esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The development of management zones in modern agriculture is of high importance when considering yield maximization, the minimization of costs, and influences on the environment. The technique based on the measurement of electrical parameters of soil is a promising option. However, the use of this technique requires knowledge of the relationships between electrical parameters and other features of the soil. In this study, the relationships between apparent soil electrical conductivity and magnetic susceptibility and the mechanical parameters of soil, soil compaction, and shear stress were investigated to determine the practical use of these relationship models in terms of their acceptable accuracy. Therefore, in this research, four modeling methods were compared. Generally, the neural networks and support vector machine forecasting models of relationships under study outperformed the multiple linear regression models. The performance of the multilayer perceptron and the radial basis function neural models were comparable. In the case of shear stress, the best performance was observed in the multilayer perceptron model. The radial basis function network produced slightly better models for soil compaction. Considering the correlation coefficients between the independent and dependent variables, error metrics, multi-criteria objective functions, and the generalization abilities of the models, it can be stated that neural models can be utilized for precision farming applications.  
The results of the present study led to an added conclusion: data from dynamic soil electrical conductivity and magnetic susceptibility probe measurements have the potential for alternative uses in estimating the mechanical parameters of soil layers. This is a premise of particular importance for future research in the field of conserving agricultural soils from natural and anthropogenic erosion, including over-compaction.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), T.W., G.N. and J.T.M.; formal analysis, [PERSON] ([PERSON]), [PERSON] ([PERSON]), T.W. and G.N.; investigation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and J.T.M.; methodology, [PERSON] ([PERSON]), [PERSON] ([PERSON]), T.W. and G.N.; writing--original draft, [PERSON] ([PERSON]) and [PERSON]; writing--review and editing, [PERSON] ([PERSON]), [PERSON] and [PERSON]; visualization, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available by contacting the authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Results
### Time-Frequency Image of Vibration Signal  
To improve the model's performance, TFCF is used to transform vibration signals into dual-channel time-frequency images containing both SCRVF and SCSVF. A sample is randomly selected from each fault type, and their STFT, TMSST and MSST time-frequency images are shown in Figure 4. It can be seen that a two-dimensional time-frequency image converted from a one-dimensional vibration signal by a time-frequency analysis algorithm can more intuitively reflect the variation rule of various frequency components in vibration signal with time.
## Materials and Methods
### Test Scene and Ground Truth Data  
We set up a test scene with a size of about \(0.4\times 0.4\times 0.4\) m\({}^{3}\) (Figure 7). It consists of several objects with different surface materials: Two ceramic busts with matt surface and slightly different reflectivities, a wooden box, a ceramic cup with a glossy surface and a felt handle, a 3D printed intricate plastic model, a cast iron disk and a rusty cast iron turbine housing. The scene is placed on an industrial fabric. We added an inhomogeneous NIR ambient light source from the left side of the scene to simulate real world conditions.  
We recorded a data set of 400 image pairs at a resolution of 1 Megapixel per image while the projector was running. Each image pair contains a different aperiodic sinusoidal stripe pattern (Figure 8). We rectified the images and worked exclusively with this rectified version throughout the survey. These rectified images are available for download as Supplementary material.  
We calculated the temporal mean (Figure 9a) and temporal standard deviation (Figure 9b). The ambient light which is temporally constant is visible in the mean but not in the standard deviation. Because the NCC and the NEBF both work on temporal brightness contrast, the standard deviation represents the signal strength for the reconstruction algorithm. We looked up the noise level (as shown in Figure 6) for each pixel in the mean image. We then divided the temporal standard deviation by it. The result is an approximated signal to noise ratio (ASNR) (Figure 9c). The more ambient light, the higher the mean and therefore the higher the noise. This means that the ambient light has a negative influence on the ASNR.  
Figure 6: Noise levels of the rectified camera images.  
Figure 7: Photograph of the test scene.  
We created an accurate reference 3D model which we consider ground truth (Figure 10). We used the reference algorithm (NCCCOS) with the full sequence of 400 image pairs to calculate it.  
Figure 8: Rectified images of the left and right camera (brightness and contrast adjusted for better visibility).  
Figure 10: Ground truth 3D model calculated from 400 image pairs.  
Figure 9: **(a)** mean of 400 images with changing projected pattern. Note the inhomogeneous ambient light coming from the left and the shadows thrown by it on the background fabric. **(b)** temporal standard deviation of the same images. Note the absence of the (temporally constant) ambient light. **(c)** approximated signal to noise ratio (ASNR). Note the negative influence of the ambient light on the ASNR value on the background fabric.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data
### Hierarchical Bayesian Modeling
#### 3.1.4 Gibbs Sampler  
The Gibbs sampler is an MCMC algorithm that generates a sequence of observations (samples) from a specific multivariate distribution of the hierarchical model parameters. It starts by simulating a sequence of random vectors \(Y_{1}^{m},Y_{2}^{m},\dots,\ Y_{n}^{m}\), for \(m=1,2,\dots\), and \(n=1,\dots,D\). Then, it choose a starting point \(p(Y_{1}=y_{1}|Y_{2}=y_{2}^{m-1},Y_{3}=y_{3}^{m-1},\dots,Y_{n}=y_{D}^{m-1})\) for which \(p(Y_{1})>0\)[57]. (Algorithm 1)  
```
initialize \(y^{(0)}\sim q(y)\) foriteration m=1,2,3,...do \(y_{1}^{m}\sim p(Y_{1}=y_{1}|Y_{2}=y_{2}^{m-1},Y_{3}=y_{3}^{m-1},\dots,Y_{D}=y_{ D}^{m-1})\) \(y_{2}^{m}\sim p(Y_{2}=y_{2}|Y_{1}=y_{1}^{m},Y_{3}=y_{3}^{m-1},\dots,Y_{D}=y_{D}^ {m-1})\) \(\vdots\) \(y_{n}^{m}\sim p(Y_{D}=y_{D}|Y_{1}=y_{1}^{m},Y_{2}=y_{2}^{m},\dots,Y_{D}=y_{D-1 }^{m})\)  end for
```  
**Algorithm 1**Gibbs Sampler  
Gibbs sampling allows us to examine each variable and calculate its conditional distribution for the random vectors \(Y_{1},Y_{2},\dots,\ Y_{n}\), and the value for each random variable \(y_{1},\ y_{2},\dots,\ y_{n}\) is initialized from the prior distribution. In each iteration \(m\), the sampler produces the samples of \(y_{1},\ y_{2},\dots,\ y_{n}\) as follows:  
\[y_{1}^{m}\sim p(Y_{1}=y_{1}|Y_{2}=y_{2}^{m-1},Y_{3}=y_{3}^{m-1}) \tag{26}\]  
\[y_{2}^{m}\sim p(Y_{2}=y_{2}|Y_{1}=y_{1}^{m},Y_{3}=y_{3}^{m-1}) \tag{27}\]  
\[y_{3}^{m}\sim p(Y_{3}=y_{3}|Y_{1}=y_{1}^{m},Y_{2}=y_{2}^{m}) \tag{28}\]  
\[y_{n}^{m}\sim p(Y_{D}=y_{D}|Y_{1}=y_{1}^{m},Y_{2}=y_{2}^{m},\dots,Y_{D}=y_{D-1 }^{m}) \tag{29}\]  
The Gibbs sampler stops when all generated sampling values have the same distribution size. Algorithm 1[57; 58] provides the procedure that the Gibbs sampler uses to generate the samples. Samples are generated by examining each random variable one at a time and obtaining samples from the conditional distributions of each variable. A sequence of pairs of random variables is generated as follows: \((Y_{1},y_{1}),(Y_{2},y_{2}),(Y_{3},y_{3})\).
## 4 Diversity Performance
### Diversity Gain (DG)  
The diversity gain is another parameter of the diversity performance of the antenna. The diversity gain can be measured as the communication link reliability in a MIMO system as it shows the reduction in the fading of the channel while combining two or more independent antennas. The diversity gain is calculated from the formula given below:  
\[\text{DG}=10*\sqrt{1-(\text{ECC})^{2}} \tag{6}\]  
The threshold of diversity gain should be nearly equal to 10. The diversity gain is related to ECC as the values of ECC reduce to zero; then, the values of diversity gain will approach 10. Figure 10 shows the diversity gain of the proposed antenna which is nearly close to ideal values.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 4 Deployment of AccFlow
### Flow Aggregation  
Flow aggregation can prevent the attackers from amplifying their attacking scale by faking huge numbers of flows. Furthermore, with protocols like ingress filter [20] and Passport [21], ASes can limit the range of their acceptable source IP addresses. As a result, the number of distinct attacking flows that can be used to attack the bottleneck link is limited. Moreover, the existing security protocols, such as MiddlePolice [22; 23], Mirage [24], Phalanx [25], Pushback [26], and DoS-limiting architecture [27], can be applied to further limit the attacking scales. For instance, Mirage adopts the concept of frequency hopping in wireless networks to \"hop\" the destination IP addresses among all available addresses. Each time a user wants to send traffic to this site, it has to solve a computational puzzle to obtain the new IP address, which will limit the volumes of traffic that the computationally limited attackers can send. MiddlePolice, on the other hand, allows the destination to determine which source IPs are allowed through self-defined traffic control policies. Since AccFlow does not cause disruption to the existing network infrastructure, it can effectively interact with these security protocols to defend the network against extremely large-scale DDoS attacks.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 4 Experiment and Analysis
### Discussion  
In this study, we used AIS data to calculate emissions from POLB-area vessels. The calculation considers variables such as vessel type, size, speed, and engine power, and incorporates factors such as emission factors, sailing patterns, and time of day. We also made assumptions to supplement unavailable data points. To verify the accuracy of our method, we compared the calculations with the POLB official report, using main engine CO2 emissions as an example, which is shown in Table 6.  
Using AIS data and a bottom-up approach, this study assesses vessel emissions in the POLB region affected by the COVID pandemic. In terms of vessel type, most vessel types showed a decline. In terms of emissions, there was a slight decrease in 2020, due to a reduction in the number of vessels. However, as the economy starts to recover in 2021, emissions showed a steep increase, with emissions from container ships doubling even more. The reason for this is that ships have to wait longer in port areas, due to the decline in production and the strict controls by the government to prevent the spread of the COVID pandemic. However, despite the longer waiting times for transmission, emissions from ships in the berthing state have decreased, which demonstrates the significance of the shore power policy. The introduction of this policy can significantly reduce emissions from ships in the port area. The main part of the reduction comes from ships in berthing status. Such vessels can switch off their ME, AE, and boilers, and switch to shore power for basic operations, thus achieving zero emissions.  
In the future, we envisage that further reductions in emissions in port areas could be achieved by increasing incentives for low-emission vessels and imposing fines or bans on high-emission vessels. As port-wide emissions from ships are caused mainly by ships sitting in port, efforts such as mandating in-port speeds for ships and lowering operational times could be implemented, in addition to shore power restrictions. Moreover, the implementation of electrification in ports can be further pushed in terms of port operations through initiatives such as the construction of marine microgrids, offshore wind power generation, and the expansion of energy storage systems, to promote the use of green energy. Ports should also promote intelligent procedures and implement techniques such as optimising port operations, decreasing vessel waiting times, and enhancing port transport efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Total Main Engine CO2** & **Our AIS-Based** & **POLB Official** & **Relative** \\
**Emission in** & **Calculation** & **Report** & **Error** \\
**Different Years** & **(Million Tonnes)** & **(Million Tonnes)** & **(Million Tonnes)** & **(**)** \\ \hline
2019 & 52,527 & 57,832 & 9.17\% \\
2020 & 42,602 & 46,822 & 9.01\% \\
2021 & 46,687 & 50,515 & 7.57\% \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of AIS-based calculation and POLB official report results [52].
## 3 System Architecture
### Preliminary: UCB / D-UCB  
The UCB-1 method [18] chooses the arm \(i\) that maximizes the upper bound of a confidence interval for the stationary expected reward. To handle non-stationary expected rewards, the Discounted UCB (D-UCB) [21; 22], a UCB-variant, provides higher weights to recent rewards than to older rewards, considering a discounted empirical average reward, \(\bar{X}_{t}^{std}(i)\) and a discounted number of arm pulls, \(N_{t}^{std}(i)\), in the computation of the confidence interval's upper bound \(U_{t}^{std}(i)\):  
\[U_{t}^{std}(i) =\bar{X}_{t}^{std}(i)+C\sqrt{\frac{\log\sum_{j=1}^{K}N_{t}^{std}(j )}{N_{t}^{std}(i)}}, \tag{2}\] \[\bar{X}_{t}^{std}(i) =\frac{1}{N_{t}^{std}(i)}\sum_{s=1}^{t}\gamma^{t-s}\mathds{1}_{ \{I_{s}=i\}}X_{s}(i),\] (3) \[N_{t}^{std}(i) =\sum_{s=1}^{t}\gamma^{t-s}\mathds{1}_{\{I_{t}=i\}},\text{ with }N_{1}^{std}(i)=0, \tag{4}\]  
where the scalar \(\gamma\in[0,1]\) is a discount factor, \(C>0\) is a constant controlling the exploration level, and \(std\) indicates that this formulation refers to the standard D-UCB. D-UCB selects the arm to be pulled at time \(t\) as the one with the highest upper confidence bound (principle known as _optimism in the face of uncertainty_):  
\[I_{t}^{std}=\arg\max_{i\in\mathcal{K}}U_{t}^{std}(i). \tag{5}\]
# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 3 Proposed Method
### Similarity Match Network  
Cross correlation operation refers to the process of obtaining the feature response map by correlation operation with the search area and the template as the kernel. Inspired by reference [7], this paper introduces deep-wise cross correlation. Deep-wise cross correlation takes the search area as the input and the template as the kernel to obtain the response map channel by channel. It can greatly reduce the amount of parameters and ensure the tracking accuracy is not affected.  
The tracking algorithm determines the positive and negative samples in the image and locates the target through the response map. By enhancing the rich feature information in the response map, the tracking accuracy can be improved. Therefore, we designed the response map enhancement module. As shown in Figure 4.  
The proposed RmE method adjusts the response map by calculating its own mean and variance. After obtaining response map with the size of 8\({}^{\circ}\)256\({}^{\circ}\)25\({}^{\circ}\)25, we adjusted the response map through encoder and decoder. In the encoder, in order to reduce the amount  
Figure 4: Response map Enhancement. The module consists of Encoder and Decoder. 8*256*25*25 in the figure represent batch*channel*height*width of the characteristic graph, respectively.  
Figure 3: Transformer Feature Pyramid.Features form Feat3-Feat5 are flattened and fed into TFE blocks. TFN consists of 4 TFE blocks.Hierarchical information are extracted by 3 separate TFE blocks, and further enhanced by 1 additional TFE block.  
of computation, we used 1 \(\times\) 1 convolution to adjust the response map and adjust the channel size from 256 to 64. In the decoder, we adjust the mean and variance of response maps in the same batch by batch normalization and restored the number of channels through 1 \(\times\) 1 convolution. Because the targets of the same batch are same in the training process, the batch normalization of the response maps enables more target information to be obtained. The output is then averaged. The result was used as the weight of convolution kernel, and channel 0 of each data was used as the bias of the convolution kernel. Through this special convolution method, we adjusted the original response map to allow it to obtain global context information and enhance the discrimination ability of this network, so as to deal with similar object interference and significant appearance changes of the target.
## 1 Introduction
### Machine-Learning Modeling  
Physical modeling approaches are the most common approaches for predicting system behaviors, but they rely on descriptions of physics concepts. Thus, they tend to be complex, as the detail of the model increased. Therefore, as the principle of Occam's razor states, physical modeling must balance complexity with assumptions in order to produce simplified and representative models [22; 23].  
On the other hand, artificial intelligence (AI) researchers have proposed several techniques that allow automatic generation of the models and equations based on measurements arranged in datasets. Furthermore, machine learning (ML), a field of AI, applies deterministic and heuristic methods to produce models with less complexity established in the raw measurements [22].  
During the last two decades, ML models have exhibited high effectivity, accuracy, and performance in several fields, including energy applications. Furthermore, ML results for modeling have motivated researchers to apply its models to accurately predict the behavior of physical phenomena [22; 23; 24; 25; 26; 27; 28; 29; 30].  
The ML modeling process can involve several stages, depending on its application, but a general description would include collecting data, preprocessing data, building a model, training, and testing. Furthermore, all the stages must be continually tuned to improve the results; i.e., the stages can repeatedly change across the entire process if the model requires efficiency improvements, as represented in Figure 1[22].
## 3 Results and Discussions
### Fuel Cell  
In this section, the aim is the selection of an appropriate fuel cell. On the daily demand per hour of the one-family house (see Figure 1), the peak maximum of electrical power is achieved at 8 pm, 1.015 kW. The selected fuel cell must be available for supplying this power demand when the solar energy is null. Considering this, the maximum power per hour that the fuel cell must supply is 1.218 kW according to a 20% of oversizing. A possible commercially available option among different manufacturers is the model Fcgen-1020 ACS, which is a proton exchange membrane (PEMFC) that can be scaled up to meet power requirements from 450 W to 3 kW. The physical characteristics per stack of fifty-six fuel cells is 363 \(\times\) 103 \(\times\) 351 mm; the fuel flow rate per fuel cell is 0.5 standard liters per minute (slpm), and the rated power is 41.1 W/cell. It is an air-cooled fuel cell stack with an open-cathode and a self-humidifying MEA, which allows eliminating the humidifier, coolant pump, and radiator. Therefore, to cover the power of 1.218 kW, thirty fuel cells are necessary in the FC stack. For more information of the fuel cell, see Figure S6 in Supplementary Materials.  
Figure 6: Coupling Solartec SST72 110 24 106W PV module \(-\)5 cm\({}^{2}\) electrolyzer.  
An estimation of the hydrogen consumption is taken into account, since the Ballard manufacturer provides the consumption of hydrogen, 0.5 L/min per cell. In terms of mass, it is 0.059 kg/day per cell. This hydrogen consumption is for a production of 30 kWh per day. As in our system, the maximum electrical demand per day is 7.635 kWh, and the fuel cell consumes 0.45 kg for obtaining this energy. For this reason, considering the design basis of four consecutive days without solar energy, the amount of hydrogen that must be stored in the tank is 1.8 kg.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 4 Conclusions  
Establishing how best to fully exploit polarization information for detection of land geophysical parameters is perhaps one of the most important tasks in the promising GNSS-R remote sensing technique. Owing to a lack of sufficient in situ measurement data suitable for such research, we relied on microwave scattering models to further such analysis. To investigate the potential of polarization properties for spaceborne GNSS-R, the random surface scattering model and the first-order RT model were employed, together with the corresponding DDM simulator (i.e., LAGRS) that is oriented for the FY-3E GNSS-R payload. The importance of these theoretical models is that they are the key mechanism tools for analyzing and interpreting satellite observations, simulation/assimilation of satellite data, development of quantitative inversion algorithms of surface parameters, and the design of new sensors. The simulations in this paper revealed obvious differences in the DDM outputs and the scattering variations at different polarizations. They indicate  
Figure 5: The vegetation specular scattering properties. The left figure is the coherent scattering at various polarizations. The right figure is the component scattering contribution to the total scattering.  
Figure 6: Vegetation DDMs at LR polarization (**left**), VR polarization (**middle**), and HR polarization (**right**).  
that polarizations of the GNSS-R remote sensing technique can potentially provide very good information for geophysical parameter retrieval. Therefore, we will conduct further theoretical and experimental work on this topic in the near future.  
Conceptualization, methodology, draft preparation and supervision X.W.; supervision and funding acquisition, X.D., F.Y. and W.B.; revision tasks and draft preparation, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42061057, and 72004017) and the innovative Teams of Studying Environmental Evolution and Disaster Emergency Management of Chifeng University (cfxykycxtd202006) and Chifeng University, Laboratory of National Land Space Planning and Disaster Emergency Management of Inner Mongolia (CFXYZD202006).  
Special thanks are given to the reviewers for their constructive comments and helpful suggestions.  
The authors declare no conflict of interest.