esa-sceva/satcom-chunk-collection huggingface dataset:
# A Passively Q-Switched Holmium-Doped Fiber Laser with Graphene Oxide at 2058 nm
## 2 Characterization and Preparation of the GO-Based Saturable Absorber  
The conventional, low-cost GO was obtained from a commercial company for this experimental demonstration. The measured Raman spectrum of the GO particles is illustrated in Figure 1a. The D band and G band were observed at 1348 and 1592 cm\({}^{-1}\), each. The D peak is known to be induced by the lattice disorder of graphene [76], while the G peak is caused by the phonon excitation at the Brillouin zone center [77]. The X-ray photoelectron spectroscopy (XPS) spectrum was also measured and the spectrum in the C 1s region of the GO particles is shown in Figure 1b. After curve fitting, it was found that the fitted C 1s peaks could be decomposed by three peaks at \(\sim\)284.8, \(\sim\)286.7, and \(\sim\)287.9 eV, which correspond to groups of C-C/C=C, C-O, and C=O, respectively. These peaks correspond to those of the previously reported values [78].  
Figure 1: (**a**) Raman spectrum and (**b**) XPS spectrum (C 1s) of the graphene oxide (GO) particles.  
Next, nonlinear transmission measurement was performed as a function of the input pulse peak power for the fabricated GO-deposited SA, using an experimental setup in Figure 3a. The incident beam was coupled into the GO-based SA in the transverse electric (TE) mode. For this measurement, we used a passively mode-locked fiber laser with a repetition rate of ~36.94 MHz and a temporal width of ~711 fs at 1902 nm. Since an ultrafast laser source operating at a 2.0-2.1-\(\upmu\)m wavelength region did not exist in our laboratory, this particular measurement was carried out at 1.9 \(\upmu\)m with our built mode-locked Tm-doped fiber laser. The nonlinear transmission curve of the GO-based SA, is illustrated in Figure 3b together with its corresponding fitting curve [79]:  
\[T(I)=1-\Delta T\cdot\exp(\frac{-I}{I_{sat}})-T_{ns}, \tag{1}\]  
where \(T(I)\) and \(\Delta T\) are the transmission and modulation depth, respectively. \(I\) and \(I_{sat}\) are the input-pulse energy and saturation energy, respectively. \(T_{ns}\) represents the nonsaturable loss. The modulation depth and saturation power were estimated at ~13.1% and ~12.5 W, respectively.
## 6 Results and Discussion
### Effect of Multi-Level and Multi-Scale Features  
To measure the effect of multi-level and multi-scale feature combination, we experimented with several settings in Table 4. The SampleCNN models are first trained on MTAT dataset, then this pre-trained networks are used as feature extractors for the MTAT dataset again. The results show that as more features are fusioned, the performance increases. This can be viewed similar to an ensemble method, however our approach is distinguished from it in that the feature aggregation is performed on activations of the hidden layers, not on the prediction values.
## 4 Overview: Signal Processing Methods
### Signals after Pre-Proccessing with Filtering  
In Figures 15 and 16, the signals are depicted after undergoing filtering, preparing them for subsequent analysis using the Discrete Wavelet Transform (DWT). Filtering plays a crucial role in enhancing signal quality by removing noise, artifacts, and extraneous components. This ensures that the application of the DWT is effective and precise.
## 3 Results
### Results of the Qualitative Study
#### 3.2.3 Environmental Perspective  
For the environmental perspective criterion, we analyzed factors such as energy efficiency, competitive pressures, image, subjective norm, industry characteristics, social impact, and privacy. Most respondents (9 out of 15) emphasized the need to preserve privacy when using cloud services. SME10 stated, \"The Montenegrin mentality has always problematized the need to ensure privacy. Despite legal solutions that clearly define norms of behavior to protect privacy, information is often exchanged informally in a way that significantly compromises the privacy of individuals and organizations\". This highlights the lack of enforcement and sanctioning of legally prescribed standards.  
Industry characteristics and social impact were also recognized as significant factors influencing the use of cloud services. Respondents from industries such as tourism highlight the importance of cloud services for efficient analysis of customer demands and specific markets, thereby enhancing their competitiveness. SME13 emphasized, \"When planning a new tourist season, we use services to analyze the tourism potential of specific markets.  
Based on that information, we set prices and make contracts. Storing data and using BI services on such cloud systems would significantly increase the risks of losing competitive position and know-how resources\".  
Competitive pressure was another important factor highlighted by two respondents. SME7 mentioned difficulties with international competitors, stating, \"Foreign companies have greater access to and use of modern technological solutions, and we are increasingly unable to keep up with this trend, thus losing certain markets\".  
The least mentioned ecological factor was energy efficiency. Only two respondents indicated that using cloud services significantly reduces electricity consumption and thus helps protect the environment. It is also understandable that many SMEs do not have a well-developed awareness of environmental preservation, considering their impact (both positive and negative) on maintaining a healthy environment to be minor.
## 3 Results and Discussion
### Sorption Study
#### 3.1.2 Effect of Contact Time  
The influence of contact time on the adsorption efficiency of Y(III) from their solution by AHIIBC was performed in the range from 5 to 120 min, whereas the adsorption factors were fixed at 50 mL volume of 200 mg\(\cdot\)L\({}^{-1}\) of Y(III), pH 6.0 at 25 \({}^{\circ}\)C temperature, and 50 mg of adsorbent dosage.  
As seen in Figure 1b, after 5 min of contact time, the amount of Y(III) adsorbent was 22.65%. There was a gradual increase of the sorption efficiency of Y(III) from 22.65 to 85.66% with increasing the agitation time to 60 min. However, increasing the contact time up to 120 min has no noticeable impact on the adsorption efficiency. Consequently, the equilibrium time for adsorption of Y(III) on AHIIBC was achieved after 60 min. In view of that, the ideal contact time considered for advanced studies is 60 min.
## 5 Design Choices for OpenFlow-Based SDN
### Control Plane: In-Band vs. Out-of-Band Signaling  
In the following, we will discuss the communication between the control elements and the forwarding devices on the data plane. This control channel has to be secure and reliable. In SDN-based data center networks, this control channel is often built as a separate physical network in parallel with the data plane network. Carrier and ISP networks have different requirements. They often span over a country or a continent. Therefore, a separate physical control network might not be cost-efficient or viable at all. Thus, two main design options for the control channel exist: in-band control plane and out-of-band control plane.  
With in-band control, control traffic is sent like data traffic over the same infrastructure. This is shown in Figure 8(a). This variant does not require an additional physical control network, but has other major disadvantages. Firstly, the control traffic is not separated from the data traffic, which raises security concerns. Failures of the data plane will also affect the control plane. Thus, it is possible that a failure disconnects the switch from its control element, which makes the restoration of the network more complicated.  
Out-of-band control requires a separate control network in addition to the data network, as illustrated in Figure 8(b). This is a common approach in data centers that are limited in geographical size. In the data center context, the maintenance and cost of an additional control network is usually acceptable. This may be different for wide-ranging networks, such as carrier networks, where a separate control network can be costly with regard to CAPEX and OPEX. The advantages of an out-of-band control plane are that the separation of data and control traffic improves security. Data plane network failures do not affect control traffic, which eases network restoration. Moreover, a separate control plane can be implemented more securely and reliably than the data plane. This ensures the high availability of the control plane and can be crucial for disruption-free network operation.  
A possible approach that combines in-band and out-of-band control planes for carrier networks is based on the control planes in optical networks, such as synchronous optical network (SONET) or synchronous digital hierarchy (SDH) and optical transport networks (OTN). In such networks, an optical supervisory channel (OSC) may be established on a separate wavelength, but on the same fiber over which data traffic is carried. In a similar way, a dedicated optical channel could be allocated for SDN control traffic when an optical layer is available. Furthermore, other lower layer separation techniques may be used to implement separated control and data networks over the same physical infrastructure.
## 2 The Proposed Method
### GSC Method with Sidelobe Neutralization
#### 2.3.2 Beamforming Sidelobe Neutralization
\(v_{3}\) is assumed to be the inaccurately estimated noise direction, and Equations (20) and (21) can be modified as follows:  
\[y_{v3} =\sum_{m=1}^{M}\omega_{(s3,m)}^{*}x_{m}=\sum_{m=1}^{M}v_{(3,m)}^{* }s_{1}v_{(1,m)}+v_{(3,m)}^{*}s_{2}v_{(2,m)}=u_{31}s_{1}+u_{32}s_{2} \tag{22}\] \[y_{v3s1} =y_{v3}-u_{31}y_{v1}=u_{31}s_{1}+u_{32}s_{2}-u_{31}s_{1}-u_{31}u_{ 12}s_{2}=s_{2}(u_{32}-u_{31}u_{12})\] (23) \[x_{m}=Sv=s_{1}\
u_{(1,m)}+s_{2}\
u_{(2,m)} \tag{15}\]0  
\[x_{m}=Sv=s_{1}\
u_{(1,m)}+s_{2}\
u_{(2,m)} \tag{15}\]1  
where \(r_{v3}\) is the ratio of the noise signal processed by the sidelobe neutralization method with the inaccurate noise direction to that by the beamforming method. \(r_{v3}\) can be analyzed qualitatively as follows:  
If \(u_{12}\) is small or near to zero, thus,  
\[x_{m}=Sv=s_{1}\
u_{(1,m)}+s_{2}\
u_{(2,m)} \tag{15}\]2  
Therefore, \(r_{v3}\) may be extremely big. If \(u_{12}\) is big, there are roughly two cases:  
\[x_{m}=Sv=s_{1}\
u_{(1,m)}+s_{2}\
u_{(2,m)} \tag{15}\]3  
\[x_{m}=Sv=s_{1}\
u_{(1,m)}+s_{2}\
u_{(2,m)} \tag{15}\]4  
Assume an instance, in which \(\
u_{1}\) is \(0^{\circ}\) and \(\
u_{2}\) is \(60^{\circ}\), to illustrate the affection of the inaccurately estimated noise direction for the residual noise. According to Equation (25), Figure 5 shows \(r_{v3}\) with different estimated noise direction (\(v_{3}\) from \(-180^{\circ}\) to \(+180^{\circ}\)). The blue gap near 60\({}^{\circ}\) in Figure 5a shows that the noise signal processed by the sidelobe neutralization method with an inaccurate noise direction was less than that by the original beamforming method in a relatively wide direction range near the actual noise direction. The ratio in the frequency band between 1000 to 1500 Hz in all directions was high, because the sidelobe attenuation coefficient of the actual noise direction in this frequency range was close to zero (\(u_{12}\), as shown in Figure 5b). When the estimated noise direction was far away from the actual noise direction, the ratio was close to one at the greatest portion of the figure, which means that the energy of the noise signal processed by the sidelobe neutralization method was almost equal to that by the beamforming method, except in the high-frequency band in several directions. Even in these areas, the ratio was still less than two. Therefore, the conclusion could be derived that the sidelobe neutralization method could work effectively when the estimated noise direction was near the actual noise direction. When the estimated noise direction was far away from the actual noise direction, the denoising performance of the sidelobe neutralization method was similar to the beamforming method, except in the frequency range, in which the sidelobe attenuation of the actual noise direction was strong.
## 2 Literature Survey  
[PERSON] et al. [12] proposed security issues and discussed a convincing solution. In Mobile Ad hoc Networks, the nodes cannot be connected between two points because they are powerless. [PERSON] et al. [13] depicted the assortment of nodes in the remote systems.  
[PERSON] et al. [14] proposed the maximum number of nodes in the network. Basically, all the nodes will communicate and connect each other one to one in a heterogeneous environment. Each node will manage itself by preventing malicious nodes and performing the recovery process of the network in a successful manner. This paper focuses on a novel approach for the detection method by preventing and detecting the system in a secure way.  
[PERSON] et al. [15] deals with the centralization and decentralization of the nodes and the networks. It deals with the routing protocol of MANET, which has the ability to connect with nodes. The protocol in the network has the ability to adapt to environmental conditions.  
[PERSON] et al. [16] proposed a sequential probability of nodes that helps to identify the malicious nodes among the network. [PERSON] [17] approached the greedy methodology to approach the nodes and deployed the mobile sensor network. The deployment methodology was discussed in detail. A node can optimize the optimal location by using data with a history of nodes.  
[PERSON] [18] observed that when a network link fails, the shortest path estimating routing scheme creates extra energy and delay for data transfer. This research presented an efficient innovative hello-based path recovery (HBPR) routing protocol for the shortest path calculation to address this difficulty. If a link fails in the network layer during transmission, the HBPR system creates an alternate path, reducing delay time and energy usage. The unique simplified honey pot optimization (SHPO) is then developed to predict the network's hazardous nodes.  
[PERSON], [PERSON], [PERSON] [19] found that to avoid the local optimal solution, the elbow approach is merged with the silhouette coefficient before clustering, and similarity inside the cluster and dissimilarity across clusters are incorporated. Finally, the nodes are clustered using the clustering algorithm. The ring model is used to optimize the cluster head selection process by taking into account the residual energy of nodes as well as the distance between nodes and the cluster center. It effectively extends the lifetime of mobile ad hoc networks, increases network performance, and lowers packet loss rates.  
[PERSON] et al. [20] showed the different routing protocols and MANET security issues. It also discusses the OSI model and the link between security systems,the expanded and precise approach to the various protocol's aids in the development of a progressive MANET security concept. Various challenges and solutions aid in the discovery of threats' flaws and it clearly describes the MANET efficiency study. The simulation analysis aids in the understanding of protocol comparisons.
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## Materials and Methods
### Model Validation Measures  
As already described, many different combinations of regression methods, variants, and configurations were applied in the experiments to the available datasets. Their results were analyzed using the root-mean-squared error (RMSE) of the obtained predictions for the test set. This error is defined as  
\[RMSE(m)=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y(i)-y_{m}(i))^{2}}, \tag{1}\]  
where \(m\) is a given regression model, \(n\) is the number of test samples, \(y(i)\) is the ETo calculated with the Penman-Monteith method for the \(i\)-th sample, and \(y_{m}(i)\) is the estimated value for that sample using the proposed model \(m\). This parameter is a common way to compare the accuracy of different methods, where a model is better when its RMSE is lower. As an alternative, the mean absolute error (MAE) can also be used to assess the accuracy of a method, which is given by  
\[MAE(m)=\frac{1}{n}\cdot\sum_{i=1}^{n}abs(y(i)-y_{m}(i)). \tag{2}\]  
However, both RMSE and MAE by themselves are difficult to interpret unless they are compared with a range of values for predicted variables. For this reason, the mean relative error (MRE) is another good accuracy measure, since it considers the errors with respect to the ground-truth values. It is defined as  
\[MRE(m)=\frac{1}{n}\cdot\sum_{i=1}^{n}\frac{abs(y(i)-y_{m}(i))}{y(i)}. \tag{3}\]Finally, another frequently used measure is the correlation coefficient, \(R\), that expresses the linearity in a scatter plot of measured and predicted values of ETo. It can be computed as  
\[R(m)=\frac{\sum_{i=1}^{n}(y(i)-\overline{y})\cdot(y_{m}(i)-\overline{y_{m}})}{ \sqrt{\sum_{i=1}^{n}(y(i)-\overline{y})}\cdot\sqrt{\sum_{i=1}^{n}(y_{m}(i)- \overline{y_{m}})}}, \tag{4}\]  
where \(\overline{y}\) is the average of \(y(i)\) for \(i\) in \((1\dots n)\), and \(\overline{y_{m}}\) is the average of \(y_{m}(i)\) for \(i\) in \((1\dots n)\).
## 5 Conclusions  
In this work, a plasmonic refractive-index nano-sensor consisting of MIM waveguides with two symmetrical rectangular baffles, coupled with a connected concentric double rings resonator (CCDRR), has been devised, and its transmission characteristics have been studied by introducing the finite element method (FEM). The transmission spectrum of the structure showed an asymmetric and sharp shape, due to Fano resonance caused by the destructive interference between the successive wide-band mode of the two rectangular baffles on the bus waveguide and the discontinuous narrow-band mode of the CCDRR. We then analyzed the effects of the refractive index and geometric arguments on the transmission performance levels of the structure. With increases of the refractive index \(n\), external radii of the outer circular ring \(R_{1}\), and the inner circular ring \(R_{2}\), the dip point of Fano resonance showed a red shift, while with increases of coupling gap g, the dip wavelength showed a blue shift. Changes to the separation between the two rectangular baffles \(H\) and to the heights of the two baffles \(h\) had a significant effect on the line shape of Fano resonance, but not on the wavelength of the dip. The designed structure achieved an optimal performance when the structural parameters were as follows: \(R_{1}\) = 240 nm, \(R_{2}\) = 130 nm, \(H\) = 540 nm, \(h\) = 150 nm, and \(g\) = 10 nm. The sensitivity of the proposed sensor could reach 2260 nm/RIU with a high FOM of 56.5. Finally, its application in the temperature sensor field with ethanol instead of air was also studied, and the sensitivity was finetuned to 1.48 nm/\({}^{\circ}\)C. The designed structure has good application prospects in temperature sensors and other photonic devices.  
Conceptualization, S.Y. and P.L.; methodology, P.L. and X.W.; software, P.L.; validation, S.Y., X.Z., and T.L.; formal analysis, X.Z. and Y.R.; investigation, T.L.; data curation, X.W. and E.H.; writing--original draft preparation, P.L.; writing--review and editing, Y.R., X.Z., and T.L.; supervision, S.Y. and L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 10: (**a**) Transmission spectrum for the disparate temperatures of the sensor; (**b**) shifts of the dip point in Fano resonance (\(\Delta\lambda_{\mathcal{T}}\)) with a changed temperature (\(\Delta T\)).  
Funding: This work was supported in part by the National NaturalScience Foundation of China, under grants 61875250 and 61975189; in part by the Zhejiang Provincial NaturalScience Foundation of China, under grant LD21F050001; and in part by the Key Research and Development Project of Zhejiang Province, under grant 2021C03019.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 3 Numerical Example
### Comparison of the Performance Between the Proposed Method and the Traditional Approach
#### 3.3.2 Damage Detection Considering the Effect of Measurement Noise  
The damage case described in Section 3.3.1 is adopted again in this section; however, in contrast to the previous comparison, the effect of measurement noise is discussed in detail. Herein, the effects of different noise levels on the damage detection results are compared, as described in Table 3. For the sake of brevity, detailed information for only one case (1.6% noise level) is presented. As shown in Figure 7a, it is difficult to directly detect damage using the displacement ILs. Furthermore, it is impossible to utilize the difference in the displacement ILs before and after damage to identify the damage, as shown in Figure 7b. It appears that the effect of measurement noise misleads the damage detection results obtained by the traditional method. Nevertheless, Figure 7c shows that damage can still be identified clearly using the proposed method, which is clearly more effective than the traditional approach at detecting damage.  
Next, random white noise is added directly to the displacement ILs. As shown in the first four columns of Table 3, the traditional method is effective at detecting the damage only in the cases in which the noise level is less than 1.6%. In contrast, the proposed method works well for all cases in which the noise level is no larger than 4.8%. The noise level of the correlation coefficient between two displacement ILs before and after damage may be greater than the damage extent, which explains why it is difficult for the proposed method to detect damage when the noise level (5.2%) is greater than the damage extent (5%) of the bridge. Similar results are shown for the other cases with damage extents of 10% and 15%, as listed in Table 3.  
Figure 6: Comparison of the damage detection performance between the traditional method and the proposed method before and after damage without the effect of measurement noise: (**a**) displacement ILs; (**b**) difference between two displacement ILs obtained by the traditional method; (**c**) metric of the damage feature obtained by the proposed method.