esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
###### Abstract  
The ear is an effective biometric feature for personal identification. Although numerous studies have attempted personal identification using frontal-view images of the ear, only a few have attempted personal identification using multi-angle-captured ear images. To expand the extant literature and facilitate future biometric authentication technologies, we explore the feasibility of personal identification using multidirectionally captured ear images and attempted to identify the direction-independent feature points that contribute to the identification process. First, we construct a convolutional neural network model for personal identification based on multi-angle-captured ear images, after which we conduct identification experiments. We obtained high identification accuracies, exceeding 0.980 for all the evaluation metrics, confirming the feasibility of personal identification using multi-angle-captured ear images. Further, we performed Gradient-weighted Class Activation Mapping to visualize the feature points that contribute to the identification process, identifying the helix region of the ear as a key feature point. Notably, the contribution ratios for ear images in which the inner ear was visible and not visible are 97.5% and 56.0%, respectively. These findings indicate the feasibility of implementing personal identification using multi-angle-captured ear images for applications, such as surveillance and access control systems. These findings will promote the development of future biometric authentication technologies.  
ear-based personal identification; multi-angle ear image; convolutional neural network; Grad-CAM; biometric authentication Article
## 4 Data Preprocessing
### Learning Data Generation  
This subsection describes the process to convert the above-mentioned application layer payload data into the learning data suitable for deep learning models.  
The overall learning data for each application are generated by arbitrarily extracting packets of eight applications (RDP, SSH, Skype, BitTorrent, Facebook, Wikipedia, Google, and Yahoo) from the application layer payload data. In each application, random flow's packets were extracted. All bits in the payload data of a packet are divided by 4 bits and grouped into one pixel of imaged data. Therefore, one pixel of the imaged data represents the decimal numbers 0 (=0x0000) to 15 (=0x1111). According to the pre-defined image size values, 36 (= 6 \(\times\) 6), 64 (= 8 \(\times\) 8), 256 (= 16 \(\times\) 16), and 1024 (= 32 \(\times\) 32), the pixels of one image data are taken from the beginning of each packet, and the size of one image data is readjusted to 36, 64, 256, and 1024 pixels. Figure 2 shows the case of the imaged packet data of 256 pixels for an arbitrary packet of each application. If the extracted payload size is smaller than the pre-defined size, the image is adjusted by zero-padding to match the pre-defined size.  
The application layer payload data is converted into the flow-based dataset. In this study, a network flow is defined as a unidirectional sequence of packets between two endpoints with five common attributes as well as the source IP/port, destination IP/port, and protocol. The flow-based learning dataset is also arbitrarily selected for each application in the application layer payload data. One selected flow-based data consists of a series of the first N packets in the corresponding flow. The number N is set to 30, 60, and 100. Each of the packets is pre-processed in the same manner as previously described and converted into imaged packet data. For each of the eight applications, we extract 2000 flows from the application layer payload data, and the flow-based learning dataset has a total of 16,000 flows.  
Lastly, for target data of eight applications, each label is expressed as a one-hot vector with eight lengths. A one-hot vector is a 1 \(\times\) 8 matrix with all 0s and a single 1 used to distinguish the label representing an application.
## 4 Results and Analysis
### The Spatial-Temporal Characteristics of Wetland Changes
From the early 1990s to 2004, the decrease of swamps was mainly concentrated in Maduo County in the Yellow River source region, Yushu, Qumalai and Zhiduo counties in the Yangtze River source region, as well as Zaduo County in the Langcang River source region, while the decrease of lakes, reservoirs & ponds mainly occurred in Maduo County in the Yellow River source region; and the expansion of lakes, reservoirs & ponds mainly occurred in western parts of Zhiduo County and northern parts of Tanggulashan County. From 2004 to 2012, the changes (including expansion and shrinkage) of all kinds of wetlands was concentrated in Maduo County in the Yellow River source region, the western parts of Zhiduo County and the northern parts of Tanggulashan county. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wetland category**} & \multicolumn{2}{c}{**The early 1990s–2004**} & \multicolumn{2}{c}{**2004–2012**} & \multicolumn{2}{c}{**The early 1990s–2012**} \\ \cline{2-7}  & **Area (km\({}^{2}\))** & **Percent (\%)** & **Area (km\({}^{2}\))** & **Percent (\%)** & **Area (km\({}^{2}\))** & **Percent (\%)** \\ \hline Streams and rivers & –8.6 & –0.1 & –29.8 & –0.4 & –38.3 & –0.5 \\ Lakes & 68.9 & 1.1 & 486.6 & 7.3 & 555.6 & 8.4 \\ Reservoirs and ponds & 11.6 & 75.3 & 0.4 & 1.5 & 12.0 & 77.9 \\ Swamps & –242.1 & –3.0 & –26.6 & –0.3 & –268.6 & –3.3 \\ Total wetlands & –170.1 & –0.8 & 430.7 & 2.0 & 260.6 & 1.2 \\ \hline \end{tabular} Note: The percent of each wetland category is the proportion of its area in total wetland. The percent of total wetland is the proportion of total wetland in total land.  
\end{table} 
Table 3: The area changes of different wetland categories from the early 1990s to 2012.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 2 System Model and Problem Formulation
### Computing and Communication Model
#### 2.2.1 Computation Model  
The training delay of user \(n\) in one iteration of FL is  
\[T_{n}^{comp}=\frac{I_{l}C_{n}D_{n}}{f_{n}},~{}\forall n\in\mathcal{N}_{m} \tag{5}\]  
where \(D_{n}\) is the data volume of user \(n\) and \(f_{n}\) denotes the CPU frequency of user \(n\), which is measured by the number of CPU cycles per second; \(C_{n}\) is the number of CPU cycles required for computing one sample data at user \(n\); and \(I_{l}\) is the local training rounds for each global round at user \(n\).  
According to [10], when the required local accuracy reaches \(\eta\), the \(I_{l}\) needs to satisfy the condition: \(I_{l}\geq\frac{2}{(2-L)\delta\gamma}\log\Big{(}\frac{1}{\eta}\Big{)}\), so \(I_{l}=\frac{2}{(2-L)\delta\gamma}\log\Big{(}\frac{1}{\eta}\Big{)}\), where \(L\), \(\gamma\) is the parameter associated with the loss function. The step must satisfy \(\delta<\frac{2}{L}\). Therefore, the training energy consumption of user \(n\) in each global round is  
\[E_{n}^{comp}=\kappa I_{l}C_{n}D_{n}f_{n}^{2},~{}\forall n\in\mathcal{N}_{m} \tag{6}\]where \(\kappa\) is the effective switched capacitance.
# Porcine Parvovirus 7: Evolutionary Dynamics and Identification of Epitopes toward Vaccine Design
## 3 Results
### CD8 T Cell Epitopes Prediction and Interaction Study of Predicted Peptides with SLA Alleles  
A total of 12 peptides from the Cap were predicted as CD8 T cell epitopes, whose antigenicities were evaluated with a Vaxijen v2.0 sever (Table S4). The 3D structures of all MHC class-I peptides were modelled via PEP-FOLD3, and a best model for each peptide was used for the subsequent molecular docking with SLA proteins. Among all 12 peptides, 3 peptides were docked to MHC class-I SLA-2*04:02, whereas 6 peptides were docked to MHC class-I SLA-3*04:01. All 9 peptides had high binding affinities. For the 3 peptide-SLA-2*04:02 molecular docking, the binding efficiency of each epitope was evaluated by the global and vdW energies, computed to range from \(-\)23.20 to \(-\)54.08 kcal/mol and \(-\)22.21 to \(-\)30.77 kcal/mol, respectively (Table 4). All 3 peptides were predicted to be able to be docked into the groove of the SLA-2*04:02 molecule and form stable hydrogen  
Figure 4: Sequence alignment of predicted PPV7 and identified PPV1 linear B cell epitopes from Cap. Blue lines represent predicted PPV7 B cell epitopes and green lines represent identified PPV1 B cell epitopes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epitope** & **Start** & **End** & **Sequence** & **Length** & **Identity** & **Vaxijen v2.0** \\  & & & & & **(59 Isolates)** & **Score** \\ \hline A & 111 & 118 & YETCyHNW & 8 & 72.88\% & 0.5888 \\ B & 135 & 150 & LVKPPTIATECBVGNS & 16 & 18.64\% & 0.4355 \\ C & 216 & 224 & IQELRPGKN & 9 & 98.31\% & 0.7908 \\ D & 255 & 272 & ESGYSHNINIREKKYQGPFG & 18 & 25.42\% & 0.6235 \\ E & 385 & 391 & KRRSRMF & 7 & 100.00\% & 1.6044 \\ F & 419 & 445 & TCGARRSWQARTRDTDRKEPQQPWVQW & 27 & 10.17\% & 0.4966 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linear B cell epitopes prediction of PPV7 Cap using BepiPred 2.0.  
bonds with the residues (within 3.1A) in the groove of the SLA. Furthermore, Asn66 and Asn70 residues from the SLA groove were most abundantly involved in bonding with various peptides (Figure 5A and Figure S5A). In addition, global and vdW energies of the 6 peptide-SLA-3*04:01 dockings ranged from \(-\)17.93 to \(-\)44.82 kcal/mol and \(-\)18.96 to \(-\)26.27 kcal/mol, respectively (Table 4). Of these 6 peptides, hydrogen bonds \(<\)3A were frequently observed in 5 docking complexes to form stable complexes (Figure 5B and Figure S5B). However, the peptide (TAPETNWTW) docked into the groove of SLA-3*04:01 and non-formed hydrogen bonds, whereas the major hydrophobic component interacted with SLA. Notably, 2 overlapped peptides (KRRSRMFAP and RRSRMFAPT) had higher immunogenicity scores (1.2953 and 0.9148), and were strictly conserved in all 59 PPV7 Cap sequences (Table 4).  
Figure 5: Molecular interaction analysis of PPV7 peptides (shown as red) to SLA-2*0402 (**A**) and SLA-3*0401 (**B**) protein (shown as gray). Interacting residues are shown and are consistent with the information in Table 4.
# Wireless and Fiber-Based Post-Quantum-Cryptography-Secured IPsec Tunnel
## 6 Results
### IPsec Tunnel  
Using the iperf traffic generator, we analyze the performance of the north-south IPsec tunnel that we set up between the Jetson as a wireless device and the 25 G DPU in the cloud, shown in Figure 7. We achieve an AES-256 GCM encrypted wireless throughput of 0.486 Gbit/s. As this scenario emulates a mobile device communicating via the cloud, the signal travels through a chain of multiple hops. We therefore do not set the maximum transmission unit (MTU) because every device in the chain between the mobile device and the 25G DPU in the cloud can modify the MTU size.  
In our intra-data-center east-west traffic scenario, however, we do have control over the MTU size. Thus, after we set up the east-west IPsec tunnel from DPU to DPU, we characterize the tunnel's throughput with different MTU sizes using the VIAVI traffic generator. The results can be seen in Figure 17. The original plots generated by VIAVI for each MTU can be seen in Figure 18. With small packets, 64 B MTU, we achieve an encrypted throughput of 34 Gbit/s. This can be seen in Figure 18a. Doubling the MTU to 128 B yields an encrypted throughput of 58 Gbit/s, as shown in Figure 18b. Setting the MTU to 256 B in Figure 18c results in an encrypted throughput of 73 Gbit/s. In Figure 18d, at 512 B MTU, we obtain an encrypted throughput of 95 Gbit/s. Ultimately, in Figure 18e,f, starting from 1024 B MTU, we observe an encrypted throughput of 100 Gbit/s line rate. For all MTU sizes that are bigger than, and including 1024 B, the encrypted throughput converges towards 100 Gbit/s. That holds true for jumbo-sized packets due to the fact that the on-board hardware accelerators of the DPU support only operation up to 100 Gbit/s.  
Figure 16: Falcon’s and Dilithium’s main steps (key generation, verification, and sign) and Kyber’s main steps (key generation, key encapsulation, and key decapsulation) executed on an Intel Xeon processor.
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication
### Neighbor Discovery Block  
On network bootstrapping or with the introduction of every new node, the Neighbor Discovery Block is activated. In this phase, every node signs and broadcasts a one-hop hello message to introduce itself to its neighboring nodes. The hello message have the following format--{Message Type = 1 [one byte], Sender ID [one byte], MAC [x bytes]} as shown in Figure 1.  
Note that x is chosen in the Initialization Block by the administrator based on the network security requirements. Upon receipt of the hello message, each node within radio range, checks the authenticity  
Figure 1: Different Message Formats.  
of the packet, if applicable, by verifying its MAC using the sender's public key (node ID) as the verification key for PBC, or using the symmetric key for RC5, as set by the administrator in the Initialization Block. If the packet is authentic, the receiver node adds the sender in its neighbors list and replies using a signed \"unicast\" hello-reply packet back to the sender in order to confirm the neighborhood between them. The hello reply message has the same format as the hello message with the Message Type = 2.  
Also, every fixed time, set and synchronized by the BS, all nodes broadcasts to its neighbors hello_keep_alive message. The period is set by the administrator in the Initialization Block, and it is chosen to be multiple of times the period of sending the activity reports. This hello-keep-alive message has the exact same format as a hello message with the Message Type = 3, and it is used to make sure that a node still exists in order to keep the BS updated with correct information. Upon receipt of a hello-keep-alive message, the receiving node just refreshes the status of its neighbors, without replying. Note that in case of an authentication scheme set, all types of hello messages must be signed and verified for trusted neighbors' identities.
## 3 System Model and Problem Formulation  
This section introduces the BWSAR system model architecture and problem formulation in detail. The UAV uses 5G-NR beam sweeping technologies for disaster area search and rescue processes to improve victim location efficiency and accuracy. On this basis, the UAV trajectory progress optimization problem with the shortest search and rescue time is established.
# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 3 Neural Network Modeling
### Correlation Analysis of Rolling Data  
In the actual production of rolled pieces, there are many production process parameters besides the rolling specification, such as the process parameters in the heating furnace area, including the discharge temperature, in furnace time, rolling rhythm, etc.; rough rolling area includes primary descaling, descaling between rough rolling passes, number of rough rolling passes and thickness of medium and thick billets; the finishing rolling area includes secondary descaling, final rolling temperature, descaling between stands, roll gap lubrication, load distribution, roll bending, rolling mileage, linear speed, etc. There are collinear variables in many rolling process parameters, and the number of neural network input variables will affect the effect and speed of model fitting, so these variables need to be eliminated before modeling. In this paper, the Adaptive-Lasso method [24] is selected for parameter estimation and variable selection. Lasso parameter estimation is defined as follows:  
\[\hat{\beta}^{*(n)}=\operatorname*{arg\,min}_{\beta}\left\|y-\sum_{j=1}^{p}x_{j }\beta_{j}\right\|^{2}+\lambda_{n}\sum_{j=1}^{p}\hat{\omega}|\beta_{j}| \tag{3}\]  
where the weight is \(\hat{\omega}=1/\left|\hat{\beta}_{j}\right|^{\gamma}(\gamma>0)\). \(\hat{\beta}_{j}\) is the coefficient obtained by the least square method.  
After preliminary screening by the Adaptive-Lasso method, according to the recommended requirements of rolling process on site, 7 items, such as width, thickness, rolling rhythm, intermediate slab thickness, final rolling temperature, furnace time, and rolling sequence in roll change cycle, are finally selected as neural network inputs, and the rolling stability score is taken as neural network output to build the neural network model of limit specification rolling. This is shown in Figure 2.
## 2 Materials  
### Aluminium Skins  
The skins of the sandwich panels were made from 0.8 mm thick low-strength, strain-hardened and cold-rolled aluminium sheets of the alloy EN AW 1050A-H14, manufactured by Norsk Hydro ASA. This alloy is considered commercially pure and is primarily intended for forming operations into wrought products. The nominal yield stress and tensile strength, given by the manufacturer, are 100 MPa and 116 MPa, respectively, while the chemical composition of the alloy is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Si** & **Fe** & **Cu** & **Mn** & **Mg** & **Zn** & **Ti** & **Al** \\ \hline
0.04 & 0.32 & 0.001 & 0.002 & 0.000 & 0.000 & 0.02 & Balance \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of AA1050-H14 (in wt. %).  
Plates with dimensions 625 mm \(\times\) 625 mm were received from the manufacturer. Dog-bone specimens having a gauge length 70 mm, width 12.5 mm and thickness 0.8 mm were cut from one of the plates, oriented at 0\({}^{\circ}\), 45\({}^{\circ}\) and 90\({}^{\circ}\) relative to the rolling direction of the material. A sketch of the specimen geometry can be found in [28]. Triplicate tests in each orientation were conducted, giving a total of nine tensile tests. Before testing, measurements of the width and thickness were taken across the gauge length of specimens. The tensile tests were performed in an Instron 5985 universal testing machine (Instron, Norwood, MA, USA) with a 250 kN load cell at an initial nominal strain rate of \(5\times 10^{-4}\) s\({}^{-1}\). The force was recorded by the load cell, while a digital camera oriented perpendicular to the specimen captured pictures at 1 Hz. Before testing the specimens were painted with a speckle pattern to enable two-dimensional digital image correlation (2D-DIC) to track displacements across the gauge area. Engineering strains were then calculated by a virtual extensometer with an initial length of 50 mm using the in-house DIC-code eCorr (v4.0, NTNU, Trondheim, Norway) [29].  
Engineering stress-strain curves until fracture from tensile tests in different material orientations are shown in Figure 1a. A slight anisotropy in flow stress is seen between the tests in the rolling direction (0\({}^{\circ}\)) and the tests angled 45\({}^{\circ}\) and 90\({}^{\circ}\) to the rolling direction. The angled tests have an increase in flow stress of about 5 % compared to the tests in the rolling direction of the plate. The elongation to fracture varies between specimen orientations and is more consistent in the 45\({}^{\circ}\) and 90\({}^{\circ}\) orientations than in the 0\({}^{\circ}\) direction. [PERSON] et al. [30] showed that the anisotropy of thin plates made of the aluminium alloy AA5083-H116 did not influence the response in low-velocity impact tests. Based on results like these, a representative test in the 0\({}^{\circ}\) direction was chosen and used to calibrate the constitutive relation and failure criterion applied in the numerical models. Details regarding the calibration process and the obtained material parameters are presented in Section 4.2.1.
## 4 Simulation Studies and Performance Assessment
### Control Effects Under SNN Fitting  
As shown in Figure 8, when the primary level only uses droop power allocation (i.e., when the power reference value in Formula (1) is 0), the system frequency varies with changes in the common load. Under the droop control method, the frequency falls below the rated value of 50 Hz and decreases further as the common load power increases. When the common load power changes, the frequency changes accordingly. This frequency deviation from the rated value is undesirable, as it prevents equipment from operating at the rated frequency, thereby reducing system efficiency.  
Figure 8: System performance under droop control. (**a**) frequency, (**b**) power sharing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Training Times**} & \multicolumn{4}{c}{**SNN**} & \multicolumn{4}{c}{**SNN with Input Enhancement**} \\ \cline{2-7}  & **Training Epochs** & **Gradient Values** & **RMSE** & **Training Epochs** & **Gradient Values** & **RMSE** \\ \hline
1 & 287 & \(6.60\times 10^{-8}\) & 3244.4896 & 267 & \(2.06\times 10^{-8}\) & 3244.4976 \\
2 & 291 & \(2.02\times 10^{-8}\) & 3244.4948 & 224 & \(6.87\times 10^{-8}\) & 3244.6135 \\
3 & 229 & \(9.26\times 10^{-8}\) & 3244.5632 & 230 & \(7.56\times 10^{-8}\) & 3244.5714 \\
4 & 241 & \(8.16\times 10^{-8}\) & 3244.4918 & 209 & \(8.70\times 10^{-8}\) & 3244.6388 \\
5 & 209 & \(8.26\times 10^{-8}\) & 3244.5627 & 226 & \(2.84\times 10^{-8}\) & 3244.5948 \\ Average & 251.4 & \(6.86\times 10^{-8}\) & 3244.5204 & 231.2 & \(5.61\times 10^{-8}\) & 3244.5832 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training performance comparison.  
The system frequency curve when using SNN curve fitting is shown in Figure 9. Specifically, the SNN forecasted power values are used as power reference values in formula (1). Compared to Figure 8, it can be clearly seen that the system frequency has returned to near its rate value. Through SNN curve fitting, the effects of common load changes have been effectively counteracted, maintaining system frequency stability. The amplitude of frequency fluctuations and power distribution unevenness are related to line impedance, with these impedance values shown in Table 2 corresponding to each DG connection.  
Analysis of the frequency operation curve shows that system frequency exhibits fluctuation characteristics when the common load changes, particularly with significant frequency fluctuations occurring after 3 s. These fluctuations mainly stem from two aspects: the deviation between SNN forecasted and actual values, and the deviation between peak values. Therefore, subsequent dynamic timing regularization is needed.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model
### Network Infrastructure
#### 3.1.2 Length of BI  
The length of the BI must ensure that all superframe durations (\(SD_{j}\)) can be scheduled during the BI. Therefore:  
\[BI\geq\sum_{j=1}^{N_{CH}}SD_{j} \tag{1}\]  
where \(BI\) is the beacon interval, \(SD\) is the superframe duration and \(N_{CH}\) is the number of Cluster-Heads (CHs). Nevertheless, higher BIs will highly increase the end-to-end communication delays. Thus, the BI should be carefully defined in order to prevent high end-to-end communication delays.  
Considering that a specific sensor node \(i\) is able to send messages during its cluster active period, and also that it generates a new message every period \(P_{i}\), the message periodicity of each node imposes a restriction upon the BI. However, as the message generation period of a MN is not synchronized with the beacon period, a new message may be generated after its active period. In that case, it will be sent just during the next cluster active period.  
Within this context, in order to guarantee that a message may be transferred before the next message generation, \(P_{i}\) must always be larger than the BI plus \(\delta\), where \(\delta\) is the maximum time required to transmit a message. Then, the following constraint must be satisfied:  
\[BI\leq min\{\mathbb{P}\}-\delta, \tag{2}\]  
From Equations (1) and (2), it follows that:  
\[\sum_{j=1}^{N_{CH}}SD_{j}\leq BI\leq min\{\mathbb{P}\}-\delta \tag{3}\]  
Therefore, Equation (3) represents a protocol constraint that must be guaranteed when setting up cluster-tree networks.