esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 5 Experimental Results and Comparative Analysis
### Comparison of Learning Curves  
In Figure 13, we compared training and validation learning curves alongside a standard augmentation and GAN-based augmentation on the Faster-RCNN model. We observed that GAN-based augmentation improves the model efficacy. In the final steps, we observed a 0.18 difference between training losses of standard augmentation and the GAN-based augmentation model. We also see a 0.204 difference between the validation losses of standard augmentation and the GAN-based augmentation model. Each curve shows the averages across five runs under an augmentation type. This gives a clear sense of augmentation methods.  
In Figure 14, we compared training and validation learning curves alongside a standard augmentation and GAN-based augmentation on the SSD model. We observed that GAN-based augmentation improves the model efficacy. In the final steps, we observed a 0.25 difference between training losses of standard augmentation and the GAN-based augmentation model. We also see a 0.22 difference between the validation losses of standard augmentation and the GAN-based augmentation model. Each curve shows the averaged across five runs under an augmentation type. This gives a clear sense of variability in these curves and the significance of the differences between the two types of augmentation methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FID** & **KID** & \
\begin{tabular}{c} **P \& R** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Recall** \\ \end{tabular} \\ \hline SG2-ADA & 26.67 & 0.08 & 0.49 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the SG2-ADA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN Models** & \begin{tabular}{c} **Rice Leaf Diseases** \\ **BB** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **RS** \\ \end{tabular} &
\begin{tabular}{c} **GAN-Based** \\ **RB** \\ \end{tabular} \\ \hline Faster-RCNN & 0.7810.90 & 0.8910.93 & 0.7910.92 & 0.8810.95 & 0.84 & 0.93 \\ \hline SSD & 0.7710.89 & 0.8510.90 & 0.7610.91 & 0.8410.93 & 0.81 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the proposed GAN-based augmentation pipeline generative model.
# Applications of Machine Learning Technology in Agricultural Data Mining
###### Abstract
However, challenges still exist in the form of data heterogeneity, lack of enough labeled datasets, and the need for interpretable models that support human decision-making [11]. The quality of animal origin products is another area where ML has made substantial contributions [12]. Consumer preferences and market standards necessitate precise quality control measures, which traditionally involve labor-intensive and subjective assessment methods. ML offers automated, non-invasive, and highly accurate alternatives, such as computer vision systems powered by CNNs that can analyze images of eggs or carcasses to detect defects, classify grades, or even predict shelf life [13]. Similarly, [PERSON] et al. [14] showed that spectroscopic data, combined with ML algorithms, has been used to assess meat quality attributes such as tenderness, water-holding capacity, and chemical composition. These advancements not only improve the efficiency and accuracy of quality control processes, but also enable the industry to meet the growing demand for the traceability and transparency of agricultural products through data mining.
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 5 Simulation Results and Analysis
### Impact of Bandwidth Utilization Rate  
The bandwidth utilization rate is defined as the amount of bandwidth utilized to the total available bandwidth in the network. It is evaluated in percentage (%).  
Table 3 illustrates the bandwidth utilization rate to the number of DPs sent, ranging from 10 to 100 during the routing in MANET. The table value shows that the proposed FFROR technique has achieved a lower bandwidth utilization rate than the existing works. When the number of DPs sent increases, the bandwidth utilization rate also increases correspondingly. However, the bandwidth utilization rate in the proposed FFROR technique is less because of its use of the FFRO Algorithm. In the FFRO Algorithm, minimum bandwidth utilization MN is selected for transmitting the data packets. From the results,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Simulator & NS 2.34 \\ Protocol & AODV \\ No, of mobile nodes & 50, 100, 150, 200, 250, 300, 350, 400, 450, 500 \\ No, of Data packets & 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 \\ Simulation time & 200 s \\ Number of runs & 10 \\ Node speed & 0-30 m/s \\ Network area & 1000 m \(\times\) 1000 m \\ Packet size & 200 Kbps \\ Bandwidth & 20 MHz \\ Initial energy & 20 J \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
the proposed FFROR technique has reduced the bandwidth utilization rate by 23% when compared to the EAER Protocol [1], 34% when compared to the MECOR Protocol [4], 13% when compared to the EESMR technique [25], and 29% when compared to the OEERA scheme [26].
## 2 Materials and Methods
### Regularization Procedure  
Regularization is a well-known technique used for presenting some initial knowledge in statistical and estimation problems. [PERSON] in [PERSON] (1988) proved that this method may be applied to the identification problems, where an optimal regularization matrix, defined as the quadratic norm, is found. Mismatch of the estimated model may be described by two types of errors:* variance error: random error, representing uncertainty in the models' parameter estimates;
* bias error: systematic error, which represents model mismatch.  
The model is then parameterized also with bias and variance errors. Perfectly matched parameters minimize the mean square error, described by: \(MSE=|Bias|^{2}+Variance\). The use of regularization allows for better control of the bias-to-variance ratio during model estimation. The cost function changes its form from (12) to (13) [38].  
\[V_{N}(\theta)=\frac{1}{N}\sum_{n=1}^{N}\epsilon^{2}(n,\theta)+\frac{1}{N} \lambda\theta^{T}R\theta \tag{13}\]  
The standard cost function form is extended by two positively defined \(\lambda\) and \(R\) matrices. Matrix \(\lambda\) defines the effect of variance error traded for bias error. These two errors are connected together as follows: increase in the \(\lambda\) causes bias to increase and variance error to decrease. Thus, it improves the estimated model fit, while affecting deterioration of its parametric uncertainties.  
\[\lambda\uparrow\Rightarrow\text{bias}\uparrow,\text{variance}\downarrow \tag{14}\]  
\(R\) matrix is defined by weights and/or rotation of particular estimated model parameters (\(\theta\) vector values). It has an impact on penalty coefficients shaping and allows for constraint (e.g., model stability) to meet. It also allows for adding information about estimated parameters, such as the reliability of particular \(\theta\) vector parameters.  
Determination of regularization matrices is an empirical process [18]. It requires a priori knowledge (\(\theta^{*}\)) about estimated parameter values. Then \(\lambda\cdot R\) represents the confidence of these parameters. However, the process of finding their values still requires carrying out cross-validation tests and having some guesses about parameter values, which is not trivial. The Marginalized Maximum Likelihood method [18] enforces \(\lambda\) matrix empirical estimation and allows for \(\mathbf{R}\) matrix parameterization, defined by (15). One may create \(\mathbf{R}(\beta)\) matrix and write the likelihood functions for each parameter \(\beta\). There are some common ways to parameterize the impulse response of the FIR filter or the ARX model. However, none of them works properly with the state-space linearized ship dynamics model. Therefore, the regularization matrices have been defined using a genetic algorithm.
## 7 Experimental Evaluation  
In this section, we evaluate the different allocation methods proposed when applied to orbit slot allocation problems encoded as V-DPAP or R-DPAP. We present the experimental setup and analyze some results obtained on synthetic realistic instances. In addition to the experimental evaluation, this section also illustrates how a concrete application can be modeled in our theoretical framework.
# Stereotactic Positioning System: Towards a Mechanism Used in Thermal Ablation Therapy
## 3 Results
### Graphic Interface  
Figure 10 shows the graphic user interface designed and used to implement the preliminary test. The interface consists of three main sections: Section (1) or the control section. The desired coordinates are introduced using two textboxes provided for the Theta-axis and Z-Axis. Additionally, four buttons were added to implement a manual control; buttons allow displacements from the position to the left and to the right over the Theta-axis and up and down in the Z-axis. Figure 10 shows the graphical user interface developed to manipulate the stereotactic positioning system. Section (2) or the section for displaying the current position. This section shows the values obtained in real-time by the infrared sensors for the Z-axis and the Theta's potentiometer axis. Section (3) or the data export section. The measurements obtained by the three sensors are stored in a table. Once the total measurements were either recorded, or the test was finished, the user can export the data table. The formats in which the table can be exported are.xls and.CSV. This information can be used for further analysis.  
The graphical interface allowed the preliminary tests to be carried out in an easier and faster way. First, the graphical interface was able to carry out the movements requested by the user. Furthermore, the interface shows the system's position, providing a way to check the system's accuracy. Finally, the interface is able to store the data obtained by the sensors, export them, and analyze the system's behavior during tests.  
Figure 9: Difference between programmed value and the measured physical value, the red line represents the error mean value and dotted red line the confidence interval.
# _remote sensing_
## 4 Satellite Architecture and System Budgets
### Link Budget  
The satellite will use a TT&C up/down link channel in the UHF band and a down link channel in X-band for high data-rate transmission of scientific data. The UHF downlink channel uses an omnidirectional antenna, whereas the X-band uses a patch antenna. The selected data rate for the link budget computation is 300 MBps, which has been demonstrated in [55; 56] and that is allowed by commercial hardware for CubeSat platforms [57]. The ground station antenna gain's values refer to conventional performances for Yagi antennas in UHF band and 4-m paraboloid antennas for X-band data down link. Whereas command uplink does not pose any problem in terms of link budget, detailed evaluation of down-link budget is necessary, due to the limited power resources available on-board. The downlink budgets are reported in Table 12, considering a worst-case condition at 5 degrees of elevation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UHF (435 MHz)** & \multicolumn{2}{c}{**X-Band (8.0 GHz)**} \\ \hline
**Parameter** & **Value (Linear)** & **Value (dB)** & **Value (Linear)** & **Value (dB)** \\ \hline RF output (spaeccraft) & 1 W & 0 dBW & 3 & 4.77 dBW \\ \hline Spaeccraft line loss & & 0.6 dB & & 0.6 dB \\ \hline Spaeccraft antenna gain & & \(-\)0.5 dB & & 13 dB \\ (and pointing losses) & & \(-\)0.5 dB & & 176.9 dB \\ \hline Free space loss & 5 degrees elevation & 151.6 dB & 5 degrees elevation & 176.9 dB \\ \hline Ionospheric/Atmospheric losses & & 2.5 dB & & 2.4 dB \\ \hline Polarization losses & & 3 dB & & 1 dB \\ \hline Ground station antenna & & & & \\ pointing loss & & 0.7 dB & & 1.1 dB \\ \hline Ground station antenna & & 14.1 dBi & & 48.3 dBi \\ gain & & & & \\ Effective noise temperature & 510 K & 27.08 dBK & 245.36 K & 23.90 dBK \\ \hline Ground station line losses & & 1 dB & & 1 dB \\ \hline Data rate & 9600 bps & 39.8 dBHz & 300 Mbps & 80.0 dBHz \\ \hline Eb/N0 & & 17.7 dB & & 6.77 dB \\ \hline  & & \multicolumn{3}{c}{Band efficient 8 PSK} \\ Eb/N0 threshold & (GMSK, BER \(10^{-5}\)) & 10.6 dB & Concatenated Viterbi/Reed & 4.2 dB \\  & & \multicolumn{3}{c}{Solomon Rate 1/2} \\ \hline
**Link margin** & & **5.2 dB** & & **3.57 dB** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Downlink link budgets (UHF and X-band).
## 4 Results and Analyses
### Effect of ACF on Hydrothermal Parameters  
Evaluating the ACF of stochastic fields of hydrothermal parameters is crucial for understanding the impact on temperature distribution of soft clay rock in underground space engineering. The ACF characterizes the spatial dependency of hydrothermal parameters for the soft clay rock. This information is vital for predicting the spatial variation of temperature within the soil mass and, consequently, the performance of freeze curtains. By analyzing the autocorrelation function, engineers can assess how thermal properties vary over different distances within the soft clay rock. This understanding enables the estimation of the range at which thermal influences propagate, guiding the design and placement of freeze curtain elements for optimal temperature control. Additionally, it helps identify areas of high spatial variability where additional monitoring or intervention may be necessary to ensure uniform freezing and prevent ground settlement. Furthermore,  
Figure 5: Variability of temperature characteristic value of soft clay rock: (**a**) Average thickness of freezing curtain; (**b**) standard deviation of average thickness; (**c**) average temperature of freezing curtain; (**d**) standard deviation of average temperature. incorporating the ACF into thermal analysis models enhances their accuracy and reliability. By capturing the spatial correlation of thermal parameters, these models can provide more realistic predictions of temperature distributions within the soil mass and along the soft clay rock. This is essential for optimizing freeze curtain designs, ensuring adequate ground freezing, and minimizing the risk of soil instability during underground space engineering. Overall, evaluating the ACF of hydrothermal parameters is essential for understanding the spatial variability of temperature within underground space engineering environments. It facilitates informed decision-making in freeze curtain design and construction, ultimately ensuring the safety, stability, and longevity of underground infrastructure. To elucidate the ACF on the variability of temperature characteristics of frozen curtains, this paper selects the DSNX, DSQX, DSMK, DCSX, and DBIN models to represent the ACF of frozen soft clay rock layers and conducts sensitivity grouping (Table 3) calculations for correlation structure models. The results are shown in Figure 6.  
Figure 6 shows that the five different random field-related structures have different effects on the SD of freezing soft clay rock temperature. When DBIN is used, the SD of freezing curtain temperature is the largest. When DSQX is used, the SD of the freezing
## 3 Results and Discussion
### Dissolution Test
The %DE (Figure 2a) and MDT values (Figure 2b) for the developed GEF carriers were significantly improved as compared to the suspension of GEF pure. It was evident that all the examined developed drug delivery systems in the present investigation exhibited faster dissolution of GEF than the GEF pure. The DE% and MDT of GEF pure were only around 28.46 \(\pm\) 2.78% and 28.40 \(\pm\) 2.15 min, respectively. In the case of the F1 (GEF-TPGS) and F2 (GEF-SLS), better dissolution properties were observed as compared to the GEF pure. The DE and MDT of GEF-TPGS (F1) were 68.32 \(\pm\) 1.64% and 33.42 \(\pm\) 2.47 min, respectively, while those of (GEF-SLS) F2 were 59.64 \(\pm\) 1.25% and 36.37 \(\pm\) 3.88 min. Thus, the dissolution parameters for F1 (GEF-TPGS) were superior to F2 (GEF-SLS) because of better release profile in the case of F1, which was attributed to the good stabilizing property of TPGS. In the case of GEF-TPGS with HP \(\beta\)-CD (F3), both the dissolution parameters were significantly improved. It represented around 78.57 \(\pm\) 1.53% of DE and 24.90 \(\pm\) 1.79 min of MDT. The improvement in the dissolution parameters for F3 might be associated with the high solubility and complexation of GEF with TPGS and HP \(\beta\)-CD, respectively. Thus, the use of a  
Figure 1: Dissolution test and release profile of GEF from GEF pure and GEF SDs in 0.1 N HCl as release medium (pH 1.2). The data were provided as the mean and standard deviation (mean \(\pm\) SD) of three measurements, where “_”_” \(p\) < 0.005, F3 against F1, F2 and GEF pure; “#” \(p\) < 0.005, F1 against F2 and GEF pure; “$” \(p\) < 0.005, F2 against GEF pure. ternary system in the present study confirms the synergistic effect on the solubility of GEF when used in combination with HP \(\beta\)-CD [22; 43].
## 3 Methods
### Numerical Validation  
Synthetic videos were obtained from a numerical model of voice production referred to as the _Modified Body Cover Model_ (MBCM), which is a modification of existing VF models [5; 37] to better serve this study. The model features posterior and membranous glottal openings (PGO and MGO) implementations and a triangular (non-plane) intraglottal mass surface representation, which allows for modeling the variations of the contact area and pressure during closure phases in the anterior-posterior direction. The model is fixed on the anterior and posterior VF locations, and the medial portions are considered point masses. The shape of each fold is defined by a linear interpolation between the anterior, medial, and posterior points. When the cover masses are in contact, the resulting overlapping shape has a distributed contact load that is used to calculate the lumped contact pressure interaction between the cover masses. In particular, the contact depth, contact area, and contact pressure values are extracted from the lower cover masses to conform to the benchmark data set. Glottal opening areas are controlled by parameters governing the throarytenoid configuration, with the possibility to define incomplete glottal closure scenarios with PGO and MGO gaps. During collision, the MBCM masses overlap as in traditional lumped element VF models [38]; the penetration depth of the MBCM is observable and serves as a target for analysis of the proposed CPA method.  
The MBCM model was used to simulate two sets of test cases, one with complete glottal closure phonation (TEST1) and the other with incomplete glottal closure phonation (TEST2). Each test has the same muscle activation, posturing, and vocal tract configurations but differ in the PGO/MGO gaps, which are zero in TEST1 and non-zero in TEST2. For each set, 53 videos with different subglottal pressure values, ranging from 700 to 2000 Pa were obtained. In both test sets, muscular activity was fixed to \(a_{ta}=0.5\) (throarytenoid), \(a_{ct}=0.1\) (cricothyroid), and \(a_{lc}=0.495\) (lateral cricoarytenoid) [39]. Grayscale frame sequences with a transverse view of the MBCM model showing a glottal gap representation were generated. Point locations of each mass were interpolated with parabolic curves to reproduce the glottal gap. Each video frame is 300 \(\times\) 300 pixels and the frame rate is 8750 fps.  
The CPA algorithm is computed on each video with the same configuration setup. The attachment points for these tests are defined by the anterior and posterior rest positions of the MBCM's low masses, and the video orientation is fixed; as such, the preprocessing stage is not needed. Validation with the synthetic videos consists of comparisons of penetration and collision pressure between the MBCM model and the CPA predictions.
## 3 Result Analysis and Discussion
### Peak-to-Average-Power Ratio (PAPR)
Figure 11 illustrates the CCDF curves corresponding to the F-OFDM signals generated by the proposed NHS-Flip-F-OFDM, traditional Flip-F-OFDM, DCO-F-OFDM, ACO-F-OFDM, DCO-OFDM, and ACO-OFDM. These curves are derived from a 16-QAM constellation with a signal length of \(N=512\). As shown in Figure 11, the CCDF diagrams for the proposed and standard F-OFDM are closely aligned, exhibiting a lower PAPR than ACO-F-OFDM and DCO-F-OFDM, respectively. However, compared with DCO-OFDM, due to the DC bias used, the proposed Flip-F-OFDM introduced a lower PAPR. In contrast, ACO-OFDM illustrates a lower PAPR than Flip-Filtered OFDM due to differences in signal structures and clipping mechanisms. ACO-OFDM inherently reduces the PAPR by clipping the signal at positive part of the signal is transmitted [37]. This clipping process removes prominent peaks, which significantly lowers the PAPR. F-OFDM filters each sub-band to confine the signal within a specified bandwidth. While this improves spectral efficiency and reduces out-of-band emissions, filtering can introduce additional peaks in the time domain signal due to the convolution process.
## 2 Related Works
### The Proposed Two-Phase Routing Algorithms  
The routing algorithm attempts to divide the complex data transmission process into two phases, which is an effective method to manage the routing and forwarding of messages in opportunistic networks. [PERSON] et al. [18] recommended a routing-delivery scheme (Explore and Wait) for profile-casting based on PRoPHET [19] algorithm. The algorithm consists of two phases: Explore phase and Wait phase. In the Explore phase, the Relcast message routes nodes to understand the state of the network. The Relcast message will be sent to a node profile that is defined as a group of users and the node profile is identified by node attributes instead of network address. During the Wait phase, the Relcast message has already built enough statistics about the network and the message carrier forwards Relcast messages to the suitable relay nodes through the statistics shared from the Relcast message. If the destination node has not appeared in the Explore phase, the carrier does not forward the Relcast message until the destination node is found during the Wait phase.  
The Spray and Wait routing algorithm is proposed by [PERSON] et al. [20]. This algorithm also includes two different phases: Spray phase and Wait phase. In the Spray phase, the source node is trying to make enough message copies and then transmits the message copies to the nodes it encountered. During the Wait phase, if the destination has not appeared in the spray phase, the nodes which obtain a message copy do not transmit the message until they find the destination node. However, the data transmission in Spray and Wait algorithm is impossible for the Bundle Protocol to implement the status routing of messages. For another routing algorithm with two phases, [PERSON] et al. [21] proposed the Predict and Relay routing algorithm based on historical meeting information. In this algorithm, the message will be sent to the nodes which have a higher probability of encountering the destination than others. The Predict and Relay routing algorithm effectively improves the delivery radio and reduces the average end-to-end delay in OppNet.