esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 2 Measurement Setup Description  
### Measurement Scenario  
The scenario where the set of measures has been carried out is similar to the one measured in [17; 18]. This is the underground level of the Technical University of Carta-gena. It mainly consists of a 50-m-long corridor, three perpendicular corridors and some laboratories used by students and researchers. The corridor walls are made of plasterboard; the floor is made of reinforced concrete, as is the ceiling. Figure 1 shows the corridor and Figure 2 shows a top view of the measurement environment, with the measurement points marked in red. The measurement points are 2 m apart. The measurement points located in the main corridor, a horizontal corridor, are what we consider to be NLOS-1 measurements,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Modulation/Transmission Technology** & **Frequency** & **Model** & **n (Slope)** \\ \hline
[46] & NB CW & 914 MHz & CI & LOS 1.8–2, NLOS 3.25 \\ \hline
[47] & CW & 914 MHz & CI & LOS 1.9, NLOS 2.4 \\ \hline
[48] & VNA & 5 GHz & CI & LOS 1.7, NLOS 3.5 \\ \hline
[49] & DPSK & 850 MHz & CI & NLOS 3 \\ \hline
[50] & SIMO & 2.4 GHz & CI & LOS 2.1, NLOS 2.959 \\ \hline
[51] & NB CW & 2 GHz & CI & LOS 2.0, NLOS 2.8 \\ \hline
[51] & NB CW & 2.9 GHz & CI & LOS 1.6, NLOS 3.1 \\ \hline
[52] & NB CW & 4.5 GHz & CI & LOS 2.31, NLOS 3.69 \\ \hline
[52] & NB CW & 4.5 GHz & FI & LOS 1.32, NLOS 4.85 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._while the measurement points that continue along the perpendicular corridor, a vertical corridor and starting from point R1 are what we consider to be NLOS-2 measurements. The aim is to measure the power and BER received at each point with different technologies in both NLOS situations.
# A Parameter Self-Tuning Decoupling Controller Based on an Improved ADRC for Tension Systems
###### Abstract  
Aiming at the problems of strong coupling and time-varying parameters in the tension systems of roll-to-roll coating machines, this paper outlines the design of a parameter self-tuning decoupling controller based on an improved active disturbance rejection controller (ADRC) for tension systems. First, we established a nonlinear coupled model for a global tension system based on the roll-to-roll coating machine tension constituent unit and working principle. Second, we introduced virtual quantities and actual control quantities; then, we applied an ADRC for decoupling control of the tension system based on a nonlinear coupling model, using a genetic algorithm (GA) to achieve parameters tuning in the ADRC. Finally, both the traditional proportional-integral-differential (PID) controller and the designed controller were simulated to evaluate their anti-interference and decoupling performance. Furthermore, the performances of several controllers were compared to the results obtained by other researchers. The result found that the parameter self-tuning decoupling controller based on an improved ADRC for tension systems exhibited better decoupling control performance and that the controller was able to achieve precise control of tension.  
roll-to-roll coating machine; tension system; decoupled control; ADRC; parameter self-tuning +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### The Effect of the Air Leaked into the Secondary Fluid on the Ejector Performance
#### 4.2.2 The Effect of Air Leaking into the Secondary Fluid on the Internal Flow Structure  
There were no significant differences in the mixing chamber and the throat (Figure 13a), which indicated that air leaking into the secondary fluid does not change the shock structure and the size of the primary fluid jet core in these two parts. In the diffuser, the shock position with the air leaking into the secondary fluid was farther downstream than that without air. Nevertheless, being located downstream of the shock position does not always mean that the ability to resist back pressure had improved. In the diffuser, we compared the Mach number of the primary fluid jet core at different air mass fractions with that without air, and found that the jet core Mach number with air leaking into the secondary fluid is smaller than that without air, and the axis Mach number is also stretched, as shown in Figure 13b.
## 2 Methods
### The Design of the Control Program  
BTTT is a passive operation terminal after receiving the command of the upper computer or the buoy main control board. The main function of the terminal in the operation of the buoy is to receive remote commands from the shore base to the buoy to remotely modify the initialization parameters of the buoy, profile motion parameters, hover parameters, sleep parameters, sampling interval parameters, and other information. After completing a profile, the shore base sends a recovery location command to the buoy, and the buoy remotely returns its positioning information. On receiving profile detection data and buoy state information transmitted by the buoy main control board, the terminal remotely sends it to the shore. During the noncommunication phases, the BDM is disconnected from the power and enters STOP mode. Controlled by the buoy's main control board, it wakes up at the appropriate time through the serial port. Under the control of the main control chip, the BDM power is turned on, and the control program flow chart of the BTTT is shown in Figure 4, and each step is divided by a dotted-lined square.  
Once the terminal receives information, it first determines whether it is the RDSS serial port or the USER serial port that receives the information, respectively, corresponding to the information sent by the shore base to the terminal and the information sent by the buoy main control board.  
Figure 4: Control program flowchart of BTTT.  
Secondly, the content of the information is judged. If it is a remote command sent from the shore base, the terminal is tasked with forwarding the remote command to the buoy control board for altering operational parameters and providing feedback on the success information to the shore base. In the event that a retrieval and positioning instruction from the shore base is received, the terminal first reads the positioning information continuously transmitted to it by the BDM before dispatching this information to both the buoy control board and the shore base.  
When the terminal receives buoy status information from the master control board of the buoy, it processes the received buoy status information for remote transmission to the shore base through a communication request. If it receives profile detection data, the terminal first compresses the data before sending the data packets to the shore base through a communication request.  
Thirdly, after the terminal sends the information once, it enters the STOP low power consumption mode and disconnects power to the BDM. After 55 s, the buoy main controller wakes up the terminal through the USER port and powers the BDM.  
Finally, determine whether all the position information or profile detection data has been sent. If the information is not sent completely, loop the sending process until the end.
###### Abstract  
Vehicular Communication Networks (VCNs) are essential to intelligent transportation systems, where real-time data exchange between vehicles and infrastructure supports safety, efficiency, and automation. However, achieving high Quality of Service (QoS)--especially under delay-sensitive conditions--remains a major challenge due to the high mobility and dynamic topology of vehicular environments. While some efforts have explored routing protocol optimization, few have systematically compared multiple optimization approaches tailored to distinct traffic and delay conditions. This study addresses this gap by evaluating and enhancing two widely used routing protocols, QoS-AODV and GPSR, through their improved versions, CM-QOS-AODV and CM-GPSR. Two distinct optimization models are proposed: the Traffic-Oriented Model (TOM), designed to handle variable and high-traffic conditions, and the Delay-Efficient Model (DEM), focused on reducing latency for time-critical scenarios. Performance was evaluated using key QoS metrics: throughput (rate of successful data delivery), packet delivery ratio (PDR) (percentage of successfully delivered packets), and end-to-end delay (latency between sender and receiver). Simulation results reveal that TOM-optimized protocols achieve up to 10% higher PDR, maintain throughput above 0.40 Mbps, and reduce delay to as low as 0.01 s, making them suitable for applications such as collision avoidance and emergency alerts. DEM-based variants offer balanced, moderate improvements, making them better suited for general-purpose VCN applications. These findings underscore the importance of traffic- and delay-aware protocol design in developing robust, QoS-compliant vehicular communication systems.  
**Keywords:** vehicular communication networks; routing protocols; delay-sensitive; traffic-oriented evaluation; delay efficiency; optimization
## 2 Proposed Method
### Task-Oriented Meta-Learning Algorithms  
Recent developments in task-oriented meta-learning have demonstrated significant advancements in optimizing learning processes through past experiences to enhance performance across a variety of tasks. Notably, task sequencing meta-learning techniques have been introduced to improve adaptability and performance in few-shot learning scenarios by optimizing the order of task presentation.  
* Task-Sequencing Meta-Learning (TSML): Introduced by [PERSON] et al. [38], TSML organizes meta-training tasks from simple to complex, enhancing a model's adaptability in few-shot fault-diagnosis scenarios.
* Task-Specific Pseudo Labelling: Developed by [PERSON] et al. [39], this method employs pseudo labelling to enhance transductive meta-learning by generating synthetic labels for unannotated query sets, significantly improving model performance.
* Task Weighting with Trajectory Optimization: Proposed by [PERSON] [40], this approach uses trajectory optimization to automate task weighting, showing improved performance over traditional methods in few-shot learning benchmarks.  
Despite these innovative approaches, challenges remain, particularly with the variability between tasks which can lead to unstable knowledge transfer, impacting learning efficacy. This issue is exacerbated in real-world applications where task heterogeneity is prevalent and tasks often deviate significantly from the training distribution. The assumption of task uniformity by these algorithms often does not hold in complex scenarios, leading to a gap between theoretical efficiency and practical applicability. The fixed nature of task prioritization may also fail to accommodate the dynamic variability of real-world data, posing further challenges to model adaptability.  
The exploration of these meta-learning algorithms underscores the necessity for a robust framework that intelligently sequences tasks and is resilient to the unpredictable nature of practical applications. The upcoming section introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) Framework, which proposes innovative solutions to address these challenges. ATASML integrates data augmentation and adversarial examples within its learning process, significantly enhancing the model's resilience and generalization capabilities. These features enable ATASML to perform effectively even under the challenging conditions presented by complex fault scenarios, thereby providing a substantial improvement over existing meta-learning frameworks.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 2 Challenges in Traditional XRD Data Analysis
### Quantitative Phase Analysis  
Quantitative phase analysis involves determining the relative proportions of different phases in a sample. Traditional methods like those described in Section 1 (RIR, whole pattern fitting procedure, or Rietveld refinement) can be computationally intensive, especially for samples with many phases, leading to long processing times.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 1 Space-Air-Ground Integrated Network  
### Overview of SAGIN  
Space-air-ground integrated networks (SAGIN) include ground networks, satellite systems, and air networks to ensure the reliability and throughput of data transmission [1]. The many combined networks within a SAGIN allow for greater coverage than a traditional ground network [2]. It can provide a secure information infrastructure for sea-, land-, air-, and space-based user activities [3]. In addition, a SAGIN expands the capability of wireless networks, making it an important part of many advanced applications such as autopilot and earth monitoring [4]. There are various complex environments and tasks in future communication. When natural disasters and public emergencies occur, ground communication facilities may be damaged and thus unable meet the needs of communication. SAGINs can achieve fast networking and flexible deployment. At the same time, SAGINs will play an even more important role in the 6G era, appearing as a new trend of future communication networks.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs  
The high energy dependence weighs on the performance of the WSN, so various techniques have been designed to save the energy. Each unit of the sensor node contributes to the power of the node, as shown in Figure 1. The optimization of energy contributes to the improvement of the performance not only of all the protocol layers of a sensor node, but also involves both the software and the hardware [6].  
In the physical layer, the energy can be minimized by reducing the data rate, packet size, and an efficient throughput and through an optimal energy model [14]. In the Medium Access Control (MAC) layer, an energy-efficient duty-cycle design and packet scheduling or an accurate analytical model for the IEEE 802.15.4 CSMA/CA protocol are used to reduce the power consumption [9; 15]. Table 1 presents different power optimization strategies and the impact of the considered parameters on the energy minimization [16].
###### Abstract  
In this paper, an event-triggered adaptive dynamic programming (ADP) method is proposed to deal with the \(H_{\infty}\) problem with unknown dynamic and constrained input. Firstly, the \(H_{\infty}\)-constrained problem is regarded as the two-player zero-sum game with the nonquadratic value function. Secondly, we develop the event-triggered Hamilton-Jacobi-Isaacs(HJI) equation, and an event-triggered ADP method is proposed to solve the HJI equation, which is equivalent to solving the Nash saddle point of the zero-sum game. An event-based single-critic neural network (NN) is applied to obtain the optimal value function, which reduces the communication resource and computational cost of algorithm implementation. For the event-triggered control, a triggering condition with the level of disturbance attenuation is developed to limit the number of sampling states, and the condition avoids Zeno behavior by proving the existence of events with minimum triggering interval. It is proved theoretically that the closed-loop system is asymptotically stable, and the critic NN weight error is uniformly ultimately boundedness (UUB). The learning performance of the proposed algorithm is verified by two examples.  
event-triggered control (ETC); adaptive dynamic programming (ADP); zero-sum game; neural network (NN); integral reinforcement learning (IRL).
## 4 Natural Antioxidants and Their Role in Cosmetics
### Carotenoids
#### 4.2.3 Lycopene  
Another carotenoid with strong antioxidant effects is lycopene (Figure 3). It is primarily responsible for the red color of vegetables and fruits. Lycopene is best absorbed in the presence of fats, and heat treatment increases its bioavailability in food products. It belongs to the group of unsaturated hydrocarbons with a linear structure consisting of 13 double bonds, including 11 conjugated bonds. As a powerful antioxidant, lycopene neutralizes free radicals through radical addition, hydrogen detachment, or electron transfer [55].  
Lycopene is used in protective creams against UV rays, as well as in cosmetics aimed at regenerating, firming, and revitalizing the skin [55]. Dietary supplements containing lycopene have been shown to significantly improve skin texture, density, and thickness. Positive effects have also been observed in wrinkle reduction [63].  
Lycopene enhances the overall appearance of the skin, significantly increases the elasticity of the stratum corneum, and improves skin hydration. Preparations containing lycopene increase pigmentation index values, suggesting that it is an effective tool for preventing skin photodamage, thanks to its photoprotective effects [64].  
Currently, lycopene used in cosmetics is primarily derived from tomato extracts or microbial fermentation. Synthetic production is less common, as consumers prefer natural and eco-friendly ingredients. Microbial fermentation using _Blakeslea trispora_ (a filamentous fungus), _Escherichia coli_ (genetically modified bacteria), and _Rlodotorula glutinis_ (yeast) is becoming increasingly popular as a method to produce a pure, stable, and sustainable product, aligning with growing trends in anti-aging and protective cosmetics [65,66].
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 5 Conclusions  
In this paper, we proposed a novel cross-correlation algorithm for the passive radar, which breaks through the optimal limits of the traditional cross-correlation algorithm under the condition of a high reference channel SNR. The algorithm uses the differential  
Figure 23: Output SNR under different reference SNR (SNR\({}_{\text{sur}}=-30\) dB: (**a**) \(N=10^{4}\), (**b**) \(N=10^{6}\)).  
to convert a single signal received by the echo antenna into multi-channel signals and then uses the differential multi-channel reference signal to eliminate the phase influence of the signal itself in the target echo. This can achieve the twice coherent accumulation of the target echo. At the same time, due to the phase and correlation difference of the noise and interference signals, only twice the incoherent accumulation can be realized. In traditional R-D processing, the target echo achieves one coherent accumulation and the target and interference also carry out one incoherent accumulation. Therefore, compared with the traditional algorithm, our algorithm can greatly improve the target detection performance. Meanwhile, the target detection results of the simulation data and measured data are basically consistent with the theoretical derivation results presented in this paper. The target detection performance of this algorithm is much better than that attained with traditional R-D processing, and the output SNR of our algorithm can be improved faster with an increase in the accumulation time.  
At the same time, it should be noted that the algorithm presented in this paper requires a large amount of computation, and parallel processing and faster two-dimensional Fourier transform may need to be introduced. This is left for future work.  
Conceptualization, D.Z. and J.W. ([PERSON]); methodology, D.Z.; software, D.Z.; validation, D.Z., [PERSON] ([PERSON]) and L.Z.; formal analysis, D.Z.; investigation, D.Z.; resources, [PERSON] ([PERSON]); data curation, L.Z. and [PERSON]. ([PERSON]); writing--original draft preparation, D.Z.; writing--review and editing, D.Z.; visualization, D.Z.; supervision, [PERSON] ([PERSON]); project administration, [PERSON]; funding acquisition, [PERSON] ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Radar Signal Processing Laboratory under Grant KGJ202102. This research was funded in part by Key Laboratory of Space Microwave Technology under Grant 6142411342104.  
This paper is supported by the National Radar Signal Processing Laboratory funded by KGJ202102 and is supported by Key Laboratory of Space Microwave Technology under Grant 6142411342104.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
The device structure is shown in Figure 1, and the fabrication process has been described in detail in a previous work [27]. We investigated three different active layer thicknesses: 350, 500 and 650 nm, three samples of each thickness, each one having four devices, that is, 12 nominally equal cells for each thickness.  
Electrical measurements were carried out using an Autolab potentiostat/galvanostat model PGSTAT204 (Eco-Chemie), equipped with the FRA32M impedance module and the Metrohm Autolab optical bench. The instrument was controlled by the NOVA 2.1.4 software. \(J\)-\(V\) characteristics were performed under 1000 W\(\cdot\)m\({}^{-2}\) illumination (equivalent to 1-sun) with an AM1.5G spectrum using an LED-based Solar Simulator (model Newport Oriel Verasol-2 class AAA).  
Reverse and forward \(J\)-\(V\) curves recorded using a scan rate of 33 mV/s showed no hysteresis behavior. For this reason, \(J\)-\(V\) curves have been registered in forward direction (from \(J_{\text{SC}}\) to \(V_{\text{OC}}\)) using this scan rate and the characteristic parameters have been determined.  
In order to study device degradation, indoor cell lifetime testing was carried out according to the International Summit on OSC Stability (ISOS) standard L-1 protocol [28]. During these experiments, cells were constantly irradiated under 1-sun illumination with controlled temperature and humidity conditions, and \(J\)-\(V\) characteristics were recorded every minute.  
During the degradation experiment, IS characterization was carried out under light conditions at different bias voltages. Impedance spectra are obtained by applying a small AC voltage superimposed to a DC voltage and measuring the resulting AC current response. Impedance is defined as the small AC voltage over the resulting AC current, resulting in a frequency-dependent complex value. Cole-Cole diagram is obtained by plotting imaginary vs. real part of the impedance, with the frequency of an implicit parameter. Frequency ranged between 1 MHz and 1 Hz, applying sinusoidal signals of 50 mV amplitude under open-circuit conditions. Experimental data were modeled using Scribner's ZView software.  
Once the samples were stabilized, \(J\)-\(V\) characterization in a broad range of DC light intensities was carried out using the solar simulator. To evaluate the dominant recombination processes in the device, the ideality factor (\(n\)) was extracted from these DC measurements and compared with that extracted from the AC measurements.  
Figure 1: Device layer structure layout. Note that scaling between layer thicknesses was not considered.