esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Interference Studies  
For the estimation of the interferences related to different species on the detection of caffeic acid at the 20 uM level, different interference studies on the SPCE/PB/HRP biosensor were set up by cyclic voltammetry. The compounds used in these studies were different phenolic compounds with a similar chemical structure to caffeic acid, namely, catechol, catechin, and gallic acid. The concentration of interfering phenolic compounds in the solutions was 20 uM.  
The response of the SPCE/PB/HRP biosensor immersed in 20 uM caffeic acid and 20 uM caffeic acid solution in the presence of electrolyte support (solid line) and the response in the electrolyte support (dashed line) are shown in the Figure 7.  
As can be observed, the peak corresponding to caffeic acid is appearing at 1.105 V and the current is 141.3 uA--values very close to those obtained in the solution containing only caffeic acid. Two peaks related to catechol were observed: one anodic (at 0.700 V) and one cathodic (0.680 V). However, the SPCE/PB/HRP biosensor is able to accurately detect the caffeic acid in the presence of catechol.  
Other experimental results obtained in the interference studies are included in Table 3. As can be seen, all the phenolic compounds studied had reduced interference in the determination of caffeic acid. The biosensor showed low sensitivity to the various interfering compounds found in multicomponent solutions and high selectivity for the caffeic acid.  
Figure 7: Cyclic voltammogram of SPCE/PB/HRP biosensor immersed in 20 uM caffeic acid and 20 uM caffeic acid solution in 10\({}^{-2}\) M PBS pH 7–10\({}^{-2}\) M hydrogen peroxide solution (solid line); cyclic voltammogram of SPCE/PB/HRP biosensor immersed in 10\({}^{-2}\) M PBS pH 7–10\({}^{-2}\) M hydrogen peroxide solution (dashed line).  
This study demonstrated that the biosensor could be used for the detection of caffeic acid in real samples in the presence of interfering compounds, even at high concentrations. These results also confirmed that the peak currents and potentials related to the presence of caffeic acid are slightly influenced by the other phenolic compounds from the analyzed solution.
## 8 Seismic Performance Evaluation
### Distribution of Internal Forces and Bending Moments  
Figure 12 indicates the distribution of the mean internal forces in the arch of a bridge subjected to a near-field earthquake. The shear forces and bending moments for different directions in the RC arch are identified by RF\({}_{\text{i}}\) and RM\({}_{\text{i}}\), respectively. The in-plane shear force and bending moment in the normal direction to the axis of the arch are denoted by RF\({}_{2}\) and RM\({}_{3}\), respectively, while RF\({}_{3}\) and RM\({}_{2}\) are, respectively, the out-of-plane shear force and bending moment in the transverse direction of the arch. Figure 12 shows that the maximum axial forces are obtained at the arch's springings (arch abutments). The axial forces at the arch crown can be up to 35% lower than those at the springings. The shear forces in both directions (in-plane and out-of-plane) are distributed approximately uniformly  
Figure 11: Deck and arch crown displacement histories for Imperial Valley-06 earthquake. (**a**) Longitudinal direction; (**b**) Transverse direction; (**c**) Vertical direction.  
along the RC arch. Figure 12b shows that the variation in the out-of-plane bending moment, RM\({}_{2}\), is accompanied by a steep gradient near the arch's springings and a local maximum at the arch crown of the bridge; the in-plane bending moment, RM\({}_{3}\), is distributed along the arch and follows the same trend, albeit with lower values. The same variation in the patterns exhibited by the shear force and bending moment distributions are produced in bridges subjected to far-field earthquake, but due to space limitation these are not presented here.
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 5 Challenges
### Dataset of Insufficient Size and Variety  
In many papers and articles, the main limitation is the dataset used to train the CNN network, which leads to a worse performance accuracy for the identification of disease. In DL, there is a need for a large dataset with a wide variety of images. The PlantVillage [23] and Image Database of Plant Disease Symptoms (PDDB) [137] dataset are the only freely available large diseases dataset at the present time. The images available are from a laboratory setup and were captured with a uniform background. However, the collection of images from the field is expensive, and it requires agriculture expertise for the accurate identification of diseases.
## 2 The Surface Energy Balance: A Cornerstone in Understanding ET and RS Derived Variables  
Enhancing the comprehension of the connections between ET and remotely sensed derived variables entails providing a concise overview of the energy balance.  
It is important to note that this overview does not cover extensively all aspects, as it specifically focuses on commonly utilized models. For a more comprehensive understanding of specific concepts, readers can refer to exhaustive overviews [9; 16].  
The surface energy balance equation is a fundamental concept in studying the exchange of energy at the Earth's surface. Accounting for four components, the equation can be expressed as follows:  
\[R_{n}=H+G+LE+\Delta H \tag{1}\]  
where  
* \(R_{n}\) is the net radiation represented as the sum of net downward and upward shortwave and longwave radiation (W\(\cdot\)m\({}^{-2}\));
* \(H\) refers to the sensible heat flux representing the transfer of heat between the surface and the atmosphere (W\(\cdot\)m\({}^{-2}\));
* \(G\) is the ground or soil heat flux, which represents the transfer of heat into or out of the ground (W\(\cdot\)m\({}^{-2}\))
* \(LE\) represents the latent heat flux, which accounts for the energy used during evapotranspiration processes (W\(\cdot\)m\({}^{-2}\))
* \(\Delta H\) (W\(\cdot\)m\({}^{-2}\)) accounts for the heat storage in biomass and canopy air space that is often neglected in calculations [9].
## 6 Computational Results of the Developed Algorithms
### Discussion of the Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**J**} & \multicolumn{1}{c}{**SL**} & \multicolumn{1}{c}{**O**} & \multicolumn{1}{c}{**M**} & \multicolumn{1}{c}{**F0 (h)**} & \multicolumn{1}{c}{**T0 (s)**} & \multicolumn{1}{c}{**F1 (h)**} & \multicolumn{1}{c}{**T1 (s)**} & \multicolumn{1}{c}{**F2 (h)**} & \multicolumn{1}{c}{**T2 (s)**} & \multicolumn{1}{c}{**F3 (h)**} & \multicolumn{1}{c}{**T3 (s)**} & \multicolumn{1}{c}{**F4 (h)**} & \multicolumn{1}{c}{**T4 (s)**} \\ \hline
110 & 110 & 402 & 29 & - & \(>\)10,800 & 986.7 & 480 & 986.2 & 720 & 1030.5 & 115 & 1018.4 & 225 \\
100 & 100 & 342 & 29 & - & \(>\)10,800 & 696.5 & 270 & 696.5 & 480 & 732.3 & 65 & 723.5 & 126 \\
90 & 90 & 324 & 29 & - & \(>\)10,800 & 704.7 & 240 & 705.1 & 360 & 740.2 & 58 & 732.5 & 112 \\
80 & 80 & 286 & 29 & - & \(>\)10,800 & 525.6 & 205 & 525.5 & 356 & 556.7 & 48 & 548.7 & 94 \\
60 & 60 & 200 & 29 & - & \(>\)10,800 & 248.8 & 140 & 248.5 & 210 & 262.4 & 34 & 258.6 & 65 \\
50 & 50 & 173 & 29 & - & \(>\)10,800 & 318.9 & 110 & 318.3 & 198 & 336.9 & 28 & 331.5 & 54 \\
20 & 20 & 74 & 29 & - & \(>\)10,800 & 158.6 & 60 & 158.2 & 108 & 165.3 & 15 & 163.9 & 28 \\
15 & 15 & 59 & 29 & - & \(>\)10,800 & 421.5 & 52 & 421.1 & 92 & 432.1 & 12 & 428.8 & 22 \\
10 & 10 & 31 & 29 & - & \(>\)10,800 & 215.4 & 20 & 215.1 & 34 & 219.8 & 4 & 217.9 & 7 \\
9 & 9 & 34 & 29 & - & \(>\)10,  
Comparing the two genetic algorithms in terms of rapidity, the first algorithm is faster than the second algorithm, while comparing them in terms of stability both algorithms are stable for the real instances of HCT and randomly generated instances, while for the randomly generated instances of the HCT type, the second algorithm is more stable than the first algorithm. Comparing the two algorithms in terms of quality of solutions, the performance of the algorithms depends on the types of instances. For some instances, the first algorithm is better than the second algorithm, while for other instances, the second algorithm is more efficient than the first algorithm. By comparing the results obtained with the genetic algorithms and the iterated local search methods for all the tested instances, we find that the ILSs are worse than the GAs in terms of the quality of solutions obtained. However, in terms of rapidity, the ILS methods are faster compared to the GAs.
## 2 Numerical Model and Verification
### Geometrical Data and Finite Element Modeling (the Case Study Tank)  
A rectangular concrete liquid storage tank was modeled with the finite element method and subjected to several near-fault pulse-like and non-pulse-like ground motions to investigate the effects of ground motion characteristics on the dynamic response of rectangular liquid storage tanks. The tank was modeled with all walls being flexible in order to take into account the effects of wall flexibility on the seismic behavior of the tank.  
The maximum pressure in rectangular concrete water storage tanks occurs at the bottom of the tank wall, where the hydrostatic pressure, a function of the liquid's density and the liquid's height, is the highest [65]. Therefore, the tank walls were modeled as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Analytical**} & \multicolumn{6}{c}{**FEM (Number of Elements vs. Frequency)**} \\ \cline{3-6}  & & **M1** & **M2** & **M3** & **M4** & **M5 (Fine)**, \\  & & **(120 Elements)** & **(240 Elements)** & **(480 Elements)** & **(1920 Elements)** & **(15,200 Elements)** \\ \hline
1 st & 0.87 & 0.85 & 0.87 & 0.87 & 0.87 & 0.87 \\
2 nd & 1.53 & 1.23 & 1.45 & 1.54 & 1.50 & 1.51 \\
3 rd & 1.98 & 1.32 & 1.75 & 2.05 & 1.91 & 1.95 \\
4 th & 2.35 & 1.39 & 1.91 & 2.51 & 2.30 & 2.33 \\
5 th & 2.64 & 1.45 & 1.99 & 2.75 & 2.55 & 2.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequency comparison (Hz).  
Figure 3: Comparison between the sloshing response of the numerical model and the experimental results published by [53].  
tapered, such that the thickness of the walls varied linearly from 0.4 m at the base to 0.2 m at the top of the tank wall. Some liquid storage tanks were constructed with tapered walls, and some numerical models of liquid storage tanks studied in the literature were modeled with tapered walls like the rectangular liquid storage tank studied by [52]. Figure 4 shows the geometrical parameters of the water-tank system.  
Tables 2 and 3 show its dimensional and material properties.
## 2 Principles and Setups  
From Figure 1, the proposed MWEDFL is mainly composed of a broadband source (BBS), a comb filter (i.e., Lyot filter) and an electrical monitoring and controlling system. In such hybrid dual-ring configuration, BBS and Lyot filter form an optical ring cavity (denoted by Ring-1) to generate and flatten multi-wavelength oscillations in the band of 1550 nm through connecting a section of SMF, coupler (C1) and isolator. And the monitor and controller form an electrical negative-feedback ring (denoted by Ring-2) to further stabilize the outputs of MWEDFL.
## 5 Conclusions  
In this paper, an MBA anti-collision protocol for handling the capture effect and the interference in RFID systems was proposed. The efficiency of the proposed MBA has been improved to 33.87% (and more as \(\alpha\) increases) with \(v=1\) m/frame, \(n=300\), \(pi=0.5\), and \(\alpha=0\) compared with the efficiency of 26.25% for the BA protocol. Consequently, the proposed protocol, have eliminated all BA drawbacks and improved its efficiency. Moreover, the results of the comparison of the same types of protocols (without bit tracking) such as GBT, TBT, GQT1, and GQT2 with the MBA indicate that the efficiency of these protocols is constant regardless of whatever tag velocity is changed (less than MBA when \(v=1\) m/frame for \(\alpha\) less than 0.7 and pi less than 0.25). MBA also outperforms the GBT and TBT with higher constant efficiency of 60% for any number of tags when \(v=1\) m/frame, while GBT and TBT only have efficiencies of 35% for any velocity of tag motion and any number of tags. It is also found that the efficiency of GQT1 and GQT2 changes depending on the number of tags and tags distribution but never exceeds 48% at \(n=500\) and uniform distribution. Moreover, GQT1 and GQT2 have efficiencies of less than 46% for \(n=300\) when tag IDs are lengthened with uniform distribution. Also, they are significantly influenced by the type of non-uniform distribution and by efficiencies much smaller than those with uniform distribution, while MBA does not depend on tag IDs, and its efficiency never decreases. Consequently, MBA exceeds the former protocols in efficiency when the tags move at low blocking velocity (such as 1 m/frame) and outperform the original BA protocol with full read rate and less distortion influence. Furthermore, the simulated results have proved that MBA is the best protocol to implement in the real world and is more suitable than other commercial implemented protocols in RFID tags and readers, particularly when tag motion is slow, the capture effect phenomenon exists, and the environment is noisy. Finally, the proposed protocol utilizes only comparison, random functions, two counters (\(pc\) and \(Ac\)), and small memory to store tag ID on the tag's side, so the implementation of the protocol can easily be done by passive tags with no more than little additional memory to represent tag counters and low computational cost for simple functions implementation. Consequently, MBA outperforms all other older protocols that do not employ bit-tracking technology in their operations by utilizing all possible techniques of blocking, capture-effect treatment, and interference distinguishing.  
Conceptualization, R.A.; Methodology, I.H.; Software, I.H.; Supervision, R.A. and B.J.  
The authors declare no conflict of interest.
## 4 Proposed MIMO Antenna for Body Contacts and Specific Absorption Rate
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Dimensions** & **Isolation** & **Max Gain** & **BW** & **Efficiency** & **Max** \\
**Number** & **(mm)** & **(dB)** & **(dBi)** & **(GHz)** & **(\%)** & **ECC** \\ \hline
[15] & 13 \(\times\) 25 & \textgreater{}20 & 4.80 & 2–12 & N/A & 0.009 \\ \hline
[31] & 50 \(\times\) 50 & \textgreater{}17 & 5.80 & 2–12 & 86.00 & 0.45 \\ \hline
[24] & 34 \(\times\) 34 & \textgreater{}25 & 6.00 & 2.2–11 & 75.00 & \textless{}0.01 \\ \hline
[23] & 38.2 \(\times\) 26.6 & \textgreater{}16 & N/A & 2.72–12 & N/A & N \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference Number** & **Dimensions** & **Isolation** & **Max Gain** & **BW** & **Efficiency** & **Max** \\
**(mm)** & **(dB)** & **(dB)** & **(dB)** & **(GHz)** & **(\%)** & **ECC** \\ \hline
[83] & 24 \(\times\) 30 & \textgreater{}16.3 & 4.80 & 3–12.6 & N/A & \textless{}0.05 \\ \hline
[84] & 50 \(\times\) 35 & \textgreater{}21 & 7.48 & 1.83–13.82 & 84.00 & \textless{}0.059 \\ \hline
[85] & 45 \(\times\) 45 & \textgreater{}15 & 7.50 & 21–30 & 78.00 & \textless{}0.03 \\ \hline
[86] & 150 \(\times\) 75 & \textgreater{}12 & 6.20 & 3.3–7.1 & \textgreater{}48 & \textless{}0.07 \\ \hline Proposed & 50 \(\times\) 45 & \textgreater{}22 & 10.70 & 3.70–3.85, 5–40 & 98 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._  
Figure 28: Simulated SAR distribution for the MIMO antenna on chest at different frequencies. More simulations were run to see how well the suggested MIMO antenna worked when held close to the CST MWS human arm and chest voxel model. When port 2 is  
Figure 29: Simulated SAR distribution for the MIMO antenna on arm at different frequencies. operational, Figure 30 depicts the antenna patterns throughout the complete resonance spectrum. The voxel model had a greater impact on vertical polarization patterns than horizontal polarization patterns because the phone primarily contacted the head and hand by its length in the presence of the voxel body. Figure 31 exhibits greater than 22 dB isolation. But there is a minor frequency shift observed when all resonances have been accomplished. Reasonably, the coupling effects from the body might influence the antenna's characteristics that shifts its frequency. In fact, the body has higher dielectric constant compared to the simulation conducted in the vacuum. Figure 30: The simulated radiation pattern of the proposed MIMO antenna on arm of voxel body at different frequencies.
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 3 Conventional Precoding Schemes  
In this section, conventional BD precoder and modified BD precoders are introduced. Before the detailed description, \(\mathbf{\tilde{H}}_{i}\), \(L\) and \(\tilde{L}_{i}\) are defined as follows,  
\[\mathbf{\tilde{H}}_{i}=\Big{[}\begin{array}{cccccc}\mathbf{H}_{1}^{T}&\cdots& \mathbf{H}_{i-1}^{T}&\mathbf{H}_{i+1}^{T}&\cdots&\mathbf{H}_{K}^{T}\end{array} \Big{]}^{T}, \tag{5}\]  
\[L=\text{rank}(\mathbf{H}), \tag{6}\]  
\[\tilde{L}_{i}=\text{rank}(\mathbf{\tilde{H}}_{i}). \tag{7}\]
## 3 Distributed Jamming System Model for LEO Constellation Downlink
### Jamming Evaluation Metrics
#### 3.1.2 Time Delay  
Distributed jamming systems operate on the principle of time delay deception. The system deceives the jamming device by receiving all currently available satellite signals, thus determining whether they are all visible to the ground receiver. After spatial separation, a time delay is applied to each visible satellite signal individually. The amalgamated  
Figure 2: Jamming resource allocation system.  
deceptive signals are then broadcast to the target receiving device on the ground. By ensuring that the peaks of the deceptive signals surpass those of the genuine signals, the tracking loop of the target-receiving device can lock onto the deceptive signals and generate incorrect position solutions.  
When the target receiving device tracks the deceptive signals, the pseudorange from the \(j\)th jamming device forwarding the signal from the \(i\)th satellite to the \(k\)th target point is given by  
\[\rho=\left|\mathbf{r}_{i}^{L}-\mathbf{r}_{j}^{Z}\right|+\left|\mathbf{r}_{j}^{Z}-\mathbf{r}_{k }^{T}\right|+c\cdot\Delta\tau_{ijk}+c\cdot\delta, \tag{2}\]  
where \(\mathbf{r}_{i}^{L}\) represents the position of the \(i\)th satellite, \(\mathbf{r}_{j}^{Z}\) represents the position of the \(j\)th jammer, \(\mathbf{r}_{k}^{T}\) represents the position of the \(k\)th target receiver, \(\delta\) represents the receiver clock bias, and \(\Delta\tau_{ijk}\) represents the delay added when the \(j\)th jammer forwards the signal from the \(i\)th satellite to the \(k\)th user.  
Assuming that the desired location is represented by the vector \(\mathbf{r}_{k}^{C}\), the pseudorange from the \(i\)th satellite to the \(k\)th fictitious target point is given by  
\[\rho^{\prime}=\left|\mathbf{r}_{i}^{L}-\mathbf{r}_{k}^{C}\right|+c\cdot\delta. \tag{3}\]  
By letting  
\[\rho=\rho^{\prime}, \tag{4}\]  
the delay added when the \(j\)th jammer forwards the signal from the \(i\)th satellite to the \(k\)th user can be determined as follows:  
\[\Delta\tau_{ijk}=\frac{\left|\mathbf{r}_{i}^{L}-\mathbf{r}_{k}^{C}\right|-\left|\mathbf{r }_{i}^{L}-\mathbf{r}_{j}^{Z}\right|-\left|\mathbf{r}_{j}^{Z}-\mathbf{r}_{k}^{T}\right|}{c}. \tag{5}\]
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 8 Performance Evaluation  
### Experimental Setup  
We performed experiments using three devices to act as a sensor pool, edge server, and cloud server. The sensor pool sends the data to the target server, and either the edge or cloud server collects the data and performs the fault detection with the trained model. We implemented the CRFS algorithm using JmetalPy [47], and the fault detection model was implemented in Tensorflow [48] with Keras [49] for the high-level API to build the model. The experiments were evaluated through the accuracy (0-1), detection time (ms) that maintains the total of data transmission time and model prediction time, and packet size (KB). Table 4 lists the parameters considered for these experiments.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{6}{*}{**CRFS**} & Population and Offspring size & 100 \\  & Mutation & Bit-flip (\(1/n\)) [50] \\  & Crossover & Single-point (0.9) [51] \\  & Selection & Binary tournament [52] \\  & Weights & \(w_{cor_{s}}=0.4,w_{cor_{f}}=0.4,w_{nb_{f}}=0.2\) \\  & Iterations & 25,000 \\ \hline \multirow{6}{*}{**LSTM-AE**} & Test data ratio & 0.2 \\  & Encoder and Decoder units & [64; 32; 16] and [16; 32; 64] \\  & Encoded features & 8 \\  & Dense neurons & 8 \\  & Dropout & 0.3 \\  & Window size & 3 \\  & Learning rate & 0.001 \\  & Optimizer & Adam [53] \\  & Batch & 30 \\  & Epochs & 500 \\ \hline \multirow{3}{*}{**Device**} & Sensors pool & Ubuntu 16, Intel i3-6x, RAM 4 GB \\  & Edge server & Windows 10, Intel i5-7x, RAM 4 GB \\ \cline{1-1}  & Cloud server & Digitalocean Ubuntu 18, 1 TB Transfer \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental parameters.
## 2 Materials and Methods
### Display Characterization  
In a completely dark room, luminance and chromaticity coordinate measurements of each device display were taken by positioning the display against the aperture of a CS-2000A spectroradiometer (Konica Minolta), aligned perpendicularly to the display surface. The display was set to its maximum brightness, with automatic brightness adjustment turned off. A series of full-screen images was shown on the display, and for each image, the chromaticity coordinates \((x,y)\) and luminance \(Y\) were measured. For instance, in order to determine the model parameters of the Tone Rendering Curve of the red channel, images were displayed with \(G=0\) and \(B=0\), while varying \(R\) from 0 to 255 in steps \((0,\ 5,\ 10,\dots,255)\). This provides a comprehensive sampling of the full Tone Rendering Curve.  
A total of 11 devices were analyzed, consisting of 6 smartphones and 5 tablets. Of these, 8 were based on LCD and 3 on OLED display technology. For LCD-based displays, even when showing a completely black image, some luminance remains visible due to the backlighting transmission in the LCD, reaching up to 0.41 cd/m\({}^{2}\). To correct for this black level, Equation (1) is adjusted as follows [27]:  
\[\begin{pmatrix}\text{X}-\text{X}_{0}\\ Y-\text{Y}_{0}\\ \text{Z}-\text{Z}_{0}\end{pmatrix}=M\begin{pmatrix}\text{Y}_{R}\\ \text{Y}_{G}\\ \text{Y}_{B}\end{pmatrix} \tag{6}\]  
Here, \(X_{0}\), \(Y_{0}\), and \(Z_{0}\) represent the tristimulus values for a completely black image. These values are straightforward to calculate, as the measurement setup closely follows the standard procedure for measuring colorimetric characteristics of electronic displays [34; 35], by measuring the chromaticity coordinates and luminance \(x_{0}\), \(y_{0}\), and \(Y_{0}\). The modified Equation (6) compensates for one type of flare caused by light leakage through the LCD when displaying dark colors [32].
## 4 Case Study: AQM Implementation  
The Crowdsourced Navigation System (CNS) [51] is a mobile crowdsourcing system that has been chosen as a case study to evaluate the effectiveness of the proposed solution. The proposed AQM was implemented for managing the data confidentiality property in CNS. The four AQM phases were incorporated in the CNS agent system. Figure 7 shows the CNS architecture that incorporates the AQM framework.