esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 2 System Model
### Queue Scheduling Mechanism for TSFs  
The topology of inter-satellite links (ISLs) in LEO networks is dynamic, influenced by satellite orbital motion, line-of-sight visibility, and relative positional changes. Consequently, routing strategies must adapt dynamically to real-time satellite position updates for efficient data transmission. This paper focuses on optimizing the node-level forwarding mechanism for delay-sensitive flows. Therefore, we employ a shortest-path routing algorithm. The SDN controller dynamically computes the optimal path, selecting one or multiple satellites as relay nodes to minimize forwarding delay. Given the distance instability caused by ISL switching and our primary focus on queue scheduling optimization, we simplify the propagation delay component. The total network delay comprises transmission delay and queuing delay, expressed as:  
\[D_{total}\ =\ D_{trans}\ +\ D_{queue} \tag{1}\]  
Queue allocation management is divided into two main phases: packet enqueuing and packet dequeuing. Data flows in the queue can be broadly categorized into TSFs and non-sensitive flows (NSFs). The enqueuing phase involves selecting the target queue for newly arrived packets, taking into account the total priority sum of available queues, queue capacity, and packet collision issues. The dequeuing phase determines the queue from which packets are sent in each time slot, addressing cases where the total priority sums are equal and dynamically adjusting priorities. To ensure the prioritized transmission of delay-sensitive flows, factors such as the urgency of the flows and scheduling strategies must be comprehensively considered during scheduling to reasonably adjust the priority and scheduling order of each flow. Therefore, the following key parameters need to be defined:  
Queue priority sum: The priority sum of queue \(Q\), denoted as \(S_{Q}\), is defined as the sum of the dynamic priorities of all waiting packets in the queue:  
\[S_{Q}=\sum_{p\in D_{Q}}\pi_{p} \tag{2}\]  
where \(D_{Q}\) represents the set of all waiting packets in queue \(Q\), \(\pi_{p}\) denotes the dynamic priority of packet \(p\), and the packet size is \(s_{p}\); if the queue is empty, \(S_{Q}=0\). When a new packet \(p1\) arrives with a priority of \(\pi_{p1}\), the queue priority sum is updated as follows:  
\[{S_{Q}}^{{}^{\prime}}={S_{Q}}\ +\pi_{p1} \tag{3}\]  
Queue capacity: The remaining capacity of queue \(Q\), denoted as \(K_{Q}\), is defined as:  
\[K_{Q}=K_{\text{max},Q}-\sum_{p\in D_{Q}}s_{p} \tag{4}\]  
where \(K_{\text{max},Q}\) represents the maximum capacity of queue \(Q\).  
Available queues: For each packet's size, the availability of queues \(F_{Q}\) is determined based on the remaining capacity \(K_{Q}\) calculated above:  
\[F_{Q}=\{K_{Q}\ \geq\ s_{p}\} \tag{5}\]
## 3 Research Model and Hypothesis Development
### Organizational Factors
#### 3.2.1 Top Management Support  
Top management support, encompassing both financial and political backing from the company's leadership [78], is crucial for the successful implementation of Industry 4.0 initiatives. The adoption of Industry 4.0 technologies often requires substantial resources, both human and physical [24; 66]. Top management plays a pivotal role in securing these resources and acting as change agents to facilitate the assimilation of new technologies into the organization [91].  
Given that Industry 4.0 emphasizes the utilization of new technologies to enhance vertical, horizontal, and end-to-end integration [104], the involvement of various functional areas is imperative [24]. Top management support becomes essential in mitigating potential conflicts or resistance that may arise during the implementation process, thereby breaking down barriers within and across departments and organizations [105]. Furthermore, the endorsement of top managers signifies their commitment to embracing new technologies, fostering an environment conducive to innovation, and encouraging employees to fully engage in implementation efforts [90].  
**Hypothesis 3 (H3)**.: _Top management support positively influences the implementation success of Industry 4.0 technologies._
## Materials and Methods
### Flux Tower Data  
Photosynthetic activity expressed through GPP was used in order to evaluate and validate the satellite-based SOS estimates in this study. Daily GPP were acquired from a SUBSET Data Product for the FLUXNET 2015 (fluxnet.fluxdata.org, 2016). FLUXNET is a global network of micrometeorological flux measurement sites measuring net CO\({}_{2}\) exchange using the eddy covariance method. The FLUXNET 2015 dataset is a new version that offers improved measurements in comparison to the previous versions--FLUXNET Marconi Dataset (2000) and the FLUXNET LaThuile Dataset (2007). The improvements consist of refined data quality, new methods for uncertainty quantification and use of reanalysis data to fill long gaps of micrometeorological variable records ([[http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)]([http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)), 2016). In this study, GPP estimates from 14 sites with a total of 81 site-years across the study area were used (Figure 1B; Appendix A Table A1). For information of time span of the different sites, we refer to Table A1.
## 2 Method
### Loss Function  
We introduce a dynamic hybrid loss function that adaptively balances MSE and SSIM losses throughout the training process. Traditional loss functions, such as MSE or mean absolute error (MAE), often prioritize pixel-wise accuracy but fail to capture perceptual or structural qualities crucial for wrapped-phase images. Conversely, using SSIM alone may preserve structural features but can neglect overall intensity consistency. To address these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Feature Map (H \(\times\) W \(\times\) C)** & **Padding** & **Dilation** \\ \hline Input & 256 \(\times\) 256 \(\times\) 1 & - & - \\ \hline Shallow Feature Extraction (Conv C ReLU) & 256 \(\times\) 256 \(\times\) 48 & 1 & 1 \\ \hline Residual Dense Attention Block 1 (RDAB1) & 256 \(\times\) 256 \(\times\) 24 & 1 & 1 \\ \hline Residual Dense Attention Block 2 (RDAB2) & 256 \(\times\) 256 \(\times\) 24 & 1 & 1 \\ \hline Residual Dense Attention Block 3 (RDAB3) & 256 \(\times\) 256 \(\times\) 24 & 1 & 1 \\ \hline Residual Dense Attention Block 4 (RDAB4) & 256 \(\times\) 256 \(\times\) 24 & 1 & 1 \\ \hline Global Feature Fusion (1 \(\times\) 1 Conv) & 256 \(\times\) 256 \(\times\) 48 & 1 & 1 \\ \hline Global Feature Fusion (3 \(\times\) 3 Conv) & 256 \(\times\) 256 \(\times\) 48 & 1 & 1 \\ \hline Global Residual Learning (Residual Connection) & 256 \(\times\) 256 \(\times\) 48 & 1 & 1 \\ \hline Final Convolution (Output Layer) & 256 \(\times\) 256 \(\times\) 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed configuration of proposed network layers.  
limitations, we employ a dynamic weighting strategy that gradually shifts the optimization focus from MSE to SSIM as training progresses. This design allows the model to initially emphasize accurate intensity reconstruction through MSE loss, then progressively transit toward structural accuracy using SSIM. Such an adaptive transition enables the network to achieve a better balance between global intensity accuracy and local structural preservation. The dynamic hybrid loss function is defined as:  
\[\mathrm{L_{DH}}=(1-\alpha)\mathrm{L_{MSE}}+\alpha\mathrm{L_{SSIM}} \tag{15}\]  
In our implementation, the parameter \(\alpha\) increases linearly over the training epochs with \(\alpha=\mathrm{e}/\mathrm{E}\), where \(\mathrm{e}\) is the current epoch and \(\mathrm{E}\) is the total number of epochs. This gradual transition allows the loss function to initially emphasize MSE for stabilizing intensity learning and progressively shift toward SSIM for enhancing structural consistency.
## 2 Materials and Methods
### Photocatalyst Characterization  
The photocatalysts were characterized via X-ray diffraction (XRD), UV-vis spectroscopy, X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy, and high-resolution transmission electron microscopy (HR TEM), including the high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) technique.  
The chemical state of cations and surface composition of the catalyst were studied via XPS using a photoelectron spectrometer (SPECS Surface Nano Analysis GmbH, Berlin, Germany) using non-monochromatized Al K\(\alpha\) radiation (hv = 1486.6 eV). The spectrometer was equipped with a PHOIBOS-150 hemispherical analyzer and an XR-50 X-ray source with a double Al/Mg anode. The calibration of the binding energy scale was performed by setting the C 1\(s\) peak at 288.1 eV, which corresponded to carbon in the g-C\({}_{3}\)N\({}_{4}\).  
The chemical state of platinum in the bulk of the catalysts was studied via X-ray absorption spectroscopy at the EXAFS spectroscopy beamline of Siberian Synchrotron and Terahertz Radiation Center (SSTRC, Budker Institute of Nuclear Physics, Novosibirsk, Russia). The X-ray absorption near-edge structure (XANES) spectra of Pt \(L_{3}\)-edge were recorded via transmission geometry. Energy calibration was performed using the first inflection point in the Pt \(L_{3}\)-edge spectrum of Pt foil at 11,564 eV. The spectra were processed using standard procedures for subtracting the background and normalizing to the magnitude of the \(L_{3}\)-absorption edge jump using the DEMETER 0.9.25 software package [48].  
The morphology of the photocatalysts was studied via HR TEM using a ThemisZ electron microscope (Thermo Fisher Scientific, Waltham, MA, USA) at an accelerating voltage of 200 kV. The microscope was equipped with a SuperX energy-dispersive spectrometer and a spherical aberration corrector. The maximum resolution of the microscope was 0.06 nm. For the HR TEM analysis, the samples were ultrasonically dispersed onto perforated carbon substrates attached to aluminum grids.  
The diffuse reflectance UV-vis spectra were measured using a UV-2501 PC spectrophotometer with an ISR-240A diffuse reflectance unit (Shimadzu, Kyoto, Japan).  
The X-ray diffraction pattern was recorded using a D8 ADVANCE diffractometer equipped with a LYNXEYE linear detector (Bruker AXS GmbH, Karlsruhe, Germany). The diffraction patterns were obtained at room temperature in the 2\(\theta\) range from 10 to 70\({}^{\circ}\) with a step of 0.05\({}^{\circ}\) using Ni-filtered Cu K\(\alpha\) radiation (\(\lambda\) = 1.5418 A).
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 3 AMR Speech Codec  
The narrowband AMR speech codec (AMR-NB) encodes speech signals sampled at 8 kHz using eight different bit rates, where \(BR\in\{4.75,\,5.15,\,5.90,\,6.70,\,7.40,\,7.95,\,10.20,\,12.20\}\) kbits/s (kpps). It employs the code-excited linear prediction (CELP) model for encoding. The CELP model parameters are extracted through 10 th order LP analysis of 20 ms speech frames, with each frame containing 160 samples. These extracted CELP model parameters are then encoded and transmitted during the encoding process. On the decoder side, the transmitted parameters are first decoded to reconstruct the excitation signal. This reconstructed excitation signal is subsequently applied to the LP synthesis filter to obtain the speech signal. For more technical details regarding the AMR-NB speech codec, readers are referred to [4].  
The Wideband AMR speech codec (AMR-WB) operates on speech signals with a sampling frequency of 16 kHz and consists of nine bit rates: 6.60, 8.85, 12.65, 14.25, 15.85, 18.25, 19.85, 23.05,.., and 23.85 kbits/s. During encoding, the WB AMR codec first applies a pre-emphasis filter to the speech signal, followed by dividing the signal into short frames of 20 ms each. Each frame is then analyzed using a 16 th-order LP analysis to obtain the code-excited linear prediction (CELP) model parameters. Unlike the AMR-NB codec, which applies LP analysis once per frame, the AMR-WB encoder further divides each frame into four subframes. For each subframe, adaptive and fixed codebook parameters are transmitted. During decoding, the transmitted parameters are decoded first, followed by synthesizing the speech signal by applying the reconstructed excitation signal to the LP synthesis filter. More details on the AMR-WB encoding and decoding steps can be found in [5].
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI and Glaucoma  
#### 4.2.1 Introduction  
Glaucoma is a set of optic neuropathies labeled by progressive degeneration of the optic nerve and retinal ganglia cell loss (RGC). Glaucoma progresses without causing symptoms until the disease is advanced, with substantial neuronal damage. When symptoms occur, up to 30-50% of retinal ganglion cells may be lost, leading to irreversible visual field (VF) loss with an associated decrease in quality of life. Glaucoma is the second leading cause of irreversible blindness worldwide, currently affecting over 80 million people globally, and is estimated to affect 110 million by 2040.  
Risk factors for glaucoma include elevated intraocular pressure (IOP), a family history of the disease, advanced age (>50), African or Asian descent, myopia, and the use of systemic or topical corticosteroids. Primary open-angle glaucoma (POAG) is the most  
\begin{table}
\begin{tabular}{l c c} \hline  & **Kane Formula** & **Hill-RBF** \\ \hline Short axial length (\(\leq\)22.0 mm) & 0.441 & 0.440 \\ Intermediate axial length & 0.322 & 0.340 \\ (>22.0 mm to \(<\)26.0 mm) & 0.326 & 0.358 \\ Long axial length (\(\geq\)26.0 mm) & 0.326 & 0.358 \\ \hline \end{tabular}
\end{table}
Table 3: Performance values of the Kane and Hill-RBF formula methods of predicting IOL power.  
common type worldwide, with the highest prevalence (3.05%), followed by primary angle-closure glaucoma (PACG, 0.5%).  
There is currently no cure for glaucoma, and it is important to detect the disease as early as possible so that IOP-lowering treatment can be initiated to avoid irreversible visual functional loss.  
Recent advances in AI, especially the advent of DL, have demonstrated a transformative impact on the healthcare industry, with outstanding performance in skin cancer classification, early diagnosis of Alzheimer's disease, glioma prognosis, detection of diabetic retinopathy [73], and, most recently, assessment of the severity of COVID-19, as well as many other applications.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 4 Results and Discussion
### Experimental Testbed  
In this study, the experimental testbed integrates an FSOs communication system simulated through OptiSystem software Version 7.0, comprising an optical transmitter, channel, receiver, Optical Spectrum Analyzer and BER analyzer components for comprehensive performance analysis. The system is coupled with a blockchain infrastructure utilizing Ganache for the development environment, smart contracts for data management, IPFS for distributed storage, and Web3.py for system integration, enabling secure storage and verification of data.
###### Abstract  
A variety of heart rate variability (HRV) indices have been reported to estimate sleep stages, but the associations are modest and lacking solid physiological basis. Non-REM (NREM) sleep is associated with increased regularity of respiratory frequency, which results in the concentration of high frequency (HF) HRV power into a narrow frequency range. Using this physiological feature, we developed a new HRV sleep index named Hsi to quantify the degree of HF power concentration. We analyzed 11,636 consecutive 5-min segments of electrocardiographic (ECG) signal of polysomnographic data in 141 subjects and calculated Hsi and conventional HRV indices for each segment. Hsi was greater during NREM (mean [SD], 75.1 [8.3]%) than wake (61.0 [10.3]%) and REM (62.0 [8.4]%) stages. Receiver-operating characteristic curve analysis revealed that Hsi discriminated NREM from wake and REM segments with an area under the curve of 0.86, which was greater than those of heart rate (0.642), peak HF power (0.75), low-to-high frequency ratio (0.77), and scaling exponent \(\alpha\) (0.77). With a cutoff \(>\)70%, Hsi detected NREM segments with 77% sensitivity, 80% specificity, and a Cohen's kappa coefficient of 0.57. Hsi may provide an accurate NREM sleep maker for ECG and pulse wave signals obtained from wearable sensors.  
heart rate variability; sleep stage; respiration; electrocardiography; power spectrum; detrended fluctuation analysis; REM sleep +
Footnote †: journal: Applied Sciences
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 5 Simulation Results and Analysis
### Energy Efficiency  
In WSN with limited energy, one of the key tasks is to improve energy utilization, i.e., to maximize the network lifetime with limited energy. Energy variance (EV) can reflect the difference of residual energy of each node. The smaller the value and the smaller the curve fluctuation, the more balanced the energy of the network. The residual energy reflects the energy distribution of all nodes in the network when the first node dies. The more balanced the residual energy, the greater the energy efficiency of the network and the longer the network lifetime.  
From Figure 4, we note that the volatility of the EV curve of DS-EERA is smaller than that of the other two algorithms, and the energy distribution between nodes is more balanced. Obviously, the network lifetime of the DS-EERA is 1027 rounds, which are 311% and 170% of the FLEOR and MCRP respectively. This is because DS-EERA considers the \"energy density factor\" of the forward neighbor nodes when routing, and avoids the data being transmitted to the energy hole region, so that the energy of each node is evenly distributed. Meanwhile, the three EV curves increase with the number of network rounds. When the first node dies (i.e., network lifetime), EV reaches the maximum and then decreases to 0 with the death of the nodes in the network. At the same time, it can be seen that from the comparison of Figure 5a-c, when the first node dies in the network, the most nodes of energy in DS-EERA algorithm is less than 25%, and the energy of most nodes in MRCP and FLEOR  
Figure 3: The packet loss rate (PLR) comparisons with varying network size.  
In addition, from the number of death rounds in the first node of the network, the network lifetime of the DS-EERA is 170% and 310% longer than the MCRP and FLEOR respectively, which means that the network can maintain a longer effective working time. At the same time, when the nodes of MCRP and FLEOR all die, DS-EERA has not yet seen the death of the first node, showing the good performance of our algorithm.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 1 Introduction
First, the hybrid statistical model of the geometric error and electrical performance was established based on second order Taylor expansion; then the statistical parameter of the performance was deduced; finally, the hybrid tolerance design was achieved based on the optimization model. The rest of this paper is organized as follows. Section 2 discusses the surrogate model of the electrical performance and hybrid geometric errors. Section 3 discusses the statistical analysis of the performance of the array antenna. Section 4 discusses the hybrid tolerance design. Section 5 discusses the numerical simulations and results. Finally, Section 6 provides the concluding remarks.
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 4 Proposed Algorithm
### Proposed Algorithm  
In order to avoid the randomness of the CH selection, a centralized clustering routing protocol is proposed. The stage of CH selection is calculated centrally by BS through the modified grey wolf optimizer (MGWO), and then the result of CH selection is notified to all sensor nodes in the network in the form of broadcast. The phases at which the cluster member joins the cluster and steady-state are the same as in the LEACH [14] protocol. In the first stage of CH establishment, the sensor nodes first send the location and initial energy to the BS, which receives the information and saves it. The BS is given the initial energy of the nodes, and the node's position is fixed. The energy consumption of the node can be estimated by clustering information of each round, and the energy information of the node in each round can be obtained. Therefore, the nodes do not need to send position and energy information to the BS in each round.
# Effect of Impact and Bearing Parameters on Bird Strike with Aero-Engine Fan Blades
###### Abstract  
Bird strikes are one of the most dangerous incidents occurring to aircraft engines and can inflict heavy casualties and economic losses. In this study, a smoothed particle hydrodynamics (SPH) mallard bird model has been used to simulate bird impact to rotary aero-engine fan blades. The simulations were performed using the finite element method (FEM) by means of LS-DYNA. The reliability of the material model and numerical method was verified by comparing the numerical results with [PERSON]'s experimental results. The effects of impact and bearing parameters, including bird impact location, bird impact orientation, initial bird velocity, fan rotational speed, stiffness of the bearing, and the damping of the bearing on the bird impact to aero-engine fan blade are studied and discussed. The results show that both the impact location and bird orientation have significant effects on the bird strike results. Bird impact to blade roots is the most dangerous scenario causing the impact force to each 390 kN. The most dangerous orientation is the case where the bird's head is tilted 45\({}^{\circ}\) horizontally, which leads to huge fan kinetic energy loss as high as 64.73 kJ. The bird's initial velocity affects blade deformations. The von Mises stress during the bird strike process can reach 1238 MPa for an initial bird velocity of 225 m/s. The fan's rotational speed and the bearing stiffness affect the rotor stability significantly. The value of bearing damping has little effect on the bird strike process. This paper presents a procedure for evaluating the strength of fan blades against bird strike in the design stage.  
bird strike; engine fan; impact parameter; bearing parameter; SPH +
Footnote †: journal: Applied sciences