamspace CMA (BSCMA) [21; 22]. We define this scheme as PI-CMA.
# A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching
## 3 Problem Formulation
### Operation Conditions Constraints  
Power balance of microgrid is the primary condition for the stable operation, and the power demand and supply in each time period need to satisfy the power balance equation. The equation is defined as  
\[\sum_{i=1}^{y}P_{\text{load }i,t}=\sum_{i=1}^{m}P_{\text{PV }i,t}+\sum_{i=1}^{x}P_{\text{dg }i,t}+\sum_{i=1}^{n}P_{\text{b }i,t} \tag{5}\]  
\[P_{dg}^{\text{min}}\leq P_{dg,t}\leq P_{dg}^{\text{max}} \tag{6}\]  
\[0\leq P_{pv,t}\leq P_{pv}^{\text{max}} \tag{7}\]  
where \(P_{dg}^{\text{min}}\), \(P_{dg}^{\text{max}}\), \(P_{pv}^{\text{max}}\) are the lower and upper limits of generator output power and the upper limits of photovoltaic output power, respectively.  
The output power of DG and PV is constrained by the upper and lower boundaries in Formula (6) and Formula (7). The working time of the energy storage battery is limited by Formula (8). Its power rating and maximum charging/discharging rate are limited by Formula (9).  
\[E_{b,t}^{\text{min}}\leq E_{b,t}\leq E_{b,t}^{\text{max}} \tag{8}\]  
\[P_{b,t}^{\text{min}}\leq P_{b,t}\leq P_{b,t}^{\text{max}} \tag{9}\]  
When the energy storage battery provides energy for \(q\) loads, the total power allocated by the transmission line must meet with the constraints  
\[0\leq\sum_{i=1}^{q}u_{i,t}\leq P_{b,t}^{\text{max}} \tag{10}\]  
Correspondingly, when the distributed power sources in the microgrid provide energy for \(f\) energy storage batteries, the power distributed by the transmission line needs to meet the constraints  
\[0\leq\sum_{i=1}^{f}u_{i,t}\leq P_{b,t}^{\text{max}} \tag{11}\]  
\[P_{t,t}=I_{h,t}^{2}R_{h} \tag{12}\]  
\[I_{h,t}=\frac{u_{h,t}}{V_{m}} \tag{13}\]  
The voltage \(V_{m}\) of the microgrid is generally 220 V. At the same time, the line loss of the \(h\)-th transmission line at time \(t\) can be calculated based on Formulas (12) and (13).
Sensors_**2012**, _12_, 9055-9097; doi:10.3390/s120709055  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Game Theory for Wireless Sensor Networks: A Survey**  
**[PERSON] 1,2, [PERSON] 1,** **, [PERSON] 3 and [PERSON] 1,** *  
1 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; E-Mail: [EMAIL_ADDRESS]  
2 School of Computer Science and Technology, Shaoxing University, Shaoxing 312000, China  
3 School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (W.-L.W.); [EMAIL_ADDRESS] (S.-Y.C.); Tel./Fax: +86-571-8529-0309.  
_Received: 22 May 2012; in revised form: 21 June / Accepted: 26 June 2012 / Published: 2 July 2012_  
**Abstract:** Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.  
**Keywords:** wireless sensor network; game theory; scheduling; optimization; mechanism
## 3 Results
### Comparison of Prediction Accuracy for All Variable Selection Approaches  
We compared the prediction accuracy and the average number of selected variables across various variable selection methods, all tuned using optimal parameters from 10-fold CV. We investigated the influence of sample size, SNR, and correlation on the model performance. We began by comparing the prediction accuracy of classical methods (BSS, BE, and FS), followed by a comparison of classical and penalized methods. To clearly observe the patterns, we plotted the average of each metric as a function of SNR.
## 4 Analysis and Results
### Practicability  
Figure 7 depicts how the decision-maker can use the model, the model response, and its use for supporting decision making in 6 steps:  
1. The smallholder sets up HMP-Coffee with the basic attributes.
2. The smallholder executes HMP-Coffee to estimate the level of profitability of his/her small coffee production.
3. The hierarchical multicriteria model responds with the estimation of profitability level \"**Favorable**,\" \"**Average**,\" and \"**Unfavorable**\". The model shows the qualitative effect (represented by colors) of each attribute on the profitability level. The red color means a negative impact, the black one a neutral impact, and the green one a positive impact.
4. The decision-maker visualizes the level of profitability of the crop.
5. If the decision-maker agrees with the profitability level, it means that the crop's current management is suitable. On the contrary, the smallholder could analyze the variables with a negative impact on the profitability level (i.e., the red color); apply one or more actions to improve the final result.  
Figure 7: HMP-Coffee practical operation.  
For instance, a smallholder decides to make a high Investment in chemical fertilization. The farmer goes to the model and enters the general information of the crop (step 1). For this particular case: Investment in chemical fertilizer (t5) is \"**Expensive**\", Time spent on fertilization (t1) is \"**High time**\", and the Coffee production volume (t17) is expected to be \"**High production**\". The model uses this information to execute its hierarchical multi-criteria feature (step 2). It responds with an \"Average\" level of profitability (step 3). The smallholder visualizes the estimation and the effect by attribute represented using colors (step 4). The smallholder notes that, in addition to (t5), (t1), and (t17), if the price of the workforce (t4) is \"**Expensive**\", the investment in crop care activities (t23) increases. This situation produces a profitability level \"**Average**\" (step 5). On the contrary, for the same investment in chemical fertilization, when the price of the workforce is \"**Cheap**\", it balances the investment in crop care activities (t23), producing a level of profitability \"**Favorable**\" (step 5). Based on this analysis, if the smallholder wants to make a high investment in chemical fertilization and maintain a \"**Favorable**\" profitability level, it should reduce labor costs, postponing some crop activities as the renewal, among other options.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 4 Effect of Cage Pocket Clearance
### Wear of the Bearing Cage  
The wear of cage-guiding surfaces and pockets from the disassembled test bearing after the same operating cycles with the three different pocket clearances is shown in Figures 9 and 10. The degrees of cage wear can be evaluated by use of wear width and color. It can be clearly seen that the degrees of the guiding surfaces' wear are significantly different. With the pocket clearance increases, the wear of the guiding surface becomes more serious, corresponding to the amplitudes of \(f_{i}\) shown in Figure 8, and then the wear of the pocket is also decreased, but the distinguishing features is smaller than the guiding surface, as shown in Figures 9 and 10. Shrinking the pocket clearance leading to the small free space of ball will impel continued impact between the cage and balls, and then push the cage towards the inner ring, which will lead to severe wear of the cage pocket and the guiding surface. Increasing the pocket clearance will weaken the impact between the cage and balls.  
Figure 7: Wear of cage pocket with different guiding clearances. (**a**) \(C_{g}\) = 0.1 mm, (**b**) \(C_{g}\) = 0.2 mm, (**c**) \(C_{g}\) = 0.4 mm.  
Figure 8: Radial motions of the bearing cage with different cage pocket clearances (\(\omega_{i}\) = 6000 rpm). (**a**) Waveform, spectra and normalized trajectory of cage motion when \(C_{p}\) = 0.1 mm; (**b**) waveform, spectra and normalized trajectory of cage motion when \(C_{p}\) = 0.25 mm; (**c**) waveform, spectra and normalized trajectory of cage motion when \(C_{p}\) = 0.4 mm.  
Figure 9: Wear of guiding surface with different pocket clearances. (**a**) \(C_{p}\) = 0.1 mm, (**b**) \(C_{p}\) = 0.25 mm, (**c**) \(C_{p}\) = 0.4 mm.
## 2 System Model and Dynamic Optimization Problem
### Dynamic Maximization Problem of the Number of Service-Guaranteed Users  
A VU is regarded as a service-guaranteed user if the sum of the service rates is always larger than that of the data arrival rates in a window denoted as \(W\). Let \(z_{n}(t)\) be the notation of user \(n\) to be service-guaranteed in slot \(t\), which is defined as follows:  
\[z_{n}(t)=\left\{\begin{array}{ll}z_{n}\left(t-1\right),\ \text{if }\sum\limits_{\tau =t-W}^{t}\mu_{n}\left(\tau\right)\geq\sum\limits_{\tau=t-W}^{t}a_{n}\left( \tau\right),\ \forall n\in\mathcal{N},\\ 0,\ \text{otherwise},\end{array}\right. \tag{24}\]  
where it is assumed that \(z_{n}\left(0\right)=1,\ \forall n\in\mathcal{N}\).  
Constrained by the limited resources, not all VUs can be allocated to a resource without suffering interference. To guarantee the quality of service, the set of served services at which the scale of demand exceeds the capability of the network has to be determined. To increase the number of service-guaranteed users, the dynamic channel allocation problem can be formulated as follows:  
\[\begin{array}{ll}&\max\limits_{\mathbf{X}}\ \ \ f(t)=\sum\limits_{n=1}^{N}z_{n}(t)&( 25a),\\ \text{s.t.}&\sum\limits_{n\in\mathcal{N}}x_{nk}(t)\leq 1,\ \forall\ k\in\mathcal{K}&( 25b),\\ &\lim\limits_{T\rightarrow\infty}\frac{1}{T}\sum\limits_{t=1}^{T}r(t)\leq\gamma& (25c),\\ &x_{nk}(t)=x_{nk}\left(t-\left(1-r(t)\right)\right),\ \forall\ n\in\mathcal{N},\ k\in\mathcal{K}&( 25d),\\ &x_{nk}(t)=\left\{0,1\right\},\ \forall n\in\mathcal{N},\ k\in\mathcal{K}&( 25e).\end{array} \tag{25}\]The optimization problem aims to maximize the number of service-guaranteed users. Constraint 1 guarantees the interference mitigation among the VUs in a platoon, where the channel allocation is orthogonal for each member. Constraint 2 is the constraint of the rate of reallocation. Constraint 3 means that the channel allocation remains the same if there is no re-allocation. Constraint 4 ensures that all the elements in the optimization problem are 0/1 variables.
## 6 Summary Analysis and Discussion  
Given the above results, we try to analyze whether representations show consistent performance in different environments. Regarding the given property measures, some representations have consistent performance while others do not. Some measures that do not have any strong relationship with the RL performance. Below we list major points summarized from the experiments.  
* We found that auxiliary tasks do help with reinforcement learning during our experiments, but not all of them. On the contrary, adding a decoder for reconstructing the observation image is consistently bad at predicting expert knowledge such as the coordinate of the agent. Although the input-decoder auxiliary task has been empirically shown effective when it is used to prevent the representation from converging to 0, adding the decoder forces the agent to include every detail in the observation, even part of them is useless. Thus, one guess is that such a constraint requires a larger capacity in the representation than necessary and hurts the representation's ability to extract useful information.
* \(GTC_{\text{mean}}\) is a good indicator of the performance that can be obtained in RL. When measuring \(GTC_{mean}\), we see that representations with a relatively high \(GTC_{mean}\) score always show a good performance in RL (a higher mean reward).  
Figure 12: Performance (mean and standard error) on RL algorithms using SRL states with mobile robot one-random-target task.  
* Auxiliary tasks generally improve the representation in terms of complexity reduction and decorrelation, regardless of whether the RL performance is improved or not. But there does not appear to be a clear relationship between the measures of these properties and the RL performance. In short, auxiliary tasks should be designed according to the task characteristics.
* Learning policies from vision-based DRL is sensitive to hyperparameter changes. For instance, hyperparameters (such as the embedded dimension) tuning of DQN is required in order to have decent results from the pixels (Figure 7 and Figure 8). However, the DRL performance is relatively stable by means of SRL methods. This can be explained by the reduced search space: the task is simpler to solve when features are already extracted.
###### Abstract  
The degradation of alginate extracted from Moroccan _Bifurcaria bifurcata_ has not been fully established to date. In this work, we report the extraction and the characterization of alginate (ASBB) from the brown algae _B. bifurcata_, as well as the production of oligo-alignates (OGABs) by using a green chemistry process. The depolymerization of ASBB was carried out by controlled radical hydrolysis through our green chemistry process using a hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) catalyst. The molecular weight (Mw) and degree of polymerization (DP) distribution of oligo-alignates (OGABs) obtained were then characterized by HPLC size exclusion chromatography (SEC) and high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Structural characterization revealed that after 6 h of depolymerization of ASBB, we obtained OGABs with Mw \(\leq\) 5.5 kDa and 2 \(\leq\) DP \(\leq\) 24. These results highlight the effectiveness of the controlled radical hydrolysis of alginate to produce good yields of alginate fractions with controlled Mw with a known polymerization degree (DP) and without altering properties of oligo-alignates. _Bifurcaria bifurcata_ can be a potential source of alginate and oligo-alignates given its abundance on the northwest Atlantic coast. The production and characterization of oligo-alignates promote their exploitation in the cosmetic, pharmaceuti, and agriculture fields.  
**Keywords:**_Bifurcaria bifurcata_; alginate; oligo-alignates; Atlantic coast; hydrogen peroxide +
Footnote †: journal: Applied Physics Letters B
## 6 Conclusions and Future Extensions  
In this work, we explore the gain that can be achieved by jointly allocating flow on multiple paths and employing successive interference cancelation (SIC) for random access wireless mesh networks. More precisely, we explore a distributed flow allocation scheme aimed at maximizing average aggregate flow throughput, while also providing bounded delay, when SIC is employed. The flow allocation scheme discussed achieves up to 15.2% higher AAT when combined with SIC, instead of treating interference as noise (IAN), for an SINR threshold (\(\gamma\)) value equal to 0.5. For larger \(\gamma\) values, this improvement either becomes negligible, or lower AAT is achieved for the topologies considered. This is because the increased interference caused by links whose success probability is significantly increased with SIC is not compensated by the gain in terms of throughput. Moreover, the improvement in terms of throughput by employing SIC instead of IAN increases with the asymmetry among interfering links.  
Future extensions of this work will include the deployment of SIC jointly with TOFRA on larger topologies. The proposed framework can be applied in large topologies, as long as the link success probabilities can be calculated for different interference handling approaches, such as SIC. However, considering SIC in such topologies, when there is more than one interfering node to decode, has its challenges. Furthermore, it would be of interest to study the case where a receiver works in a hybrid manner by treating interference as noise or applying SIC based on the network conditions.  
The first and second author have contributed equally. The third author has proofread and supervised the work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the Editor [PERSON] for improving the quality of this work with his suggestions.  
The authors declare no conflict of interest.  
Figure 14: Simulated annealing vs. brute force, test case 6: Topology = 1, \(\gamma=2.0\), Interference handling: SIC(R,D).
###### Abstract  
A three-dimensional (3D) skeletonization algorithm extracts the skeleton of a 3D model and provides it for many applications, such as 3D model classification and identification. There are three major skeletonization methodologies used in the literature, distance transform field-based methods, Voronoi diagram-based methods, and thinning-based methods. However, the existing algorithms cannot preserve the connectivity of the skeletons of the 3D mesh models. In this paper, we propose a 3D skeletonization algorithm for 3D mesh models using a partial parallel thinning algorithm and a 3D skeleton correcting algorithm. The proposed algorithm uses pre-defined removing and recovering templates. The partial parallel 3D thinning algorithm separates 62 symmetrical removing templates into two groups based on symmetry. It thins a model with the templates of each group in each thinning procedure. The 3D skeleton correcting algorithm uses six correcting templates to inspect the disconnected voxels in the skeleton and corrects them. The experimental results show several comparisons of skeletons extracted by different skeletonization algorithms. The proposed algorithm can extract the skeleton of each branch of a model and preserve the connectivity.  
3D model; voxelization; thinning; skeletonization; correcting +
Footnote †: journal: Applied Sciences