esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Enzymatic Hydrolysis of Mechanically Disrupted Cells  
Enzymatic hydrolysis of microalgal biomass was carried out on different scales. The preliminary experimentation with various enzyme doses was performed in 5 mL screw-cap glass vials in triplicate using a thermal shaker (Thermomixer Basic, CellMedia GmbH & Co. KG, Zeitz, Germany) incubating the reaction mixture for 72 h with an agitation rate of 550 rpm. Scale-up experiments on a 60 L scale were performed in a stirred-tank reactor (LP 75 L, Bioengineering AG, Wald, Switzerland) with an agitation rate of 550 rpm. Scale-up to 200 L was done in a tempered stirred tank (M500 L, Bioengineering AG, Wald, Switzerland). The commercial enzymes used for biomass hydrolysis are listed in Table 5.  
Cell dry weight (CDW) concentration of the microalgal biomass to be hydrolyzed was adjusted to 150 g L\({}^{-1}\) in all cases. The cells were always mechanically disrupted by high-pressure homogenization using an industrial HPH before performing enzymatic hydrolysis unless stated otherwise. Instead of heat sterilization, antibiotics (each 100 mg L\({}^{-1}\) of kanamycin sulfate and tetracycline hydrochloride) were added to the biomass at the beginning of hydrolysis to inhibit the growth of contaminants unless specified otherwise.  
In some cases, pretreatment with proteases was performed before adding polysaccharide-degrading enzymes to determine if this would increase the accessibility of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Enzyme** & **pH** & **Temperature** \\ \hline Protamex\({}^{\circledR}\) & 7.0 & 37 \({}^{\circ}\)C \\ Protease from _Aspergillus saitoi_ & 3.5 & 37 \({}^{\circ}\)C \\ Other enzymes & 5.0 & 50 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hydrolysis conditions used for different enzymes for optimal enzyme activity according to the manufacturer’s specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Enzyme** & **Activity** & **Manufacturer** \\ \hline Cellic\({}^{\circledR}\) CTc3 HS & Cellulae, \(\beta\)-glucosidase, hemicellulose & Novozymes (Bagsvaerd, Denmark) \\ \hline Remicellulose from _Aspergillus niger_ & Hemicellulose & Sigma-Aldrich (Taufkirchen, Germany) \\ \hline Rohament\({}^{\circledR}\) CEP & Cellulae, \(\beta\)-glucosidase, hemicellulose & AB Enzymes (Darmstadt, Germany) \\ \hline Rohalase\({}^{\circledR}\) GMP & Endo-mannanase & AB Enzymes (Darmstadt, Germany) \\ \hline Protamex\({}^{\circledR}\) & Endo-protease & Novozymes (Bagsvaerd, Denmark) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Commercial enzymes used for enzymatic hydrolysis after mechanical cell disruption.  
carbohydrates for the latter enzymes. Since the optimal pH and temperature conditions to achieve maximum activity differed for each enzyme, these parameters were adjusted differently for the proteolysis and the following polysaccharide hydrolysis, as listed in Table 4.
## 3 Experimental Analysis
### Average Precision Analysis  
The R-P curve demonstrates the relationship between model precision (P) and recall (R) and enables the evaluation of model performance. When the classification threshold interval is taken from 0.001 to 1.000 with a total of 1000 threshold nodes, the corresponding P and R of the model are obtained. Using 1000 sets (R, P), the R-P curve is plotted with recall on the horizontal axis and precision on the vertical axis. The area under the line of the resulting R-P curve is the average precision (AP) of the model.  
The R-P curves of YOLOv7 and GCB YOLOv7 are shown in Figure 6. As can be seen from the figure, the area under the line of the GCB YOLOv7 model is significantly larger than that of the YOLOv7 algorithm, so the average precision is higher. In Hemercallis citina Baroni maturity detection, the average precision of the YOLOv7 and GCB YOLOv7  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **+ Ghost** & **+ CBAM** & **+ Bi FPN** \\ \hline YOLOv7 & & & & \\ Model\_1 & \(\surd\) & & & \\ Model\_2 & \(\surd\) & \(\surd\) & & \\ Model\_3 & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Network Layers** & **Parameters/Millions** & **Flops/G** & **Training Time/h** & **Model Volume** \\ \hline YOLOv5 & 468 & 46.14 & 107.9 & 8.067 & 92.7 \\ YOLOv7 & 415 & 37.20 & 104.8 & 8.025 & 74.8 \\ Model\_1 & 500 & 34.64 & 96.7 & 7.776 & 69.7 \\ Model\_2 & 511 & 65.17 & 97.5 & 7.722 & 70.8 \\ Model\_3 & 511 & 35.17 & 97.5 & 7.903 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model lightweight analysis.  
models is 0.913 and 0.922, respectively. It can be seen that the improved GCB YOLOv7 algorithm is superior.  
In the R-P curve, the balance line of the R-P curve (red solid line) is plotted by connecting the points (0,0) and (1,1), which produces two intersections with the R-P curves of the YOLOv7 and GCB YOLOv7 algorithms, (0.8326, 0.8326) and (0.8364, 0.8364), respectively. From the enlarged view inside the red circle, we can see that when models P and R are balanced in Hemercollis citrina Baroni maturity detection, the GCB YOLOv7 algorithm is able to take into account both P and R, and its balanced performance is more desirable.
# A Multimode Fusion-Based Aviation Communication System
## 5 Conclusions  
The ongoing evolution of aviation electronics necessitates the development of more integrated, modular, and intelligent systems. This paper presents a multimode fusion communication system designed to enhance aircraft communication capabilities. By incorporating ad hoc networks, 5G public networks, BeiDou satellite communication, RTK high-precision positioning, and ADS-B air broadcasting, the system achieves a balance between communication quality and system volume expansion, providing data communication, aggregation, sharing, analysis, display, and other services for modern aviation communication.  
The system's design emphasizes structural integrity, electromagnetic compatibility, and thermal management, featuring various modules, including ad hoc network, public network, BeiDou, RTK, ADS-B, and a fusion control module. This platform supports a range of functions, from data and image transmission to remote control and emergency communication. With advancements in artificial intelligence and big data, this system could further enhance air traffic control, monitoring, and early warning capabilities, laying the foundation for a comprehensive and interconnected aviation communication network.  
Conceptualization, J.Q. and M.L.; methodology, J.Q.; validation, J.Q. and F.X.; formal analysis, Y.B.; investigation, J.Q.; resources, J.K.; writing--original draft preparation, J.Q.; writing--review and editing, J.Q.; visualization, F.X. and D.O.; supervision, M.L. and J.K.; project administration, J.K. and D.O.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities.  
Data Availability Statement: Data are contained within the article.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Shanghai Aerospace Electronics Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions
### Physical Characteristics  
Satellite and terrestrial channel conditions such as Rician fading and Rayleigh fading can be exploited to improve security. By analyzing the Doppler shift and shadowing, an improved security mechanism can be created to improve the system performance. PLS techniques for inter-satellite link security can be considered for specific channel characteristics and potential threats. Terrestrial channel characteristics, such as urban and rural models, can be investigated to optimize the system performance. Large- and small-scale fading is another possible way to distinguish between channels.  
Joint channel estimation and beamforming in diverse ISTN channels can be considered for improved security. Channel estimation based on deep learning would be helpful in such scenarios. Considering the diverse channel model, specific security metrics can be devised. Physical layer key generation based on different fading models can be considered for secure communication. By exploiting the channel characteristics, PLKG offers the potential for information-theoretic security, eliminating the need for traditional cryptographic algorithms that rely on computational hardness assumptions.  
Advancements in signal processing techniques are crucial for enhancing the resilience of satellite communications to a range of physical layer attacks, including jamming, spoofing, and eavesdropping. Researchers are developing adaptive signal processing algorithms that can dynamically adjust to changing threat environments, employing machine learning to detect and mitigate attacks in real time [110]. Physical layer authentication methods are also being explored, which utilize the unique characteristics of the communication channel, such as the propagation delay, Doppler shift, and angle of arrival, to authenticate signals and detect potential intrusions. This approach adds an additional layer of security by ensuring that the physical properties of the signal match expected parameters, making it difficult for attackers to successfully impersonate or alter legitimate communications.
## 2 Reliability Analysis Method of 3D Slopes
### Model of Random Field
#### 2.1.2 K-L Expansion Method  
The K-L expansion method, recognized for its superior computational precision and efficiency, is widely used to simulate cross-correlated non-Gaussian random fields [6,30]. Using this method, the log-normal random fields related to cohesion \(c\) and friction angle \(\varphi\) can be represented as [25]  
\[H_{c}(x,\theta)=\exp\left(\mu_{\ln c}+\sum_{i=1}^{M}\sigma_{\ln c}\sqrt{\lambda _{i}}\varphi_{i}(x)\chi_{ci}(\theta)\right) \tag{2}\]  
\[H_{\varphi}(x,\theta)=\exp\left[\mu_{\ln\varphi}+\sum_{i=1}^{M}\sigma_{\ln \varphi}\sqrt{\lambda_{i}}\varphi_{i}(x)\left(\chi_{ci}(\theta)\cdot\rho(c, \varphi)+\chi_{\varphi i}(\theta)\cdot\sqrt{1-\rho^{2}(c,\varphi)}\right)\right] \tag{3}\]  
In Equations (2) and (3), \(\mu_{\ln}\) is the mean of \(\ln(H)\) and equal to \(\ln\mu-(\sigma\ln)^{2}/2\).  
\(\sigma_{\ln}\) is the standard deviation of \(\ln(H)\) and equal to \((\ln(1+(\sigma/\mu)^{2}))^{0.5}\).  
\(\chi_{i}(\theta)\) is a set of orthogonal random coefficients, i.e., uncorrelated random variables with zero mean and unit variance.  
\(\lambda_{i}\) and \(\varphi_{i}(x)\) denote the eigenvalues and eigenfunctions associated with the autocorrelation function, respectively.  
\(M\) denotes the count of truncated terms within the series expansion.  
\(\chi_{ci}(\theta)\) and \(\chi_{\varphi i}(\theta)\) are independent random vectors.  
\(\rho(c,\varphi)\) is the cross-correlation coefficient between \(c\) and \(\varphi\).  
The precision of the random field is evaluated by the ratio of the expected energy \(\varepsilon\), which is formulated as  
\[\varepsilon=\sum_{i=1}^{M}\lambda_{i}\bigg{/}\sum_{i=1}^{\infty}\lambda_{i} \tag{4}\]
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 3 Results and Discussion  
### Monitoring of Control Parameters in Photobioreactors  
The temperature in the PBRs ranged from 24 to 26 \({}^{\circ}\)C. This parameter is directly related to light incidence and is a key control parameter for the system's balance, as well as DO concentrations, pH, species dynamics, and the total efficiency of photobioreactors using the microalgae-bacteria consortia. This factor stayed constant during the period of the 14-day experiment, for the fact that microalgae grow best at temperatures around 25 \({}^{\circ}\)C. According to [PERSON] et al. [36], high temperatures in microalgae systems can directly damage all of the enzymes in the microalgae cells, as well as generate a thermal stress that primarily affects the proteins and membrane structures.  
The initial pH of the systems, after of inoculation of microalgae and aerobic sludge, was in the range of 7.89 \(\pm\) 0.16 in the six PBRs, as shown in Figure 2. After two days of monitoring, the species started to emerge in the system, and pH increased to 8.92, 8.85, 9.13, 9.23, 9.18, and 9.53 in PBR1, PBR2, PBR3, PBR4, PBR5, and PBR6, respectively. This circumstance indicates that the medium's photosynthetic activity was increasing, allowing for more organic matter degradation [37]. To avoid nutrient removal mechanisms such as ammonia volatilization and phosphorus (P) precipitation, the pH was maintained with 1M HCl during the operative time. Because of the alkaline pH range, the process of spontaneous autofloculation of microalgae could occur [38]. On the other hand, some microalgae species are unable to resist acidic conditions [39]. The pH of all photobioreactors was kept close to neutral for these reasons.  
Higher values of DO were observed in the photobioreactors, with values of 17.06 mg/L, 17.44 mg/L, and 17.73 mg/L for PBR4, PBR5, and PBR6, respectively. These values indicate that the medium has a higher photosynthetic activity, implying that microalgae in PBRs are growing at a faster rate. The high concentration of DO in the system can have a direct impact on biomass productivity as well as macroscopic compositions, including proteins, carbohydrates, and lipids [40]. When DO is above 4 mg/L, it may favor biomass synthesis, whereas when it is below, it may result in an incomplete breakdown of organic molecules [41]. PBR1, PBR2, and PBR3 had the lowest concentration of organic carbon in the medium, as well as the lowest microalgae growth rate and the lowest concentration of suspended particles in the medium; these latter two characteristics will be examined further in later sections.
## 3 Classification
### Type of Applications
#### 3.4.2 Convenience Applications  
Convenience applications are the V2P applications that assist the VRUs by improving their travel efficiency through various services. Examples of such convenience applications are ride-sharing, green light for bicycles, traffic information for VRUs etc. [PERSON] et al. [40] propose a V2P-based convenience application that helps electric bicycles save battery power. This is achieved in real-time by calculating the bike's recommended speed based on the received traffic light timer information. The TIMON project [42] provides various convenience services for VRUs that include current traffic status, information about vehicle collisions, and re-routing assistance. Table 1 lists various V2P convenience applications.
## 2 Peer-to-Peer Approach for Decentralized MG's Control
In this strategy each peer has a local table that is used as a lookup algorithm to route the request data according to node tables [57]. DHT allows the peers to find the addressed data using flat identifiers (IDs). This kind of P2P system improves the network communication usage, and it is shown in Figure 3.  
Figure 3 compares the performance of broadcasting and DHT lookup algorithms for distributed peer to peer architectures. The complexity of each of these lookup algorithms can be evaluated by analytical metrics [58]. Analytical metrics for structured DHTs and Broadcasting-unstructured P2P designs have been proposed in [59]. The computational behavior of these lookup algorithms is described by means of O, which describes the asymptotic behavior of the algorithms [58]. In a structured DHT overlay, each node maintains information about the O (logn) value of other nodes and solves lookup via O (logn) messages per lookup. In broadcasting, the metric is O (logn) messages per lookup. It is worth noting that O notation expresses the asymptotic upper bounds, since it bounds the growth of the message exchange for a large enough number of input nodes. This means that the exchange messages grows no faster than a certain constant value (in the case of broadcasting) or a logarithmic one (in the case of DHTs). In fact, it grows slower. From a practical point of view, the value of O(n) agrees with n [58]. Figure 3: Numerical analysis for comparison of lookup P2P algorithms. Message counts are considered as a metric value for communication overhead [60] and can provide evidence about the network and the bandwidth usage, as well as about the end-to-end latencies. O(logn) and O(n) indicators depict the worst case scenario for n nodes in the network. As Figure 3 shows, the DHT lookup technique is more efficient than Broadcasting. Thus, it will be used in the proposed solution. In addition with DHT the resources discovery can be satisfied in a bounded number of steps even for large scale distributed systems.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 3 Results and Discussion  
Using time series analysis, we developed forecasting and estimation models for the behavior of NVDI using satellite measures. We considered the time series obtained by the average values of available data in each month. However, the same methodology can be applied for the series obtained considering the maximum and minimum values. We considered an aggregated final model to forecast the time series values in the short term, considering 10 months of horizon planning (forecast values from January 2018 to September 2018). For model validation, we used the cross-validation method; divided the samples into training time series, comprising the available data from January 2013 to January 2015; and tested time series from February 2015 to December 2017. In consequence, we present final model equations avoiding non-significant parameters. The motivation for using cross-validation techniques is that when we fit a model, we fit it to the training data set. Without cross validation, we will only obtain information about how the model works with the sample data. Ideally, we want to see the performance of the model when new data are available based on the accuracy of the prediction.  
Exponential smoothing models and ARIMAX were combined to include the meteorological measurement variables, namely, temperature (T), precipitation (P), humidity (H) and radiation (R), whose prediction errors were greater than the combination of their predictions using different methods.  
For the elaboration of forecasts of the different crops with the ARIMAX structure, considering the dynamic structure of the time series and by means of transference functions, the variables that have an impact on their behavior must be identified, and in turn, the methodology used for the construction of the prediction model. The first step is the development of the iterative process to analyze the variables whose coefficients are significant to explain the behavior of the response variable.  
In all the ARIMAX models that we present below, the meteorological measurements are included, and after adjusting and verifying the significance of the coefficients, the results of the diagnosis are presented below.
## 4 Discussion
### The Determination of Lag Phase  
In this study, by comparing the maximum or minimum value of Pearson's correlation coefficient (\(r\)), we determined the lag phase, which is inspired by the studies of [PERSON] et al. [56] and [PERSON] et al. [57]. Our results showed a delayed response or time lag effect which impacted the correlation between SIF and the climatic factors. However, there were some uncertainties in this method. More specifically, Figure 6 could be considered as correcting the \(r\) of SIF with the four factors through adjusting the time series of SIF and the four factors to a reasonable response period. Compared with Figure 4, the spatial maps of \(p\) were noticeably improved in Figure 6. In fact, we adjusted the time series within 6 months, but  
Figure 7: Histogram comparison between the spatial Pearson’s correlation coefficient (\(r\)) of deseasonalized solar-induced chlorophyll fluorescence (SIF\({}_{\text{ds}}\)) with deseasonalized precipitation (P\({}_{\text{ds}}\)), vapor pressure deficit (VPD\({}_{\text{ds}}\)), evaporative stress (ES\({}_{\text{ds}}\)), and root zone soil moisture (RSM\({}_{\text{ds}}\)) and that of the corrected data. (**–d**) were the histogram comparisons between the \(r\) of SIF\({}_{\text{ds}}\) with P\({}_{\text{ds}}\), VPD\({}_{\text{ds}}\), ES\({}_{\text{ds}}\), and RSM\({}_{\text{ds}}\) and that of corrected data, respectively.  
in some areas the \(r\) of SIF with the climatic factors were insignificant at any adjustment. For this situation, we cannot determine the lag phases between variables, or even analyze the response between them. In addition, the correlation coefficient is a measure of the overall relationship between two variables. However, in the study period, some events, such as droughts and floods, made the relationship between SIF and the climatic factors abnormal or even deviant. This also affected the assessment and qualification of the mechanism of the relationship between variables. For determining a more accurate lag phase or response period, we believe that it is more effective to study the response between the variables from the perspective of events and utilize higher temporal and spatial resolution data.
## 4 Materials and Methods
### Characterization
#### 4.3.4 Small Angle X-Ray Scattering (SAXS)  
SAXS measurements were performed on the CREDO instrument [125] equipped with a GeniX3D Cu ULD beam delivery system with integrated FOX 3D parabolic multilayer optics (Xenocs SA, Sassenage, France) and a 30 W microfocus Cu anode X-ray tube (Xenocs SA, Sassenage, France). The produced X-ray beam (\(\lambda\) = 0.154 nm) was cut to the required cross-section and divergence using a 3-pinhole collimation scheme [126]. After interacting with the samples mounted on a ladder sample holder inside the evacuated sample chamber, scattered X-rays were detected with a Pilatus 300K CMOS hybrid pixel detector (Dectris Ltd., Baden, Switzerland). From each sample, several scattering patterns were recorded to assess the stability of the sample and the instrument. The sample-to-detector distances were nearly the same for each sample, covering the range of 0.05 \(<q<\) 2 nm\({}^{-1}\), where the \(q\) is the magnitude of the scattering vector, defined as \(q\) = 4\(\pi\) sin \(\theta/\lambda\), where 20 is the scattering angle and \(\lambda\) = 0.154 nm is the X-ray wavelength of the Cu K\(\alpha\) radiation used. The individual scattering patterns were subjected to the standard correction procedure of the instrument as soon as they were ready. Geometric effects, such as detector flatness, X-ray absorption in the sample, etc., were accounted for. The angular range was calibrated (through determining the sample-to-detector distance) using a mixture of silver behenate and a batch of SBA15 mesoporous silica which was previously calibrated in the same instrument on first principles basis. Although the intensity is routinely calibrated into absolute units of differential scattering cross-section using a piece of glassy carbon, due to the uncertainty of determining the sample thickness, in this case, we were only able to express the intensity on a relative scale of arbitrary units.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 2 Early History and Definitions
The re-ordering also reduced the bit error probability at the decryptor input thereby reducing the probability of decryption introducing long error bursts into the PCM data. In summary, while deep-space applications were in a position to use channel codes almost immediately after the appearance of PCM, the applications in aeronautical mobile telemetry occurred later. For this reason, the history of deep-space telemetry is described first, followed by the history of aeronautical mobile telemetry.