esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 3 Prevention of DFA
### Differential Distribution Table
The results show that one-dimensional structure cannot learn the distribution of the S-box well, and there will be problems such as mode collapse and gradient disappearance. We realized that the S-box is more like a 16 \(\times\) 16 single channel picture. Therefore, the convolution dimension of the generator and discriminator is selected as two-dimensional. The training times of the generator and the discriminator in a single iteration are modified to balance their capabilities. The outputs of the generator were changed into integers by data processing and modified the structure of the discriminator. Because the JS divergence between integers could not be calculated, it will be changed to the Wasserstein distance. Mode collapse may be caused by the few training samples. To solve this problem, in the generating training samples stage, the number of training samples is significantly increased by removing the restriction of the fixed points parameter of the samples. Although this reduces the quality of the samples, we believe that GAN has low sensitivity to fixed points and this will not affect its training effect. We can limit and screen the fixed points parameter of GAN output results to improve their quality. The model parameters are shown in Table 1. In training, a 100-dimensional noise is used as the input of the generator. The output of the generator and the S-box of the data set are used as the input of the discriminator, as shown in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item 1** & **Item 2** \\ \hline Convolution layers of generator/discriminator & 3/3 \\ Filter, kernel and step of generator & 128, 2, 2/64, 2, 2/1, 2, 2 \\ Filter, kernel and step of discriminator & 64, 3, 2/128, 3, 2/256, 3, 2 \\ Lr of generator/discriminator & 0.0001/0.0001 \\ Optimizer of generator & SGD(lr = 0.0001, momentum = 0.0, decay = 0.0, \\  & \
\begin{tabular}{c} \text{resetrov = False} \\ \end{tabular} \\ Optimizer of discriminator & SGD(lr = 0.0001, momentum = 0.0, decay = 0.0, \\  &
\begin{tabular}{c} \text{resetrov = False} \\ \end{tabular} \\ Iterations of generator in single round epoch & 3 \\ Iterations of discriminator in single epoch & 1 \\ BatchSize & 64 \\ Epoch & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WGAN-GP model parameters. Figure 3: WGAN-GP structure. The training of GAN does not participate in the encryption and decryption process, and its training is independent. With the increase of iteration, GAN reaches Nash-equilibrium and outputs S-box with good performance. At this time, we stop the training and save the network structure. When there is a need to replace the S-box, a batch of S-boxes are generated by using the saved GAN and the usable S-boxes are selected for replacement after detecting some parameters. This process will not affect the encryption and decryption process.
## 3 Application to Total Hip Arthroplasty with UHMWPE Liner
For all considered geometries, the generated mesh consisted of four-noded axisymmetric quadrilateral elements with a minimum element size of 0.05 mm at the contacting interfaces. In case of a 12 mm head with an 8 mm liner, this resulted in approximately 27,000 elements for the head and 40,000 elements for the liner. Using approximately twice as many elements yielded an increase of less than 1% in normal force, contact angle and maximum contact pressure. The loading was applied by prescribing a displacement to the cross section of the lower hemisphere of the head. It is assumed that the plastic liner is bonded to a much stiffer metal backing. Thus, the displacements at the outside of the liner were fixed [18; 23]. In agreement with the low coefficient of friction measured in the lubricated contact in THA (\(\mu\approx 0.06\)[13]), the contact was assumed to be frictionless.
## 2 The Electro-Optical Trap
### Loading Ions into the Electro-Optical Trap  
In typical ion trapping experiments, ions are produced by ionizing atoms from a hot vapor created by a small oven. Therefore, the depth of the trapping potential must be adequate (100 s of kelvins) so that a hot ion could be confined and then laser-cooled. In an electro-optical trap, however, the depth is limited mainly by the optical component of the potential, the depth of which is typically well below 1 K. For this reason, a different strategy for loading ions in an electro-optical trap must be devised. One possibility could be to photo-ionize atoms that have already been laser-cooled in a magneto-optical trap (MOT) [18; 19]. However, since we are planning to use Ba\({}^{+}\) ions, this solution is not practical as a Ba MOT requires three infrared repumping lights [20]. Our plan, instead, is to use a Paul trap as a first trapping stage in which the hot ions are trapped and laser-cooled and then transferred from the  
Figure 2: Electro-optical trap “stability diagram”. The shaded region shows all the values \(\{P,\phi\}\) for which the trapping frequencies in the three directions are simultaneously positive, thus indicating a stable trapping potential. The laser wavelength, the beam waists, and the crossing angle chosen for trapping Ba\({}^{+}\) are \(\lambda=451.7\,\text{nm}\), \(w_{0x}=w_{0y}=40\,\upmu\)m, and \(2\theta_{c}=10\,^{\circ}\), respectively. The red line indicates the parameters \(\{P_{\text{iso}},\phi_{\text{iso}}\}\) for which the confinement is equal in each direction. The region above this line corresponds to a disk-like-shaped potential.  
Paul trap to the EO trap. This strategy has the advantage of using standard ion trapping techniques for producing ions at the Doppler temperature, for which the EO trap potential is deep enough to ensure a confinement of the particles. In order to facilitate the swapping from the Paul trap to the EO trap, the shape of the two trapping potentials should match as much as possible. This request sets a number of constraints on the shape and size of the electrodes and on the method for creating the deep optical lattice.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model
#### 4.1.1 Noise Filtering Module  
Since our system works upon application layer, the input of this model \(I_{n}(v_{i},v_{j})\) is expected to contain noises brought by the application, e.g., garbage collection or thread scheduling process. This module is used to preprocess the input to eliminate the impact of noises. For different scenarios, different implementations of noise filtering modules can be implemented, e.g., removing a part of highest numbers from collected RTTs. Algorithm 1 shows an implementation of noise filtering process. We denote the function of this process as follows,  
\[NF:\mathbb{R}^{N_{w}}\rightarrow\mathbb{R}^{[N_{w}\times(1-F_{S})]} \tag{4}\]  
Then the output \(\widehat{I_{n}}(v_{i},v_{j})=NF(I_{n}(v_{i},v_{j}))\) will be used in next steps.  
```
Input: a vector of collected RTTs denoted by \(Rts\), \(Rts\in\mathbb{R}^{N_{w}}_{+}\) Initialize: \(\mathcal{R}_{f}\gets Rts\) \(k\leftarrow[N_{w}\times F_{S}]\) \(i\gets 0\) while\(i<k\)do \(m_{v}\leftarrow\max\mathcal{R}_{f}\) \(i_{m}\gets FindFirst(\mathcal{R}_{f},m_{v})\) \(\mathcal{R}_{f}\gets RemoveAt(\mathcal{R}_{f},i_{m})\) \(i\gets i+1\) Output:\(\mathcal{R}_{f}\)
```  
**Algorithm 1**Simple noise filtering algorithm.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Cargo Port Drayage
#### 4.1.6 Dynamics  
Figure 7 describes the dynamic aspects of the Secure Cargo Port Drayage pattern using an activity diagram for the following use case.  
_Use Case: Drop-off Export Container:_  
Summary: a truck requests entrance to the maritime cargo port. GateAccess verifies the identity of the driver, the truck, and its load. GateAccess also verifies that the driver's transaction is a legitimate one. The driver obtains the location and proceeds to drop the container off in the storage yard.  
Actors: TruckDriver, GateAccess.  
Precondition: The drayage operation information has been previously entered into the system.  
Description:  
TruckDriver arrives at the terminal entrance.  
GateAccess authenticates the driver.  
GateAccess authenticates the truck.  
GateAccess authenticates the container load.  
Figure 6: Class Diagram of Cargo Port Drayage.  
Postcondition: A container has been deposited in the port, and the transaction is recorded.  
Other typical use cases include picking up import containers, assigning locations to the containers, and assigning drivers to the trucks. Typical roles include gate attendant, storage yard supervisor, storage yard worker, truck driver, and gate access worker.
## 3 Materials and Methods
### Overall Research Workflow
The FL architecture is depicted in Figure 6, and a comprehensive explanation of FL is provided in Section 3.5. FedAvg (Algorithm 1) served as the aggregation method, and the FL hyperparameters are detailed in Table 2 (refer to Section 4.2.2). * **FAT with FGSM Samples:*
* This step encompassed various experimental scenarios, exploring distinct client settings, dataset distributions, and the number of attacks. Scenario names included fl-fgsm-25, fl-fgsm-50, fl-fgsm-75, fl-mixed-fgsm, and fat-fgsm (refer to Table 3 for clarity). * FGSM adversarial samples were employed as a dataset for conducting attacks. The attacking process is illustrated in Figure 7 (Section 4.3). A comprehensive presentation of results and discussions for this step is available in Section 4.4.2. * **FAT with PGD Samples:*
* Similar to the previous step, this step utilized PGD adversarial samples for attack scenarios. The scenario names encompassed fl-pgd-25, fl-pgd-50, fl-pgd-75, fl-mixed-pgd, and fat-pgd (refer to Table 3 for clarity). * The comprehensive results and discussions pertaining to this step are provided in Section 4.4.3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Communication Round & 30 \\ Aggregation & FedAvg \\ Client’s Local Epoch & 35 if server round \textless{} 6 else 15 \\ Client’s Batch Size & 16 \\ Client’s Early Stopping Patience & 7 \\ Client’s Early Stopping Monitor & Validation Loss \\ Client’s Model Checkpoint Monitor & Validation Loss \\ Client’s Model Checkpoint Trigger & Minimum \\ Client’s Save Best Weight Only & True \\ Client’s Loss Function & Categorical Crossentropy \\ Client’s Optimizer & ADAM \\ Client’s Learning Rate & 0.0001 (\(1\times 10^{-4}\)) \\ Number of Clients & 4 \\ Client Selection & All \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameter settings in normal FL and FAT. \begin{table}
\begin{tabular}{c c c} \hline \hline  
Figure 5: Model architecture. Figure 6: Federated learning architecture in general. Figure 7: Attack scenario in federated learning.
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 1 Introduction
Although a single-camera HS-3D-DIC effectively avoids the high cost and precise synchronization issues, in turn, it introduces two new problems: (1) the resolution of each view is reduced in half due to the scene splitting; and (2) the optical arrangement of a single-camera HS-3D-DIC is relatively complicated and the imaging parameter becomes hard to adjust. This work developed a high-speed-2D-DIC (HS-2D-DIC)-based full-field vibration measurement method. The method uses measured in-plane virtual strains to sense the out-of-plane displacements, which inherits the advantages of a single-camera HS-3D-DIC and overcomes its remaining problems. In the remainder of this manuscript, the principle of the HS-2D-DIC-based full-field vibration measurement method is first introduced. Then, validation experiments are carried out, including in-plane and out-of-plane translation tests, out-of-plane displacement sensitivity assessment, and vibration frequency measurement. Finally, the movements of a rectangular cantilever aluminum panel under the impulse load were measured, and the panel's natural frequencies and mode shapes were successfully estimated.
## 6 V2X Testbed Setup and Algorithm Implementation
### Algorithm Implementation
#### 6.2.1 The CAMINO Framework  
The CAMINO framework [16] running inside NUCs serves as the central system for managing V2X communication technologies and services. It can seamlessly incorporate with present and forthcoming V2X technologies, new sensing elements, Human Machine Interface (HMIs), and external service providers. It can integrate with both current and future short and long-range V2X technologies such as DSRC, C-V2X PC5, and C-V2X Uu (5G/4G), as well as vehicle or roadside infrastructure sensors, actuators, Human Machine Interface (HMIs), and third-party service providers. Currently, the CAMINO-Core supports multiple ITS applications and provides basic implementation of Cooperative Awareness (CA), Decentralized Environmental Notification (DEN) and In-Vehicle Information (IVI). This allows sending and receiving CAM, DENM and In-Vehicle Information Message (IVIM). Prior to this study, V2X technology selection in CAMINO platform is based on the configuration settings without intelligence support. The code of CAMINO is modified in order to support intelligent hybrid V2X technology selection, which is described in detail in Section 5.
## 3 Multi-Sensor Fusion
### Fusion of Horizontal Lidar and Vertical Lidar
#### 3.3.1 External Parameter Calibration of Horizontal Lidar and Vertical Lidar  
Assuming that \(\mathbf{L}_{1}\) is the horizontal lidar coordinate system and \(\mathbf{L}_{2}\) is the vertical lidar coordinate system, the horizontal lidar coordinate system is used as the frame coordinate system and the vertical lidar coordinate system \(\mathbf{L}_{2}\) is registered in this frame coordinate system \(\mathbf{L}_{1}\) through the rotation matrix \(\mathbb{R}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}\) and translation matrix \(\mathbf{H}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}\), which can be computed by Equation (5):  
\[\mathbf{L}_{1}=\mathbb{R}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}\times\mathbf{L}_{2}+\mathbf{H}_{\mathbf{L} _{2}}^{\mathbf{L}_{1}} \tag{5}\]  
where \(\mathbb{R}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}\) is the rotation matrix and \(\mathbf{H}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}\) is the translation matrix which can be obtained by external parameters. From the hardware structure of the scanning arm, the vertical lidar coordinate system first rotates around the \(\mathbf{y}\)-axis and then translates to the horizontal lidar coordinate system, where  
\[\mathbb{R}_{\mathbf{L}_{2}}^{\mathbf{L}_{1}}=\left[\begin{array}{ccc}\cos\mathbf{\theta }&0&\sin\mathbf{\theta}\\ 0&1&0\\ -\sin\mathbf{\theta}&0&\cos\mathbf{\theta}\end{array}\right] \tag{6}\]  
Figure 2: An overview of our proposed framework.  
where the angle of the rotation matrix \(\theta=-77.94^{\circ}\) and the translation matrix \(\mathbf{H}_{L_{2}}^{L_{1}}=(x,0,z)\), with \(x=-0.177\) m and \(z=-0.213\) m.  
The point cloud of the lidar after external parameter calibration in the frame coordinate system is shown in Figure 3, with the point clouds of the vertical and horizontal lidars not affecting each other.
## 3 Experimental Results
### Performance Comparisons
Figure 6 shows another example of detected face areas with various merging thresholds, while the minimum and maximum sizes of the detectable object are 20 \(\times\) 20 and 85 \(\times\) 85, respectively. By observing Figure 5(a), all face areas can be successfully detected when the merging threshold is set to unity. However, many segmented blocks without the human face have also been classified into face blocks in the VJ algorithm [5]. If the merging threshold is selected to two, many non-human blocks can be excluded, as shown in Figure 5(b). Only one face cannot be successfully detected. However, a few segmented blocks without the human face are still identified as face blocks. If the merging threshold is set to five, the detected results are shown in Figure 5(c). The red bounding box highlights the differences among Figure 4(a)-c. One can find that all of the segmented blocks without the human face are excluded. However, the VJ algorithm cannot successfully detect one face block on the left-hand side of the photo cannot be successfully detected. This result is due to the value of the merging threshold being set too high.
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 2 Shear Strength Equations
### British Standard Code  
In BS 8110, it is assumed that the critical section is estimated as 1.5d away from the column face in the concrete flat plates without shear reinforcement. The shear reinforcement is placed around the critical section, which increases by 0.75d. Figure 2b shows the critical section suggested by BS 8110. The shear strength equation is determined by the sum of the shear strength of the concrete and shear reinforcement (Equation (1)). The shear strength of the concrete is described by considering the compressive strength of the concrete, the critical section, the effective depth of the slab and the tension reinforcement ratio as shown in Equation (4). [PERSON] and [PERSON] [16] reported that the concrete strength was overestimated in concrete slabs using high strength concrete. The concrete  
Figure 2: Critical sections provided by ACI 318-19, BS 8110–97 and Eurocode 2: (a) ACI 318–19; (b) BS 8110–97; (c) Eurocode 2.  
compression strength is represented as a square root. BS 8110 assesses the concrete shear strength as the value of the cubic root of the concrete compressive strength. It considers the size effect attributed to the effective depth of the slabs and the tensile reinforcement ratio. Equation (5) describes the shear strength of the GFRP vertical grids, which can be computed by calculating cross-sectional areas of the shear reinforcement.  
\[V_{c,BS}=\frac{0.79}{\gamma_{m}}(100\cdot(\frac{A_{s}}{b_{v}\cdot d})^{1/3}.( \frac{400}{d})^{1/4})b_{0}d. \tag{4}\]  
\[V_{f,BS}=0.95\sum A_{f}*f_{fu} \tag{5}\]  
where \(V_{c,BS}\) is the nominal shear strength provided by the concrete (BS 8110-97) (kN) and \(V_{f,BS}\) is the nominal shear strength provided by the GFRP vertical grid (BS 8110-97) (kN).
## 2 Materials and Methods
### Analytical Methods and Data Interpretation  
Sugar composition (including glucose, xylose, and arabin), inhibitor concentrations (furfurfural and 5-hydroxymethylfurfural, HMF), and organic acids were analyzed by high-performance liquid chromatography (HPLC) on a Shimadzu HPLC system (LC-2050C, Shimadzu Europa Analytical Instruments, Kyoto, Japan) equipped with a refractive index detector (RID-20A, Shimadzu RID 20A, Kyoto, Japan) coupled to a UV detector (214 nm). For the analysis of sugars, a Rezex chromatographic column (RHM-Monosaccharide H+, Phenomenex, Aschaffenburg, Germany) was used at a column temperature of 60 \({}^{\circ}\)C and a flow rate of 0.6 mL/min of eluent (5 mM H\({}_{2}\)SO\({}_{4}\)).  
For the analysis of organic acids and inhibitors, an Aminex HPX-87H chromatographic column (Bio Rad, Hercules, CA, USA) was used, with the column temperature maintained at 75 \({}^{\circ}\)C and the eluent consisting of 25 mM H\({}_{2}\)SO\({}_{4}\) at a flow rate of 0.7 mL/min. The H\({}_{2}\) content of the gas was determined using gas chromatography with an Agilent GC system (8860-GC, Agilent, Wilmington, DE, USA) equipped with a thermal conductivity detector and a packed column (28.0 \(\times\) 30.5 \(\times\) 16.5 cm). This analytical method was also capable of detecting and quantifying carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), and hydrogen sulfide (H\({}_{2}\)S), if present.  
Total organic carbon (TOC) and total nitrogen (TN) were determined using a Shimadzu total organic carbon analyzer (TOC-Vcsh, Shimadzu, Kyoto, Japan). Total solids (TS), volatile solids (VS), and COD were determined according to standard methods [24]. Total and reducing sugars were quantified using the phenol-sulfur method and the dinitro-salicylic acid (DNS) method, respectively [25; 26].  
The microbial community structure was analyzed during the steady state of the fermentation reactor. DNA extraction was carried out using the FastDNATM SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer's protocol. The extracted DNA samples were sent to Novogene (Cambridge, UK) for 165 rRNA gene amplicon sequencing. Specifically, the V4-V5 region of the 165 rRNA gene was amplified by PCR using the primer pair 515F/907R. PCR products of the correct size were selected via agarose gel electrophoresis. Equal volumes of these PCR products from each sample were pooled, end-repaired, A-tailed, and ligated with Illumina adapters. Library quantification was performed using Qubit and real-time PCR, and the size distribution was assessed using a bioanalyzer. The quantified libraries were then pooled and sequenced on an Illumina paired-end platform. Amplicon Sequence Variants (ASVs) were generated using the DADA2 pipeline implemented within the QIIME 2 software (Version 202202) package and annotated with the Silva 138.1 database.  
Finally, a comparative analysis of H\({}_{2}\) productivity at different concentrations of FeNPs was performed using a one-way ANOVA conducted with STATGRAPHICS Centurion XVI, version 16.1.03.
## 3 Related Work
### Underwater Monitoring
#### Underwater Acoustic Sensor Networks
Each U-sensor sends its data to the GW or the sink according to its next hop information. GWs send collected sensing data to the AUV when they become close enough for high rate data transmission. For example, the AUV receives from \(GW1\) the data generated by _US1_, _US3_, and _US4_. The AUV forwards the received data from GWs to the sink. Note that _US5_ directly transmits its data to the sink according to is next hop information. Now, we describe the algorithms of each network element in more detail.
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 4 Conclusions  
The application of ultra-high-performance concrete for conventional steel rebar has a great effect on the improvement of the bond-slip behavior. The excellent surface bonding with minimized porosity on the steel rebar caused a significant change in the de-bonding mechanism before and after the maximum load. Based on the results of the experimental study, we proposed reasonable prediction models of the monotonic bond performance by applying existing models. The following conclusions can be drawn:  
1. The thin layer of UHPC coating on the surface was strongly attached to the reinforcing bar and bond failure due to slippage was observed at the UHPC-OPC interface (interface 2) by inducing the integral behavior;
2. The UHPC-coated reinforcing bar significantly improved the bond strength and pullout performance by forming a new interface, while the addition of steel fibers improved the overall performance of the adhesion-slip behavior. Among the fiber types, the crimped steel fiber had the highest effect on the bond performance;
3. The bond failure modes were sensitive to the thin coating of UHPC, creating an additional interface between the steel rebar and concrete matrix. Most of specimens had high residual bond stresses compared to the control specimen. For the 0.8% and 1.0% fiber volumes in both CSF and CSF specimens, the strain hardening of the steel rebar against the pullout load was created, which delayed the bonding failure mechanism;
4. The prediction models were proposed by analyzing existing models (BPE, CMR). The CMR model fitted the ascending branches well compared to the BPE model before the maximum load. After the maximum load, a prediction model for the residual load was proposed by modifying the existing BPE model through a comparison with the experimental data.  
B.K., I-H.E. and [PERSON] conceived and designed the experiments. [PERSON] and B.K. performed the experiments. [PERSON], B.K. and [PERSON] analyzed the data. B.K., [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Seoul National University of Science and Technology.  
Not applicable.  
The authors declare no conflict of interest.