esa-sceva/satcom-chunk-collection huggingface dataset:
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 1 Introduction
After receiving these analytics, the service provider communicates with the cloud operator to request an appropriate allocation of resources for each service in the forthcoming period. To minimize the OPEX of the service provider while ensuring that the average response time for services remains within specified requirements, a constrained Markov decision process (CMDP) problem was formulated. We derived the optimal stochastic policy by transforming the CMDP model into a linear programming (LP) model. The evaluation results indicate that PASPF can reduce over 50% in the service provider's OPEX compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement. Moreover, it has been found that the PASPF can dynamically adjust its operational policy based on the environment, which can be utilized as the guidelines for the service providers to design the service provisioning framework. The followings are this paper's contributions: (1) We construct the popularity estimation model, which can be used to generate the analytics in NWDAF. (2) The PASPF derives the optimal policy on how much resources are requested for each service. The optimal policy can be derived from a low complexity and practical LP model. (3) The evaluation results under various conditions offer valuable insights for cost-efficient service provisioning. The rest of this work is organized as follows: Section 2 provides a summary of related works, Section 3 introduces the PASPF. Section 4 describes the CMDP model. Section 5 includes the evaluation results. Section 6 offers the conclusion of this paper.
## 2 Materials and Methods
### Procedure and Variables  
An optical tracking system collected match physical demands data (ChyronHego(r); TRACAB, New York, NY, USA). Eight super 4K High Dynamic Range cameras are used in this multicamera tracking system to follow and track the soccer players on the pitch. The cameras capture video from various angles and offer real-time tracking with 25 Hz data. According to an analysis of the video-tracking system's validity and reliability, the TD covered showed average measurement errors of 2% [32]. Additionally, a few studies have examined the agreement between GPS devices and the Mediaoach(r) system [33]. Specifically, the intraclass correlation coefficient (ICC) magnitude was \(<\)0.90.  
The match physical demands variables analyzed were categorized according to the ball possession as follows [34,35]: with possession (WP) and without possession (WOP). Two variables were studied for each of these categories: total distance (m) covered by players (i.e., TD) and total distance covered at more than 21 km\(\cdot\)h\({}^{-1}\) (i.e., TD \(>\) 21 km\(\cdot\)h\({}^{-1}\)).  
To examine if opponent quality influenced match running performance, the difference in the final ranking of the analyzed team and the opponent was included in the analysis (i.e., match balance = TA-TB), where TA is the final ranking of the analyzed team, and TB is the final ranking of the opponent team [21]. For example, if team A ranked 6 th and team B ranked 14 th, the opponent quality would be \(-8\); likewise, in that same match considering the opponent team, opponent quality would be \(+8\).  
The value range was set between \(-19\) and \(+19\) in L1 and \(-21\) and \(+21\) in L2. So, the less the value, the easier the match was for the analyzed team; likewise, the greater the value, the harder the match was.  
Finally, due to the match variability, opponent quality was divided into five different groups according to match balance values: match balance 1 (MB1; matches where the match balance value was \(+10\)); match balance 2 (MB2; matches where the match balance value was between \(+4\) and \(+9\)); match balance 3 (MB3; matches where the match balance was between \(+3\) and \(-3\)); match balance 4 (MB4; matches where the match balance value was between \(-4\) and \(-9\)); match balance 5 (MB5; matches where the match balance value was \(-10\)).
## 2 System and Channel Models
### RF Link  
The first hop is the RF link, and the received signal at R can be expressed as:  
\[y_{R}=h_{RF}x+n_{SR} \tag{1}\]  
where \(h_{RF}\) is the channel coefficient, \(x\) denotes the signal from the transmitter, and \(n_{SR}\) is the additive white Gaussian noise (AWGN) with zero mean and \(N_{01}\) variance.  
The instantaneous SNR for the first hop is \(\gamma_{RF}=\frac{E_{s}|h_{RF}|^{2}}{N_{01}}=|h_{RF}|^{2}\gamma_{RF}\). \(E_{s}\) is the average power and \(\gamma_{RF}=E_{s}/N_{01}\) denotes the average SNR. The RF link is characterized by Rayleigh fading, and the PDF and CDF of \(\gamma_{RF}\) can be given as [6]:  
\[f_{\gamma_{RF}}(\gamma)=\frac{1}{\gamma_{RF}}\text{exp}\left(-\frac{\gamma}{ \gamma_{RF}}\right) \tag{2}\]  
and  
\[F_{\gamma_{RF}}(\gamma)=1-\text{exp}\left(-\frac{\gamma}{\gamma_{RF}}\right) \tag{3}\]
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 4 Mobile Element Scheduling Scheme
```
1:procedureMain algorithm
2: Apply Initialization Algorithm 1
3:main loop:
4: Solve the Optimization Problem (from Section 4) and find the routes for all \(M\) MoEIs
5:if Optimization Problem has solution then
6:for\(\forall vroute\ of\ M\ routes\)do
7:for\(\forall node\ j\in route\)do
8: Calculate the traveling duration from depot to node \(j\) (\(depot2 node\_duration\))
9: Reset \(e_{j}\) and \(priority_{j}\) (\(P_{r}\) set) (presented in Algorithm 3)
10: Reset profit \(p_{j}\) (presented in Algorithm 4)
11:endfor
12:endfor
13:goto\(\mathit{main}\ loop\)
14:else
15:case loop:
16:while Optimization Problem has no solution do
17: Take a subset of the prioritized nodes \(\in P_{r}\) sorted by their \(e_{j}\)
18: Solve the Optimization Problem and find the routes for all \(M\) MoEIs
19:if Optimization Problem has solution then
20:for\(\forall route\ of\ M\ routes\)do
21:for\(\forall node\ j\in P_{r}\ and\in route\)do
22: Calculate the traveling duration from depot to node \(j\) (\(depot2 node\_duration\))
23: Reset \(e_{j}\) and \(priority_{j}\) (\(P_{r}\) set) (presented in Algorithm 3)
24: Reset profit \(p_{j}\) (Algorithm 4)
25:endfor
26:endfor
27:for\(\forall node\ j\in P_{r}\ but\
otin\ in\ M\ routes\)do
28: Regard its data as data loss
29:endfor
30:elseif(Optimization Problem has no solution and there are still subsets of the prioritized nodes \(\in P_{r}\))
31:goto\(\mathit{case}\ loop\)
32:else
33:for\(\forall node\ j\in P_{r}\)do
34: Regard its data as data loss
35:endfor
36: Break
37:endif
38:endwhile
39:goto\(\mathit{main}\ loop\)
40:endprocedure
```  
**Algorithm 1** Initialization phase of Mobile Element Scheduling scheme  
```
1:procedureReset nodes' expiration time and priority
2:for\(\forall node\ j\in route\)do
3:\(e_{j}=e\_initial\_) - charging_time - (route_duration - depot2 node_duration\({}_{j}\))
4:endfor
5:for\(\forall node\ j\in P_{r}\) but \(\
otin route\)do
6:\(e_{j}=e\_initial\_) - charging_time
7:endfor
8:for\(\forall node\ j\)do
9:if\(e_{j}\ \leq(1+\epsilon)\times(budget+charging\_time)\) (\(\epsilon\ \simeq\ 0.2\) in our simulation studies) then
10: Include node \(j\) in the priority set \(P_{r}\) for the next route
11:endif
12:endfor
13:endprocedure
```  
**Algorithm 3** Reset nodes' expiration time and priority function
## 4 \(\alpha\)-Stability
### Finding the Lex-Male-Optimal \(\alpha\)-Stable Marriage  
It is possible to find optimal \(\alpha\)-stable marriages by adapting the Gale-Shapely algorithm. Given an SMW, \(P\), and a natural number, \(\alpha\), by Theorem 1, to find an \(\alpha\)-stable marriage, it is sufficient to find a weakly stable marriage of \(\alpha(P)\). This can be done by applying the GS algorithm to any linearization of \(\alpha(P)\). Given an SMW, \(P\), a natural number, \(\alpha\), and two orderings, \(o_{m}\) and \(o_{w}\), over men and women computed by applying a voting rule to \(P\), as described in Definition 3, it is possible to find the \(\alpha\)-stablemarriage that is lex-male-optimal w.r.t \(o_{m}\) and \(o_{w}\) by applying the GS algorithm to the linearization of \(\alpha(P)\), where we order incomparable pairs, _i.e._, the pairs that differ for less than \(\alpha\) in \(P\), in accordance with the orderings, \(o_{m}\) and \(o_{w}\).  
**Proposition 3**: _Given an SMW \(P\), a natural number \(\alpha\), \(o_{m}\) (resp., \(o_{w}\)), an ordering over the men (resp., women), algorithm Lex-male-\(\alpha\)-stable-GS returns the lex-male-optimal \(\alpha\)-stable marriage of \(P\) w.r.t. \(o_{m}\) and \(o_{w}\)._  
```
Input: \(P\): an SMW, \(\alpha\): a natural number, \(r\): a voting rule Output: \(\mu\): a marriage \(o_{m}\leftarrow\) the strict total order over the men obtained by applying \(r\) to the preference values given by the women over the men \(o_{w}\leftarrow\) the strict total order over the women obtained by applying \(r\) to the preference values given by the men over the women \(P^{\prime}\leftarrow\) the linearization of \(\alpha(P)\) obtained by ordering incomparable pairs of \(\alpha(P)\) in accordance with \(o_{m}\) and \(o_{w}\); \(\mu\leftarrow\) the marriage obtained by applying the GS algorithm to \(P^{\prime}\); return\(\mu\)
```  
**Algorithm 2**_Lex-male-\(\alpha\)-stable-GS_
## 3 Results and Discussion
### Training on limited variants and generalization  
Search and detection dog handlers are keenly aware that while dogs are able to generalize from the few individual targets they are trained on to new and unfamiliar variants, the more target variants the dogs are trained on, the more unfamiliar variants they will correctly identify [19]. During this study the dogs were trained on, or learned through encounters in trials, tortoises that were at least 89 mm in length, called juveniles, up to large adults. After the detection distance trials were completed, we conducted some additional trials that are not included in this study [38]; during these additional trials the dogs found five very small (30, 40, 50, 50 and 80 mm) tortoises on the surface using olfaction. Dogs were observed to follow scent to these tiny tortoises and perform their trained alert, indicating they recognized the small tortoises as belonging to the general class 'tortoise'. This is significant because the dogs had not been trained on tortoises this small yet they were able to correctly recognize them as being 'tortoise'. We cannot say how many small tortoises (i.e. \(<90\)mm) were present in any of the pens in which dogs were fielded, so we cannot say how many small tortoises the dogs missed. However, we can report that at a certain point (be it after a set number of encounters with reward, after small tortoises began to surface, or due to specific weather conditions, none of which with we can cite with any certainty) the dogs generalized small tortoises; this further lends evidence to our theory that evaporative rate of tortoise odor is effectively independent of tortoise size.  
Another important result of this work is the demonstration of dogs' abilities to be trained on residual scent from a relatively small number of live tortoises kept in captivity in a highly unnatural environment, fed a non-natural diet and maintained in unnatural enclosures with unnatural bedding material - and then go on to find completely different and unrelated live tortoises maintained outdoors in their native habitat with minimal to no subsidy by humans. This also demonstrates the dogs' ability to generalize. Each encounter with a new tortoise over the course of the research trials increased the dogs' pool of variants and generalization is enhanced with exposure to more variants [19]. This finding lends support to the validity of collecting scent articles or training aids through residual scent transfer in other detection dog disciplines.
## 3 Results
### Anova-Pdr  
ANOVA-PDR is able to isolate the interferences between factors and to analyze the differences between various classes of interest by each factor. ANOVA-PDR delivered better separation of EVOO origin, as well as adulteration level and adulteration type. The important variables, i.e., spectral peaks, can also be identified by the corresponding loadings. Meanwhile, ANOVA-PDR directly quantifies the distinction between factors within classes. Figure 4 shows the PDR mapping with detailed between-class separations. Compared with the direct PDR in the initial UV-Vis spectrum, ANOVA-PDR resulted in a considerably greater value, thus a clearer distinction between each factor class.  
Figure 3: Projected difference resolution (PDR) mapping of conventional PDR results. (**A**) EVOO Origin; (**B**) Adulteration Level; (**C**) Adulterant Type. \(R_{s}\), geometric average of all PDR scores. P, pure samples.  
Detailed relationships of classes and their corresponding influential components can be further analyzed from ANOVA-PDR. Figure 4A corresponds to the ANOVA-derived PDR mapping of the EVOO origin. In this plot, there are significant differences in the samples from different EVOO origins. Specifically, samples A1 produced in Australia were particularly further away from the counterparts produced in other locations, probably due the fact that they were the only olive oils produced in a non-Mediterranean country. Such difference may suggest the concentration of different pigments may be different with respect to originations. Due to the fact that the UV-Vis shows general disadvantages in characterization of compounds, the compositional variations of pigments is rather indicative than exhaustive and complete. Further investigation is necessary. The PDRs between samples increased as the level of adulteration rises, as shown in Figure 4B. Although part of the PDRs between two adjacent adulteration levels was less than 1.5, the average PDR between the concentrations is 1.78, a clear difference. The PDRs suggested that it is foreseeable to establish an effective adulteration detection model, provided that a proper treatment to exclude or reduce the influence from other factors such as EVOO origin is performed. Furthermore, the PDRs values between the various adulteration types in Figure 4C are all less than 1.5, as well as the average PDR of the adulteration types is also less than 1.5, indicating that different adulteration types are difficult to classify. Since all the oils that we used as adulterants were only from China, it may be possible that there are limited variances between oils. Therefore, different types of adulterants carry unnoticeable variances in the overall compositions compared to other factors.  
In summary, PDR mappings and average PDR can intuitively and quantitatively conclude that the differences between the classes from large to small are the origin of EVOO, the level of adulteration, and the type of adulteration, reflecting the degree of influence of factors on the adulterated samples. Compared to PCA, PDR is more effective in distinguishing between classes within these three factors.
## 5 Experimental Results
### Experimental Setup  
Our tests evaluate the existing datasets, the audio features and the classifiers. All the experiments have been firstly executed on an Apple MacBook Pro (2.3 GHz Intel Core i5), in order to evaluate the impact of the features' selection and to compare the achievable performance of the classifiers. The experimental measurements on the embedded system have been obtained after 10 executions on a Raspberry Pi 3B+.  
Firstly, the feature extraction is performed by using the Python 2.7 package LibROSA 0.6.3 [25]. The features' selection can be done by importing Weka [13] on Python after converting the features' files in an.arff format. However, in most of our experiments, all the features mentioned in Section 3.2 are used. Secondly, the classifiers are implemented using the Python library scikit-learn 0.21.2 [33]. The classification is performed by splitting the data into 20% testing and 80% training. Each execution of the Python scripts is iterated 10 times to obtain the average of the time extraction per feature. Notice that the feature extraction and the sound classification are operations independent of the approach since both use the same extracted features for the sound classification at all stages. Therefore, our experiments target the profiling of the sound classifiers in terms of accuracy and timing.
## 3 Results
### Aphid Distribution in Preference for Contact Cues  
We also assessed the host preference of alate _M. persicae_ based on visual and contact cues from plant leaves (Figure 3). At 30 min after release, 46.3% aphids _M. persicae_ (\(df\) = 1, \(F\) = 4.697, \(p\) < 0.05) were found on MMWV-infected plants and 39.1% on healthy plants (Figure 3A). Also, 14.6% of _M. persicae_ made no choice. Similarly, 48.1% were found on infected plants relative to 42.7% on mock-inoculated plants (\(df\) = 1, \(F\) = 6.122, \(p\) < 0.05) (Figure 3B). However, at the 60 min time point, for the infected vs. healthy plant pair, aphids on the MMWV-infected plants dropped to 26.7% (\(df\) = 1, \(F\) = 35.317, \(p\) < 0.001) compared to an increase to 61.2% on the healthy plants (\(df\) = 1, \(F\) = 45.493, \(p\) < 0.001) (Figure 3A). Likewise, on the infected vs. mock-inoculated plant pair, more aphids preferred mock-inoculated (50.1%) to MMWV-infected plants (40.9%) (\(df\) = 1, \(F\) = 10.038, \(p\) < 0.05) after 60 min (Figure 3B). In contrast, no significant difference was found between aphid numbers when choices were made between healthy and mock-inoculated plants at both 30 min (\(df\) = 1, \(F\) = 3.824 \(p\) = 0.061) and 60 min (\(df\) = 1, \(F\) = 2.523, \(p\) = 0.123) time points (Figure 3C). These results indicated that, although MMWV-infected plants were more attractive initially, more _M. persicae_ emigrated to healthy plants once they probed or fed on infected plants.  
Figure 2: The response of _Myzus persicae_ to different odor stimuli. For each pair of stimuli, a total 150 _M. persicae_ were tested by the Y-shaped olfactometer. Data were presented as the mean \(\pm\) SE. Bars denoted by asterisk indicate a significant preference for the treatment (\({}^{*}\)\(p\) < 0.05, \({}^{**}\)\(p\) < 0.01, \({}^{***}\)\(p\) < 0.001). No significant difference (\(p\) > 0.05). Healthy plants are shown in green; MMWV-infected plants are shown in red; Mock-inoculated plants are shown in blue. Numbers of individuals that responded or did not respond are given.  
Figure 3: Distribution of alate _Myzus persicae_ in response to contact cues of healthy, mock-inoculated and MMWV-infected plants after 30 min and 60 min. (**A**) Healthy vs. MMWV-infected plants, (**B**) Mock-inoculated vs. MMWV-infected plants, (**C**) Healthy vs. mock-inoculated plants. Each replicate (n = 15) consisted of fifty alate _M. persicae_. Asterisks indicate significant differences (* \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\)).
# Effect of Laser Surface Treatment on the Corrosion Resistance of Zircaloy-4 at High Temperature
## 5 Conclusions  
LST was conducted on the Zircaloy-4 plate specimen, the LST layer of the specimen was also the \(\alpha\)-Zr phase, no oxidation was observed, no ZrH\({}_{x}\) formation was noted, and the Fe-Cr precipitates were dissolved in the Zr matrix. The corrosion resistance of the LST Zircaloy-4 specimen was better than that of the NLST Zircaloy-4 specimen. The main reason could be attributed to the higher solid solutions of Fe and Cr and there being fewer Fe-Cr precipitates in the LST layer. This work was an attempt to study the surface treatment technology rather than the coating technology for the development of ATF cladding. It could be necessary to carry out a systematic study of the corrosion resistances of the different kinds of zirconium alloys corroded in high-temperature steam after conducting tests using the different laser parameters in future. This technology may be an option for the development of ATF cladding.  
Conceptualization, S.X. and R.M.; Methodology, R.M.; Validation, T.S.; Formal analysis, T.S., Y.Y., J.L., Y.G. and J.Q.; Investigation, S.X., T.S., Y.Y., J.L. and Y.G.; Resources, Y.G.; Data curation, Y.Y., J.L. and Y.G.; Writing--original draft, S.X. and W.L.; Writing--review & editing, S.X. and W.L.; Visualization, J.Q. and W.L.; Supervision, R.M., W.L. and D.Y.; Project administration, J.Q. and D.Y.; Funding acquisition, S.X. and D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (12375272).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank [PERSON] at Changsha University of Science and Technology for his assistance with TEM analysis.  
The authors declare no conflict of interest.
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 2 Method: The Optical Efficiency Calculation Model and Optimization Method for Combined Disk System  
### Combined Spherical Mirrors Dish System Design  
Small-sized square spherical mirrors were installed on the frame structure of the solar dish system. The size of each mirror was 0.5 m \(\times\) 0.5 m, and the total mirror area of the system was 62.25 m\({}^{2}\). The system had nine layers in both vertical and horizontal directions, with a total of 249 mirrors. An octagonal solar dish system centered on the bottom mirror is shown in Figure 1.  
Figure 1: _Cont._  
The distance from the reflection point P to the center of the receiver is  
\[f_{p}=\frac{2f_{0}}{1+\cos(\varphi_{rim})}, \tag{1}\]  
Figure 1: Schematic diagram of reflected mirrors in a combined dish system: (**a**) main view; (**b**) top view; (**c**) spatial view.  
where \(f_{0}\) is the system focal length, and \(\varphi_{rim}\) is the rim angle of the mirror.  
\[sin\varphi_{rim}=\frac{x_{0}}{f_{p}}, \tag{2}\]  
where \(x_{0}\) is the vertical distance from the reflection point p to the principal axis.  
Therefore, it can be obtained from Equations (1) and (2) that  
\[\varphi_{rim}=2\times\tan^{-1}\frac{x_{0}}{2\times f_{0}}. \tag{3}\]
## 4 Dynamic Response Characteristics of the TDP-VSG  
To reconcile the dynamic and static characteristic requirements of VSG outputactive power, while mitigating dynamic power oscillations and eliminating the steady-state deviation induced by damping characteristics, an optimized transient damping control strategy based on a first-order lead-lag compensator for VSG has been developed. However, most of the existing literature focuses on analysing the performance of transient damping in suppressing transient oscillations and eliminating steady-state deviation [26]. It seldom analyses the difference between its transient response characteristics under given perturbation and grid frequency perturbation, nor does it analyse the overshoot problem caused by the implementation of transient damping. To tackle these issues, this chapter develops a frequency domain model of transient-damped VSG control based on a small-signal model. It analyses the transient oscillation mechanism and power overshoot issues in the transient damping VSG control system, focusing on the distribution trends of the closed-loop pole trajectories and zero points to understand the system's dynamic behavior.