esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm
## 5 Battery Model Parameter Identification Based on Improved Sparrow Search Optimization Algorithm
### Experimental Results
#### 5.2.1 Parameter Identification Results under HPPC Test  
Table 5 shows the battery parameters identified by six different algorithms under the HPPC test. Table 6 shows the estimation errors of the terminal voltage with six different algorithms. The MAE, RMSE, MaAE and SSE are used to reflect the accuracy of the cell model. Clearly, in terms of SSE, MAE, RMSE and MaAE, CQSSA has the best accuracy while GA has the worst accuracy. Compared with SSA, CQSSA has 4.3%, 5.9% and 11.5% improvement in MAE and MaAE, respectively. Figure 5 shows the graphical comparison of terminal voltages. It can be seen that the CQSSA has the smallest error, which is consistent with the above analysis. In addition, we observed that the maximum absolute error usually occurs when the input current has a sudden change. Therefore, the smallest MAE and MaAE states that the model parameters identified by CQSSA not only perform optimally in the average error but also maintain a smaller error in the current loading and unloading stage.  
Figure 6 shows the iterative processes of the six algorithms. Obviously, the fitness values (SSE) all gradually converge. However, QSSA converges to under 0.4 V\({}^{2}\) with the least number of iterations. Furthermore, the final fitness value is also the smallest, revealing the highest convergence accuracy.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 6 Conclusions  
Most of the papers related to WSN do not include quantitative results about the impact of the WSN's deployment in the field [41]. In this paper, we presented a low-cost system based on LoRa technology capable of taking measurements of the parameters most used in precision agriculture, such as humidity and ambient temperature, light received by the plant, and soil temperature, soil moisture, and EC.  
The results obtained show better consumption than that of commercial wireless sensors. The consumption of the node was measured in its different operating states, and the battery discharge curve was obtained, achieving, if measurements were taken every 30 min, an approximate battery duration of 724 days. All the consumption required by data acquisition and sending to the gateway was taken into consideration.  
In order to increase the battery life and take into account the discharge effect, a solar panel could be installed in the node and evaluation performed on how it affects the consumption of the battery.  
Another line of work is related to the type of battery installed. In this case, the operation of the node can be compared with another type of power, such as alkaline or lithium-ion batteries.  
We are aware that more work is needed on energy consumption, both from a theoretical and practical point of view. The analysis of gateway consumption is another interesting topic for agriculture, especially in remote areas or developing regions.  
Finally, a more detailed study on the evolution of NB-IoT technologies versus LoRaWAN and SigFox should be analyzed.  
Conceptualization, J.D.B.; Methodology, J.D.B.; Funding acquisition, A.Z.; Implementation and prototyping, G.F.; Writing-original draft, J.D.B. and Writing-review and editing, J.D.B., A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was founded by Department of Agroforestry Science - University of Huelva.  
The authors thank Bo True Activities SL -a spin-off of Huelva University- for supporting the materials and equipment used for experiments, and [PERSON] for his helpful advices to build the device.  
The authors declare no conflict of interest.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Communication Range for Lossless Transmission Mediums
#### 3.2.2 Derivation of the Coil-to-AMR Transmission Range  
If an AMR detector is used, the maximum transmission distance depends on the magnetic flux density \(B\) at the receiver side [17]:  
\[B=\frac{\mu_{0}\mu_{\rm r}NIr_{\rm T}^{2}}{2d_{\rm cAMR}^{3}}, \tag{7}\]  
where \(\mu_{0}=4\pi\times 10^{-7}\) is the magnetic field constant, \(N\) the number of turns of the transmitter coil, and \(I\) the electrical current through the transmitter coil. Accordingly, the transmission range \(d_{\rm cAMR}\) is  
\[d_{\rm cAMR}=\sqrt[3]{\frac{\mu_{0}\mu_{\rm r}NIr_{\rm T}^{2}}{2B}}. \tag{8}\]  
If \(B\) is replaced by the detection threshold of the magnetic sensor, \(B_{\rm min}\), the maximum communication range is obtained.
# Application of Ultrasound Homogenization in Milk Ice Cream Mixes
## 3 Results and Discussion
### The Stability of Milk Ice Cream Mixes
It is proven that not only the conditions of ultrasound should be taken into consideration but also the exact amount of stabilizers to obtain a stable emulsion. Figure 1a-c present an overview of the stability of emulsion during 24 h of maturation. Based on results from backscattering, it can be observed that in the milk ice cream mixes after ultrasound homogenization, one destabilization phenomenon occurred--coalescence/flocculation (Figure 1c). This means that the sizes of the particles increase or they create the aggregation of particles. On the other hand, samples after the mechanical homogenization were prone to sedimentation, which is usually identified with the migration of particles (Figure 1b). Interestingly, in control samples (CH and CU) without stabilizers, only the coalescence/flocculation was noted regardless of the sort of homogenization (Figure 1a). Therefore, it can be concluded that the addition of stabilizers in the recipe of milk ice cream mixes may have contributed to the type of destabilization. Different authors, [PERSON] and [PERSON] [32], measured peanut protein-stabilized emulsions after HUI (high-intensity ultrasound), which remained stable for three weeks. Only a slight phase separation was observed while the same emulsions after HPH (high-pressure homogenization) creamed more notably. The reason for such observations was the fact that after ultrasound treatment, emulsions formed a higher interfacial protein concentration and viscosity. Other researchers have highlighted that the addition of emulsifiers may lead to an increase in fat destabilization. This is on the grounds that if emulsifiers in the emulsions of ice cream mixes are incorporated into the fat globule membrane instead of proteins, the emulsions become more unstable. However, this effect can be improved by the maturation step while fat crystallization and protein desorption have occurred. It causes the weakening of the whole structure of the fat globule [18; 20]. Therefore, in the presented paper, the addition of an emulsifier might have been a reason for the destabilization of all prepared samples of ice cream mixes.
## 1 Introduction
First, all of them are focused on different regions of Africa, and some of them include data from Asia and the Middle East, using data from the FAO desert locust database [8] as the main target source of data. Second, all these previous approaches have formulated the prediction problems as classification tasks, in many cases as binary classification problems (appearance or non-appearance of locusts or locust breeding areas, etc.). In all the problems reviewed, the small number of registers in the database and the scarce observations present important problems, which have been attempted to be solved by including \"pseudo-absenses\" [23], an attempt to extend the available databases with synthetic data, such as in [20; 23]. However, this method may introduce biases into the prediction models, as pointed out by some authors [28]. Finally, all the works previously described use similar predictive variables, related to weather variables and soil moisture data. In this paper, an alternative methodology to tackle desert locust prediction problems is proposed. For this purpose, classification-regression tasks are considered (and not only classification approaches) with ML schemes. The problem is focused on the Western Africa region, and the presence-only data are considered in three large breeding zones, in which different ML algorithms are applied to reanalysis and NDVI data. Firstly, classification approaches are applied in order to predict the presence/non-presence of locusts in each zone, in a monthly time horizon. Secondly, a regression step is addressed, in which the number of monthly visualizations of desert locusts in each zone is predicted. The results obtained show that a successful joint prediction of the occurrence and number of locust visualization events is possible with ML classification-regression schemes. The main contributions of this work can be summarized as follows:  
* An ML-based classification-regression approach is proposed to deal with the problem of desert locust detection in Western Africa. * The classification task involves a binary detection problem (yes/no) based on weather variables from reanalysis and NDVI data. * A regression task which deals with the number of locust sightings in each study zone is proposed, using similar predictive variables to those of the classification problem. * Experiments based on real sighting data from Western Africa have shown the potential of the proposed classification-regression approach. The rest of the paper has been structured in the following way: The next section describes the methodology proposed, including the data available, the predictive variables and a summary of the ML algorithms considered. Section 3 presents the experiments carried out and the results obtained, with a discussion on different aspects of the application of ML classification-regression algorithms to the problem at hand. Section 4 closes the paper with some final remarks on the research carried out.
## 3 Results
### Natural Frequencies and Damping Ratios  
Figures 10 and 11 show the variations in the first four natural frequencies and their corresponding damping ratios for all the specimens in the different groups, respectively. The fundamental natural frequency of Group 1 (with central hole) is slightly lower than for Group 2 (no central hole) for all the samples, but Group 1 shows a slightly higher damping, due to the presence of the center holes in the middle of the specimens. In both groups, a frequency increase is observed as the specimen width also increases. This indicates that the W/D aspect ratio does not play a significant role and the modal behavior is governed by specimen stiffness and mass in spite of the fact that the size effect reduces nominal strength up to 15% [19].  
Figure 8: Mode shapes for a single specimen (W = 24 mm, D = 8 mm) in Group 3.  
Figure 9: Mode shapes for a single specimen (W = 30 mm, D = 8 mm) in Group 4.  
Interestingly, the change in center-hole diameter at constant width in Groups 3 and 4 shows a very little difference in frequency for mode 1 in all the specimens. The vibration mode 4 in all the groups show high damping values as compared to other modes. This behavior can also be seen in the waterfall plot that shows low vibration amplitudes in this mode.
## 2 Theoretical
### Clausius-Clapeyron Equation for the Dissociation Enthalpy of CO\({}_{2}\)-CH\({}_{4}\) Hydrates  
In order to investigate the thermodynamic stability of hydrates formed under different CO\({}_{2}\) mole fractions, this study used the Clausius-Clapeyron equation (Equation (8)) combined with the established multi-parameter empirical model to calculate the dissociation enthalpy (\(\Delta H_{\text{d}}\)) of CO\({}_{2}\)-CH\({}_{4}\) hydrates. In \(P\) and 1/T always exhibit a clear linear relationship under different CO\({}_{2}\) mole fractions, and the slope can be obtained through linear regression, which can then be used to calculate the dissociation enthalpy of CO\({}_{2}\)-CH\({}_{4}\) hydrates.  
\[\frac{\text{d}\text{ln}P}{\text{d}(1/T)}=-\frac{\Delta H_{\text{d}}}{\text{Z} \text{R}} \tag{8}\]  
where \(P\) represents the equilibrium pressure, in MPa; \(T\) represents the equilibrium temperature, in K; \(\Delta H_{\text{d}}\) represents the enthalpy of hydrate dissociation, in kJ/mol; \(Z\) represents the compressibility factor of the CO\({}_{2}\)-CH\({}_{4}\) mixed gas, calculated by the BWRS equation [30]; and R represents the universal gas constant, in J\(\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\).
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 3 Building Technologies
### OpenStack, Open Source MANO, and ONOS  
SN4I makes use of various software products that orchestrate and manage the physical components (physical servers and interconnected switches) that make up the infrastructure, according to the previously explained NFV and SDN technologies. These software components are OpenStack, Open Source MANO (OSM), and Open Network Operating System (ONOS).  
OpenStack implements the Virtualized Infrastructure Manager (VIM) of the NFV architectural framework previously shown in Figure 2. It also provides a common virtualization layer to the hardware servers in the NFVI it manages in order to abstract their physical resources into virtual resources. OpenStack is ultimately capable of controlling a pool of virtual resources and allocate different kinds of resources from it in order to instantiate Virtual Machines (VMs). OpenStack can also manage the life cycle of these VMs, as well as set up their internal means of interconnection. A VM holds a component of a VNF (a VNF can contain one or more VMs) and can be exposed and perceived by an end user just as a real dedicated computer system.  
Open Source Mano (OSM) is, as its name implies, the MANO of SN4I. OSM is an ETSI NFV-aligned Management and Orchestration software that provides an upper layer to the OpenStack services. OSM provides the SN4I infrastructure managers with a tool to design and build NSs and VNFs composed of eventual VMs, as well as Virtual Links and Connection Points between their components. OSM is then responsible for interacting with OpenStack in order to realize the components and interconnections of the designed NSs into the NFVI. OSM exposes interfaces to manage the life cycle of these NSs and VNFs and includes rich features for their monitorization, as well as for the configuration and automation of their functionality.  
Finally, Open Network Operating System (ONOS) performs as the controller of the SDN network infrastructure, which comprises both inter-VIM and intra-VIM connections. In SN4I, ONOS, through a protocol named OpenFlow, populates into the data plane devices the forwarding rules that make possible the interconnection of all the components of a given process (i.e., a given NS), while simultaneously ensuring that they are isolated from other processes' traffic. Furthermore, ONOS can be integrated with OSM in order to automatically install these forwarding rules that correspond to the NSs' Virtual Links.
## 3 Frequency Response  
We employed the method of separation of variables to decompose \(W_{0}\) into time and spatial domain, defining it as:  
\[W_{0}(x,\tau)=X(x)Y(\tau) \tag{28}\]  
By substituting Equation (28) into Equation (26) and incorporating the boundary conditions, we derived the characteristic equation:  
\[\alpha^{3}(-1-\cos\alpha\cosh\alpha)+M(-\sin\alpha\cosh\alpha+\cos\alpha\text {sinh}\alpha)=0 \tag{29}\]  
where \(\alpha\) represents the system eigenvalue. The mode shape can then be expressed as:  
\[\phi_{n}(x)=-\sin\alpha_{n}x-\text{sinh}\alpha_{n}x+\frac{\sin\alpha+\text{ sinh}\alpha}{-\cos\alpha-\cosh\alpha}(-\cos\alpha_{n}x-\cosh\alpha_{n}x) \tag{30}\]  
Then, the general solution of \(W_{0,1}(x,\tau)\) can be expressed in terms of mode shape and generalized coordinate as follows:  
\[W_{0}(x,\tau)=\sum_{n=1}^{\infty}\xi_{0n}(\tau)\phi_{n}(x) \tag{31}\]  
\[W_{1}(x,\tau)=\sum_{n=1}^{\infty}\xi_{1n}(\tau)\phi_{n}(x) \tag{32}\]  
where \(\xi_{0n}(\tau)=B_{n}(T_{1})e^{-i\zeta_{n}\varepsilon^{i}\omega_{n}T_{0}}+ \overline{B}_{n}(T_{1})e^{-i\zeta_{n}\varepsilon_{n}}e^{-i\omega_{n}T_{0}}\), \(\xi_{n}\) is the phase angle, and \(B_{n}\) represents the amplitude of the nth mode. Substituting Equations (31) and (32) into Equations (26) and (27) and then using orthogonal property, the following equation can be obtained:  
Order of \(\varepsilon^{1}\):  
\[2\xi_{0m}{}^{**}+\alpha_{m}{}^{4}\xi_{0m}=0 \tag{33}\]  
Order of \(\varepsilon^{3}\):  
\[ \begin{array}{l}2\xi_{1m}{}^{**}+\alpha_{m}{}^{4}\xi_{1m}=\!\!-4\frac{ \partial}{\partial T_{1}}\xi_{0m}{}^{*}-\mu\xi_{0m}{}^{*}\\ -\frac{1}{Q_{m}}\xi_{0i}\xi_{0j}\xi_{0k}(\int_{0}^{1}\phi_{n}\phi_{n^{\prime}} \phi_{j^{\prime}}\phi_{k^{\prime\prime}}dx+\int_{0}^{1}\alpha_{m}{}^{4}\phi_{ m}\phi_{j}\phi_{k^{\prime}}\phi_{mk}dx+4\int_{0}^{1}\phi_{m}\phi_{i^{\prime}} \phi_{j^{\prime\prime}}\phi_{k^{\prime\prime\prime}}dx)\\ -\frac{1}{2Q_{m}}(\xi_{0i}\xi_{0j}{}^{**}\xi_{0k}+\xi_{0i}\xi_{0j}{}^{*}\xi_{0k }{}^{*}+\xi_{0i}\xi_{0j}{}^{*}\xi_{0k}{}^{*}+\xi_{0i}\xi_{0j}\xi_{0k}{}^{**}) \left\{\int_{0}^{1}\phi_{m}[\phi_{n^{\prime}}\!\int_{1}^{x}\int_{0}^{x}\phi_{j ^{\prime}}\phi_{k^{\prime}}dxdx+\phi_{i^{\prime}}(\int_{1}^{x}\int_{0}^{x} \phi_{j}^{\prime}\phi_{k}^{\prime}dxdx)^{\prime}]\right\}\\ +F_{M}\!+\!\frac{1}{Q_{m}}\frac{\hbar q^{2}}{e^{kpt}}\int_{0}^{1}\phi_{m}[\int_ {b}^{a}\phi_{i^{\prime}}\xi_{0i}dx\int_{0}^{x}(\int_{b}^{a}\xi_{0j^{\prime \prime}}\phi_{j^{\prime\prime}}dx)e^{k\tau}d\tau]dx\end{array}  \tag{34}\]  
where \(Q_{m}\) represents \(\int_{0}^{1}\phi_{m}{}^{2}dx\). By examining Equations (33) and (34), it becomes apparent that the \(\xi\) term involves the interplay of modes within this beam. Consequently, we will delve into the analysis of coupling effects among the first three modes of this elastic beam.
# THz Rectennas and Their Design Rules
## 5 Power Level
### Signal Amplitude
While geometric diodes are by nature asymmetric, tunneling diodes, for example, can be made asymmetric by using different metals for the electrodes [50; 56; 87]. Lastly, power level is closely connected to the frequency of operation. Therefore, the design should be aware of the both as shown in Section 3.
## 5 Findings and Discussion, Contribution, Limitations and Future Work of The Study
### Limitations and Future Work of The Study  
This research explores the spatial distribution of the community structure. However, the analysis leaves two issues, which can be addressed in future research. First, there is no commonly accepted standard to evaluate the detection of communities [22]. Second, this study targets the dynamics of a static network. A study on the temporal network will become more meaningful in evaluating the epidemic outbreak and traffic dynamics, especially during the pandemic [40]. Therefore, further research is necessary to fill these gaps.
## 4 Evaluation Results
### Grout-Injection Material Physical Property Change Measurement Comparison Results
#### 4.2.2 Epoxy-Resin Grout-Injection Material  
The physical property changes of the three different epoxy-resin grout-injection specimens were mutually compared. The results are shown that the three epoxy resin products are all relatively susceptible to physical property changes under the effect of chemical exposure and substrate behavioral stress. Product A, in particular, showed a higher stability than the other two products with regards to the material degradation factors. Refer to the below radar chart in Figure 14.
## 5 Results and Discussions
### Performance Measures  
The performance measures, such as encryption time, decryption time, downloading time, uploading time and reputation, are discussed in the following section.  
* Encryption time is the time taken for the encryption algorithm to create the cipher text from the plain text. It is employed to compute the throughput of an encryption method. It represents the speed of the encryption.
* Decryption time is the time taken to convert the encrypted data into the original data is called decryption time. It is the reverse scheme for the encryption. Decryption decodes the encrypted data so that the authorized user can only decrypt the data using a secret key or password.
* Uploading time is time taken to transmit the data from one computer system to another system through the network.
* Downloading time is the time taken to download any page linked with the services, including the entire content contained in the page.
# Novel Aromatic Estolide Esters from Biobased Resources by a Green Synthetic Approach
## 3 Results and Discussion
### Structural Characterization of the Estolide Ester Products  
#### 3.3.1 FT-IR Analysis  
FT-IR spectrometry can provide structural information that is needed to certify the formation of the ester bonds, particularly of the aromatic esters at the carboxy or hydroxy end of the ricinoleic acid oligomer. Figure 2 presents the superimposed FT-IR spectra of the reaction product of RCA with caffeic acid and of the starting materials. The FT-IR spectra of the other two ricinoleic estolide aromatic esters, with sinapic acid and cinnamyl alcohol, respectively, are shown in the Supplementary Materials (Figures S2 and S3), together with the assignment of the relevant absorption bands.  
The broad v_(O-H) stretching bands confirm the presence of aliphatic and aromatic hydroxyl groups in the product. The phenolic OH stretch is usually recorded at higher wavenumbers compared to the OH stretching vibration of aliphatic alcohols. The two rounded shape peaks in the 3200-3400 cm\({}^{-1}\) region of the product can be assigned to the aromatic OH group of the caffeic acid moiety, demonstrating the esterification with this acid. In the same time, the strong v_(C-H) stretching bands at 2923.8 cm\({}^{-1}\) in both the RCA and product spectra indicate the presence of the aliphatic chains of the ricinoleic acid moiety also in the product. The shift of the v_(C = O) stretching vibration band to 1731.9 cm\({}^{-1}\) from 1708.7 cm\({}^{-1}\) of the carboxyl group in the RCA spectrum and 1641.2 cm\({}^{-1}\) of the carboxyl group in the caffeic acid spectrum confirms the formation of an ester product. The bands in the 1450-1600 cm\({}^{-1}\) region can be assigned to the aromatic v_(Ar) and olefinic v_(C = C) stretching vibrations as also being identified in the FT-IR spectrum of caffeic acid. The v_(C-C-O) stretching vibrations in the ester linkages were identified at 1211.17 cm\({}^{-1}\) in the product's spectrum.  
The FT-IR spectra demonstrate the enzymatic synthesis of oligoesters from ricinoleic acid and caffeic acid. The changes in the spectra, such as the shift in the carbonyl stretching, the decrease in hydroxyl group intensity and the appearance of new ester C-O stretching peaks provide clear evidence of esterification. This transformation suggests that the product contains ester linkages, confirming the successful formation of oligoesters.