esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Yearly Cropland Suitability Prediction Based on Machine Learning  
Prediction of peak LAI and FAPAR values based on machine learning regression was performed by using the peak LAI and FAPAR of ground-truth soybean parcels from the APPRRR database as training and test data, while a total of 186 environmental covariates per study year were used as predictors, including climate, soil, spectral, and topography data. A 10-fold cross-validation in 10 repetitions was performed to evaluate predictive accuracy of machine learning regression methods for peak LAI and FAPAR prediction. Training data from each study year, consisting of peak LAI and FAPAR values, along with relevant environmental covariates, were utilized as inputs to various machine learning regression algorithms. This predictive model aims to assess soybean vegetative potential across the entire agricultural landscape within the study area. Four machine learning regression algorithms, including random forest (RF) [34], support vector machine (SVM) [35], and extreme gradient boosting (XGB) [36], as well as their combination, were implemented, which are known for their high prediction accuracy as evidenced in previous studies [37, 38, 39]. In theory, one of the key benefits of ensemble approaches is increased accuracy, as ensembles can effectively mitigate the biases and errors inherent in individual models [40]. This is particularly beneficial in complex datasets, where different models may capture different patterns or relationships within the data. In addition, ensemble methods exhibit improved robustness to outliers and noise, as the different perspectives of multiple models help to smooth out individual errors, resulting in more reliable predictions [41]. Hyperparameters of each evaluated machine learning model were selected based on the highest prediction accuracy of ten evaluated iterations, each having unique set of tuning hyperparameters. Predictions were executed in R using the \"caret\" library [42]. The performance of these models was evaluated using test data specific to each study year, with a focus on maximizing the coefficient of determination (R\({}^{2}\)) and minimizing the root mean square error (RMSE) to ensure the most reliable LAI and FAPAR predictions.
## 3 Artificial Porosity Images Creation for SLM Parts
### Measuring the Similarity between Artificial and Real Porosity Images  
The created artificial porosity images were compared with real CT scan slices of finished SLM parts [26] to verify that the simulated images are close the reality. The real images are only used as a demonstration and are not directly linked with the pore formation equations mentioned in Section 3.1. The following Figure 18 shows an illustrative example of the slices before and after processing, which was required to focus on the bulk of the material and to obtain images of the same size as the artificial images.  
Figure 17: Illustrative example of the final version of artificial porosity images.  
Figure 19 presents the real image along with the first version of the artificial image.  
Samples of 100 slices of real images were taken and compared with each of the first versions of 100 artificial images created in the previous section. The comparison was made using a quantitative method called Structural Similarity Index (SSI). It is a measure that assesses the images based on three computational terms, namely the luminance (l), the contrast (c), and the structural terms (s). Multiplying the three terms results in the overall index. If two images are exactly the same, the index should be 1, and if they are totally different the index should be close to zero. The mathematical equations for calculating SSI for images x and y are as follow [28]:  
\[\text{SSI(x,y)}=[\text{l(x,y)}]^{\alpha}\times[\text{c(x,y)}]^{\beta}\times[ \text{s(x,y)}]^{Y} \tag{3}\]  
where:  
\[\text{l(x,y)}=(2\times\mu_{\text{x}}\times\mu_{y}+\text{C1})/({\mu_{\text{x}}} ^{2}+{\mu_{\text{y}}}^{2}+\text{C1}) \tag{4}\]  
\[\text{c(x,y)}=(2\times\sigma_{\text{x}}\times\sigma_{y}+\text{C2})/({\sigma_{ \text{x}}}^{2}+{\sigma_{\text{y}}}^{2}+\text{C2}) \tag{5}\]  
\[\text{s(x,y)}=(\sigma_{\text{xy}}+\text{C3})/(\sigma_{\text{x}}\times\sigma_{y }+\text{C3}) \tag{6}\]  
\(\mu_{\text{x}}\) and \(\mu_{\text{y}}\) are local means, \(\sigma\)x and \(\sigma\)y are standard deviations, and \(\sigma\)xy is cross-covariance for images x and y. The exponents for luminance, contrast and structural are alpha(\(\alpha\)), beta(\(\beta\)), and gamma(\(\gamma\)), while C1, C2, and C3 are constants added to avoid instability for image regions with local mean or standard deviation close to zero.  
MATLAB software was used to calculate SSI between the real and first version of artificial porosity images, the average similarity index for 3000 slices was 0.9586. The same  
Figure 19: Real image (**Left**) vs. first version of artificial image (**Right**).  
Figure 18: Illustrative example of the real existing porosity images [26].  
100 samples of real images were used to compare them, with the final version of artificial porosity images shown in the following Figure 20:  
The average similarity index for 3000 slices was improved to 0.9967, which means that the final version of artificial porosity images mimics better real images since the index is closer to 1.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### Congestion Control in IoT Networks
#### 4.2.2 Congestion Avoidance  
Congestion avoidance aims to prevent the network from entering a congested state. It includes proactive techniques like:  
* Traffic Shaping: Proactively smoothing out traffic bursts through buffering and rate control avoids sudden traffic spikes that lead to congestion.
* Load Balancing: Distributing traffic across multiple paths avoids overloading particular links and nodes. This prevents localized congestion hotspots.
* Adaptive Transmission: Nodes can dynamically adjust the transmission rates based on the network conditions to stay within the capacity limits.
* Caching and Offloading: Caching popular IoT data at the edge of the network alleviates traffic in the core network, avoiding congestion. Computation offloading also reduces network loads.  
Machine-learning predictive models can forecast traffic and detect anomalies to activate avoidance measures before congestion sets in. However, this requires collecting sufficient training data. Overall, a combination of avoidance tactics is necessary for robust congestion control.
## 4 Stronger Versions of LLL  
### Strengthening the Symmetric Version  
A natural question in connection with the original symmetric LLL (Lemma 3) is this: how large can \(\Pr(A_{i})\) be, such that the conclusion \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) is still guaranteed to hold? Note that a larger value of \(\Pr(A_{i})\) makes \(\Pr(\overline{A}_{i})=1-\Pr(A_{i})\) smaller, so the nonemptiness of \(\overline{A}_{1}\ldots\overline{A}_{n}\) becomes less likely. To formalize it, let us introduce a parameter \(p>0\) and replace the condition (3) with  
\[\Pr(A_{i})\leq p\qquad(\forall i). \tag{5}\]  
Then, the largest value of \(p\) for which the conclusion \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) still remains valid provides us with the strongest from of the symmetric LLL. Note that the value of \(d\) is kept fixed.  
[PERSON] [4] investigated the above question and computed a specific function \(f(d)\), which is the supremum of \(p\) values in (5) for which the conclusion \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) remains valid, for a fixed \(d\):  
**Lemma 2** ([PERSON]'s lemma).: _For any fixed integer \(d\geq 1\), let \(f(d)\) be the supremum of \(p\) values, for which the the conclusion \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) remains valid if \(p\) is used in place of \(1/(4d)\) in (3). Then,_  
\[f(d)=\left\{\begin{array}{cl}\frac{1}{2}&\text{if}\quad d=1\\ \\ \frac{(d-1)^{d-1}}{d^{d}}&\text{if}\quad d\geq 2\end{array}\right. \tag{6}\]  
Since \(f(d)\) is the _supremum_ (not necessarily the maximum) of \(p\) values in (5) for which the conclusion \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\) holds, therefore, we can formulate the strongest version of the symmetric LLL this way:  
**Lemma 3** (Strongest symmetric LLL).: _Let \(A_{1},\ldots,A_{n}\) be events, such that each event is mutually independent of all the others, except at most \(d\geq 1\) of them. Let \(f(d)\) be given by (6). Then, if_  
\[\Pr(A_{i})<f(d)\quad\quad(\forall i)\]  
_holds, then \(\Pr(\overline{A}_{1}\ldots\overline{A}_{n})>0\)._  
For example, if \(d=3\), then \(1/(4d)\approx 0.0833\), but \(f(3)=4/27\approx 0.148\), so in this case the latter allows an about \(77.7\%\) larger value for \(\Pr(A_{i})\), than (3). Since, however, the formula (6) for \(f(d)\) is relatively complicated, one may look for a simpler bound. [PERSON] [5] proved that \(p=\frac{1}{\mathrm{e}(d+1)}\) suffices in place of \(\frac{1}{4d}\) in (3), where \(\mathrm{e}=2.718\ldots\) is the base of the natural logarithm. [PERSON] and [PERSON] [6] further improved it to \(\frac{1}{\mathrm{e}d}\).
## 2 Materials and Methods
### Methodology
#### 2.3.4 Identification of Impairment Profiles  
The GSOM generates a two-dimensional grid of nodes, with each node denoting a group of similar data patterns. Nodes located in closer proximity to each other on the map are indicative of clusters. As a result, creating GSOM maps enables the automatic detection of data point clusters (participants) through iterative analysis of participant data within each node. This process unveils unique patterns that differentiate these clusters from the larger population. Consequently, by visually inspecting the map, we can identify regions where nodes are densely clustered, as these areas generally correspond to clusters of similar patterns. Therefore, the framework enables the automatic identification of prominent clusters by computing the intracluster similarity using the patient data of each cluster. The GSOM algorithm was used for this task with a 0.8 spread factor, while the Euclidean distance [31] was used as the distance measure to calculate the difference in input data between two participants. This resulted in marked regions that indicated different subgroupings (profiles) of survivors of mild stroke.  
The analysis was conducted to identify the following:  
1. Profiles (subgroupings) within NIHSS assessment on day 3-7 after stroke;
2. Profiles across measures on day 3-7 after stroke;
3. Profiles across measures at 3 months after stroke;
4. Profiles across measures at 12 months after stroke.  
In this study, we created an interactive visualization tool using the GSOM maps that can be used by clinicians to monitor each participant's impairment profiles over time. Appendix A presents screen captures of the developed tool.
# Changes of Gait Patterns after Correction of Refractive Error in the Elderly with Cataracts
## 3 Results
### Analysis of Correlation between Change in Step Length and Each Gait Pattern  
Correlation analysis was performed in order to examine possible associations between the changes in other factors affecting gait pattern, including stride length, cadence, width, and walking velocity, and change in step length after the participants wore correction glasses (Figure 2). The results showed a strong positive correlation between step length and stride length (r = 0.779/\(p\) = 0.000, Figure 2a), with a decrease in cadence (r = 0.686/\(p\) = 0.000, Figure 2b) along with increasing step length. Despite an increase in step length, no effect on step width was observed; however, a proportional correlation was observed between walking velocity and increasing step length (r = 0.416/\(p\) = 0.022, Figure 2d).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Condition of** \\ **Refractive Correction** \\ \end{tabular} } & \multicolumn{6}{c}{**Factors Affecting Gait Patterns**} \\ \cline{2-7}  & **Step Length** & **Stride Length** & **Cadence** & **Step Width** & **Walking Velocity** & **\#** \\  & **(cm)** & **(cm)** & **(Steps/min)** & **(cm)** & **(m/s)** & \\ \hline Before & 45.57 \(\pm\) 5.37 & 89.09 \(\pm\) 10.41 & 108.16 \(\pm\) 12.59 & 10.21 \(\pm\) 2.07 & 0.84 \(\pm\) 0.16 & each \\ \hline After & 47.78 \(\pm\) 5.81 & 94.09 \(\pm\) 12.57 & 102.67 \(\pm\) 19.74 & 10.93 \(\pm\) 1.68 & 0.84 \(\pm\) 0.17 & 30 \\ \hline \(t/p\)-value & \(-\)2.363/0.025 \({}^{*}\) & \(-\)2.43/0.022 \({}^{*}\) & 2.467/0.020 \({}^{*}\) & \(-\)3.410/0.002 \({}^{*}\) & 0.070/0.945 & \\ \hline cogen’s d (effect size) & 0.395 & 0.433 & 0.332 & 0.203 & - & \\ \hline \hline \end{tabular} Data are expressed as mean \(\pm\) SD. \({}^{*}\)\(p\) < 0.05: a significant difference according to the paired \(t\)-test.  
\end{table}
Table 1: Change in gait patterns before and after the participants wore correction glasses.
# Vibration Response Law of Aircraft Taxing under Random Roughness Excitation
## 2 Random Vibration Models
### Pavement Roughness Models  
The Federal Aviation Administration (FAA) gathered and published longitudinal profile elevation data for 37 runway centerlines [26]. Our team also collected pavement roughness data for 20 runways in China using a vehicle-mounted laser profile meter and a Beidou high-precision positioning system, which can obtain the true full-wave runway profile [27]. Figure 4 presents the pavement roughness and the data collection system used by our team. The basic information for the 20 Chinese runways collected by our team is presented in Table 1. Upon analyzing the data, we discovered that asphalt pavement and concrete pavement display distinct digital characteristics in their roughness sequences.  
Figure 5 illustrates the power spectrum of measured roughness data for the asphalt pavement (a) at Atlantic City International Airport and the concrete pavement (b) at Taizhou Luqiao Airport. In Figure 5, \(n\) is the spatial frequency, and its unit is cycle/m. Dividing \(n\) by the unit spatial frequency (1 cycle/m), we can obtain the unitless spatial frequency \(n_{\text{u}}\). Leg\(n_{\text{u}}\) is the logarithm of \(n_{\text{u}}\) to the base 10. There are notable differences in the power spectrum between asphalt pavement and concrete pavement. The PSD distribution curve of asphalt pavement roughness is relatively smooth, while that of concrete pavement decreases significantly faster at high frequencies. This phenomenon can be attributed to the fact that concrete runways consist of slabs as units, with joints and uneven surfaces between two adjacent slabs, while the surface within a single unit is generally flat. In  
Figure 4: Pavement roughness and data collection: (**a**) Pavement roughness; (**b**) data collection.  
contrast, asphalt runways are flexible pavements and are relatively more continuous in terms of roughness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**International Air Transport**} \\ \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**Association**} & \multicolumn{1}{c}{**Runway Type**} & \multicolumn{1}{c}{**Runway Length/m**} \\ \multicolumn{1}{c}{**(IATA) Code**} & & \\ \hline
1 & HGH & cement & 3560 \\
2 & DOY & 2/3 asphalt + 1/3 cement & 3600 \\
3 & WUX & asphalt & 3180 \\
4 & JGS & cement & 2590 \\
5 & HYN & cement & 2490 \\
6 & NGB & cement & 3160 \\
7 & LCX & cement & 2390 \\
8 & A military airport & cement & 2210 \\
9 & A military airport & cement & 2280 \\
10 & FOC & cement & 3580 \\
11 & SWA & cement & 2480 \\
12 & YNZ & cement & 2810 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information for the 20 Chinese runways.  
Figure 5: Power spectrum and roughness model of measured data of asphalt and cement pavement: (**a**) Atlantic City International Airport (asphalt pavement); (**b**) Taizhou Luqiao Airport (cement pavement).
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 4 EdgeX over Kubernetes  
In this section, we present EoK to overcome the fundamental limitations of EdgeX Foundry when building an IoT edge computing infrastructure. First, the problems of EdgeX Foundry can be defined as follows:  
(1) EdgeX Foundry offers poor application service manageability. As EdgeX Foundry uses Docker Compose to deploy and manage microservices on a single node, it does not provide the functionality to remotely deploy and manage microservices to edge nodes. In other words, as Figure 1a shows, each service of EdgeX Foundry is deployed and operated locally in the form of a microservice using Docker Compose. As Docker Compose is installed on a single host, an administrator must enter commands directly at the edge node when they want to update or deploy new services to edge nodes. Accordingly, when the service requested by the user is not deployed at the corresponding edge node, it is impossible to deploy and use the service in real-time.  
(2) EdgeX Foundry does not allocate adequate computational resources based on resource status. In edge computing infrastructure, the demands on the application service can vary depending on the user distribution and their requests. For example, if the allocated computational resources are insufficient to process the requests, the QoS, such as in terms of response time and throughput, must be lowered. Therefore, allocating appropriate resources to services based on real-time demands and current resource status presents a cost-efficient approach toward improving QoS. However, EdgeX Foundry lacks features such as real-time resource monitoring and dynamic resource management for application services; therefore, the service cannot be scaled up even when resource usage by the Virtual Pod (device service) increases, as shown in Figure 1a.  
To address the problems concerning low manageability and service quality discussed above, we propose EoK. EoK is based on EdgeX Foundry; however, it can increase manageability by efficiently managing edge nodes and services with help from Kubernetes container orchestration. As shown in Figure 1b, EdgeX Foundry's edge nodes become worker nodes of a Kubernetes cluster. In this cluster, EdgeX Foundry's edge nodes can deploy and manage microservices remotely through Kubernetes, not Docker Compose, providing increased manageability. Moreover, service resource management and service quality can be improved by enabling autoscaling of microservices through the HPA--one of the features of Kubernetes. For example, if the resource usage of the Virtual Pod is above a given threshold, EoK can adapt its resource usage by dynamically deploying replicas of the _Virtual_ Pod, as shown in Figure 1b. To summarize, the proposed EoK provides functions such as remote service deployment and autoscaling with the help of Kubernetes to improve the manageability and QoS of EdgeX-based IoT edge gateways.  
In the following subsection, we describe in detail how Kubernetes functions can be used to design the EoK system. Then, we describe two common-use cases that benefit from the EoK system.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 1 Introduction
This paper expands the investigation to higher order Ambisonics. The methodology of diffuse-field equalisation is explored to determine which quadrature method to use and the optimal number of measurements necessary to produce a sufficient approximation. DFE of Ambisonics is evaluated in three ways; spectral difference over the sphere, predicted sagittal plane localisation and perceptual listening tests. Three orders of Ambisonics: \(M=1\), \(3\) and \(5\) are investigated.
# Types and Performances of Polymer Gels for Oil-Gas Drilling and Production: A Review
## 2 Types of Polymer Gels Used for Oil-Gas Drilling and Production
### Pre-Crosslinked Polymer Gels
used nano-silica and polymers to prepare the emulsion polymerization of organic-inorganic polymer microspheres as a profile control agent, which achieved a plugging efficiency of up to 93.87% for high-permeability zones [51]. Figure 6: Schematic of the mechanism of stabilizing gel particles by silica nanoparticles. (Reprinted/adapted with permission from Ref. [46]. 2016, [PERSON], et al.). Ground crosslinked polymer gels have the following advantages compared with in situ polymer gels. (a) Depending on the preparation process, the particle sizes of PPGs are adjustable from nanometers to millimeters or even centimeters. So, the gels can be prepared in different particle sizes according to the specific plugging requirements of different pores and fracture sizes in the formation. (b) PPGs do not undergo chromatographic separation between the different components during the injection process and are minimally affected by factors such as temperature and salinity. In addition, (c) PPGs can migrate long distances in the formation and are minimally influenced by the shearing effect of the formation pores; thus, they are suitable for in-depth plugging and oil displacement in formations. However, there are the following problems. For example, (a) the particle sizes of the PPGs must match well with the pores and fractures size in the formation; thus, PPGs have poor injectability performance for formation with pores and fractures of different sizes. (b) The PPG concentration will decrease substantially during migration under the influence of factors such as dilution of formation water, making it difficult to achieve effective blocking of in-depth pores and fractures in the formation. In addition, (c) for water shutoff or lost circulation control in formations, PPG has difficulty forming bridges between macroscopically large fractures, as well as fractures and cavities, and forming a plugging zone; thus, PPG is mainly applicable to plugging low-permeability formations with microscopic pores and fractures. Therefore, ground PPGs should be further studied to address the above problems.
## 3 Laboratory Methodology
### Power Electronics  
The power amplifier box includes a signal generator that splits into two channels, where each channel is connected to two amplifiers and each amplifier is connected to a load box providing two outputs. Overall, this results in 8 outputs as shown in Figure 5.  
Power amplifier specification:  
* Power 4 \(\times\) 100 W Root Mean Square (RMS), High Voltage Amplifier
* Power Supply control 100-300 V, adjustable voltage via dimmer and QT_Sendwave
* Maximum current output 5 A RMS
* Operating Frequency 10-300 kHz
* Load Box with current protection, allowing off-resonance voltages up to 1000 V  
Figure 4: Impedance characterization of 40 kHz HPUT before and after marinization.  
Figure 5: Power electronics system schematic.  
* Programmable 2 channel output signal generator
* 2 channel signal generator--two different outputs simultaneously
* Each signal generator drives 2 amplifiers
* 8 continuous-wave output channels (2 per channel 4 per output)
* Single sinusoids and chirp functions
* Arbitrary function generator possible with a software upgrade
* Power supply monitoring functions
* Logging, auto restart, and parameter persistence functions
* Raspberry pi with a touch screen interface
* 2 USB ports
* Fan cooled
* Isolated transformer  
Table 4 displays the final system features and shows that each channel is driven by a signal generator that can be controlled by the user using the Raspberry Pi touchscreen interface, allowing the user to change the signal wave, frequency, power, and sampling. The final hardware is developed and shown in Figure 6.