esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure
## 1 Introduction
If we use security metrics based on the overall network assessment and observe improved security, it could be misleading as the security improvement may be limited to certain parts of the network. As mentioned earlier, asymmetry in network attacks and defenses exists, particularly in large-scale networks. The location from which attackers launch their attacks is difficult to predict, and the scope of protection provided by defense strategies is often limited. In such cases, global metrics fail to clearly reflect the defensive performance. In other words, metrics can reflect the overall defensive performance but cannot pinpoint the exact location of changes in defensive information. Therefore, it is necessary to adopt a location-aware method. The main contributions of our work are summarized as follows. Firstly, to capture variations in attack-defense performance within specific local networks, we utilize a network partitioning method (i.e., Louvain algorithm) to segment the extensive network into smaller segments and perform correlation metrics on these segments to achieve a more fine-grained assessment. Subsequently, we establish relevant security metrics within the partitioned objective space for cyber mimic defense. These metrics encompass various quantitative measures such as the common-mode index and failure risk, which are tailored to accurately reflect the effectiveness of cyber mimic defense systems. This results in the creation of an innovative approach for effectively assessing cyber mimic defense deployed in complex networks. Finally, simulated attacks are carried out to verify the applicability and effectiveness of the proposed metrics. This paper is organized in the following way. In Section 2, we introduce the preliminaries; in Section 3, we give the network partitioning method; in Section 4, we define metrics in the constructed objective space; in Section 5, we perform simulation experiments and give results; in Section 6, we list related work; and in Section 7, we make a conclusion of our work and propose a vision for future development.
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
###### Abstract  
This paper proposes a field probe to measure the power density (PD) at a millimeter-wave (mmWave) frequency band. The proposed probe is composed of a loop antenna in which one end is terminated with a load resistor. Such a structure enables the simultaneous measurement of electric (E)- and magnetic (H)-fields: the E-field is measured at a gap where the load resistor is placed, and the H-field is measured through the loop antenna. The simultaneous measurement makes it possible to measure PD for an incident wave even in the near field region where the E- and H-fields have different phases from each other. The proposed probe is fabricated and evaluated for its PD measurement performance. The measurement results show that the probe measures PD with an error less than 1.3 dB. Owing to the near field measurement, the proposed probe is useful in measuring the human body exposure to electromagnetic fields (EMFs) that are generated by 5G mmWave base stations.  
radiofrequency exposure; 5G; base stations; millimeter-wave measurements +
Footnote †: journal: _aciences_
## 1 Introduction  
Tissue engineering and regenerative medicine is a field that involves replacing or regenerating human cells and tissues to restore or even improve the function of the human body [1]. Human mesenchymal stem cells (hMSCs) are derived from the bone marrow, and are often considered to be highly promising therapeutic agents in this field due to their self-renewal and multilineage differentiation capabilities [2]. The hMSCs are known to have the potential to differentiate into osteoblasts, chondrocytes, adipocytes, etc. [3; 4; 5; 6], but the control of their differentiation still remains to be resolved, which will be the key to successful tissue engineering and regenerative medicine in the future.  
The use of a non-thermal atmospheric pressure plasma jet (NTAPPJ) has drawn a great deal of interest from researchers, especially because of its possible applications in biological fields such as the treatment of biomaterial surfaces [7,8] and the direct treatment of both microorganisms [9,10] and living cells and tissue [11, 12, 13]. Therefore, NTAPPJ has advantages in terms of the production of electrons, ions, and free radicals below body temperature, as it ensures no thermal damage is done to the target [12,14].  
Recent studies have indicated that a certain duration of exposure to plasma may have bioactive effects, such as improved cell attachment and proliferation [13, 14, 15]. A study that considered the use of adipose-tissue-derived stem cells (ASCs) indicated that there was an increase in the proliferation of cells by NTAPPJ exposure, which did not affect their stemness [16]. In another study in which the effect of NTAPPJ exposure was considered on human mesenchymal stem cells isolated from periodontal ligaments (hPDL-MSCs), it was evident that the hPDL-MSCs possessed all of the typical MSC properties, including the capacity for multilineage differentiation [17]. Finally, in a study in which human peripheral blood mesenchymal stem cells (hPB-MSCs) were exposed to an NTAPPJ, the effects were variable in accordance with their distance from the plasma plume, where both dead cells and viable cells were evident [18]. Despite these few papers describing the investigation of the effects of NTAPPJs on stem cells, studies have been limited in terms of investigating the proliferation and multilineage potential rather than considering the direction of differentiation. Additionally, no study has investigated the effects of NTAPPJs on the differentiation of human mesenchymal stem cells.  
Hence, the aim of the study was to investigate the effects of an NTAPPJ on the differentiation of hMSCs for possible applications in the field of tissue engineering and regenerative medicine.
###### Abstract  
Purpose: This study aimed to determine the changes in lower extremity biomechanics during running-induced fatigue intervention. Methods: Fourteen male recreational runners were required to run at 3.33 m/s until they could no longer continue running. Ground reaction forces (GRFs) and marker trajectories were recorded intermittently every 2 min to quantify the impact forces and the lower extremity kinematics and kinetics during the fatiguing run. Blood lactate concentration (BLa) was also collected before and after running. Results: In comparison with the beginning of the run duration, (1) BLa significantly increased immediately after running, 4 min after running, and 9 min after running; (2) no changes were observed in vertical/anterior-posterior GRF and loading rates; (3) the hip joint range of motion (\(\theta_{\text{ROM}}\)) significantly increased at 33%, 67%, and 100% of the run duration, whereas \(\theta_{\text{ROM}}\) of the knee joint significantly increased at 67%; (4) no changes were observed in ankle joint kinematics and peak joint moment at the ankle, knee, and hip; and (5) vertical and ankle stiffness decreased at 67% and 100% of the run duration. Conclusion: GRF characteristics did not vary significantly throughout the fatiguing run. However, nonlinear adaptations in lower extremity kinematics and kinetics were observed. In particular, a \"soft landing\" strategy, achieved by an increased \(\theta_{\text{ROM}}\) at the hip and knee joints and a decreased vertical and ankle stiffness, was initiated from the mid-stage of a fatiguing run to potentially maintain similar impact forces.  
Keywords:fatiguing run; instrumented treadmill; joint range of motion; stiffness +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Improved Feature Extraction Algorithm  
This article proposes an improved feature extraction method for SAR images to resolve the question of pseudo-feature points in SAR-SIFT. The method consists of three main steps: Firstly, the Shi-Tomasi [31] response function is used to replace the SAR-Harris response function to overcome the problem of feature point extraction accuracy and quantity caused by the artificially set empirical threshold. Secondly, the Harris-Laplace algorithm and a multi-scale feature point screening method are employed to eliminate points that do not possess spatial scale invariance. At the same time, the SUSAN operator with strong noise resistance is applied to remove points affected by image noise. The STSU-SAR-SIFT strategy is illustrated in Figure 2.
# Towards Hierarchical Workflows in SysML to Support Virtual Validation of Technical Systems
## 7 Conclusions  
In order to efficiently validate whether a system's solutions fulfill their intended functionalities at the early development stage, this work provides a standardized method in the function-oriented system architecture to build well-organized virtual validation workflows. The workflows can support the system's virtual validation across all functional hierarchy levels. System engineers can carry out virtual validation of the necessary system by utilizing the workflow. This work covers the following core content: a nested structure for the virtual validation workflow is developed that corresponds to the functional hierarchy of the system. The decomposed solutions can be virtually validated at all hierarchy levels to satisfy the corresponding functions. The virtual testing workflows are arranged in parallel to each other as the criteria to provide evidence that the solution fulfills the corresponding function. Furthermore, this work presents a design approach for a standardized virtual testing workflow framework, which is grounded in domain model classification. The following key statements can be made: each domain model corresponds to a simulation activity. The simulation activity is designed modularly, which increases its reusability. Simulation activities are nested based on the system scope, model purpose, and model fidelity within the framework. In a virtual testing workflow, engineers execute the corresponding simulation activities in the framework step by step through an iterative cycle. One of the case studies of this work validates the WT system, focusing on the scope of \"Wind Turbine\" and its sub-systems \"Bearing\". The impact on the system caused by design changes is observed by performing the automated virtual validation workflows, and \"Bearing\" solutions can be easily reused in different system contexts.  
The contribution of this work is to develop automated virtual validation processes for complex technical systems, providing automated validation, early failure risk detection, and ease of redesign. The function-oriented hierarchical architecture allows for synchronous workflow creation, while the standardized design ensures function reusability. Modular design, based on model classification, improves design agility and adaptability in different system contexts. Future improvements involve the introduction of dependency detection mechanisms for arranging simulation sequences, extending workflows to support optimization algorithms, and automating the creation of workflow structures corresponding to functional architecture.  
Conceptualization, Y.Z., G.J., J.B. and G.H.; methodology, Y.Z., G.J., J.B. and G.H.; software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z., G.H. and J.B.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., J.B. and G.H.; supervision, G.J. and J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### Tomek Links  
Tomek Links (TL) [24] is another effective undersampling technique used for balancing the data. TLs are pairs of samples that are very close two each other, but they belong to different classes. These samples are contiguous to the borderline between classes. In mathematical language, given a pair of samples \(\big{(}\mathcal{S}_{i},\mathcal{S}_{j}\big{)}\) from the dataset, we say that there is a TL between the two samples if at least one of the two following inequalities is satisfied:  
\[\delta(\mathcal{S}_{\mathrm{i}}\mathcal{S}_{\mathrm{l}})<\ \delta\big{(} \mathcal{S}_{\mathrm{i}}\mathcal{S}_{\mathrm{j}}\big{)},\ \delta\big{(}\mathcal{S}_{\mathrm{j}}\mathcal{S}_{\mathrm{l}}\big{)}<\ \delta\big{(}\mathcal{S}_{\mathrm{i}}\mathcal{S}_{\mathrm{j}}\big{)} \tag{1}\]  
where \(\delta(x.y)\) is the distance between \(x\) and \(y\)[54].  
Generally, one of the two samples that form a TL is considered a noisy sample, or the two samples together are considered borderline [4]. In this case, by eliminating the samples of the majority class that belong to the pairs forming TLs, the distance between the two classes increases, and the dataset becomes more balanced [23]. See Figure 6, which shows how TLs can be used to reduce the number of samples in the majority class.  
Figure 4: Illustration of SMOTE Oversampling for Imbalanced Classification. By interpolating between samples of the minority class that is close to one another, SMOTE generates new samples from the minority class.  
Figure 5: Illustration of Random Under-Sampling technique. This method selects examples from the majority class at random and removes them from the training dataset.  
One-Sided Selection (OSS) [25] is proposed as an undersampling technique whose main idea is to combine TL and Condensed Nearest Neighbor Rule. In order to address the issue of imbalanced datasets, this approach leaves the minority class samples completely intact. It filters out the redundant samples in the majority class through a modification of the condensed nearest-neighbor rule [55].  
In OSS, \(\delta(x.y)\) is supposed to be a distance value that meets the requirements for being a TL, where \(x\) is chosen from the majority class, and \(y\) is selected from the minority. This way, two scenarios can happen: (1) a TL is found to be on the class boundary when both \(x\) and \(y\) exist in the right class regions (2) a TL is found to be inside one of the class regions when either \(x\) or \(y\) lies in the wrong region. OSS was introduced to decrease the number of majority class samples by omitting the data points which are borderline or noisy [56]. Figure 7 illustrates a diagram of the OSS technique.
## 5 Stability Analysis of TSWs
### Analysis of Influencing Factors
#### 5.2.3 Influence of Sidewall Length and Height on TSW Stability  
To study the relationship between SL, SH, and TSW stability, the MPS and the number of different yield zones for the weaker TSWs were calculated. For an SH of 20.0 m, 40.0 m, 60.0 m, 80.0 m, and 100 m, the average ST was set to 3 m, the SL to 50 m, the LP to 25 m, and the LW to 4 m. Additionally, for an SL of 20.0 m, 40.0 m, 60.0 m, 80.0 m, and 100 m, with the SH set to 50 m and other conditions remaining unchanged, the MPS, the number of different yield zones, and the percentage of yield zones for the weaker TSWs were calculated. The variation in the TSW's MPS and yield zones under the different SH is shown in Figure 9a. The variation in the TSW's MPS and yield zones under the different SL is shown in Figure 9b.  
In Figure 9a, as the SH increases, the MPS at each point rises and eventually stabilizes. The MPS at (0, h/2) is consistently the highest. When SH > 70 m, MPS at (LP, 0) exceeds that at (LP, h/2). The yield zones increase in number and proportion with SH, reaching 39.5% at SH = 100 m.  
Figure 8: Variation in the TSW’s MPS and yield zones under a different LW.  
In Figure (b)b, increasing the SL leads to a reduction in the MPS, with diminishing rates of decrease. MPS at (0, h/2) remains highest; MPS at (LP, 0) is higher than (LP, h/2) when SL < 40 m. The yield zone counts decline as the SL increases, but their relative proportion increases, peaking at 49.5% for SL = 20 m. When SL \(\geq\) 60 m, no yield occurs in HBZ and CZ.  
Figure 9: Variation in the TSW’s MPS and yield zones under the different TSW structural parameters: (**a**) a different SH; (**b**) a different SL.  
This occurs because the lateral load is distributed as a narrow strip in the vertical direction. A shorter SL increases bending deformation and stress concentration, weakening stability. Conversely, a longer SL reduces bending deformation and stress, improving stability. Thus, a longer SL and a lower SH contribute to a more uniform stress distribution and enhanced TSW stability.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 3 Method
### System Overview  
Figure 1 illustrates an overview of our proposed FSAD mobile-based continuous authentication system. For data acquisition, the system collects the touch screen and motion sensor data from the accelerator and gyroscope when a user touches a smartphone screen.  
These sensors capture 11 raw touch-gesture readings with 100 Hz. The touch screen gives x-y coordinates of touched points, touch pressure, and horizontal-vertical touched area information. The accelerometer and gyroscope give x-y-z axis values of velocity and rotation, respectively. Each sensor reading is min-max normalized.  
The captured sensor readings are then converted into data samples by the sliding window with the window size of \(T_{w}\) seconds (\(100T_{w}\) readings) and overlapping of \(T_{o}\) seconds. Each raw reading is concatenated along the time dimension. We obtain a raw data matrix \(X=\{f_{t},f_{a},f_{g}\}^{\top}\in\mathbb{R}^{11\times 100T_{w}}\), where \(f_{t}\), \(f_{a}\), and \(f_{g}\) are the raw readings from the touch screen, accelerometer, and gyroscope, respectively.  
In the training phase, the system utilizes \(M\) genuine samples, \(X_{1}^{G},X_{2}^{G},\ldots,X_{M}^{G}\), and \(K\)(=10) samples from each of N(=10) impostors, \(X_{1}^{I},X_{2}^{I},\ldots,X_{NK}^{I}\). After that, the random pattern mixing augments the impostor samples to \(M\) samples, \(X_{1}^{I},X_{2}^{I},\ldots,X_{NK}^{I},\tilde{X}_{NK+1}^{I},\ldots,\tilde{X}_{M} ^{I}\), so that the number of genuine and impostor samples is balanced up. Finally, they are used to train the LFP-TCN with the binary cross entropy loss.  
In the authentication phase, the sensor data are continuously captured, and a claimant is verified every \(T_{r}\) seconds for re-authentication. Here, \(n=1+\frac{T_{r}-T_{w}}{I_{w}-T_{o}}\) samples can be acquired by the sliding window for each verification. For instance, we obtain five samples when \(T_{r}=3\), \(T_{w}=1\), and \(T_{o}=0.5\). For \(n\) samples, the LFP-TCN returns \(n\) anomaly scores, \(s_{1},s_{2},\ldots,s_{n}\). Finally, the decision-making module yields a decision of \"accept\" or \"reject\" based on the decision or score-level fusion of the \(n\) scores.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Parallel Hole-Cutting Based on Two-Step Nodes Classification Scheme  
As shown in Equation (2), the distance relationship and the coverage relationship jointly define the property of the nodes. The implicit assembly method [12; 14; 15] calculated the wall-distance parameters when searching donor cells. The distance of a given node to its own component (denoted by \(ds\)) is directly calculated according to the original definition, while the distance to other components (denoted by \(dt\)) is obtained by locating donor cells on the corresponding grids and performing a linear interpolation. For a rigid moving grid system, this method avoids the repeated calculation of the invariant \(ds\) at different physical time steps and improves the parameter-reuse rate of \(ds\) during the calculation of \(dt\). From the perspective of parallel computation, however, it has the following two disadvantages. (1) The communication cost is extremely high for large-scale multi-component grids. When calculating \(dt\), all the nodes of the current grids are sent to the processors owning target grids to perform the searching operation, and the interpolated distance is then returned backwards [16]. (2) For deformed or adaptive meshes, the \(ds\) data cannot be reused.  
To address the aforementioned issues, we propose a two-step scheme for nodes classification, which consists of a preliminary screening step (Section 3.3.1) and a verification step (Section 3.3.2).
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
###### Abstract  
The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2 Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.  
V2 Verifier; postquantum cryptography; vehicle-to-vehicle communication; digital signature algorithm +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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