esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Pre-Processing  
Firstly, the CT image pixel values were converted from radiodensity values to Hounsfield Units (HU) using the Rescale Slope and Rescale Intercept attributes stored in the metadata of the scans. Then, the entire dataset (including the tumor masks) was resampled to standardize image representations. The space between slices and pixel spacing were set to 1 mm and [1.0, 1.0] mm, respectively, and each slice dimension was calculated to match this new spacing, obtaining the resampled image by interpolation [24]. Additionally, all CT images were normalized between \(-\)1000 HU and 400 HU using the _min-max_ normalization method. CT values below \(-\)1000 HU, corresponding to the radiodensity of air, were set to 0; values above 400 HU, representing hard tissues, were fixed to 1. A linear transformation was computed to map all the intermediate HU values to the [0,1] range. Lung binary masks were segmented using a 2D lung segmentation model based on the U-Net architecture [25]. These lung regions underwent the same resampling operation to match the dimensions of the corresponding CT, and all images were rescaled to a size of \(N\times 256\times 256\) pixels, with \(N\) representing the number of slices of the correspondent CT scan. To obtain the CT images with only the lung containing the nodule, the mask of the nodule was superimposed on the lung segmentation in order to identify the lung where the nodule is located in the selected axial CT slice. Then, the image was transformed to consider half of the opposite side as background.  
Figure 2: Representation of lung axial slices with nodule from the NSCLC-Radiogenomics dataset of patients with (**a**) mutant _EGFR_ and (**b**) wild-type _EGFR_.
## 4 Performance Evaluation
### Image Acquisition  
In this study, the digitized mammogram images from DDSM [56], INbreast [57] and MIAS [58] datasets are used to evaluate the performance of the proposed method. In fact, all the databases are utilized to show the reliability and effectiveness of the proposed method in diagnosing breast cancer. The DDSM database consists of 1500 mammogram images having 519 normal images and 981 abnormal images. The INbreast database contains 336 mammogram images having 69 normal images and 269 abnormal images, whereas the MIAS database consists of 326 total mammogram images having 207 normal images and 119 abnormal images. In this work, a total of 1369 breast mammogram images were utilized for abnormal, in which the DDSM dataset have 479 benign and 502 malignant out of 981 mammogram images, the INbreast dataset contain 269 mammogram images having 220 benign and 49 malignant, and the MIAS dataset contain 119 mammogram images having 68 benign and 51 malignant [56]. To evaluate the supremacy and effectiveness of the proposed method. Experts collect ground truth labels and diagnose these cases (via labels) using a variety of tests including experimental screening and mammography. Tables 4 and 5 illustrated the mammography images distribution from the used databases. During the assessment, the images in each database are divided into two sets, a training set and a testing set.
# Waterproof Performance of Sealing Gasket in Shield Tunnel: A Review
###### Abstract  
Rubber gaskets are commonly adopted as the waterproof component in shield tunnels for their outstanding sealing performance. The contact pressure between surfaces generated by the assembly stress ensures that the gaskets resist certain water pressure without leaking. However, with the continuous occurrence of leakage accidents, attention has been drawn to the topic of the waterproof performance of gasketed joint shield tunnels. In this article, prominent contributions to the waterproof performance of sealing gasket in shield tunnels are listed and sorted into four sections: (1) structural behavior of lining and joint; (2) material constitutive model and durability; (3) numerical simulation methods; (4) thermal-mechanical coupling analysis. First, examples of leakage are discussed and tests on gaskets are elucidated, which is followed by a summary of the progress on material mechanical properties and durability. Then, the development of the simulation methods is presented. Finally, the existing research on the thermal-mechanical coupling analysis is summarized. It is found that the contributions to gaskets' waterproof performance are fruitful, however, with stringent construction conditions, such as the material constitutive model and aging mechanism under special conditions, such as high temperature, numerical simulation, and laboratory test methods, which need to be further explored.  
shield tunnel; sealing gasket; sealing performance; thermal-mechanical coupling +
Footnote †: journal: Applied
# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
## 2 Forward Problem
\[G_{1}\big{(}x,y;x^{\prime},y^{\prime}\big{)}=\frac{1}{2\pi}\int_{-\infty}^{\infty} \frac{j}{\gamma_{1}+\gamma_{2}}e^{j\tau_{1}(y+A)}e^{-j\tau_{2}(y^{\prime}+A)}e^ {-j\mu(x-x^{\prime})}d\alpha. \tag{8}\]  
The Green's function matrix can be expressed as follows:  
\[G_{f}\big{(}x,y;x^{\prime},y^{\prime}\big{)}=\frac{j}{4}H_{0}^{(2)}\Big{[}k_{2 }\sqrt{\big{(}x-xt\big{)}^{2}+\big{(}y-yt\big{)}^{2}}\Big{]} \tag{8a}\] \[G_{\rm s}\big{(}x,y;x^{\prime},y^{\prime}\big{)}=\frac{1}{2\pi}\int_{-\infty}^{ \infty}\frac{j}{2\gamma_{2}}\bigg{(}\frac{\gamma_{2}-\gamma_{1}}{\gamma_{2}+ \gamma_{1}}\bigg{)}e^{-j\tau_{2}(y+2A+y^{\prime})}e^{-j\mu(x-x^{\prime})}d\alpha\] (8b) \[\gamma_{i}^{2}=k_{i}^{2}-a^{2},\ i=1,2,\ Im(\gamma_{i})\leq 0,\ yt>A. \tag{9}\]  
Conceptually, the scattered field \(E_{\rm S}(x,y)\) can be regarded as the surface induced current \(J_{\rm s}\) on the conductor radiating in the half-space. By means of and the two-dimensional Green's function, the scattered field outside the conductor can be expressed as follows:  
\[E_{\rm s}= \begin{cases}-\int_{0}^{2\pi}G_{1}(\stackrel{{\rightharpoonup}}{{r}};F( \theta^{\prime}),\theta)I(\theta^{\prime})d\theta^{\prime}\,,y\leq-A\\ -\int_{0}^{2\pi}G_{2}(\stackrel{{\rightharpoonup}}{{r}};F( \theta^{\prime}),\theta)I(\theta^{\prime})d\theta^{\prime}\,,y>-A\end{cases}  \tag{10}\]  
where  
\[\stackrel{{\rightharpoonup}}{{r}}=(x,y),\ J(\theta)=-j\omega\mu_ {0}\sqrt{F^{2}(\theta)+F^{2}(\theta)}J_{\rm s}(\theta). \tag{11}\]  
The boundary condition for a perfect conductor is that the total electric field in the tangential direction on the surface of the conductor is zero. According to this boundary condition, we can obtain the integral equation of \(J(\theta)\):  
\[E_{2}(F(\theta),\theta)=\int_{0}^{2\pi}G_{2}\big{(}F(\theta),\theta;F\big{(} \theta^{\prime}\big{)},\theta^{\prime}\big{)}I\big{(}\theta^{\prime}\big{)}d \theta^{\prime}. \tag{12}\]  
The scattered field in Region I is as follows:  
\[E_{\rm s}(x,y)=-\int_{0}^{2\pi}G_{1}(x,y;F(\theta^{\prime}),\theta^{\prime})I (\theta^{\prime})d\theta^{\prime},\ y\leq-A. \tag{13}\]
## 4 Potentials and Challenges
### Challenges  
It turns out that the novel design concepts offer a number of benefits compared to conventional bridge crane systems. However, the concepts face further challenges, which are to be illustrated in the following and mainly cover:  
* Mechanical issues;
* Costs;
* Acceptance of potential operators, customers.  
As previously shown in Table 3, additional mechanical challenges have to be overcome in the context of segmented structures. These include in particular the weld seams, since the structure is by definition not capable of absorbing tensile stresses. For this reason, the frictional connection at the joints must be maintained at all times. This applies especially to the box section design; see Section 3.1. Furthermore, the area of axial load application is subject to high stresses. To compensate local stress increases, relief notches are provided to relieve the adjacent weld seams connecting the web plates to the bottom flange. A detailed design of the relief notch is the subject of current research and development work. In this context, a method will also be developed to monitor the applied prestressing force during operation. Maintaining the prestressing force is essential for the reliability of the structure, as it is the only way to provide the load-bearing capacity. Further research work covers the design of the joints in the box section design. Here, the previously defined serviceability has to be ensured in any case. For this reason, the development of an additional mechanically designed safety function is planned. A major challenge is the cost of the segmented design. The aim here is that the costs should not be particularly higher than for conventional crane systems, otherwise the competitiveness is not given. From this requirement, it is also already possible to determine a field of application for segmented cranes. These can only compete with box-type crane bridges, i.e., for spans of approx. 12 m and more. A replacement of rolled I-shaped girders, on the other hand, seems unlikely due to the low production costs. Although the acceptance of potential users can be classified as a soft criterion compared to the mechanical challenges and the costs, it is nevertheless crucial for a successful market launch. For this reason, a future analysis of stakeholder management is to be considered as well.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 2 System Model  
### NOMA System  
NOMA facilitates the simultaneous transmission of symbols by multiple users on the same frequency. Power domain and code domain multiplexing are two distinct types of NOMA. Focusing on power domain multiplexing, it enables the user's signal to be superimposed and encoded in the power domain, allowing a resource block to serve multiple users concurrently. Considering an uplink NOMA scheme with \(N\) users, the channel gain for the \(i\)-th user is \(h_{i}\). Assuming the channel gain from user 1 to user \(N\) is arranged in descending order, which is \(h_{1}>h_{2}>\cdots>h_{n}\), the power allocation \(p_{i}\) should be \(P_{1}>P_{2}>\cdots>P_{n}\). It is because the difference in user symbol gain should be more pronounced to make the station distinguish signals accurately as it receives the user's superimposed signal. Consequently, users with stronger channel gains are allocated higher power. The superimposed signal \(y\) at the BS can be written as [29]  
\[y=\sum_{i=1}^{N}\sqrt{P_{i}}h_{i}s_{i}+w_{0}\,. \tag{1}\]  
In the SIC receiver, signal detection occurs based on the user's symbol gain, progressing from larger gains to smaller gains. When detecting users with strong \(h_{i}\), the receiver treats the signals of users with weak \(h_{i}\) as interference signals.  
In the uplink NOMA scenario, the SINR of the \(i\)-th user can be calculated by the superimposed signals from the base station receiver, which is written as [30]  
\[\text{SINR}_{i}=\frac{P_{i}{|h_{i}|}^{2}}{\sum_{k=i+1}^{N}P_{k}{|h_{k}|}^{2}+N _{0}}\,. \tag{2}\]  
Assuming the bandwidth of the transmission time-frequency block is \(B\), the throughput \(R_{i}\) of the \(i\)-th user in the uplink NOMA system can be written as  
\[R_{i}=B\log\left(1+\frac{P_{i}{|h_{i}|}^{2}}{\sum_{k=i+1}^{N}P_{k}{|h_{k}|}^{2 }+N_{0}}\right). \tag{3}\]  
Assuming the transmission power of two users is fixed at 1 W, the bandwidth is 1 Hz, the channel noise at the base station end is 10 mW, the power distribution factor of user 1 and user 2 is \(\alpha\) and \(1-\alpha\). Due to the higher \(h_{i}\) of user 1 than user 2, the base station receiver first detects the symbols of user 1 and then detects the symbols of user 2. At this point, the throughput of users 1 and 2 in the uplink NOMA scenario can be represented as [6]  
\[R_{1}^{NOMA}=\log\left(1+\frac{\alpha P|h_{1}|^{2}}{(1-\alpha)P|h_{2}|^{2}+N_{0} }\right). \tag{4}\]  
\[R_{2}^{NOMA}=\log\left(1+\frac{(1-\alpha)P|h_{2}|^{2}}{N_{2}}\right). \tag{5}\]
## 4 Experimental Results
### Weighting Analysis between SSIM and DEC Loss Functions  
The SSIM loss function affects the reconstruction similarity on the normal area and the DEC loss function affects the repair effect on the defective area. The optimization directions of the two for the model training are opposite. A higher weight of SSIM loss function improves the reconstruction similarity on the normal area, but it will cause the defect area to be unable to be repaired. This phenomenon is shown in Figure 20. Since the defect area cannot be completely repaired back to the normal image as shown in Figure 20b, the difference in the defective area is not obvious in the difference image shown in Figure 20c, and then it will be judged as a normal image. Increasing the weight of the DEC loss function improves the repair capability of the defective area but reduces the reconstruction effect of the normal area, and many noisy regions, as shown in Figure 21, appear. It can be seen that a balance needs to be struck between the weights of SSIM and DEC loss functions.  
Figure 19: The subjective evaluation of repair quality using SSIM and MSE as the training and testing functions on defective grid images.  
Figure 20: Simulation result for higher weight of SSIM loss function. (**a**) The defective carpet image. (**b**) The reconstructed image for image (**a**). (**c**) The difference image between images (**a**,**b**) obtained by SSIM function.  
In Section 2, we show that the Inception Autoencoder has a much higher accuracy in normal area reconstruction than the Simple Autoencoder. This also means that when the Inception Autoencoder is used as a defect-inspection model, it must be assigned a lower weight of SSIM loss function than the Simple Autoencoder to achieve better normal area reconstruction and defect area repair effects. In Table 6, we suggest weighting ratios between SSIM and DEC loss functions for Simple Autoencoder and Inception Autoencoder.  
In addition, in the DEC method, the number of clusters has a significant impact on the repairing effect on the defective area. In Figure 22, we illustrate the clustering effect for different cluster numbers by using the latent vectors of carpet images. Figure 22a shows that some large clusters can be further grouped into more clusters. However, Figure 22c shows there are many empty clusters when the cluster number is too large. Hence, we set the number of clusters \(k\) to 40 that can have a better repairing effect on the defective area.
## 2 Materials and Methods  
### Materials and Membrane Preparation  
A porous PE film (Hipore, \(t\) = 25 \(\upmu\)m) was supplied by Asahi Kasei (Japan) and used as a reinforcing material for preparing the PFIEMs. The specifications for the commercial porous PE substrate used in this work were found from the literature and are summarized in Table 1[29]. As described previously, VBC and/or Sty were used as main monomers for introducing ion-exchange groups. BPO and DVB were employed as a thermal polymerization initiator and a cross-linking agent, respectively. For the quaternionic and sulfonation of base membranes, trimethylamine (TMA) and chlorosulfonic acid (CSA) were used, respectively, and 1,2-dichloroethane was employed as a solvent. As a monomer containing a fluorine moiety, OFPMA was purchased from TCI (Japan) and employed. All reagents except OFPMA were purchased from Sigma-Aldrich (USA) and were used as received without any purification. In addition, AMX and CMX (Aatom Corp., Japan) were  
Figure 3: Reaction scheme of (**a**) quaternized anion-exchange polymer and (**b**) sulfonated cation-exchange polymer.  
chosen as the commercial hydrocarbon-based AEM and CEM, respectively, for membrane property comparison.  
For the membrane fabrication, a porous PE film was first impregnated with a monomer mixture. At this time, the monomer mixture was prepared with VBC and/or Sty and OFPMA at molar ratios of 1:0, 2:1, 3:1, and 4:1, respectively. In addition, 20 wt% of DVB as a cross-linking agent and 2 wt% of BPO as an initiator were added and fully mixed using a magnetic stirrer. The detailed composition of the monomer mixture is summarized in Table 2. After that, the monomer-filled substrate film was heated at 80 \({}^{\circ}\)C for 3 h in an oven for the thermal radical polymerization, producing a base membrane. For the fabrication of the PFAEM, the base membrane was then immersed in 1.0 M TMA aqueous solution, followed by a quaternization reaction at 60 \({}^{\circ}\)C for 5 h. Similarly, the PFCEM was prepared by reacting the base membrane in 10 wt% CSA (in 1,2-dichloroethane) solution at 50 \({}^{\circ}\)C for 5 h. The prepared PFIEMs were washed with distilled water and ethanol and stored in 0.5 M NaCl aqueous solution before use.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 3 The Crypto-Engine
### Hardware Acceleration  
The Crypto-Engine has been specifically designed to support internal and external performance-wise HW acceleration for cryptographic computations. In the case of ECTAKS, the Crypto-Engine supports ECC hardware acceleration for the primitive operation on elliptic curve points, e.g., _Point Addition_, _Point Doubling_, and _Point Multiplication_. This enables mixed hardware-software Crypto-Engine implementations that can be adapted to different scenarios, platforms, and requirements. Some example implementations are the following ones:  
* A software-only solution, in which ECTAKS and the other services are implemented in pure software for maximum flexibility across different HW platforms;
* An \"internally-accelerated\" software solution, within which the cryptographic operations are accelerated through platform-specific HW features and instructions (e.g., ARM/Intel cryptographic instructions or custom in-HW cryptographic implementations [63]);
* Using an external HW platform (e.g., a separated FPGA platform), towards which a software-based solution can offload cryptographic operations (Figure 3) for improved flexibility and re-usability;
* Adopting a hybrid programmable platform (e.g., ZYNQ (Xilinx ZYNQ platform architecture, available: [[http://www.ioe.nchu.edu.tw/Pic/Courseltem/4468_20_Zynq_Architecture.pdf](http://www.ioe.nchu.edu.tw/Pic/Courseltem/4468_20_Zynq_Architecture.pdf)]([http://www.ioe.nchu.edu.tw/Pic/Courseltem/4468_20_Zynq_Architecture.pdf](http://www.ioe.nchu.edu.tw/Pic/Courseltem/4468_20_Zynq_Architecture.pdf)) (accessed on 27 November 2023))) to implement the ECTAKS primitives in SW, while the hardware acceleration for modular arithmetic operations and ECC is in the programmable logic (HW).  
As described in Section 4, in this paper we report the results of a software implementation that is partially accelerated by exploiting the OpenSSL support for ARM and Intel HW-specific cryptographic instructions for the AES and SHA-256 library functions. Additionally, as reported in Section 5, we are currently working on releasing a Crypto-Engine implementation that features an on-demand, customizable external ECC accelerator for FPGA platforms to provide the Crypto-Engine with faster ECC computations.
## 2 Model of the PV System
### Solar PV Cell Model
The voltage-current characteristic equation of a PV module becomes as follows:  
\[I=N_{p}.I_{ph}-N_{p}.I_{sd}\left[exp\left(\frac{q\left(\frac{Vpv}{N_{s}}+\frac {I.R_{s}}{N_{p}}\right)}{K.T.n}\right)-1\right]-\frac{\left(\frac{N_{p}.Vpv}{N _{s}}+I.R_{s}\right)}{R_{p}} \tag{8}\]  
The following Table 2 gives the parameters of the PV module used in this work. Figure 4 shows the proposed block diagram of the PV system. In this model. We include the intelligent MPPT algorithm (based on GWO and PSO), which will be described in Sections 3 and 4. The proposed algorithms aim to compensate for the limitations of conventional maximization algorithms, which cannot find the overall maximum, especially during a partial shading phenomenon. Regardless of the partial shading profile, it can find the maximum power point on the power-voltage characteristic (\(Ppv\)) of a photovoltaic system. This characteristic is usually referred to as \(Ppv=\) f (\(Vpv\)). The algorithm is applied to the semiconductor switch of the boost converter installed to connect the PV panels to the DC-link. Specifically, the duty cycle of this device is adjusted according to the output of the Intelligent MPPT algorithm. In our implementation, \(V_{out}\) is the voltage that will be used for feeding the battery with the required power for its charge. \begin{table}
\begin{tabular}{c c} \hline \hline
**Maximum Power (Pmax)** & **249 W** \\ \hline Voltage at maximum power point (Vmpp) & 30 V \\ Current at maximum power point (Impp) & 8.3 A \\ Open Circuit Voltage (Voc) & 36.8 V \\ Short Circuit Current (Isc) & 8.83 A \\ Number of cell per module & 60 \\ Temperature coefficient of Voc(\%/deg.c) & \(-\)0.33 \\ Temperature coefficient of Isc(\%/deg.c) & 0.063805 \\ Nominal Voltage V\({}_{\text{battery}}\) & 100 V \\ Rated Capacity Battery & 10 Ah \\ Initial Stat of Charge & 50\% \\ Battery response time & 0.001 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of PV Module “Tata Power Solar Systems TP250 MBZ”. Figure 3: A general model of a Photovoltaic Module.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 2 Problem Description
### Measurement Noise Analyses  
The distribution of the time-varying noise caused by GNSS signals interference is most similar to Gaussian distribution with a time-varying mean value. However, Inv-Gamma distribution or Inv-Wishart distribution is assumed as the prior distribution of the MNCM, when estimating the MNCM by VBAKF algorithm. All of this is to ensure the prior distribution and the likelihood distribution conjugate. Therefore, it will affect the estimation accuracy of the MNCM.  
In order to prove the correctness of the above discussion, KLD was used to calculate the degree of similarity between time-varying noise and each distribution. We took a piece of trial data from the GNSS as an example. The time-varying noise of the GNSS east position is shown in Figure 3, where the data is derived from near the Airport Road. The distribution of the MNCM of the east position is shown in Figure 4. The Gaussian, Inv-Gamma, and Inv-Wishart distributions are shown in Figure 5.  
Figure 4: The distribution of GNSS east position noise.  
Figure 3: The time-varying noise of the GNSS east position.  
From Figures 4 and 5, we can see that the time-varying noise still conforms to the Gaussian distribution, it is just that the mean value has changed. However, the distribution of time-varying noise is not similar to the Inv-Gamma and Inv-Wishart distributions. Next, we analyze the similarity between the noise distribution and each approximate distribution based on KLD.  
The KLD formula is as follows  
\[\text{KL}(p(x_{i})||q(x_{i}))=\sum_{i=1}^{N}p(x_{i})\cdot\log\frac{p(x_{i})}{q( x_{i})} \tag{12}\]  
where \(p(x_{i})\) denotes the distribution of the GNSS east position time-varying noise. \(q(x_{i})\) denote the Gaussian, Inv-Gamma, or Inv-Wishart distribution. N is the number of samples.  
According to (12), we can obtain the average Kullback-Leibler divergence (AKLD), which be defined as  
\[\text{AKL}(p(x_{i})||q(x_{i}))=\frac{1}{M}\sum_{m=1}^{M}\text{KL}_{m}(p(x_{i}) ||q(x_{i})) \tag{13}\]  
where \(M\) = 500 denote the numbers of experiment.  
The AKLD values for each distribution and time-varying noise are shown in Table 1.  
It can be seen from Table 1 that the Gaussian distribution is more similar to the distribution of the time-varying noise. However, the Inv-Gamma and Inv-Wishart distribution have poor similarity with the time-varying noise distribution.