esa-sceva/satcom-chunk-collection huggingface dataset:
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spectral Signal-to-Noise Ratio
These corrections are not applied here, while the resulting SNR is still close to results from earlier simulations.
# Airport pavement Maintenance Decision-Making System with Condition Cases Optimization
## 2 Airport Pavement Management System Architecture
### Decision Making Process for Airport Pavement Management System
#### 2.4.4 Sensitivity Analysis
After the analysis period, SL showed a similar decrease rate for all alternatives. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Input Value**} & \multicolumn{4}{c}{**Total B/C Difference (\%)**} & \multicolumn{2}{c}{**SL at the End of the Analysis**} \\  & & & \multicolumn{2}{c}{**Period Difference (\%)**} \\ \cline{3-6}  & & **NO** & **CCO** & **DN** & **NO** & **CCO** \\ \hline \multirow{4}{*}{**Budget**} & 20\% & 100.03 & 107.88 & 100.00 & 100.53 & 100.21 \\  & 10\% & 100.03 & 107.88 & 100.00 & 100.53 & 100.21 \\  & **0\%** & **100.00** & **100.00** & **100.00** & **100.00** \\  & \(-10\%\) & 99.94 & 54.33 & 100.00 & 99.09 & 99.47 \\  & \(-20\%\) & 99.90 & 2.44 & 100.00 & 98.67 & 98.93 \\ \multirow{4}{*}{Definition of \"Life Extension\" \"Life after M\&R & 250.81 & 266.68 & 100.00 & 91.29 & 91.91 \\  & **Life extension** & **100.00** & **100.00** & **100.00** & **100.00** \\  & **5 years** & **100.00** & **100.00** & **100.00** & **100.00** \\ Analysis period & 7 years & 343.07 & 369.99 & 95.06 & 96.02 & 95.71 \\  & 10 years & 426.99 & 233.42 & 87.63 & 89.79 & 88.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensitivity analysis results (difference with original value). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Airport** & **A** & **B** & **C** & **D** & **E** \\ \hline Annual Departures & 12,000 & 8000 & 4500 & 3000 & 2000 \\ Number of & Rumway = 15 & Rumway = 15 & Rumway = 15 & Rumway = 15 & Rumway = 15 \\ Sections per Branch & Taxiway = 10 & Taxiway = 8 & Taxiway = 2 & Taxiway = 1 & Taxiway = 1 \\  & Apron = 8 & Apron = 2 & Apron = 1 & Apron = 1 & Apron = 1 \\ \(\Lambda\) & 0.7 & 0.4 & 0.4 & 0.4 & 0.6 \\ CC I Sections & 3 & 2 & 1 & 0 & 0 \\ CC II sections & 7 & 3 & 2 & 4 & 0 \\ CC III sections & 23 & 20 & 15 & 13 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Information about sections used for the sensitivity analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Input Value**} & \multicolumn{4}{c}{**Total B/C Difference (\%)**} & \multicolumn{2}{c}{**SL at the End of the Analysis**} \\  & & & **Period Difference (\%)** & \\ \cline{3-6}  & & **NO** & **CCO** & **DN** & **NO** & **CCO** \\ \hline \multirow{4}{*}{**Budget**} & 20\% & 100.03 & 107.88 & 100.00 & 100.53 & 100.21 \\  & 10\% & 100.03 & 107.88 & 100.00 & 100.53 & 100.21 \\  & **0\%** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & \(-10\%\) & 99.94 & 54.33 & 100.00 When adjusting the costs for maintenance methods, a 20% cost reduction resulted in an approximately 1.3-fold increase in total B/C. However, when costs increased by 20%, particularly in the case of CCO, it showed a significant decrease of 8% compared with the initial value, indicating high sensitivity.
## 4 Experimental Results and Discussion
### Performance Metrics Curves  
To check for overfitting, compare the training and validation losses shown in Figure 10:  
* Convergence of Losses: if both decrease and stabilize closely, the model generalizes well.
* Divergence of Losses: if training loss decreases while validation loss increases, it likely indicates overfitting. Based on the performance metrics curves for YOLOv5s6--Yarn Hairiness in Figure 10, we can assess potential overfitting. Key observations from the graphs include:
* **train/box_loss and val/box_loss:*
* both box loss curves decrease consistently, with no significant difference, indicating good generalization.
* **train/obj_loss and val/obj_loss:*
* object loss curves decrease with minor fluctuations in validation, but no significant difference, suggesting no clear overfitting.
* **train/cls_loss and val/cls_loss:** classification losses decrease similarly for both curves, indicating good model generalization.
* **metrics/precision:** accuracy steadily increases and stabilizes for both training and validation, with no large differences, indicating no overfitting.
* **metrics/recall:** recall increases and stabilizes similarly in both sets, with a minimal difference between training and validation, indicating good generalization.
* **metrics/mAP_0.5 and metrics/mAP_0.50.95:** mean accuracy (mAP) metrics increase steadily, with close proximity between the training and validation curves, indicating good generalization and no clear overfitting.  
In conclusion, based on the graphs in Figure 10, there is no clear evidence of overfitting. The training and validation loss and metric curves are close, indicating that the YOLOv5s6 model generalizes well to the validation data. If overfitting exists, it is minimal. The metrics  
Figure 10: Performance metrics for the YOLOv5s6-Hairiness approach.  
Figure 9: Confusion Matrix for the proposed object detection model.  
demonstrate the quality and performance of the neural network during training and testing, specifically for yarn hairiness detection.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 2 Methods and Materials
### Regolith Simulate  
Due to a lack of original material from the Moon, synthetic lunar soils, so-called regolith simulants, are used for earthly research. For the experiments described in this paper, two advanced regolith simulants, LX-T100 and LX-M100, from the TU Berlin were used. Each simulant consists of one of the dominant bedrocks of the Moon, basalt and anorthosite. In addition to the chemical and mineralogical composition, the simulants have the same particle shape and particle size distribution as the lunar regolith. LX-T100 is based on anorthosite rock. It consists predominantly of plagioclase (>95%). This material, thus, corresponds to the terrae material of the Moon in its pure form. LX-M100 is a simulant made of basalt, i.e., the material of the mare regions. It consists of 39% plagioclase, 42% pyroxene and 19% olivine. In terms of their particle morphology and particle size distribution, both simulants correspond to the average values of lunar regolith, which were determined from the samples from the Apollo and Luna missions. The chemical composition in the form of total oxides is shown in Table 10.
## 3 Signalized Intersection Traffic Flow Model for Intelligent Connected Environment
### CAV Passing Decision Model  
1. Free-driving area evolution rules: CAV in the free-driving area is not influenced by traffic signals, applying only relevant car-following models.
2. Passing decision area evolution rules: Based on the vehicle's status and interaction with the driving environment, we determined scenarios such as green light with long remaining time, green light with short remaining time, red light, and consider headway constraint to optimize speed guidance.
3 CAV as the leading vehicle  
Scenario e: The vehicle enters the intersection when the traffic light is green and the remaining time is relatively long. During this green light phase, it can pass the stop line without exceeding the speed limit, thereby accelerating to provide the maximum passing time for subsequent vehicles.  
Figure 2: Schematic diagram of traffic zone partitioning.  
Scenario f: The vehicle enters the intersection when the traffic light is green, and the remaining time is relatively short. During this green light phase, it cannot pass the stop line. If accelerating allows it to pass during this green light phase, it accelerates; if accelerating does not allow it to pass during this green light phase, it decelerates to pass the stop line just as the next green light phase starts.  
Scenario g: The vehicle enters the intersection when the traffic light is red. If it can reach the stop line by the end of this phase, it decelerates to pass the stop line just as the next green light phase starts; if it cannot reach the stop line by the end of this phase, it accelerates.  
All other situations must comply with the following two constraints:  
1. Solve for the recommended acceleration based on the control strategy when the CAV is the leading vehicle.
2. To ensure driving safety, the solved acceleration must meet the constraints of the improved CACC and ACC car-following models on speed.
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 5 Discussion  
### Decoy State and the _Nack State Protocol_  
At first glance, the _nack state_ protocol seems to be similar to the _decoy state_ protocol. However, they differ due to some main issues. For example, the states of the _decoy state_ protocol are \(BB84\) non-orthogonal states, which produce two photon distributions that differ from each other with respect to the expected photon number of the source (\(\mu_{1}\
eq\mu_{2}\)).  
Thus, in the _decoy state_ protocol, single detection events come from non-orthogonal states. As described by [PERSON], if [PERSON] lets an abnormally high fraction of multiphotons reach [PERSON]'s station, then decoy states, which have a high weight of multiphotons, will have an abnormally high transmission probability [23]. Unfortunately, _decoy state_\(QKD\) is vulnerable to the \(IRFS\) attack [13; 20]. On the other hand, in the _nack state_ protocol, [PERSON] interleaves randomly parallel and orthogonal quantum states. Unlike the _decoy state_ protocol, in the _nack state_, the photon source uses a unique \(\mu\) value. However, single and double detection events come from parallel and orthogonal states randomly.
# Influence of Cracking on the Durability of Reinforced Concrete with Carbon Nanotubes
## 3 Results and Discussion
### Chloride Ion Penetration
The incorporation of CNT led to a relevant reduction of the D\({}_{\text{cl}}\) in the uncracked area of about 35%, regardless of the target crack width. Two main reasons may be attributed to this improvement: the fact that the uncracked region is also affected by the crack and its surrounding area; the greater contribution of the CNT near the natural cracks, which, as mentioned, is more disturbed than the vicinity of artificial cracks. However, the D\({}_{\text{cl,CR}}\) was less affected by the incorporation of CNT. The reduction of this property was only 8-10% for the different crack widths, compared to plain concrete. As mentioned, the CNT are not able to participate within the crack alignment due to their large width. The contribution may essentially occur in the microcracked area of the crack vicinity. This is better shown in Section 3.5.
## 5 Discussion  
The key components in our experiment included an RF amplifier (SHF 100 AP) with a bandwidth of 30 KHz-25 GHz, Bias-Tee (PE1611) with a bandwidth of 10 MHz to 2.5 GHz, blue LD with a bandwidth of 1.25 GHz, and lens and avalanche photodiode (APD) with a bandwidth of 1 GHz. The bandwidth in the link was enough, so it was not the factor that affected the experiment's performance. The wavelength of the LD was 451 nm, and the output power was fixed to 30 mW. The wavelength of LD was one of the key factors that affected the performance. For underwater optical communications, the wavelengths of blue (around 450 nm) and green (around 532 nm) were two transmission windows; the light with these two wavelengths had less attenuation brought by the water and produced better BER performance than light with other wavelengths. Another key factor was the lens at the transmitter side. If the light beam is expanded by the lens, a big light spot will be produced; then, the optical power reaching the receiver lens will reduce, which will lead to worse BER performance.  
In optical wireless communications, OFDM was widely researched because it could bring higher spectra efficiency and better tolerance of multipath effect. But in turbulent atmosphere channels, when it encountered the scintillation of light, OFDM was not superior to a single-carrier scheme. Because the high-order modulation format used by OFDM, like QAM, carried information both with amplitude and phase, when the light was distorted by the turbulence, both the amplitude and phase of information symbols were affected, which led to performance degradation.  
For FOFDM, the information was mapped to ASK symbols; only the amplitude was used, so when distortion appeared, only the amplitude was affected. In order to investigate the performance of 4-ASK-FOFDM, we transmitted a 4-ASK-FOFDM signal over a turbulent air-water channel and a non-turbulent air-water channel. Through comparison, it can be concluded that the 4-ASK-FOFDM has a better tolerance in the turbulent channel. Moreover, because of low complexity and high SE, the FOFDM scheme could have more potential applications in future optical wireless communications.
## 1 Introduction
(1994) studied the permeability of Yinzhuang sandstone in the full stress-strain relationship-testing process and analyzed the relationship between the permeability and the full stress-strain evolution of this rock [23]. [PERSON] et al. (2014) studied the influence of water pressure on the time-dependent deformation of surrounding rock in the process of deep rock mass excavation under high stress by performing triaxial creep tests of deep sandstone from Chongqing and found that water pressure can enhance the performance of rock creep deformation [24]. [PERSON] et al. (2019) studied the variation in sandstone permeability in the loading-unloading process of confining pressure [25]. [PERSON], S.B. and [PERSON], M.A. (2009) carried out research on the variation in the permeability of carbonate rocks and found that the permeability began to decrease when the temperature increased from 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C; the permeability decreased by approximately 59% when the temperature decreased from 100 \({}^{\circ}\)C to 50 \({}^{\circ}\)C and the confining pressure from 28 MPa to 5 MPa [26]. [PERSON] et al. (2020) found that the increase and decrease in the seepage flow discharge and permeability were consistent with the damage evolution trend under high temperatures and high confining pressures through an experimental study on the permeability characteristics of low-permeability Yunnan red sandstone [27]. The above studies mainly focused on the relationship between sandstone pore permeability and the development process of rock fracture and mostly on ultralow-permeability rock, such as marble, granite and tight sandstone. Although these studies systematically analyzed the effects of high temperatures and high confining pressures on rock permeability and other physical properties, the test conditions were extreme; for example, the test temperatures were mostly 100-800 \({}^{\circ}\)C, and the effective stress was more than 80 MPa (sample fracture occurs). The \"three-high\" conditions (high confining pressure, high water pressure, high temperature) are quite different from those that are encountered in deep coal mines at present or that will be countered in the next 20 years. This study focuses on the problem of sandstone pore water seepage that affects coal mining in deep coal mines at present or within the next 20 years. The research aims to solve the engineering problems concerning the realistic \"three-high\" conditions of deep coal mines, namely, confining pressures of 1-40 MPa, water pressures of 1-12 MPa, and temperatures of 25-65 \({}^{\circ}\)C. This study considers the permeability differences of sandstones with different pore structures under the actual \"three-high\" conditions of deep coal mines. The aim of this study is to explore a classified permeation grouting method and optimize grouting parameters for permeation grouting in deep-mine sandstone.
## 3 Results
### The Weighting Factor and the Results  
The mean group weighting factors of the two carriage conditions were determined for the NASA TLX rating scales after the experiment, as shown in Figure 11. The results of the paired \(t\)-test illustrate that the MD and PD weights are significantly different in the two carriage conditions. The MD weight in the with-EXO condition is significantly higher (\(p\) = 0.037) than the one which is in the conventional carriage. It means the assistive exoskeleton increases the mental demand of the subjects. According to the experiment process, the reason may be the subjects have to stabilize the human-robot system not only by providing force and torque, but by providing mental control strategies. As far as PD was concerned, the PD weight in the with-EXO condition is significantly lower (\(p\) = 0.005) than the one in the conventional carriage. It means the assistive exoskeleton successfully shared the loadings in the experiment process. There were no significant differences for the weighting factors TD, OP, and EF. For the weighting factor FR, it indicated that subjects were more insecure, discouraged, irritated, stressed, and annoyed with exoskeleton assisted loading walking. This indicates a poor ergonomic user interface for the exoskeleton.  
According to the comparison of weight factors, the discomfort C, mental demand MD, physical demand PD, and frustration FR were selected into the two-way ANOVA analysis. The results are shown in Table 4. Although there is no interaction effect in the statistical results of Discomfort, Mental Demand, and Physical Demand with carrying condition and load level, the ordinal interactions existed in the interaction plot, as shown in Figure 12. In reality, soldiers often carry loads over 55 kg. If the load level was increased, a disordinal interaction may have appeared. The with- or without-EXO main effects on the three workload factors were significantly different (\(p\) < 0.0001). The load level main effects on the Physical Demand were significantly different (\(p\) < 0.0001), and also on Discomfort and Mental Demand. The \"with or without\" main effects were pair-wise compared. The unweight marginal mean values were 31.208 \(\pm\) 5.502, \(p\) < 0.0001. The with-Exo discomfort level was higher 21.438 (95%CI is from 20.890-42.328) than the without-Exo.  
Figure 10: **(a)** The subjective discomfort performance. **(b)** The comparisons of mean rating value plots of the six workload factors.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 3 Salt-Stress Treatment for Caroenoid Production from Microalgae
### Salt Stress Strategies for Carotenoid Accumulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resposes** & **Low-Dose NaCl** & **High-Dose NaCl** \\ \hline \multirow{2}{*}{Physiology} & Growth \(\uparrow\); & Growth \(\downarrow\); Chlorophyll content\(\downarrow\); \\  & Photosynthesis \(\uparrow\) & Photosynthesis \(\downarrow\) \\ \hline \multirow{2}{*}{Morphology} & \multirow{2}{*}{No significant changes} & Cell size \(\uparrow\); Cell wall \(\uparrow\) \\  & & & Color change \\ \hline \multirow{2}{*}{Main carbon sinks} & \multirow{2}{*}{Carbohydrate \(\uparrow\)} & Carbohydrate (providing building blocks) \(\downarrow\) \\  & & & TGs \(\uparrow\) \\ \hline \multirow{6}{*}{Gene expression} & \multirow{6}{*}{\(\bullet\)} & \multirow{2}{*}{\(\pi\) gene (for N assimilation) \(\uparrow\)} & \multirow{2}{*}{\(\bullet\)} & TFs (WRKY, MYB, bHLH\_). \\  & & & Antioxidant enzymes genes (SOD, \\ \cline{1-1}  & & & Contayment \(\uparrow\) \\ \cline{1-1}  & & & Contayment \(\uparrow\) \\ \cline{1-1}  & & & Secondary metabolites genes \(\uparrow\) \\ \cline{1-1}  & & & Fatty acid synthesis genes \(\uparrow\) \\ \cline{1-1}  & & & Starch catabolism genes (PK) \(\uparrow\) \\ \cline{1-1}  & & & Gluconeogenesis gene (PECK) \(\downarrow\) \\ \hline \multirow{2}{*}{Metabolites} & \multirow{2}{*}{Lutein \(\uparrow\)} & \multirow{2}{*}{Secondary carotenoids \(\uparrow\)} \\  & & & TFA \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the effects of salinity on microalgae at different doses. the presence of 20% NaCl (\(w/v\)) [92]. Strategies with salinity treatment to boost carotenoid accumulation in microalgae are summarized in Table 4.  
In summary, moderate salt stress can increase biomass productivity and induce accumulation of carotenoids, depending on microalgae species, while strict stress will be toxic and even fatal to microalgae. As both high cell density and carotenoid productivity are desirable for biotechnological goals, an optimal salt concentration should be chosen cautiously according to different cultivation goals and microalgae species. Two-stage or multi-stage cultivation and strategies combining salinity with other stress factors also have potential in microalgae cultivation for carotenoids.
## 2 Structure of Present and Future Detectors  
Terrestrial gravitational wave detectors are kilometer-scale Michelson interferometers with arms equipped with Fabry-Perot cavities to extend their effective optical length. The Fabry-Perot mirrors are heavy test masses suspended from threads, so that they can be freely accelerated along the beamline by the space-time fluctuations caused by passing gravitational waves. Present surface detectors (Virgo, LIGO, KAGRA [17; 18; 19]) have arms 3 to 4 km long, while future detectors currently being designed will be three to ten times longer. In the current surface L-shaped detectors, input test masses, recombination and input-output optics are housed in a single large building. The present design of the Einstein Telescope has three 10 km-long tunnels oriented at 120\({}^{\circ}\) from each other. The six nested detectors are intended to record signals from both polarizations of gravitational waves. They intersect in three large vertex halls, each containing eight of the twenty-four test masses, including at least one of all six detectors, as well as the input/output optics of two Michelson interferometers and their ancillary equipment. This configuration has several drawbacks including excessive crowding and mixing of optical components and seismic isolation systems, that require quiet conditions, with ancillary equipment, such as cryogenic chillers that cause reverberating acoustic noise, which is known to inject noise into the interferometer. The worst concern is that access for maintenance or upgrade on a single detector will affect, and in most cases impede, the operation of all others, thus reducing or interrupting astronomical observations every time an access is made.
## 2 Theoretical Approach
By using Equations (1) and (2), the approximate peak lateral resistance requirements of the CWR track can be obtained, as shown in Figure 6. In Figure 6, the \(x\) and \(y\) axes represents the peak lateral resistance of the CWR track and \(f_{all}\), respectively. Firstly, for a given curve radius (1500 m, 700 m, and 300 m), \(f_{b,min}\) was calculated using Equation (2) for various values of the peak lateral resistance. Then, \(f_{all}\) could be obtained using Equation (1), where \(\Delta T_{R}\) is set as 35 \({}^{\circ}\)C (=60 \({}^{\circ}\)C (the max. temp. of the rail) \(-\)25 \({}^{\circ}\)C (the rail laying temp.)) according to KR C-14050 [11]. UIC 60 rail is used for the calculation and \(I_{R}\) of UIC 60 rail is 513 cm\({}^{4}\). From Figure 6a-c, it can be seen that the minimum peak lateral resistance requirement of girder-sleeper fastener is approximately 17 kN/m/track, 14 kN/m/track, and 10 kN/m/track for \(R\) = 1500 m, 700 m, and 300 m, respectively. These results can be utilized as reference values for the preliminary design of the fastener. However, the effect of the bridge is not considered in this result. Furthermore, in-depth CWR track buckling behavior on the open-deck steel plate girder bridge should be discussed to determine the proper lateral resistance requirements of a girder-sleeper fastener, and this appears in later sections. Figure 5: Track buckling shape.