esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Influence of the LS-SVM Parameters on Identification Performance  
The performance of LS-SVM with RBF kernel function is influenced by the regularization constant \(C\) and kernel bandwidth \(\sigma\). \(C\) is introduced to measure the trade-off between complexity and losses. It adjusts empirical risk and confidence interval to lead to a good generalization performance. If the value is too small, the punishment of error is small, which makes big training error and poor generalization ability. If the value is too big, then only the risk of experience is minimized but the complexity of the model is not considered, which will also bring poor generalization ability. Smaller \(\sigma\) might yield the SVM over trained and make the kernel matrix trend toward unit matrix. On the contrary, bigger \(\sigma\) will make the kernel function tend to be constant.  
In order to contrast the influences of different \(C\) and \(\sigma\), simulated tests are carried out four times respectively. The sampling set is from an actual pressure control system of chlorine in a salt industry plant which will be introduced in Section 5. Six hundred sample data are randomly divided into five groups. Among them, four groups are the training data and the other group is the test data. The mean squared error (MSE) is used to estimate the forecast accuracy.  
In the first set of experiments \(\sigma\) is fixed at 0.04 while the value of \(C\) is varied. Then in the second set of experiments \(C\) is set to 200 while the different \(\sigma\) values are tested. The relationships of MSE _versus_\(C\) and \(\sigma\) are displayed in Figure 0(a,b respectively. The values of \(C\) and \(\sigma\) that minimize MSE are listed in Table 1. It can be observed from Figure 1 and Table 1: Firstly, for the fixed sampling data set, different parameter values (e.g., \(C\) of the first test in Figure 0(a)) make the performance of LS-SVM different. There is an optimal parameter value minimizing the MSE. Secondly, the test data is different among the four times experiments because of they are fetched randomly from the sample set. Moreover, the optimal parameter values of four times are also different. Thereby, we can draw the conclusion that the optimal parameter value varies with the sample data, even for the same predicting object. In brief, to get the best modeling performance, it is necessary to optimize the parameters \(C\) and \(\sigma\) before every regression for any data set.
# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 4 Experiments
### Model Complexity Analysis  
Running time and the number of parameters are important indicators to evaluate the complexity of a model. In this section, we compare MDNCL with BM3D [7], TWSC [45], MCWNNM [46], IRCNN [47], DnCNN-B [9], FFDNet [10], BUIFD [37], ADNet [20], SADNet [48], CycleISP [49], AirNet [42], VDN [50], AINDNet [51], VDIR [52], RDUNet [12], ATDNet [53], CTNet [54], and TBSN [44] in terms of running time and model parameters. Among these models, BM3D, TWSC, and MCWNNM were implemented in the Matlab (R2020a) environment, and other denoising methods were implemented in PyCharm (2021.2.1) software. We randomly selected images with sizes of \(256\times 256\), \(512\times 512\), and \(1024\times 1024\) at a noise level of 25. Table 8 records the average running time of  
\begin{table}
\begin{tabular}{l l l l} \hline \(\mu\) & \(\sigma=15\) & \(\sigma=25\) & \(\sigma=50\) \\ \hline
0.4 & 31.90/0.8419 & 29.56/0.8449 & 26.47/0.7553 \\
0.6 & 32.77/0.8814 & 29.46/0.8577 & 27.45/0.7690 \\
0.8 & 32.57/0.8763 & 29.80/0.8593 & 27.54/0.7723 \\
1.0 & **33.14/0.9065** & **30.80/0.8679** & **27.67/0.7955** \\
1.2 & 31.78/0.8801 & 29.40/0.8470 & 26.45/0.7529 \\ \hline \end{tabular}
\end{table}
Table 7: Average PSNR (dB)/SSIM results of ablation experiments on Set12 (The superior results are emphasized in bold).  
Figure 11: Image denoising result with a noise level of 25 on Set12 when \(\mu\) takes different values. (a) clean image; (b) noisy image; (c) \(\mu=0.6\); (d) \(\mu=0.8\); (e) \(\mu=1.2\); (f) \(\mu=1.0\).  
20 implementations of each tested model, and we ignore the memory transfer time between the CPU and GPU. Table 9 shows the complexity of the comparison models.  
The performance of the MDNCL algorithm is highlighted on multiple dimensions, showing its superiority over the compared denoising models. For color images, the running times for each image size are 0.316 s, 0.574 s, and 2.174 s. When processing large-size color images, MDNCL is much faster than models such as AINDNet, BM3D, and MCWNNNM; AINDNet took 9.573 s, BM3D took 12.788 s, and MCWNNNM took 1120.332 s, indicating the efficiency of MDNCL when processing large-size images.  
As can be seen from Table 9, MDNCL has 6.71 M parameters, indicating that it can still achieve excellent noise reduction performance while maintaining low computational complexity. In contrast, the number of parameters of CTNet is as high as 49.03 M. While the denoising performance of CTNet may be better, it can be prohibitive in practical applications due to its huge computing requirements. MDNCL provides a balanced solution that optimizes performance and computational efficiency for environments with limited computing resources.
## 4 Results
### Forest Aboveground Biomass Update  
#### 4.3.1 Carbon Pools Initialization  
The Landsat-based AGB (\(x\)) (kg/m\({}^{2}\)) values effectively captured the variation of wood biomass (\(y_{\mathrm{s}}\)) (kg/m\({}^{2}\)) with the linear regression statistical model as \(y_{\mathrm{s}}=0.985x-0.049\) (R\({}^{2}=0.99\), \(n=35\), \(p<0.001\)). Furthermore, it predicted 93.8% of variation in the coarse root biomass (\(y_{\mathrm{r}}\)) with the exponent regression statistical model as \(y_{\mathrm{r}}=0.293x^{1.051}\) (\(n=35\), \(p<0.001\)) and explained 87.3% of variation in the leaf biomass (\(y_{\mathrm{i}}\)) with the exponent model as \(y_{\mathrm{1}}=0.032x^{0.810}\) (\(n=35\), \(p<0.001\)). Based on these three statistical models, the wood, leaf, and root carbon pools were initialized by determining the coefficients of wood, leaf, and root biomass to AGB from the above models. Assuming the fine root carbon pool and the leaf carbon pool were similar [55; 56; 57; 58], the initial fine root carbon pool can also be determined.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**ALS Metrics** & \(\mathbf{h_{m}}\) & \(\mathbf{d}\) & \(\mathbf{h_{10}}\) & \(\mathbf{h_{20}}\) & \(\mathbf{h_{30}}\) & \(\mathbf{h_{40}}\) & \(\mathbf{h_{50}}\) & \(\mathbf{h_{60}}\) & \(\mathbf{h_{70}}\) & \(\mathbf{h_{80}}\) & \(\mathbf{h_{90}}\) \\ \hline
**AGB** & 0.91 & 0.56 & 0.78 & 0.85 & 0.87 & 0.88 & 0.89 & 0.89 & 0.88 & 0.88 & 0.87 \\ \hline \hline \end{tabular} Note: Here AGB is the forest aboveground biomass (kg/m\({}^{2}\)); \(h_{\mathrm{m}}\) is the mean canopy height (m); d is forest plot density (trees/hm\({}^{2}\)); h\({}_{10}\), h\({}_{20}\), \(\ldots\), h\({}_{90}\) are the 9 different percentile heights computed based on the ALS-based point cloud data.  
\end{table}
Table 4: The correlation analysis between field-based AGB and ALS-based canopy metrics at forest plot level.  
Figure 4: The validation of ALS-based forest AGB in 2012 using the results obtained from forest field-based AGB at the plot level.
## 7 Simulation Results
### Discussion
The mathematical expressions of these indicators are provided in Appendix A. Notice that the indicator CEI\((u)\) evaluates the control effort applied by the proposed control law \(u\) to the exoskeleton at the knee joint. Moreover, the indicator MATI\((u)\) indicates the maximum value realized when applying the controller \(u\). It is important to mention that in all previous simulations, the controller \(u\) reaches its maximum/minimum at the beginning of the simulation, and this reached value depends entirely on the initial condition and also on the feedback gain \(\mathcal{K}\). As noted previously, the same initial condition was adopted for all the simulations. The different values of these indicators evaluated for the four scenarios/approaches are given in Table 7. It is clear that for the four approaches, the first three indicators, MAE, MSE, and RMSE, are small. However, what is comparable is the value of the last indicator; that is, MATI\((u)\). For the first and third approaches, this indicator was found to be very high: approximately 37 in the first approach and 22.5 in the third approach. As a result, the control effort indicator CEI\((u)\) was found to be around 1.34 for these two cases, which is relatively high compared to the values obtained via the two other approaches. In contrast, for the second and fourth approaches, the values of the indicator MATI\((u)\) are very small: approximately 1.37 for the second approach and 2.26 for the fourth approach. Hence, these results show the superiority of these two approaches over the first and third approaches. Furthermore, by comparing the second and fourth approaches, we reveal that there is a difference between the results of the remaining indicators. Indeed, it is obvious that the values of the indicators MAE\((\phi)\), MSE\((\phi)\), and RMSE\((\phi)\), were decreased for the fourth approach. Thus, the position control of the knee exoskeleton was improved in this approach compared to that in the second approach.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 3 Materials and Methods
### Study Area  
Figure 2 shows the distribution of the ground stations in the Dong Thap Province. In this study, the Tram Chim National Park located in Dong Thap Province was selected as the site for the deployment and testing of the proposed system. This choice was made because the area represents all the vital attributes of the Mekong Delta region. Tram Chim National Park, situated in Dong Thap Muoi in Dong Thap Province, Vietnam, is one of the few remaining wetlands in the threatened Plain of Reed's ecosystem. This park holds exceptional biodiversity and tourism value, being home to over 230 bird species and 130 fish species, including the endangered Sarus Crane that is listed on the IUCN Red List. Tram Chim has been designated as a Ramsar site of international significance under the Ramsar Convention, emphasizing its importance to the global community.  
However, despite its significance, the current environmental management and research efforts at Tram Chim are hampered by the lack of sufficient and consistent survey data. This is due to the challenges of accessing remote sites and the frequent flooding that occurs in the area. The absence of adequate data collection hinders the efforts to preserve and protect the park's valuable ecosystem and wildlife. In light of this, the proposed smart environmental management system aims to overcome these challenges by utilizing innovative data collection and processing techniques.  
In our system, we deploy five IoT stations in the national park area for collecting multiple telemetry parameters, as mentioned in Section 2. However, only PM10, PM2.5, and land surface temperature values are used for this work.
# Food Allergies: Immunosensors and Management
## 3 Peanut Allergy
### Management  
The solvency of nut protein is influenced by warm handling techniques and preparation times. The water-solvent protein yield of crude peanuts was 72% of complete protein. In any case, the dissolvability of the protein was diminished alongside expanded handling time. Fricasseeing for 8 min resulted in the least water-solvent protein yield of 6.4% of the overall protein (dry premise), which diminished 66.5% of the dissolvability. The allergen's quantitative assurance depended on the band forces and favorability to vein substance. The nut preparation impacts water-solvent protein (allergen). Water-solvent Ara h 1, Ara h 2 and Ara h 3 substance, estimated by the strategy of [PERSON] (1976) for crude peanuts, are 3.2, 1.9, and 9.5 g/100 g nut (dry premise), respectively. Bubbling, HP bubbling, Steaming, HP steaming, and profound browning all diminished the water-dissolvable Ara h 1 and Ara h 2 substances to almost zero (under 0.1 g/100 g nut for Ara h 1 and under 0.2 g/100 g nut for Ara h 2) before the cooking was complete. These discoveries were predicted by a new investigation by [PERSON], [PERSON], and [PERSON] (2016), in which they found that bubbling and searing could diminish the solvency of Ara h 1 and Ara h 2 because of the compliance changes [35].  
One way to decrease the nut sensitivity reaction is to remove the allergens in peanuts; expulsion of the allergens of Ara h 2 and Ara h 6 from a concentrate brings about a deficiency of their strength [39]. Peanuts go through warm preparation before utilization. They are eaten bubbled, singed, or cooked by different culinary practices. These preparation techniques appear to affect the pervasiveness of nut sensitivity. Allergenicity of nut proteins is additionally influenced by primary changes prompted by warm handling. Browning of peanuts, yet not bubbling or cooking, changed the optional design of Ara h 2 drastically by diminishing the particle's substance of \(\alpha\)-helices and expanding its \(\beta\)-sheets, \(\beta\)-turn and arbitrary loop, in this manner modifying Ara h 2 epitopes and decreasing its allergenicity [40].
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### Tdl4  
TDL4, which emerged around 2007 as a sophisticated rootkit malware, representing a significant evolution in the PDoS threat landscape, is a specific strain of the Alureon banking trojan. While Alureon is a banking trojan that occasionally caused a Blue Screen of Death in 32-bit Microsoft Windows systems on a restart loop (power cycle), TDL4 malware is notable for its complex multi-component architecture and its ability to embed deeply within the host system, making detection and removal particularly challenging. TDL4, as a rootkit, primarily targets the boot process of a system by infecting the MBR to ensure its persistence and stealth.  
The malware's advanced capabilities include kernel-mode rootkit techniques, encrypted communication with command and control servers, and a modular framework allowing it to download and execute additional payloads. TDL4 is also capable of data-flow manipulation, redirecting DNS requests, and performing man-in-the-middle attacks, thereby compromising the integrity of the system and the data passing through it.  
Recovery from a TDL4 infection often requires specialized tools and techniques, as standard antivirus software struggle to effectively detect and remove the rootkit components. In some cases, the damage inflicted by TDL4's manipulation of system processes necessitates a complete system reinstallation or restoration from a clean backup.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 1 Introduction
The emissions from forest fires have been calculated in Mexico mainly from statistics supplied by CONAFOR (the National Forestry Commission) and cartography from INEGI (the National Institute of Statistic and Geography) [19; 20; 21; 22]; these calculations followed the guidelines of the Intergovernmental Panel on Climate Change (IPCC). Nevertheless, the accuracy of the estimation can be enhanced by using satellite products to extract temporal and spatial variations. The objective of this paper is to calculate the emission of gases (CO\({}_{2}\), CO, CH\({}_{4}\), NOx, NH\({}_{3}\)) and fine particulates (PM\({}_{2.5}\)) caused by forest fires in three ecoregions of Mexico using remote sensing products and auxiliary data. Such knowledge regarding the spatial and temporal distribution of emissions can identify the most sensitive areas. The purpose of this paper is also to demonstrate the use of indirect techniques supported by remote sensing products with a high temporal resolution, together with a machine learning approach, in order to estimate the burning efficiency.
# Performance-Based Reliability Estimates for Highway Bridges Considering Previous Inspection Data
## 2 Basic Resistance Deterioration and Reliability Analysis  
The bridge under consideration was to be used as a two-lane expressway and comprised a single span that was 15 m in width. Table 1 and Figure 1 present the data specifications and cross-sectional profiles of the representative bridge at the mid-span [8]. The basic resistance capacity of the pre-stressed concrete-I (PSC-I) girder bridge was evaluated by using the flexural strength equation from the load-and-resistance factor design (LRFD) bridge design specifications [9].  
The initiation time for the performance of the structure to deteriorate was calculated using the improved chloride diffusion equations of [PERSON] et al. [10]. Table 2 presents the corrosive parameters used in the analysis [7].  
Research Program 368 (NCHRP 368) [12]. The material properties of concrete and steel structures were taken from Nowak and Szerszen [13]. The probabilistic characteristics of the resistance capacity were estimated through a Monte Carlo simulation, which was run 100,000 times. The estimated basic deterioration model of resistance capacity was used as the basic model in Sections 3.2 and 4.1. In this study, to apply the live load effect, an extreme annual load that reflects the actual traffic characteristics was referenced, as listed in [14; 15]. Table 3 presents the probabilistic characteristics of the vehicle weight model: mobile cars (P), buses (B), mid-size trucks (T), heavy trucks (TT), and semi-trailers (ST). The first mode (1) indicates the empty or lightly-loaded condition, and the second mode (2) represents the heavily-loaded condition. An annual average daily traffic (AADT) of 10,000 along with heavy vehicle proportion of 15% was set as the light traffic characteristics; an AADT of 20,000 with heavy vehicle proportion of 25% was set as the normal traffic characteristics; an AADT of 40,000 with heavy vehicle proportion of 35% was set as the heavy traffic characteristics.  
The reliability of a representative bridge was analyzed based on the limit state equation by [PERSON] et al. [7] given below. The load factor in Equation (1) represents the moment exerted by each load, and the resistance factor represents the flexural strength of the structure:  
\[g=R_{f}-D_{1}-D_{2}-LL(1+IM), \tag{1}\]  
where \(R_{f}\) is the resistance capacity (flexural strength), \(D_{1}\) is the load effect due to the girder, \(D_{2}\) is the load effect due to the barrier, sidewalk blocks, and pavement, \(LL\) is the load effect due to the live load, and \(IM\) is the impact coefficient.
## 3 Methods
### Sea Fog Identification
#### 3.1.3 Sea Fog Recognition Method Based on MLP  
As a mature classification method, Multilayer Perceptron (MLP) method has been applied to various remote sensing image classification tasks [35; 36]. In this paper, sea fog identification is taken as a binary-classification task to train the MLP classifier. The low clouds, sea surface, and medium-high level clouds are unified into a non-sea-fog category. The objective function of the MLP binary classification method can be described as shown in Equation (2) [37]:  
\[\min_{w,b}\frac{1}{2}||h_{w,b}(f(wx+b))-y||^{2}, \tag{2}\]  
where \(w\) and \(b\) represent the weight and bias of each layers, \(y\) represents the sample labels, \(x\) represents the sample values, \(h_{w,b}\) represents the output of the output layer, and \(f\) represents the activation function. The kernel objective in this paper is the sigmoid function, which can be described as given in Equation (3):  
\[f(x)=\frac{1}{1+\exp(-x)}\, \tag{3}\]  
Through training the MLP classifier, each parameter of the layers is obtained. The main training process consists of four steps:  
* Step 1: Normalize the sample values, which are scaled between \([0,1]\);
* Step 2: Divide the training set and testing set, and train the MLP classifier using the former;
* Step 3: Cross validation to determine the optimal parameters; and
* Step 4: Complete MLP training and verify the accuracy of the classifier.  
The partition ratio of training set to test set is 7:3, and the hidden layer sizes in this study is 100 \(\times\) 100, and the random state is 10, the optimizer solver is \"adam\". Through training, a sea fog identification model based on AHI data is obtained, which is used to obtain a large amount of historical sea fog information in the research area. The verification method and accuracy results of sea fog identification are described in Sections 3.3.1 and 4.1.
## 4 System Simulation Analysis
### Analysis of Output Stage
An oscilloscope was used to observe the difference in upper and lower capacitance and the data were imported into Origin, as shown in Figure 12.  
As illustrated in Figure 12, the voltage difference between the upper and lower capacitors fluctuated within the range of \(-2\) V to \(2\) V, indicating that both capacitors exhibited good stability. Figure 11: (a) Output phase voltage waveform. (b) Output line voltage waveform. Figure 12: Difference between upper and lower capacitance. The output stage of the three-phase inverter primarily comprised a dual closed-loop control system utilizing the SVPWM modulation algorithm, an NPC three-level inverter circuit, an LC filter circuit, and a three-phase load module. Based on the SVPWM algorithm, the maximum amplitude of the three-phase voltage output was \(U_{\text{dc2}}/3^{\text{th}}\). The modulation ratio m was the ratio of the output phase voltage amplitude to the DC bus voltage. Theoretically, the maximum output line voltage amplitude was equal to the DC bus voltage. Therefore, the modulation ratio m was set to a maximum of \(3^{\text{th}}\); otherwise, overmodulation occurred. \(U_{\text{dc2}}\) was input into the NPC three-level inverter circuit and the modulating wave frequency in the 120\({}^{\circ}\) SVPWM algorithm was varied to obtain the three-phase output phase voltage waveforms with different frequencies (\(f_{1}\)-\(f_{3}\)), as shown in Figure 13. The three-phase output phase voltages were symmetrical and had good waveforms, so the simulation results met the expected requirements.
## 4 Simulation Results
Especially, if all the contents are the same size, no matter how the request probability changes, the expectation of the content size will not change, and the curve mentioned above tends to be a horizontal straight line. In general, we can observe that the optimization of the caching strategy is more effective when the Zipf parameter is larger. Furthermore, the impact of UAV altitude is considered. We vary the height of UAVs from 100 m to 500 m, and the latency performance is shown in Figure 4. The curves of three algorithms are generally flat and very slowly ascending. We consider that all the interference between UAVs exists simultaneously in the system, and accordingly, the interference has a significant impact on the system latency. As the UAV height increases, the received signal weakens and the interference also decreases; as a result, the overall transmission delay remains almost stable. Note that the curves show a slight upward trend because the AWGN will not decrease as the height increase, which causes a decrease in SINR. The proposed Algorithm 1 performs better than the other two in different UAV height situations. Figure 4: The latency performance versus UAV height. Figure 3: The latency performance versus Zipf parameter \(\alpha\). After a comparison and analysis, we found that the performance of Algorithm 2 is usually better than Algorithm 3, excluding the situation that the UAV capacity is extremely large. This indicates that the caching strategy plays a more important role than UAV locations in this system model. Moreover, the proposed DC-based algorithm performs better in situations where the Zipf parameter of content popularity is large or the UAV caching capacity is limited.