esa-sceva/satcom-chunk-collection huggingface dataset:
# Al-Fe-Ni Metallic Glasses via Mechanical Alloying and Its Consolidation
## 2 Materials and Methods  
Elemental powders of Al (\(-200\), \(+325\) mesh) and \(>\)99.5% purity, Fe (\(-325\) mesh) and \(>\)97% purity and Ni (\(-325\) mesh) and \(>\)99.5% purity were used as the raw materials. An alloy with a nominal composition of Al\({}_{82}\)Fe\({}_{14}\)Ni\({}_{4}\) (as given in Table 1) was milled by using an AGO-2 planetary ball mill. A total of 10 g of the mixture powder and 200 g of WC hexagonal balls (3-5 mm in diameter) was put into hardened steel vials. Additionally, 50 mL of n-hexane was added as a process control agent into the vial before milling to reduce the sticking phenomena between the powder and the milling tools. The milling process was conducted at rotation speeds of 250 and 350 rpm and under an argon atmosphere to prevent oxidation.  
The X-ray patterns of the as-milled powders and bulk samples were measured using Panalytical X'pert Pro diffractometer (Malvern Panalytical, Almelo, The Netherlands) equipped with Cu K\({}_{\alpha}\) radiation. The MDI Jade version 6.5 (associated with the ICDD PDF2 database, 2007) was used to find the peaks matching the reference sample. The microstructures of the samples were analyzed by scanning electron microscopy HITACHI TM4000 PLUS (Hitachi High-Tech Corporation, Tokyo, Japan). The particle size distributions of the alloyed powders were determined by a Horiba LA-960 laser diffraction particle size analyzer (Horiba Ltd., Kyoto, Japan) in distilled water. The differential scanning calorimeter (DSC) curves were measured for the milled powders using DSC-1150B with a heating rate equal to 20 K/min in a protective purity N\({}_{2}\) atmosphere. The magnetic properties were examined by vibrating system magnetometers (VSM, MicroSense, EV9, Lowell, MA, USA), in which each sample was measured five to ten times and an average value was obtained.  
After being milled at 350 rpm for 60 h, the amorphous Al\({}_{82}\)Fe\({}_{14}\)Ni\({}_{4}\) powders were compacted by SPS under a high-vacuum state at sintering temperatures of 450, 500 and 600 \({}^{\circ}\)C for 180 s, with a heating rate of 10 K/min and applied pressures of 300 MPa using LABOX-210 (Spark Plasma Sintering System). The relative densities of the bulk samples were obtained by Olympus PMG3 optical microscope (Olympus Corporation, Tokyo, Japan) with computerized image analysis with a magnification of 100\(\times\). The compression tests of the sintered samples were performed with a normal displacement rate of 0.1 mm/min in an Instron 4469 Advanced Tensile Tester (Instron(r), Norwood, CO, USA).
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup  
This section describes the GNSS receiver under test, the configuration of each presented adaptive algorithm, the metric used to determine the tracking and system performance, and the simulated scenarios.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification  
We verified our analytical model using computer simulation and investigated the communication performance under various configurations of \(p_{b}\), \(\lambda\), and \(N\). We used the open source vehicular network simulation framework Veins which is frequently used in academic research. It consists of two parts: the mobility characteristic of vehicles was modeled using SUMO and the communication networking characteristic was modeled using OMNeT++.  
The simulation scenario is based on a real map of the city of Shenyang in China, which is shown in Figure 4. Figure 4a shows a real map extracted from OpenStreetMap and Figure 4b illustrates the road topology without buildings. The size of the scenario is 1 km \(\times\) 1.6 km. The number of vehicles in a platoon varied within the interval [2; 10]. The speed of vehicles was set to be 60 km/h based on the typical motorways speed limitation. Many DSRC studies have employed 6 Mb/s as the data rate since it seems to be a good choice for safe message transmission in most cases. As a result, 6 Mb/s was selected in our simulations. As for wireless communication simulation, propagation signal modeling is one of the most fundamental issues. The Nakagami model has proven to be the best fading radio propagation model for simulation of a VANET environment. Consequently, we adopted this propagation model in our experiments. Transmission power was 20 dBm and noise-floor was \(-\)89 dBm. The values of other parameters are listed in Table 1. The parameters were chosen from the latest draft IEEE 802.11p standard. To guarantee the accuracy of the experimental results, we repeated each independent experiment thirty times.
# Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles
## Results
### Comparison of Leaf Morphology  
We compared the shapes of the measured ChIF spectra across species for the OC and FW methods. For practical purposes, we present the results as normalized spectra, such that the level of the red region (685 nm) illustrates the red/far-red ratio. Only a very small effect of leaf morphology or sample arrangements could be seen in the far-red region of the spectra. Conversely, there were larger changes between methods in the red region of the spectra. Therefore, in addition to the various factors which control the intensity of the ChIF signal, differences in the red/far-red ratio highlight the presence of factors that affect the spectral shape of the ChIF.  
Lingonberry (green) presented the lowest red/far-red ratio, with similarly low values in both OC (Figure 7a) and FW (Figure 7b). The one needle (light grey) and three needles (dark grey) configurations tended to yield systematically higher red/far-red ratios than NM (black) in both methods. Regarding red/far-red ratios, no statistically significant differences were found between the OC and FW method.  
Figure 7: Chlorophyll fluorescence spectra of birch (violet), linguonberry (green) and pine needles (1 needle—light grey, three needles—dark grey, and needle mat—black): (**a**) in optical chamber (and zoomed in at the 675–700 nm range (**c**)); and (**b**) in FluoWat (and zoomed in at the 675–700 nm range(**d**)).
## 2 Materials and Methods
### Experimental Setup  
Figure 2 shows the free-space optical setup used for the characterization of the WGs provided by FBK and the University of Trento. The samples available for measurement had a common thickness size (770 nm), with a length of 6 mm and five different widths (7, 8.3, 8.6, 9.4, and 10 mm--see also the last figure for info about stress for every sample). The goal of this experiment was an initial evaluation of the WG properties (measuring the transmitted power value at one WG end) at different wavelengths and to compare those results with the FEM analysis described in next sections.  
We used four different laser sources to monitor the waveguiding behavior from the visible to the MIR range: a laser diode emitting 0.9 mW at 532 nm (Thorlabs CPS532), a  
Figure 1: Cross section of the Waveguided model comprising some SiN/SiO\({}_{2}\) layers. During the simulation, we changed the number of layers, layer width (w), and layer thickness (dSiO, dSiN).  
Figure 2: Schematic of the free-space optical system assembly for the characterization of the waveguiding behaviour (in text and figure, M is for mirror and C is for collimator). Inset: Optical pictures of behaviour at 635 nm of a 6 mm-length WG with a width of 9 μm.  
laser diode with 1.2 mW of power at 635 nm (Thorlabs CPS635), an Er-doped fiber laser with 0.8 W at 1550 nm (IPG Photonics), and a DFB laser with 7 mW at 2640 nm (Nanoplas).  
Our experimental setup (Figure 2) enabled a fast switch of the different sources and detectors. Flip mirrors were used to easily switch between visible (M7) and IR (flip mirror) input light in the tested WGs. The remaining propagation optics, except WG-input and WG-output collimators were shared at all tested wavelengths by using gold-coated mirrors. For the collimation optics (C1 and C2 in the figure), a couple of collimation lenses were used, anti-reflective coated between 350 and 700 nm for the visible sources and between 1.6 and 3 \(\upmu\)m for the IR sources. Input laser beams at the different wavelengths were optically shaped to match the WG' mode field diameters. In Figure 2 lines of different colors are depicted according to the different spectral regions: visible (green line), NIR (red line), and MIR (brown line). Half-wave plates were placed in the NIR and MIR lasers' input paths in order to control beam polarization. Fine realignment of the input and output optics to optimize waveguiding and detection was performed when switching between laser sources.  
At the WG output, the light was sent to three different detectors, depending on the source in use. In addition, to analyze, check, and optimize the waveguiding behavior of the samples, as well as to control the correct IR beam line-up, an additional branch could be selected, by means of the flip mirror, after M5 to send the output light to a charge-coupled device (CCD) camera (an Si sensor for visible light, and an InSb camera for NIR/MIR light).
## 6 DIS-Trickle: A Mechanism for Accelerating Network Convergence  
This section proposes and evaluates a mechanism for leveraging DIS messages in order to accelerate RPL network convergence. In some scenarios a node may have to wait for a long time to receive a DIO message (and thus, to join a DODAG). As we described in Section 3.1, a node may discover nearby DODAG nodes in a short time by sending DIS messages, which will trigger a quick response (in the form of a DIO message) from neighbors that have already joined a DODAG. However, the RPL specification does not state any rule or scheduling algorithm for the transmission of DIS messages [5]. In this section we propose and evaluate DIS-Trickle, a mechanism for accelerating the network convergence process by using DIS messages. We first present DIS-Trickle and then evaluate its influence on network convergence performance.
# Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks
## 3 Methodology
### Network Description  
The modeled network comprises seven circuits connected to the company's medium-voltage network through a 15 kV/400 V/230 V three-winding transformer. Among these circuits, there are linked to the 400 V output, while the remaining four are connected to the 230 V output of the transformer. The 400 V output is utilized for supplying three-phase loads, as well as single-phase loads with a phase-neutral connection at 230 V. On the other hand, the 230 V output supplies single-phase loads connected between two phases. A network equivalent represents the medium-voltage network, as the study primarily focuses on analyzing the behavior of the low-voltage network downstream of the transformer. Figure 1 shows the analyzed network, where each color represents a different circuit.  
The low-voltage network comprises insulated cables with aluminum conductors ranging in sections from 25 mm\({}^{2}\) to 150 mm\({}^{2}\) across all lines. Distances between nodes vary, as they are based on actual network data, and the spacing between connection points is not uniform. The maximum distance between two nodes is 221 m, found at the end of Circuit 4,  
Figure 1: Analyzed low-voltage power grid.  
while the minimum distance is 5 m. The network comprises 499 terminals interconnected by 509 cables, with a total of 251 loads connected to these circuits. These loads are characterized based on the contracted power at installation nodes and usage factors, which depend on the load type, whether residential, service, or industrial, according to the circuit distribution indicated in Table 1. Industrial and service loads are small workshops, shops and offices in the village. These loads have been considered three-phase loads with delta connection. Domestic loads are typically single-family detached or semi-detached houses with some small blocks of flats. These domestic loads are considered single-phase or three-phase regarding the contracted power. If the contracted power is smaller than 5.5 kW, it has been assumed that this is a single-family house whose connection is single-phase. However, if the contracted power is higher than that value, it has been assumed that there is more than one home, and therefore, it has been assumed that the load is three-phase.  
Furthermore, the variation in consumed power has been determined from current measurements at the transformation centre provided by the distribution company. The measurement campaign was carried out over a month, and from these data, a typical week curve was determined for that season (Figure 2a). The figure shows that the power demanded by the loads remains within a range of 50 to 100 kW throughout the day, except for the period between 18:30 and 21:30, coinciding with the typical arrival time at homes in Spain, when demand increases, reaching the highest peak at 20:00.
## 4 Results
### Transfer of Multi-Component Diffusion Simulations to the Host Rock Scale  
The applicability of \(K_{d}\) and \(D_{e}\) calibrated from the 1 m-model to the host rock scale (50 m) was evaluated by comparison with a MC simulation (MC\({}_{4 Clay}\)). The other scenarios (MC\({}_{max}\), MC\({}_{min}\) and MC\({}_{2 Clay}\)) were calculated using the SC approach and the calibrated parameters given in Table 3 to quantify the influence of the mineral composition and total amount on the host rock scale. All results on the host rock scale are shown in Figure 6.  
After a simulation time of one million years, diffusion lengths of uranium for the MC\({}_{4 Clay}\) scenario varied in the facies between 13 m (Figure 6a) and 24 m (Figure 6d). For all facies, the MC simulations could be reproduced on the host rock scale with the \(K_{d}\) and \(D_{e}\) calibrated on the small scale with an average deviation of 3% (MC\({}_{4 Clay}\) and rRMSE, Figure 6). Considering two (MC\({}_{2 Clay}\)) or four (MC\({}_{4 Clay}\)) clay minerals for sorption did not significantly influence the migration of uranium in the shaly and carbonate-rich facies (Figure 6a-c), whereas, in the sandy facies, it reduced uranium migration by 8 m, from 24 m (MC\({}_{4 Clay}\), Figure 6d) to 16 m (MC\({}_{2 Clay}\), Figure 6d). The scenarios for the minimum and maximum total amount of minerals per kg porewater led to differences in the diffusion lengths between 2 m (Figure 6b) and 13 m (Figure 6d). Depending on the total amount  
Figure 6: Concentration of uranium in the porewater of the shaly (**a,c**), carbonate-rich (**b**) and sandy facies (**d**) for the scenarios with minimum (MC\({}_{min}\), light, green line), maximum (MC\({}_{max}\), dark, green line) and average (MC, blue line) total amount of rock per kg porewater as well as for the concepts considering four (MC\({}_{4 Clay}\)) and two clay minerals (MC\({}_{2 Clay}\)) for sorption. Dotted lines mean that the SC approach with the calibrated \(K_{d}\) and \(D_{e}\) was used, with red representing the results for the MC\({}_{4 Clay}\) scenario for better visibility. Only MC\({}_{4 Clay}\) (blue line) is based on the MC diffusion approach.  
of rock, maximum diffusion lengths of uranium ranged from 10 m in the shaly (MC\({}_{max}\), Figure 6a) to 35 m in the sandy facies (MC\({}_{min}\), Figure 6d).  
The computing times for the individual scenarios in Table 3 varied on the metre-scale between 1 h and 2 h for the MC approach and a few seconds for the SC calculations. On the host rock scale, the MC simulations required a computing time between 5 h and 6 h (6 h = 21,600 s), whereas the SC simulations were done in a few seconds (\(<\)10 s). This provides a time saving of four orders of magnitude (factor of 2\(\times\)10\({}^{4}\)).
## 2 Materials and Methods
### Dynamic Selection Adaptive Genetic Algorithm (DSAGA)
#### 2.3.5 Mutation Operation  
The mutation operation emulates the occurrence of genetic mutation in the context of biological evolution [48]. It stochastically alters the gene values within an individual's chromosome based on a specific probability. The mutation operation introduces novel combinations of genes, enhancing the variety of the search space and preventing the occurrence of locally optimal solutions.  
For FJSP, given the relatively lower propensity for variability in the inheritance process, setting the mutation rate as [49; 50]  
\[p_{m}=0.3 \tag{21}\]  
is advised, a value derived from copious experimental validation, which ideally suits the problem's nature.  
The decision to set a mutation rate at 0.3 strikes a balance between the need for introducing new genetic variations and preserving effective solutions within the genetic algorithm framework. An increase in the mutation rate elevates the likelihood of incorporating new genetic information into the population, fostering diversity and supporting optimization efforts. Nevertheless, an excessively high mutation rate might destabilize successful configurations and decelerate the convergence process. A mutation rate of 0.3 is thus considered optimal as it ensures adequate diversity without impacting the performance of well-adapted individuals [46]. The mutation occurs when a randomly generated number is less than or equal to the probability. This procedure initiates with the newly created entity undergoing mutation, followed by an evaluation of its fitness. The process entails selecting a specific workpiece operation for mutation at random, affecting the overall procedure. A machine for the selected operation is then randomly chosen from the available pool, with subsequent adjustments to machine allocations and process durations for the affected individual. This iterative mechanism repeats until mutation has been applied across all individuals.  
In summary, DSAGA signifies a progression in digital twin technology for solid-wood-panel manufacturing, differentiated by its adaptive selection strategy. This approach enables DSAGA to adjust selection pressure based on the algorithm's development stage, offering flexibility that is absent in GA. This adaptability enhances exploration within the search space and the quality of solutions generated. Furthermore, DSAGA, incorporating PMX operation and a calculated mutation strategy, effectively merges advantageous traits from multiple parents and introduces new genetic variations. By maintaining sufficient genetic diversity and preventing early convergence, these mechanisms collectively improve the algorithm's capacity to explore the search space, elevate solution quality, and sustain population diversity. Consequently, DSAGA emerges as a more effective and reliable approach for addressing complex manufacturing challenges in solid-wood-panel production, ensuring optimal operational performance and efficiency.
## 4 Discussion
The variations seen in SCFAs' levels in patients in comparison with healthy controls led to an increased interest in their use as potential biomarkers in a wide spectrum of clinical settings [63]. The remarkable results of lyophilised forms of FMT turned them into an interesting tool for quantification of SCFAs as a supportive method for better evaluation of quality of processed FMT. Despite the availability of several standardised and tested formulations of products based on FMT, there are still patients that may benefit from drinkable, fluid FMT formulation or a lyophilised, non-capsulated form. This includes adult and paediatric patients who may have problems with swallowing the capsules, particularly when the therapeutic procedure requires multiple repeating dosages. The proof of the clinical effectiveness of the lyophilised FMT for recurrent CDI was provided by two randomised controlled studies [67; 68]. In the study [67], the authors reported no significant differences in the speed of recovery between groups of patients treated with lyophilised versus frozen FMT by means of colonoscopy. In spite of this, there was an insignificantly lower speed of recovery observed when using lyophilised product. No significant difference was observed between patients treated per os by lyophilised FMT and by enema administration of frozen FMT, with respect to safety or the results of 2-month cure rate [68]. From the beginning of observation, the viability of microorganisms in lyophilised forms decreased at a higher rate in comparison with the frozen form of FMT stored at \(-70\)\({}^{\circ}\)C. All three lyophilised forms of FMT (\(-70\)\({}^{\circ}\)C, 4 \({}^{\circ}\)C and 20 \({}^{\circ}\)C) showed a similar trend of viability of faecal microbiota that decreased to the range of 11-23% by the 2 nd month of storage. Between months 3 and 7 of storage, the metabolic activity of microorganisms in all lyophilised forms continued to decrease to below the unacceptable level of 10%. It is important to note that the number of viable microorganisms throughout the storage period was significantly higher (\(p<0.001\)) in the frozen FMT form (\(-70\)\({}^{\circ}\)C) in comparison with all three lyophilised forms (\(-70\)\({}^{\circ}\)C,4 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C). With its viability at the level of 20-30%, thus the highest observed metabolic activity of FMT, this form appeared most suitable for the purpose of transplantation. After determination of the influence of methods of FMT processing on the viability of bacteria during storage, future investigations could focus on observation of the level of fermentation activity in long-term stored lyophilised forms of FMT. This could contribute to better understanding of the relationship between the viability of microorganisms and the level of their fermentation activity during aerobic processing of FMT and conservation by lyophilisation. Additional studies are needed to connect the viability of bacteria with the effectiveness of treatment with FMT, and the use of compact new formulations could facilitate such efforts.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## Abbreviations  
**AAA**: Authentication, Authorization, and Accounting
**AES**: Advanced Encryption Standard
**AI**: Artificial intelligence
**ADASs**: Advanced Driver Assistance Systems
**AUTOSAR**: Automotive Open System Architecture
**CAN**: Controller Area Network
**CIA**: Confidentiality, Integrity, and Availability
**CRC**: Cyclic Redundancy Check
**DEM**: Data Encapsulation Mechanism
**DMS**: Driver Monitoring System
**DoS**: Denial of Service
**ECU**: Electronic Control Unit
**GPIO**: General Purpose Input/Output
**GDPR**: General Data Protection Regulation
**HDMI**: High Definition Multimedia Interface
**IDS**: Intrusion Detection System
**IoT**: Internet of Things
**IPS**: Intrusion Prevention System
**ISO**: International Organization for Standardization
**JTAG**: Joint-Test Action Group
**KEM**: Key encapsulation mechanism
**MAC**: Message authentication code
**ML**: Machine learning
**OEM**: Original Equipment Manufacturer
**PCIe**: Peripheral Component Interconnect Express
**SPI**: Serial Peripheral Interface
**SAE**: Society of Automotive Engineers
**SSL**: Secure Sockets Layer
**TARA**: Threat Analysis and Risk Assessment
**TLS**: Transport Layer Security
**UART**: Universal Asynchronous Receiver Transmitter
**USB**: Universal Serial Bus
**V2X**: Vehicle-to-Everything
**VSA**: Vehicle Status Analysis