esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 5 Conclusions
One important reason is the low recognition accuracy of WBFM signals, according to Figure 11. About half of WBFM signals are misclassified as AM-DSB signals. It can also be understood through signal simulation images that the IQ time-domain waveforms of these two signals are very similar, according to Figure 1. Therefore, the next step is how to distinguish these two signals more effectively to improve the overall accuracy of the model. 2. Many existing algorithms generally have poor recognition performance at low SNRs. Improving the network recognition performance at low SNR is also a problem to be solved. In the next step, we consider adding an attention mechanism to build an improved network model to improve the signal recognition accuracy at low SNRs [27; 38; 39]. Conceptualization, S.H. and Y.F. ; methodology, Y.F. ; software, S.H. ; validation, S.H. and Y.L. ; formal analysis, Y.F. ; resources, S.F. ; data curation, S.H. ; writing--original draft preparation, S.H. ; writing--review and editing, S.H. ; supervision, B.H. ; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript. This research was funded by Key Basic Research Projects of the Basic Strengthening Program, grant number 2020-JCJQ-ZD-071 and National Key Laboratory of Science and Technology on Space Microwave, No. HTKJ2021 KL504012. Not applicable. The authors declare no conflict of interest.
## 1 Introduction
### Motivations  
The BGV-type scheme, as a representative of leveled FHE schemes, offers several advantages over other types of schemes, such as flexible noise management, the ability to avoid expensive bootstrapping by predefining the homomorphic evaluation depth for any polynomial size, and not relying on unreliable circular security assumptions. However, existing mainstream BGV-type MKFHE schemes still have some drawbacks. First, these schemes only support basic multi-hop features, requiring a predefined maximum number of participating parties during the initialization phase. Second, the tensor product in homomorphic multiplication results in further expansion of the dimensions of the extended ciphertext. After this tensor product, a key switching process is necessary to convert the expanded ciphertext back into a standard extended ciphertext. Due to the special and complex structure of the evaluation keys needed for key switching in a multi-party setting, existing BGV-type MKFHE schemes require an embedded extendable auxiliary ring-GSW scheme (which can be regarded as a separate GSW-type MKFHE scheme) to compute the evaluation key generation materials. These materials are then used to extend a large joint evaluation key at each level, and any change in the set of participating parties necessitates the recalculation of these joint evaluation keys. In simple terms, the presence of this auxiliary scheme leads to redundant and complex computations as well as significant storage overhead. Moreover, after eliminating the bootstrapping procedure, the tensor product and key exchange process become the new performance bottlenecks, introducing a square-level complexity related to the number of parties.  
Additionally, the most efficient FHE schemes today are those that support batch techniques and allow SIMD (Single Instruction, Multiple Data) style operations. By utilizing ciphertext packing, such schemes pack multiple plaintexts into an equal number of independent \"slots\" within the plaintext space. However, existing mainstream MKFHE schemes [16; 17; 19] that theoretically support batching only make a simple claim that their schemes inherit the batch-processing capability of basic SKFHE schemes, without providing a detailed explanation of how to design ciphertext packing techniques to aggregate multiple plaintexts or how to specifically construct a batched MKFHE scheme.
## 4 Types of Catalysts Used in Transesterification
### Homogeneous Catalysts
#### 4.1.2 Homogeneous Acid Catalysts  
These catalysts are more tolerant to the moisture content in reaction mixture. In order to decrease the FFA content of oil and fats, the homogeneous acid catalysts are used before transesterification with alkaline catalyst. The reactions catalyzed by these catalysts require high alcohol to oil ratio and high temperature, hence they are undesirable over alkali catalysts. The most common homogeneous acid catalysts are sulfonic acid (HSO\({}_{3}\)R), sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) and hydrochloric acid (HCI). These catalysts reduce high FFA content of cooking oil and animal fat, and they require specialized but expensive processing equipment is required due to the corrosive nature of these catalysts. When the FFA content is above 5%, the separation of methyl esters and glycerol is inhibited due to soap and then emulsion formation. In order to overcome this issue, an acid catalyst such as H\({}_{2}\)SO\({}_{4}\) is used in process called esterification, as shown by Equation (4).  
\[\text{RCOOH }+\text{CH}_{3}\text{OH }\xrightarrow{\text{H${}_{2}$SO${}_{4}$}} \text{RCOOCH}_{3}+\text{ H${}_{2}$O} \tag{4}\]  
This pretreatment process lowers FFA content in feedstock, which are thus converted into methyl esters. Palm oil contains 51% free fatty acid (FFA) and it was treated with Sulfuric acid H\({}_{2}\)SO\({}_{4}\) 0.75% \(w/w\) of oil and methanol CH\({}_{3}\) was mixed with oil under optimum conditions of 70 \({}^{\circ}\)C temperature and agitation at 600 rpm for 1 h and then sodium methoxide ion (1.25%) was added. At 50 \({}^{\circ}\)C, the mixture was stirred again for an hour. This procedure led to over a 97% yield and an incomplete reaction occurred due to water content and its accumulation stops the reaction before completion. These acid catalysts used in the process of esterification can synthesize biodiesel from products of refining industry that are in fact low grade raw material, for example soap stock. It is dried and esterified with alcohol by a catalyst; in this reaction, an inorganic acid. A sufficient amount of homogeneous acid catalysts, methanol and long duration are required for the reaction to take place; reaction time can be helpful in achieving desirable yield and good performance of biodiesel through homogeneous acid catalyst. Reactions were optimized at the methanol to oil molar ratio from 3:1 to 30:1, reaction temperature (25 and 60 \({}^{\circ}\)C), acid (H\({}_{2}\)SO\({}_{4}\) from 1-3.5 weight percent), 48-96 h of reaction time, from 1 to 5 wt. % water content and FFA content from 2 to 30 wt. % was investigated. Even in case of FFA \(\sim\)5, the ester yield was approximately 90%.
## 3 Numerical Results
### Definition of the Reference Case  
The reference case is described in Table 1 which includes all the necessary input parameters. They are quite similar as the ones in [17] except for the anisotropic stress state which was not considered. For a better understanding of the Burger model, excavation and convergence parameters, and their value selection, readers are referred to [12; 13; 17]. A quasi-instantaneous installation of both liners is assumed by profiting the advantage of the presence of the outer flexible liner. In fact, each liner stress state in the tunnel support system depends strongly on their rigidity as well as their installation time. The discussions in [17] highlighted that an early installation of the liners induces the higher compressive stress, particularly in the inner concrete liner which is also amplified by the rigidity (i.e., higher elastic moduli) of the outer elastic liner. However, in the case of flexible liner, the low elastic moduli of this support allow it to absorb the increase in time of the rock convergence and hence reduce the transmitted load to the concrete liner. This advantage allows to install at the same time two liners that facilitates the excavation and installation of a tunnel support system by TBM.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Parameter** & **Symbol** & **Value** \\ \hline Initial stress & Vertical stress & \(P_{0}\) & 6.8 MPa \\  & Anisotropic coefficient & \(\lambda\) & 0.66 \\ \hline Burger model & Viscosity of Kelvin & \(\eta_{K}\) & 2. 07 \(\times\) 10\({}^{7}\) GPa.s \\  & Viscosity of Maxwell & \(\eta_{M}\) & 4. 14 \(\times\) 10\({}^{6}\) GPa.s \\  & Shear modulus of Kelvin & \(G_{K}\) & 0.34 GPa \\  & Shear modulus of Maxwell & \(G_{M}\) & 3.45 GPa \\ \hline Geometry & Tunnel radius & \(R\) & 4.5 \\  & Thickness of 1 st liner & \(l_{1}\) & 0.2 \\  & Thickness of 2 nd liner & \(l_{2}\) & 0.45 \\ \hline Two liners & Elastic modulus of 1 st liner & \(E_{1}\) & 0.1 GPa \\  & Elastic modulus of 2 nd liner & \(E_{2}\) & 30 GPa \\  & Poisson coefficient of 1 st liner & \(v_{1}\) & 0.001 \\  & Poisson coefficient of 2 nd liner & \(v_{2}\) & 0.2 \\ \hline Others & Installation time of the two liners & \(t_{0}\) & 0.001 day \\  & Exavation rate & \(v_{1}\) & 0.75 m/day \\  & 1 st parameter of deconfinement rate function & \(m_{1}\) & 0.7 \\  & 2 nd parameter of deconfinement rate function & \(m_{2}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters for the analytical model.  
For the reliability analysis, two failure modes will be considered for analyzing the tunnel support system: the exceeded volumetric strain in the outer flexible liner (Equation (2)) and exceeded equivalent stress in the inner concrete liner (Equation (1)).
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Relationship between Forest Stand Age and Canopy Spectral Characteristics
#### 3.2.2 Relationship between Vegetation Indices and Forest Stand Age  
To eliminate the effects of forest background reflectance and topographic effects, vegetation indices were introduced to study the canopy spectrum and forest stand age. A vegetation index can eliminate the effects of forest background reflectance and topographic effects on the spectrum by means of band combination methods. The linear (black fitting line) and nonlinear (red fitting line) relationships between \(SR\), \(NIR_{v}\), \(NDVI\), \(GVI\) and forest stand age were analyzed, and the results are shown in Figure 5. For the relationship between \(SR\) and forest stand age, the \(R^{2}\) of the nonlinear relationship is 0.55, and the \(R^{2}\) of the linear relationship is 0.49. The nonlinear relationship is significantly higher than the linear relationship, indicating that when the forest stand age reaches the age threshold, the change in structural parameters slows down, and the nonlinear fitting curve approaches saturation. For the relationship between \(NIR_{v}\) and forest stand age, the \(R^{2}\) of the nonlinear relationship is 0.45, and the \(R^{2}\) of the linear relationship is 0.42; in this case, the nonlinear relationship is also significantly higher than the linear relationship. Similarly, when the forest stand age reaches the age threshold, the change in photosynthetic parameters slows down, and the non-fitting line slowly approaches saturation. For the relationship between \(NDVI\) and forest stand age, the \(R^{2}\) of the nonlinear relationship is 0.57, and the \(R^{2}\) of the linear relationship is 0.53; the nonlinear relationship is significantly higher than the linear relationship. When the forest stand age is 45 years, \(NDVI\) shows a peak and a sharp decline trend. For the relationship between \(GVI\) and forest stand age, the \(R^{2}\) of the nonlinear relationship is 0.37, and the \(R^{2}\) of the linear relationship is 0.33; the nonlinear relationship is significantly higher than the linear relationship, and \(GVI\) has a saturation trend, but it is not sensitive enough.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Reflectance** & \(B_{1}\) & \(B_{2}\) & \(B_{3}\) & \(B_{4}\) & \(B_{5}\) & \(B_{6}\) & \(B_{7}\) & \(B_{8}\) & \(B_{8A}\) & \(B_{11}\) & \(B_{12}\) \\ \hline \(r\) & 0.19 * & 0.37 ** & 0.35 ** & 0.38 ** & 0.24 ** & 0.24 ** & 0.39 ** & 0.53 ** & 0.53 ** & 0.19 * & 0.21 ** \\ \hline \end{tabular}  
* the significance level of 0.01,* the significance level of 0.05.  
\end{table}
Table 2: [PERSON]’s correlation coefficient for the relationship between maximum canopy reflectance and forest stand age in the growing season.  
Figure 5: \(Cont.\)
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 4 Optimization Programming and Simulation Setup  
### PSO Heuristic Algorithm  
The initiation of the PSO is done using a group of randomly positioned particles whose ultimate goal is to locate the best point. The PSO are defined by four parameters: position array, denoted as \(X_{i}\) = (\(x_{i1}\), \(x_{i2}\),..., \(x_{in}\)); velocity array, \(V_{i}\) = (\(v_{i1}\), \(v_{i2}\),..., \(v_{in}\)); best previous position, \(P_{i}\) = (\(p_{i1}\), \(p_{i2}\),..., \(p_{in}\)); and the global best position, \(P_{g}\) = (\(p_{g1}\), \(p_{g2}\),..., \(p_{gn}\)) [46]. The velocity and position of each particle are updated as follows:  
\[v_{new}=w\times v_{old}+c_{1}\times r_{1}(p_{in}-x_{in})+c_{2}\times r_{2}\big{(} p_{gn}-x_{in}\big{)}, \tag{21}\]  
where \(w\) is the inertia weight; \(r_{1}\) and \(r_{2}\) are random numbers; \(c_{1}\) is the self-recognition component coefficient; \(c_{2}\) is the social component coefficient; and the choice of the values \(c_{1}\) = \(c_{2}\) = 2 is generally referred to as the learning factors. The following weighting function is usually utilized in Equation (21):  
\[w=\frac{w_{\max}-[(w_{\max}-w_{\min})\times iter]}{iter_{\max}}, \tag{22}\]  
where \(w_{\max}\) is the initial weight; \(w_{min}\) is the final weight; \(iter\) is the current iteration number; and \(iter_{\max}\) is the maximum iteration number.  
Next, the new position is determined using the previous position, and then the new velocity can be written as follows:  
\[x_{in\_new}=x_{in\_old}+v_{new}. \tag{23}\]
## 1 Introduction
In the cotton industry in Australia, this is sometimes referred to as irrigation water use index (IWUI) and relates cotton production only to the amount of irrigation water used [5]. In Australia, surface irrigation is the main irrigation method used, and in 2013-2014, it accounted for 59% of the total irrigated land [2]. However, the system in general is associated with high labour requirement and low WUE. This explains why modernisation and automation projects (discussed later in this paper) have tended to focus on this irrigation system. Conversely, the pressurised irrigation methods (sprinkler and drip) are generally less labour-intensive and have significantly higher WUE. With the advancement of technology, thanks largely to the many years of investment and research and development in agriculture, there are new and emerging opportunities for further improving the WUE in irrigated agriculture. Examples of these include use of remotely sensed data (from drones or satellites), communication networks and the availability of cheap sensors. It is clear from the above discussion that in order to improve the irrigation WUE, losses that occur along the conveyance and distribution channels must be minimised, and the timing and the quantity of water applied (or irrigation scheduling) must be optimised. Improvement of the irrigation WUE may lead to water savings which may be used to irrigate more land, which is particularly relevant where water is the limiting factor of production. The purpose of this paper was to review the advancements that have been made to improve the irrigation WUE, document the challenges encountered as well as exploring opportunities for further development. Although the bulk of the review is on Australia's irrigated agriculture, examples from other countries are also used, and it is anticipated that the findings will inform researchers and policy makers in general. The paper starts by looking at the nexus between irrigation modernisation and automation in Australia, particularly focusing on irrigation distribution channels and on-farm development. The review then discusses the role of irrigation scheduling in improving the WUE, and the concept of real-time control and optimisation that is still under development. The emerging and potential opportunities for improved WUE through remote sensing techniques, and sensors and communication networks are discussed. In the final section, we discuss the challenges to the achievement of higher WUE, with focus on water consumption at the basin scale and factors affecting trends in WUE which are broadly categorised as: engineering and technological; environmental; advancements in plant and pasture science; and socio-economic.
## 2 Theory
### Inertia and Friction Coefficient  
Inertia and the friction coefficient determine the relationship between output torque and rotor speed. That is, when the output torque and rotor speed are known, inertia \(J\) and friction coefficient \(B\) can be further obtained. Assuming that the torque only causes the rotor speed to change, in time domain, \(t\), the relationship between rotor speed and torque conforms to the following differential equations:  
\[J\dot{\omega}_{r}(t)+B\omega_{r}(t)=T_{out}(t). \tag{42}\]  
In discrete data, this can be expressed as  
\[J(\omega_{r}(n)-\omega_{r}(n-1))+B\omega_{r}(n)=T_{out}(n),\;n=1,\cdots,N. \tag{43}\]  
When considering the inertia and friction coefficient is constant, to obtain the most appropriate parameters, set the objective function \(E_{T}\) as  
\[E_{T}=\sum\limits_{n=1}^{N}\left(T_{out}(n)-J[\omega_{r}(n)-\omega_{r}(n-1)]- B\omega_{r}(n)\right)^{2},\;n=1,\cdots,N. \tag{44}\]When the objective function is the minimum, the most appropriate \(J\) and \(B\) are obtained, i.e., the gradients for Equation (44) are both zero, and thus \(J\) and \(B\) can be obtained by  
\[\left[\begin{array}{c}J\\ B\end{array}\right]=\left[\begin{array}{cc}\frac{N}{n-1}(\omega_{r}(n)- \omega_{r}(n-1))^{2}&\sum\limits_{n=1}^{N}\omega_{r}(n)(\omega_{r}(n)-\omega_ {r}(n-1))\\ \sum\limits_{n=1}^{N}\omega_{r}(n)(\omega_{r}(n)-\omega_{r}(n-1))&\sum\limits_ {n=1}^{N-1}(\omega_{r}(n))^{2}\\ &n=1,\cdots,N.\end{array}\right]^{-1}\left[\begin{array}{c}\sum\limits_{n= 1}^{N}T_{out}(n)(\omega_{r}(n)-\omega_{r}(n-1))\\ \sum\limits_{n=1}^{N}T_{out}(n)\omega_{r}(n)\end{array}\right], \tag{45}\]
## 3 Results and Discussion
### Characterization of the Developed Electrospun Membranes
#### 3.1.2 EDS, ATR-FTIR, GSDR and TGA Analyses
Compared to the PCL spectrum, the spectrum of PCL/Ag NPs reveals the appearance of the absorption band characteristic of Ag NPs formation, resulting from the surface plasmon resonance (SPR) of Ag NPs, peaking at 413 nm [26; 28]. This result indicates the successful synthesis and attachment of Ag NPs onto PCL membranes. On the other hand, a new absorption band at approximately 380 nm, characteristic of TiO\({}_{2}\) NPs [41; 42], is observed in the spectrum of PCL/TiO\({}_{2}\) NPs membranes, once again confirming the presence of these NPs onto the PCL surface. Finally, a very small absorption band seems to appear around 277 nm in the spectrum of PCL/MgO NPs when compared to the PCL spectrum, which is probably related to the presence of the MgO NPs on the membranes' surfaces [43; 44]. Finally, TGA analysis was performed in order to estimate the amount of NPs impregnated in PCL electrospun membranes (Figure 4f). The samples were heated to 600 \({}^{\circ}\)C and the residues obtained at the end of the heating process were considered, being the amount of each type of NPs calculated using equation 2 [27; 28]. PCL membranes presented 2.48 wt% of residues while PCL/Ag NPs, PCL/TiO\({}_{2}\) NPs and PCL/MgO NPs revealed 2.98, 5.49 and 6.18 wt%, respectively. Assuming that the final sample is composed of PCL and NPs, the percentage of NPs presented onto the membranes' surface is approximately 0.512, 3.09 and 3.79%, respectively. The estimated amount of commercial NPs (TiO\({}_{2}\) and MgO) NPs detected in PCL membranes was higher than the amount of synthetized Ag NPs. This finding could be related with the in situ synthesis of Ag NPs. Contrary to the impregnation solutions containing the commercial NPs, where the added amount of NPs is known, the impregnation solution of Ag NPs was the synthesis solution, where the amount of synthetized NPs is unknown. Since during the synthesis process some losses can occur, including the possibility of not all the precursors being reduced to produce the NPs, it is expected that the PCL/Ag NPs electrospun membranes present the lowest amount of NPs when compared to membranes containing the commercial NPs. All of these techniques were able to confirm the successful functionalization of PCL electrospun membranes with three different types of NPs.
## 4 Conclusions  
This work proposes an HT-forming technique for multiband array antennas. We evaluated the far-field performance of linear arrays consisting of 6, 8, 12, 16, and M \(\times\) N elements. The findings of our simulations and measurements demonstrate that the proposed method can achieve an ONF pattern in these array antennas, and using the HT linear array and comparing it with the uniform array increases the power under the base station tower by over 20 dB. Overall, the whole cell's power distribution is improved. Additionally, the proposed HT linear array antenna can scan at an angle of \(\pm 42^{\circ}\), and the sidelobe suppression (dp) weighting parameter allows for flexible adjustment of the anti-power fluctuation effect and gain: compared to the uniformly excited one, for example, the maximum ONF gain of an eight-element linear array operating at 3.5 GHz is only reduced by 0.5 dB when \(\mathrm{dp}=-30\) dB and by 2 dB when \(\mathrm{dp}=-60\). This can be used to improve communication quality near a BS and can also be applied in MIMO systems to optimize the number of beams in order to reduce cost and increase efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper/Year** & **Method** & **Complexity** & **Far-Field Pattern** & **Beam Scanning** \\ \hline
[7]/2013 & Schelkunoff roots & high & Null-filled & \\
[12]/2014 & TI-IWOA & high & Filling 1 null & \\
[13]/2021 & DRA and AFSA & high & Arbitrary & yes \\
[14]/2014 & GA & high & Null-filled & no \\
[23]/2020 & Decoupling & high & Null-filled & no \\
[24]/2016 & Complex control & high & Filling 1 null & \\
[25]/2022 & Coupling & high & Existing nulls & \(\pm 60^{\circ}\) \\ This work & HT method & low & Null-free & \(\pm 42^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with recent achievements.  
Figure 16: Comparison between simulated and measured normalized patterns: (**a**) 3.5 GHz; (**b**) 26 GHz.  
**Author Contributions:** Writing--original draft preparation, L.D.; writing--review and editing, [PERSON], [PERSON] ([PERSON]) and Y.Z.; methodology, L.D.; software, L.D.; formal analysis, L.D.; validation, D.C.; data curation, J.D.; investigation, X.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 62271063, in part by the Fundamental Research Funds for the Central Universities under Grant 2019 XD-A07, in part by the Director Fund of Beijing Key Laboratory of Space-Ground Interconnection and Convergence, and in part by the National Key Laboratory of Science and Technology on Vacuum Electronics.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.