esa-sceva/satcom-chunk-collection huggingface dataset:
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 2 Materials and Methods
### Precipitation Products: TAMSAT3, CHIRPS, and MSWEP
#### 2.1.2 CHIRPS  
CHIRPS is a precipitation dataset with quasi-global coverage (50S-50N, only over land pixels) and a high space resolution (0.05\({}^{\circ}\)), available on a daily, five day, and monthly time scale from 1981 to present [28]. It is designed especially for studying extreme precipitation events, from droughts to floods and climatic trends. Similar to TAMSAT3, CHIRPS is based on TIR observations from the geostationary satellite constellation and uses linear relationships between CCD and precipitation. However, substantial differences among the two products exist in their exploitation of rain gauge data. In this case the calibration of the CCD vs. precipitation relationships is carried out by means of the TMPA 3B42 product [29]. Furthermore, unlike TAMSAT, the temperature threshold chosen to determine the CCDs is fixed and equal to 235 K. The TIR-based estimates are converted into percentage anomalies and subsequently multiplied by the Climate Hazards group Precipitation climatology (CHPClim) dataset to obtain the CHIRP estimates. In this way, the CCD data are used to estimate variations around the CHPClim mean, reducing CHIRP's systematic bias. In case of a lack of satellite observations, CHIRP estimates are replaced by percentage anomalies calculated using reanalysis data from the Coupled Forecast System (CFS) version 2. The last step in the algorithm involves the merging of CHIRP with precipitation data from ground stations on the basis of a modified inverse distance weighting algorithm. Various rain gauge datasets are used from both private and publicly available archives in order to guarantee the best possible rain gauge spatial coverage.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 4 Multi-Channel Routing in WSNs  
In _data collecting_ wireless sensor networks the forwarding scheme follows a tree structure connecting the nodes to the sink. With single-channel operation, a node overhears all nodes that are in the receiving range of that node. Our first objective is to use a multi-channel tree so that the overhearing problem is reduced. We propose a multi-channel scheme in which the available channels (which is much smaller than the number of nodes) are distributed among the nodes and each node listens on its selected channel by default. For data transmissions and forwarding, each node temporarily switches to the channel of its selected parent and switches back to its designated channel when the transmission is completed. Selection of designated channels as well as parents are based on a battery health parameter \(H\) and a link quality parameter (ETX), as explained below. While channel selection builds a multi-channel tree that is the primary mechanism for overhearing reduction (see illustration in Figure 5, where different channels are shown in different colors), it also builds the framework for dynamic route and channel selection to achieve load balancing, which is designed to meet our second objective of lifetime equalization.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 4 Discussion
### Limitations and Challenges of the Study  
While this study provides a structured and comparative evaluation of communication strategies for UAV operations in air mobility corridors, several limitations and challenges must be acknowledged. These factors highlight areas where further research and refinement are needed to fully translate the findings into real-world deployments.  
All strategies assume identical repeater performance characteristics, such as coverage radius, failure rates, and repair times. In practice, these parameters may vary due to hardware diversity, installation conditions, or power supply differences, introducing heterogeneity that could alter availability outcomes.  
The Markov models rely on a stationary (steady state) Poisson arrival process and exponential service times. However, UAV communication demand may vary dynamically with time, mission type, and traffic density. These non-stationary effects are not captured in the current framework and may influence short-term availability fluctuations.  
The reliability models assume that repeater station failures and repairs are statistically independent. Environmental disruptions (e.g., power outages and weather events) or coordinated cyberattacks could affect multiple stations simultaneously, reducing the effectiveness of spatial redundancy.  
While the number of required repeater units is used as a proxy for infrastructure cost, the study does not incorporate installation, maintenance, energy consumption, or operational costs. A full techno-economic assessment would be necessary for decision-making in real-world projects.  
These limitations do not diminish the value of the findings but instead underscore the need for a multi-disciplinary, iterative approach to corridor design.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Transmitter  
Active radars require Power Amplifiers (PAs), which should be small, efficient and low-cost. We implemented a C-band PA centered at 5.56 GHz with an output peak power of 5 kW (67 dBm) from a 30 dBm drive with a Power Added Efficiency (PAE) of 67%. GaN  
\begin{table}
\begin{tabular}{c c} \hline
**Optical Configuration** & \multicolumn{1}{c}{Cassegrain} \\
**Mount configuration** & \multicolumn{1}{c}{Alt-Azimuthal} \\
**Primary aperture** & 9.0 m \\
**Primary depth** & 1.53 \\
**Secondary aperture** & 1.17 \\ F/D & 0.368 \\
**Surface rms (static)** & 0.5 mm \\
**Azimuth Travel** & 0\({}^{\circ}\) to 360\({}^{\circ}\) \\
**Azimuth Travel Rate** & 4 rpm \\
**Elevation Travel** & 35\({}^{\circ}\) to 90\({}^{\circ}\) \\
**Elevation Travel Rate** & 2\({}^{\circ}\)/s \\
**Maximum Operational Wind (5 GHz)** & 25 km/h \\
**Survival Winds** & 150 km/h \\
**Reflector Weight** & 1900 kg \\
**Pedestal Weight** & 3500 Kg \\
**Foundation Size** & 4.5 \(\times\) 4.5 \(\times\) 0.75 m \\ Concrete Volume & 12 ms \\
**Beamwidth @ @ 5 GHz** & 44 arcmin \\
**Beamwidth @ 10 GHz** & 22 arcmin \\
**Pointing Accuracy (wind limited)** & \textless{}1/20 beamwidth \\
**Gain @ 5 GHz (G)** & 46 dB \\
**G/T (5 GHz)** & 39 dB/K \\ \hline \end{tabular}
\end{table}
Table 2: Antenna specifications.  
Figure 2: ATLAS Antenna.  
technology is suitable for these requirements and can do so with high efficiency. The PA was implemented by readily available COTS technology. Compact Radio Frequency (RF) power sources now exist with the recent release of high-power solid-state RF amplifiers such as the Wolfspeed/Cree CGHV59350 high-electron-mobility transistors (HEMTs) [27]. These HEMTs are capable of 500 W of RF power each. Our design follows the traditional approach of combining a large number of solid-state RF amplifiers into a high-power all-solid-state RF system [28]. The transmitter, depicted in Figure 1 in blue, is responsible for outputting the RF signal. All the relevant operating parameters are summarized in Table 3. The signal generation starts with a frequency synthesizer at 5.160 GHz, which is fed to a frequency translator. Along with the 5.160 GHz signal, the frequency translator receives an intermediate frequency of 400 MHz from the reference module and generates the desired 5.560 GHz signal. This signal is fed into a PIN modulator (PIN diode based) responsible for modulating the 5.560 GHz carrier in amplitude. The PIN modulator is also connected to the controller board in order to receive the user-defined pulse shape. The modulated signal goes to a driver, to achieve a 30 dBm minimum signal to be provided to the PA. We selected the previously mentioned Cree HEMTs at 5-6 GHz because these HEMTs provide the highest available RF power as required (67 dBm). The target performance requirements for the amplifier are listed in Table 3.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 1 Introduction
For the past two years, several researchers have reviewed deep learning-based modulation recognition algorithms for the period from 2017 to 2021 [18; 19], where research by [18] focuses on the application of classical neural networks (e.g., CNN, RNN, and FFNN) in deep learning for modulation recognition; and studies by [19] focuses on a review of datasets, signal representation methods, and deep learning-based approaches in the field of modulation recognition. Although research by both [18; 19] have made relevant contributions to deep learning-based AMR algorithms, most research in the literature summarizes AMR methods using classical neural networks and ignores the relevant descriptions of deep learning modulation recognition methods in difficult environments, e.g., small sample environments. Therefore, in order to provide a complete reference for subsequent research, it is necessary to summarize the new deep learning modulation recognition algorithms and further summarize the related algorithms in the small sample environment. This review fills a gap in the analysis of deep learning-based modulation recognition algorithms in difficult environments. The contributions of this paper are as follows:  
1. The traditional modulation identification algorithm is investigated, and the characteristics of the signal and the advantages and disadvantages of the traditional modulation identification algorithm are summarized in detail;
2. This paper introduces the application of CNN, RNN, combined neural network, and other types of neural networks in modulation recognition, and makes a comprehensive summary of new algorithms for modulation recognition based on deep learning from 2020 to 2022;
3. This paper describes the challenges faced by deep learning in identifying modulated signals in a small sample environment and summarizes the solutions to these challenges. The remainder of this paper is organized as follows: In Section 2, the traditional modulation recognition algorithms are described, and the advantages and disadvantages of the two algorithms are summarized at the end; the modulation recognition algorithm based on deep learning is discussed along with the problem that the current deep learning algorithm has poor recognition accuracy in the case of small samples; in Section 4, we introduce the application of deep learning in modulation recognition in a small-sample environment; in Section 5, the challenges and future research directions for automatic modulation recognition are described; finally, the conclusions are summarized in Section 6. The structural layout of the article can be illustrated in Figure 1.
## 3 Simulation Results and Analysis  
### Strength and Deformation Characteristics of Rock-like Samples  
Figure 3 shows the results of the axial stress-strain curve of samples with non-parallel overlapping flaws. The numerical simulation results show that the sample with a larger flaw inclination angle has higher uniaxial compressive strength (UCS) and elastic modulus. It is worth noting that the axial stress of the samples with different flaw inclination angles decreases suddenly after reaching the peak value, which indicates that this material is brittle. The analysis results show that there is a close relationship between the flaw inclination angle and the mechanical properties of the rock-like materials with non-parallel overlapping flaws.  
To quantitatively understand the relationship between UCS, elastic modulus and flaw inclination angle, the numerical simulation results of UCS and elastic modulus changing with flaw inclination angle are shown in Figure 4. It can be seen that the UCS and elastic modulus of the sample increase monotonously with the increase of the flaw inclination angle. When the flaw inclination angle increases from 15\({}^{\circ}\) to 75\({}^{\circ}\), the UCS increases by 33.2%, and the elastic modulus increases by 5.5%. Although the changing trend of UCS and elastic modulus with flaw inclination angle is consistent, the UCS is more sensitive to the change of flaw inclination angle. From the overall trend, a large flaw inclination angle can weaken the deterioration effect of non-parallel overlapping flaws on the UCS and elastic modulus of samples.
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 3 Experimental Results and Analysis  
### Datasets  
The dataset used in this study is sourced from the PlantVillage dataset [13] and the Tomato-Village dataset [14]. The PlantVillage dataset contains a large number of images depicting plant diseases and pests. There are ten different classes in this dataset of 18,161 tomato leaves from PlantVillage. Nine of those ten classes are for diseases, while the final class is for health. The ten classes are healthy, bacterial spot, early blight, late blight, leaf mold, Septoria leaf spot, spider mites, target spot, tomato mosaic virus, and tomato yellow leaf curl virus. In tomato villages, there are eight different classes in this dataset of 4064 tomato leaves. Seven of eight classes are for diseases, while the final class is for health. The eight classes are early blight, late blight, leaf minor, magnesium deficiency, nitrogen deficiency, potassium deficiency, and healthy. To enhance the reliability of the model and the generalization of training, this study utilizes data from both the PlantVillage and Tomato-Village datasets, mixing images of the same categories of diseases and pests. We selected the 13 most common types of tomato leaf disease images and healthy tomato leaf images and randomly selected images from each category at a ratio of 4:1. These categories included early blight, bacterial spot, late blight, leaf mold, Septoria leaf spot, spider mites, target spot, tomato mosaic virus, tomato yellow leaf curl virus, magnesium deficiency, nitrogen deficiency, potassium deficiency, and healthy, totaling 17,043 images, as illustrated in Figure 4.  
Figure 4: \(Cont.\)
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Evaluation Metrics
A lower RMSE is preferable. This value is minimized when the predicted value matches the true observed value from the environment. Evaluation metrics such as PPV, ACC, ERR, and F1-score are computed using values from the confusion matrix in both columns, and as such are sensitive to any change, especially with an imbalanced dataset. These metrics change as the distribution of data changes, even if the classifier's performance does not [96]. However, GM can be used with both balanced and imbalanced data, even if its calculation involves values from both columns of the confusion matrix, because the changes in the class distribution cancel each other out. Other evaluation metrics commonly used to assess the ability of ML models to detect cyberattacks targeting WSNs are related to the required memory usage, buffer size, computational complexity, processing time, and prediction time, which are other elementary evaluation metrics. On-chip memory usage considers the random access memory (RAM) and Flash memory in the microcontroller unit, usually measured in kilobytes (KB). The amount of RAM directly affects processing speed. A larger amount can handle more data; however, WSN nodes have relatively low on-chip memory, which means that ML models must require low amounts of on-chip memory and be optimized for efficiency. Buffer size affects the rate of false alarms, as the node buffer usually stores certain fields of monitored traffic data that can be used as input for the detection model running within the nodes. In certain scenarios, a specific MN is responsible for monitoring its neighbors, listening to messages within radio range, and continuously examining traffic to look for intruders.
## 6 Optimization and Simulation Results  
Different average daily solar radiation values of 4.0, 4.5, 5.0 and 5.5 kWh/m\({}^{2}\) are used to simulate the application of solar energy across a wide range of South Korea areas (as shown in Figure 7)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Parameters** & **Value** \\ \hline Renewable energy & Solar radiation & 4.0, 4.5, 5.0, 5.5 kWh/m\({}^{2}\)/day \\ resources & Wind speed & 4.0 m/s \\ \hline \multirow{4}{*}{Control parameters} & Annual real interest rate & 1.25\% \\  & Project lifetime & 10 years \\  & Dispatch strategy & cyclic charging \\  & Apply setpoint state of charge & 80\% \\  & Operating reserve: as percent of load, hourly load & 10\% \\ \hline \multirow{4}{*}{SPV} & Sizes considered & 4, 4.5, 5.5, 6.5, 7 kW \\  & Operational lifetime & 25 years \\  & Efficiency & 90\% \\  & System tracking & Two axis \\  & Capital cost & $1/W \\  & Replacement cost & $1/W \\  & O&M cost per year & $0.01/W \\ \hline \multirow{4}{*}{WTG} & Sizes considered & 1, 2, and 3 kW \\  & Operational lifetime & 15 years \\  & Hub & 50 m \\  & Capital cost & $0.6/W \\  & Replacement cost & $0.6/W \\  & O&M cost per year & $0.05/W \\ \hline \multirow{4}{*}{Inverter} & Sizes considered & 0.1, 0.15, 0.2, 0.25, \& 0.3 kW \\  & Efficiency & 95\% \\  & Operational lifetime & 15 years \\  & Capital cost & $0.4/W \\  & Replacement cost & $0.4/W \\  & O&M cost per year & $0.01/W \\ \hline \multirow{4}{*}{Trojan L16P Battery} & Number of batteries & 32, 40, 48, 56, 64, 72 \\  & Round trip efficiency & 85\% \\  & Minimum state of charge & 30\% \\  & Nominal voltage & 6 V \\  & Nominal current & 360 Ah at 20 h \\  & Nominal capacity & 6 V \(\times\) 360 Ah = 2.16 kWh \\  & Lifetime throughput & 1075 kWh \\  & Max. charge rate & 1 A/Ah \\  & Max. charge current & 18 A \\  & Self-discharge rate & 0.1\% per hour \\  & Min. operational lifetime & 5 years \\  & Capital cost & $300 \\  & Replacement cost & $300 \\  & O&M cost per year & $10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: HOMER simulation setup.  
with 4.0 m/s wind speed. Additional configuration details are given in Table 2. The energy output, the economic analysis of the proposed hybrid power system, and the related sensitivity analysis are provided in the following paragraphs.
## 2 Results and Discussion
### Relationship between Extraction Efficacy and Analyte Characteristics
Of this group of descriptors, two-way significant positive correlations were only observed between the extraction efficacy of the 45 um particles and the pKa value (strongest acidic) of the analytes. The median coefficient value was r = 0.593 (0.551-0.602, \(p\leq 0.01\), \(n=5\)). However, two-way significant negative correlations were present with the extraction yield and pKa value (strongest basic) for the 5 um particles with a median value of r = \(-\)0.488 (\(-\)0.545-- 0.486, \(p\leq 0.009\), \(n=3\)), the 10 um particles with a median value of r = \(-\)0.516 (\(-\)0.517--0.502, \(p\leq 0.007\), \(n=4\)), and the 10 um polar particles with a median value of r = \(-\)0.513 (\(-\)0.525--0.497, \(p\leq 0.007\), \(n=3\)). Correlations between the extraction efficacy and number of donor spots for hydrogen bonds were also negative for all particle types. Stronger correlations were observed with data from ChemAxon (number of pairs = 28 analytes) than with data from the ACD/Labs and Cactvs programs (\(n=30\) both). With the 5 um particles, the median correlation value was r = \(-\)0.537 (\(-\)0.601-- 0.480, \(p\leq 0.01\), \(n=8\)). With the 10 um particles, r = \(-\)0.544 (\(-\)0.643-- 0.484, \(p\leq 0.009\), \(n=9\)). With the 10 um polar particles, r = \(-\)0.529 (\(-\)0.563-- 0.493, \(p\leq 0.008\), \(n=9\)). With the 45 um particles, r = \(-\)0.611 (\(-\)0.698-- 0.560, \(p\leq 0.002\), \(n=9\)). All of the two-way significant correlations discussed above are summarised in Table 3.
## 4 Parametric Study on HCR Internal Spur Gear Drives  
To investigate the influence of root deflection angle, modification angle, and addendum coefficient, other HCR gear-drive design parameters are kept constant and listed in Table 1. According to the control variable method, nine groups of comparisons are listed in Table 2, in which numbers 1-3 discuss the influence of deflection angle, numbers 4-6 the influence of modification angle, and numbers 7-9 the influence of the addendum coefficient. Different tooth profiles of the HCR pinion for use in parametric study are shown in Figure 10. The maximum bending stress \(\left(\sigma_{f}\right)_{max}\) at the filet region and maximum contact stress \(\left(\sigma_{h}\right)_{max}\) at the tooth flank are both estimated through FEA, and the stress maps of 2D FEMs are shown in Figure 11.  
Figure 10: Different tooth profiles of HCR pinion for parametric study.  
Figure 9: Two-dimensional 20-teeth finite-element model.  
bending stress of the first stress peaks in groups 1-3 are 93 Mpa, 83 Mpa, and 68 Mpa, respectively. The second group is 10.8% lower than the first group, and the third group is 26.9% lower than the first group (Figure 12b). The maximum contact stresses of the first stress peaks in groups 1-3 are 280 Mpa, 210 Mpa, and 142 Mpa, respectively. The second group is 25% lower than the first group, and the third group is 49.3% lower than the first group (Figure 12c). It can be seen that the maximum bending stress and contact stress gradually decrease with the increase in tooth root deflection angle. The larger the tooth root deflection angle is, the smaller the maximum bending stress and contact stress are. It can be found that all stresses decrease significantly as the deflection angle increases due to the decrease in root bending stiffness. However, the deflection angle should not be too large, otherwise it will reduce the root bending strength. Furthermore, in the middle of a mesh cycle, the bending stress of a single tooth decreases clearly and then increases again; namely, two stress peaks appear in a mesh cycle for a single tooth.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Problem Definition: Hybrid Flow Shop and Parallel Machine Scheduling  
Job scheduling problems encompass a wide range of models, including Flow Shop Scheduling (FSS) and Parallel Machine Scheduling (PMS) [3]. Our research focuses on a hybrid scheduling problem that integrates characteristics of both [4; 5; 6; 7]. In this model, jobs consist of multiple tasks with predefined precedence constraints, similar to traditional FSS. However, unlike conventional FSS, each task is not restricted to a single machine but can be executed on one of several parallel machines, introducing characteristics of PMS [8; 9; 10]. This hybrid approach improves both machine utilization and fault tolerance, making it well-suited for dynamic production settings.