esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Methods  
TBNPA and DBNPG concentrations were determined by high-performance liquid chromatography (HPLC; Agilent 1100 series, Palo Alto, CA, USA) using a SupelcosilTM LC-18 column (Supelco, Bellefonte, PA, USA), with an eluent of 45:55 acetonitrile to water ratio at a flow rate of 1.5 mL min\({}^{-1}\). The concentrations of both compounds were quantified at 210 nm using a calibration curve. The detection limit of 1 mg L\({}^{-1}\) was determined along with an analytical error of \(\pm\)2%. Carbon and bromine isotope analyses were performed according to the methods described earlier [38]. Carbon isotope ratios were expressed relative to an international standard (V-PDB) in % units. \({}^{81}\)Br/\({}^{79}\)Br ratios were obtained from the coupling of a gas chromatograph (Hewlett-Packard 5890) to a multicollector-inductively coupled plasma mass spectrometer (MC-ICPMS Nu Instruments, Wrexham, UK) analysis after correction by a Sr external spike as previously reported [39]. Bromine isotope ratios during the experiment (specific time points--\(R_{sample}\)) in per mil units were expressed relative to the bromine isotope ratio of the sample at time zero according to Equation (1):  
\[\delta^{81}Br,\ \%_{\rm e}=\left[\frac{R_{sample}-R_{t0}}{R_{sample}}\right]\times 1000 \tag{1}\]  
where \(R_{t}\) is the isotopic ratio in the sample at a given time in the experiment and \(R_{t0}\) is the ratio at time zero. The samples were analyzed three times each for carbon and bromine isotope compositions.
## 5 Result and Analysis
### Recognizing Critical Locations with Large Tensile Stress in JPCP under Dynamic Load  
It is important to determine the critical locations where the tensile stress might reach its maximum and to study the effect of the pavement structure parameters and the dynamic load parameters on the dynamic responses of JPCP. According to engineering practice, bottom-up transverse cracks normally occur at the middle of the slab, while top-down cracks normally occur at the corners of the slab. The following locations are normally recognized as the key locations where the maximum tensile stress might be generated, denoted as P1, P2, P3, and P4, respectively, for the bottom midpoint of the longitudinal slab edge, the bottom of the slab center, the top of the longitudinal slab corner edge, and the top of the transverse slab corner edge, as shown in Figure 9. For simplification, \(\sigma_{\text{x-d}}\) and \(\sigma_{\text{x-s}}\) are denoted as the dynamic and the static stress in the traveling direction, respectively, and \(\sigma_{\text{y-d}}\) and \(\sigma_{\text{y-s}}\) are denoted as the dynamic and the static stress in the transverse direction, respectively. In this study, the magnitude of stress is defined as positive in tension and negative in compression.  
Figure 7: Pattern of contact between axle load and the road (in cm).  
Figure 8: _Cont._  
To determine the critical location where the tensile stress might reach its maximum, the case of \"100 km/h-30 tons-D\" is selected for computation. The values listed in Table 4 are used as the input for pavement structure parameters. The calculated stresses at the four locations of P1-P4 are plotted in Figure 9, with the position of the dynamic moving quarter-car model.  
It is seen that the maximum tensile stresses are generated at locations P1 and P2. The tensile stress reaches maximums of 3.13 MPa and 2.67 MPa, respectively, when the axle load moves to the middle of Slab2. Under well-supported slab conditions, the tensile stresses \(\sigma_{\text{x-d}}\) and \(\sigma_{\text{y-d}}\) at P3 and P4 are small and negligible, and are not in the critical location to develop top-down corner cracks. Considering the actual engineering scenario, it is believed that the top-down cracks developed at the slab corner are mainly caused by the loss of slab support either from voids beneath the slab or slab curling/warping due to temperature/moisture gradient. Comparing the tensile stresses at the four potential critical locations, \(\sigma_{\text{x-d}}\) at P1 is the largest, and thus the bottom midpoint of the longitudinal Slab2 edge (P1) is selected as the location to be monitored for dynamic response analysis.  
Figure 8: The influence lines of dynamic axle load for different loading cases: (**a**) 30 km/h-10 tons, (**b**) 30 km/h-20 tons; (**c**) 30 km/h-30 tons, (**d**) 60 km/h-10 tons, (**e**) 60 km/h-20 tons, (**f**) 60 km/h-30 tons, (**g**) 100 km/h-10 tons, (**h**) 100 km/h-20 tons, and (**i**) 100 km/h-30 tons with the other parameters of quarter-car model fixed as shown in Table 3.
# Towards the Implementation Process of Accessible Virtual
## 3 Technological Accessibility Requirements in Higher Education Institutions
### Answers to Research Questions Related to Technological Accessibility Requirements in HEIs
These indicators form part of the detected strategies that seek to bring the required support during college. Actions that support the technological development of accessible virtual platforms in HEIs include the founding of IT departments for the purpose of developing of virtual platforms, and the digitalization of educational material while complying with accessibility standards [44]. For instance, ISO/IEC 40500 and the Web Content Accessibility Guidelines (WCAG) 2.0 [45; 46] by the World Wide Web Consortium (W3C) are a set of popular guidelines for the creation of accessible websites that focus on the development of systems designed to include people with disabilities as users. There are strategies oriented towards attending to the lack of sensitized personnel, pedagogical barriers, and attitudes that limit the integration of students with disabilities to a college environment. Identifying requirements is not enough to foster accessible learning environments. Nonetheless, this can be helpful for detecting points for evaluation to determine accessibility levels within an educational institution, thereby promoting improvements that tear down existing barriers and facilitate the integration of students with disabilities into the HEI [43]. 3.4 NRQ4: What Are the Results Documented in the Literature That Support the Functionality of Technological Accessibility Strategies Implemented by HEIs? By implementing technological accessibility strategies in HEIs, positive results are observed, such as a tendency to enhance the integration of students with disabilities and the quality of services offered to them. One of the benefits identified in literature includes the use of accessibility techniques for online courses by adding subtitles to audio elements and alternate texts to visual elements for the understanding of those with sight or hearing disabilities. Involving students with disabilities in the processes that compose an educational institution tends to increase adaptability and accessibility in physical spaces as well as learning material; as a result, this fosters inclusive environments within classrooms. Commissions and groups are created that contribute to the strengthening of strategies, sensitivity training, employee training, and equality. Other positive impacts identified are a decrease in discrimination, alongside an increase in inclusive counseling and physical spaces for those with disabilities in the student community. These resulting benefits serve to better integrate this vulnerable minority into academic activities.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Turbulence-Induced Fading for Airborne Nodes
Utilizing this, the formula in (14) can be modified for a slant link where \(C_{n}^{2}\) changes with altitude as (the turbulence strength may change as a result of a change in the transmission distance, due to the movement of the UAV over the predefined track. This small change is neglected here. )\[f_{I}(I)=\frac{1}{I\sqrt{2\pi\sigma^{2}}}\exp\left(-\frac{\left(\ln(I)-\mu \right)^{2}}{2\sigma^{2}}\right),\qquad I>0 \tag{7}\]0  
In Figure 9, we present the losses associated with turbulence-induced fading, assuming typical daytime and nighttime \(C_{n}^{2}\) profiles. It is observed that the power losses are limited to only a few decibels for vertical distances in the order of km. For example, the maximum turbulence loss for a vertical link with a transmission distance of 10 km is observed at daytime, being 3.3 dB at a wavelength of 350 nm. This drops to 1.75 dB for a wavelength of 1550 nm. In other words, higher wavelengths are less affected by turbulence. \[f_{I}(I)=\frac{1}{I\sqrt{2\pi\sigma^{2}}}\exp\left(-\frac{\left(\ln(I)-\mu \right)^{2}}{2\sigma^{2}}\right),\qquad I>0 \tag{7}\]1 Losses due to the propagation of FSO signal through a turbulent channel at propagation distance of (**a**) 0.1 km, (**b**) 1 km, and (**c**) 10 km.
## 2 Materials and Methods
### Phantom Test  
We subsequently tested the system on a solid mechanically switchable phantom [44] that was realized at the Physics Department of Politecnico di Milano in collaboration with the Physikalisch-Technische Bundesanstalt of Berlin. This phantom allows to rapidly change the optical properties from homogeneous to multiple degrees of heterogeneity. It is composed by a block of material (approximately a parallelepiped of 104 \(\times\) 80 \(\times\) 45 mm\({}^{3}\)), the background, with a cylindrical hole from one lateral side to the opposite surface, and whose center is at 15 mm of depth and several cylinders, the rods, slightly lower in diameter than the hole, but longer than its height. These rods can be adjusted inside the background and interchanged between them. Since each rod embeds a different purely absorption inclusion, by modifying their position, a corresponding different perturbation is induced in the phantom itself and in changeable positions. The homogeneous background is in epoxy resin (optical properties at 690 nm: \(\mu_{a}=0.01\ \text{mm}^{-1}\)  and \(\mu_{s}^{\prime}=1\ \text{mm}^{-1}\)), while the inclusions are totally absorbing black polyvinyl chloride cylinders with different volumes. The suitability of the totally absorbing objects to mimic realistic optical inhomogeneities was rigorously demonstrated on Monte Carlo simulations for a wide range of experimental conditions [43]. The constructed phantom revealed discrepancies up to 30% with respect to the theoretical expected perturbation, which was possibly ascribed to uncertainties in the fabrication process. Still, within this limitation in accurate quantitation, the phantom is effective in testing the sensitivity of a measurement system.  
The probe is placed on the upper surface of the phantom, while the inclusion is translated at a depth of 15 mm, traversing the whole region beneath the detectors' circle. Measurements are taken at steps of 5 mm for a total of 20 positions, repeating the acquisition 10 times for 1 s at each position. A homogeneous measurement (i.e., with the inclusion far from the detector area) is always taken as reference for unperturbed state.
## 6 AOG Downtime Reduction and Implementation Results
### Pair plot of Continuous Variables  
The pair plot (Figure 16) visually compares continuous variables related to AOG incidents, including part failure rate, Maintenance Schedule Compliance, total flight hours, downtime hours, and delay time hours. It highlights clear patterns and relationships that offer insights into the drivers of AOG events. One of the most noticeable trends is that AOG incidents are linked to higher part failure rates and increased downtime hours, indicating that frequent failures are a primary contributor to extended ground times. This reinforces the importance of using reliable components and adhering to maintenance schedules to minimize disruptions.  
Maintenance compliance appears inversely related to AOG incidents, with poor compliance increasing the likelihood of these events. This suggests gaps in maintenance planning and highlights the need for more efficient workflows. While total flight hours has a minimal direct impact on downtime, aircraft with higher usage tend to experience more downtime when AOG incidents occur, likely due to accumulated wear and tear. Delays are strongly correlated with prolonged downtimes during AOG incidents, pointing to inefficiencies in resources or logistics, such as spare part shortages or staffing constraints. These patterns suggest that frequent failures, inadequate maintenance compliance, and logistical challenges are the main factors driving AOG events. Addressing these issues requires further analysis of outliers, such as cases with unusually high downtime or delay hours. Investigating these incidents using predictive models and historical records can help  
Figure 15: Maintenance Schedule Compliance vs. AOG incidents.  
identify root causes, whether they stem from parts quality, inventory gaps, or procedural inefficiencies.
## 4 Simulation and Experiment  
### Simulation Results  
The effectiveness of the improved algorithm is verified on a simulated scenario implemented in MATLAB, involving a micro-amplitude wave model caused by underwater sound sources. The underwater sound source excites signals with two frequencies of 100 Hz and 103 Hz on the water surface, which is also affected by wind. Table 1 reports the FMCW transmitted signal and the simulation parameters.  
The echo matrix was obtained from the simulation accumulation of Equation (6). The simulation environment is that the underwater sound source excites 100 Hz and 103 Hz signals to the water surface, superimposed with the sea wave disturbance caused by wind. Since the WSAW amplitude and the sea wave disturbance cannot exceed one range bin, these are represented as a straight line in the echo matrix.  
Regarding the method, when a single frequency has fewer sampling data points, the improved algorithm attains a higher estimation accuracy than a periodogram, which requires encoding more frequencies within a certain period, thus increasing the communication system's transmission rate. Additionally, when the measured signal contains signals with a strong amplitude, the side lobe of this signal overwhelms the main lobe of the weak signal, affecting the accurate estimation of the WSAW frequency. The height of the WSAW is inversely proportional to the frequency of the underwater sound source. If the frequency of the underwater sources is higher, the WSAW reaches the submicron level, making it challenging to detect the WSAW signals. Therefore, the underwater communication frequency band is narrowband. The frequency interval setting for the underwater sound source coding affects the communication performance of the cross-media communication system. However, the improved algorithm can separate the two closer frequencies, thereby improving the underwater acoustic frequency band utilization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Quantity** & **Value** \\ \hline \(R_{\text{radar}}\) & distance from the radar to the water surface & 1.5 m \\ \(R_{\text{speaker}}\) & distance from the underwater sound source to the water surface & 0.8 m \\ \(L_{p}\) & sound pressure level & 110 dB \\ \(f_{\text{w1}}\) & transmitting frequency 1 of the underwater source & 100 Hz \\ \(f_{\text{w2}}\) & transmitting frequency 2 of the underwater source & 103 Hz \\ \(B_{\text{r}}\) & bandwidth & 1.2 GHz \\ \(T_{\text{r}}\) & pulse repetition time & 8 us \\ \(M\) & fast-time sampling number & 400 \\ \(N\) & slow-time sampling number & 40,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 1 Introduction
The longitudinal and lateral-directional motion modes were analyzed through digital flight simulation based on decoupled dynamic equations of motion. The eigenvalue equations were formulated in the form of polynomials and solved. The eigenvalues for short-period, phugoid (long-period), Dutch roll, spiral, and roll modes of motion were estimated based on the damping ratio and undamped natural frequency. A positive real part of the eigenvalues is to indicate unstable motion. The dynamic stability characteristics during sudden plunging motion are easily judged by the values in positive or negative. The present approach is an innovation to examine possible mitigation concepts of accident prevention for airlines to enhance aviation safety and operational efficiency.
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 4 Experiments  
### Datasets  
In the experimental part, we mainly validate our model using three public datasets and one private dataset. The three public datasets are called Electricity, Weather, and ETTh2, and the private dataset is named BCAT. The Electricity dataset collated the electricity consumption from 2012 to 2014 of 321 customers; the Weather dataset contains 21 weather metrics such as temperature and humidity; the ETTh2 dataset contains six load features and one oil temperature feature, recording two years of data from July 2016 to July 2018; BCAT is a taxi parking lot departure statistics dataset from Beijing Capital International Airport, where the statistics interface records the number of taxis leaving the parking lot every half hour. BCAT recorded the departure data of the terminal taxi parking lot from 25 August 2022 to 31 December 2022, totaling 6600 items. A portion of the BCAT dataset is shown in Figure 4. Detailed descriptions of the four datasets are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Electricity** & **Weather** & **ETTh2** & **BCAT** \\ \hline Variables & 321 & 21 & 7 & 1 \\ Timesteps & 26,304 & 52,696 & 17,420 & 6600 \\ Frequency & 1 h & 10 min & 1 h & 30 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information description of the four datasets.  
Figure 3: Calculation process of the merge matrix attention mechanism.  
Figure 2: Calculation process of independent matrix attention mechanism.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 3 Research Methodology
### Image Segmentation
#### 3.2.2 Image Hue Thresholding  
The second stage of image segmentation is to determine the suitable threshold value to distinguish between the foreground and background. For this purpose, the hue image obtained in the first stage is used, as it provides a suitable grayscale image that can be used to classify objects based on color content. The upper and lower range of the hue channels is obtained from the color bar. This range is used to define an upper and lower threshold value for lettuce foliage in a hue image in the form of a mask. This mask is then applied to the R, G, and B channels of the original image, which are then stacked to obtain the segmented image. The final segmented image is saved in RGB format. In order to save time, the segmentation process is automated, and by the end of the process, each segmented image is saved in a common directory.
## 4 Results and Discussion
Since the DORAHP needs to reform the link frequently in the marine environment with time-varying topology, the packet delivery ratio of DORAHP first increases and then decreases when the node's communication radius grows. If the node's communication radius is relatively small, the number of hops for the data packet transmission would increase. In other words, more energy should be taken for the transmission in terms of the node. Figure 7b shows that the energy consumption is the lowest among the considered methods. In particular, when the node's communication radius changes dynamically, compared with  
Figure 5: (**a**) Comparison of packet delivery ratio. (**b**) Comparison of end-to-end delay. POR, E-Ant-DSR, and DORAHP, the average node energy consumption of the proposed opportunistic routing protocol is decreased by 13.7%, 19.9% and 4.8%, respectively. To further verify the performance of the algorithm, we use variable \(\sigma^{2}\) to simulate changes in sea conditions (the larger the \(\sigma^{2}\), the worse the sea conditions). It can be seen from Figure 8 that as \(\sigma^{2}\) increases, the performance of all algorithms gradually deteriorates. Figure 6: Comparison with variable node moving speed. (**a**) Packet delivery ratio. (**b**) Average energy consumption of nodes. Figure 7: Comparison with variable node’s communication radius. (**a**) Packet delivery ratio. (**b**) Average energy consumption of nodes. Since the proposed algorithm comprehensively considers the link quality, distance metric and the optimal expected packet advancement, it is better than the benchmark algorithms in terms of packet delivery ratio and node's energy consumption. When \(\sigma^{2}\) is 50 dB, the packet delivery ratio of the proposed opportunistic routing protocol is 72%, which basically meets the MSR requirements.
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Company** & **No. Sats** & **Sats Size** & **Orbit** &
\begin{tabular}{c} **Year** \\ **(Operative)** \\ \end{tabular} & **Reference** \\ \hline Sky and Space Global & 200 & Nano & LEO & 2020 & [74] \\ GeoOptics & 24 & Nano & LEO &? & [75] \\ NOAA & 12 & mini & LEO & 2020 & [76] \\ PlanetIQ & 18 & Micro & LEO & 2020 & [77] \\ Zhuhai Orbita Control Engineering & 34 & Micro & LEO & 2020 & [78] \\ Ltd. & 100 & Micro & LEO &? & [79] \\ Canon & 100 & Micro & LEO &? & [79] \\ Helios Wire & 28 & Micro & LEO & 2023 & [80] \\ Swarm Technologies & 100 & Pico &? & [81] \\ Iceye (BridgeSat) & 18 & Micro & LEO & 2020 & [82] \\ Analiltical Space &? & & LEO &? & [83] \\ Hiber & 48 & Nano & LEO &? & [84] \\ Fleet Space & 100 & Nano & LEO & 2022 & [85] \\ Audacy & 3 & & MEO & 2020 & [86] \\ ELSE & 64 & Nano & LEO & 2021 & [87] \\ AlSTech & 102 & Nano & LEO &? & [88] \\ AlSTech & 18 & Nano & LEO &? & [88] \\ HawkEye360 & 21 & & LEO &? & [89] \\ Axelspace & 50 & Micro & LEO & 2022 & [90] \\ Capella Space & 36 & Micro & LEO &? & [91] \\ Karten Space &? & Nano & LEO &? & [92] \\ UnseenLabs &? & & LEO &? & [93] \\ NSLComm & 60 & Nano & LEO &? & [94] \\ EightyLEO &? & Mini & LEO & 2022 & [95] \\ UrtheCast & 24 & & LEO & 2021 & [96] \\ Orbital Micro System & 40 & Micro & LEO &? & [97] \\ Lacuna Space & 32 & Nano & LEO &? & [98] \\ Hera Systems & 50 & & LEO &? & [99] \\ CASC (xinwei) & 300 & & LEO & 2025 & [100] \\ SRT Marine &? & & LEO & * & [101] \\ SatRevolution & 1024 & Nano & LEO & 2026 & [102] \\ Commsat Technology Development & 72 & & LEO & 2022 & [103] \\ Co. Ltd. & 72 & & LEO & 2022 & [104] \\ Aerial and Maritime & 80 & Nano & LEO & 2021 & [104] \\ Harris & 12 & Nano & LEO &? & [105] \\ Earth-i & 15 & Mini & LEO &? & [106] \\ LinkSure Network & 272 & & LEO & 2026 & [107] \\ Synspective & 25 & Mini & LEO &? & [108] \\ Space Systems Engineering Ukraine &? & & &? & [109] \\ Astrome & 200 & Mini & LEO & 2023 & [110] \\ Cloud Constellation Corp. & 10 & & LEO &? & [111] \\ Transcelestial &? & Nano & LEO &? & [112] \\ Kleos Space & 4 & & LEO & 2019 & [113] \\ HyperSat & 6 & Micro & LEO & * & [114] \\ Galaxy space & 1000 & & LEO &? & [115] \\ ChinaRS & 10 & Micro & LEO & 2021 & [116] \\ Laser fleet &? & & LEO & 2022 & [117] \\ XpressSAR & 4 & & & 2022 & [118] \\ Orbital oracle Technologies & 100 & Nano & LEO & 2024 & [119] \\ Methera Global & 16 & & MEO & 2022 & [120] \\ Trident Space & 48 & Mini & LEO & 2026 & [121] \\ VEOWARE &? & & LEO & 2022 & [122] \\ Umbra Lab & 12 & & LEO &? & [123] \\ EarthNow &? & & LEO &? & [124] \\ OQ Technology &? & Nano & &? & [125] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._