esa-sceva/satcom-chunk-collection huggingface dataset:
[PERSON]'s fully homomorphic encryption (FHE) scheme [19] based on the lattice hard problem is resistant to quantum computer attacks, and homomorphic addition and homomorphic multiplication operations can be used for the computation of ciphertexts. [PERSON] et al. [20; 21] and [PERSON] et al. [22] studied the search for FHE encrypted data. By considering the KGA problem prevalent in PEKS cryptosystems, the verification problem of untrusted cloud servers returning incomplete results, and the quantum attack problem, in this paper, we propose a new ciphertext retrieval scheme based on FHE, called VR-PEKS. Our main contributions are as follows. * We propose a new PEKS scheme based on FHE, called VR-PEKS, and make the scheme resistant to keyword guessing attacks by internal and external adversaries by using the oblivious pseudorandom function (OPRF) to blind keywords. The OPRF keys are securely stored and used by data owners and data users, so that malicious cloud servers cannot generate a valid keyword trapdoor for _Test_ algorithm. * We design an encrypted authentication index structure, which is created and disclosed by the data owner, so that the data user can verify the correctness and integrity of the search results, so as to prevent the malicious cloud server from forging, tampering with, or discarding the stored cloud data, or performing the search task incorrectly. * In the random prediction model, we prove that our scheme satisfies keyword ciphertext indiscriminability and keyword trapdoor indiscriminability under an adaptive keyword selection attack, and compare the security of the scheme with that of other PEKS schemes. The rest of this paper is organized as follows. In Section 2, the work related to PEKS is presented. In Section 3, the relevant background knowledge that is used in this paper is presented. Section 4 presents the system model, scheme definition, and security model of this scheme. Section 5 describes the proposed scheme in this paper in detail and proves the security and correctness of the scheme. In Section 6, the proposed scheme in this paper is compared and analyzed with other PEKS schemes. Finally, Section 7 concludes this paper.
## 6 Numerical Results
### Performance of Full-Size Sandwich Element
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Connector** \\ **Spacing [m]** \\ \end{tabular} & \begin{tabular}{c} **Load** \\ **Case** \\ \end{tabular} & \begin{tabular}{c} **SLS** \\ \end{tabular} & \
\begin{tabular}{c} **ULS** \\ \end{tabular} &
\begin{tabular}{c} **Failure** \\ \end{tabular} \\ \hline \multirow{4}{*}{0.5} & \multirow{4}{*}{**Failure Mode**} \\ \cline{3-10}  & & \(\mathbf{U_{\text{max}}}\) **[mm]** & **Cracks** & \(\mathbf{H_{\text{EL,d}}}\) & \(\mathbf{H_{\text{Eu,d}}}\) & \(\mathbf{F_{\text{Ep,d}}}\) & \(\mathbf{W_{\text{max}}}\) & \(\mathbf{W_{\text{max}}}\)/\(\mathbf{W_{\text{ULS}}}\) \\ \cline{2-10}  & & \(\mathbf{LC1}\) & 0.8 & Minor & –3.5 & –2.6 & 1.0 & 1420 & 1.3 & Lower anchor \\ \cline{2-10}  & & \(\mathbf{LC2}\) & –1.1 & Minor & 3.5 & 4.3 & 1.8 & –1590 & 1.2 & Upper anchor \\ \cline{2-10}  & & \(\mathbf{LC3}\) & –0.8 & Minor & 2.1 & 2.9 & 1.4 & –1590 & 1.8 & Upper anchor \\ \hline \multirow{4}{*}{1.0} & \multirow{4}{*}{_LC1_} & \(\mathbf{LC1}\) & 1.1 & Minor & –3.5 & –2.7 & 1.5 & 1460 & 1.4 & Lower anchor \\ \cline{2-10}  & & \(\mathbf{LC2}\) & –1.6 & Minor & 3.5 & 4.3 & 2.7 & –1610 & 1.2 & Upper anchor \\ \cline{2-10}  & & \(\mathbf{LC3}\) & –1.0 & Minor & 2.1 & 2.9 & 1.9 & –1620 & 1.9 & Upper anchor \\ \hline \multirow{4}{*}{2.0} & \multirow{4}{*}{_LC1_} & \(\mathbf{LC1}\) & 1.5 & Minor & –3.4 & –2.7 & 1.9 & 1470 & 1.4 & Lower anchor \\ \cline{2-10}  & & \(\mathbf{LC2}\) & –2.0 & Minor & 3.5 & 4.2 & 3.3 & –1550 & 1.1 & Conn. pull-out \\ \cline{1-1} \cline{2-10}  & & \(\mathbf{LC3}\) & –1.2 & Minor & 2.1 & 2.8 & 2.4 & –1450 & 1.7 & Conn. pull-out \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of results for the different connector distance configurations.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 2 Preliminary  
Although there are many possible routes to connect a source node and a destination node, only one route will be selected for each packet; thus, line networks are the fundamental building blocks to describe the transmission. A line network is a sequence of network nodes where network links only exist between two neighboring nodes. A recoding scheme for line networks can be extended for general unicast networks and certain multicast networks [63, 64]. On the other hand, in scenarios such as bridging the communications of remote areas, street segments of smart lamppost networks, deep space [79, 80], and deep sea [82, 83] communications, the network topologies are naturally full or segments of multi-hop line networks. Therefore, we only focus on line networks in this paper. Although there are works on dependent packet loss models [75, 76] and time-variant channels [87, 88], we consider independent packet loss at each link to further simplify the analysis. This assumption was made in many BNC studies such as [61, 63, 64, 70, 89].
## 2 Materials and Methods
### Determination of Exposure Concentration (EC)  
Annual mean concentrations of individual PAHs were measured at the three sampling sites representing the roadside, urban background and industrial environment in the BMA. Integrated 24-h PAHs in PM10 filters were quantified in 43 samples from the roadside site, 46 samples from the urban background site and 37 samples from the industrial site. Annual mean concentrations of FLU, PHE, ANT, FLT, PYR, BaA, CHR, BbF, BkF, BaP, DBA, IP and BP were multiplied by corresponding TEFs to estimate the concentration equivalent to BaP (BaP-TEQ). BaP-TEFs were considered as per [PERSON] and [PERSON] (1998). The total sum BaP-TEQ of 13 PAHs represents the contaminant concentration in air (CA) at each site, which is then converted to the exposure concentration of the population. CA and EC were calculated from the following equations. Carcinogenic risks were calculated using age-specific factors given in Table 2.  
\[\begin{array}{c}\text{CA = 0.0005 * FLU + 0.0005 * PHE + 0.0005 * ANT + 0.05 * FLT}\\ + 0.001 * PYR + 0.005 * BaA + 0.03 * CHR + 0.1 * BbF + 0.05 * BkF \\ + 1 * BaP + 1.1 * DBA + 0.1 * IP + 0.02 * BP \\ \end{array} \tag{1}\]  
\[\text{EC = (CA \times ET \times EF \times ED)/AT} \tag{2}\]  
where:  
CA = contaminant concentration in air as BaP-TEQ concentration (ng m\({}^{-3}\))  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Receptor** & \begin{tabular}{c} **BW** \\ **(kg)** \\ \end{tabular} & \begin{tabular}{c} **IR** \\ **(m\({}^{3}\) d\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **ET** \\ **(h d\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **EF** \\ **(d y\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **ED** \\ **(y)** \\ \end{tabular} &
\begin{tabular}{c} **AT** \\ **(h)** \\ \end{tabular} \\ \hline Resident (child) & & & & & & \\ \hline
1 to \textless{} 2 years & 11.4 \({}^{\text{a}}\) & 5.4 \({}^{\text{a}}\) & 24 & 350 & 2 & 17,520 \\ \hline
2 to \textless{} 3 years & 13.8 \({}^{\text{a}}\) & 8.9 \({}^{\text{a}}\) & 24 & 350 & 3 & 26,280 \\ \hline
3 to \textless{} 6 years & 18.6 \({}^{\text{a}}\) & 10.1 \({}^{\text{a}}\) & 24 & 350 & 6 & 52,560 \\ \hline
6 to \textless{} 11 years & 31.8 \({}^{\text{a}}\) & 12.0 \({}^{\text{a}}\) & 24 & 350 & 11 & 96,360 \\ \hline
11 to \textless{} 16 years & 56.8 \({}^{\text{a}}\) & 15.2 \({}^{\text{a}}\) & 24 & 350 & 16 & 140,160 \\ Adult (16 to 70 years) & 70 \({}^{\text{b}}\) & 16.0 \({}^{\text{a}}\) & 24 & 350 & 54 & 473,040 \\ Worker (16 to 70 years) & 70 \({}^{\text{b}}\) & 16.0 \({}^{\text{a}}\) & 8 & 250 & 54 & 473,040 \\ \hline \hline \end{tabular}  
* US EPA, 2011; \({}^{\text{b}}\) US EPA, 2014.  
\end{table}
Table 2: Age-specific exposure factors for the assessment of lung cancer risk from long-term PM10-bound PAHs exposure related to body weight (BW) and total lifetime hours of exposure (AT).
## 2 CFD Methodology and Numerical Modeling  
### CFD Numerical Model  
#### 2.1.1 Governing Equations and Mathematical Models  
The three-dimensional ventilation flow field of an indoor plant factory has been numerically simulated and predicted by the computational fluid dynamics (CFD) method. In this research, it is assumed that the airflow within the plant factory is steady and incompressible. The airflow is assumed to be fully turbulent throughout the interior due to the high inertial effect of the inlet flow and the obstruction of the airflow by the indoor cultivation racks and plants. The k-\(\varepsilon\) model is widely applied to airflow and heat transfer in agriculture [34; 35]. Therefore, numerical models based on incompressible Reynolds-averaged Navier-Stokes (RANS) equations and standard k-\(\varepsilon\) turbulence model are used in this research to simulate the airflow within the plant factory [30]. The governing equations for conservation of mass, momentum, and energy are used to predict the velocity, pressure, and temperature of the fluid inside the plant factory room as shown below [36].  
\[\frac{\partial\rho u_{i}}{\partial x_{i}}\ =\ 0 \tag{1}\]  
\[\frac{\partial\big{(}\rho u_{i}u_{j}\big{)}}{\partial x_{i}}\ =\ -\frac{ \partial p}{\partial x_{j}}+\frac{\partial\rho}{\partial x_{i}}((\mu+\mu_{t}) \frac{\partial u_{j}}{\partial x_{i}})+\rho g_{i} \tag{2}\]  
\[\frac{\partial(\rho u_{i}h)}{\partial x_{i}}\ =\ \frac{\partial}{\partial x_{i}} \bigg{(}\bigg{(}\frac{\mu}{Pr}+\frac{\mu_{t}}{\sigma_{t}}\bigg{)}\frac{ \partial h}{\partial x_{i}}\bigg{)}+S_{h} \tag{3}\]  
where \(u_{i}\) represents the \(i\)-axis component of the time-averaged velocity, \(x_{i}\) is the Cartesian coordinate. \(\rho\) and \(p\) denote air density and air pressure, respectively. \(\mu\) and \(\mu_{t}\) are the viscosity and turbulent viscosity, respectively. \(h\) and \(S_{h}\) denote the heat released per unit mass and the heat source term, respectively. \(P_{r}\) and \(\sigma_{t}\) represent the Prandtl number and the turbulent Prandtl number, respectively.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion
### Effect of Agitation on Cellulase and Xylanase Production  
The result of this study is in accordance with that of [PERSON] et al. [50], who reported agitation of 150 rpm as optimal for xylanase production by _Streptomyces_ sp. Ab 106. On the contrary, [PERSON] et al. [51] reported 200 rpm as optimal for xylanase production by _Bacillus mojavensis_ AG137 cultured on agricultural waste. Whereas [PERSON] et al. [31] reported static condition as optimal for endocillusae production by _Streptomyces_ sp. MDS using carboxymethyl cellulose as carbon source, this was not the case in this study as the least cellulase production was recorded at stationary condition (Figure 4b). An optimal agitation of 300 rpm was recorded for cellulase production by _Streptomyces_ sp. T3-1, a transformant strain cultivated in 50 L fermenter [52]. Agitation affects the rate of oxygen mass transfer, and is crucial for maximal production in microbial enzyme fermentation [52]. However, high agitation speed may also result in high shear stress and impact negatively on mycelia growth [53; 54]. In this study, low agitation speed seemed favorable for cellulase and xylanase production.
## 2 Materials and Methods  
In the industrial production process, the tasks undertaken by edge PLCs can be divided into two categories based on their real-time requirements.  
The first category is real-time tasks. These tasks typically require less memory for allocation but have high real-time requirements. Real-time tasks often involve control instructions that are either automatically generated or manually executed, with unpredictable intervals between them. Failure to process these tasks in a timely manner can result in production accidents, which highlights the importance of ensuring their real-time processing. Common real-time tasks include the execution of control commands and fault diagnosis.  
The second category is non-real-time tasks. Non-real-time tasks, which primarily involve data processing at the edge, have relatively lower real-time requirements. Due to the large scale of edge data, non-real-time tasks require more memory for allocation. The execution time of non-real-time tasks is directly correlated with the scale of the data and usually cannot be completed within a short period. Examples of common non-real-time tasks include data storage and log recording.  
Due to constraints of space and cost, the memory resources in edge PLCs are limited. Therefore, the efficient utilization of these limited memory resources is crucial for improving task processing efficiency. The objectives of memory allocation decisions in edge PLCs can be summarized as follows: for real-time tasks, ensure real-time processing while conserving memory space; for non-real-time tasks, allocate memory efficiently to enhance task processing efficiency. We divide the memory allocation zone into two parts: the real-time zone and the non-real-time zone. These zones are responsible for processing real-time tasks and non-real-time tasks, respectively. When allocating memory, the memory pools used by these two zones are relatively independent.  
The memory allocation process based on the source of memory requests is illustrated in Figure 2. The allocation strategies in the real-time zone and the non-real-time zone will be discussed in the following two sections.
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
## 3 Overall System Control
### PV Array and ESS  
The proposed shipboard electric power system involves the integration of PV modules and storage batteries into the LVDC bus of the proposed DC distribution network. The PV-battery system supports the main generators by supplying part of the power to the LVDC bus loads and the HVDC bus propulsion motors in times of PV peak power, which adds to the efficient operation of the whole system. The PV system employed a maximum power point tracking (MPPT) control algorithm applied to the PV converter to deliver the maximum power from the PV panels. Meanwhile, the control algorithm of the ESS is shown in Figure 7, which is considered a power management control scheme that depends mainly on PV power generated, \(P_{PV}\), state of charge \(SoC\), and the power limit specified by the possible average power that can be supplied from the PV and battery system, \(P_{Lim}\). In this algorithm, batteries switch between charging and discharging modes defined by the SoC and the PV power status, which determine the power supplied by the ESS to the load, \(P_{ESS}\). Therefore, the LVDC bus powers are kept balanced. When the limit of the batteries is reached, the power balance is ensured by the power supplied by the DAB converter, \(P_{DAB}\), from the HVDC bus side regardless of the case of the PV power, \(P_{PV}\). If the PV power, \(P_{PV}\), is higher than the limit power, \(P_{Lim}\), the storage element is to be charged depending on the \(SoC\) state of the batteries. When the batteries reach the maximum \(SoC\) state at 90%, the charging process stops and the excess PV power is transferred back to the HVDC bus side to feed the propulsion system. On the other hand, the batteries are discharging when the PV power, \(P_{PV}\), is less than the limit power, \(P_{Lim}\). In that case, the load is supplied by the batteries until its \(SoC\) reaches the minimum limit at 30%. Accordingly, the excess load is supplied from the HVDC bus side through the DAB converter, \(P_{DAB}\).  
Figure 6: V/f control of asymmetrical six-phase machine.
# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 5 Conclusions and Future Work  
In this paper, an enhanced 3D U-Net segmentation model was proposed to address the shortcomings of 2D networks in handling brain tumor segmentation tasks. While retaining the original U-shaped network framework, the structure of the encoder and decoder is recompiled, i.e., the same convolution is used in the coder-decoder module instead of the valid convolution used in the original network to enhance the re-utilization of the feature map. The network model is optimized by adding a \(1\times 1\times 1\) convolution to reduce the dimensionality and the computation of the network while finding the optimal hybrid loss function to improve the learning ability of the network model and further improve the segmentation accuracy of the lesion region. The results showed that the model's segmentation performance of both TC regions and ET regions was superior to those of other 2D U-Net models, and its sensitivity reached 90.6%, 92.6%, and 94.6% for the WT region, the TC region, and the ET region, respectively.  
The algorithm designed in this article has good segmentation performance for the tumor core area and tumor enhancement area, but there are shortcomings in the dice index for the entire tumor area. In the future, further improvements are needed to the network structure, such as adding attention mechanisms to improve the segmentation of the entire tumor area. This article uses a skip connection method to concatenate the extracted feature maps from the encoding module directly to the corresponding layer of the decoding module. However, the extracted low-level features have redundant information. The use of attention mechanism can effectively suppress activation in irrelevant regions and reduce redundant parts of skip connections. At the same time, the network model should be optimized; the encoding and decoding modules should be simplified; and the network training speed should be improved.  
Conceptualization, Z.L., X.W. and X.Y.; Methodology, Z.L., X.W. and X.Y.; Software, X.W.; Data Curation, X.W.; Writing--Original Draft, X.W.; Writing--Review and Editing, Z.L. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated from this study are included in this published article. Raw data are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
WT Whole Tumor  
TC Tumor Core  
ET Enhanced Tumor  
MRI Magnetic Resonance Imaging  
HGG High-Grade Gliomas  
LGG Low-Grade Gliomas  
BN Batch Normalization  
ReLU Rectified Linear Unit
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Sensor Deployment
#### 5.2.3 Varying the Perception Probability of Sensors  
Figure 6 shows the effect of increasing \(c_{th}\) on the number of deployed sensors. Due to the changing value of \(c_{th}\), according to Equation (1), the maximum effective coverage radius of the sensor also changes, which results in a change in the distance between the candidate sensor and the PoI, and the distance between the candidate sensors. When \(c_{th}\) = 90% and \(c_{th}\) = 80%, the maximum effective coverage radiuses of the sensor are 2.3992 m and 2.2444 m, respectively, so the distance between the candidate sensors should not be greater than 0.1548 m, and if the distance is too small, the solving time will be too long. To eliminate the effect of spacing, we uniformly set the spacing between candidate sensors to 0.1 m.  
As shown in Figure 6, the number of sensors deployed by the two solutions increases with the growth of \(c_{th}\), and the increasing trend slows down. According to Equation (1), as \(c_{th}\) increases, the effective coverage radius of the sensor decreases and the rate of decrease slows down, which results in an increase in deployed sensors and a decrease in the growth  
Figure 5: Deployed sensors with the increase in \(L_{s}\). (**a**) The number of deployed sensors; (**b**) Solving time.  
Figure 6: Deployed sensors with the increase of sensor perception probability. (**a**) The number of deployed sensors; (**b**) Solving time.  
trend. As \(c_{th}\) increases, the solving time gets shorter and shorter. Because of the high discretization accuracy, the higher the \(c_{th}\), the fewer candidate locations there are for placing sensors, so the solving time decreases. In summary, both algorithms are scalable as the perception probability grows.  
To sum up, the proposed solutions for sensor deployment are robust as the number of PoIs, the accuracy of sensor discretization and the probability of sensor perception grow. The number of sensors deployed by the GHDSAE solution is comparable to that of the GHDS solution. Although the GHDSAE solution takes longer than the GHDS solution, its execution time is still within seconds, making it acceptable.
## 4 Experimental Validation
### Case 2
#### 4.2.5 Comparative Experiments with Different Models  
The results of the model comparison experiments on the JNU dataset are presented in Table 16 and Figure 15. KAResNet achieved a fault diagnosis accuracy of 100% across  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **0-1** & **0-2** & **1-0** & **1-2** & **2-0** & **2-1** & **Average** \\ \hline Method A & 99.667 & 100 & 100 & 100 & 99 & 100 & 99.778 \\ Method B & 99.33 & 98.67 & 100 & 99.67 & 97 & 99.67 & 99.06 \\ MCR-KAResNet-TLDAF & 99.667 & 100 & 100 & 100 & 99.667 & 100 & **99.889** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Experimental results of different domain adaptation algorithms on the JNU dataset.  
Figure 14: Average accuracy of different domain adaptation algorithms on the JNU dataset.  
multiple operating conditions. There was a slight decrease in accuracy in transfer tasks 0-1 and 2-0. Specifically, in transfer task 0-1, the speed changed from 600 rpm in condition 0 to 800 rpm in condition 1, which is a small range of speed variation. Due to the high complexity of the MCR-KAResNet-TLDAF model, its performance on the test data may be inferior to the relatively simpler MCR-2D-CNN-TLDAF in this small-range speed variation task. In transfer task 2-0, the accuracy of MCR-KAResNet-TLDAF declines. One possible reason is that although KABlock provides a high smoothness, handling large speed variations may affect its adaptability to data of different speeds, leading to inferior performance in the 2-0 task compared to MCR-2D-CNN-TLDAF. Although its accuracy declined in the 0-1 and 2-0 transfer tasks, where it was surpassed by the other models, KAResNet still maintained the highest overall fault diagnosis accuracy. This fully demonstrates the powerful capability of KAResNet in handling complex transfer tasks.  
The Vgg16 model's performance on the JNU dataset is noticeably inferior to the other models, especially in the 2-0 and 2-1 transfer tasks, with accuracies of 70.333% and 71%, respectively. This could be due to its fixed convolutional and fully connected layer structure, which lacks the flexibility needed for complex bearing fault signals and struggles with effective domain alignment and feature adaptation, despite incorporating domain adaptation methods. Additionally, the high-speed Condition 2 (1000 rpm) introduces more high-frequency noise and complex features, interfering with Vgg16's ability to extract accurate fault information, thereby impacting its performance in the target domain. These insights highlight the necessity of using more adaptable and robust models, like KAResNet, for effectively managing the intricate dynamics present in bearing fault diagnosis tasks under varying operational conditions.