esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications and Services
### Services  
One of the promising applications is utilizing UAVs as aerial support for ground vehicles in dense environments, heavy roads, and traffic jams. In traffic monitoring, the UAV-BS can be deployed for malfunction or congestion relaxation for terrestrial cellular networks. However, the ground backhauling through direct LoS links for UAV-BSs may be randomly blocked by buildings due to the UAV-BS movement, whereas the backhaul rate must be high enough to support the traffic. Therefore, aerial backhaul system has been proposed through a high-altitude aerial platform (HAP) balloon RISeUAV [95], as shown in Figure 6. The passivity of the RIS element facilitates energy-efficient aerial LoS backhauling. The other advantage is that the aerial balloon RISeUAV can provide isotropic (\(0\sim 360^{\circ}\)) reflection to cover a wider area, which is energy efficient from the UAV propulsion perspective. The 3D placement as well as phase alignment of aerial RISs are optimized so that the source transmit power is minimized. The optimization problem is solved through alternating optimization and using [PERSON]'s algorithm [137].  
As another application, the UAV-relayed, RIS-assisted link was used to enhance satellite-terrestrial communication [138], accounting for hardware impairments and interference when direct LoS links were unavailable. A closed-form expression for the outage probability was derived to assess the impact of RIS and key parameters on system performance. RISeUAV also has been utilized for covert communication [109] to transmit information secretly.
## 2 Rule-Based Strategies
### Deterministic Rule-Based Strategy  
Deterministic rule-based strategies are mainly based on several predetermined threshold parameters to make engines and motors operate in their high-efficiency area, which also takes battery charging and discharging efficiency into consideration in order to properly distribute the demand torque. According to power requirements, battery SOC, and vehicle speed or acceleration, the vehicle can be divided into several operation modes, which mainly include motor-only mode, engine-only mode, power-assist mode, regenerative braking mode, mechanical braking mode, recharging mode, and so forth. The mode switches when the operating state meets threshold switching condition. As detailed in [10], it takes four operation modes into consideration, namely, motor-only mode, engine-only mode, regenerative braking mode and hybrid mode, and the threshold of mode-switching is determined by desired torque and battery SOC.  
Generally speaking, threshold selection of the deterministic rule-based strategy is mainly based on engineering experience and the efficiency characteristics of the related power components, and it requires considerable parameter debugging time to acquire satisfactory results. Thus, some optimization methods are required to automatically obtain global optimal threshold parameters to more reasonably distribute the torque between engine and the motors.  
The main objective of mode-switching is to obtain minimum fuel consumption under the premise of meeting driver's demand and maintaining battery SOC. To make deterministic rule-based strategies more effective, mode-switching thresholds can be optimized using several intelligent algorithms, such as the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), quadratic programming (QP), and so forth. For instance, mode-switching thresholds were optimized by simulated annealing-particle swarm optimization (SA-PSO) in [26] to obtain the ideal mode-switching sequence. In [37], a hybrid algorithm combining GA with SA was applied to simultaneously optimize powertrain and control parameters, resulting in a better convergence speed and offering a global searching ability to obtain the best comprehensive performance for a plug-in hybrid electric bus (PHEB). In order to have good real-time performance, the direct algorithm has been used to optimize extracted key parameters globally due to its low computational burden and rapid convergence [38].  
In addition, the method of operation-mode prediction can also be introduced to optimize the deterministic rule-based strategy. A torque correction strategy based on operation-mode prediction is proposed in [39]. The current operation mode of HEV is determined by demand power, vehicle speed, and battery SOC, and then the operation mode in the immediate future time-horizon is predicted based on the Markov probability matrix [9; 24; 25]. In the end, based on ECMS algorithm, the optimized factor of torque correction is selected as the control parameter to correct engine torque and motor torque in real-time.
# Preface: Land Surface Processes and Interactions--From HCMM to Sentinel Missions and Beyond
## 2 Recent Advances in Monitoring Land Surfaces and Interactions
### Surface Energy Fluxes
To take into account the influence of water salinity on the evaporation rate, [PERSON] et al. [20] adapted the SEBS (Surface Energy Balance System) model for large water bodies (AquaSEBS) via parameterizing the water heat flux (the analogy of ground heat flux over water) and the sensible heat flux by adapting the roughness heightsfor momentum and heat transfer. Furthermore, they introduced a salinity conversion factor over saline water bodies. The verification of AquaSEBS over Lake Ijsselmeer (the Netherlands) as well as Victoria and Tana freshwater lakes (Africa) showed that the calculated latent heat fluxes agree well with eddy covariance measurements. The comparison with ECMWF (European Centre for Medium-Range Weather Forecasts) data showed that the salinity reduced the evaporation by up to 27% in the Great Salt Lake and by 1% in Open Ocean. These findings indicate the importance of salinity to the evaporation rate and the suitability of the AquaSEBS for fresh and saline waters.
## 3 Results and Discussion
### Physicochemical Properties of Flour and Baking Mix
The tested mixtures exhibited significant differences in ash, with the highest ash content in _A. diaperinus_ powder (3.67%) and the lowest in wheat flour (0.49%). Consequently, increasing the proportion of insect powder led to a higher ash content in the baking mixture. According to the literature, the ash of insect powders varies based on the developmental stage (larva or adult), ranging from 2.95% to 5.22% [16; 31; 58; 59; 60; 61; 62; 63]. The ash primarily consists of calcium, iron, magnesium, phosphorus, potassium, and sodium [64; 65]. Regarding the Lab* color components of the baking mixtures, significant differences were observed between wheat flour and insect powder mixtures (Table 1), with variations depending on the proportion of insect powder. The insect powder had the darkest color, with color components of L* = 58.59, a* = 6.12, and b* = 25.57. The brightest color was found in wheat flour (average L* = 98.89). Thus, as the proportion of powdered insects in wheat flour increased, the L* value decreased and the mixture became darker. With an increasing proportion of powdered insects, the yellowness (a*) and redness (b*) of the mixtures also increased. An additional analysis of the \(\Delta\)H* and \(\Delta\)C* parameters revealed that the tested mixtures exhibited a brownish hue (H: 77.04-80.56) with low saturation (C parameter value: 13.49-16.58). The calculated browning index (BI) value (Figure 2) significantly increased with a higher proportion of _A. diaperinus_ powder in the produced baking mixtures. Furthermore, changes in the \(\Delta\)E values (\(\Delta\)E > 6 compared with the control sample and between the analyzed mixtures) indicated that the differences in the color, browning level, and degree of darkening of the tested mixtures would be noticeable to consumers and may influence their perception. The color and intensity of _A. diaperinus_ powder may result not only from the natural pigmentation of live larvae but also from the production process. Key processing steps before grinding include blanching [61], freezing, lyophilization [4; 16; 60; 63], and drying using airflow, contact, or microwave methods [66; 63; 67; 68]. High temperatures during the initial processing of _A. diaperinus_ larvae can influence the final color of the powder. This effect is primarily attributed to the Maillard reaction, which occurs between amino acids and reducing sugars under heat, leading to the formation of melanoidins--compounds responsible for the brown color and characteristic flavor [63; 69]. However, the manufacturer of the insect powder used in this study did not provide details on the larvae's cultivation or production technology, which could also impact the nutrient composition [58].
## 3 Wavelet Analysis
### Morlet Wavelet  
The selection of the mother wavelet function is the key step of wavelet analysis. Morlet wavelet is a complex wavelet, which is more advantageous than a real wavelet in application. The phase difference between the real part and the imaginary part of the Morlet wavelet is \(\pi/2\), which can eliminate the oscillation caused by the modulus of the real wavelet transform. In addition, the mode and phase can be easily separated from the wavelet coefficients. When analyzing time series data, the Morlet wavelet is employed as the parent wavelet, enabling the simultaneous extraction of amplitude and phase information through the modulus and real part of complex wavelet coefficients. This facilitates a visual representation of the oscillation period inherent in the time series. A Morlet wavelet is usually defined as follows [36]:  
\[\varphi(t)=\pi^{-1/4}e^{i\omega t}e^{-t^{2}/2} \tag{3}\]  
where \(\pi^{-1/4}\) is the normalization parameter, and \(\omega\) is the dimensionless angular frequency.  
When \(\omega=6\), the scale of the wavelet transform is approximately equal to the frequency of the Fourier transform. At this time, the real part of the wavelet coefficient \(R\{W_{x}(a,b)\}\) can accurately reflect the energy distribution of the time series, and the real part modulus square of the wavelet coefficient \(|W_{x}(a,b)|^{2}\) can reflect the intensity of the periodic vibration in the time series, which is usually called the wavelet power spectrum.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Comparative Analysis
#### 4.2.2 Throughput of the Network  
Average network throughput is a significant metric in this comparison as our MIDS algorithms deal with the packet-dropping attacks. Figure 11 shows the comparison of all three techniques based on average network throughput. MS-LEACH has the worst average network throughput among all. The cryptographic key management process takes a very large amount of time process and causes high communication overhead on both the sender and receiver side. The attacker recognition process is slower in cryptographic key management mechanisms. Hence, the packet delivery ratio is much less in these mechanisms.  
Figure 10: Average energy consumption.  
The reference technique deals with only one packet-dropping attack. Also, it lacks a secure CH selection technique which makes it more vulnerable to attacks. Hence, when more prone to packet-dropping attacks the average throughput of the network decreases.  
The throughput of the network increases by 65% when the MIDS algorithm is used. The MIDS algorithm has a strong mechanism of CH selection, after that the malicious nodes are efficiently detected and isolated, which increases the packet delivery ratio of the algorithm. A strong IDS mechanism makes it quicker in detecting and preventing malicious nodes. Multipath routing mechanism helps in optimal path selection towards the destination which reduces the time for packet transmission, and in the case of the failure of a link, the alternative path is ready which also makes it more time efficient.
## 2 Literature Review
### Machine Learning Techniques for Water Quality
After the comparison, they stated that the quantile regression forest (QRF) model showed excellent performance among the learning models, and the integrative PCA-QRF model performed better than all the other models, with the statistical criteria of the coefficient of determination (R\({}^{2}\)), the root mean squared error (RMSE), and the mean absolute error (MAE) of PCA-QRF being 0.94, 0.12, and 0.05, respectively. [PERSON] et al. [25] used the principal component regression (PCR) technique to predict the water quality in the area of Gulshan Lake. Following this process, after conducting WQI, they extracted important parameters using PCA, and then different regression algorithms were used on these parameters. Finally, they determined the water quality status (WQS) using a Gradient Boosting Classifier. PCR achieved 95 % accuracy, whereas the Gradient Boosting Classifier made 100 % accurate predictions. Moreover, researchers have determined different types of models in various regions on the basis of creative methodologies. Some of them have found that the SVM model yields more accurate WQI predictions than other methods, observed in an R\({}^{2}\) score of 0.98 in Iran [13]. In Malaysia, the application of a Least Square Support Vector Machine (LSSVM) achieved an R\({}^{2}\) value of 0.9 [26]. Conversely, superior results were observed in Poland, where neural networks attained an R\({}^{2}\) value of 0.99 [27]. Similarly, reliable outcomes have been reported in Ethiopia, Vietnam, and Brazil, as documented in references [28; 29; 30; 31].
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 2 Theory and Model
### Transmission of Bowel Sounds
#### 2.2.1 Principle of Sound Propagation
The spatial discretization divides the abdominal cavity into a mesh of smaller elements, each representing an approximation of the sound pressure and its gradient. The temporal discretization divides the simulation time into small time steps, allowing the tracking of dynamic changes in sound pressure over time. By discretizing both space and time, COMSOL enables a detailed simulation of the sound wave's propagation and its interaction with the heterogeneous tissues in the abdominal cavity.
## 5 Implementation  
In our implementation and experiments, we use identical hardware resources and a WiFi connection to resemble real scenarios as best as possible.  
Specifically, we use Docker (v.20.10.23) [15] to emulate a 6-core (2 GHz) CPU with 8 GB of RAM and no GPU for our drone platform since we assume the drone is equipped with limited computing power. In comparison to desktop and server-rack GPUs, the total available RAM and memory bandwidth are significantly lesser on the drone. The cloudlet is emulated using a single NVIDIA T4 GPU, which is an entry-level GPU supported by most cloud providers including AWS and GCP. The Tesla T4 GPU is optimized for AI workloads and is sufficiently powerful to demonstrate the effectiveness of our system. To emulate the actual wifi communication between a drone and a cloudlet, we also use wifi for the transmission of feature maps and predictions between them, using PyZmq (v.26.2) to implement the messaging protocol. When we deploy the system, the onboard and cloudlet models are run in a client-server manner. As a result, the inference time of a split model includes the time taken to extract features, encode features and metadata into a packet, transmission time (to and from the drone), and the time taken to compute bounding boxes using the cloudlet model. Docker allows near-perfect emulation of lower-end drone capabilities since CPU cores, RAM, ports, etc. can be precisely allocated.
# Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm
## 5 Results and Discussions
### Comparison of Various Algorithms on the Artificial Generation Network  
In order to test the clustering accuracy of different algorithms, this paper uses the widely used random network GN (4, 32, 16, Zout). It can be seen that as Zout increases, the network community  
Figure 2: Comparison of results of various algorithms on the real network.  
structure becomes more and more blurred, and it also brings more and more challenges to community detection algorithms. When Zout \(>\) 8, the community structure of the network is considered to be very vague. The experiment uses PSO-GA hybrid algorithm, GN algorithm, Fastgreedy algorithm and LFM algorithm to cluster 50 random networks, respectively, and the average accuracy obtained is shown in Figure 3.  
As can be seen from Figure 3, the PSO-GA algorithm has the highest NMI accuracy on the GN reference network, the LFM algorithm is slightly lower, and the Fastgreedy and GN algorithms are much lower. The LFM algorithm considers the idea that the community structure is a local structure conforms to many large-scale networks. Therefore, its NMI accuracy is higher on the GN reference network, while the module-based community detection algorithms GN and Fastgreedy are relatively low. The results show that the clustering accuracy of the PSO-GA algorithm proposed in this paper is better than the three algorithms.  
Computing speed is another important index to evaluate the performance of community detection algorithms. This paper uses a random network GN (\(C\), 100, 16, 5), where the number of nodes s in each community is 100, the degree d of each node in the network is 16, and the number of links between each node and nodes outside the communities Zout is 5. As the number of communities \(C\) in the network increases, the PSO-GA hybrid algorithm is tested, and the experimental results obtained are shown in Figure 4.  
It can be seen from Figure 4 that as the network scale increases, the running time of the PSO-GA algorithm increases. When the size of the word community is constant, the running time of the algorithm is approximately proportional to the network size. The size of the community in a large-scale real network is generally much smaller than the entire network scale. Therefore, for a large-scale complex real network, the efficiency of the PSO-GA algorithm is very high. The results show that the method in this paper has good feasibility in large-scale community networks.  
Figure 3: Comparison of results of various algorithms on the artificially generated network.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 2 System Model and Acquisition Method
### Acquisition Method
#### 2.2.1 CAF Evaluation
If the code in the received signal is the same as local replica code, \(\rm{R(\tau_{e})}\) is called autocorrelation function which can be expressed as Equation (6). \[\rm{f_{f}}=f_{d,min}+if,\ i=1,2,\ldots,S_{f} \tag{8}\]  
\[\rm{\tau_{l}=\tau_{min}+j\tau,\ j=1,2,\ldots,S_{c}} \tag{9}\]  
The CAF results defined over a bi-dimensional grid that is usually referred to as the search space. Equations (4)-(6) indicates that CAF \(\rm{P_{MF}(0,0)}\) or \(\rm{P_{PMF-FFT}(0,0)}\) have biggest value. Therefore, positioning receiver can obtain the coarse estimation of \(\rm{f_{d}}\) and \(\rm{\tau}\) by testing CAF values corresponding to different \(\rm{f_{i}}\) and \(\rm{\tau_{l}}\). Besides the difference in hardware implementation, the difference between match filter method and PMF-FFT method in CAF value is the power attenuation caused by frequency bias and the picket fence effect of FFT. The proposed method in this paper focuses on the search space reduction and the improvement of detection strategy. Therefore, the differences of CAF evaluation methods have no influence on the analysis of the proposed method. The size and the number of search space have significant impact on acquisition time. Smaller size and number lead to shorter acquisition time. Multiple dwell technique can be used to reduce the search space size and number. Double dwell is taken as an example. first dwell gives the coarse estimation with big search size while second dwell gives the fine estimation. On the other hand, in the signal detection stage, the existence of interference may lead to poor detection performance, which leads to longer acquisition time. Coherent and non-coherent integration are commonly used to improve the SNR, and a fine detection strategy is also utilized to improve the detection performance.
## 2 Materials and Methods
### XUV Optimization Studies
#### 2.2.4 Filtering  
Although the pairs of XUV multilayer mirrors were specially designed for the geometry of our setup and for the wavelengths of 32.2 nm and 47.6 nm, their transmittance was additionally tested in realistic experimental conditions. For this, an optical setup in the chamber of Stage C was developed, so that the generated XUV beam is first filtered by the 400 nm Al foil, then propagates through the pair of multilayer mirrors and, finally, is spectrally analyzed by the flat field grating. The recording of the XUV spectrum was performed via the MCP scanning mode in order to obtain the full spectrum. The results of these measurements for both pairs of multilayer mirrors are presented in Figure 5.  
It can be seen that the pair of low-value wavelength mirrors transmits at a central wavelength of 32.2 nm with an FWHM of 2.5 nm, while the pair of high-value wavelength mirrors transmits at a central wavelength of 47.6 nm with an FWHM of 1.5 nm. The ap  
Figure 4: XUV harmonic spectral images measured for various laser pulse durations and Ar gas pressures. The negative/positive signs of the laser pulse durations correspond to the negative/positive chirp, respectively. The nominal spectral locations of the harmonics order, estimated by ray-tracing calculations, are noted at the bottom of the XUV spectral images.  
Figure 5: Overall transmission spectra of the generated XUV radiation obtained for both pairs of multilayer mirrors. (**a**) Pair for 32.2 nm. (**b**) Pair for 47.6 nm. The peaks at \(\sim\)65 nm in (**a**) and \(\sim\)95 nm in (**b**) correspond to the second order of diffraction.  
pearance of peaks at lower-value wavelengths in both spectra is due to the second order of diffraction, as can be inferred from their twice in magnitude wavelength value. Finally, it should be noted that for 32.2 nm, Ar gas was used, while for 47.6 Kr, gas was used, as resulted from the study on the gas types.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 2 Load Period Partitioning Method Based on Improved Fuzzy C-Means Clustering
### Comprehensive Similarity Calculation Based on Load Characteristics and Optimal Network Structure
\[S_{ab}^{base}=\alpha S_{ab}^{l}+(1-\alpha)S_{ab}^{N} \tag{6}\]  
To balance the influence of temporal similarity and optimal network structure similarity in the comprehensive similarity index, we introduce a weight coefficient \(\alpha\), whose value ranges from 0 to 1. A higher value of \(\alpha\) increases the emphasis on temporal features, while a lower value emphasizes structural similarity. In this study, \(\alpha\) is empirically set to 0.5 to equally reflect both aspects. This setting was selected based on empirical sensitivity analysis, which showed that it provides a robust trade-off between clustering accuracy and adaptability under varying load patterns. The final comprehensive similarity \(S_{ab}^{base}\) reflects the overall similarity between any two time periods \(a\) and \(b\), providing a basis for subsequent clustering. The comprehensive similarity calculation method based on conforming features and optimal network architecture proposed in this article only requires static reconstruction for each initial time period, which significantly reduces the computational complexity compared to the method of partitioning and reconstructing at different time periods to minimize network loss. Compared with the clustering method based solely on load characteristics, the method proposed in this paper also considers the situation where the load differences are large but the optimal network structure is the same, which increases the possibility of applying the same network structure after merging time periods.