esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Generalized Algorithm
### Main Flowchart  
The main flowchart of the developed generalized algorithm is shown in Figure 10. SE calculations start with setting the excitation source parameters, the observation point position, as well as the characteristics of the enclosure, its apertures, and the inhomogeneities that fill the enclosure or apertures. The parameters are fed to the \(f\) loop input, which  
Figure 9: Enclosure filled with a conductive plate (**a**), equivalent circuit for calculating SE (**b**).  
performs frequency sweeping from \(f_{s}\) to \(f_{e}\). First of all, the expression for calculating \(Z_{ap}\) impedance is selected within the \(f\) loop. In addition, the \(Z_{ap}\) values can be calculated for a single aperture, an array, or a group of apertures. This is performed using flowcharts that can be found in the next section. Then, based on the calculated \(Z_{ap}\) value and [PERSON]'s theorem, the values of voltage \(V_{1}\) and impedance \(Z_{1}\) are calculated using Equations (4) and (5). To take into account the higher-order modes excited in the enclosure, all subsequent transformations of the equivalent circuit are performed inside the \(M\) and \(N\) loops. Based on the results of these loops, we calculate the voltage \(V_{p}\) or current \(i_{p}\) at the observation point. These values are then used to determine the electric and magnetic SE. When the \(f\) loop is complete, the SE values are output as a frequency dependence.
## 1 Introduction
Specifically, the pixel size of a CT scan can vary from 0.5 \(\times\) 10\({}^{-3}\) to 1.0 \(\times\) 10\({}^{-3}\) m, while the slice thickness can vary from 2.0 \(\times\) 10\({}^{-3}\) to 6.0 \(\times\) 10\({}^{-3}\) m [20]. This means that a slight shift in the orientation or position of the electrode or the grid could result in different discretization of the needle electrode (see Figure 1), resulting in an inaccurate electric-potential distribution and, consequently, an inaccurate electric-field distribution. This can be solved by increasing the grid resolution. However, due to the uniformity of the rectangular grid, a decrease in the voxel size will be at the expense of the computer/GPU memories and speed. This would especially be the case when large models are used (i.e., additional memories would be required), and/or when model properties are updated in time due to the change in tissue properties as a function of the electric field and temperature (i.e., additional calculation time would be required). In this study we present a method to increase the accuracy of the electric-field distribution within a low-resolution grid by estimating the electric-potential values in the non-metallic 'estimation set' voxels neighboring the IRE-needle electrode voxels based on an analytical model. Electric-Potential Estimation (EPE) is a method that considers the circular surface of a needle electrode regardless of its size, position, and orientation in a uniformly distributed rectangular grid. In this study, we will determine and validate the effects of EPE on the electric-potential distribution and on the electrical resistance. This will be performed for needle shaped models such as coaxial cable (e.g., for modeling of radio-frequency ablation [21]) and cylindrical electrode pair (e.g., for modeling of electroporation [22; 23]). In addition, we will present a case of rotated needle electrodes around a tumor within a low-resolution grid to illustrate the improved distribution of the electric field.
## 3 Proposed Methods
### Similarity Measurement Between RGB Image and CAD Rendering
As the rotational pose between the RGB object and the CAD rendering becomes similar, matrix \(A_{sim}\) should have only scale elements in Equation (18). In the ideal case, if the rotational poses in both RGB and CAD rendering images are the same, \(\cos\theta=1\) and \(S_{x}=S_{y}=S\). Here, scale \(S\) can be obtained from Equation (19). To normalize \(A_{sim}\), we derive the scale-normalized affine matrix \(A_{normal}\) as in Equation (20). \[R= \begin{bmatrix}S_{x}&0\\ 0&S_{y}\end{bmatrix}  \tag{18}\]  
\[S=\frac{\sqrt{{a_{1}}^{2}+{a_{4}}^{2}}}{2}=\frac{\sqrt{\left(S_{x}\cos\theta \right)^{2}+\left(S_{y}\cos\theta\right)^{2}}}{2} \tag{19}\]  
\[A_{normal}= \begin{bmatrix}a_{1}/S&a_{2}/S\\ a_{3}/S&a_{4}/S\end{bmatrix}  \tag{20}\]  
From the scale-normalized matrix \(A_{normal}\), we define \(e_{rot}\) and \(s_{AT}\) using Equations (22) and (23). Finally, the rank for CAD model retrieval is determined by the \(Rank_{AT}\) value as in Equation (24), where a higher value means a higher rank. Additionally, by defining \(Rank_{AT}\) as in Equation (24), we consider not only the error for the rotation angle but also the affine consistency for retrieving the matching CAD model. In Equation (24), \(N_{inter}\) is the number inlier in deriving the affine transformation matrix \(A\). \[E=A_{normal}- \begin{bmatrix}1&0\\ 0&1\end{bmatrix}  \tag{21}\]  
\[e_{rot}=\sum\lvert E_{i}\rvert \tag{22}\]  
\[s_{AT}=\left(1-2*\left(\frac{1}{1+e^{-e_{rot}}}-\frac{1}{2}\right)\right) \tag{23}\]  
\[Rank_{AT}=s_{AT}*(N_{inter}) \tag{24}\]  
As the second measurement, we only use the number of feature-matching inliers (SM-NI). With this measurement, the CAD retrieval ranking is determined from the highest number of inliers to the lowest number. Using these two measurements, the similarity between every pair of RGB image and CAD rendering image within a detected upper category is evaluated. For the evaluation of the two measurements, we have manually generated the annotations of CAD subcategories for all 2000 RGB images. In the experiments, we show quantitative analysis of CAD model retrieval based on SM-AT and SM-NI with the ground truth.
# Estrogen Induces c-myc Transcription by Binding to Upstream ERE Element in Promoter
###### Abstract  
Estrogen Receptor \(\alpha\)(ER\(\alpha\)) is reported to regulate the expression of many target genes by binding to specific estrogen response elements (EREs) in their promoters. c-myc is known to be over-expressed in most of the human carcinomas due to dysregulated transcription, translation, or protein stability. Estrogen (E) can induce the c-myc expression by binding to an upstream enhancer element in its promoter. This suggests that elevated estradiol (E2), a potent form of estrogen, levels could induce the expression of c-myc in breast cancer (BC). The expression of c-myc and estradiol were induced at Stage III and Stage IV of breast cancer. c-myc and estradiol expression was also associated with the established risk factors of breast cancer, such as BMI. Age at the time of the disease was also uncorrelated with the relative expression of c-myc and estradiol (\(p<0.0007\) and \(p<0.000001\)). The correlation coefficient (R = 0.462) shows a positive relationship between estradiol bound ER, ER, and c-myc. Docking energy \(-\)229 kJ/mol suggests the binding affinity of estradiol bound ER binding to 500 bp upstream of proximal promotor of c-myc at three distinct positions. The data presented in this study proposed that the expression of c-myc and estradiol are directly correlated in breast cancer. The prognostic utility of an induced level of c-myc associated with the normal status of the c-myc gene and estradiol for patients with metastatic carcinoma should be explored further.  
c-myc; estrogen; estradiol; estrogen response elements (EREs)
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 3 Proposal for PLC Control Logic Embedding and Anomaly Detection
### Control Logic Learning and Anomaly Detection  
The control logic anomaly detection model learns the structure of control logic and predicts normal control logic. It consists of fully connected layers that train on embedded normal control logic and detect anomalous control logic. Figure 4 shows the architecture of the control logic anomaly detection model.  
The control logic anomaly detection model embeds and learns control logic before it is deployed to the PLC. The trained anomaly detection model extracts control logic from field PLCs, performs the embedding process, and uses it as input. Anomaly detection operates by predicting the \(t+1\)th IL code based on the \(t\) input IL codes. The model then calculates the error between the actual \(t+1\)th IL code and the predicted \(t+1\)th IL code. Since the anomaly detection model learns the patterns of normal control logic, it predicts normal control logic. If the error between the predicted control logic and the input control logic exceeds a predefined threshold, it is classified as an anomaly. This process determines whether the control logic uploaded to the PLC is anomalous. The proposed control logic anomaly detection model detects control logic that deviates from the normal range, rather than merely identifying whether modifications have occurred. Additionally, since the anomaly detection model consists of fully connected layers capable of detecting even subtle code changes, it can identify previously unseen patterns in control logic.  
Figure 4: Control logic anomaly detection model configuration.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Quantitative Evaluation  
The comparison results between the method proposed in this paper and the above methods on the UIEB, EUVP, and RUIE datasets are listed in Tables 2-4. These values represent the average results over five trials.  
**Full-Reference Evaluation:** The UIEB and EUVP datasets were used for this evaluation. The statistical results and visual comparisons are summarized in Tables 2 and 3. As the two tables show, our DeMBFF-Net demonstrates the best performance for both the PSNR and SSIM metrics. Specifically, our method outperforms other methods in terms of image quality and recovery. It also illustrates that our method achieves the highest values for all the indicators except for UICM, indicating exceptional recovery effects in terms of sharpness and contrast. The overall effect of the recovered image is closer to that of the real image according to the following subjective results.  
**Nonreference Evaluation:** The UIEB, EUVP, and RUIE datasets were used for the nonreference evaluation, in which the statistical results are represented in Tables 2-4. The results for the RUIE dataset were obtained using the model trained on the UIEBD dataset. Visual comparisons are shown in Figures 7-9. Our method achieved the highest  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metric** & **DCP** & **UDCP** & **DeepSesr** & **Funie-GAN** & **MLFCGAN** & **UWCNN-SD** & **USUIR** & **SCNET** & **Ours** \\ \hline UIQM\({}^{\dagger}\) & \(2.254\pm 0.415\) & \(2.311\pm 0.296\) & \(3.113\pm 0.254\) & \(3.909\pm 0.235\) & \(2  
score for the UIQM metrics for these three datasets, which confirmed its ability to generalize to various real-world underwater scenes. For the three items of the UIQM, our method has the highest values for UISM and UIConM, indicating exceptional recovery effects in terms of sharpness and contrast. The following visual comparison results are consistent with the objective results.
# Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
## 4 Previous Works
### Obtaining Recommendations
#### 5.1.1 K Nearest Neighbors (KNN)  
The objective of this method is to group users or items with similar preferences and interests. Based on similarities, a rating \(r^{\prime}\) is predicted for an item which has not yet been valued [23; 24]. That is, given users \(u\in U\), then \(k\) users \(v\) closest to \(u\) (called the neighborhood of \(u\)\(N\) (\(u\))) are obtained, and rating \(r^{\prime}_{ui}\) is obtained from the ratings from users \(v\in N(u)\) (Equation (1)).  
\[r_{ui}=w_{u}+\frac{\sum_{v\in N}sim(u,\ v)(r_{vi}-w_{v})}{\sum_{v\in N}sim(u,\ v)} \tag{1}\]  
Rating \(r^{\prime}\) is the weighted sum of the ratings of the \(k\) most similar users, where \(w\) is the average score of each user \(u\).  
In this work, [PERSON]'s correlation (Equation (2)) was used in the KNN method to obtain the 50 closest neighbors.  
\[sim(u,\ v)=\frac{\sum_{i\in I}(r_{ui}-w_{u})(r_{vi}-w_{v})}{\sqrt{\sum_{i\in I }(r_{ui}-w_{u})^{2}\sum(r_{vi}-w_{v})^{2}}} \tag{2}\]
## Appendix A Detailed Description of CSES Electron Density Data Processing  
In this appendix, we describe the CSES electron density data analysis in detail. The first step is the extraction of all the data acquired by the CSES-01 satellite in quiet geomagnetic conditions (\(\mathrm{1\,Dst\,|\,\leq 20\ nT}\) and \(\mathrm{a_{p}\leq 10\ nT}\)) inside a circle centred on the volcano and with a radius of 420 km (see Figure 11). Such a radius has been selected, taking into account the extension of La Canarias caldera volcano system, and also that a too-small area is not appropriate due to the altitude of the satellite (about 510 km above sea level).  
A first time-series of CSES Ne was obtained with the daily median of the data collected inside the research area for day-time or night-time, which was analysed separately. The median has been selected instead of the mean, as it is less affected by eventual spikes in the original data. Figure 12 shows such time series for night-time, as well as Figure 13 for day-time. Due to the standard seasonal variation of the ionosphere, the obtained time series are affected by seasonal trends [93]. In order to detrend the time series from the seasonal trend, a polynomial curve has been fitted to the data. The order of the polynomial has been selected as 5 for night-time and 7 for day-time. The reason for a different selection is due to the more complex structure of the ionosphere during day-time for the direct input of the solar irradiance that, for example, creates the \"fountain\" effect of the electron charges that move upward at the geomagnetic equator and downflows Southern and Northern, but only when the Solar irradiation is present (or in the following hours for the inertia of the phenomenon [94]).  
The final time series is obtained after polynomial detrending, and it is presented in the result section of the paper. The median and interquartile range are computed on such time-series, defining the threshold to detect eventual anomalies as the median plus 1.5 times the interquartile range corresponds to two standard deviations in case the residuals follow a Gaussian distribution.
## 4 Discussion
2023 [54] utilized a comprehensive HPTLC-derived database to identify antioxidant compounds in these honeys via an HPTLC-DPPH assay. Hesperitin was found to contribute to the antioxidant activity of WA Jarrah and NZ Manuka honeys, and m-coumaric acid was identified as an antioxidant compound in Coastal Peppermint honey as well as in WA Manuka honey 1 and WA Manuka honey 2. WA Manuka honey 2 and its formulations showed the highest antioxidant as found in both the FRAP assay (6.56 mmol Fe\({}^{2+}\) equivalent/kg of pure honey) and HPTLC-DPPH assay (band activity equivalent to 29.30 \(\upmu\)g gallic acid/g pure honey) among all analyzed WA honeys (Tables 2 and 3). In the FRAP assay, WA Manuka honey 2 and its formulations (i.e., pre-gel and wet sheet) showed higher activity (\(p\) = 0.029) compared to the NZ Manuka honey and its pre-gel solution and wet sheet (Table 2). Moreover, WA Manuka honey 2 and its pre-gel solution and wet sheet formulations showed higher band activities (\(p\) < 0.0001) compared to the NZ Manuka comparator honey and its formulations (Table 3). The antibacterial activity of the honeys and the honey-loaded formulations was measured via the optimised agar overlay assay. It was found that WA Jarrah, WA Manuka 2 and NZ Manuka honeys and their corresponding formulations showed growth inhibition zones against all Gram-positive and Gram-negative bacteria. In the case of Jarrah honey, the activity might be related to its hydrogen peroxide component and osmotic pressure, while this activity recorded in WA Manuka 2 and NZ Manuka honey might be due to their MGO content as a non-peroxide antibacterial factor along with osmotic pressure [10; 12; 25]. The MGO level of WA Jarrah, WA Manuka 2 and NZ Manuka honey was reported in previous study [29]. Coastal Peppermint honey and its corresponding formulations did not show any zones of growth inhibition against _S. aureus, E. faecalis, S. epidermidis and A. baumannii_ (Figures 7 and 8). The carrier, sodium alginatedissolved in deionised water, was investigated as a blank, and it was found that the alginate solution itself had no antibacterial activity. Interestingly, it could be demonstrated that an artificial honey and its formulations produced zones of growth inhibition against _E. coli, A. baumannii_ and _M. luteus_ (Figures 7 and 8). The osmotic pressure exerted by its high sugar content might be responsible for the antibacterial activity seen [10; 27]. However, the hypothesised osmotic pressure-induced antibacterial activity appears selective to particular bacterial strains as it was only seen for _M. luteus, E. coli_ and _A. baumannii_ but not for _S. aureus, E. faecalis, S. epidermidis, P. aeruginosa, K. pneumoniae_. The findings of the overlay assay allow us to draw conclusions on the antibacterial activity of the prepared formulations in the actual form they would be applied in an in vivo setting. Thus, this study generated clinically relevant data as the formulations did not undergo any further processing, such as dissolution, during the antibacterial analysis.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 4 APT Analysis Model  
In the previous section, we summarized the APTs faced by each layer of IIoT and their defense methods. In this section, we will use modeling analysis to consider factors such as the attacker's motivation, the characteristics of the target, and the attacker's technical and organizational capabilities. Here are some possible reasons for modeling and analyzing APTs.  
1. High target value: APT attacks typically target high-value targets such as government agencies, financial institutions, and military units. These targets often have a large amount of confidential information and financial resources, and once breached they can bring huge benefits.
2. Adaptive capability: APT attackers usually use highly organized and coordinated methods to monitor and study targets for a long time and use flexible methods to launch attacks, such as social engineering or phishing emails, in order to bypass traditional security defenses.
3. Vulnerability exploitation capability: APT attackers usually can discover and exploit vulnerabilities in the target network, such as insecure applications or incomplete network isolation, to gain more privileges and control during attacks.
4. Persistence: APT attackers can usually exist in the target network for a long time and continue to collect and steal confidential information, making it more difficult to detect and remove APT attacks.
5. State support: Some APT attacks are suspected to be supported by states, such as inter-state espionage activities. These attackers usually have more resources and technical support, making attacks more complex and difficult to defend against.  
By establishing models and conducting analyses, we can better understand the nature and mechanisms of APT attacks and help formulate more effective defense strategies. This section will analyze APTs through six models (the threat intelligence model, attack chain model, diamond model, risk assessment model, machine learning model, and network simulation model) and make a comparison between these models.
## 3 Parametric Study
### Deck Properties  
The parametric analysis is performed considering five deck typologies typical of the Italian scenario [25].  
The materials and geometry of the deck are defined based on the length of the spans. Specifically, for the simply supported scheme, three decks are considered:  
* RC slab with four T beams, span 20 m;
* RC slab with three V beams, span 35 m;
* mixed section with two steel beams and a concrete slab, span 20 m;
* While for the continuous deck scheme, the decks are
* RC slab with lightening tubes, span 20 m;
* RC box girder, span 35 m.  
The assumed cross-sections are shown in Figure 8, while the assigned geometric and material properties and design loads are provided in Table 1.  
Figure 7: Bearing layouts for continuous deck bridges: (**a**) with seismic restraints on each axis; (**b**) with fixed constraints on one abutment. M = Multidirectional bearing; U = Unidirectional bearing; F = Fixed bearing; S = Seismic restraint engaged during the ground motion.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**RC Slab with**} & \multicolumn{2}{c}{**RC Slab with**} & \multirow{2}{*}{**Mixed Deck**} & \multicolumn{2}{c}{**RC Slab with**} & \multicolumn{2}{c}{**RC Box**} \\  & & **T Beams** & & **V Beams** & & **Lightening Tubes** & **Gider** \\ \hline A\({}_{c}\) & m\({}^{2}\) & 5.434 & 6.732 & 3.18 & 10.02 & 12.54 \\ I\({}_{x}\) & m\({}^{4}\) & - & - & - & 2.45 & 33.8 \\ I\({}_{y}\) & m\({}^{4}\) & - & - & - & 98.42 & 123.4 \\ A\({}_{beam}\) & m\({}^{2}\) & - & - & 0.57 & - & - \\ A\({}_{dioph}\) & m\({}^{2}\) & - & - & 0.072 & - & - \\ Y\({}_{c}\) & kN/m\({}^{3}\) & 25 & 25 & 25 & 25 & 25 \\ Y\({}_{s}\) & kN/m\({}^{3}\) & - & - & 78.5 & - & - \\ S\({}_{1}\) & kN/m & 135.85 & 168.30 & 129.92 & 250.5 & 313.5 \\ S\({}_{2}\) & kN/m & 47 & 47 & 47 & 47 & 47 \\ L & m & 20 & 35 & 20 & 20 & 35 \\ G\({}_{span}\) & kN & 3657 & 7355.5 & 3538.3 & - & - \\ G\({}_{dock}\) & kN & - & - & - & 23,800 & 50,470 \\ Q\({}_{span}\) & kN & 1380 & 2190 & 1380 & - & - \\ Q\({}_{dock}\) & kN & - & - & - & 4620 & 7860 \\ \hline \hline \end{tabular} A\({}_{c}\) = area of concrete cross-section; I\({}_{x}\), I\({}_{y}\) = area moment of inertia of deck cross-section (only for continuous deck); A\({}_{beam}\) = area of steel beam cross-section; A\({}_{dioph}\) = area of steel diaphragm cross-section; Y\({}_{c}\) = concrete density; Y\({}_{s}\) = steel density; g\({}_{1}\) = permanent structural load; g\({}_{2}\) = permanent non-structural load; L = span length; G\({}_{span}\) = span weight (simply supported scheme); G\({}_{dock}\) = deck weight (continuous deck scheme); Q\({}_{span}\) = variable load on the single span (simply supported scheme); Q\({}_{dock}\) = variable load on the deck (continuous deck scheme); “-”: not relevant/not applicable.  
\end{table}
Table 1: Deck characteristics.  
Figure 8: Deck cross-sections examined in the parametric study: (**a**) RC slab with T beams; (**b**) RC slab with V beams; (**c**) mixed deck; (**d**) RC slab with lightening tubes; (**e**) RC box g girder. Measures in cm.