esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Model  
### Establishment of Finite Models  
This numerical simulation was adopted mainly to study the influence of pipeline parameters, the relative position of the tunnel, construction parameters and other factors on the stress and deformation of adjacent underground pipelines during the excavation of shallow buried double-arch tunnel. A three-dimensional model including stratum, tunnel and underground pipeline, and the tunnel section form, support structure and excavation process were established which was consistent with the actual construction.  
Figure 4 shows the numerical model established by using the finite difference method software FLAC\({}^{\text{3D}}\). In the model, the Y direction was the direction of tunnel excavation. In the XOZ plane which was perpendicular to the direction of tunnel excavation, the study area was expanded 3-5 times of the tunnel diameter [36] in order to avoid the influence of boundary conditions on the simulation results. Therefore, the model size was set as 120 m in the X direction and 60 m below the surface in the Z direction, and the excavation length in the Y direction was set as 60 m. According to the actual situation of the construction site and survey report, the stratum was divided into three parts: 4 m thick miscellaneous fill, 22 m thick completely weathered granite, and 24 m thick fragmentary strongly weathered granite. According to the construction scheme, the three pilot tunnels plus three steps reserved core soil method was adopted for tunnel excavation. The surrounding rock of the tunnel was simulated by using solid elements [37] and its mechanical behaviour conformed to the Mohr-Coulomb failure criterion. The support structure of the three pilot tunnels adopted shell elements, and the lining of the main tunnel adopted solid elements. Their thickness and strength parameters were consistent with actual situation. The middle partition wall and two side partition walls were simulated by using solid elements, and their shape, size and strength parameters were established according to the actual situation. Underground pipelines were simulated by using shell units with a certain thickness [38]. The numerical model was divided into 303,500 elements. The material parameters were obtained according to survey report, surrounding rock classification, and numerical reverse analysis, as shown in Table 1. According to site conditions, the horizontal displacement of all sides around the model was constrained, the horizontal and vertical displacements of the bottom surface of the model were constrained, and the top surface of the model was set as a free surface. The load on the pipeline mainly included earth pressure around the pipelines, internal pressure and force caused by self-weight. The earth pressure included earth covering load and additional earth pressure caused by tunnel excavation.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 6 Conclusions
### RSA's Dead-End  
It is particularly apparent that RSA's viability is increasingly challenged, even before the potential impact of quantum computing on its integrity is taken into account. Key sizes for RSA already need to be 2048-4096 bits and beyond for best practice (NIST Level 3 to Level 5), due to the impact and roadmap of classical computing progression (Moore's Law [20]), and the cryptographic attacks this enables.  
The energy used for RSA-4096 key generation was orders of magnitude greater than for NIST security level 5 'equivalents' such as Elliptic Curve P521 and ML-KEM-1024 (see Figure 4). When the potential of quantum computing algorithms is taken into account, even the Level 3-Level 5 key sizes for RSA do not provide sufficient protection. Larger RSA key sizes (towards 1 TB) that offer additional resilience to PQC attacks [29] quickly become unsustainable in terms of their requirements for memory and consumption of computational resources and energy.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Segmentation Results Based on Object Cropping
#### 4.3.2 Comparison with Other Segmentation Methods  
Additionally, we conducted a comparative analysis between the PSO segmentation method and alternative segmentation techniques, such as Otsu, Watershed, and K-means. The findings are presented in Table 5, and they showcase a substantial difference in the segmentation outcomes achieved by our proposed method compared to other techniques. The results suggest that the PSO segmentation approach, when coupled with preprocessing techniques, delivers images with superior clarity and accuracy.  
In particular, the Dice similarity coefficient (DSC) and Jaccard index values highly indicate the segmentation method's performance. The values approaching one indicate a remarkable similarity between the segmented objects and their ground truth, demonstrating the high accuracy and precision of the proposed PSO approach. This reinforces the potential of our method for application in medical image segmentation, where precise delineation of anatomical structures is paramount.  
The results suggest that preprocessing techniques, especially HE and the proposed adjustments, contribute significantly to the quality of medical image segmentation. Moreover, when compared to conventional approaches, the PSO segmentation method demonstrates its prowess at producing images with enhanced clarity and accuracy. This is particularly valuable in medical imaging scenarios, where precise segmentation is crucial for diagnosis and treatment planning.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 2 Feature Extraction Method  
### _Fft_  
FFT is a common tool for signal processing and is widely used in feature extraction, radar signal processing, etc. The specific implementation is shown as follows.  
For a finite-length discrete signal \(x(n)\), \(n=0,1,\ldots,N-1\), its discrete Fourier transform (DFT) can be expressed as:  
\[X(k)=\sum_{n=0}^{N-1}x(n)W_{N}^{kn} \tag{1}\]  
where \(k=0,1,\ldots,N-1\), \(W_{N}=e^{-j\frac{2n}{N}}\). FFT decomposes \(x(n)\) into an even sequence \(x_{1}(n)\) and an odd sequence \(x_{2}(n)\):  
\[x(n)=x_{1}(n)+x_{2}(n) \tag{2}\]  
where \(x_{1}(n)\) and \(x_{2}(n)\) are both of length \(N/2\). In addition, then we can obtain:  
\[X(k)=\sum_{n=0}^{N-1}x_{1}(n)W_{N}^{2 kn}+\sum_{n=0}^{N}x_{2}(n)W_{N}^{(2k+1)n} \tag{3}\]  
The following formula can be obtained:  
\[X(k)=\sum_{n=0}^{N-1}x_{1}(n)W_{N}^{2 kn}+W_{N}^{k}\sum_{n=0}^{N}x_{2}(n)W_{N} ^{2 kn} \tag{4}\]  
Because \(W_{N}^{2k}=e^{-j\frac{2n}{N}2 kn}=e^{-j\frac{2n}{N/2}kn}=W_{N/2}^{kn}\):  
\[X(k) =\sum_{n=0}^{N-1}x_{1}(n)W_{N/2}^{kn}+W_{N}^{k}\sum_{n=0}^{N-1}x_ {2}(n)W_{N/2}^{kn} \tag{5}\] \[=X_{1}(k)+W_{N}^{k}X_{2}(k)\]  
where \(X_{1}(k)\) and \(X_{2}(k)\) are the DFTs of \(x_{1}(n)\) and \(x_{2}(n)\) at \(N/2\), respectively. Since both \(X_{1}(k)\) and \(X_{2}(k)\) have a period of \(N/2\), \(X(k)\) can be expressed as:  
\[\left\{\begin{array}{c}X(k)=X_{1}(k)+W_{N}^{k}X_{2}(k)\\ X\left(k+\frac{N}{2}\right)=X_{1}(k)-W_{N}^{k}X_{2}(k)\end{array}\right. \tag{6}\]where \(k=0,1,\cdots,\frac{N}{2}-1\). The frequency spectrum obtained by performing FFT on a signal is shown as follows.
# Cybersecurity Access Control: Framework Analysis in a Healthcare Institution
## 4 Discussion
### TCO and ROSI
The solution adopts a hybrid approach by combining cloud-born services with on-premises solutions. Therefore, a hardware CAPEX similar to the PBSM remains essential. The TCO for a 5-year ZTA solution is calculated at USD 68,684,762.04. The highest cost is assumed in year one at USD 18,145,160.00 due to the initial CAPEX purchases and 5-year support contract. The Return on Security Investment (RSOI) (Figure 8), which is a form of cost-benefit analysis, is computed as described by [PERSON] [22]. ROSI serves as one metric to quantify the value of investing in cybersecurity, hence assessing the bottom line. In other words, it serves to ascertain the returns an organization garner from its cybersecurity protection investments, as outlined by [PERSON] [23]. The SLE is assessed at USD 11 million, derived from a healthcare institution's typical data breach cost. The ARO value used is 40, reflecting the average number of cyberattacks experienced by medical centers annually [24]. The MR values fluctuate depending on the model, with the MR for the existing PBSM at Company X standing at 60% and the ZTA at 90%. The MR for the PBSM is averaged from the data for Company X, indicating that the current production model enables the capability to conduct deep packet inspection at the infrastructure level on approximately 60% of the enterprise's traffic. Additionally, the average risk of internal threat is valued at 40%, indicating a mitigation ratio of 60% [25]. The ZTA MR values are established based on ZTA solutions' capability to each endpoint, facilitating 100% endpoint detection and response and the potential for deep packet inspection covering approximately 90% of enterprise traffic. Each cost figure is drawn from the preceding TCO calculations in Figure 6.  
Figure 8: ROSI formula. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \multicolumn{2}{c}{ZTA} & \multicolumn{2}{c}{**Quantity**} & \multicolumn{2}{c}{**CARTEC**} & \multicolumn{2}{c}{**J1.7\({}^{\circ}\)**} & \multicolumn{2}{c}{**Inflation**} \\ \hline
**Headspace** & **Difference** & **Co  
The PBSM ROSI analysis in Figure 9 returns a positive percentage of 1052.5% return on investment, equating to a savings of USD 241,094,607.83. The ZTA ROSI analysis in Figure 10 returns a positive percentage of 476.5% return on investment, equating to a savings of USD 327,282,891.12. PBSM ROSI = [$31,000,000.00 - 40] \(\times\) 0.60 - $22,906,851.10] / $22,906,851.10] PBSM ROSI = 1052.546 PBSM SANTOS - $21,906,851.10 + $0.525 - $21,094,607.83] PBSM SANTOS - $21,906,851.10 + $0.525 - $21,094,607.83]  
PBSM ROSI calculation. PBSM and ZTA demonstrate favorable returns, suggesting that both models offer financial benefits in enhancing cybersecurity postures. PBSM generates a higher ROSI when considering the cost-to-financial impact ratio. The incremental cost of implementing the ZTA solution over PBSM translates to USD 2,289,492.07 per 1% increase in mitigation ratio.
The HS-SPME technique employs a small fibre coated with an adsorbing phase, and has found widespread applications in qualitative and quantitative analyses of VOCs in various food, environmental and biological samples [24; 25; 23]. Comparing with other extraction methods, HS-SPME is less destructive to the samples and can capture the VOCs in a short time. These advantages make HS-SPME a promising technique for rapidly extracting VOCs that are rapidly released from fresh vegetables upon cell rupture. It is known that plant VOCs are chemically instable and the profiles may change rapidly after leaf and stem damage [26]. To elucidate the related metabolic pathways in vegetables, it is crucial to obtain VOCs in a fast and gentle way. For this purpose, direct HS-SPME may be a good choice and deserves further investigation. In particular, the combination of HS-SPME with gas chromatography-high resolution mass spectrometry (HRMS) can provide reliable and high-quality VOCs profiles in a simple and less tedious way. The objectives of this study are (i) to develop an effective sampling method for determining the VOCs released from 12 different Brassicaceae vegetables, using HS-SPME combined with GC-HRMS; (ii) to find out whether the VOCs profiles can be used to distinguish various Brassicaceae vegetables; and (iii) to elucidate the possible metabolic pathways, and their relationships associated with the metabolism of VOCs.
## 2 Background  
### Electrical Capacitance Tomography Fundamentals  
Electrical Capacitance Tomography (ECT) is a non-intrusive and non-invasive imaging technique used to monitor industrial processes in pipelines and reactors, particularly where mixtures of non-conductive dielectric materials are involved. Common applications include monitoring gas-oil flows, solid particle flows, and reservoirs [5]. An ECT system typically measures changes in mutual capacitance between pairs of electrodes arranged around the circumference of a process container or pipe. The amount of measurement data, usually ranging from 66 to 496 data points, depends on the hardware used, and the acquisition rate can vary from a few to hundreds of images per second. The collected data are then processed by a high-performance PC system using mathematical modeling and specialized algorithms for image reconstruction, raw data analysis, and making effective and robust diagnostic decisions for process control and automation [6]. Industrial processes generally exhibit a three-dimensional nature, making it necessary to sense and measure phenomena in the 3D space occupied by these processes [7; 8]. Consequently, ECT is evolving from Z-axis averaged measurements recorded in a cross-sectional plane to 3D scanning of the entire volume. Conventional (cross-sectional, planar) ECT systems utilize a single layer of evenly spaced electrodes to exclusively capture cross-sectional data. This limitation restricts image reconstruction to a two-dimensional space, averaging static or dynamic data along the Z-axis. Conversely, 3D ECT sensors extend the capabilities of planar cross-sectional imaging by using multiplane electrode arrangement and enabling full volumetric reconstruction of material distribution within the entire sensing space. This advancement provides a more comprehensive and detailed understanding of both static and dynamic  
Figure 1: Overall structure and logic of the paper.  
objects under examination. The 3D ECT sensor and sensing system concept, with the main components, are illustrated in Figure 2.
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
## 2 NFC Energy Harvesting
### Tag Antenna Design Considerations
(7)  
The tag quality factor, therefore, decreases and the tag bandwidth (BW) increases with the increase in distance. When the reader-tag range decreases, the input voltage increases, and the rectifier gradually becomes conductive. On the other hand, the voltage-dependent parasitic capacitance increases by about 1.5% [43]. Therefore, the resonance tends to decrease due to the variation of the chip capacitance. Moreover, taking into account the presence of metal (smartphone case), the variation in the resonance frequency is masked since the inductance is significantly reduced due to the proximity to the metal. Consequently, an overall increase in the resonance frequency (see Figure 7) is produced. This result can be interpreted by analyzing Equation (5). However, because \(R_{pa}>>R_{IC}\), the tag \(Q\) factor is mainly fixed by the chip resistance. Figure 8 shows the quality factor and the BW as a function of the tag-to-mobile distance. We can see that the BW is higher than that required for ISO15693 (968 kHz) and that shape distortion is due more to the reader than to the tag. Figure 8: Tag quality factor and bandwidth (BW) as a function of the distance to metal depending on chip resistance \(R_{c}\). The BW limit for ISO 15693 is also shown. Figure 7: Measured S\({}_{11}\) of the test antenna as a function of frequency for different distances between the tag and the mobile [42].
## 2 Optimal Trajectory Formulation
### The Variational Formalism
\begin{split} I_{i}=&-\varepsilon\delta t_{i}F( \dot{\mathbf{q}}(t_{i}),\dddot{\mathbf{q}}(t_{i}))+2\varepsilon\Big{[}\frac{ \partial F}{\partial\dot{\mathbf{q}}}\ddot{\boldsymbol{\eta}}\Big{]}_{t_{i}}^{ t_{i+1}}\\ &-2\varepsilon\left[\left(\frac{\mathrm{d}^{2}}{\mathrm{d}t} \frac{\partial F}{\partial\dot{\mathbf{q}}}\right)^{\top}\dot{\boldsymbol{ \eta}}\right]_{t_{i}}^{t_{i+1}}+2\varepsilon\left[\left(\frac{\mathrm{d}^{2}}{ \mathrm{d}t^{2}}\frac{\partial F}{\partial\dot{\mathbf{q}}}-\frac{\partial F}{ \partial\dot{\mathbf{q}}}\right)^{\top}\boldsymbol{\eta}\right]_{t_{i}}^{t_{i +1}}\\ &-2\varepsilon\int_{t_{i}}^{t_{i+1}}\left(\frac{\mathrm{d}^{3}}{ \mathrm{d}t^{3}}\frac{\partial F}{\partial\dot{\mathbf{q}}}-\frac{\mathrm{d}}{ \mathrm{d}t}\frac{\partial F}{\partial\dot{\mathbf{q}}}\right)^{\top} \boldsymbol{\eta}\mathrm{d}t+\varepsilon\delta t_{i+1}F(\dot{\mathbf{q}}(t_{i+ 1}),\dddot{\mathbf{q}}(t_{i+1})).\end{split}  \tag{14}\]  
After substituting (10) in (14), we can rearrange such an equation as a linear combination of the variations \(\boldsymbol{\eta}\), \(\delta t_{i}\), \(\delta\dot{\mathbf{q}}_{i}\), \(\delta\ddot{\mathbf{q}}_{i}\). Such variations being arbitrary, they are linearly independent [27]. It follows that the necessary stationarity conditions of \(I\) at \(\mathbf{q}\) are defined in the intervals \(J_{i}=[t_{i},t_{i+1})\) for \(0\leq i<N\) and \(J_{N}=[t_{N},t_{N+1}]\) as:  
\[\frac{\mathrm{d}^{6}\mathbf{q}}{\mathrm{d}\,t^{6}}+\frac{\alpha}{(1-\alpha)} \frac{\mathrm{d}^{2}\mathbf{q}}{\mathrm{d}\,t^{2}}=0 \tag{15}\]  
\[\mathbf{q}\in C^{4}(T,\mathbb{R}^{n}) \tag{16}\]  
\[\left[\alpha\frac{\mathrm{d}^{6}\mathbf{q}}{\mathrm{d}\,t^{6}}(t_{i}^{+})+(1- \alpha)\frac{\mathrm{d}\mathbf{q}}{\mathrm{d}\,t}(t_{i}^{+})\right.\left.- \alpha\frac{\mathrm{d}^{6}\mathbf{q}}{\mathrm{d}\,t^{6}}(t_{i}^{-})-(1-\alpha )\frac{\mathrm{d}\mathbf{q}}{\mathrm{d}\,t}(t_{i}^{-})\right]^{\top}\dot{ \mathbf{q}}(t_{i})=0, \tag{17}\]  
where \(t_{i}^{-}\) and \(t_{i}^{+}\) represent the limits of \(t\) when approaching \(t_{i}\) from the left and the right, respectively, and (15) are the Euler-Lagrange equations at each interval \(J_{i}\).
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 4 Experiment Results and Discussion
### Discussion  
In Section 4, the highest test accuracy (0.83) and smallest _FNR_ (0.144) obtained by AKPCA (fusion) shows that AKPCA is the best UAD method among OCSVM (together), OCSVM (fusion), AKPCA (together), AKPCA (fusion), ISF (together), and ISF (fusion). Similarly, the highest accuracy (0.996) and highest _f\({}_{1}\)-score_ (1) of SVM (fusion) shows that SVM (fusion) is the best model among 1 DCNN (together), 1 DCNN (fusion), SVM (together), and SVM (fusion). The test results prove that selecting a model based on the highest validation accuracy is reliable. In addition, mean fusion is a good choice for multi-channel same-type signal processing and is helpful for UAD and FD.  
The results in Table 10 illustrate that only AKPCA results in a lack of specific fault information when an anomaly is detected, while using only SVM may lead to misdiagnosis. These issues are resolved when AKPCA and SVM operate in parallel through the information integration algorithm. Therefore, within a framework where UAD and FD function in parallel, the information integration algorithm is essential for the comprehensive and precise condition monitoring of complex equipment, including but not limited to ESWPs. To our knowledge, there is no research on ESWP condition monitoring. The condition monitoring of other types of pumps generally only includes anomaly detection [38] or only includes fault diagnosis [10]. Research on the simultaneous use of UAD and FD has appeared. Reference [39] uses pressurized water reactor simulator data to perform AKPCA and SVM, equivalent to UAD and FD working in series. The design of UAD and FD in series is similar to [23]. This method does not consider the impact of the UAD algorithm's false negatives on the monitored object. Our novel features and innovation lie in the parallel design of UAD and FD algorithms. Compared with the probability-weighted voting decision [40] for the same output form of models, the information integration algorithm focuses on fusing the outputs of models with similar functions but different forms of results. Similar to the ensemble learning mentioned in reference [41], the information integration algorithm is fault-tolerant.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
###### Abstract  
In this paper, an antenna with a multiple-input, multiple-output (MIMO) configuration is demonstrated for mm-wave 5G-based Internet of Things (IoT) applications. The two antenna elements are arranged next to each other to form a two-port antenna system such that significant field decorrelation is achieved. Moreover, a dielectric layer is backed by an eventual multiport system to amend and analyze the radiation characteristics. The overall size of the MIMO configuration is 14 mm \(\times\) 20 mm, and the operation bandwidth achieves ranges from 16.7 to 25.4 GHz, considering the \(-10\) dB criterion with a maximum isolation of more than \(-30\) dB within the operating band. The peak gain offered by the antenna system is nearly 5.48 dB, and incorporating a dielectric layer provides an increase in the gain value to 8.47 dB. Within the operating band, more than 80% total efficiency is observed, and analysis shows several MIMO performance metrics with favorable characteristics. The compactness of the proposed design with high isolation, improved gain, and wideband features make it a suitable candidate for mm-wave-based 5G applications.  
5G; mm-wave; IoT; MIMO; communication networks +
Footnote †: journal: Applied Sciences  
_Applied_  
_sciences_  
_Article_  
A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 3 Experiments and Results  
### Evaluation Metrics  
In order to comprehensively evaluate the model's detection ability in scenarios such as long and narrow water bodies commonly found in farmland irrigation areas, fragmented distribution, and strong background interference (building occlusion, vegetation coverage, topographic shadows). We noticed that the number of water-body pixels is usually much smaller than that of non-water-body background pixels, resulting in a serious data imbalance between the target and background categories. This kind of imbalance will undermine the effectiveness of traditional semantic segmentation evaluation metrics, such as Overall Accuracy (OA) and Mean Intersection over Union (mIoU), in reflecting the actual performance of water body segmentation.  
To compensate for the evaluation bias caused by the excessively high proportion of background pixels, this study adopted a refined index system to comprehensively evaluate the accuracy of water body segmentation in remote sensing images. The evaluation indicators used in the experiment are as follows:  
\[IoU=\frac{TP}{FP+FN+TP} \tag{13}\]  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
Figure 9: Two-stage transfer learning framework based on pretrained LKF-DCANet.  
where \(TP\) denotes the number of samples correctly classified as water body, \(FP\) refers to background samples incorrectly predicted as water body, and \(FN\) refers to water body samples misclassified as background.  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{16}\]  
\[Params=k_{h}\times k_{w}\times C_{in}\times C_{out} \tag{17}\]  
\[FLOPs=[(C_{in}\times k_{w}\times k_{h})+(C_{in}\times k_{w}\times k_{h}-1)] \times C_{out}\times W\times H \tag{18}\]  
where \(k_{h}\) and \(k_{w}\) denote the kernel's height and width, \(C_{in}\) is the number of input channels, and \(C_{out}\) is the number of output channels.