esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 4 Relative State Measurement Technology Trends and Optimization
### Multidisciplinary Optimized Architecture for Relative State-Measurement Payloads
In order to improve the accuracy and robustness of relative state measurement payloads, based on the overall trade-off of the formation design and the comprehensive consideration of the coupling relationship between subsystems, this paper proposes a multidisciplinary design optimization architecture by which to select relative state measurement payloads and optimize their performance parameters to find the best design solution. The coupling relationship between the distributed spacecraft formation flying subsystems is shown in the design structure matrix in Figure 32. The boxes represent the  
Figure 32: Design structure matrix of the overall design problem of distributed spacecraft formation flying. subsystem modules and the horizontal and vertical lines are the input and output of each subsystem module; the nodes symbolize the coupling and dependencies between the subsystems. The key issues of overall formation design decomposed by the design structure matrix (DSM) in this paper are allocated to the system and subsystem levels for optimization, as shown in Figure 32. The system-level target selection can be decided according to the mission requirements and the designer's focus. The architecture can process coupled sub-problems, such as the formation configuration design, load performance, platform performance, and spacecraft cost analysis, both in parallel and independently, to solve the coupling and competition effects between sub-problems. Under the given design constraints, at the system level, each discipline independently completes the analysis and design of this discipline and returns the design results to the system level. Finally, the system level is coordinated and optimized to obtain the optimal design that meets all the requirements. An optimal design that achieves a comprehensive balance of performance, cost, reliability, and maintainability is obtained; then, the type selection and parameter optimization of relative measurement payloads are conducted, as shown in Figure 33. At present, the researchers in this paper have used the visual monocular sensor, combined with multi-disciplinary collaborative optimization, to develop the ultra-precision short-range visual spacecraft formation flying project.
# Intelligent Environment Monitoring System for University Laboratories
## 2 System Design and Implementation
### Each Module of Proposed System
#### 2.3.2 Data Acquisition Module  
The system acquisition module uses digital sensors without external A/D conversion so that it is convenient to design. To guarantee the launch order of the GPRS module, the development board adopts power DC12V/2A. Meanwhile, 3.3 V and 5 V voltage chips are embedded. This can ensure that other data gathering modules work normally. The selection process of data acquisition sensor modules is shown in Table 1.  
Figure 2: System telecommunication framework.  
DHT11 (temperature and humidity collection module): Application of this composite module can monitor indoor humidity and temperature and provide the test results in the form of a digital signal. This module has better anti-interference ability and single-wire serial interface, which is helpful to system integration. Considering the impact of laboratory space, the appropriate pull-up resistor is selected based on the actual size of the laboratory.  
GY-30 (light intensity acquisition module): Through the application of the module, the sensor can measure a wide range of brightness and cut off the indoor light. Its internal 16-bit A/D converter can directly output a digital signal, independent of external A/D conversion unit and ambient light. The light spectrum is similar to that of the human eye. Thus, the impact on the infrared, which is small, does not influence the work of other modules.  
MQ-9 (fire monitoring module): Application of this detection device can monitor methane, carbon monoxide and other flammable gas. The module has a signal output, 0-5 V voltage. The larger the concentration is, the higher the voltage.  
HC-SR501 (human body infrared sensor module): Using the infrared control technology, the HC-SR501 can achieve automatic sensing. It is set in the induction block time, in which the sensor cannot receive any induction signals. This module can be applied to the interval detection to inhibit the load switching process caused by a variety of interference.
## 5 Experimental Evaluation
### Comparison Study and Discussion  
We compared our approach MTD3 CO with existing methods: (1) the improved deep reinforcement learning algorithm in [27], (2) the HEFT-based heuristic algorithm, and (3) the greedy algorithm. These methods were chosen because they are common algorithms when solving task offloading and are similar to our study.  
To train the MTD3 CO strategy, the DAG task generator was used to generate 20 training datasets with different DAG parameters and 100 DAG tasks with the same width and density in each set, each with 20 subtasks and density and width values of \(\{0.5,0.6,0.7,0.8\}\). Each dataset represented a mobile device application preference, and finding an effective offloading strategy for each dataset was used as a learning task in the MTD3 CO algorithm. Lastly, a training dataset was used to train the MTD3 CO algorithm. In Algorithm 1, the number of sampling tasks n was set to 10, i.e., 10 training datasets were selected from 20 training datasets. Each dataset sampled N trajectories for gradient update with gradient number k = 6. Every two times the value network was updated, the target network and the strategy network were updated once.  
Figure 4 shows the average reward during the training process. When the number of training times reached 200, it can be seen that the average reward increased substantially. This means that the strategy started to work and moved in a better direction. Finally, the average reward stabilized around \(-6\) and converged. When the meta-strategy converges, the meta-strategy network at this point summarizes the commonality of different mobile application offloads so that effective task offloads can be performed for different applications. However, since all applications are considered at this time, the strategy is not the optimal strategy for a specific application at this time. For a specific application, only a small number of inner-loop learning iterations are needed to get the best strategy. Below, we make a few different changes to the environment that can be used to compare how well the meta-strategy adapts to the new environment.  
When the meta-strategy training was completed, in order to verify the adaptability of the MTD3 CO algorithm to new tasks and new environments, some test datasets different from the training dataset were randomly generated for testing. For a task that needs to be scheduled, the data points that users are most concerned about are latency and mobile device energy consumption performance. Thus, the experiments mainly compare the performance of the algorithm on these two metrics under the new task; three relevant experiments are described below.
###### Abstract  
In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and \(R^{2}\) ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.  
agriculture; deep learning: LSTM; support decision-making algorithms; irrigation management +
Footnote †: journal: Applied Sciences  
15 April 2021  
Accepted: 27 May 2021  
Published: 29 May 2021
## 2 Materials and Methods
### The Measurement Setup for Outdoor Testing  
The measurement setup was installed on the INCEMC building (on a platform at a height of 5 m from the ground). Photovoltaic tiles based on yellow and green DSSCs were tested on 29 June 2022 from 10:00 a.m. to 6:00 p.m. in the evening. This time interval was chosen to be able to observe their behaviour in conditions of maximum temperatures. The tilt angle of the cells was set to 30\({}^{\circ}\), which is approximately the optimal angle for summer in Timisoara, Romania (45.4\({}^{\circ}\)N, 21.1\({}^{\circ}\)E) with the azimuth to the south.  
On the experimental stand (Figure 1), the temperature sensors (type k thermocouple, referred to as PV thermocouples) were attached under each individual tile using Kapton tape and a thermal compound for better heat transfer. For the ambient environment, a temperature sensor (DHT22 Temperature Sensor) was also used and installed on the platform at a height of 1.5 m. The monitoring of the data from the temperature sensors was carried out using the SPER SCIENTIFIC 800025 device (Scottsdale, AZ, USA), which was connected to the computer interface. The measurement error of the temperature sensors was \(\pm\)0.1%.  
The SOLAR-4000 (Solar analyzer) and Wireless sensor (SOLAR-4000 SENSOR, Beha-Amprobe, Thalwil, Switzerland), were used to monitor and record the solar radiation that fell on the surface of the photovoltaic tiles. The irradiance sensor was positioned at the same tilt angle as the cells. The J-V curves of the three tiles and the meteorological parameters (temperature on each tile, outside temperature, and solar irradiance) were recorded every 15 min.  
The photovoltaic tiles were mounted on an aluminum platform with an adjustable tilt angle. All cables were placed next to the experimental stand and connected to the measuring instruments and the computer where all the experimental data were recorded, these being placed inside so as not to be affected by the weather conditions during the measurements. It was taken into account that the wires needed to measure the electrical parameters had a resistance as low as possible so as not to influence the measurements.  
To measure the performance of the photovoltaic tiles, a Keithley 2450 source meter (Cleveland, OH, USA) was used, and an Agilent 34970A (Santa Clara, CA, USA) was used as a multifunctional switching unit. Both instruments were controlled from a computer using the Python interface. The PV tiles were kept in open circuit during the test due to the automatic measurement limitations of the system. The P\({}_{\mathrm{max}}\) parameters were extracted from the J-V curves. The measurement time for each individual tile was approximately  
Figure 1: Photograph of assembled yellow, green and Si tiles on aluminum platform.  
20 s. The UV-visible-NIR analysis (transmission and reflection modes) of the samples was performed using a UV/Vis/NIR (Lambda 950 UV-Vis-NIR from Perkin Elmer, Waltham, CT, USA) spectrophotometer in the range of wavelengths of 300-1000 nm at ambient temperature. For collecting the diffuse reflectance spectra, a 150 mm integrating sphere was utilized.
## 4 Proposed Model  
Existing work regarding scheduled beacon node broadcasting in underwater network provide the location using adaptive and opportunistic methods formulated under different schemes such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), and receiving signal strength indicators without introducing the scheduled mechanism [17]. Additionally,  
Figure 1: Conceptual overview of system model.  
many existing techniques offer linear algorithm control parameters, such as centroid and range-based or range-free algorithms, to modify and expand the criteria of beacon-based broadcasting. In GA, FA, LFA, BPSO, BFS, BFA [28; 29; 30; 31; 32] algorithms also give ideas to use a metaheuristic approach in TWNS and provide motivation for the application of UASNs. To address these challenges, a range-free metaheuristic-based beacon node broadcasting algorithm is proposed with the following key parameters, beacon frequency, transmission power, and beacon interval, optimized in UASN algorithms to ensure network performance while conserving energy. Algorithms can incorporate adaptive scheduling to adjust beacon behavior based on node status, balancing accuracy, and energy efficiency. The proposed method does not require the adjustment of algorithm-specific control parameters but the control parameters like population size of control, fitness function, lifetime of the nodes, and impact of scheduled beacon nodes need to be modified when and where required. The scheduling of the beacon nodes problem, defined as a multi-objective scheduling problem, is solved by the metaheuristic-based method. The goal of the multi-objective scheduling issue is to optimize the data packet forwarding ratio while decreasing localization error, false identification of beacon nodes, and shortest scheduled route for data forwarding. Many metaheuristic-based approaches, like BFA, GA, FA, LFA, and BPSO [28; 29; 30; 31; 32] with their hybridization of EHO, are implemented for computation analysis in order to calculate the performance of the suggested method. The formulation of beacon-based broadcasting and its parameter modification are discussed in detail in the next section.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion
### Simulation Results
#### 4.2.5 Experiment Five  
Aim: A comparative analysis has been done with an optimization model dealing with the recovery of HLR mobility databases presented in [40]. In this work, analytical performance results evaluation for the location updating scheme have been done for the mobility database failure recovery under some assumptions. The lost incoming calls cost is calculated versus the system probability distribution for the recovery time. Therefore, in our proposal, a simulation for a traffic analysis with random log file sizes is done into failure recovery time to calculate the lost packet rates.  
Main Results: The results in Figure 8 show a significant decrease in the average lost packets in the proposed model compared to the comparative protocol under different failure recovery time.  
Discussion: That is because the proposed model switched between a pool of recovery algorithms that may suitable for any environment rather than using a fixed recovery method that may not be suitable, which leads to the retrieval system not constantly updating its database in a proper time. Thus, it reduces the packet loss rate.  
Figure 7: Comparison results in terms of total payoff.
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 3 Results and Discussion
### _Phaeodactylum_ Total Lipid Extract-Fish Oil Mixtures (Part II)  
#### 3.2.1 Characterization (Fucoxanthin/n-3 LC-PUFA Ratio)  
Building further on the amount of fucoxanthin relative to the amount of n-3 LC-PUFA, Table 3 gives the n-3 LC-PUFA content (in mg/g), fucoxanthin content (in mg/g), and fucoxanthin/n-3 LC-PUFA ratio (in \(w/w\)) of the different mixtures of _Phaeodactylum_ total lipid extract and fish oil. Fucoxanthin was further highlighted in this because of its abundance and its importance in former research [18; 29]. While other antioxidants might also play their role [31], fucoxanthin was thus considered the major antioxidant. Information on the full fatty acid and carotenoid composition of both _Phaeodactylum_ total lipid extract and pure fish oil can be found in Appendix A.  
Since the fish oil has a higher n-3 LC-PUFA content than the _Phaeodactylum_ total lipid extract (between 227 and 117 mg/g, respectively), it should not surprise that the n-3 LC-PUFA content increases as relatively more fish oil were used. The fucoxanthin content, on the other hand, decreases by blending more fish oil due to its absence of carotenoids. Therefore, also the fucoxanthin/n-3 LC-PUFA ratio decreases, from which it is clear that mixing the _Phaeodactylum_ total lipid extract and fish oil in the amounts that were described in Table 1 led to different orders of magnitude of the fucoxanthin/n-3 LC-PUFA ratio. This formed an interesting starting point to investigate the oxidative stability of the different systems.  
\begin{table}
\begin{tabular}{l c c} \hline
**Carotenoid** & **Content (ug/g Oil)** & **Carotenoid/n-3 LC-PUFA Ratio (\(w\)/\(w\))** \\ \hline Fucoxanthin & 17 (\(\pm\)4) & 0.00007 (\(\pm\)0.00002) \\ Diatoxanthin & 4.4 (\(\pm\)0.7) & 0.000018 (\(\pm\)0.00003) \\ \(\beta\)-carotene & \textless{}LOQ (=5) & \textless{}0.00002 \\ \hline \end{tabular}
\end{table}
Table 2: Oil phase carotenoid content of the ‘Fish oil + _Phaeodactylum_ biomass’ system.  
\begin{table}
\begin{tabular}{l c c c} \hline
**System** & **N-3 LC-PUFA** & **Fucoxanthin** & **Fucoxanthin/n-3 LC-PUFA Ratio** \\  & **(mg/g)** & **(mg/g)** & **(\(w\)/\(w\))** \\ \hline Pure _Phaeodactylum_ total lipid extract & 117 (\(\pm\)1) & 32.4 (\(\pm\)0.4) & 0.277 (\(\pm\)0.003) \\ Mixture 79/21 & 145 (\(\pm\)3) & 24.06 (\(\pm\)0.04) & 0.1663 (\(\pm\)0.0003) \\ Mixture 49/51 & 165 (\(\pm\)4) & 16.7 (\(\pm\)0.3) & 0.101 (\(\pm\)0.002) \\ Mixture 6/94 & 222 (\(\pm\)3) & 1.73 (\(\pm\)0.04) & 0.0078 (\(\pm\)0.0002) \\ Mixture 1/99 & 232 (\(\pm\)3) & 0.211 (\(\pm\)0.004) & 0.00091 (\(\pm\)0.00002) \\ Pure Fish oil & 227 (\(\pm\)6) & - & - \\ \hline \end{tabular}
\end{table}
Table 3: Characterization of the different _Phaeodactylum_ total lipid extract–fish oil mixtures in terms of n-3 LC-PUFA and fucoxanthin.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 2 All-Digital True Random Number Generators
### Digital TRNGs
#### 2.2.2 Metastable Ring Oscillator  
Metastable Ring Oscillator (Meta-RO) [19] is another variant of a ring oscillator that harvests entropy from analogue metastability of inverter gates. A CMOS inverter reaches a metastable state if its output is short-circuited to the input with a closed switch. In this configuration, the output voltage oscillates around a metastable level due to a superimposed noise contribution. The complete circuit is depicted in Figure 2. The oscillator operation evolves through different phases:  
* Initialization: the oscillator is brought into metastable state with \(mux\_sel=0\). The metastable voltage is perturbed by low amplitude noise.
* Transition: signal \(mux\_sel\) transitions to _logical 1_ and the ring is recomposed. The low amplitude noise is amplified by inverters. The ring starts in a random state.
* Sampling: once the oscillator stabilizes to full-logic levels, sampling can take place using a D flip-flop. To accurately control the sampling instant, the author proposes to generate \(clk\) from the signal \(mux\_sel\) using a delay line.
## 4 Results
### Identification of Sensitive Features
#### 4.2.1 Crop and Grassland Areas  
In Zhengzhou City, for 99.7% of the total area for the low-sensitive area, the occurrence of low-sensitive features to buildings and most of the anomalies are mostly subsidence and crops, and the grassland area accounted for only 7%. However, in the highly sensitive area, there was 72% of the area for the crop area, which occurred three times in the anomaly settlement of the crop area, which accounted for 65% of the total sensitive area. The reason for this result is that a strata house surrounded by farmland in the northern part of Zhengzhou City experienced three obvious anomaly deformations during the rainy season of 2021 (Figure 6b), but since the surrounding features are farmland with poor backscattering, it is not possible to compute its PS points, and, thus, a large number of blank zones of PS points are generated around the building, but since the inverse distance weight interpolation method assigns a distance based on its positional weights, it spreads these three anomalies to a large number of blank zones around the building (Figure 6a), resulting in an error. Similarly, similar agricultural land patches are distributed in a large number of sparsely built-up areas in Zhengzhou City, but the detection of their anomalies may be subject to error, and, therefore, is only used as a reference. After measurement, the area of this blue area is about 1,520,000 m\({}^{2}\). After correction, the highly sensitive area is dominated by anomalous subsidence phenomena, and the area of highly sensitive agricultural land and grassland is slightly higher than that of the built-up area, which is the main contributor to anomalous subsidence features.