esa-sceva/satcom-chunk-collection huggingface dataset:
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 1 Introduction
To address the high complexity and computational overhead of end-to-end models, which can cause latency in real-time systems, we propose an ultra-lightweight MFF network with minimal computational overhead. We adopt a decision-map-based fusion paradigm to achieve this goal. Our major contributions are summarized as follows:  
1. We introduce a novel task transformation paradigm that reframes MFF from direct focus classification to a decision map refinement process. By providing an initial coarse decision map and explicit edge maps as prior guidance and boundary cues, our approach simplifies network optimization and improves fusion quality. 2. We present ULU-Net, a novel U-Net variant designed specifically for MFF. This architecture achieves competitive performance with an ultra-low parameter count, significantly reducing model complexity. 3. We propose LightMFF, an ultra-lightweight MFF framework that combines our novel task transformation paradigm with the ULU-Net architecture. Extensive quantitative and qualitative experiments demonstrate that LightMFF achieves state-of-the-artfusion quality while requiring significantly fewer parameters and computational resources than existing methods. The remainder of this paper is organized as follows: Section 2 reviews related works, covering traditional, deep learning-based, and lightweight MFF methods. Section 3 details our proposed LightMFF method, including its theoretical foundations, network architecture, and training strategy. Section 4 presents comprehensive experimental results and comparisons with state-of-the-art methods. Section 5 discusses the limitations and future directions of our work. Finally, Section 6 concludes the paper.
## 4 Performance Evaluation
### RNN Model Configuration and Hyperparameters  
The RNNs used in the experiments were LSTM, GRU, and RNN-SH. Additionally, the number of hidden units was set to 64, 128, and 256. RNN-SH used tanh or relu as the output activation function. We also experimented when the RNNs are stacked in two layers. We used RAdam [27], which is a kind of stochastic gradient descent algorithm for learning, and set RAdam parameters to recommended values in Adam [28]. The dropout ratio and parameter of weight decay were set to 0.5 and 0.01, respectively, to prevent overfitting. The number of epochs was 50, and the mini-batch size was 27. Regarding the initialization of weight parameters, they were initialized to a Gaussian distribution with a mean of 0 and a variance of \(1/n\) (\(n\): the number of input units) [29], and all biases were initialized to 0. Additionally, CNN weights were initialized to a uniform distribution \([-v,\ v]\) where  
Figure 6: The examples of preprocessed voice data and generated RPs. The left image (**a**) is an example of a healthy person, and the right image (**b**) is an example of a PD patient. (**a1,b1**) are voice waveform, (**a2,b2**) are normalized waveform for 0.280 to 0.290 s. (**a3,b3**) are generated RPs from normalized waveform for 0.280 to 0.290 s. PD patients have a faint voice by dysarthria and there are differences between the two recurrence plots. The RP was rotated 90 degrees in this figure.  
\(v=\sqrt{3/n}\)[29]. The scalar value \(a\) of gate structure of RNN-SH was initialized with 1.0. Finally, Softmax cross entropy was used as the loss function. This loss function (\(L\)) is expressed by the following Equation (17):  
\[L=-\frac{1}{m}\sum_{i,j}t_{ij}\log\Biggl{(}\frac{\exp\bigl{(}y_{ij}\bigr{)}}{ \sum_{k=1}^{o}\exp(y_{ik})}\Biggr{)} \tag{17}\]  
where \(m\) is the batch size, \(o\) is the output size, \(y_{ij}\) is the output from output layer, and \(t_{ij}\) is the correct answer label. In addition, \(i\) is the data number, \(j\) is the dimension, and \(t_{ij}\) is the correct answer label of \(j\)-th dimension in the \(i\)-th data.
## 4 Simulations and Discussion
### The Influence of Oblique Incidence Angle on the Characteristics of MSON  
For oblique incidence, the light intensity distributions in the \(r\)-z plane at oblique incidence angles of \(0^{\circ},10^{\circ},15^{\circ}\), and \(20^{\circ}\) are illustrated in Figure 4a-d, respectively. Normally, the DOF of the MSON is measured by the axial length whose axial intensity is more than 80% of the maximum axial intensity, and the resolution is characterized by the maximum full width at half-maximum (FWHM) [20] of all longitudinal plane optical fields. Moreover, when the light intensity is more than 0.8 times the maximum light intensity, the MSON is considered to have a homogeneous intensity. The intensity distribution in the transverse plane with \(r=0\) (marked by a black dashed line in Figure 4a) is plotted in Figure 4e. For the longitudinal plane (marked by white dashed line in Figure 4a), intensity distribution at \(z=0.7\lambda\) is plotted in Figure 4f. As expected, as the angle of oblique incidence increased, both the DOF and the uniformity of the MSON deteriorated. When the oblique incidence angle was \(10^{\circ}\), the DOF and uniformity changed little. However, when the oblique incidence angle was \(20^{\circ}\), the DOF was nearly attenuated by half compared with normal incidence. In addition, the resolution of the MSON remained the same as the oblique incident angle increased. However, the MSON had a tendency to focus to the left. This is because as the oblique incidence angle increases, the peak intensity on the longitudinal plane at \(z=-0.7\lambda\) remains almost constant, while the peak intensity of the longitudinal plane at \(z=0.7\lambda\) decreases, especially when the oblique incidence angle is larger than \(10^{\circ}\).
## 3 Measurement, Control, and Communication Architecture
### Software and Communication
#### 3.2.1 Measurement and Data Acquisition Layer: Arduino(r)  
The Arduino(r) MEGA 2560 acts as the core microcontroller responsible for low-level data acquisition and real-time control in the system. It continuously monitors three key analog variables: DC voltage, DC current, and wind speed. These signals are sampled using the board's integrated 10-bit analog-to-digital converters (ADCs) at a fixed rate of 1 kHz, providing sufficient temporal resolution for the relatively slow dynamics of small wind systems.  
Each analog input is mapped to a dedicated ADC channel. The measured values are scaled according to sensor characteristics (e.g., transducer gain and offset), digitized, and temporarily stored in internal variables. The wind speed sensor, which delivers an analog voltage proportional to rotational speed, is processed in the same acquisition loop using similar ADC-based sampling.  
To ensure consistent and reliable communication with the embedded host system (Raspberry Pi(r)), the Arduino(r) organizes the acquired sensor data into structured UART frames. Each frame includes:  
* A start delimiter (0x02) to indicate the beginning of a new message;
* A sequence of encoded sensor readings in hexadecimal format;
* A checksum value for basic error detection; and
* An end delimiter (0x03) to mark the end of the transmission.  
The UART link is configured at 9600 bps with an 8N1 format (8 data bits, no parity, 1 stop bit), balancing communication speed and reliability. The serial transmission is handled in a non-blocking loop that runs continuously in parallel with the acquisition and control routines.  
In addition to measurement tasks, the Arduino(r) generates a PWM signal that serves as the control input to the DC-DC boost converter. Although the specific control strategy (e.g., MPPT or power limitation) depends on the application context, the duty cycle of this signal is dynamically adjusted based on sensor feedback and commands received from the supervisory system.
## 4 Applications
### Photocatalytic  
Beside the applications based on the piezoelectric properties of bismuth layered ferroelectric materials, efforts have been carried out to explore other characteristics of this family of materials as the photocatalytic activity. TiO\({}_{2}\) has been widely used as a photocatalyst for energy conversion and pollutant elimination because of its high activity and stability, low cost, availability and nontoxicity [86]. However, it is only activated under UV radiation, because of its large band gap (about 3.2 eV). Thus, alternatives are considered. Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\) has been proposed, with a relatively high photocatalyst activity attributed to the effective utilization of interlayer space as reaction sites [87]. Examples that show the potential of Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\) for this type of applications can be found in the photodegradation of methyl orange [84; 88]. The photocatalytic activity is high, although lower than for TiO\({}_{2}\). In can be improved by selective doping, as in the case of Cr [86]. Further improvement could be achieved by reducing the electron-hole combination (promoting the appearance of oxygen vacancies, for instance) or increasing the light absorption. It has been also shown that the use of Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\) nanofibers can also give a better photocatalytic activity [89].
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 2 Related Work  
In the field of securing software-defined networks (SDNs) against DDoS attacks, researchers have employed various deep learning techniques. Some studies have utilized single algorithms, such as convolutional neural networks (CNNs) or long short-term memory (LSTM) approaches, while others have employed hybrid algorithms combining multiple techniques. According to [9; 10], hybrid deep learning algorithms are the most widely used approaches for detecting and mitigating attack traffic within SDNs. In this section, we will discuss different approaches to demonstrate the significance of deep learning in the context of securing SDNs against DDoS attacks.
## 2 Lossless Transmission Circuit Equations with Terminal Conditions
### Transmission Line Circuit Equation
: _If \(1>\frac{r}{Z}>\zeta>0\) and Nagumo's 3 SPWL Resistor \((k=N)\) is \(\eta>-1\) or TD 3 SPWL Resistor \((k=T)\) is \(\eta>1\), then \(v(\tau+\frac{1}{2},\frac{1}{2})\) is explicitly determined by 1-D map \(g_{k}\) of \(v(\tau-\frac{1}{2},\frac{1}{2})\) such that the following is the case._  
\[v(\tau+\frac{1}{2},\frac{1}{2}) = g_{k}(v(\tau-\frac{1}{2},\frac{1}{2}),\zeta,\eta,\xi), \tag{18}\] \[\psi(\tau-1) = \frac{v(\tau-\frac{1}{2},\frac{1}{2})+f_{k}(v(\tau-\frac{1}{2}, \frac{1}{2}),\zeta,\eta,\xi)}{2},\] \[v(\tau+\frac{1}{2},\frac{1}{2}) = h_{k}(\psi(\tau-1))-\frac{1+\frac{R}{Z}}{1-\frac{R}{Z}}h_{k}( h_{k}(\psi(\tau-1)))\] \[+ \frac{E}{1-\frac{R}{Z}},\text{(in the case of $1>\frac{R}{Z}\geq 0$), or}\] \[= -\chi_{k}(\psi(\tau-1))+\chi_{k}(-\chi_{k}(\psi(\tau-1))),\] \[\text{(in the case of $R\to\infty$)},\]  
_Remark that \(i(\tau,\frac{1}{2})=f_{k}(v(\tau,\frac{1}{2}),\zeta,\eta,\xi)\), for any \(\tau\)._  
Proof in Appendix A.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 2 Related Works  
The first step towards the proper development of a CR-based UAV is choosing a suitable SDR-UAV combination. With a particular emphasis on SDR hardware and software that can be used for aerial wireless exploration and analysis, [PERSON] et al. [14] provides a comparative overview of SDRs. They address specifications for SDR hardware, features of available SDR hardware that are acceptable for small UAVs, and measurements of power. They also present SDR software specifications, open-source SDR software available, and SDR software calibration/benchmarking. Finally, the authors present Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) as a case study, and address various different experiments that can be sponsored by SDRs on that platform to verify /test possible wireless advancements, protocols, and technologies.  
CR aircraft applications are also the subject of recent surveys. [PERSON] et al. [15] carried out a survey on CR for aeronautical applications. Although their survey is not directly related to UAVs, they present valuable information about studies involving CR and UAS. Notwithstanding, [PERSON] et al. [9] undertook a survey on the integration of CR with UAVs. They discussed and highlighted a variety of challenges, issues, and future research in the field.
###### Abstract  
The term deviation frequency (fd) denotes the boundary between the variable part of the amplitude and phase scintillation spectrum and the part of uninformative noises. We suggested the concept of the \"characteristic deviation frequency\" during the observation period. The characteristic deviation frequency is defined as the most probable value of the deviation frequency under current local conditions. Our case study involved GPS, GLONASS, Galileo and SBAS data under quiet and weakly disturbed geomagnetic conditions (geomagnetic storm on 16 April 2021, \(\mathrm{K}\mathrm{P}\mathrm{m}\mathrm{x}\) = 5, \(\mathrm{S}\mathrm{M}\mathrm{+}\mathrm{H}_{\mathrm{min}}\) = \(-\)57 nT) at the mid-latitude GNSS station. Our results demonstrated that the deviation frequency for all signal components of GPS, GLONASS and Galileo varies within 15-22 Hz. The characteristic deviation frequency was 20 Hz for the mentioned GNSS signals. The SBAS differs from other systems: deviation frequency varies within 13-20 Hz. The characteristic deviation frequency is lower and equal to 18 Hz. We suggest the characteristic deviation frequency to determine the optimal sampling rate of the GNSS carrier phase data for the ionospheric studies. In turn, the deviation frequency can be considered as a promising index to estimate the boundary of non-variability of the ionosphere.  
Keywords:ionosphere; scintillations; carrier phase; GNSS; GPS; GLONASS; Galileo; SBAS; GNSS signals; deviation frequency +
Footnote †: journal: 2021
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 4 A Delay-Constrained Duty-Cycle Scheduling Algorithm  
In this section, a delay-constrained duty-cycle scheduling (DDS) algorithm is described. First, we present a group-based forwarding algorithm that consists of two phases. In the first phase, the sink collects network topology information. The second phase implements a scheduling algorithm for data transmission. For duty-cycle scheduling, using the information gathered during the first phase, the one-hop delay distribution of each group is formulated as a function of the duty cycle interval, the delay bound, and the required DSR. Then, the distribution of E2E delay, which is the sum of the one-hop delays of the groups, is estimated. Finally, the maximal duty cycle interval that satisfies the given delay bound requirement is determined by using the obtained E2E delay distribution.
## 4 Numerical Examples
### Crack Growth under Quasi-Static Loading  
#### 4.1.1 Quasi-Static Tensile Test of Notched Specimens  
This example was considered to demonstrate the capability of the staggered scheme with the hybrid formulation to predict the experimental crack trajectory obtained in the  
Figure 5: Flowchart of the staggered scheme with hybrid formulation for dynamic fractures.  
Figure 6: Flowchart for the phase field solution for a given increment using a staggered scheme at each integration point.  
previous section for different 'h' values between the pre-crack and the hole under quasi-static loading. The boundary conditions are a fixed edge and an imposed axial displacement on the opposite edge (Figure 7). The domain was meshed with triangular elements with a characteristic size s = 0.1 mm. The material properties are E = 210 GPa, G\({}_{\text{c}}\) = 0.03 kN/mm, and \(\
u\) = 0.3. The length scale parameter was chosen as \(\eta\) = 2 \(\times\) s = 0.2 mm.  
The crack paths given by the numerical simulations are illustrated in Figure 8. The obtained crack paths are in agreement with those found experimentally (see Figure 2). In fact, in the case of h = 5 and 6 mm, the cracks changed direction and pass through the hole and, in the case of h = 9 mm, the cracks propagated directly to the opposite edge.
## 4 Discussion
### Bilateral Ratio  
Our results identified a non-significant relationship between the bilateral strength ratio (Q:Q and H:H) at a low angular velocity and the incidence of ACL and hamstring injuries in soccer players. Contrary to our conclusions is the study of [PERSON] et al. (2018), where they demonstrated a significant increase in the risk of knee injury if asymmetry between the extensors and knee flexors reached up to 10% at a low angular velocity (\(60^{\circ}\cdot\mathrm{s}^{-1}\)) in soccer players. It should be added that we lack the mean values and standard deviations of the isokinetic peak torque in the study, and therefore we cannot determine with certainty whether the difference between the injured and uninjured players achieved these criteria. The research observed greater bilateral asymmetries in the knee flexors than in the extensors in soccer players of varying ages and levels of performance [29; 49; 50]. Another of our studies detected higher bilateral differences in the knee flexors than in the extensors (H:H = 7.94 \(\pm\) 11.47%, Q:Q = 7.97 \(\pm\) 9.29%), in 70 young elite soccer players, which may be a potential risk for hamstring injuries [29]. Preseason screening of strength asymmetries consisting only of isokinetic strength tests seems to be insufficient. For testing, we should include more methods that can provide a better picture of the current state of the player [45; 46]. Furthermore, low lateral strength asymmetry in the quadriceps (up to 10%) is necessary for a safe return after injury [51; 52]. [PERSON] et al. [53] found similar pre-injury isokinetic strength and isokinetic strength values after hamstring injuries in soccer players, but they found side-to-side asymmetry in pre- and post-injury testing. In contrast, [PERSON] et al. [21] identified a lower eccentric hamstring peak torque below 2.4 N\(\cdot\)m\(\cdot\)kg\({}^{-1}\) and a lower concentric hamstring-to-quadriceps ratio below 50.5% as being related to an increased risk of hamstring injury during the season. Strength asymmetry between the knee flexors is the most important feature in the prevention of hamstring injuries [54]. [PERSON] et al. [55] proposed that the pre-season period should be used to increase muscle strength and reduce strength bilateral asymmetries between the flexors and extensors of the knee, especially by using unilateral and eccentric strength training.
## 5 Feasibility Study
### Multi-Site Support  
The LTC system can support multi-site enterprises by connecting the embedded Asterisk servers of multiple PoT gateways using virtual SIP trunks, as depicted in Figure 4. Moreover, by connecting the PoT gateway to an Internet Telephony Service Provider (ITSP) (SIPStation, Flowroute, etc.), as depicted in Figure 4, the LTC system can even be expanded to a greater extent. Even if the called person does not exist within any of the enterprise's premises, the embedded Asterisk server can automatically seize the SIP trunk from the ITSP to make an outbound call to reach the registered mobile phone of the target person.
## 2 A Multi-Source Real Data Model for Intercity Link Communication Propagation Computing
### Extraction of Digital Elevation Data on Intercity Link  
This paper used DEM digital elevation data with a resolution of 90 m for the analysis and calculation. The data set was provided by geospatial data cloud site, Computer Network Information Center, Chinese Academy of Sciences ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn))). We accessed the data in 17 July of 2022. The data came from SRTM (Shuttle Radar Topography Mission) and were jointly measured by the National Aeronautics and Space Administration (NASA) and the National Imagery and Mapping Agency (NIMA).The data volume of radar images acquired by the SRTM system was about 9.8 trillion bytes. After various pre-processing steps, such as editing, gross error removal, determining water surface elevation, and defining coastlines, etc., the results were cut into 15,000 pieces according to geographic coordinates. Each map frame was stored in latitude and longitude, and a digital elevation model (DEM) was made. The data accuracy that could be used for scientific research was the STRM-3 version, which was sampled every 3 arc seconds, and the data of 3 arc seconds were collected and generated from 1 arc second of data. They were divided in \(1^{\circ}\times 1^{\circ}\) of the latitude and longitude grid in the geographic projection, and the horizontal resolution was about 90 m. SRTM terrain data obtained by CIAT (the International Center for Tropical Agriculture) using a new interpolation algorithm, this method better fills the data gap of SRTM 90. The interpolation algorithm is from [PERSON] et al. (2023).  
Figure 2: The nested grids of the simulated areas in WRF modes.  
SRTM-3 data are divided into files every 5 degrees of longitude and latitude, with a total grid data of 24 rows (including the area that latitude ranges from 60\({}^{\circ}\)N to 60\({}^{\circ}\)S), 72 columns (longitude ranged from \(-180^{\circ}\)W to 180\({}^{\circ}\)E). According to DEM data product, the elevation data of several intercity links from the area of interest (25.846\({}^{\circ}\)N-36.224\({}^{\circ}\)N, 117.472\({}^{\circ}\)E-123.528\({}^{\circ}\)E), could be determined with a resolution of 90 m. Interpolating to fill the null zone, the global elevation distribution of the selected area is obtained as shown in Figure 6.  
We extracted the link elevations from four surrounding cities to city A. Considering that the minimum distance between cities was about 90 km, which was about 0.81\({}^{\circ}\) when it was represented by the longitude and latitude coordinates. To not reduce the accuracies of DEM data on intercity links, we extracted the required link DEM data using traversal and interpolation. The processing steps are in Figure 7.  
Figure 3: The distribution of ducts in selected plain areas by seasons, where the yellow area represents the surface duct and the red area represents tge elevated duct. (**a**) March. (**b**) June. (**c**) September. (**d**) December.