esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In summary, the properties of the maraging steel metal materials before and after LPBF polishing were compared and analyzed. A continuous laser and a pulsed laser were used to polish the surface with double laser beams, which remarkably reduced the surface roughness. LPBF polishing roughness reduction is an important factor affecting the LPBF component performance. The polished layer was characterized by a surface roughness measurement instrument, XRD, SEM, and nano hardness tester. We studied the microstructure and mechanical properties of the surface that had undergone laser surface polishing for the metal additive manufacturing of high-strength maraging steel metal material. Compared with the initial surface, the peak value of martensite on the laser-polished surface decreased, but the peak value of the austenite phase increased, indicating that the microstructure of the laser-polished layers changes with the occurrence of grain refinement during the rapid melting of the workpiece surface, and that the microstructure was different from that of the matrix. After polishing, the martensite content in the horizontal plane decreased, with the differences in both processes influencing the composition, morphology. and content of the martensite and austenite. This study is a part of ongoing research for improving the surface quality of the high-strength maraging steel metal materials.  
Conceptualization, H.X. and W.Z.; methodology, Y.Z. and M.L.; investigation, C.D. and H.X.; writing--original draft preparation, H.X. and Y.C.; writing--review and editing, Y.Z., C.D. and W.Z.; supervision, M.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Science and Technology Project of Guangdong Province (grant no. 2021A0505030013), the Scientific Research Project of General Universities in Guangdong Province (grant nos. 2021 KCXTD058, 2022 ZDZX3073), the Shenzhen Science and Technology Plan (grant nos.JSGG20191230162001814, JSGG20210420091802007), and the School-Enterprise Collaborative Project of Shenzhen Institute of Information Technology (grant no. SZIIT2022 KJ075).  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data underlying the results presented in this paper are available upon request from the corresponding author.  
**Acknowledgments:** We would like to acknowledge the contributions to the theory of metal additive manufacturing and double-laser-beam polishing described in this paper. We acknowledge [PERSON] and [PERSON] for their help with the supervision and investigation.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 16: XRD analysis of the as-fabricated and polished MS specimens.
# Compact Fuzzy Systems Based on Boolean Relations
## 6 Application Example of Architecture II: PID Control
### Optimization Process  
The optimization process includes an operation that permits the calculation of the control system dynamics. The optimization parameters \(X\) defines the response of the control system \(y_{out}(X)\). The reference output \(y_{ref}\) (desired behavior) is found from the reference model given by the transfer function display in Equation (72). Figure 26 shows the reference model response for different values of \(r(t)\) where the settling time is the 1800 s namely 30 min (time scale in minutes). The reference model is proposed to reduce the settling time of the open-loop plant presented in Figure 22.  
Figure 27 shows the scheme used for the optimization process. The result of \(y_{out}\) and \(y_{ref}\) are employed to calculate the performance index to optimize \(J(X)\). Thus, the optimization process employs the performance index function. In addition, \(X\) corresponds to the controller's set of parameters, \(n\) is the discrete-time variable, and \(N\) is the number of data employed. In this way, the performance function is obtained, which corresponds to mean squared error:  
\[J=\frac{1}{N}\sum_{n=1}^{N}(y_{ref}(n)-y_{out}(n,X))^{2} \tag{73}\]  
The Quasi-Newton method of BFGS (Broyden-Fletcher-Goldfarb-Shanno) is used in the optimization process; this method performs a successive Hessian approximation [65].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \(\mu_{N,1}\) & \(\mu_{P,1}\) & \(\mu_{N,2}\) & \(\mu_{P,2}\) & \(\mu_{N,3}\) & \(\mu_{P,3}\) & \(\mu_{N,
## 4 Numerical Modeling: Mass-Spring Waveguides  
We develop a numerical model based on mass-spring waveguides that can be used to shed light on the complex behavior of nonseparable states in coupled rod systems. For this, we assume that each rod can be represented by a 1D harmonic crystal composed of connected identical masses and springs and that no pre-compression exists in the chain. We further assume that the masses of each chain are constrained to move only in the horizontal direction. Therefore, the coupled rod system can be represented as a set of 1D harmonic crystals coupled along their length by longitudinal harmonic springs. Dissipative effects in the medium are modeled by linear viscous damping elements.
## 3 Secure FSO Link in GNU Radio Simulation
### Receiver  
The receiver has the same stages as the transmitter but in reverse, as given in Figure 6. The data coming into the receiver must first pass through the FSK Packet Receive block before being depacketised and decrypted.  
Figure 6: Secure FSO link Receiver blocks in GNU Radio.  
As explained in Section 3.2, the access code is part of the header that consists of four bytes. The FSK Packet Receive block locates and discards the access code and any data before it (this includes the preamble). Then, it passes the rest of the binary data to the next block. Lastly, the bits are repacked into bytes and then the CRC is verified and passed on. Now the data will consist of just the nonce data and the encrypted packet data.  
To extract the packet data and prepare the data for decryption, a reverse custom formatter was created. The reverse custom formatter will add the tag 'nonce' to the nonce data and pack it into a PMT list, and the encrypted message is assigned the tag'msg_encrypted' and is also packed into a PMT list, this is so that the decryption block locates the nonce and the encrypted data.  
Next, the message is verified and decrypted by the Decrypt Public block from gr-nacl using the crypto_box_open_easy function from libsodium. If the message is properly decrypted, then the message is sent to the formatter to be written to the log, and it is also written to the terminal. If the message is not decrypted, then the block returns a message to indicate that the message cannot be decrypted and provide a reason, e.g., no nonce detected or the encryption key was invalid. In the last stage, the message is logged before the reverse formatter, after the reverse formatter and finally, after the decryption.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 2 Background
### Measuring Oxygen in Packaging
#### 2.2.2 Potentiometric Detectors  
Oxygen concentration can also be detected potentiometrically, leading to higher sensitivities than in traditional colorimetric methods. A gaseous flow with an unknown oxygen concentration contacts a platinum electrode on the inside of an electrolyte tube, usually alumina or yttria stabilized zirconia (YSZ) for high temperatures [68; 69]. The oxygen acts as an electron acceptor and generates positively charged holes in the electrode, creating the potential for electron flows between the measuring electrode and the reference electrode on the other side of the electrolyte. The reference electrode is in contact with a known oxygen concentration (most often air) [68]. The electrical voltage can then be measured and is directly related to the oxygen concentration.  
There are two main methodologies in practice to measure oxygen permeation employing these detectors. The first is a destructive method in which a package is punctured in order to achieve contact between the packaged atmosphere and the electrode. In the course of the shelf life, the real oxygen concentration of the packaging is measured several times. The disadvantages of such a destructive method is that every data point requires a different packaging sample and that the measurements span a large time period [70].  
As a second method, the inside of a package is in contact with a gas flow of known composition, while it remains in contact with air on the outside. The outgoing gas stream is analyzed potentiometrically to indicate the rate of oxygen ingress [71; 72; 73; 74]. Limitations here include not being able to simulate real life conditions, meaning no liquids can be present. This is especially disadvantageous for active barriers that are only active in contact with water.
## 4 Multicast Routing Approaches in DTN
### Geographical Multicast Routing in DTN
#### 4.4.2 Location-Based  
Collaborative Time-Stamp based Multicast Routing: Collaborative Time-Stamp based Multicast Routing (CTMR) [72] is a probabilistic routing protocol based on historical encounters and implements  
Figure 12: Ferry-based interdomain multicast routing (FBIMR) scheme [67].  
a time-stamp to limit recently communicated nodes from further communication attempts within a predefined time-stamp threshold. The authors make the assumption that recently communicated nodes are not likely to have new bundles for forwarding, and thus energy in the nodes is preserved from unnecessary communication overhead. CTMR employs a customized bundle format, which includes the GPS coordinates of nodes when it creates the bundle. This location information is stored as one bundle for the metadata for every bundle in each node. For any encounter of two nodes after the time-stamp limit, they first exchange the metadata of the bundles and calculate the location differences of the same type of bundles from both nodes using the Haversine formula. The sender only forwards bundles when the location difference is greater than the predefined location difference threshold to minimize redundancy.
# Ultra-Small Fiber Bragg Grating Accelerometer
## 2 Theory  
### Relationships among the Sensitivity, Resonant Frequency and Length of the Spring  
The resonant frequency of an accelerometer \(\omega_{0}=\sqrt{\frac{K}{m}}\), where \(K\) is the spring constant and \(m\) the mass of the inertial object [1]. It shows that as the mass increases, the resonant frequency will decrease; as the spring becomes shorter, the spring constant and resonant frequency will increase.  
The Static Sensitivity in terms of the Relative Displacement (SSRD) is \(\left|\frac{x-z}{a}\right|=\frac{1}{\omega_{0}^{2}}\), where \(x\) and z are the displacements of the inertial object and shell from their equilibrium positions, respectively [1]. SSRD is the length change of the spring at unit acceleration, and serves as a bridge to find the static sensitivity.  
Assume that the length of the spring is halved (\(K\) is doubled) and the mass of the inertial object is doubled (\(m\) is doubled); the resonant frequency and SSRD will be unchanged. However, the static sensitivity will be doubled, if the sensing element is based on strain change instead of length change. Strain is the ratio of the length change to the original length. Even though SSRD (length change) is the same, the strain will be doubled if the original length is halved.  
For example, FBG is based on its strain change. Assume that the length of an FBG accelerometer's vibration arm is quartered (\(K\) is quadrupled). If the mass of the inertial object is the same, the resonant frequency will be doubled, the SSRD quartered, and the static sensitivity the same; if the mass of the inertial object is also quadrupled, the resonant frequency and SSRD will be the same, but the static sensitivity quadrupled.
# Robust Control for a Slung-Mass Quadcopter Under Abrupt Velocity Changes
## 4 Experimental Setup  
This section presents the experimental setup used to perform outdoor flight tests. The experimental platform is shown in Figure 6. The slung mass is attached to the quadrotor with a rigid rod at 20 cm below the airframe.  
The slung-mass \(\beta\)-angle has been mechanically locked, hence it can only balance the \(\alpha\) angle and a bearing is used to reduce friction on the hinge. Furthermore, the slung mass cannot rotate about the yaw moment axis of the rigid rod. An external IMU attached to the slung mass is used to measure the angular position and velocity \((\alpha,\dot{\alpha})\). These measurements are transmitted to the autopilot through the Inter Integrated Circuit (I2C) communication interface. Robust control algorithms are implemented on a CubePilot Cube Orange\({}^{+}\) flight controller PX4 Firmware compatible. The autopilot has triple redundancy IMU's  
Figure 4: Abrupt changes in velocity simulation data.  
Figure 5: Simulation result of the slung-mass angular position (\(\alpha\)).  
for attitude measurements (accelerometer, gyroscope, and compass). Real-Time Kinematic (RTK) positioning data are obtained by a Global Navigation Satellite System (GNSS) and a differential ground station reference for centimeter accuracy.  
Figure 7 illustrates the outdoor flight test of the quadcopter in forward navigation tracking a speed reference. Table 2 lists the environmental conditions during the outdoor experimental tests. It can be observed that the angular position of the suspended mass is opposite to the direction of travel of the quadrotor.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Hyperspectral Imaging Technology  
Hyperspectral imaging technology is another commonly used technique for obtaining the spectra of a target [111; 112]. The hyperspectral image containing two-dimensional (2D) spatial and 1D spectral information comprises a 3D data cube [113]. Notably, different objects exhibit different spectra, which can be used for the identification and detection of such objects. In the 1980s, [PERSON] et al. [114] began a revolution in remote sensing by developing an airborne visible infrared imaging spectrometer (AVIRIS) [113], which initiated the development of hyperspectral imagers. The number of bands in a multispectral image is usually less than five, while that in a hyperspectral image is more than 100; moreover, continuous spectral information is obtained from a hyperspectral image.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 7 Discussion
### Ethical Implications of Cycle-Based EMRs  
Implementing a cyclical data structure in EMRs introduces significant ethical considerations that must be carefully addressed. A primary concern is balancing the need for standardized cycle templates (which enable cross-institutional learning and enhance clinical decision-making) with the imperative to safeguard patient confidentiality and ensure data privacy. While some structured elements of treatment cycles (such as generalized data forms and standardized fields) can be shared across systems to enhance collective medical knowledge and improve decision support, it is crucial to ensure that only non-private, non-identifiable information is used for broader applications. Data anonymization and selective filtering must be employed to protect patient confidentiality before sharing data [73; 74].  
Furthermore, transparency in CDSS recommendations derived from cyclical data is essential. Physicians must explicitly understand how patient data across treatment cycles shapes AI-driven or system-generated recommendations, mitigating the risk of over-reliance on automated outputs. It is crucial to underscore that the ultimate responsibility for medical decisions rests with the physicians [75; 76]. The proposed approach is intended to support clinicians by offering structured insights that improve the decision-making accuracy while preserving the primary of professional judgment.  
Finally, the growing structure and data-driven nature of EMR systems necessitate continuous evaluation to ensure that patient safety, data privacy, and data ownership rights are fully respected in all implementation and data utilization stages [77].
# Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay
###### Abstract  
Concrete pavement proportions are increasing in Korean expressways, resulting in increased maintenance cost. The length of degenerate concrete pavements that have exceeded the design life (20 years) was 1150 km in 2015 and 2605 km in 2020 and is expected to rapidly increase. To extend the service life of concrete pavements, life-cycle cost (LCC) analysis was conducted on asphalt and concrete overlays, based on the different maintenance methods. LCC analysis was performed when the shoulder was used and when it was not used between 6000 and 35,000 AADT traffic according to the two-lane and four-lane traffic. During overlay, one lane was completely blocked, and the value per vehicle was converted into the user cost using the Construction Analysis for Pavement Rehabilitation Strategies software, according to whether the shoulder was used to maintain the number of lanes. In addition, LCC analysis was conducted by examining the construction cost and life-cycle according to each overlay method. When the shoulder was used, the total construction cost decreased, owing to the reduction in user cost, indicating that the implementation of the traffic measure of using the road shoulder improves user satisfaction and cost. The asphalt overlay was observed to be more favorable than concrete overlay in terms of the initial total construction cost. However, under a 20-year cycle, the economic efficiency of concrete overlay was higher than that of asphalt overlay. After repairing the deteriorated target sections of concrete pavements, the overlay method (asphalt or concrete) ought to be selected according to the target service life for beneficial economic effect. Concrete overlay was to obtain about 20% or greater LCC effect compared to asphalt overlay, and at least 5% or more additional LCC effect obtained when the shoulder was used.  
concrete pavement; pavement rehabilitation; asphalt overlay; concrete overlay; traffic volume; traffic control; life-cycle cost (LCC) +
Footnote †: journal: Applied  
Article  
Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay  
1 Department of Civil Engineering, Kangwon National University, 346 Jungang-ro, Samcheok-si 25913, Korea; [EMAIL_ADDRESS] (H.J.); [EMAIL_ADDRESS] (C.O.)  
1 Department of Infrastructure Safety Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero, Ilsansoe-gu, Goyang-si 10223, Korea; [EMAIL_ADDRESS]  
2 Department of Civil Engineering, Kangwon National University, 1 Kangwonadehak-gil, Chuncheon-si 24341, Korea; [EMAIL_ADDRESS]  
3 Department of Civil Engineering, Kangwon National University, 1 Kangwonadehak-gil, Chuncheon-si 24341, Korea; [EMAIL_ADDRESS]  
4 Correspondence: [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (C.P.); Tel: +82-33-570-6511 (S.K.); +82-33-570-6515 (C.P.)
## 5 Results
### Scalability Simulation Analysis  
The following is a compilation of the scalability simulation results from monitoring smart IoT device usage in the smart building. For the benefit of this study, an experiment was conducted to evaluate real-world IoT device data in layers 0, 1, and 2, as shown in Figure 1 above, with IoT device scalability for smart office building use. The number of devices simulated was on scaled up in increments of 20 devices to capture energy consumption, latency, and network bandwidth usage and monitor performance metrics to demonstrate the benefits of Fog computing. Table 7 below highlights the simulation results for the impact on energy consumption, focusing on managing CCTV IoT device data, CO\({}_{2}\) IoT device data, and lighting sensor IoT device data processed at both the Cloud and Fog layer at the network's edge in the smart office building. Furthermore, Table 7 shows varied numbers of devices, ranging from 20 to 200, in 20 device increments, simulating scalability for Cloud-only and Fog-only scenarios in the different (Cloud and Fog) columns. The difference % column represents the percentage difference between the Cloud-only and Fog-only simulations, comparing the energy consumption measured in megajoules (MJ). In summary, the results highlighted in Table 7 show that the level of energy consumption for all three IoT devices was steady for both Cloud-only and Fog-only simulations with minimal changes, as the number of devices increased from 20 to 200. However, there was a higher difference from 100 to 200 devices. The results for CCTV in the Fog-only simulation clearly showed a slightly lower energy consumption rate compared to the Cloud-only simulation with a percentage difference between \(-0.47\%\) at 20 devices and \(4.35  
expected. However, the energy consumption figures stabilised with subsequent increases in devices between 140 and 160 devices. Furthermore, the light sensor results also follow the same pattern of a steady level of energy consumption for both Cloud and Fog scenarios, with the Fog scenario consuming a lower energy rate, as shown in Figure 4. The percentage difference for the light sensors ranged between \(-0.58\%\) at 40 devices and 3.89% at 180 devices. In addition, Figure 5 below highlights the simulation results for latency with a focus on the management of CCTV IoT device data, CO2 IoT device data, and light sensor IoT device data processed with computation and storage resources at both the data centre (Cloud) and the network's edge (Fog) in the smart building. Table 8 highlights the number of devices in 20 increments ranging from 20 to 200. The simulation was conducted for Cloud-only and Fog-only, respectively, with results in the different (Cloud latency and Fog latency) columns, and the difference % column represents the percentage difference between the Cloud-only and Fog-only simulations, comparing system latency measured in milliseconds (ms).
###### Abstract  
Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users' biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.  
biometrics; security; privacy; deep learning Article  
[PERSON], [PERSON]-[PERSON], [PERSON], [PERSON], [PERSON][PERSON], Q. A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application  
1