esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
## Appendix A Results of Classification Precision, Sensitivity, Specificity, F1 Score Values
Classification of the WDBC dataset by ELM and metaheuristic algorithms is shown in Table 12. It was found that IWSO was 98.34 in the precision values, IWSO was 98.09 in the sensitivity values, IWSO was 98.90 in the specificity values, and IWSO was 98.18 in the F1 score values. In the studies conducted with these data, when the accuracy results of IWSO were compared, higher than MNHO [8], ABC [12], that is, it is seen that it is more successful in classification and lower than PSO [10]. It is seen that the sensitivity results of IWSO are higher than GWO [8], and the specificity results are lower than MNHO [8]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SPECTF**} & \multicolumn{2}{c}{**ALO**} & \multicolumn{2}{c}{**DA**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**GWO**} & \multicolumn{2}{c}{**WSO**} & \multicolumn{2}{c}{**OWSO**} & \multicolumn{2}{c}{**IWSO**} \\ \cline{2-13}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline Precision & 96.19 & 11.32 & 81.44 & 29.89 & 89.34 & 23.22 & 91.40 & 20.78 & 83.95 & 24.17 & 90.68 & 18.32 & 93.77 & 17.62 \\ \hline Sensitivity & 84.95 & 3.25 & 86.09 & 6.06 & 85.32 & 3.74 & 84.72 & 5.30 & 87.73 & 6.21 & 87.51 & 4.66 & 88.56 & 4.74 \\ \hline Specificity & 86.64 & 13.13 & 89.61 & 9.49 & 85.59 & 11.49 & 91.35 & 10.14 & 86.84 & 10.84 & 87.05 & 12.51 & 90.88 & 10.46 \\ \hline F1 Score & 89.72 & 7.24 & 79.52 & 19.97 & 85.12 & 16.08 & 86.17 & 14.33 & 83.11 & 16.30 & 87.66 & 11.13 & 89.53 & 12.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of ELM with IWSO and ELM with other algorithms on the SPECTF dataset.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 3 Experimentation and Performance Evaluation
### Experimental Results and Analysis
#### 3.2.2 Detection of Sparsely Distributed Targets  
As can be seen in Figure 11, the diseased areas of the maize leaves were sparsely distributed, mainly concentrated in the upper right part and the lower left part of the leaves, and there was a certain distance between the two diseased areas. DETR and CenterNet mainly detected disease in the upper right part of the leaf, and there were a large number of missed detections. Although the transformer used in DETR focuses more on local key information, the transformer model often requires a large amount of data and a long training period to make the model converge. While the maize leaf dataset uses data enhancement technology to mitigate the over-fitting, the dataset for this experiment is much less massive than ImageNet, resulting in not fully meeting the data volume requirements of the transformer model. This is the main reason why DETR has a large number of missed detections in the lower left part of the leaf, as well as the leaf edges. Although YoLov5s detects most of the diseases, the detection confidence is lower than that of the MFF-CNN, and the detection effect is not satisfactory in the case of sparse disease distribution.  
The MFF-CNN algorithm proposed in this paper uses SoftPool to reduce the loss of information of features in the pooling process, thus maximizing the assurance of the comprehensible extraction of disease feature information in plant leaves. Thus, the MFF-CNN performs best even in the complex case of sparse disease distribution in maize leaves.  
Figure 10: Detection results of the overlapping shading of diseased leaves.  
3.2.3. Detection of Target and Background Texture Similarity  
In Figure 12, the texture and color of the maize leaves are very similar to the background, and it is hard to detect disease. YoLov5s does not have the same detection confidence as our proposed MFF-CNN on the maize leaf dataset. The main reason is the relatively large image resolution of the maize leaf dataset used in this experiment, which makes YoLov5s ineffective at detecting small targets with inadequate multi-scale information.  
Figure 11: Detection results of the sparse distribution of diseased leaves.  
Figure 12: Detection results of similar textures and backgrounds of maize leaves.  
Our proposed MFF-CNN enhances the multi-scale features of the CA module and prevents information loss as much as possible with the help of Softpool, which provides a good foundation for the detection of multi-scale targets afterward. It is experimentally demonstrated that with the joint efforts of the CA module and Softpool pooling, the MFF-CNN obtains the optimal detection accuracy, with a 1.6% improvement compared to YoLov5s and a 1.9% improvement compared to DETR.
## 3 Cooperative Schemes
### Cooperative Schemes for VLC
#### 3.2.4 Relay-Based Cooperative Techniques  
In this context, relay-assisted techniques are considered as suitable alternatives to support the main link and improve the system throughput. In [125], a cooperative VLC system is proposed using an intermediate light source that acts as a relay terminal. A relay technique using an asymmetrically clipped direct current biased optical OFDM (ADO-OFDM) is proposed in [126]. This way, with the aim of relaying information and transmitting its own data using odd and even subcarriers, respectively, a relay-assisted VLC system comprising a main light on the ceiling and one task light on a table is presented in [127]. Notice that these relay-based cooperative techniques can be considered as special cases of the single footprint scenario.  
The applicability of these schemes to more flexible scenarios, in which even portable sources of light are involved, may not be feasible. A cooperative VLC system based on relay-assisted techniques where reflections are used as relaying links is proposed in [45] assuming that a PD is added per each AP. This proposal increases the reliability of the VLC networks in the presence of blocking elements, as it is common in indoor scenarios. That is, it provides an additional link that allows us to maintain the system performance even with the blockage of the LoS link. The basics for reflection-based cooperative techniques are presented in [45]. These techniques comprise a source S, a relay R and a destination user D, as depicted in Figure 16. Note that \(H^{\text{SR}}\) is the reflection-based channel where neighbouring APs are communicated. Then, \(R\) can relay the information to \(D\) after adequate processing based on amplify-and-forward and decode-and-forward methods and half-duplex and full-duplex modes.
## 4 Conclusions  
New configurations of a magnetoinductive device with directional filter properties have been introduced based purely on magnetically coupled LC resonators. Design rules have been established, and methods for calculating scattering parameters when filters are  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Manufacturer** & **Model** & **OD** & **ID** & **Height** & **Initial \(\mu_{i}\)** & **F\({}_{\text{max}}\)** & **T\({}_{\text{C}}\)\({}^{\circ}\)C** & **Material** & **Loss Factor** \\ \hline Ferroxcube & - & - & - & - & 125 & 20 & \textgreater{}350 & 4C65 & 130 @ 10 MHz \\ Fair-Rite & 5961001801 & 22.10 & 13.70 & 06.35 & 125 & 25 & \textgreater{}300 & 61 & 10 @ 10 MHz \\ Ferroxcube & - & - & - & - & 25 & 100 & \textgreater{}400 & 4E2 & - \\ Fair-Rite & 5968001801 & 22.10 & 13.70 & 6.35 & 16 & 150 & \textgreater{}500 & 68 & 300 @ 100 MHz \\ National Magnetics & - & - & - & - & 7.5 & 400 & \textgreater{}320 & M5 & \textless{}3500 @ 100 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of a selection of ferrite materials and toroids.  
Figure 9: Power spectral densities of (**a**) input signal to port 1 of MI single-notch directional filter; (**b**) output signal at port 2 normalised by the input carrier power \(P_{c}\). Dashed lines: filter bandwidth; red: harmonics at multiples of carrier frequencies; green: harmonics at message sidebands. (**c**) Attenuation of sidebands and carrier.  
used in MI systems (which have complex, frequency-dependent impedance) or conventional systems (with real impedance) have been clarified. The circuit equations have been simplified and solved using wave analysis. Analytic expressions have been developed for S-parameters and bandwidth, a simple method for introducing tuneability has been proposed, and extensions to allow infinite rejection at a chosen frequency or multiple stopbands with high rejection have been described.  
Device construction and operation have been verified at HF. Ceramic ferrite cores have been used to ensure nearest-neighbour coupling in a compact layout, and simple mechanical methods of tuning resonant frequency and coupling (essential in blocker applications) have been proposed and demonstrated. Experimental results for tuneable filters, filters with infinite rejection, and filters with multiple bandstop frequencies have all been shown to agree with theoretical models. Power handing capability sufficient for blocker applications in HF RFID has been demonstrated, with little harmonic generation due to ferrite nonlinearity. Further development will involve direct connection to a magnetoinductive antenna, and this work is in progress.  
Methodology, A.V., R.R.A.S. and O.S.; software, A.V.; validation, A.V., R.R.A.S. and O.S.; formal analysis, A.V., R.R.A.S. and O.S.; investigation, A.V.; writing--original draft preparation, A.V., R.R.A.S. and O.S.; writing--review and editing, A.V., R.R.A.S. and O.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### PCA Analysis
Figure 3c shows a noticeable difference in the intensities of the vibrational modes of the samples around 4304-8763 cm\({}^{-1}\), and this could explain why PC-1 obtains 78% of the variance present in the data without the presence of any sample overlap (Figure 4d). The first four components for the pre-treatment SNV + detrending + 1 st derivative with 19 smoothing points explain almost 100% of the variance present in the samples. Likewise, PC-2, PC-3, and PC-4 contribute 12%, 8%, and 1%, respectively, of the variance explained in the data. But according to Figure 6a,b, these last PCs generate overlaps between a certain group of samples for the remaining score plots, which makes it difficult to observe the  
Figure 5: Loading plot for PC-1 to PCA model with SNV + detrending (2-order) + Savitzky–Golay first-derivative with 19 smoothing points. differences between the samples and identify the associated variable. Accordingly, the PCA model of pre-treatment SNV + detrending (2-order) + Savitzky-Golay first-derivative with 19 smoothing points using PC-1 and PC-2 contained sufficient information to explain the separation between the samples. On the other hand, Table 2 describes WI of the samples according to the data obtained by [PERSON] et al. in the analysis of physical properties using the Whiteness Index between the samples of the H1-H4 group using the values of Hunter scale data, the H1, H3, and H4 samples present very close WI values, visually indicating that the colors of their surfaces are very similar. This is of crucial importance for quality control in the process of mixing powdered flour products [54].
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Altimetry  
GNSS-R altimetry using interferometry processing was proposed by [PERSON] in 1993 as the first application of GNSS-R [5]. Several studies have demonstrated the capabilities of cGNSS-R and iGNSS-R in producing altimetry products. The first GNSS-R altimeter concept was conceived of in [98] and used the interferometric technique to achieve a better accuracy. The proof of concept was extended into a satellite instrument [56], but it was never launched into space. The GNSS-R altimetry theoretical precision using the interferometric mode depends on the signal/band used. For GPS L1 this can be ~13 cm, and for Galileo E5 this can go down to 6.3 cm [99]. The measured error of other space-borne altimeters, such as CryoSat-2 or Envisat over lakes, was between 5-68 cm and 17-54 cm [100], respectively, and the surface wave height altimetric product of other altimeters as Jason-3 and Sentinel-3 was 23-27 cm as compared to buoy data [101]. Results showing similar RMSE to CryoSat-2 or Envisat have been reported in actual GNSS-R experiments [102], where the RMSE was found to be ~1 m using long integration times of 2-5 s.  
Due to the lack of GNSS-R instruments capable of performing altimetry (e.g., capturing direct and reflected waveform simultaneously), other techniques were developed using propagation models that relied on only the reflected GNSS signal. However, results presented large errors and biases with respect to iGNSS-R techniques, with RMSEs on the order of 10 m [103].  
Recently, a thorough study on GNSS-R altimetry was published using the microwave interferometric reflectometer (MIR) technique to assess the GNSS-R precision bounds using experimental data, and using different bands and processing techniques [24]. The study presented results similar to those obtained [101], albeit using different combinations of bands and peak tracking techniques. It was found that the best achievable accuracy, compared to CryoSat-2, using the GPS L5 band, and tracking the first peak of the waveforms' derivative, was one of 85 cm.  
GNSS-R has been proposed as a low-cost altimeter, with a performance usually one to two orders of magnitude worse than that of radar altimeters. For GNSS-R methods, large integration times are required to lower the error, even using iGNSS-R or high-bandwidth signals, such as GPS L5. An alternate approach was presented in [104], where the authors proposed to use the coherent scattering as this allowed them to track the phase of the carrier signal to provide precise ranging measurements. As per study [104], a grazing anglegeometry maximized the ideal conditions for coherent scattering over the ocean. Authors used CYGNSS measurements of grazing angles to implement the first grazing angle carrier phase system for sea surface salinity, obtaining a precision of 3-4.1 cm in mean at 50 ms integration, i.e., cm at 1 Hz, comparable to radar altimeters.
## 4 Discussion
Thus, the calculations indicate that at the multipulse laser processing, the vapor parameters are considerably less favorable for plasma ignition while the thermal impact on the target is essentially the same. This is why the multipulse processing can be applied to avoid plasma ignition. The present work proposes a modeling tool, which gives tendencies and can be a guideline in experiments. To the best knowledge of the authors, the present work is the first where gas-kinetic processes are taken into account and analyzed for sub-microsecond laser pulses. The two examples considered show the contribution of different physical processes. Based on the achieved understanding of the competing processes, one can qualitatively estimate the influence of the laser parameters. For the given laser setup, only two parameters are variable: the pulse energy and the repetition rate. The modeling indicates that increasing the pulse energy sharply increases the optical thickness of the vapor, which results in optical breakdown. This is why increasing the pulse energy is not desirable. On the other hand, increasing the repetition rate above the 100 kHz considered here (corresponds to the 10 \(\upmu\)s inter-pulse separation) will increase heat accumulation in the target at a much lower value of the optical thickness. Thus, the thermal impact on the target will increase while the probability of plasma ignition will be still low. Therefore, increasing the repetition rate above 100 kHz seems to be promising in order to increase the efficiency of the laser processing.
## 2 Materials and Methods
### Biological Characterization  
Indicators related to microbial biomass (MB) and enzyme activity were analyzed. The MB was determined using the fumigation-incubation method, which involves the fumigation of soil samples with chloroform [26]. This method was used for the indicators of microbial biomass C (MBC) and microbial biomass N (MBN). To measure the N mineralization capacity of microorganisms, the MBN indicator was analyzed using the methodology established by [PERSON] [27], reported in ug N\({}_{\text{mic}}\) kg\({}^{-1}\) soil. For the quantification of the evolution of the microbial community through C mineralization by microorganisms, the MBC indicator was analyzed using the extraction-fumigation methodology [28], reported in ug C\({}_{\text{mic}}\) kg\({}^{-1}\) soil. The soil microbial respiration (SMR) was determined using the soil incubation technique [26]. During the incubation of soil in a closed system, the amount of C-CO\({}_{2}\) generated was estimated and trapped in a 1.0 M NaOH solution, then determined with 1.0 M HCI [26] and reported as mg C-CO\({}_{2}\) kg\({}^{-1}\) soil. To measure the general enzyme activity--which includes proteases, lipases and esterases--the fluorescein diacetate (FDA) method was used [29], reported in mg fluorescein kg\({}^{-1}\) soil h\({}^{-1}\). The metabolic coefficient (qCO\({}_{2}\)) was calculated from the quotient of the SMR and MBC indicators [30], reported in mg C-CO\({}_{2}\) g C\({}_{\text{mic}}\)\({}^{-1}\) h\({}^{-1}\). Enzyme activity tests were conducted using the API ZYM\({}^{\text{\text{\textregistered}}}\) enzyme system, which analyzes 19 specific enzymes related to the N, C and P cycles, generating color patterns with different intensities and hydrolyzed substrate values (level 0 to 5, in increasing order of intensity) after an incubation period of four hours at 37 \({}^{\circ}\)C; the results are reported in nmol [31; 32].
## 4 Performance of SSA with GREATEM Field Data  
In November 2015, we used the GREATEM system for field detection experiments in the Daxing'an Mountain range, Heilongjiang Province. The system's working parameters were as follows: the transmitting current was 50 A, the receiving coil area was 1080 m\({}^{2}\), the half-wire length was 744 m. The field exploration experiment site is in the base rock uplift area of the Yishu Basin. The low-resistance area is composed of sand and gravel, and the high-resistance area is horn shale. The lower resistivity part is the Yitong Basin, and the higher resistivity part is the bedrock uplift area.  
We took two measured points as an example. After superimposing the measured attenuation curves, we used WT, EMD, VMD, and SSA to de-noise the attenuation curve. The results are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **SNR (dB)** & **RMSE** \\ \hline Synthetic GREATEM signal & 29.3049 & 0.0031 \\ Signal de-noised by WT & 51.8377 & 2.3079 \(\times\) 10\({}^{-4}\) \\ Signal de-noised by EMD & 49.3527 & 3.0723 \(\times\) 10\({}^{-4}\) \\ Signal de-noised by VMD & 47.5233 & 3.7892 \(\times\) 10\({}^{-4}\) \\ Signal de-noised by SSA & 64.5133 & 5.3631 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of indexes of different methods to eliminate measured noise.  
Figure 11: Comparison of measured noise de-noising results by different methods. (**a**) Overall view; (**b**) partially enlarged view.  
Figure 12: Comparison of two measured attenuation curves after de-noising.  
Through comparison, we can see that using SSA can more effectively eliminate considerable power frequency noise, which verifies the effectiveness of the SSA method in eliminating noise in GREATEM field data and improves signal performance.  
Figure 13 shows the resistivity inversion results at a depth of 150 m in Fei Hu Mountain Area, Daxing'an Mountain range measured data. Figure 13a is the investigation result of the survey area provided by Heilongjiang Institute of Geophysical and Geochemical Exploration. As shown in Figure 13b, because the signal is submerged by noise in the late stage, the low resistivity region D1 is not apparent, not effectively reflecting the real underground geological results. From Figure 13c, we can see that using SSA can effectively eliminate the false anomalies, clearly demonstrating the distribution of low-resistivity regions, and improving the imaging results' resolution. This further verifies the effectiveness of using SSA de-noising GREATEM field data.
## 5 Evaluations
### Performance Analysis of the Object Detection Function
#### 5.2.2 Precision, Recall, and mAP Validation of the Object Detection Function  
The evaluation of a _YOLOv8_ model is measured by the values of the _precision_, _recall_, and _mAP_ parameters compared between the training and testing dataset. These parameters refer to the model's ability to accurately identify and classify classes [54]. The _precision_ measures the proportion of correctly predicted data (true positives) compared to all data  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter Name** & **Hyperparameter Value** \\ \hline Optimizer & SGD \\ Initial Learning Rate (\(lr\)0) & 0.01 \\ Final Learning Rate (\(lrF\)) & 0.01 \\ Momentum & 0.937 \\ Weight Decay Coefficient & \(5\times 10^{-4}\) \\ Random set & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter specifications for _YOLOv8_ model training.  
Figure 6: (a) Validation training from box loss.; (b) validation training from class loss.  
classified as positive. The _recall_ measures the proportion of correctly predicted data (true positives) of all actual positive data in the dataset [55]. The \(mAP\) metric evaluates the accuracy and coverage of the object detection bounding box by using the _Intersection over Union (IoU)_ threshold [56]. The _IoU_ threshold represents the tolerance level for the overlap area between the detected bounding boxes and the ground truth bounding boxes. In this study, we employed an _IoU_ threshold at 0.5 and 0.5-0.95.  
In the metrics of _precision_ and _recall_, the _YOLOv8_ model demonstrates a _precision_ value of 99.99% and achieves a 100% _recall_ value. This indicates that the model is robust and accurate in detecting the _sign image_ from an input image, as illustrated in Figure 7. The model achieved a final \(mAP\) of 0.995 at _IoU_ 0.5 and a final [EMAIL_ADDRESS]-0.95 value of 0.978. Based on [57], these \(mAP\) values demonstrate the consistent and reliable capability of the _YOLOv8_ model in detecting the trained object. The corresponding \(mAP\) results are shown in Figure 7. A high \(mAP\) value indicates that the model could accurately and confidently detect objects with performance similar to the expected result.