esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed ASVMR Protocol
### The Framework of SVM  
To minimize latency and extend the overall lifespan of the network, four factors, namely the ratio of the depth difference and the maximum communication range, the ratio of the depth difference and the distance of the sensor nodes, the residual energy function, and the neighboring node function, are selected as features for training the SVM model for routing. These factors collectively form a four-dimensional sample, represented by \(x_{k}\).  
Suppose that node \(n_{i}\) sends a data packet to node \(n_{j}\), \(\left(x_{i},\ y_{i},z_{i}\right)\) and \(\left(x_{j},\ y_{j},z_{j}\right)\) represent the positions of nodes \(n_{i}\) and \(n_{j}\), respectively. The ratio of the depth difference and the maximum communication range \(R\) can be defined as  
\[drRatio\big{(}n_{i},n_{j}\big{)}=\frac{z_{j}-z_{i}}{R}. \tag{14}\]  
The ratio of the depth difference and the distance between nodes \(n_{i}\) and \(n_{j}\) can be defined as  
\[ddRatio\big{(}n_{i},n_{j}\big{)}=\frac{z_{j}-z_{i}}{\sqrt{\big{(}x_{j}-x_{i} \big{)}^{2}+\big{(}y_{j}-y_{i}\big{)}^{2}+\big{(}z_{j}-z_{i}\big{)}^{2}}}. \tag{15}\]  
The residual energy function at node \(n_{j}\) can be defined as  
\[f_{\rm e}\big{(}n_{j}\big{)}=\frac{e_{\rm res}\big{(}n_{j}\big{)}}{e_{\rm ini }\big{(}n_{j}\big{)}}, \tag{16}\]  
where \(e_{\rm ini}\big{(}n_{j}\big{)}\) and \(e_{\rm res}\big{(}n_{j}\big{)}\) denote the initial and residual energy of node \(n_{j}\), respectively.  
The neighboring node function at node \(n_{j}\) can be defined as  
\[f_{\rm n}\big{(}n_{j}\big{)}=\frac{nei\big{(}n_{j}\big{)}}{nei_{\rm max}}, \tag{17}\]  
where \(nei\big{(}n_{j}\big{)}\) denotes the number of neighboring nodes connected to node \(n_{j}\), and \(nei_{\rm max}\) is the maximum number of neighboring nodes among all the sensor nodes in the network.  
Therefore, the sample \(k\) can be expressed as \(x_{k}=\{drRatio,\ ddRatio,f_{\rm e},f_{\rm n}\}\).  
Here, we employed a portion of the publicly available ASUNA dataset [30], which consists of 11,000 sample groups, including 6561 positive and 4439 negative samples. The partial training samples are listed in Table 1.  
Here, the value of \(drRatio,\ ddRatio\), and \(f_{\rm e}\) is set within \((0,\ 1]\), and the value of \(f_{\rm n}\) is set within \([0,\ 1]\). The value of \(y\) is determined according to the result provided by ASUNA and manual judgment. For example, in the first sample of Table 1 the node has no neighboring nodes with \(x_{4}=0\), and thus, the label \(y\) is set to \(-1\). We randomly selected 2500 samples for the training of the model, normalized them, and employed 5-fold cross-validation with an RBF kernel function. The remaining 8500 test samples were utilized to evaluate the trained SVM model. The test results showed that out of 8499 samples, the predicted values of the labels \(y\), calculated by the SVM model, matched the true values. Therefore, the test accuracy achieved 99.988%.
## 5 Future Perspectives
### Expansion of Application Fields  
As SiC powder 3D printing technology continues to evolve, its potential applications to emerging fields are becoming increasingly important. In addition to traditional industrial applications, SiC materials show great potential for use in the biomedical field, renewable and clean energy, and the aerospace sector [199; 200; 201; 202].
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 3 Results
### Analysis of Trend
#### 3.2.3 Difference in Drought Area  
The differences between the proportion of drought area on TVDI and SRHI are calculated (Figure 8). In the Huanghuai region, the difference between the drought area on TVDI and SRHI, reaches a maximum (62.03%) on 29 June 2022, and minimum (\(-\)10.21%) on 7 May 2022, mainly in [5.16-28.3%]. The differences in Yunnan Province peaked at 44.96%, mainly [16.93-25.22%]. The differences in Guangdong Province peaked on 7-10 January 2021, with the difference mainly distributed in [6.66-18.93%]. The proportion of drought area on TVDI was greater than the proportion on SHRI, with the most concentrated distribution of the difference in Yunnan and the most dispersed distribution in the Huanghuai region.  
Figure 7: Trends of drought area in different drought intensities (Column **(A)**: Huanghuai region; Column **(B)**: Yunnan Province; Column **(C)**: Guangdong Province region).
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 3 Proposed Proxy-Based Adaptive MP-QUIC Transmission
### Connection Management  
In the proposed scheme, the connection manager is used to manage all connection requests by clients and to determine the MP-QUIC for each client by considering the concerned priority. The connection manager uses the Hash ID to identify each client, as shown in Table 2.  
Hash ID for a client is allocated by the proxy. For each Hash ID, the mapping information between client (QUIC) and server (MP-QUIC) will be maintained and updated, which includes the IP address and port number of the client, the IP address, and port number of the server, and the concerned priority type.  
In this paper, we assume that each client determines its priority (0 or 1), as per the pre-configuration and an appropriate negotiation with the proxy (or service provider). In Step 4 of Figure 2, a client requests a Hash ID to the proxy with its priority information. In Step 5, the proxy creates a new entry for the client in the connection management table, in which the priority information is recorded, as shown in Table 2. In Step 6, the server responds to the client with Hash ID information.  
In the connection management, the proxy will determine the MP-QUIC path for the client among all available network paths, as indicated in the path management table of Table 1. The client with priority packets will be assigned to the best path with the smallest RTT among available MP-QUIC paths between the proxy and the server. On the other hand, the client with non-priority packets will be assigned to the path with the lowest _hits_ value for traffic load balancing among the available MP-QUIC paths while it is available. If the two MP-QUIC paths have the same _hits_ value, the path with a smaller RTT is preferred. In Step 6 and 7, the connection manager determines this MP-QUIC path information by inter  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Path ID** & **RTT (ms)** & **Hits** \\ \hline Path\_1 & 100 & 10 \\ Path\_2 & 200 & 4 \\ Path\_3 & 300 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Path management table.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Hash ID**} & \multicolumn{2}{c}{**Client**} & \multicolumn{2}{c}{**Server**} & \multicolumn{1}{c}{**Priority**} \\ \cline{2-5}  & **IP Address** & **Port Number** & **IP Address** & **Port Number** & \\ \hline
123 ABC & 1.1.1.1 & 5389 & 10.10.10.10 & 443 & 0 \\
456 DEF & 1.1.1.2 & 4573 & 10.10.10.5 & 443 & 1 \\
789 CHI & 1.1.1.3 & 5482 & 10.10.10.4 & 443 & 0 \\... &... &... &... &... &... \\
754 KCH & 1.1.1.10 & 5678 & 10.10.10.5 & 443 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Connection management table by Hash ID.
## 4 Discussion  
By improving the retrieval window and correcting the frequency shift of satellite spectra, we achieved better separation of the SIF signal at the FLs band near 757 nm wavelength. The new retrieval window addresses the issue of overfitting of singular vectors, significantly enhancing the sensitivity of the data-driven method to Fs. Furthermore, the correction for the Doppler effect can further enhance the intensity of the retrieval results. In this paper, we illustrate this through the comparison of retrieval results and SVD results before and after the correction of satellite spectrum frequency shift. In addition to the higher correlation with GPP and VI, our retrieval results also exhibit a more reasonable intensity  
Figure 10: Maps of the mean 2019 annual intensity distribution for the two SIF products at 2\({}^{\circ}\) grid cells and of the mean annual results for EVI at 0.1\({}^{\circ}\) grid cells. The color–intensity relationship is the same for each row of subplots. The maps contain four regions: Northern South America, the United States and southern Canada, Western Europe, and southern Africa.  
distribution, further demonstrating the improvement in sensitivity of the data-driven algorithm due to the enhanced methods.
## 2 Experimental Setup
### Vehicle Driving Cycle and Experimental Condition  
IM240 mode was performed for the operating mode for analyzing charging characteristics in regenerative braking operation according to the initial SOC state as shown in Figure 4. IM240 cycle is a chassis dynamometer schedule developed and recommended by the U.S. EPA for emission testing of in-use, light-duty vehicles in inspection and maintenance (I&M) programs implemented in a number of states. The EPA has also developed a guidance document specifying IM240 emission standards for use in I&M testing programs.  
If the hybrid vehicle does not operate the accelerator pedal according to SOC management strategy, there is a situation wherein the battery is charged at the time of deceleration during inertia running. Since the deceleration section in the middle of the mode as shown in Figure 4 is the deceleration section due to inertia running, in this study, it is not regenerative braking during braking, and is excluded from the target.  
This is because regenerative braking due to brake actuation is a clear reference to characterizing battery charging separately from hybrid operating modes.  
The energy of the hybrid vehicle used in this study is calculated by calculating \(J\) (Joule, kWh) to calculate the actual charged electric energy \(E\) (Energy, kWh), obtained by the following formula:  
\[1\ J=2.778\times 10^{-4}\ Wh\]  
\[1431\ Wh=1431\times\frac{1}{2.778\times 10^{-4}}\ J\]  
\[=5,151,188\ J=5151.188\ kJ\]  
\[Gain\ E:\frac{Regeneration\ J}{5151.188}\times 100\]  
Figure 4: Driving profile of IM240 mode.  
In this study, regenerative braking period is an energy recovery mechanism that slows a vehicle into a form that can be stored in a secondary battery, and is ranged to the point wherein the vehicle speed is 0 after the brake is applied. Data interval of SOC and current (OBD, PicoScope) are extracted and analyzed in 350 ms units.  
Experimental conditions are shown in Table 3, and initial SOC state was 35% and 65% (tolerance of IM240 mode is within \(\pm\)3 km/h). IM240 is a 240 s test representing a 3.1 km route with an average speed of 47.3 km/h and a maximum speed of 91.2 km/h.
Previous studies have established and solved optimal problems with different objective functions and sets of variables in specific applications. In 2001, [PERSON] introduced a procedure called \"enumeration method\" to select optimal process parameters in grinding operations [2]. In this procedure, the user tests different values of process variables and assesses the corresponding values of the desired target function while meeting the process constraints [2]. In 2002, a multi-parameter optimization problem in the cylindrical grinding process was developed by [PERSON] [3]. The optimal grinding control parameters were obtained through computer simulation. In addition, the actual grinding process was controlled by these optimal parameters [3]. In 2015, a methodology for the design of optimal multivariable stepwise cycles internal grinding processing was introduced by [PERSON] [4]. This method provides multi-parameter optimization of the control program for CNC machines on internal grinding operations [4]. In 2018, the influence of grinding parameters on surface temperature and burn behaviors of grinding rail were investigated by [PERSON] et al. [5]. In 2018, [PERSON] [1] analyzed the influences of cutting mode parameters on surface roughness of cylindrical traverse cut grinding of aluminum bronze material. In addition, the optimal cutting mode parameters were determined by the Taguchi method. The wear of the grinding wheel and the conditioning during creep feed grinding of aeronautical alloys were analyzed in [6]. A method of modeling and calculating the material removal of abrasive belt grinding process using ANFIS (An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system) is reported in [7]. Recently, a study on optimization of dressing parameters for grinding tablet shape punches 90 CrSi by CBN wheel on CNC milling machine was carried out [8]. However, none of the previous researches had studied the problem of optimizing cutting parameters for grinding tablet shape punches by cubic boron nitride wheels (CBN wheels) on CNC milling machine. For grinding tablet shape punches by cubic boron nitride wheels on CNC milling machine, setting up optimal cutting conditions increases machining productivity and ensures quality of the grinding part as well. Thus, the economic and technical targets of the machining process are directly affected by setting up optimal cutting conditions. The optimization of cutting regime to determine and set up suitable cutting mode parameters is the most basic and effective method to control product quality and enhance machining productivity. In this study, the input process parameters, including the cutting depth (\(\mathrm{a_{ed}}\)), the wheel speed (\(\mathrm{R_{pm}}\)) and the feed rate (\(\mathrm{F_{e}}\)), were investigated for evaluating their influence on the grinding time as well as for finding the minimum grinding time when grinding tablet punches with CBN wheel on CNC milling machine by using the Taguchi method. Based on that, the optimum set of cutting parameters for grinding tablet shape punches by cubic boron nitride wheels on a CNC milling machine has been determined.
## 2 Related Work
### Problem Formulation  
In 3-D wavelet-based SVC, the temporal decomposition is efficiently refined by adopting the MCTF technique. The ENH-MC-EZBC scalable video coder utilizes an adaptive lifting-based MCTF framework with switching between LeGall and Tabatabai (LGT) 5/3 and Haar 2/2 filters to exploit the temporal redundancies between successive frames [41]. During the MCTF process, video frames are filtered into low-frequency (L) and high-frequency (H) subbands. The process to generate the temporal high- and low- subbands is called the \"prediction\" and \"update\" step, respectively. Figure 2 illustrates the prediction and updates steps for the analysis stage of the three-level adaptive MCTF decomposition. MC and IMC are motion compensation and inverse operators, respectively. Scene change information is reflected in the choice of the filter bank, which propagates to the lower temporal levels. The coefficients \(\alpha_{\tau}\) and \(\beta_{\tau}(\tau=1,2,3)\) along the branches are filter-based weighting factors. Since 3-D wavelet-based SVC is usually based on the underlying open-loop MCTF structure, its RD performance is further complicated by the inherent problem of the propagation of the quantization errors along the temporal wavelet decomposition tree [42]. Therefore, the selection of the Lagrange multiplier for wavelet-based SVC should be addressed differently.
# Effects of Nonlinear Damping on Vibrations of Microbeam
###### Abstract  
The present paper develops a new Bernoulli-Euler theory of microbeams for the consideration of small-scale effects and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin-Voigt damping. The non-resonance and primary resonance of microbeams are researched through the application of Galerkin and multiple scale methods to the new model. The results suggest the following: (1) Nonlinear damping slightly affects the vibration amplitudes under the non-resonance condition; (2) nonlinear damping can significantly change the bifurcation points that induce a jump in the vibration amplitudes under the primary resonance condition. The current researches indicate that nonlinear damping is necessary for an accurate description of microbeam vibrations.  
Bernoulli-Euler microbeam; modified couple stress theory; nonlinear vibrations; damping; multiple scale method +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Methods
#### 2.2.1 VPD Control Development
The main characteristic of an NN is that each unit that composes it can learn and train itself, therefore, it can be applied in areas where the detection of parameters is difficult to achieve [44]. An NN was designed for the humidity system. This control has a hidden layer with two neurons. The training algorithm used was backpropagation [45]. During training, a mean square error of \(3.58\times 10^{-4}\) was obtained. The training values were taken in a range from 1 to \(-1\) for the present error in intervals of 0.2. For the desired output, values in the range from 0 to 0.033 were taken, which correspond to the minimum and maximum value that \(\dot{m}_{a}\) can take. After carrying out the VPD control design by modifying the values of temperature and humidity in the micro-greenhouse, a transfer function was obtained that describes the soil moisture dynamics through an identification process. The Matlab tool \"System identification toolbox\" was used, which works with the data input obtained experimentally [46]. For this, a voltage of 12 V was applied to the water pump. When the system activates the irrigation, the soil moistens quickly and the dynamics have a fast behavior. A reference of 100% moisture in the soil was established and an average of 7 runs was calculated [47]. The transfer function that describes the behavior of soil moisture in the micro-greenhouse is shown in Equation (14). \[VP_{sat}=0.61078\bigg{(}e^{[[\frac{[27,27](7)}{29,37+1}]}\bigg{)} \tag{2}\]6  
At present, PID control has been used to control irrigation systems in greenhouses [48]. The PID control is made up of three actions: proportional, integral and derivative [49]. A PID control with one input and one output (SISO) was designed for soil moisture control in this work. The first Ziegler-Nichols method was used to determine the PID controller gain value [50]. The values to calculate \(K_{p}\), \(T_{i}\) and \(T_{d}\) were obtained from the rules proposed by [PERSON], where it was determined that \(L=6\) and \(T=12.5\). The PID controller parameter values for soil moisture are \(K_{p}=2.5\), \(K_{i}=0.2\) and \(K_{d}=7.5\). The control algorithms implemented by FL and the NN for VPD and PID for soil moisture were performed in the LabVIEW programming environment. The control system implementation diagram can be seen in \[VP_{sat}=0.61078\bigg{(}e^{[[\frac{[27,27](7)}{29,37+1}]}\bigg{)} \tag{2}\]7 An FL and NN control was implemented for temperature and humidity. The FL algorithm controls the pump flow water in the heat exchanger to decrease or increase the micro-greenhouse internal temperature when it exceeds the indicated reference value. The NN controls \(\dot{m}_{a}\) through a fan, saturating the air with water vapor to humidity the micro-greenhouse. For irrigation, a PID control with an input and an output was implemented. The PID control regulates the submersible pump speed, which functions to transport water through the irrigation pipes installed inside the micro-greenhouse.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 3 Proposed Framework--AMAN  
The AMAN is a breast cancer detection system aiming to aid doctors and specialists facing difficulties while detecting breast cancer tumors, as shown in Figure 1. The goal of this system is to help cancer doctors and specialists make a critical decision in diagnosing breast cancer, especially in ambiguous cases when the mammogram images of the breast are not clear enough to make the decision.  
Moreover, the clinical report that goes with these images will be used to enhance decision support. To perform our system, the authors will use a combination of AI tools on the cloud and the Saudi Arabian dataset from the King Fahad University Hospital to develop a system that will detect breast cancer early and reduce mortality. The system's main target is to help the relevant breast cancer doctors and specialists in the field.  
Figure 2 displays a block diagram of the proposed AMAN'S system. Mammogram images will be inserted into the system model, which will apply a pre-processing procedure in which the image will be enhanced and formatted using deep learning techniques, and it will segment the image into multiple parts to detect and find the tumor. Furthermore, the BI-RADS descriptors will be inserted into the system model that will apply data preprocessing to classify the tumor. Moreover, the system will implement the most correct model, the Xception model, to classify the mammogram images and the gradient boost to classify the  
clinical data based on our experiments to predict the tumor of the imported mammogram image and display the detection result as either normal, benign, or malignant.  
The AMAN system (Figures 2-4) is expected to be usable and acceptable to the end-user; it should have a straightforward and interactive interface. It must also be robust and secure to prevent system failures and damage. Credibility is a vital attribute of the system since it needs to use sensitive patient information and keep it safe from loss or damage.  
Figure 3: AMAN system procedure.  
Figure 2: Proposed AI model.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 5 Performance Analysis  
The current reaching the PIN photodetector for the required user is expressed as in [33]:  
\[I_{Ch}=\frac{\mathcal{R}P_{Rx}K}{N} \tag{10}\]  
where \(\mathcal{R}\) is the responsivity of PIN photodetector and \(P_{Rx}\) is the received power which in FSO transmission is expressed as in [27]:  
\[P_{Rx}=P_{Tx}\bigg{(}\frac{d_{Rx}}{d_{Tx}+\mathcal{O}L}\bigg{)}^{2}10^{\frac{- \mathcal{R}L}{26}} \tag{11}\]  
where \(P_{Tx}\) represents the transmitted power, \(\beta\) is the attenuation due to weather conditions (\(\gamma\) in the case of fog and \(\propto\) in the case of snowfall), \(d_{Rx}\) is the diameter of receiver aperture while \(d_{Tx}\) is the diameter of transmitter aperture, \(L\) is the range of the signal transmitted in the FSO channel, and \(\mathcal{O}\) represents the beam divergence. The SNR is expressed as in [33; 34]:  
\[\text{SNR}=\frac{\left(\frac{\mathcal{R}P_{Rx}L}{N}\right)^{2}}{\left(eB_{e} P_{Rx}\frac{W+L-1}{N}\right)+\frac{4k_{B}T_{abs}B_{e}}{R_{Load}}} \tag{12}\]  
where \(e\) and \(B_{e}\) are charge of electron and electrical bandwidth, respectively, \(k_{B}\) is Boltzmann constant, \(T_{abs}\) refers to absolute temperature of receiver noise, and \(R_{Load}\) is the load resistance of receiver.  
The BER is given as in [27]:  
\[\text{BER}=0.5 erfc\bigg{(}\sqrt{\frac{\text{SNR}}{8}}\bigg{)} \tag{13}\]  
Figure 2: Setup of PDM-SAC-OCDMA-enabled FSO system.  
Alternatively, the \(Q\)-factor is given as in [27]:  
\[\text{BER}=0.5 erfc\left(\frac{1}{\sqrt{2}}Q\right) \tag{14}\]