esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The GPR Result  
The GPR results are presented in Figure 4. The results of Figure 4 are based on fitting with real data, the fitted lines appeared like Figure 4 when the GPR was applied in real data. The fitted lines indicate the trend of the non-corroded potential data and reflect well the characteristics of the data. The SQEXP kernel is a key function in the GPR, and the 95% confidence intervals in Figure 4 indicate that the kernel worked. Similar works with observational data fitting of the kernel in the applied GPR showed magnificent performance, with high coefficients of determination (above 0.95) [29,38]. The GPR with kernels usually showed the level of ML performance, and these results also showed high agreement with Table 2.  
On the basis of these results, the fitting trend flow is presented in Figure 5 and indicates that the GPR results are well fitted. When predicting in the range of temperature, the trend was similar to real data fitting, and the grey areas showed small gaps from the trend line. The grey areas in Figure 5 are the 95% confidence intervals and the dots are the predicted data in the range of the given data [39]. The given data refers to the data used in the GPR fitting. The interval is decreased in the range of the given data and follows the trend line and the predicted data well. However, the interval is widened in the range of ungiven data. Although this is a limitation of the GPR method [26], only the given data  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & \(\overset{\wedge}{\beta}\) & **Coefficients of Determination (R\({}^{2}\))** & **Root Mean Square Error (RMSE)** \\ \hline Air temperature & \(-5.385\) & 0.99 & 0.00366 \\ D10 & \(-4.415\) & 0.97 & 0.0570 \\ D13 & \(-5.447\) & 0.97 & 0.0657 \\ D16 & \(-5.048\) & 0.95 & 0.0394 \\ D19 & \(-4.639\) & 0.96 & 0.0502 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Gaussian process regression (GPR) fitting results.  
Figure 3: The corrosion potential results: (a) D10; (b) D13; (c) D16; (d) D19.  
was used in this study. Therefore, the fitting trend and intervals are the main elements in the given data we used when evaluating the fitting performance. Hence, the GPR performed well in each case.  
For understanding using TPI, Figure 4a indicates how the GPR derives with the real data. Additionally, Figure 5a shows how the GPR fits well with predicted data and 95% confidence interval. When performing the work of removing the temperature effect from corrosion potential, the value of _Temperature GPR_ of TPI is calculated based on this result.  
Figure 4: The fitting results: (**a**) air temperature; (**b**) D10; (**c**) D13; (**d**) D16; (**e**) D19.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Discussion  
The growing implementation of IoT-enabled applications, devices, and smart systems emphasizes the need for enhancing the security of these networks. In this research paper,  
Figure 16: Comparison of MCC.  
Figure 15: Comparison of [PERSON]’s kappa.  
the focus is on RPL-based IoT networks, particularly, the detection of PS-R and SN-W attacks. The ML-based approach is selected because of the various advantages that it provides such as automation, pattern recognition, and feature extraction as well as selection of useful features. Based on the critically chosen parameters from different parameter categories, including the core, learning control, input and output workflow, objective, and metric parameters, an ML-based model is designed and developed. The performance evaluation is carried out using the testing set of the proposed LIoTN-RPL dataset which plays the role of model testing environment. The proposed ProSenAD model outperforms the benchmarks and provides security against PS-R and SN-W attacks in terms of number of attacks detected, attack classification accuracy, PPV, recall, CE, reliability (CK), and MCC.  
The novelty and usefulness of the proposed ML-based model is summarized, as follows:  
* It considers both the categories of attacks prevalent in RPL-based IoT networks and provides summateity of attack detection using a multiclass approach.
* The LIoTN-RPL dataset used in the development of the model is prepared such that it allows the detection of multiple attacks from both attack categories.
* It is comparatively lightweight as compared to the other models due to the usage of lightweight boosting methods and feature bundling techniques that it employs to build the model.
* It addresses the CIA triad-related security compromises that occur due to the attacks including confidentiality, integrity due to PS-R attacks and confidentiality, availability due to SN-W attacks.
* It overcomes the accuracy bias and regularization-related issues by employing appropriate metric parameters and learning control parameters, respectively.  
Overall, the performance of ProSenAD is significantly better when compared with the model-related benchmarks including GB, XGBoost, and LGBM, as well as research-related benchmarks including GAN-C [14] in terms of accuracy, precision, and recall, and ML-LGBM [12] and GRU-DL [12; 13] in terms of the number and simultaneity of the attacks addressed.
## 3 Results and Discussion
### Effects of the Protein Density Ratio Inside/Outside Cluster on the Accuracy of Peak Analysis Method  
Beyond cluster geometrical parameters, protein density ratio inside/outside cluster also proved to be an important factor influencing the accuracy of peak analysis [16]. To investigate the effects of protein density ratio on the estimation of cluster radius, we varied the protein ratio from 2 to 15 while holding cluster separation constant at 1000 nm and cluster radius constant at 50 nm. The selected [PERSON]'s H calculations for different ratios are shown in Figure S5. An interesting feature of [PERSON]'s H calculation is that the maximum of \(\mathrm{L(r)}~{}-~{}\mathrm{r}\) increases as the protein density ratio increases. This phenomenon shows that high degree of protein aggregation inside a cluster is helpful to predict the radius of a cluster. To evaluate the effects statistically, we again counted 100 simulations for each case in Figure 4a. We found that a protein ratio threshold of four is essential to validate the cluster analysis by peak analysis. Under this value, it is difficult for the peak analysis method to identify the protein clustering. Moreover, as the protein ratio value varies from 5 to 11, the SD monotonically increases, yet after the value of 12, the peak analysis method predicts the cluster radius with nearly stable errors [29].
## 2 Materials
### The Properties of the MODIS Satellite  
The MODIS multispectral sensor of the NASA Earth Observing System (EOS) is being provided atmosphere, land, and ocean products. The overpass time of the Terra satellite is approximately 10:30 AM when descending and 10:30 PM when ascending. The Aqua satellite overpass time is approximately 1:30 AM when descending and 1:30 PM when ascending. The MODIS 17 GPP and 16 global ET products from the Terra satellite were employed to estimate the WUE during 2007 and 2008. The provided datasets in the Hierarchical Data Format (HDF)-EOS with Sinusoidal (SIN) projection was reprojected using Transverse Mercator (TM) coordinate system.
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 4 Experiment and Analysis
### Joint Tracking Results  
To further compare the accuracy of the three combination tracking schemes for the B1C and the L1C signals, Figure 11 shows the DLL and PLL results of all real-life satellite signals involved in the positioning solution. The code loop bandwidth was set to 5 Hz, the carrier loop bandwidth was set to 30 Hz, and the phase detector interval was set to 0.15 chips. For ordinary receivers, the BOC (1,1) component, which accounts for most of the power, was used for tracking in the pilot channel, which can reduce the complexity of the receiver. Therefore, low-frequency components were used for pilot tracking in subsequent experiments. The results show that the accuracy of the \"amplitude ratio Combination\" was the highest for the B1C and L1C in the code and carrier loop. The accuracy of \"power ratio combination\" was the second highest, and the accuracy of \"1:1 Combination\" was the lowest.
## 4 Discussion
### Effect of Training on Sprint Performance  
To the authors' knowledge, this study is the first that investigated effects of loaded byometric with short sprints and change-of-direction training on sprint performance in young soccer players. In our study, we could show relevant improvements in both type of sprints. The interaction effects (d) differed from 0.52 (30 m) to 0.94 (10 m). The improvements in the EG (d range: 1.93-2.51) were markedly higher than in the CG (d range: 0.05-0.70). Improved sprint performance observed herein are in agreement with those of [PERSON] et al. [10] who examined effects of a 10-week loaded and unloaded combined pyometric and short sprint training in elite junior male soccer players (U17). These researchers reported improvements in sprint performance over 5 (\(p<0.001\)), 10 (\(p<0.001\)), 20 (\(p<0.01\)), 30 (\(p<0.001\)), and 40 m (\(p<0.05\)) sprint times, after both training programs. Similarly, [PERSON] et al. [12] reported improvement in sprint performance over distance of 10 m (\(p<0.05\)), but no gain over a distance of 30 m after a 6-week pyometrictraining and change-of-direction exercises in prepubertal male soccer players. [PERSON] et al. [50] reported increases in 5, 10, 20, and 30 m sprint times (9%, d = 0.14; 7%, d = 0.21; 7%, d = 0.10; and 9%, d = 0.90, respectively), following combined pylemetric and short sprint with change-of-direction training in male U15 handball players. Contrary to our results, few studies [51; 52; 53] found no gain in sprinting performance following pylemetric training in young male soccer players and physical education students. Previous studies have shown that the sprint training charge may increase eccentric strength of extensor muscles during the braking phase of the contact with the ground and increase the rigidity of the springs and leg muscles, potentially increasing the ability of muscle to store elastic energy and improve power output [54; 55]. In addition, the contradictory results may be explicated by divergence in training programs and sample populations.
## 4 Results and Discussion
### The Inverse Distance Weighting (IDW)  
For the IDW interpolation, which uses a simple algorithm based on distance, the closest measured values had the most influence. Typically, the spatial distribution evaluated with this method showed marked areas in correspondence of the high or low rainfall values (Figure 3). In fact, generally, this method produces spikes around the measured points [63], although it can depict well local detailed changing as shown by [PERSON] et al. [64]. On the other hand, [PERSON] and [PERSON] [65], using IDW in estimating the spatial distribution of rainfall in the middle of Taiwan, obtained an higher accuracy in the dry season, also revealing a better ability of this estimator to predict the spatial distribution of small events.  
For the several months, the coefficient of determination (R\({}^{2}\)) of the IDW of measured versus predicted rainfall, showed the lowest values among the different interpolation methods, ranging between 0.5318 and 0.7324 (Figure 2 and Table 2). Moreover, as a consequence of the cross-validation test, this method did not result, in any analysed month, the best interpolating rainfall method (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **IDW** & **OK** & **KED** & **COK** \\ \hline January & 0.5393 & 0.6375 & 0.7021 & 0.7089 \\ February & 0.5329 & 0.6406 & 0.6711 & 0.6816 \\ March & 0.5318 & 0.6551 & 0.6901 & 0.7098 \\ April & 0.6178 & 0.7449 & 0.7748 & 0.7921 \\ May & 0.6266 & 0.7515 & 0.7748 & 0.7766 \\ June & 0.7151 & 0.8236 & 0.8492 & 0.8486 \\ July & 0.7324 & 0.8277 & 0.8320 & 0.8332 \\ August & 0.7043 & 0.8074 & 0.8194 & 0.8408 \\ September & 0.6569 & 0.7825 & 0.7956 & 0.8055 \\ October & 0.6097 & 0.7459 & 0.7723 & 0.7900 \\ November & 0.5712 & 0.6963 & 0.7168 & 0.7422 \\ December & 0.5562 & 0.6811 & 0.7064 & 0.7319 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficient of determination (R\({}^{2}\)) of measured versus predicted monthly rainfall for the different interpolation methods (inverse distance weighting, IDW; ordinary kriging, OK; kriging with an external drift, KED; ordinary cokriging, COK).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Month** & **MAE** & **RMSE** \\ \hline January & COK & COK \\ February & COK & COK \\ March & COK & COK \\ April & KED & KED \\ May & KED & COK \\ June & KED & COK \\ July & KED & COK \\ August & COK & COK \\ September & COK & COK \\ October & COK & COK \\ November & COK & COK \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best interpolation method (inverse distance weighting, IDW; ordinary kriging, OK; kriging with an external drift, KED; ordinary cokriging, COK) based on the cross-validation test (mean absolute error, MAE; root-mean-square error, RMSE).  
Figure 3: Mean monthly precipitation interpolated using inverse distance weighting (IDW) method.  
Considering that the OK method tends to underestimate mean rainfall values, as well as IDW, it is not the best interpolator for spatially analysing the rainfall observation in any considered month. It is confirmed by the MAE and RMSE results.
## 4 Evaluation
### Effect of Malicious Attacks on Positioning Accuracy  
In Section 4.1, we verified the performance of the positioning system within the service layer of the proposed framework using the enhanced positioning module. In this section, we demonstrate the impact of malicious attacks on the performance of a positioning system through experiments in a real environment. In this section, we show how the performance of an entire positioning system can be seriously damaged when a malicious attack is inflicted on one of its constituent nodes. In other words, it shows that measures to strengthen security in the positioning system are necessary.  
The experimental configuration can then be summarized in terms of localization as follows. In Section 4.3, we evaluated the data processing performance of the consensus algorithm of the proposed framework. There is a risk of delay in the positioning system due to additional system load caused by the blockchain, which was applied to defend against malicious attacks. We showed that this risk is not a problem in practice through the verification of the new consensus algorithm in terms of the time to reach consensus. Section 4.4 evaluated the consensus algorithm using the race attack scenario, which is a common malicious attack scenario considered in DAG-based distributed ledger systems. Our simulations demonstrated that our proposed solution can disable malicious transactions faster than IOTA [59], the most-widely adopted DAG-based distributed ledger system. Afterwards, we verified that the proposed framework is a security-stable system by showing that it maintained excellent data processing performance even when the intensity of the attack increased.  
We conducted two experiments to examine the impact of tampering with the data of one node in the cluster, formed in the experimental environment of Section 4.1, on the positioning performance.
## 3 Comparative Analysis of Numerical Forward Simulation and Soil Trench Modeling Experiments
### Analysis of Experimental Results
Numerical forward Simulation of Chromium-Contaminated Field Migration for Dual Frequency IP Method Monitoring  
Numerical Simulation of Dual Frequency Induced Polarization Monitoring of Contaminated Sites in Different Terrains  
The most commonly used profiling device in the dual frequency IP method is the intermediate gradient detection device, which has flexible pole placement and reduces the number of moves of the supply electrodes [29]. The data obtained from the sideline measurements can be fully interpreted for the target anomaly. The symmetrical quadrupole sounding device requires a small supply current [29]. However, it can obtain a large potential difference, is more advantageous in complex terrain conditions, and is often used as the main device for dual frequency IP method sounding. Given both advantages, this paper will use an intermediate gradient detection device and a symmetric quadrupole sounding device to perform numerical forward simulations of contaminant site migration detection. In the simulation, an ultra-low frequency sinusoidal current was used as the excitation and the frequency ratio \(S=f_{\mathrm{H}}/f_{\mathrm{L}}=13\) was selected for the dual frequency current, with a high frequency of 4 Hz and a low frequency of 0.308 Hz and a current amplitude of 0.25 A. The numerical forward simulation is based on the migration model of the contaminated field from 10 to 400 days in 1.2. The distribution of the contaminated field at 500 days is similar to that at 400 days, but the field has already migrated out of the model at 500 days, so it is not taken into consideration. Since the Cr contamination field is mostly a complex geological condition with undulating terrain, two geological conditions, flat and undulating terrain, need to be considered. Numerical forward simulations are carried out using the intermediate gradient detection device and a symmetric quadrupole sounding device, respectively.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 5 Protocol Analysis
### Effectiveness of Burst Transmission  
The burst transmission strategy can be effective at improving the transmission at the hot terminal that buffers more packets or in networks with higher traffic rates. We compare the normal transmission and the burst transmission strategy as below.  
We calculate the average waiting time for transmission of the data packets in the queue of a node as below.  
\[\tau_{wNT}=\frac{1}{N_{BT}}\sum_{i=1}^{N_{BT}}\left(\tau_{buf}(i)+i\cdot\tau_{ ngt}+(i-1)\tau_{trans}+\tau_{cmpt}(i-1))\right) \tag{31}\]  
where \(\tau_{buf}(i)\) is the buffering time of the \(i_{th}\) data packet in the queue and \(\tau_{cmpt}(i)\) is the time interval of the \(i_{th}\) transmission competition; \(\tau_{D}\) is the transmission delay of a data packet; \(\tau_{ngt}\) and \(\tau_{trans}\) are the time intervals of the negotiation phase and the transmission phase, which are calculated as:  
\[\left\{\begin{array}{l}\tau_{ngt}=\tau_{R}+\tau_{C}+2\tau_{p}\\ \tau_{trans}=\tau_{D}+\tau_{A}+2\tau_{p}\end{array}\right. \tag{32}\]  
where \(\tau_{R}\) and \(\tau_{C}\) are the transmission delays of an RTF packet and a CTF packet, respectively; \(\tau_{p}\) is the propagation delay between the sender and the receiver; and \(\tau_{A}\) is the transmission delay of an _ACK_ packet.  
When using the burst transmission strategy, it can have a shorter average waiting time as below.  
\[\tau_{wBT}=\frac{1}{N_{BT}}(\sum_{i=1}^{N_{BT}}\left(\tau_{buf}(i)+(i-1)\tau_ {D}\right)+\tau_{ngt}) \tag{33}\]  
We use \(\overline{\tau}_{cmpt}\) to represent the average time interval of competition for the transmission. Then, the difference between the average transmission times of these two strategies can be represented as:  
\[\Delta\tau_{wTX}=\tau_{wNT}-\tau_{wBT}=\frac{N_{BT}-1}{2}(\tau_{ngt}+\tau_{ trans}+\tau_{cmpt}-\tau_{D}) \tag{34}\]  
The burst transmission strategy improves the effectiveness of transmission but introduces network overhead. The selection of burst size \(N_{BT}\) influences the transmission performance. If \(N_{BT}\) is set to a small value, many packets queued in a node cannot be sent in time. In contrary, if a large value of \(N_{BT}\) is set, the channel is occupied by a transmission pair over a long time, and the unequal distribution of transmission in the network may degrade the network performance. So, \(N_{BT}\) should be adaptively set for each transmission.
## 4 Experiment and Results  
### Experimental Data Set  
The data set used in this paper is the AI classification and identification data set of high-resolution remote sensing images provided by Jiage Data. The source data is collected from southern China in 2015 from April to August by a crewless aerial vehicle. The spatial resolution of the remote sensing image is sub-meter level, and the images have no corrections and preprocessing. The samples of the data are manually marked. The data set consists of five large-scale high-resolution remote sensing images, which contain more than 180 million pixels. The objects provided by the data set are divided into five categories: Vegetation, buildings, water-body, roads, and others. Those classes are labeled 1, 2, 3, 4, and 0. Vegetation includes common cultivated land, grassland and woodland. The cultivated land is also marked as vegetation after harvesting or falling. The building category includes different types of buildings and differences between urban and rural areas. Water bodies have different water-body characteristics, and some water bodies are greenish, and some have blue water. Different road widths and spectral characteristics in road types, such as national roads, provincial roads, and county roads. The location differences, time-segment differences, and differences in feature categories in the dataset sample make the data diverse and representative. These features make the models obtained by the learning algorithm have greater generalization performance. Partial dataset image local regions and their markup visualization are shown in Figure 5.  
Due to a large number of parameters regarding deep convolution neural networks, the training process consumes a large amount of memory, which makes it impossible to directly process such large-sized remote sensing images. Here, we use the image cutting method to obtain the training set. First, the coordinates of a point are determined on the original image, and then a window of size \(256\times 256\) pixels is cut at the lower right of the coordinate. A boundary image supplementation method is used in the boundary region of the image to cut an image with a size of \(256\times 256\). The method of random cutting and cutting can not only obtain a small image, but also make the object of the object appear anywhere in the small image. This method enables the network to learn the object features at different locations and increase the translation invariance of the network. The images of the training set after partial cutting are shown in Figure 6.