esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experiment 1 in Bukavu, Eastern Highlands DR Congo

Table 2: Experiment 1 in Bukavu, eastern highlands DR Congo—F-values of fixed effects for final body weight (FBW), condition factor (CF), initial body weight (IBW), weight gain (WG), feed conversion ratio (FCR), hepato-somatic index (HSI), gonads-somatic index (GSI), protein efficiency ratio (PER), and lipid efficiency ratio (LER). FBW and CF calculated using data from individual fish, while the other parameters based on hapa data. All parameters were analyzed using Equation (1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature** & & **Dy Matter (K)** & **Code Protein (Y-dim)** & **Ach (Y-dim)** & **Cross Energy (Kooling data)** & **Code Light (Y-dim)** & **Total** & **Carbohydrate (Y-dim)** \\ \hline \hline Strain & Lake Kors & 28.3 \(\pm\) 5.7 & 36.2 \(\pm\) 3.6 & 6.04 \(\pm\) 1.06 & 4.1 \(\pm\) 0.1 & 14.6 \(\pm\) 1.8\({}^{*}\) & 0.3 \(\pm\) 4.3 \\  & Nyababens & 31.1 \(\pm\) 3.6 & 36.7 \(\pm\) 5.4 & 7.48 \(\pm\) 1.38 & 4.2 \(\pm\) 0.2 & 12.6 \(\pm\) 1.1\({}^{*}\) & 0.3 \(\pm\) 5.3 \\  & Photo-based feed & 30.7 \(\pm\) 5.6 & 37.2 \(\pm\) 3.4 & 7.09 \(\pm\) 1.26 & 4.1 \(\pm\) 0.1 & 13.3 \(\pm\) 2.2 & 42.5 \(\pm\) 3.4 \\  & Commercial feed & 28.5 \(\pm\) 4.1 & 36.7 \(\pm\) 5.7 & 6.48 \(\pm\) 1.38 & 4.2 \(\pm\) 0.1 & 13.3 \(\pm\) 1.2 & 42.7 \(\pm\) 5.6 \\  & Lake Kors & Enhanced based & 30.6 \(\pm\) 4.8 & 35.8 \(\pm\) 4.1 & 6.48 \(\pm\) 1.09 & 4.10 \(\pm\) 0.04 & 14.9 \(\pm\) 0.04 \\  & Lake Kors & Enhanced free feed & 26.2 \(\pm\) 3.7 & 36.9 \(\pm\) 5.3 & 5.84 \(\pm\) 0.24 & 4.1 \(\pm\) 0.1 & 13.7 \(\pm\) 1.5 & 42.0 \(\pm\) 6.4 \\  & Nayakawa & Enhanced free feed & 30.6 \(\pm\) 3.2 & 36.7 \(\pm\) 2.4 & 7.81 \(\pm\) 1.09 & 4.2 \(\pm\) 0.2 & 11.6 \(\pm\) 0.2 & 42.2 \(\pm\) 5.1 \\  & Nyababens & Enhanced free feed & 31.3 \(\pm\) 3.6 & 36.7 \(\pm\) 7.1 & 1.12 \(\pm\) 4.3 & 4.0 \(\pm\) 0.2 & 13.0 \(\pm\) 0.6 & 42.0 \(\pm\) 5.8 \\  & Nyababens & Commercial feed & 31.4 \(\pm\) 4.3 & 34.6 \(\pm\) 6.9 & 1.45 & 42.2 \(\pm\) 0.2 & 32.1 \(\pm\) 1.5 & 44.6 \(\pm\) 7.6 \\  & Strain & 0.271 & 0.833 & 0.300 & 0.284 & 0.008 & 0.008 & 0.007 \\  & Date & 0.796 & 0.750 & 0.140 & 0.605 & 0.278 & 0.711 \\  & Strain \(\times\) Feed & & 0.649 & 0.750 & 0.344 & 0.831 & 0.120 & 0.862 \\  & Powder-SIM & & 3.02 & 2.96 & 0.13 & 0.08 & 0.75 & 3.05 \\ \hline \hline \end{tabular}  
* \({}^{\dagger}\): Data within the same column with different letters are significantly different (see Table 2).
* \(n\) = \(p\) \(\pm\) 0.01  
\end{table} 
Table 4: Experiment 1 in Buxuxux, eastern highlands DR Congo—mean \(\pm\) standard deviation for whole body composition of Nile Alpha (Procedrum _in silico_) fits of the Lake Kivu and the Nyakabens strains fed on the commercial feed and the fish-based and fish-free feeds formulated using local ingredients \({}^{\dagger}\).
## 4 Numerical Setup
### Computational Model and Mesh  
The thermal-fluid coupling numerical computational model that includes both fluids and solids, and is shown in Figure 6. Its fundamental parameters are the same as those of the test intermediate gearbox (Tables 1 and 2). The model was divided into a tetrahedral unstructured mesh using the ANSYS meshing software, as shown in Figure 7. The bearings and gears are the key heat-generating regions; therefore, the mesh was refined locally. Analyzing the mesh independence, as shown in Table 5, we concluded that the air flow rate change of the air vent could be controlled to within 2% when the number of mesh elements was between 10,589,597 and 15,206,765. A mesh density of 10,589,597 mesh elements was selected, because this satisfied the required calculation accuracy without wasting calculation resources. The final number of mesh elements in the computational model was 10,589,597, and the number of mesh nodes was 1,787,247.  
Figure 6: Computational model.  
Figure 7: Mesh diagram for thermal-fluid coupling computational model.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 6 Conversion of Halophytes into Biogas
### The Biogas Potential of Halophytes
[71], the variation in the anaerobic degradation of halophytic biomasses are related to variations in the concentration of nutrients and some chemical compounds that are deemed inhibitory for AD. Furthermore, the differences in anaerobic decomposition can also relate to the variation in lignin content and due to its recalcitrant nature, which is considered to be non-degradable in anaerobic conditions [71, 83]. The biogas and methane potential of different halophyte species grown under hydroponic conditions using different concentrations of NaCl and artificial seawater were also investigated under AD [73, 74]. The halophyte, _Tripolium panonicum_, achieved the highest specific biogas yield grown under non-saline conditions of 476 mL g\({}^{-1}\) VS, whereas the highest specific methane yield was obtained from plants grown under 15 g L\({}^{-1}\) and 22.5 g L\({}^{-1}\) sea salt of 309 mL g\({}^{-1}\) VS and 313 mL g\({}^{-1}\) VS. _Tripolium panonicum_ obtained higher specific biogas and methane yield grown with 30 g L\({}^{-1}\), with an average of 554 mL g\({}^{-1}\) VS and 347 mL g\({}^{-1}\) VS, respectively [73]. Similarly, _Chenopodium quinoa_ was cultivated with varying concentrations of NaCl and evaluated for specific biogas and methane yield on a fresh biomass and dry material basis. Based on fresh biomass, the highest specific biogas and methane yield was achieved with plants grown at 20 g L\({}^{-1}\) NaCl with 470 mL g\({}^{-1}\) VS and 305 mL g\({}^{-1}\) VS, respectively. Likewise, based on dry matter, cultivation with 20 g L\({}^{-1}\) NaCl obtained both the highest specific biogas yield of 459 mL g\({}^{-1}\) VS and methane yield of 289 mL g\({}^{-1}\) VS [74]. The biogas yields from this halophytic species are higher in comparison with many other conventional crops, which show the favorability of using of this halophyte species for biogas production. For instance, [PERSON] et al. [84] reported an average specific methane yield of 250-450 mL g\({}^{-1}\) VS from maize silage, 200-250 mL g\({}^{-1}\) VS from straw, 300-450 mL g\({}^{-1}\) VS grass and 230-380 mL g\({}^{-1}\) VS from sugar beet. In the same direction, [PERSON] et al. [85] reported an average specific methane yield of 398 mL g\({}^{-1}\) VS from maize, 140-343 mL g\({}^{-1}\) VS from wheat, 128-392 mL g\({}^{-1}\) VS from different grasslands and 428-454 mL g\({}^{-1}\) VS from sunflowers in the first harvest. In essence, based on specific biogas/methane yields in conjunction with desirable cellulose/hemicellulose and low lignin content, halophytic biomass is an optimal candidate for energy exploitation (Figure 3).
## 3 Performance Analysis
### Average Capacity  
In a generalized fading environment, statistical averaging over the probability density function (PDF) of an instantaneous SNR is required to assess the average capacity. However, this PDF is often either unidentified or presented in such a convoluted manner that it does not lend itself to this averaging.  
For a signal's (s) transmission bandwidth (BW) and energy over an AWGN channel, Shannon capacity can be expressed as  
\[C_{\gamma}=B\log_{2}(1+\gamma). \tag{12}\]  
Because \(C_{\gamma}\) is directly coupled to \(\gamma\) in the Shannon capacity, it may also be seen as an RV (6). By averaging \(C_{\gamma}\) across the PDF of \(\gamma\) at the receiver's output, it is possible to determine the average channel capacity [40], that is,  
\[\overline{C}_{\gamma}=B\int\limits_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma}( \gamma)d\gamma. \tag{13}\]
## 4 Finite Element Model and Verification
### Model Verification  
A mesh study is conducted to observe the convergence of buckling stress as the mesh size increases. Take plate with No. 8 in Table 2 for example, the Eigenvalues with respect to element numbers depended on mesh size, as shown in Figure 9. It can be seen that the curves starts to converge after an element number of 400, which with the general mesh sizes of \(b/10\) and with mesh sizes in hole edge of \(\pi d/40\). In order to reduce computational time, those meshes are adopted in the subsequent buckling analysis.  
In order to verify FE models used in this study, a comparison with the authors' experimental results [19] on the critical stress of perforated plates strengthened with FRP has been performed, and the results are in Table 2. It can be seen that the FE models have a very limited relative absolute error in \(f_{\mathcal{C}}\), in the consider cases, the errors are 13.0% on maximum, 4.05% on average, and 0.108 on the coefficient of variation. Therefore, the FE models are very accurate and enough for the studies in the next section.  
Figure 8: Examples of four typical plate models’ meshes. (**a**) case of \(\beta=1\); (**b**) case of \(\beta=2\); (**c**) case of \(\beta=3\); (**d**) case of \(\beta=4\).
## 2 Algorithm Theory and Method
### Dynamic Warning Lines at All Levels
#### 2.4.2 Dynamic Adaptive Threshold Update Method  
During the normal operation of the equipment, because of the change in field signal transmission and environment, the data are subject to constant dynamic fluctuations. The traditional fixed threshold method will cause false alarms or missing alarms, which will affect the early warning results. The _dynamic-\(\sigma\)_ method is adopted to update the thresholds.  
According to the Chebyshev inequality, the normal interval of any random variable with finite expectations and a finite variance is \([\mu-\lambda\sigma,\;\mu+\lambda\sigma]\), where \(\mu\) and \(\sigma\) are the mean and variance of the random variable, respectively, and the bandwidth coefficient \(\lambda\) is related to the error detection rate \(\alpha\), i.e., \(\alpha=1/\lambda^{2}\).  
\[\mu=\frac{1}{n}\sum_{i=1}^{n}z_{i} \tag{13}\]  
\[\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(z_{i}-\mu\right)^{2}} \tag{14}\]  
where \(z_{i}\) is the measured value of the variable at the \(i\)-th moment. To reflect the dynamic nature of the data, it is necessary to input the data at each subsequent time point into the above equation for updating purposes. The mean value and variance are subsequently derived to further reduce the calculation amount and reduce the data storage space:  
\[\mu_{M+1}=\mu_{M}+\frac{1}{M+1}(z_{M+1}-\mu_{M}) \tag{15}\]  
\[\sigma_{M+1}=\sqrt{\frac{M-1}{M}(\sigma_{M})^{2}+\frac{1}{M+1}(z_{M+1}-\mu_{M })^{2}} \tag{16}\]  
according to the calculation of the mean value \(\mu_{M+1}\) and variance \(\sigma_{M+1}\) at time \(M+1\), only the mean value U and the variance Q at time \(M\) and the measured value \(z_{M+1}\) at time \(M+1\) need be considered.
## 5 Simulation Results
The quadrature all-pass filter in [16] and the one-stage transformer-based quadrature hybrid in [19; 20] can only provide a matched I/Q signal over a narrow bandwidth, limiting their applications in wideband 5G transceivers. An RC-CR filter can generate an I/Q signal with a high IRR over wide bandwidth but with substantially high signal loss and vulnerability to process variation. The network proposed in [18] can provide I/Q signal with an IRR > 30 dB over a wider bandwidth. However, the scheme, which is composed of three stages of transformer-based poly phase network, results in extra signal loss and chip area. Compared with wideband I/Q signal network presented in [12], the ultra-wideband  
Figure 14: Magnitude (red line) and phase (blue line) mismatch of the proposed ultra-wideband I/Q signal generation network. quadrature signal generation network proposed in this paper can generate matched I/Q signal with a higher IRR over a wider bandwidth. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Frequency Range (GHz)**} & \multirow{2}{*}{**Gain (dB)**} & \multirow{2}{*}{**I/Q Mag/Phase Error (dB/deg)**} & \multirow{2}{*}{**IRR (dB)**} & \multirow{2}{*}{**Chip Size (\(\mathbf{\mu}\mathbf{m}^{2}\))**} \\  & & & & & & \\ \hline \multirow{4}{*}{This Work a  } & \multirow{2}{*}{21.5–53.5} & \multirow{2}{*}{\(-2.74\) (\(-1.9\) c)} & \multirow{2}{*}{0.13/1.2\({}^{\circ}\)} & \multirow{2}{*}{\(>\)40} & \multirow{2}{*}{\(845\times 495\) d  } \\  & & & & & & \\ \hline \multirow{4}{*}{[12] b  } & Two-stage transformer-based transformer-based differential I/Q network & 25–50 & \(-2.1\) & \(0.15/1.8\)\({}^{\circ}\) & \(>\)35.2 & \(330\times 425\) \\ \hline \multirow{4}{*}{[16] a  } & Quadrature all-pass filter & 17–41.3 & +3 & \(1/5^{\circ}\) & - & 240 \(\times\) 410 \\ \hline \multirow{4}{*}{[17] b  } & Three-stage RC-CR polyphase filter & 2.5–10 & \(-10\) & - & \(>\)35 & - \\ \hline \multirow{
## 2 Materials and Methods
### Image Acquisition of Different Apple Orchards  
The acquisition process has been as close as possible to the conditions of a video captured by a drone. The videos were recorded in five orchards of Red Delicious apples in Kermanshah, Iran (34 deg18'48.87'', 147 deg6.92'') E using a color GigE industrial camera model DFK-23 GM021 (Imaging Source Europe GmbH, Bremen, Germany), with a 1/3 inch Aptima CMOS MT9M021 sensor (ON Semiconductor, Aurora, CO, USA) and a spatial resolution of 1280 \(\times\) 960 pixels. The mounted lens was model H0514-MP2 (Computer CBC Group, Tokyo, Japan), with f = 5 mm and F1.4. As shown in Table 1, the videos were recorded at 11 different light intensities, always under natural outdoor conditions. The distance from the trees was between 0.5 and 2 m, the speed was around 1 m/s, and the viewing angle was nearly parallel to the ground. The camera was manually held, simulating a low-medium height flight of a drone. With the system described, which has a horizontal viewing angle around 80 deg, an apple about 7 cm would be observed with a size of 20 pixels at a distance about 3 m over the trees.  
Figure 1: Scheme of the proposed algorithm for apple ripening estimation using color features.  
As indicated in Table 1, there is a total of more than 8000 images in the dataset, showing a great variety in luminosity, background, size of the apples and maturity stage. More specifically, four possible ripening stages are defined: (1) unripe, (2) half-ripe, (3) ripe, and (4) overripe. This classification has been used in many previous studies [23]. Figure 2 shows four sample images of the different stages of apple ripeness (_Malus Domestica_ L., var. Red Delicious). As can be seen, since maturation is a natural process that occurs continuously and progressively, the colors of the samples change gradually. In this way, the colors of unripe and half-ripe apples are very similar, and the same happens for ripe and overripe stages.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 5 Results of the Current Hard SyDR Implementation
### Results of the Tracking Stage
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Tasks** & **Time** & **Ratio** \\ \hline Reading of Inputs & 994 μs & 2.25\% \\ Retrieve Channel States & 5.1 μs & 0.01\% \\ Tracking & 43,100 μs & 97.7\% \\ Preserve Channel States & 5.1 μs & 0.01\% \\ \hline Total & 44,104 μs & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Task execution time of the Hard SyDR design on the FPGA. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Resource** & **LUTs** & **FFs** & & **DSPs** & & **BRAMs** \\ \hline
**Available** & 117,120 & — & 234,240 & — & 1248 & — & 144 & — \\
**Hard SyDR** & 37,552 & 32.1\% & 21,923 & 9.36\% & 293 & 23.5\% & 30.5 & 21.2\% \\ \hline Tracking & 31,827 & 27.2\% & 17,201 & 7.34\% & 293 & 23.5\% & 14.5 & 10.1\% \\ Early-Prompt-Late & 10,587 & 9.04\% & 4254 & 1.82\% & 198 & 15.9\% & 1 & 0.69\% \\ double Multiplication & 245 & 0.21\% & 475 & 0.20\% & 16 & 1.28\% & 0 & 0.00\% \\ double Cosine/Sine & 8087 & 6.90\% & 2349 & 1.00\% & 182 & 14.6\% & 0 & 0.00\% \\ Others & 2255 & 1.93\% & 1430 & 0.61\% & 0 & 0.00\% & 1 & 0.69\% \\ double Add. and Sub. & 2891 & 2.47\% & 852 & 0.36\% & 6 & 0.00\% & 0 & 0.00\% \\ double Multiplication & 612 & 0.52\% & 561 & 0.24\% & 16 & 1.28\% & 0 & 0.00\% \\ double Division & 6999 & 5.98\% & 4370 & 1.87\% & 0 & 0.00\% & 0 & 0.00\% \\ double Square Root & 3432 & 2.93\% & 2428 & 1.04\% & 0 & 0.00\% & 0 & 0.00\% \\ double Power & 2948 & 2.52\% & 1353 & 0.58\% & 73 & 10.5\% & 10.5 & 7.29\% \\ double Modulo & 766 & 0.65\% & 336 & 0.14\% & 0 & 0.00\% & 0 & 0.00\% \\ double Inverse Tangent & 2548 & 2.18\% & 606 & 0.26\% & 0 & 0.00\% & 2 & 1.39\% \\ Others & 1044 & 0.89\% & 2441 & 1.04\% & 0 & 0.00\% & 1 & 0.69\% \\ Channel Retrieve & 2324 & 1.98\% & 514 & 0.22\% & 0 & 0.00\% & 0 & 0.00\% \\ Channel Store & 1542 & 1.32\% & 514 & 0.22\% & 0 & 0.00\% & 0 & 0.00\% \\ Channel Init & 1276 & 1.09\% & 513 & 0.22\% & 0 & 0.00\% & 0 & 0.00\% \\ Others & 583 & 0.50\% & 3181 & 1.36\% & 0 & 0.00\% & 16 & 11.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Post-implementation resource utilization of one tracking channel compared to the total resources available in the XCK26-C device used in the KV260 development board [35]. Percentages are relative to the total available resources of each type. sufficiently similar to render these disturbances negligible compared with the dynamic power consumption of the PL. Table 5 provides the power measurements and energy consumption estimates based on 1000 runs of the tracking stage. The energy consumption is computed by multiplying the power numbers by the total processing time. Figure 9 provides an overview of the power consumption over time in both idle and processing modes.
## 2 Experimental Setup  
Figure 1 shows a schematic of the setup, which consists of a moving frame holding three vertical tubes of a 20 mm \(\times\) 20 mm square cross-section forming a horizontal array. The center-to-center distance is S and the side length of the square cross-section is D (equivalent to hydraulic diameter) as shown in Figure 1. The experiment is performed on five S/D of 1.75, 2.75, 3.25, 3.75, and 4.25. Each tube is made from steel (polished mild steel) and filled with sand to insure uniform lateral conduction of heat, while a uniform heat flux heating element (\(\phi\) = 6.6 mm) is inserted at the center of the cross-section of each tube, as shown in Figure 2a. Each tube has a 1.0 m length and is capped with Bakelite insulation with thermal conductivity of 0.15 W/m K [14] to reduce any leakage in the axial heat transfer from each tube. Self-adhesive thermocouples (type K, 30 AWG, 0.01-inch diameter) are used to measure the surface temperature of each tube at three different surfaces; right, left, and the front surface only for similarity. The thermocouples are placed 100 mm apart on each surface, as shown in Figure 2b. Therefore, each tube has 27 thermocouples, 9 at each surface area. The test rig is placed in a 4.4 m \(\times\) 3.0 m room with no air conditioning or ventilation to ensure that the heat will be transferred from each tube surface only by natural convection. The room has only one entrance covered by a thick curtain. Thermocouples are connected to a data acquisition system, which in turn is connected to a computer, where the surface temperatures are stored for further analysis. A voltage regulator is used to control the input electrical power, which is measured by a Wattmeter and is shared equally between the tubes. It should be mentioned that the electrical power is increased such that the tube surface temperature does not exceed 100 \({}^{\circ}\)C.  
Figure 1: Experimental setup; (**a**) schematic and (**b**) real photo.  
Figure 2: Specification of each tube; (**a**) cross-section showing the internal heating element at the center and (**b**) thermocouple locations along each tube.
## 2 Brain-Computer Interfaces
### BCI Control Paradigms
#### 2.1.1 Evoked Potentials  
Evoked Potentials (EPS) are changes in the electrical potentials that are locked in time to certain events (i.e., visual or tactile). Normally, these brain signals are averaged over one second to be used as control signals. The main techniques are **P300 (P3) wave of visual evoked potentials** and **Steady State Visual Evoked Potential or SSVEP**. The P300 was first described by [PERSON] [40] as events that occur that have an alteration at 300 milliseconds after a visual event is presented (Figure 2a). The most used P300 was described by [PERSON] [41], where a matrix of letters and numbers (or symbols) are presented on a six-by-six grid (Figure 2b) and flashed horizontally and vertically, and when a line that has the corresponding symbol blinks, the P300 stimulus appears. Further, P300 can be employed as a lie detector by presenting a visual stimulus related to the lie that occurs if the subject knows the stimuli [42; 43].  
Similarly, SSVEPs are brain responses to visual stimuli, such as flickering lights, that manifest frequency-locked signals with an increased amplitude of the stimulated frequency located over the occipital lobe. Due to that, they do not require eye movement, and they can be used for people that still have eye acuity but cannot move their eyes [44].  
Figure 1: Basic components of a BCI. The image illustrates the map between the input and output through the translating algorithm. Signals are acquired by electrodes and then translated into a control signal for an external device (e.g., wheelchair, neuro-prosthesis or exoskeleton) using processing steps.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 1 Introduction
We also provide the waveform parameter selection criteria for maximizing correlation under various optimization constraints. The processing of the system received waveforms is summarized in Section 3, and system performance optimization criteria are provided in Section 4. Finally, Section 5 provides simulations to demonstrate the proposed joint waveform design approach.
## 2 Materials and Methods
### Desorption Studies  
The possibility to use sorbent more than once is a very important parameter. To obtain the information about the reusability of the sorbent 3 cycles of sorption/desorption were carried out. The acetic acid, citric acid, sodium chloride, and potassium chloride at a concentration of 0.1 M were used as eluents (additionally water as a control). The test was prepared as follows: To Erlenmeyer 250 cm\({}^{3}\) flasks containing 1 g of AP and PAP, 100 cm\({}^{3}\) of Cr(VI) and Cd(II) solutions (500 mg/dm\({}^{3}\)) were added, respectively. After 24 h, the probes were filtered and dried. Subsequently, the obtained samples were treated with the chosen eluent for 90 min. After a certain time, the ion concentration in the solution was checked by means of AAS. With the use of the equation presented below, the desorption degree was calculated:  
\[R_{des}=\frac{M_{sol}}{M_{sor}}.100\% \tag{11}\]  
where \(M_{sol}\)--metal content in solution after desorption (mg); \(M_{sor}\)--metal content in sorbent after desorption (mg).
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 4 Results and Comparison with Existing Models  
This section presents the main findings obtained after implementing and evaluating the CNN-NLP model. First, the experimental results derived from different dataset sizes are analyzed, including their impact on commonly used metrics. Furthermore, the model's behavior during training is examined, with a focus on its stability and convergence capacity. Second, a comparison with representative models is conducted to assess the performance of the proposed approach in quantitative terms.