esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a factor analysis of ground subsidence was conducted using seismic motion, GIS, and ground data. Machine-learning models were constructed using two ensemble learning algorithms, XGBoost and LightGBM, and feature extraction was performed. The XGBoost model demonstrated higher predictive accuracy than the LightGBM model, with absolute errors of \(\pm\)0.14, \(\pm\)0.41, and \(\pm\)0.71 cm at cumulative frequencies of 50, 70, and 80% on the test set, respectively. The coefficient of determination \(R^{2}\) for the XGBoost model was 0.97, and there was a good fit between the predicted and actual values, proving its effectiveness in predicting the amount of ground subsidence due to liquefaction. Subsequently, using the XGBoost model, the contribution of each explanatory variable to the prediction results was determined using SHAP impact analysis.  
In terms of global explanations, the JMA seismic intensity at the engineering bedrock and AVS30 were the key variables affecting the amount of liquefied ground subsidence, suggesting a significant impact on liquefaction phenomena. In addition, borehole data represented by the SPT-N value, which indicates the hardness of the ground, were confirmed to contribute significantly to ground subsidence. The limitations of GIS data and geomorphological classification in representing ground information suggest that including borehole information in the machine-learning model could improve prediction accuracy.  
In terms of local explanations, it was able to provide an engineering explanation for the cause of ground subsidence in any given instance. In this study, factor analysis was conducted for Urayasu City, Chiba Prefecture, and it was confirmed that the contribution to the amount of ground subsidence could be evaluated using the SHAP method. Consequently, this suggests that predictions with flexibility and interpretability are possible.  
Conceptualization, K.K.; Formal analysis, K.K.; Funding acquisition, Y.M.; Project administration, W.L. and Y.M.; Writing--original draft, K.K.; Writing--review and editing, W.L. and Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Number 23H01652 and 22K18303.  
Not applicable[PERSON]  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# A Survey on Cyber Risk Management for the Internet of Things
## 5 Cyber Risk Treatment For IoT Survey Results
### IoT Risk Control
#### 5.1.3 Optimise Control Strategies
To this end, a security administrator needs to set risk threshold values (the maximum cumulative threat value in any attack path) in a way that allows for the optimal set of vulnerabilities to be patched. Meanwhile, [PERSON] et al. [55] use a developed software toolkit to formulate attack graphs that allow for an iterative search of optimal countermeasures based on the number of vulnerabilities that need to be eliminated and priority. By using criticality indicators that characterise IoT security, the model selects security measures based on reducing risk levels based on resources that can be used during an intrusion. However, recalculating indicators to select the most optimal security measure takes a large amount of time, then more connected components than there are, which the authors suggest need to be further optimised to reduce the operating time expense. Finally, [PERSON] et al. [67] consider the optimal smart home security practices that need to be implemented by users, with controls being selected based on the reduction in high risk behaviours, increasing a user's ability to perceive and prevent attacks, as well as ensuring security familiarity. For example, in an optimal scenario, users should be risk averse and be highly familiar with security practises, showing the requirements of a secure smart home. The authors suggest that optimising the awareness towards and knowledge of good security practices allows for enhanced IoT security and the assurance that basic good practices are being enforced. _Insight 15:_ Optimisation for IoT control within the surveyed papers is limited, despite its importance to the implementation of controls and the justification of doing so. Within RQ3 and prior sections, we discussed the need for resources and security objectives when choosing controls. Finding the best controls for IoT depends on several factors defined by the IoT domain, with resources and security objectives being some of the most common. The key component of optimisation for IoT is the balance and trade-off between identified factors and the effectiveness of each control. Different types of IoT controls (e.g., prevention, detection, and recovery [97]) mitigate risks in different ways, with varying degrees of effectiveness that may degrade over time. While not specific to IoT, game theory approaches attempt to find the best strategies with the most beneficial outcome, considering the trade-offs. However, of the surveyed papers, few use this approach. One challenge that optimisation solves is the estimation of security control effectiveness alongside many attack paths and resource requirements [97] to achieve an equilibrium strategy.
## 2 Materials and Methods
### Simulation Geometry  
Using the Geant4-DNA simulation model, the experimental setup is described in Figure 1. The setup consisted of a spherical iron-gold nanoparticle heterojunction with a radius of 5 nm positioned at the centroid of a cubic container measuring \(1\times 1\times 1\)\(\upmu\)m\({}^{3}\). To emulate the conditions of a human cell and adhere to established conventions from previous studies, the cubic container was filled with water [20]. In radiotherapy, since LINAC heads are rotating to cross-fire the tumor in the patient, a radial beam was used in the simulation. The photon source was positioned to emit photons directly at the outer edge of the nanoparticle heterojunction, covering all possible radial angles around the nanoparticle. In the simulation, photon beam energies of 50, 100, and 150 keV were used. These energy levels are commonly utilized for treating different types of cancers, reflecting the varying requirements in clinical practice. By comparing this simulation with previous studies conducted using photon beams, we can gain insights into the contrasting effects of gold nanoparticles and iron-gold nanoparticle heterojunction when exposed to photon radiation. In this simulation using Geant4-DNA, we specifically utilized gamma rays as the form of radiation.  
The iron-gold nanoparticle heterojunction simulation involved a comparison of different volumes of iron and gold within the nanoparticle. Table 1 provides an overview of the percentages of iron and gold used in the tested iron-gold nanoparticles.  
Figure 2 presents a visual representation of the fabrication process of the iron-gold nanoparticle in the simulation. The calculation of the different volume percentages was determined based on the volume of the gold nanoparticle with radius equal to 5 nm, which measured 523.6 nm\({}^{3}\). The specific calculations utilized to determine the volume percentages can be found in Equations (1) and (2).  
\[Volume\ of\ Iron=\frac{\%\ of\ Iron}{100}\times 523.6\ nm^{3} \tag{1}\]  
\[Volume\ of\ Gold=\frac{\%\ of\ Gold}{100}\times 523.6\ nm^{3} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Percentage of Gold** & **Percentage of Iron** \\ \hline
0\% & 100\% \\ \hline
25\% & 75\% \\ \hline
50\% & 50\% \\ \hline
75\% & 25\% \\ \hline
100\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The volume percentages of gold and iron used in the iron-gold nanoparticle simulation as shown in Figure 1.  
Figure 2: Schematic diagram of the iron-gold nanoparticle heterojunction with gold on the outer layer and the iron embedded inside.
## Appendix A Transmitters  
To compensate for errors caused during transmissions, a channel encoder with rate \(c(u)\) adds redundancy digits to a stream of binary data of user \(u\). The produced bits are interleaved and projected to complex data symbols using a predefined signal pattern \(\Omega(u)\) of unit energy. User \(u\) optimizes its broadcasting modulation technique \(\Omega(u)\) on time-varying running circumstances in order to increase net throughput and minimize energy usage while maintaining a predetermined level of service. A small number of training symbols are embedded into the sent information to kick off the identification mechanism, as explained later. The final series comprises of \(Z(u)\) sequences, each of which contains \(\langle\Phi(u)\rangle\) symbols. Here, \(\langle\Phi(u)\rangle\) denotes the cardinality of \(\Phi(u)\). We define \(\mathbf{d}_{z}^{(u)}=\left[d_{z}^{(u)}(0),\cdots,d_{z}^{(u)}(\langle\Phi(u) \rangle-1)\right]\) as the \(z\)th sequence, where \(d_{z}^{(u)}(m)\) is the \(m\)th element of the sequence. After performing a fast Fourier transform (FFT) algorithm on \(\langle\Phi(u)\rangle\) points, we acquire frequency-domain observations \(\mathbf{F}_{z}^{(u)}=\left[f_{z}^{(u)}(0),\cdots,f_{z}^{(u)}(\langle\Phi(u) \rangle)-1\right]\), which we express as  
\[f_{z}^{(u)}(l)=\sum_{m=0}^{\langle\Phi(u)\rangle-1}d_{z}^{(u)}(m)\exp(-j2\pi ml /\langle\Phi(u)\rangle), \tag{1}\]  
where \(f_{z}^{(u)}(l)\) is the \(l\)th sample of \(\mathbf{F}_{z}^{(u)}\) and \(j=\sqrt{-1}\). The proposed approach can be employed with either frequency-interleaved or frequency-localized translation [48]. The vector \(\mathbf{F}_{z}^{(u)}\) is turned into a time-domain SC-FDMA symbol, \(\mathbf{s}_{z}^{(u)}=\left[s_{z}^{(u)}(-v),\cdots,s_{z}^{(u)}(\mathcal{N}-1)\right]\), by employing an \(\mathcal{N}\)-point inverse FFT algorithm with attaching a cyclic prefix of length \(v\) as  
\[s_{z}^{(u)}(m)=\sum_{l=0}^{\mathcal{N}-1}f_{z}^{(u)}(l)\exp(j2\pi ml/\mathcal{ N}), \tag{2}\]  
where \(m=-v,\cdots,\mathcal{N}-1\). User \(u\)'s time-domain symbols, \(\mathbf{\bar{s}}^{(u)}=\left[\mathbf{s}_{0}^{(u)},\cdots,\mathbf{s}_{Z^{(u)} -1}^{(u)}\right]\), are input into a space-time encoder, which broadcasts each \(Z_{1}^{(u)}\) symbol through \(P^{(u)}\) antennas
# Plant Extracts for the Control of _Listeria monocytogenes_ in Meat Products
## 3 Results
### Assessment of Growth Speed Using MicroFit Software(c)  
The growth rate of _L. monocytogenes_ on cooked ham was estimated through a nonlinear regression using the MicroFit(c) software. The behavior in the two lines treated and untreated with plant extracts were different, and this outcome was also to be attributed to the initial bacterial contamination level. The statistical data show that the use of plant extracts, in samples inoculated with 103 cfu/g (average 3.74 log cfu/g), caused a reduction of about 1.50 log in the 24 h following the treatment, but did not affect the duration of the lag phase or the growth rate in the subsequent days. In samples inoculated with 102 cfu/g (average 2.05 log cfu/g), the treatment, in addition to an initial reduction of about 1.00 log, produced an extension of the lag phase of the surviving cells (from 1.40 to 7.70 days) and a reduction in the growth rate. It can therefore be assumed that the activity of the plant extracts is also related to the initial level of _L. monocytogenes_ contamination. Considering that the degree of recontamination in the shearing phase and/or slicing of the cooked ham usually does not exceed 10 cfu/g, our results support the use of the plant extracts as an adjuvant in containing the pathogen growth.
## 3 System Model
### Power Allocation  
The power allocation in NOMA differs from that in OFDMA; for sub-channels, assume a single beam scenario with (\(c=1\)); let, mathematically, \(h_{1}>h_{2}>\ldots>h_{n}\) be the channel condition for users \(1,2,3,\ldots,n\).  
Figure 3: Sub channel assignment.  
Considering the user deployments as given in Figure 1, there will be two users, \(u_{1}\) and \(u_{2}\), where \(u_{1}\) is the near user, and \(u_{2}\) is the far user. The user rate for the two users will be given as:  
\[R_{u1}=log_{2}(1+\alpha_{u1}|h_{u1}|^{2}p) \tag{10}\]  
\[R_{u2}=log_{2}(1+\frac{\alpha_{u2}|h_{u2}|^{2}p}{\alpha_{u1}|h_{u2}|^{2}p+1}) \tag{11}\]  
where \(\alpha_{u1}+\alpha_{u2}=1\)  
If the total sum rate is defined as \(R_{n}=R_{u1}+R_{u2}\).  
Furthermore, \(R_{n}\) represents the sum rate.  
Maximize '\(R_{n}\)',  
Subject to:  
\[\sum_{k=1}^{n}\alpha_{k}p\ \leq p \tag{12a}\] \[\alpha_{k}p>0 \tag{12b}\]  
where  
\[R_{n}=\sum_{k=1}^{n}log_{2}(1+\frac{\alpha_{k}p\ |h_{k}\ |^{2}}{N_{o}+\sum_{t=1, \ t\
eq k}^{n-1}\alpha_{t}\ p\big{|}h_{k}\big{|}^{2}})\ >\ R_{min} \tag{12c}\]  
where \(R_{min}\) is the minimum required sum rate by the system. The power allocation is carried out so that the power is allocated to each user if the preceding user has been assigned the power [27; 32]. Therefore, if '\(k\)' is a user in the tagged cluster nearest to the SBS, therefore:  
\[\alpha_{k+1}p>0\ \textit{only if}\ \alpha_{k}\ p>0 \tag{13}\]  
It can be concluded from the above-given expression that an improved sum rate is achieved by increasing the power of every proceeding user in comparison to the succeeding one. Thus, shifting the power from farther to closer users will improve the system capacity. The shifting process continues until the farthest user reaches its minimum threshold '\(\tau_{min}\)' [27; 32]. However, the nearest user should not obtain power more than the maximum defined threshold '\(\tau_{max}\)'. The power shifting process is repeated from the farther user to the closer user as follows:  
\[u_{1}\{\alpha_{1}p\}\ \leftarrow\ u_{2}\{\alpha_{2}p\}\ \leftarrow\ \cdots u_{k}\{\alpha_{k}p\} \tag{14}\]  
As MBS employs OFDMA and SBS NOMA to achieve considerable connectivity in the proposed network, the received power approach is incorporated to regulate user association. Figure 3 plots the user association probability of the unified NOMA-enabled dense network with three layers. However, it is assumed that NOMA users prefer HSBS or MBS if LSBS availability is relatively low. With the densification of HSBS and LSBS, NOMA users show a great possibility of being associated with the SBSs deployed within the clusters.  
However, it is also assumed that NOMA-associated users have a higher probability of being associated with LSBS, even though HSBS transmits at a higher power level, due to the close deployment of LSBS within the clusters, and reveals the effectiveness and benefits of densely deploying LSBSs in the clustered HetNets.
## 3 Map Projection
### Equirectangular Projection  
Equirectangular projection maps all of the locations on the sphere onto a flat image. Generally, a simple geometric model is used to represent the sphere, for example, the Earth. Equirectangular projection maps parallels to equally spaced straight horizontal lines and meridians to equally spaced straight vertical lines. It is neither conformal nor equal in area. Figure 1(a) shows Tissot's indicatrices on the sphere that are used to illustrate the projection's distortions. Figure 1(b) shows the projected equirectangular results from Tissot's indicatrices.  
Two standard parallels are equidistant from the Equator on the map at latitudes of 30 degrees north and south. If the location is too close to the Equator, the scale is too small. If the location is farther away from the Equator along the parallels, the scale increases. Area and local shape are highly distorted at most locations except for latitudes of 30 degrees north and south. Therefore, the objects in the 360-degree image are difficult to recognize.
## 2 Study Area, Data and Methods
### Data
#### 2.2.3 MODIS Data  
Land cover maps were available in a 500 m spatial resolution of the MODIS MCD12Q1 product for the year 2016 (Figure 1, ref. [38]). The 16-day Normalized Difference Vegetation Index (NDVI) data during the period of 2014-2018 were obtained from the MODIS MOD13Q1 product with a 250 m resolution [39]. The 8-day fraction of photosynthetically active radiation (FPAR) and LAI data were derived from the 500 m spatial resolution of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Station** & **Location** & **Altitude (m)** & **Period** & \(\mathbf{T_{\text{ave}}}\) (c) & \(\mathbf{P_{\text{ave}}}\) (mm) & **Reference** \\ \hline Shouxian & (32.44\({}^{\circ}\)N, 116.79\({}^{\circ}\)E) & 27 & 15 July 2015–24 April 2019 & 16 & 1115 & [26] \\ Dongtai & (32.76\({}^{\circ}\)N, 120.47\({}^{\circ}\)E) & 2 & 1 December 2014–30 November 2017 & 13 & 1484 & [29] \\ Dafeng & (33.21\({}^{\circ}\)N, 120.28\({}^{\circ}\)E) & 1 & 16 November 2015–29 November 2016 & 15 & 1060 & [27] \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the three rice–wheat-rotation eddy covariance sites (\(T_{\text{ave}}\), annual mean air temperature; \(P_{\text{ave}}\), annual cumulative precipitation).  
MODIS MOD15A2H [40]. The MODIS GPP product MOD17A2H (version 6) had a 8-day temporal resolution and 500 m spatial resolution [41]. All of these datasets were downloaded from [[https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)]([https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)), accessed on 10 July 2021. These MODIS products were quality-controlled to exclude anomalous pixel interference.
## 4 Experimental Results and Discussion
### Experimental Setup and Evaluation Metrics
#### 4.1.2 Data Sets  
The images in the training set and test set are captured by the capture device shown in Figure 4. We collected a set of color checker images for objective evaluation. For each color square, we have captured more than 52 images at different locations in the imaging field. We randomly selected two images for each color to construct the test set, and the remaining images are used to form the training set.  
For each color square in a captured image, we manually select five patches from the center and four corners of the color square. The size of each patch is \(30\times 30\) pixels. To enhance the robustness of the CNN network, we augmented each patch by flipping, rotation, and slight translation, to eight patches. Then, 140 color patches for a color checker were stitched into the image sample. Therefore, about 2000 images for each experimental scenario are synthesized into our training set.  
For the test set, we constructed it from the selected test images. We selected color squares called the area of interest in Color Checker SG composed of three regions and shown in Figure 5. The first region is the original color checker colors the same as which is used in [11; 15]. The second region is the expanded skin tone, and these are similar to the colors of tongue coating. Colors in the third region are  
Figure 4: Appearance of the capturing device.  
close to those of the tongue body and tongue coating. Without argumentation, there are 10 synthesized images with 40 colors in our test set. Examples are shown in the upper row of Figure 6.  
The standard RGB and Lab values for each color in the color checker are employed as the labels for image samples in the training and test sets.
## 3 System Model  
### Network Model  
The proposed network model consists of base stations, sensor nodes and cluster head (CH). The end-user is connected to the base station through the internet. The sensor nodes in the network gather all the information needed and forward it to the base station through the cluster head (CH). Figure 2 shows the framework of clustering. The considered system model makes the following assumptions:  
* All the sensors nodes in the network are mobile after the deployment.
* All three different categories of nodes have different energy levels. We consider picocells as advanced cells, femtocells as intermediate nodes, and other nodes as normal.
* All sensor nodes in the network have different mobility speeds and energy levels.
* The battery in each node in the network has a different initial energy level, and it is not rechargeable or replaceable.
* All sensor nodes in the network have their unique ID.  
Figure 2: Cluster architecture of a 5G-based network.
## 3 Methodology
### The Proposed System Architecture for Glaucoma Detection  
As shown in Figure 4, the glaucoma detection system consists of the following three stages:  
Stage 1. Fundus to OCT Generator Training  
In order to judge whether there are glaucoma symptoms through the fundus image, the system will first train the fundus to OCT generator with the fundus image provided by the ophthalmologist and the corresponding OCT image. The trained generator can output a realistic OCT image corresponding to the input image based on the input fundus image. Stage 2. Glaucoma Classifier Training  
In order to correctly classify glaucoma from OCT images, at this stage, we train the glaucoma classifier with OCT images marked by ophthalmologists. The trained glaucoma classifier can judge whether it is glaucoma according to the input OCT image.  
Figure 4: Architecture of the proposed glaucoma detection system.  
Stage 3. Incremental Learning  
When deploying the trained Fundus to OCT Generator and Glaucoma Classifier to a new field, the image quality may be inconsistent due to different brands and settings of the fundus photography equipment, resulting in low accuracy of the model. Therefore, we use fundus images from new fields to fine-tune the glaucoma classifier based on the spirit of incremental learning. The general process is as follows: when the fundus image obtained from the new field is input to the generator, the generator will output the corresponding OCT image and then feed the OCT image to the glaucoma classifier. When the classifier's output is inconsistent with the doctor's diagnosis, a small expert meeting will be held. Through discussions with other doctors, the fundus image and the generated OCT image will be re-judged, labeled, trained, and the glaucoma classifier will be adjusted.