esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Collection of Exhaled Breath Samples from Participants  
The method of collecting human exhaled breath was reviewed and approved by the institutional review board (IRB) of the Kyungpook National University Hospital (IRB code KNUH_12-1002). Signed consent forms were obtained from all of the participants recruited in this study. The personal information of participants was protected by giving each exhaled breath sample a specific code number before being used in the study.  
Sixty human exhaled breath samples, including 40 from the AD and PD patient groups and 20 from healthy individuals, were collected from Kyungpook National University Hospital. Participants were requested to fast for at least 4 h before providing 3 L of their exhaled breath into a Tedlar bag through a mouth-piece filter (Figure 1A). All AD and PD group participants were regular follow-up patients from the hospital, whereas the control group members included individuals with good general health and no history of other neurological, psychiatric, or major medical diagnosis that could contribute significantly to cognitive impairment or dementia. All participants were also clinically examined using neuropsychological and clinical tests prior to their inclusion in the study. The results of the neuropsychological tests, such as the mini-mental state examination (MMSE) and the clinical dementia rating scale sum of boxes (CDR-SOB), conducted on the patient and control groups were distinctively different. The patients with AD had an MMSE score of 17.95 \(\pm\) 4.86, whereas the scores for patients with PD and healthy individuals were 22.85 \(\pm\) 4.65 and 28.55 \(\pm\) 1.05, respectively. The patients with AD had the highest CDR-SOB score of 6.1 \(\pm\) 2.6 compared to those with PD and healthy individuals, for which the scores were 2.24 \(\pm\) 2.67 and 0.25 \(\pm\) 0.30, respectively. The Hoehn-Yahr (H-Y) score was only applicable for PD patients, at 2.31 \(\pm\) 0.56. The details of participant data and clinical scores are listed in Table 1.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Dynamic Spectrum Access and Resource Management  
With the increasing demand for spectrum resources in vehicular networks, future research should explore dynamic spectrum access and resource management techniques to optimize spectrum utilization and mitigate interference. Cognitive radio and spectrum-sharing approaches can enable intelligent spectrum allocation and adaptive modulation schemes, enhancing the efficiency and reliability of V2V communication in dynamic and congested environments.  
In conclusion, addressing these future directions and open research challenges is essential for advancing the state-of-the-art in V2V communication and realizing the full potential of connected and autonomous vehicles. By leveraging emerging technologies, interdisciplinary collaborations, and innovative research methodologies, researchers can pave the way for safer, more efficient, and more Intelligent Transportation Systems of the future.
## 5 Energy Dissipation Analysis
### Energy Dissipation  
Combined with the equations, the energy dissipation curves of the final three profiles were obtained, as shown in Figure 18. The highest total energy was generated in Section B, and the dissipated energy reached a maximum value of 37 MJ (Figure 18). The maximum dissipated energy in Sections C and Sections A was 28.5 MJ and 13 MJ, respectively. The data reflect three stages of erosion damage:  
The catchment-fracture stage is the initial one, which was mainly caused by the catchment on both sides of the slope and the top of the slope to the center of the slope. The contact model between the particles in the center of the slope was preferentially fractured. The energy dissipation rate in the valley was comparably high,  
Figure 17: Variation in Kinetic Response in the three studied sections. (**a**) Section A (**b**) Section B (**c**) Section C.  
Figure 18: Comparison of Energy Dissipation in the three studied sections.  
dissipated energy reached 22.5 MJ. The dissipated energy on both sides of the gully grew slowly, with a growth amount of 4-7 MJ.  
The erosion-accumulation stage represents the second phase. The fluid reduced the strength between the particles during this stage, and the fracture phenomenon in the center of the slope gradually extended to the upper and lower sides, accompanied by the accumulation of particles. The dissipative energy moderately increased, by 2.5-5 MJ, during this stage.  
The third phase is the piping-penetration failure stage, which comprises the formation of a complete gully. The piping phenomenon at the top of the slope and the fracture-accumulation-collision at the bottom of the slope led to a sharp increase in the dissipated energy growth rate of Section C. Therefore, the dissipated energy of profile C increased rapidly from 7.6 to 28 MJ (Figure 18). In contrast, the dissipated energy in Sections B and A increased slowly, from 27.7 MJ to 37 MJ in Section B, and from 6 MJ to 14.6 MJ in Section A.  
The three stages simultaneously reflect the progressive cumulative energy consumption mechanism in the development of erosion damage. In the first two stages, the dissipated energy curves for Sections C and A were similar. However, due to the formation of lateral tributaries, the dissipated energy of Section C increased sharply in the last stage, with its final value exceeding that of Section A by 15 MJ. The course of the energy dissipation curves is closely related to the failure mode. The extent of the overall energy dissipation is controlled by the position of the slope and the different cross-sections of the gullies, and is additionally affected by the fluid. Moreover, striking differences in the changes in the dissipated energy were recognized in the three stages. Furthermore, the energy dissipation of the No. 3 gully during water flow erosion was concentrated in the catchment-fault stage (the first stage), in the central section of the slope (profile B). Another part of the energy dissipation was concentrated in profile C, regarding the slope bottom accumulation and the slope top piping-penetration failure stage (the third stage).
## 5 Experiment, Results and Discussion
### Experiment Set Up and Data Collection
#### 5.1.3 Data for Testing Position System  
To test our designed localization system, we designed static and dynamic experiments to examine our system, respectively. Figure 14 shows the concept diagrams for our static and dynamic experiments.  
For the static experiments, we fixed the three anchors in the positions shown in Figure 14a, The tag is placed in one of the three positions (red position, blue position, green position) at a time. The bidirectional arrows in the same color as the tag indicate the anchor ranging from the tag (dotted line indicates the NLOS, solid line indicates the LOS). When the tag is in each position, we collect the data transmitted by the three anchors. Each sampling data includes ranging value and eight features, and the laptop records the sample data from each anchor separately.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NLOS/LOS** & **Number of Set** & **Total** \\ \hline NLOS & 12 & 1812 \\ LOS & 54 & 6967 \\ \hline total & - & 8779 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The amount of sample data needed to train the model network.  
Figure 13: An experimental scenario for collecting data for training the model network. The black thick solid line indicates the boundary of the environment, and the red bidirectional arrows indicate the mutual ranging devices (dotted line indicates the NLOS, solid line indicates the LOS). The red five-pointed star indicates the position of the tag. (**a**) Data collection for non-line-of-sight situations, (**b**) Data collection for line-of-sight situations.  
As shown in Figure 13(b), for the dynamic experiment, we have the same setup as in the static experiment, where a person carries a tag from the red five-pointed star position as far as possible along the path of the dotted black arrow at a uniform speed until the green pentagram position ends. During the movement process, each anchor transmits the sampling data containing the ranging value and eight features of each ranging to the host computer.  
Table 3 shows the amount of the data we collected from the static and the dynamic experiments.
## 3 Methods
### AR Configuration Software
#### 3.2.3 AR Display Configuration Module Based on SLAM  
The AR display configuration module based on SLAM is used to save the SLAM map of the scene and configure the parameters of the virtual model in the scene. This module is developed based on ORB_SLAM2 and OpenSceneGraph (OSG) [29], where the former is used for scene localization and mapping, and the latter is used for real-time rendering of 3D models in the scene. OSG has advantages of high performance, scalability, portability, and rapid development. This paper is the first to combine OSG with ORB_SLAM2 for rendering virtual models in marker-less AR.  
Figure 5: Industrial virtual instrument planning interface.  
Figure 6: The flow chart of the QR code information configuration module.  
ORB_SLAM2 can locate and map unknown scenes and realize AR registration. However, ORB_SLAM2 positioning must be run online. Once the system is shut down, the scene map built by the system and the virtual model in the scene is deleted. Thus, to save the scene map and display the virtual model in the scene when the map is loaded again, this study designed an AR display configuration module based on SLAM. This module builds a scene map under the online positioning mode of ORB_SLAM2 and places a virtual model on the map. After the mapping is completed, the system can save the scene map and the virtual model parameter configuration as binary SLAM map scripts and SLAM-AR display content scripts.  
Figure 7 shows the flow chart of the AR display configuration module based on the SLAM designed in this study. In the AR configuration software, the SLAM system supports an online positioning working mode. The local mapping thread, tracking thread, and loop detection thread of the SLAM system run synchronously. Developers can initialize the SLAM system by scanning the industrial scene with a mobile camera.  
In the tracking thread, the system performs feature matching with map points by finding feature points in the scene and minimizes the reprojection error of these features to the previous frame to estimate the current camera pose. Different from AR registration based on QR code, AR registration based on SLAM does not rely on prior markers. The system adopts the Random Sample Consensus (RANSAC) [38] algorithm to fit the optimal plane from the map points in the current frame. Based on this, developers can click the graphic interaction button in the system interface to configure the 3D virtual model on the plane, and set the translation, rotation, and scaling parameters of the model. After the system is shut down, the parameter configuration of the 3D model is saved as a SLAM-AR display content script file.  
Figure 7: The flow chart of the AR display configuration module based on SLAM.  
In the local mapping thread, the system processes the image frame data and inserts information such as keyframes and map points into the map. Meanwhile, local Bundle Adjustment (BA) is required to optimize the entire local map. After the mapping thread ends, the keyframes, 3D map points, covisibility graph, and spanning tree in the map are saved as binary SLAM map script files.
## 2 A Cochlea-Inspired Artificial Filter Bank (CAFB) for Structural Health Monitoring
### Optimal Design for CAFB
Regarding the three design elements, [PERSON] et al. [13; 14] have established a procedure to optimize them, which contains Step 3, Step 4, and Step 5, as shown in Figure 4.  
At first, in Step 3, the number of filters shall be preliminarily assumed. The number of filters can be determined by the designer's empirical judgment considering the price and performance for the filter configuration, and the accuracy of the filtered signals. In the previous study, the number of filters is preliminarily assumed as 10. Next, in Step 4, the interval and bandwidth of the filters are decided using the number of filters assumed in Step 3. In the previous study, under the assumption that the number of filters is 10, the RE value is calculated with the condition of bandwidth for 100 intervals. The interval and bandwidth of the filter are determined corresponding to the least-calculated RE values. As the result, a 1.0 Hz interval and 0.6 Hz bandwidth are obtained. Next, in Step 5, using the interval and bandwidth derived from Step 4, the number of filters assumed in Step 3 is finally determined. In the previous study, RE and CR values are calculated under the condition of the 1.0 Hz interval and 0.6 Hz bandwidth, and 20 filters. The number of filters is determined for the position where the difference between the RE and CR value is minimized. As a result, the number of filters turned out to be six. Finally, in Step 6, Matlab(r2015b) from The Mathworks, Inc. (Natick, MA, USA) is utilized to program the optimal design condition (six band-pass filters, 1.0 Hz interval, and 0.6 Hz bandwidth) determined in Steps 1 to 5. Additionally, the result has been realized in a wireless measuring system using LabVIEW (2012) from National Instruments (Austin, TX, USA), which is shown in Section 3. Furthermore, in the previous study, an analytic verification (numerical study) for the optimized CAFB is carried out so that CAFB is able to compress the dynamic response within the intended frequency range without distortion of the signal. In conclusion, the method to optimize the CAFB using the El-Centro seismic waveform can be efficient to acquire only sub-10 Hz signals from various responses of structures. If the structure to be tested has been already selected, or if it is necessary to acquire the response of a specific frequency band by filtering, the CAFB can be optimized using responses by actual measurements for the structure, or using another reference signal concentrated on a specific frequency band.
## 3 The Proposed Solution
### Detailed Protocol Description
#### 3.3.1 Set Up  
The protocol is described in Figure 1. On input a security parameter \(n\), the Credential Authority CA generates the bilinear groups \((\mathbb{G}_{1},\mathbb{G}_{2},g,\hat{g},p)\), the private/public AoN-PKEET encryption pair \(\hat{\zeta},g^{\xi}\mod p\) and the trapdoor information \(tk=(\hat{p},\hat{\phi}=\hat{\beta}^{\xi})\), as described in Section 3.1. The CA will securely transfer to the SA the trapdoor information. The SA cannot decrypt messages since it has no access to the secret key or the randomness used for message encryption. It may use the trapdoor information to check only for the equality of messages encrypted with the AoN-PKEET scheme of the CA.  
Finally, each authority possesses a public/private key pair, say \(PK_{CA},SK_{CA}\) (resp. \(PK_{SA},SK_{SA}\)) to be used for signing and/or communication encryption (In practice each authority may use different key pair for each operation).  
For the digital signatures, an algorithm that supports a partially blinded setting can be used. We implement the scheme presented in [57]. Let \(SK_{SA}=x\) and \(PK_{SA}=y\) (\(=g^{x}\mod p\)) denote the private/public key pair of SA, using a typical ElGamal setting, where \(p=2\,q+1\), for sufficiently long primes \(p,q\).  
Figure 1: Set Up.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \((\xi,h=g^{\xi})\) & The AoN-PKEET encryption key pair of the CA (see Section 3.1) \\ \hline \(AEnc(h,r,m)\rightarrow(K_{1},K_{2})=(g^{r},mlr^{\prime})\) & The AoN-PKEET encryption of \(m\) with key \(h\) and random \(r\) \\ \hline \(enc_{X}\mid dec_{X}(\cdot)\) & Typical encryption (decryption) with the public (private) key of \(X\) \\ \hline \(sig_{X}(\cdot)\mid ver_{X}(\cdot)\) & Signature (verification) functions with the private (public) key of \(X\) \\ \hline \(H(\cdot)\) & A cryptographic hash function \\ \hline \(NIZKP(\cdot)\) & The NIZKP of \\ \hline \(H(m,b)\rightarrow\overline{m}\) & Blinding of message \(m\) using \(b\) as blinding factor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and cryptographic functions used.
## 4 Compact, High Efficiency Laser System
### Diode -Pumped Nd-Doped Mixed Scandium Garnets Ceramic Laser  
One of the common high power pulsed lasers are based on a Nd-doped laser, but the pulses are not in the femtosecond region. Recently, the development of a new ceramic laser medium has been reported, namely the Nd-doped mixed scandium garnets Nd\({}^{3+}\):(GSAG\({}_{0.67}\):YSGG\({}_{0.33}\)). The Optical properties of Nd\({}^{3+}\):(GSAG\({}_{0.67}\):YSGG\({}_{0.33}\)) ceramic have already been reported by [PERSON] _et al._[113]. The Nd\({}^{3+}\): (GSAG\({}_{0.67}\):YSGG\({}_{0.33}\)) has a significantly broader spectral bandwidth (5 nm) and it is about four times broader than that of Nd:YAG ceramic. The stimulated emission cross section of the Nd\({}^{3+}\): (GSAG\({}_{0.67}\):YSGG\({}_{0.33}\)) at the peak wavelength is 7.8 \(\times\) 10\({}^{-20}\) cm\({}^{2}\) which is about two times higher than that of Nd-doped glasses. The thermal conductivity is 4.7 W/mK which is the four times higher thermal conductivity than Nd-doped glass materials. The ceramic which is fabricated based on vacuum sintering and nanotechnology method has a potential for a high peak, high average power laser system because they have a size scalability and stronger fracture toughness than single crystal.  
We employed a simple \(Z\)-shaped cavity configuration. The laser oscillation experiment on this ceramic was performed in continuous wave operation. The ceramic was pumped with a 35 W fiber-coupled LD (100 \(\upmu\)m core diameter, 0.12 NA) at the center wavelength around 808 nm. As the gain medium, a 3 mm thick Nd\({}^{3+}\): (GSAG\({}_{0.67}\):YSGG\({}_{0.33}\)) (C\({}_{\rm Nd}\) = 1 at. %) ceramic was used. It was mounted in a copper holder with water cooling and placed at the Brewster angle. It absorbed about 70% of the lunch pump power. The output couplers of 1%, 5% and 10% transmittances were used. The output powers as functions of the absorbed pump power are shown in Figure 25. With the 10%, 5% and 1% output couplers, the slope efficiency with respect to the absorbed pump power reached 26%, 24% and 10%, while the obtained optical-to-optical efficiency was 24%, 23% and 9%, respectively. With the 10% output coupling, the maximum output power of 1.74 W was obtained. The measured optical spectrum for the 10% output coupler and the fluorescence spectrum are shown in Figure 26. The optical spectrum consists of two peaks and these two peaks correspond to the peaks of  
Figure 24: Contrast of the recompressed pulse.  
fluorescence spectrum. In all cases, the measured laser profiles were multi-moded. We believe that further improving the fabrication method of the ceramic to reduce the scattering loss enable to increase the optical-to-optical efficiency. In the future, we aim for generation of mode-locked femtosecond pulses.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Key Performance Metrics  
#### 6.4.1 Insertion Loss  
Insertion loss quantifies the signal power attenuation when the switch is in the \"ON\" state (or through-path). In dB form:  
\[L_{\text{ins}}(f)=10\log_{10}\Bigl{(}\frac{P_{\text{input}}}{P_{\text{output}}} \Bigr{)} \tag{52}\]  
Equivalently, if \(S_{21}\) (or \(S_{31}\)) is the transmission parameter from input to the active output port, the insertion loss (in dB) can also be expressed as:  
\[L_{\text{ins}}(f)=-20\log_{10}\bigl{|}S_{21}\bigr{|}. \tag{53}\]
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 2 Related Works
### Cryptography-Based Identity Authentication Protocol  
[PERSON] et al. [6] proposed a conditional privacy-preserving authentication scheme (PA-CRT) based on the Chinese Remainder Theorem (CRT) to secure communications in vehicular ad hoc networks (VANETs). By using CRT to assist TAs in the dynamic generation and distribution of group keys, the scheme enhances both the security and efficiency of the system. Subsequently, [PERSON] et al. [7] developed a CRT-based authentication protocol that supports dynamic group membership. The protocol addresses key issues such as message authentication, anonymity, and conditional privacy, while also ensuring forward and backward security, thereby responding effectively to the limitations of previous approaches. In 2021, [PERSON] et al. [8] introduced a lightweight authentication protocol designed to improve the efficiency of emergency vehicle clearance in the IoV. After completing initial authentication with an RSU, an emergency vehicle can subsequently authenticate with other RSUs more quickly, thereby reducing computational overhead during transit. Subsequently, [PERSON] and [PERSON] [9] proposed a secure and efficient message authentication protocol (SEMA), which combines the advantages of pseudonym-based and group-based schemes to support efficient bidirectional authentication between vehicles and RSUs in VANETs. Despite their advantages, these protocols are still exposed to certain security risks, including desynchronization attacks, attacks by privileged insiders, and the compromise of RSUs. To ensure secure wireless communication between unmanned aerial vehicles (UAVs) and ground stations, [PERSON] et al. [10] developed a lightweight key agreement protocol capable of maintaining security even in the event of temporary session information leakage, while keeping computational costs low. To address the performance overhead during protocol execution, [PERSON] et al. [11] proposed an authentication and key agreement scheme based on ECC. Their approach employs a dynamic credential synchronization framework, using dynamic authentication credentials in place of temporary public keys or static sender credentials to provide both efficiency and anonymity.
## 2 Materials and Methods
### Membrane Performance Assessment  
Membrane porosity (\(\varepsilon\)) was calculated from the difference between the bulk membrane volume (V\({}_{m}\)) and the polymer volume (V\({}_{p}\)). The porosity was calculated using Equation (3) [27].  
\[\varepsilon(\%)\ =\frac{\text{V}_{m}-\text{V}_{p}}{\text{V}_{m}}\times 100 \tag{3}\]  
where \(V_{m}\) is the bulk membrane volume, and \(V_{p}\) represents the polymer volume. \(V_{m}\) was obtained by multiplying the membrane area by its thickness. The polymer volume \(V_{p}\) was obtained by the ratio \(\frac{W_{m}}{\rho_{p}}\), where \(W_{m}\) is the membrane weight, and \(\rho_{p}\) is the polymer's density.  
The mean pore radius was calculated using the Guerout-Elfort-Ferry equation (Equation (4)).  
\[\text{r}_{m}\ =\ \sqrt{\frac{(2.9\ -1.75\varepsilon)8\eta\delta_{0}\text{Q}}{ \varepsilon\text{A}\ \triangle\text{P}}} \tag{4}\]  
where \(\eta\) is the pure water viscosity (8.9 \(\times\) 10\({}^{-4}\) Pa s), Q is the pure water volume permeated per unit of time (mm\({}^{3}\)s\({}^{-1}\)), A is the effective area of the membrane (mm\({}^{2}\)), \(\delta_{0}\) is the thickness of the membrane (mm), and \(\Delta P\) is the transmembrane pressure (Pa).