esa-sceva/satcom-chunk-collection huggingface dataset:
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 10 Conclusions  
This paper studies and formulates an NFV-Enabled energy management model for renewable and non-renewable energy sources in order to analyze the requirements and concerns about service scheduling algorithms. Strategies, OptTs, and FastTs to enable efficient utilization of available power are proposed for small and large-scale IoT scenarios. The evaluation results demonstrate that the proposed algorithmic solutions that are supported by NFV result in improvements that range from small increases in the consumption and processing of services up to the full utilization of \(P_{ES}\) and the processing of all demands (e.g., in _Case B_, \(AR=100\%\)). This better \(P_{ES}\) utilization is reflected in a decrease of \(P_{RES}\) and \(P_{LACK}\) and in an increase of \(AR\) with respect to the non-application of the energy management solution (i.e., \(T_{s}^{k}=0\)); specifically, we have verified that the greatest improvements in energy consumption are achieved with small \(T_{s}^{k}\) values (e.g., \(T_{s}^{k}=1\)). In terms of complexity, it is verified that the \(P_{RES}\) minimization problem, when considering management strategies, such as the optimal workload scheduling and service rejection, is \(\mathcal{NP}\)-hard and that its optimal solution, OptTs, has an exponential complexity that depends on the values of \(T_{s}^{k}\) and \(N\) (Equation (30)). This demands a robust and scalable computing infrastructure like the one offered by NFV. In this context, in order to propose less complex and more scalable solutions, some heuristic approaches have been analyzed, and a pre-partition strategy, FastTs, has been evaluated. Regarding the comparison between the exact and the heuristic solutions, in small scenarios, FastTs is at least three orders of magnitude faster that OptTs, producing similar or equal results (e.g., in _Case B_). In the worst case, the heuristics solution is within only \(\sim\)1.4 times the optimal result. In addition, in large-scale scenarios, the evaluation of FastTs, for different services and partitioning schemes also demonstrates improvements over 13% in all cases. Future work will be devoted to the analysis of the incorporation of an energy storage component for ensuring the execution of CS, the study of the service prioritization capability in the context of network slicing, and the development of more scalable and faster approaches that are based on the rest of the heuristic strategies presented in Table 4.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 3 Denoising Based on the Speech Segment Detection
### Fuzzy Clustering  
Fuzzy clustering is used for clustering data based on a probability to be included in each cluster [39]. We present a method using fuzzy clustering to detect the speech segments. As the criteria for fuzzy clustering, we use both the energy and entropy because they can be used complementarily in the speech segment detection. That is, the entropy reflects the change of the signal while the energy considers the absolute value of the signal.  
For each time point, fuzzy clustering outputs the probability of belonging to a certain cluster--speech or non-speech segments. Here, we establish the threshold as a probability to determine if a given sample is in speech or non-speech segments.  
Figure 13 shows the accuracy variation of fuzzy clustering as the threshold is varied from 10% to 100%. In the result, we note that the precision is relatively constant while the recall decreases as the threshold increases. The F1 score is the highest when the threshold is 30%. Figure 14 illustrates the result of fuzzy clustering where the audio signal is presented in orange and the speech segment is in blue.  
Figure 11: The accuracy variation of entropy-based filtering as \(\lambda\) is varied.  
Figure 10: Entropy of a sample audio signal.
## 3 Results and Discussion
### Conservation Quality of the SE Margin of the Ria de Arosa
#### 3.1.1 Quality of Geomorphological Domains  
Based on the geomorphological domains thematic map (Figure 3A), the conservation quality of geomorphological domains map is developed (Figure 3B). High and very high conservation quality ranks of the geomorphological domains are associated with lithostructural relief morphologies (ridges, summits, and hills) and fluvial morphobstructural system morphologies (alluvial fans and cones).  
The southwestern margin of the Castrove Peninsula holds the highest conservation ranks due to the presence of pronounced residual metamorphic relief, which gradually becomes gentler through the occurrence of adjacent alluvial fan and cone systems. These overlapping alluvial fans are particularly well-represented along the southern edge of the Umia-Grove Bay.  
The El Grove Peninsula also exhibits high conservation quality in its interior due to the presence of dome-shaped residual morphologies associated with immature stages of granite landform evolution. These features constitute one of the most characteristic elements of the relief around the Ria. The western margin of the peninsula displays more mature morphologies of the berrocal type.  
High-quality zones associated with granite landforms are also found on Arosa Island and in the southeastern margin of the study area (north of Sangenjo).  
Low values of this factor are observed in anthropized areas, both near and within urban centres. Similarly, low conservation quality is recorded on the La Lanzada tombolo and coastal sectors featuring beaches, dunes, abrasion platforms, or marine terraces. This is due to their low topographic prominence, especially when compared to the more pronounced morphologies mentioned above.
## 3 Results and Discussion
### Head Loss Trends  
This part of the study determines the effect of WDNs' pipe diameters on the head losses. Figure 8 demonstrates the head (pressure) losses based on variations in pipe diameter. As the pipe diameter increases, there is a decrease in head losses. To predict the head losses according to different pipe diameters, based on the Equation (4), it is found that 3-degree polynomial equations describe the trend of head losses with R squared values calculated to be 0.9958. It is also noted that WDNs 1 and 2 have a similar and close trend but WDN 3 behaves quite differently (in WDN 3, most values for the pressure losses are close to zero). Significantly lower pressure losses can occur when there are more water tanks for storing water and more pipes with small lengths and large diameters. On the other hand, it has become evident that when a looped WDN (WDN 1) changes to an unlooped WDN (WDN 2) with the same conditions, the pressure losses may rise slightly.
## 7 Performance Analysis  
We will now provide the comparisons of our scheme and some other classical schemes regarding security and storage efficiency. Table 2 shows the safety comparison. STD stands for standard model, ROM stands for random oracle model, FS stands for full security, GSD stands for general subgroup decision assumption, DBDH means the decisional bilinear Diffie-Hellman assumption, DBDHE indicates the asymmetric DBDH exponent assumption, and n-BDHE indicates the decision n-bilinear Deffie-Hellman exponent problem.  
The model column indicates whether the scheme achieved security under the standard model (STD) or the random oracle model (ROM). The assumption column describes the difficult problem assumption on which the scheme depended. Anonymity indicates whether the scheme was anonymous. Leakage-resilience indicates whether the scheme had the feature of resisting private key disclosure. Table 2 shows that our scheme had security, anonymity, and leakage resilience under STD. This is the best security at present.  
Table 3 shows the storage efficiency comparisons. \(t\) is used to represent the number of all users, \(d\) is the number of the intended receivers, \(\left|G_{T}\right|\) represents the length of the elements in group \(\left|G_{T}\right|\) (others are similar), and \(n\) is a value that can be changed and is related to the leakage rate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & **Model** & **Assumption** & **Anonymity** & **Leakage Resilience** \\ \hline
[16] & STD & DBDHE & YES & NO \\
[17] & ROM & DBDH & YES & NO \\
[19] & ROM & DBDH & YES & NO \\
[21] & ROM & DBDH & YES & NO \\
[25] & STD & GSD & YES & NO \\
[26] & ROM & n-BDDH & NO & NO \\
[28] & ROM & DBDHE & YES & NO \\
[29] & STD & LWE & YES & NO \\
[35] & STD & GSD & NO & YES \\
[63] & STD & GSD & YES & NO \\ Ours & STD & GSD & YES & YES \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security comparisons of some related schemes.  
Table 3 indicates that our scheme had the shortest ciphertext length, like that in [62]. Compared with the same type of schemes [35,62] and based on a composite order group, our scheme also had the shortest public key length. Because the schemes in [19,21] were constructed using a prime order group, their public key was relatively short. In terms of the private key length, our scheme adopted state partitioning technology, so the private key length was twice that of [62], on which ours was based.
## 1 Introduction  
Charge-transfer (CT) complexes are formed as a result of the interaction of a donor with an acceptor through the formation of hydrogen bonds, which was first introduced by [PERSON] and coworkers [1, 2]. This type of interaction (donor-acceptor) has a worth attention for chemical reactions including addition, substitution and condensation [3]. It also has been reported that CT complexes can formed by the reactions of \(\sigma\)- and \(\pi\)-electronacceptors with different donors such as amines, crown ethers, polysulfur bases and oxygen-nitrogen mixed bases [4; 5; 6; 7; 8]. The photometric methods used to study the interactions of CT complexes are usually simple and feasible due to the fast formation of the complexes.  
As it is well known that, most of the CT complexes having broad applications in chemical analysis including quantitative drug estimation by formation of donor-acceptor complexes [9; 10; 11]. CT complexes having excellent electrical and electronic conductivities [12; 13; 14; 15] and can be applied to explore drug-drug interaction, drug-acceptor linking properties [16; 17]. In addition, interaction of these types of CT complexes can be used to understand action mechanism of photo-catalysts. Due to the above properties of CT complexes it has been attracted considerable attention to researcher for further development of synthesizing new CT complexes. It should be noted that, determination of above properties using CT complexes are considered a simpler, cheaper and more effective analysis tool compared to other reported methods [18; 19]. Because of the simple preparation method of CT complexes and large number of applications in biological/material fields we aimed to conduct this study.  
Here, we report, in-suite synthesis of quinine-CT complexes with DDQ and TCNQ, characterized using different spectroscopic techniques (IR and NMR) and the conductance (Figure 1). The stability of the quinine with DDQ and TCNQ complex in the solution is characterized using the Benesi-Hildebrand equation to determine the formation constant and molar extinction coefficient. In addition, the stoichiometry of the Q-DDQ and Q-TCNQ is assessed using [PERSON]'s method. Different spectroscopic parameters are determined including oscillator strength, dipole moment, ionization potential, complex energy, resonance energy, dissociation energy, and standard free energy. Furthermore, a theoretical study of Q-DDQ and Q-TCNQ is presented based on density functional theory (DFT).
## 4 Results and Discussion
### Sound Transmission Loss
As an example, Figure 14 reports the bending stiffness curves (\(x\)-direction, \(y\)-direction and averaged) obtained for a strongly-orthotropic honeycomb material with \(h_{l}\) = 1 mm, \(h_{c}\) = 8 mm and \(\mu\) = 6.2 kg m\({}^{-2}\), whose honeycomb footprint on the laminate can be observed in Figure 15b, as opposed to the almost isotropic structure tested in the rest of the work (Figure 15a). Figure 14: Apparent bending stiffness (\(x\)-direction, \(y\)-direction and composition) for a strongly-orthotropic structure. Figure 13: Sound transmission loss predicted (solid line) vs. measured in sound transmission suites (bullets). Figure 16 shows the sound transmission loss computed by considering the structure as an isotropic panel with an equivalent, 'average' apparent bending stiffness (Figure 16a) or by computing a \(\theta\)-dependent apparent bending stiffness and integrating the sound transmission coefficient over the in-plane angle (Figure 16b). It can be observed that the latter approach allows one to account for the composition of the direction-dependent effects, resulting in a flatter, wider coincidence region.
## 3 Results and Discussion
### The 2018 \"Plowing\" Event
Figure 5: **(a)** Google Earth overview of the area where the 27/01/2018 incident occurred (VHR image © 2018 DigitalGlobe), and **(b,c)** comparison of Sentinel-2B: **(b)** pre-event (18/01/2018); and **(c)** post-event (28/01/2018) true colour images (Copernicus Sentinel-2 data © 2018 European Space Agency, ESA). The deep tire marks left by the truck during the incident are indicated in the post-event scene, which shows a local increase in surface reflectance.
## Materials and Methods
### Optical Visualization of Cancer Vascular Remodeling after Antiangiogenic Therapy  
Angiogenesis is a key driver of growth and metastatic cancer spread [23]. However, the anti-angiogenic concept of cancer therapy posits that anti-angiogenic drugs should restore oxygenation in the presence of proper vascular remodeling [24]. Visualization of vascular remodeling and oxygenation is, therefore, an intriguing approach for exploring normalization in the cancer microenvironment after initiation of antiangiogenic drugs [25]. TRS can be a powerful tool to monitor vascular remodeling, since visualization of breast cancer hemodynamics can help elucidate the mechanisms underlying changes in vascularity and tissue oxygenation [26]. Bevacizumab, an antibody against endothelial growth factor, is well-known as a key drug in patients with advanced and/or metastatic breast cancer [27; 28]. Thus, we conducted a clinical study in 28 patients with advanced stage 3/4 breast cancer using the standard regimen of bevacizumab (10 mg/kg body weight), administered intravenously on days 0 and 14, in combination with paclitaxel (90 mg/m\({}^{2}\) body surface area), administered on days 0, 7, and 14 in every cycle (Figure 2B) [29]. To evaluate the physiological effect of bevacizumab alone, paclitaxel was omitted on the first day of the first cycle. Patients continued the regimen for six cycles unless disease progression or unacceptable toxicity precluded chemotherapy continuation. Patients underwent serial FDG-PET/CT scans at baseline and after two cycles of chemotherapy. Then, the change in tumor maximal standard uptake value (SUVmax) was evaluated. The tumor metabolic response was classified based on the change in SUVmax, with a cutoff value of \(-20\%\) used to categorize patients as either responders (change in SUVmax > 20%; \(n=18\)) or non-responders (change in SUVmax < 20%; \(n=10\)). Patients also underwent repeat TRS measurements every day during single-agent bevacizumab, and we observed changes in Hb and SO2 in the tumor at baseline, and on days 1, 3, 6, 8, and 13. For the clinical study, we hypothesized that if vascular normalization occurs after successful remodeling, tumor tHb should decrease with a simultaneous increase in SO2 levels.
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 3 The Proposed Method
\[Rt_{12}(r,c)=\frac{N_{12}}{Card(R_{1})+4} \tag{4}\]  
\[N_{12}=Card((x_{l},y)\in R_{1}|I_{DoG}(x_{l}-1,y)>I_{DoG}(x_{l},y))+\] \[Card((x_{r},y)\in R_{1}|I_{DoG}(x_{r}+1,y)>I_{DoG}(x_{r},y))+\] \[Card((x,y_{u})\in R_{1}|I_{DoG}(x,y_{u}-1)>I_{DoG}(x,y_{u}))+\] \[Card((x,y_{d})\in R_{1}|I_{DoG}(x,y_{d}+1)>I_{DoG}(x,y_{d})) \tag{5}\]  
where \((x_{L},y)\) and \((x_{R},y)\) represent pixels of the first and the last columns in \(R_{1}\), and \((x,y_{U})\) and \((x,y_{D})\) are the pixels of the first and the last rows in \(R_{1}\). Equation (5) indicates that each corner element in \(R_{1}\) has been compared two times, so the total number of pixels in (4) is given by \(Card(R_{1})+4\) rather than \(Card(R_{1})\). Having the ratios of \(Rt_{10}\) and \(Rt_{12}\), we compare them with two predetermined thresholds \(th_{10}\) and \(th_{12}\), and use _Rule_ 2 to detect a true small target. Finally, _Rule_ 3 is applied to both target regions and non-target regions to further enhance targets while suppressing non-targets. _Rule_ 2: If \(Rt_{10}>th_{10}\) and \(Rt_{12}>th_{12}\), then the corresponding pixel position (\(r\), \(c\)) is the center of a small target. Figure 4: The intensity distribution of two real small IR targets in a DoG-filtered image. **Rule 3**: If \((r,c)\) is the center of a small target, then use (6) to enhance it; otherwise use (7) to suppress it  
Figure 2:0  
Figure 2:1  
where \(I_{Final}(r,c)\), \(I(r,c)\) and \(I_{DoG}(r,c)\) represent the final enhanced IR image, the raw IR image, and the DoG-filtered image, max(.) and min(.) are the maximum and the minimum functions. Since small targets in both a raw IR image and DoG-filtered image are usually brighter than their surroundings, the term \(I(r,c)\times I_{DoG}(r,c)\) in (6) and (7) can enhance targets while suppressing backgrounds. Moreover, since both \(Rt_{10}\) and \(Rt_{12}\) are positive numbers smaller than 1, the term \(\sqrt{\max(Rt_{10},Rt_{12})}\) in (6) is always bigger than the term \((\min(Rt_{10},Rt_{12}))^{2}\) in (7). Therefore, by applying (6) and (7) to targets and non-targets, we can get a final image with salient targets. As a result, the target is easily detected by assigning the pixel with the maximum intensity as the target center. Algorithm 2 gives the details of our method for detecting small IR target. ```
1:Input : \(I\), \(I_{DoG}\)--raw IR image and the corresponding DoG image \(th_{10}\), \(th_{12}\)--thresholds \(H\),\(W\)--the height and the width of \(I\)
2:Output : (\(r_{tar}\), \(c_{tar}\))--target center
3:for\(r=1\) to \(H\)do
4:for\(c=1\) to \(W\)do
5:if\((r,c)\) is the center of a candidate target then
6: compute \(R_{10}\) and \(R_{12}\) with (3)-(5). 7:if\(R_{10}>th_{10}\) and \(R_{12}>th_{12}\)then
8: use (6) to get \(I_{Final}(r,c)\)
9:else
10: use (7) to get \(I_{Final}(r,c)\)
11:endif
12:endif
13:endfor
14:endfor
15:\([r_{tar}\)\(c_{tar}]=\max\limits_{1\leq r\leq H,1\leq c\leq W}\left(I_{Final}(r,c)\right)\)
```  
**Algorithm 2** Our method for detecting small IR target.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 2 System Architecture
### Key Parameters Analysis
#### 2.1.3 System Attenuation  
The number of switch elements (\(1\times 2\) switch), splitter elements (\(1\times 2\) splitter), and the delay value of the true-time delay units in a given optical path determine the attenuation. The worst-case attenuation was for the highest path loss. Before the comparison, we assumed that the insert loss caused by the true-time delay line was constant and negligible. We set the insert loss of the switch and splitter elements as \(X\) and \(L\), respectively.  
In the structure DP, the number of splitter elements in the optical path depends on the current working mode, and the number of switch elements depends on the sequence number of the output ports. When the system works on the \(m\)-beam mode, the number of splitter elements in the path is \(\log_{2}(m)\), the maximum and the minimum number of switch elements is \(2\cdot\log_{2}[\log_{2}(M)+1]\) and \(2\cdot\log_{2}[\log_{2}(M/m)+1]\), respectively. Thus, the highest loss is \(L\cdot\log_{2}(M)+2X\cdot\log_{2}[\log_{2}(M)+1]\) when the system is working on the \(M\)-mode and the beam signal outputs from ports 1. But the lowest-loss path depends on the relationship between \(X\) and \(L\). Although the path within the output has the same attenuation, the path between the output ports has a large difference. The differential attenuation between output ports becomes larger when the system works on the mode which divides the optical power into more parts.  
In the structure EP, the lowest-loss path includes \(\log_{2}[\log_{2}(M)+1]+1\) switch elements when the system works in the single-beam mode. The highest-loss path includes \(\log_{2}[\log_{2}(M)+1]+1\) switch elements and \(\log_{2}(M)\) splitter elements when the system works in the \(M\)-beam mode. Thus, the differential attenuation in the structure DP can be expressed as \(L\cdot\log_{2}(M)\). Compared to the structure DP, the structure EP has less differential attenuation, which is caused only by splitters and is inevitable.
## 3 Results and Discussion
### Study of Geometric Parameter Distortion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Cross Unit Cell**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Jerusalem-Cross** \\ **Unit Cell** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Split Ring Resonator** \\ **Unit Cell** \\ \end{tabular} } \\ \hline \(t\in\)0.5-2.5\(\cdot\) mm & \(f_{\tau\,\mathrm{max}}\) [GHz] & 6.020 & 6.156 & 6.600 \\ \(t\in\)0.5-2.5\(\cdot\) mm & \(f_{\tau\,\mathrm{min}}\) [GHz] & 4.626 & 4.486 & 4.358 \\  & \(\Delta f_{\tau}\) [GHz] & 1.394 & 1.670 & 2.243 \\  & \(f_{\tau\,\mathrm{max}}\) [GHz] & 5.181 & 5.236 & 5.698 \\ \(t=\) 1.5 mm & \(f_{\tau\,\mathrm{min}}\) [GHz] & 4.825 & 4.675 & 4.455 \\  & \(\Delta f_{\tau}\) [GHz] & 0.356 & 0.561 & 1.243 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Resonance frequency variation under the influence of unit cells geometric parameters. Figure 11: Resonant frequency, \(f_{\tau}\), as a function of the strain caused by mechanical excitation (dielectric laminate thickness \(t=\) 1.5 mm). Figure 10: Reflection coefficient modulus in the resonance state \(|\)s\({}_{11}\)(\(f_{\tau}\))\(|\) distribution as a function of slot width, \(w\); and resonator to resonator distance, \(d\), in cases of dielectric laminate thickness \(t=\) 1.5 mm: (a) cross cell geometry, (b) Jerusalem cross cell geometry, and (c) split-ring resonator cell geometry. for example, metasurface fabrication quality. Despite this, the analysis was carried out, and its results were presented in Figure 10. One can observe similar distributions of \(|s_{11}(f_{\rm r})|\) in all of the evaluated structures. The resonant amplitude decreased (tends to 0) quasi-linearly with the increase of distance \(d\). This effect can be easily explained--a lower spatial density of unit elements in the metasurface causes a lower power/amplitude response. For the CR and JCR structures, an increase in \(w\) causes a higher amplitude \(|s_{11}(f_{\rm r})|\). A similar relation can be observed in the case of the SRR structure, only for lower values of \(d\), this relation was inversed.