esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Basal Leaf Removal at Different Sides of the Canopy Improves the Quality of Aglianico Wine
## 3 Results and Discussion
### Phenolic Composition
Compared to the control, leaf removal had no effect on flavonoids in the 2016 wine; in 2018 they were slightly increased and in 2017 they were almost doubled, with a more significant increase when defoliation was carried out on the south side of the canopy (Figure 2a). Defoliation did not influence anthocyanin concentration in 2016, while the highest content was observed in wines from vines defoliated on the south side in 2017 and defoliated on the north and south sides in 2018 (Figure 2b). Evidently, the higher level of defoliation (i.e., on two sides) in 2018 favored greater exposure to the sun and, consequently, greater anthocyanin accumulation. Regarding flavans reactive with vanillin, which potentially contributes to the bitterness of wines [37], leaf removal caused an increase in the south side of the canopy. Figure 2: Interaction between season and leaf removal and its effect on the phenolic parameters of wines: F—flavonoids (**a**); A—anthocyanins (**b**); FRV—flavans reactive with vanillin (**c**); P—proanthocyaniations (**d**); FRV/P—flavans reactive with vanillin/proanthocyaniations ratio (**e**); TP—total polyphenols (**f**); AA—antioxidant activity (**g**); CI—color intensity (**h**). C—control; N—leaf removal on the north side of the canopy; S—leaf removal on the south side of the canopy. only in 2017 for defoliation performed at the south and north-south sides (Figure 2c). The results concerning proanthocyanidins were much more complex: defoliation on the north side led to lower concentrations in 2016, a value comparable to the other defoliated wines in 2017 and the highest content in 2018 (Figure 2d). Defoliation performed on the north side led to a significant decrease in the FRV/P ratio only in 2017 and, consequently, likely led to the lower bitterness of the wine. (Figure 2e). Defoliation had no effect on total polyphenols in 2016, while it led to an increase in 2017--more significant on the south and north sides--and 2018, where the increase was more significant when carried out on both sides (Figure 2f). As expected, the effect on antioxidant activity was similar to that of total polyphenols: this parameter increased in both the 2017 and 2018 seasons (Figure 2g). Finally, the effect of defoliation on the wine color intensity was similar to that found for anthocyanins: compared to the control, color intensity increased in 2017, especially on the south side, and 2018, with greater prevalence on the north-south and north sides (Figure 2h). Figure 3: Interaction between season and leaf removal and its effect on the main anthocyanins of wines: Dp—delphinidin-3-glucoside **(a)**; Cy—cyanidin-3-glucoside **(b)**; Pt—petunidin-3-glucoside **(c)**; Pn—peonidin-3-glucoside **(d)**; Mv—malvidin-3-glucoside **(e)**; TA—total anthocyanins **(f)**. C—control; N—leaf removal on the north side of the canopy; S—leaf removal on the south side of the canopy; NS—leaf removal on both sides of the canopy.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Parameters for Measuring the Performance and Quality of the Data Transmission Network
#### 2.2.3 Packet Loss  
Packet loss is the number of failed packets in the delivery process through the transmission media. Different factors can contribute to this issue, such as signal degradation in network channels, corrupted packets and failures in the network device or in the network routing process. The packet loss is calculated by Equation (3),  
\[\zeta=\frac{\alpha-\beta}{\alpha}100\% \tag{3}\]  
where \(\zeta\) is the packet loss percentage, \(\alpha\) is the number of sent packets and \(\beta\) is the number of delivered packets [41].
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 1 Introduction
Although, References [13; 14; 15; 16; 17] used intelligent algorithms to improve the completion rate of TT&C tasks effectively, which do not control the time fragments of TT&C equipment during the task-planning process, resulting in untimely responses to emergency tasks. Therefore, this paper designs a low time fragmentation-oriented TT&C task-planning method (LTFT&C) based on DDQN (Double Deep Q-network) to minimize time fragmentation and maximize task revenue. The algorithm uses deep reinforcement learning to mine the distribution rules of TT&C task resources of the space network. It constructs a common TT&C task feasible scheme that considers the task completion rate and emergency task response speed. Unlike other traditional TT&C task-planning algorithms, LTFT&C can reduce the time fragments in the task-planning process while ensuring the success rate of the task. The basic idea is as follows: Firstly, aiming at the problem of high time fragmentation and low utilization rates of TT&C equipment in the existing TT&C task-planning results, a mathematical model of TT&C task planning is developed with the objective function of minimizing the total time fragments and maximizing the total task revenue. Then, based on the conflict graph model, the task-planning problem is transformed into a maximum independent set problem. Finally, the task-planning algorithm based on DDQN and the mathematical model are used alternately to complete the entire solution process. Comparing the LTFT&C algorithm with the existing methods, the simulation results show that the proposed algorithm in this paper reduces the time fragments of TT&C equipment while ensuring the revenue of common TT&C tasks and effectively improves the response speed to subsequent emergency tasks. The remainder of this paper is organized as follows: Section 2 gives a description of the TT&C task-planning scenario and problem. Section 3 has established the mathematical model of the TT&C task-planning problem in a space network. Section 4 transforms the TT&C task-planning problem into an independent set problem on the graph. Section 5 introduces a low time fragmentation-oriented TT&C task-planning algorithm. Section 6 conducts simulation experiments to verify the effectiveness of the proposed algorithm. The last section gives the research conclusion.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Fuzzy Logic-Based Safe Energy Level Determination
#### 4.4.3 Fuzzy Controller  
There are two input variables; each is converted to three fuzzy sets, so we have a total of \(3^{2}=9\) rules in the _Knowledge Base_, which are listed in Table 4. The rules are designed to reflect the observation described in Section 4.4.1. Our rules have the form of \"**IF** (\(L_{r}\) is \(A\)) **AND** (\(E_{min}\) is \(B\)) **THEN** (\(\theta\) is \(D\))\", in which \(A,B\) obtain the values of _low, medium_, or _high_, and \(D\) is either _very low, low, medium_, or _high_. For the ease of presentation, we use the following notations: _VL = very low, \(L=low\), \(M=medium\), \(H=high\)_. As the Fuzzy rules are based on the **AND** operator, the output membership degree is defined by  
\[\mu_{R_{i}}=\min\{\mu_{A}(L_{r}),\mu_{B}(E_{min})\},\forall i=1,\ldots,9. \tag{9}\]  
Figure 5: Fuzzy input membership functions.  
Figure 6: Fuzzy output membership functions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{R_{\#}}\)} & \multicolumn{2}{c}{**Input**} & \multicolumn{2}{c}{**Output**} \\ \cline{2-4}  & \(\mathbf{L_{r}}\) & \(\mathbf{E_{min}}\) & \(\mathbf{\theta}\) \\ \hline
1 & \(L\) & \(L\) & \(H\) \\ \hline
2 & \(L\) & \(M\) & \(M\) \\ \hline
3 & \(L\) & \(H\) & \(L\) \\ \hline
4 & \(M\) & \(L\) & \(M\) \\ \hline
5 & \(M\) & \(M\) & \(L\) \\ \hline
6 & \(M\) & \(H\) & \(\textit{VL}\) \\ \hline
7 & \(H\) & \(L\) & \(L\) \\ \hline
8 & \(H\) & \(M\) & \(\textit{VL}\) \\ \hline
9 & \(H\) & \(H\) & \(\textit{VL}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fuzzy rules for safe energy level determination.
# Three-Dimensional Scaffolds Designed and Printed Using CAD/CAM Technology: A Systematic Review
## 4 Discussion
### Analysis of the Bone Volume Obtained  
In the work of [PERSON] et al. [23], bone regeneration of large maxillary zygomatic defects was performed in eight patients, and a measurement of the volume gain achieved after grafting was performed. The mean volume of the preoperatively planned graft was 11.32 mm\({}^{3}\), ranging from 2.16 mm\({}^{3}\) to 30.37 mm\({}^{3}\). The mean postoperative volume of the actual graft was 10.21 mm\({}^{3}\), ranging from 1.84 mm\({}^{3}\) to 28.22 mm\({}^{3}\). After superimposing the two images using software, the average volume was 79.71%, ranging from 70.89% to 86.31%. In the postoperative period, the formation of new bone was calculated and the mean was 2.15 mm\({}^{3}\) (0.22 mm\({}^{3}\) to 7.15 mm\({}^{3}\)). The bone volume fraction was obtained as the ratio between the bone volume measured after healing and the graft volume measured postoperatively, and a mean volume gain of 23.34% (7.81% to 66.21%) was obtained. The mean bone density in the grafted areas was 188.84 HU (151.48 HU to 291.74 HU), indicating type IV bone quality according to the [PERSON] and Zarb classification [28].  
In the study by [PERSON] et al. [24], there was a mean vertical bone gain of 2.1 \(\pm\) 0.9 mm (range 0-3.3 mm, median 2.4 mm, 95% CI: 1.7-2.5 mm) and a mean horizontal bone gain of 3.0 \(\pm\) 1.0 mm (range 0-4.5 mm, median 3.2 mm, 95% CI: 2.5-3.5 mm).The 2022 study by [PERSON] et al. [25] assessed vertical and horizontal bone gain 8 months after graft placement with radiographic evaluation, revealing mean vertical and horizontal bone gains of 2.10 mm (\(\pm\)0.35) and 2.96 mm (\(\pm\)0.45), respectively. They also found a statistically significant difference between vertical and horizontal bone gain (\(p<0.001\)). This work also evaluated crestal bone loss in implants placed in the regenerated area.  
The 2014 study by [PERSON] et al. [27] evaluated the reduction in the size of the defect in height and width 8 months after graft placement through the use of a periodontal probe, observing a bone increase in height of up to 5 mm in two of the patients and in width of up to 3 mm in two of the patients. However, they do not perform any type of statistical analysis of the bone gain achieved, nor evaluation with CBCT. In this paper, a result of improved maintenance of crestal bone height was obtained after 6 months in those alveoli in which scaffolding was placed compared with those in which nothing was placed.  
The 2013 study by [PERSON] et al. [26] did not assess bone volume gain. They simply performed a CBCT 6 months after the graft and verified that the bone volume gained was sufficient for the placement of the implants without performing any quantitative measurement.
# Smooth Fractional Order Sliding Mode Controller for Spherical Robots with Input Saturation
## 3 Preliminaries  
This section presents some basic definitions and results of fractional calculus, which will be used later.  
Among three common ways of defining fractional calculus, i.e., Grunwald-Letnikov, Riemann-Liouville, and Caputo [45], this paper adopts the Riemann-Liouville definition. The Riemann-Liouville fractional integral of a continuous function \(f(t)\) is defined as:  
\[{}_{t_{0}}D_{t}^{-p}f(t)=\frac{1}{\Gamma(p)}\int_{t_{0}}^{t}\frac{f(\sigma)}{( t-\sigma)^{1-p}}d\sigma,\ \ (t>t_{0},p>0\ ) \tag{8}\]  
Also, the Riemann-Liouville fractional derivative of the continuous function \(f(t)\) is defined as:  
\[{}_{t_{0}}D_{t}^{p}f(t)=\frac{1}{\Gamma(q-p)}\frac{\text{d}^{q}}{\text{d}t^{q} }\int_{t_{0}}^{t}\frac{f(\sigma)}{(t-\tau)^{p-q+1}}d\sigma,\ q-1<p<q \tag{9}\]  
where \(p\in R^{+}\) is the derivative order or integral order, \(q=[p]\in Z\); and \(t_{0}\) is the initial value.  
**Property 1 ([45]):**_For Riemann-Liouville fractional calculus, if function \(f(t)\) is continuous and \(p>q>0\), then_  
\[{}_{t_{0}}D_{t}^{p}[_{t_{0}}D_{t}^{-q}f(t)]={}_{t_{0}}D_{t}^{p-q}f(t),\ \ t>t_{0} \tag{10}\]  
**Property 2 ([46]):**_If 0 \(<q<1\), the following equality holds for Riemann-Liouville fractional calculus_  
\[{}_{t_{0}}D_{t}^{q}[_{t_{0}}D_{t}^{-q}f(t)]={}_{t_{0}}D_{t}^{-q}[_{t_{0}}D_{t} ^{q}f(t)]=f(t) \tag{11}\]  
**Lemma 1 ([47]):**_The fractional integration operator \(D^{-q}\) with \(q>0\) is bounded_  
\[\|D^{-q}f(x)\|\leq K\|f(x)\|_{\gamma},1\leq\gamma\leq\infty \tag{12}\]  
_where \(K\) is a positive constant._  
**Lemma 2** ([48]): _Consider a fractional linear time-invariant system with a different differential order as follows:_  
\[D^{qi}x(t)=Ax(t) \tag{13}\]  
_where \(x\in R^{n}\) is the state vector, \(qi=[q1\ q2\ \dots\ qn]\in Rn,0<q\ i<1\) and \(A\in R^{n\text{\tiny{even}}}\) is a constant matrix. The system is stable if the following condition is satisfied_  
\[\left|\arg(\text{eig}(A))\right|>\frac{1}{2\kappa}\pi \tag{14}\]  
_where the parameter \(\kappa\) is the lowest common multiple of \(q_{i}\)._  
**Assumption 1**.: _The state \(x_{i}\) in Equation (3) is assumed to satisfy the following inequality_  
\[\left\|D^{p}(x_{i})\right\|\leq\delta,i=1,2,3,4 \tag{15}\]  
_where \(\delta\) is a positive constant._
## 3 Digital Signage Systems' Design and IoT Technologies Comparison
### Comparison of IoT Technologies
#### 3.1.5 Wide Area Networks (WAN)  
Cellular WANs are a form of wireless networks which differ from WLANs, as they use cellular network technologies like 2G, 3G, LTE/4G, and 5G networks for transferring data. The 1G cellular technology was introduced in the 1980s to offer voice services and was called Nordic Mobile Telephone (NMT). The 2G introduced in the 1990s was digital and was called GSM. This generation provided more services, such as Short Message Services (SMS), Multimedia Messaging Services (MMS), and General Packet Radio Services (GPRS), which offer internet services to the users. The 3G cellular technologies were introduced in the 2000s to provide faster voice, SMS, and MMS services, as well as video calling and internet services. In the third generation, there was also an exponential growth of data bandwidth and throughput from which customer services improved. Nowadays, LTE/4G cellular technologies offer improved communication links, higher data rates, and better quality of service in comparison to the earlier cellular generations. They were launched in 2010, and the improvement over the third generation and other technologies was the higher bandwidth and the better data throughput. The 5G infrastructure, which is typically based on the previous generation, has been designed to offer fast and reliable communication services, secure banking transactions, and reliable industrial IoT applications. 5G coexists with 4G/LTE technologies and it is based on Code Multiple Access (CDMA) and Beam Division Multiple Access (BDMA) technology standards, hence it supports the core network for the internet [29]. In Table 1, we see a performance comparison of the most common IoT technologies that we have described [26].
## 7 Applications and Prospects  
Two fields where copper nanoparticles can be used to prevent infections and diseases are hospitals--as seen below in Figure 4--and transportation. Copper nanoparticles can be used in wall coatings, clothing, equipment and bedding to help fight against the spread of infections especially against microorganisms that have developed a resistance to standard antibiotics and disinfect solutions [67].  
GURE:S4.F4][ENDFIGURE]  
Agarose polymer films with CuNPs can be used with great success in antimicrobial filters and coatings. Agarose can form gels and films; therefore, the resulted materials can be successfully used as food packaging, sanitation and fabrics [85].  
Another use for materials improved with CuNPs is to extend food shelf life and improve food quality by using biopolymer films such as chitosan. They are effective in reducing fluid concentration of two microorganisms that affect food quality [86].  
Fibers such as cotton cellulose loaded with CuNPs can be used as wound dressing, personal care products, protective suits, different clothing and also in military and bio-defense [87].  
Copper nanoparticles were demonstrated to be effective in fighting a range of bacterial pathogens and to stop infections. The number of potential applications is enormous, and it allows for further development of this field of study. For example, in combination with other nanoparticles, e.g., AgNp, copper nanoparticles have demonstrated an important effect in curing bovine mastitistiis [88]. Further studies need to be conducted in order to assess the safe use of copper nanoparticles, considering their potential toxicity.  
Additionally, copper nanoparticles may also be considered for agricultural purposes--for the growth of wheat crops, in order to match the high demand given by the increase in population. Thus far, it has been demonstrated that the application of 30 parts per million on the soil may increase the growth of wheat crop. Nonetheless, extensive research is needed in order to determine what is the best concentration, application mode and time [89].  
Given copper nanoparticles' high oxidation rates, recent attention has been given to copper nanoparticles carbon encapsulation--CECNp. They may be able to resist oxidation and temperatures of up to 900 \({}^{\circ}\)C and also show good compatibility with organic materials, making them feasible to use as conductive fillers, electromagnetic wave shielding materials and catalysts [26].  
Using stabilized or polymer dispersed CuNPs has better control on ion releases. Another aspect that helps with better control is if the nanoparticles are interacting with the polymer or further stabilization using a core shell structure [90].  
Figure 4: Antibacterial nanoparticles applications [84].
# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
###### Abstract  
Sleep stage classification is of great importance in sleep analysis, which provides information for the diagnosis and monitoring of sleep-related conditions. To accurately analyze sleep structure under comfortable conditions, many studies have applied deep learning to sleep staging based on single-lead electrocardiograms (ECGs). However, there is still great room for improvement in inter-subject classification. In this paper, we propose an end-to-end, multi-scale, subject-adaptive network that improves the performance of the model according to the model architecture, training method, and loss calculation. In our investigation, a multi-scale residual feature encoder extracted various details to support the feature extraction of single-lead ECGs in different situations. After taking the domain shift caused by individual differences and acquisition conditions into consideration, we introduced a domain-aligning layer to confuse the domain. Moreover, to enhance the performance of the model, the multi-class focal loss was used to reduce the negative impact of class imbalance on the learning of the model, and the loss of sequence prediction was added to the classification task to assist the model in judging sleep stages. The model was evaluated on the public test datasets SHHS2, SHHS1, and MESA, and we obtained mean accuracies (Kappa) of 0.849 (0.837), 0.827 (0.790), and 0.868 (0.840) for awake/light sleep/deep sleep/REM stage classification, which confirms that this is an improved solution compared to the baseline. The model also performed outstandingly in cross-dataset testing. Hence, this article makes valuable contributions toward improving the reliability of sleep staging.  
multi-scale inter-subject network; sleep staging; single-lead ECG 202323133474233  
in a free-living environment and in a non-obtrusive way to ensure that the sleep captured is as representative of typical sleep as possible, in order to understand the role of sleep in health and illness [7], generally, PSG can only be conducted in a specific laboratory or hospital. In recent years, there has been a significant expansion in the development and use of low-cost [8; 9], wearable sleep detection systems to study sleep architectures in free-living conditions among individuals at a population level [10; 11; 12]. Electrocardiograms (ECGs) are one of the most commonly used diagnostic tools in medicine and healthcare, and sleep detection systems allow individuals to be monitored by ECG signals from a wearable device, which has little effect on sleep monitoring under comfortable conditions. This potential to obtain sleep information from a single-lead ECG is of great significance.
## 1 Introduction
To better provide direct information on temperature tides, a constellation of small satellites has been suggested [22], while long-term observations from space clearly show difficulties in providing continuity using successive sensors [17; 18]. However, the location and nature of all type of tides will help in defining characteristics of the required constellation. Initial global modeling generally involves migrating tides [19] and assumes a zonally homogeneous atmosphere. Developments associated with the global atmospheric tidal model [20] have shown good overall agreement with observations with, however, lower modeled amplitudes compared to observations [21], which is probably due to intermittency effects as observed in continuous airglow [23]. Comparisons with European meteorological analyses assimilating radiosondes and operational satellites to the upper stratosphere show semi-diurnal fluctuations consistent between ECMWF (European Centre for Medium-Range Weather Forecasts) and theoretical estimates given by the Global-Scale Wave Model (GSWM) described in Section 2.2. However, the observed diurnal fluctuations reveal significant geographical differences and, sometimes, different characteristics of migrating tides, probably due to ground and tropospheric effects. If the atmospheric fields for composition and dynamical parameters show large variability, tidal characteristics may vary [2], and non-migratory components may appear. Migrating tides propagate westward in longitude synchronously with the rotation of the sun (sun-synchronous), while non-migrating tides can propagate either westward or eastward, or be stationary and lie in a regional zone. In the latter case, the tides do not propagate with the rotation of the sun but according to the passage of the sun. They should be generated by a zonal asymmetric thermal forcing induced by large longitudinal variations in tropospheric humidity and have been observed in the lower stratosphere using GPS radio occultation observations from space [24]. The meteorological analyses have been extended to the mesopause (80 km) with a larger data set (ERA5), allowing for the study and update of tidal characteristics and the specific contribution of non-migrating tides. To better provide direct information on temperature tides, a constellation of small satellites has been suggested [22], while long-term observations from space clearly show difficulties in providing continuity using successive sensors [17; 18]. In this study, we propose to investigate meteorological analyses (ERA5) to extract migrating and non-migrating tides in the middle atmosphere while analyses are performedup to the mesopause. In Section 2, data are presented. The methodology to extract local tides is described in Section 3, as well as the separation of migrating and non-migrating modes. In Section 4, migrating tidal amplitudes and phases are compared with the Global Scale Wave Model (GSWM), while Section 5 presents non-migrating components. Section 6 discusses the results and their impact on observation requirements. Section 7 provides conclusions.