esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
This paper presents an optimized placement scheme for UAV access points providing network connectivity to GNs with differentiated data rate requirements. The goal of the proposed algorithm is to deploy as few as possible connected UAVs to cover and simultaneously satisfy the aforementioned requirements of the GNs. In order to attain this goal, we have mathematically formulated the problem and used an MOEA named NSGA-II to run the simulations. In order for NSGA-II to work, we proposed a chromosome structure, crossover scheme, and mutation procedure. Simulations were performed considering Wi-Fi (802.11g) technology, where GNs would request to turn to a given transmission mode within a set of available ones. Simulation results show that the algorithm optimizes the UAV placement given the requirements and positions of the GNs, considering the trade-off between the number of UAVs and the quality of the coverage.  
In future work, we will consider additional constrains such as limited inter-UAV link capacity and interference. We will also consider joint topology and routing optimization.  
Conceptualization: S.S., N.H. and A.G., Writing-Original Draft Preparation: S.S, Writing-Review: S.S., N.H. and A.G., Supervision and Validation: N.H and A.G.  
This work was partially supported by Fundacao Calouste Gulbenkian and by Portuguese national funds through Fundacao para a Ciencia e Tecnologia (FCT) with reference UID/CEC/50021/2013.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
A2A Air-to-Air A2G Air-to-Ground BER Bit Error Rate BFS Breadth First Search BS Base Station D2D Device-to-Device FSPL Free Space Path Loss GN Ground Node GPS Global Positioning System IEEE Institute of Electrical and Electronics Engineers LoS Line-of-Sight MOEA Multi-Objective Evolutionary Algorithm MOP Multi-objective Optimization Problem
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy
### Role of OCTA in Diabetic Retinopathy
#### 2.2.3 OCTA for Diabetic Macular Edema  
OCTA imaging in eyes with DME allows for comparison of changes in capillary density, dropout, and to study flow signals in relation to intraretinal cystic spaces caused by capillary leakage. [PERSON] et al. also showed that most microaneurysms are found in the deep capillary layer and in areas with DME [42]. However, in the presence of severe DME, anatomical distortion of the retinal layers makes accurate segmentation of the SCP and DCP challenging. Cystoid spaces can be differentiated from areas of non-perfusion by their shape and through comparison on structural and en face images. In DME, cystoid spaces are surrounded by areas of capillary dropout on OCTA. [PERSON] et al. showed disruption of the deep plexus pattern in DME and that the deep capillary plexus is more involved than the superficial capillary plexus. In addition, they found and that no reperfusion occurred in areas of cystoid spaces after resolution of DME [43]. Similarly, [PERSON] et al. demonstrated that retinal ischemia and loss of normal vasculature contributes to disorganization of retinal inner layers after resolved DME [44].
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## 1 Introduction
* A detailed algorithm has been designed for implementing the reliability mechanism of CoAP-based mobility management. In particular, the retransmission mechanism of CoAP has been utilized to implement the reliability mechanism. This greatly simplifies the design of the efficient reliability mechanism. The confirmable (CON) message of the CoAP protocol is extended for reliable transmission of the signaling message under an unreliable IoT network environment. * We have mathematically derived the performance measures of the reliability mechanism such as average handover delay and total handover delay. In deriving the performance measures, we have taken into account the packet fragmentation effect, which is caused by the constrained packet size of the IEEE 802.15.4 low data rate wireless network. Based on this mathematical analysis, the performance of the reliability mechanism has been evaluated by simulation. It is found that the simulation results are consistent with the mathematical results. The remainder of this paper is organized as follows. In Section 2, the architecture and procedure of reliable mobility management are described. In Section 3, the detailed algorithm for reliable mobility management is presented. Mathematical analysis for the performance of this algorithm is also presented. In Section 4, we describe the performance results of the proposed reliability mechanism for IoT mobility management. Finally, in Section 5, we provide some concluding remarks regarding this research.
# Radar Voxel Fusion for 3D Object Detection
## 2 Related Work
### 2D Sensor Fusion for Object Detection  
This section reviews 2D fusion methods. The focus is on methods that fuse radar data as part of the input data.  
[PERSON] [15] is the first to use a neural network to fuse low level radar and camera for 2D object detection. The network fuses the data on a feature level after projecting radar data to the 2D image plane. The object detection scores of the fusion are higher than the ones of a camera-only network, especially for distant objects.  
CRF-Net [16] develops a similar fusion approach. As an automotive radar does not measure any height information, the network assumes an extended height of the radar returns to account for the uncertainty in the radar returns origin. The approach shows a slight increase in object detection performance both on a private and the public nuScenes data set [1]. The paper shows further potential for the fusion scheme once less noisy radar data are available.  
YOdar [17] uses a similar projection fusion method. The approach creates two detection probabilities of separate radar and image processing pipelines and generates their final detection output by gradient boosting.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
###### Abstract  
Plant phenology is closely related to plant function, ecosystem services, and climate balance. Solar-induced chlorophyll fluorescence (SIF) offers new perspectives on plant phenology at regional and global scales. However, the effect of SIF products at different scales on phenology extraction is still unclear. Understanding of the mechanisms underlying phenological responses to environmental factors remains incomplete. Therefore, in this study, two phenological metrics for the Start of Growing Season (SOS) and the End of Growing Season (EOS) were extracted from the phenology of deciduous forests in the middle and high latitudes of the Northern Hemisphere, utilizing SIF products at scales of 1 km, 5 km, and 50 km, and applying the Savitzky-Golay filtering method along with the dynamic threshold method. Our results showed that the 1-km resolution SIF had a significant advantage over the 5-km and 50-km resolution SIFs in terms of consistency with the extracted phenology results from the Gross Primary Productivity (GPP) sites, with mean absolute errors (MAEs) of 4.48 and 15.49 days for SOS and EOS, respectively. For the 5-km resolution SIF, the MAEs for the same phenological metrics were 9.2 and 21.07 days. For the 50-km resolution SIF, the MAEs were 58.94 and 42.73 days. Meanwhile, this study analyzed the trends of phenology utilizing the three scales of SIF products and found a general trend of advancement. The coarser spatial resolution of the SIF data made the trend of advancement more obvious. Using SHapley Additive exPlanations (SHAP) analysis, we investigated the phenological responses to environmental factors at different scales. We found that SOS/EOS were mainly regulated by soil and air temperature, whereas the scale effect on this analysis' results was not significant. This study has implications for optimizing the use of data, understanding ecosystem changes, predicting vegetation dynamics under global change, and developing adaptive management strategies.  
p 2025 17 2059
# Context-Encoder-Based Image Inpainting for Ancient Chinese Silk
## 4 Experiments
### Qualitative Evaluation  
Figure 7 illustrates a qualitative comparative analysis of the LISK, MEDFE, and MADF methodologies using an ancient Chinese silk dataset under irregular random masking conditions. The observations indicate that the MEDFE model, which harmonizes deep structural features with shallow textural features, effectively synthesizes realistic textures, though some artifacts remain evident. By contrast, the LISK model, which integrates a multitask learning framework with an attention mechanism, leverages similar patterns within the images to refine the synthesized structure and content, demonstrating superior performance in generating more authentic structures and textures. Furthermore, the MADF model outperforms the previous models in detail enhancement, maintaining edge clarity and smoothness even in the presence of extensive and complex damage. This improvement can be attributed to MADF's implementation of mask-aware perception and its cascaded refinement approach. By employing mask perception, MADF enhances its ability to generate more accurate feature representations for image content decoding. Additionally, during the decoding phase, point-wise normalization (PN) is applied to mitigate the effects of covariate shifts during feature normalization, combined with an advanced refinement of the decoding process, rendering the outcomes more credible.  
Figure 8 illustrates several exemplars of restoration using square random masks. The results indicate that the efficacy of all three models is compromised in scenarios characterized by extensive missing regions. In such cases, the models predominantly interpolate the voids with homogeneous content derived from adjacent areas; deterioration in structural fidelity and textural resolution is more pronounced toward the center of the voids. Owing to the scant contextual data, the restoration algorithms fail to effectively reconstruct the lost segments.
# A High-Performance Piezoelectric Micropump with Multi-Chamber in Series
## 2 Structure and Working Principle  
Single-chamber, double-chamber series, and four-chamber series piezoelectric micropumps are designed in this paper. The multi-chamber series piezoelectric micropump is fabricated on the basis of the structure of the single-chamber micropump. Figure 1a,b provide exploded views of the single and four-chamber series piezoelectric micropumps, respectively. Specifically, they include the piezo-vibrator, pump body, cantilever valves, inlet, and outlet. There is a limit support at the water outlet of the pump body to change the opening of the valve, and it can also prevent the cantilever valve from deforming too much and losing its performance when it is subjected to large pressure.  
Figure 1: Exploded diagram of single-chamber and four-chamber series piezoelectric micropump. **(a)** Exploded diagram of single-chamber micropump. **(b)** Exploded diagram of four-chamber micropump.  
Figure 2 is the working principle diagram of the piezoelectric micropump. When working, the piezoelectric vibrator will generate periodic bending vibration under voltage excitation, together with the valve action, thus forming a unidirectional flow of fluid. Due to the rigidity of the valve, it is necessary to generate negative pressure in the pump chamber to open the valve; the greater the negative pressure, the greater the opening angle of the valve. The critical pressure that makes the valve open is called the valve opening pressure. Generally, the pressure in the pump chamber is set to 0 at the moment of the initial working state.  
In detail, at the first stage and the second stage, the piezo-vibrator is bent upward. When the negative pressure in the pump chamber exceeds the opening pressure of the inlet valve, the inlet valve is opened, the outlet valve is closed, and the fluid enters the pump chamber. At this time, the negative pressure in the pump chamber is greater than the opening pressure of the inlet valve to keep it open. At the third and the fourth stage, the piezo-vibrator first bends upward until the limit state and then bends downward until the back pressure is zero. During this time, more and more fluid flows into the pump chamber because the flow in the pump chamber will offset part of the volume. The pump chamber back pressure is less than the valve opening pressure; the inlet valve is completely closed. At this point, the outlet valve is still in the closed state; in the fifth stage and the sixth stage, the piezo-vibrator is bent down to the limit from the position of zero back pressure, and the outlet valve is opened until the end of drainage work; in the seventh stage, the piezo-vibrator is restored to a flat position and the piezoelectric micropump returns to its initial state. From a macro perspective, the fluid forms a continuous unidirectional flow in the pump chamber.
# Review of sEMG for Robot Control: Techniques and Applications
## 3 Processing and Pattern Recognition Methods
### Datasets  
Generally, the specific fields of research are different, and most researchers have conducted unique experiments on their topics. Thus, the datasets in different studies are quite different and are not universal. However, studies on biceps brachii, triceps brachii,and hand gesture-related muscles are more popular. Some authors have contributed to open-source datasets of these muscles for future work.  
[PERSON] provides a database of kinematics and sEMG of the forearm and hand, called the KIN-MUS UJI Dataset, recording 572 forearm angles and muscle activities of 22 subjects during activities of daily living [73]. The sEMG and Inertial Measurement Unit (IMU) signals of the forearm during typing, push-ups, weightlifting, and rest were collected using the Myo Thalamic band in [PERSON]'s study [53]. Al-Timemy AH provided EMG signals for patients with radial artery amputation for prosthetic control [27]. A dataset related to the hand movements of amputees provided in [125] can be used to study the relationship between the sEMG signal, hand kinematics, and hand force. [PERSON] used the Myo band to collect datasets under non-ideal conditions, including electrode displacement, individual differences, muscle fatigue, daytime differences, arm posture, and other factors, for robust research on the sEMG control system [66]. The literature [72] records 12 high-density sEMG signals of gestures for gesture recognition and the development of muscle-computer interfaces (MCIs). In [39], gesture recognition was based on the CapsMyo and CSLHDEMG datasets. [PERSON] provides a record of high-density EMG signals of five muscles of the upper limb [89]. Cene provides an open-source dataset of the upper limb for an Extreme Learning Machine (ELM) limit learning machine for intention detection [50]. [PERSON] provided a method for simulating and generating EMG data from healthy individuals [114].
## 2 System Description
### Simultaneous Conjugate Impedance Matching
\[X_{2}=-\frac{1}{2}X_{1}-X_{P}-\frac{1}{2}b\sqrt{X_{1}^{2}-4R_{P}^{2}} \tag{22}\]  
where \(b=1\) for the pLsL and pLsC configurations and \(b=-1\) for the pCsL and pCsC configurations. Finally, the component types and sizes can be determined and calculated by  
\[\left\{ \begin{array}{ll}L=\frac{X_{n}}{2\pi f_{mch,\;\max}},&\text{if }X_{n}>0,\;n=1,\;2\\ \\ C=-\frac{1}{2\pi f_{mch,\;\max}X_{n}},&\text{if }X_{n}<0,\;n=1,\;2\\ \end{array} \right. \tag{23}\]  
Figure 6 shows the power transfer efficiency of the unmatched channel, \(\eta_{ch}\), and the theoretical power transfer efficiency of the matched channel, \(\eta_{mch}\), which are calculated from the channel's scattering matrix, \(S\). It can be seen from Figure 6 that the maximum efficiency of the unmatched channel is 33.1%, which occurs at 1157.2 kHz (denoted as \(f_{ch,\;\max}\)). Under the conjugate matched condition, the channel's maximum efficiency occurs at 945.0 kHz and increases to 56.0% (denoted as \(\eta_{mch,\;\max}\)). The curves of \(\eta_{ch}\) and \(\eta_{mch}\) have a common periodicity. \(f_{ch,\;\max}=1157.2\) kHz differs greatly from \(f_{mch,\;\max}=945.0\) kHz, the authors believe that it is mainly because the resonant frequencies of the commercial off-the-shelf transducers are not best matched with the thickness of the steel plate. As \(f_{mch,\;\max}\) has been determined, the matching networks are designed as Figure 7 shows. After applying the matching networks to the channel, the scattering matrix of the matched channel, \(S^{\prime}_{m}\), was measured using the vector network analyzer, in order to verify the effectiveness of the matching networks. The measured power transfer efficiency of the matched channel, \(\eta^{\prime}_{mch}\), can be calculated from \(S^{\prime}_{m}\) as  
\[\eta^{\prime}_{mch}=\left|S^{\prime}_{m21}\right|^{2}\times 100\% \tag{24}\]  
and is also plotted in Figure 6. The maximum value of \(\eta^{\prime}_{mch}\), 41.7% (denoted as \(\eta^{\prime}_{mch,\;\max}\)), occurs at 944.6 kHz (denoted as \(f^{\prime}_{mch,\;\max}\)). The differences between \(\eta^{\prime}_{mch,\;\max}\) and \(\eta_{mch,\;\max}\), \(f^{\prime}_{mch,\;\max}\) and \(f_{mch,\;\max}\), are mainly caused by the non-ideality of the commercial off-the-shelf components, as well as the rounding error introduced when selecting available commercial off-the-shelf components. However, \(\eta^{\prime}_{mch,\;\max}\) is still 8.6% than the peak value of \(\eta_{ch}\), therefore the matching networks is effective. In addition, \(\eta^{\prime}_{mch,\;\max}\) is smaller than \(\eta_{mch,\;\max}\) over the entire frequency band (excluding points near \(f_{mch,\;\max}\)), because \(\eta_{mch,\;\max}\) was calculated at each frequency point while the matching networks were designed at the single point, \(f_{mch,\;\max}\). Figure 6: Power transfer efficiencies of the unmatched and matched channel.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 2 Materials and Methods
### Possible Earthquakes Perturbations  
We reviewed the occurrence of earthquakes (EQs) around the world with moment magnitudes over 5 Mw and depth less than 70 km between the 9 and 17 of May 2021 (DoYs 129 to 137). The data were obtained from [[https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)]([https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)) (accessed on 3 December 2021). Five strong EQs (6-6.9 Mw) and twenty moderate EQs (5-5.9 Mw) occurred in this analyzed period. However, none of them produced noticeable effects on TEC during the geomagnetic storm period. The differential TEC was analyzed, taking care of the potential minor effects in the other days of the analyzed period.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 1 Introduction
### Motivations  
This survey paper took a particular look at the security features that related to authentication and key agreement (AKA) techniques based on PKI, in the field of mobile communication systems, and precisely aims to present a comprehensive review of the most concerning security issues, which are AKA based on PKI security techniques in the mobile communication networks, because they cannot be solved easily by symmetric cryptosystem for some vulnerabilities such as man in the middle attack (international mobile subscriber identity (IMSI) catcher attack) and replay attack. These two attacks are considered to be the most common weaknesses for mobile communication systems. However, this survey paper, in order to be more practical, excluded other security features and requirements such as confidentiality, integrity, privacy, etc. as a priority because most have been covered by symmetric cryptosystems, and this survey only includes the most recent and important works in the security-based PKI for 2G-GSM and 3 GPP mobile systems. Moreover, this paper studies and analyzes the existing security solutions using PKI in order to confirm how the previous proposed schemes meet the security requirements, and it suggests some new solutions as well as proposes some new directions and future scopes for compatible and secure cellular systems.  
The rest of this paper is organized as follows: Section 2 introduces some background notions on mobile communications security for each cellular generation: 2G-GSM, 3G-CDMA and 4G-LTE respectively. Section 3 discusses the previous work and gives a brief literature review for some of the proposed work in the field of mobile communications security. Section 4 describes the Security requirements and performance analysis for some of the previous work with comparisons. In Section 5, we present security solutions with new directions in the field of PKI-based security for mobile communications. Finally, Section 6 draws some conclusions.  
The general architecture for the operation and security processes is shown in Figure 1. There are five operation processes and three security processes in the mobile communications architecture for all the cellular systems, which are shown in (Figure 1) with some clarifications.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Overview  
The emergency communication network deployment model we propose is based on an improved Double DQN (DDQN) [22] algorithm, with a core architecture comprising three major parts: agent, experience replay buffer, and environment, as illustrated in Figure 3.  
The value network is constructed using a lightweight fully convolutional neural network (FCN). This network takes the current deployment state \(S_{t}\) as input, extracts features through multiple convolutional layers, and ultimately outputs a Q value matrix \(Q_{w}(S_{t},a)\) aligned with the input resolution. Each position in the matrix corresponds to the value of a different deployment location, thereby achieving an end-to-end mapping from the high-dimensional state space to action values.  
Then, the agent adopts an \(\epsilon\)-greedy strategy to dynamically balance exploration and exploitation. Based on the Q value matrix generated from the current state, the agent  
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Explanation** \\ \hline \(t,T\) & the index of an element, the total number of elements \\ \(G_{t},G_{r}\) & transmit and receive antenna gains \\ \(C_{r},f\) & speed of light, carrier frequency \\ \(c_{1},c_{2}\) & standard deviation of the Gaussian function \\ \(R_{i}\) & quantified reward function for each factor \\ \(S_{t},a_{t},R_{t}\) & state, action, and reward in \(t\)-th epoch \\ \(\alpha_{i},\beta_{i}\) & adjustment coefficients of the subtotal and total reward function \\ \(D\) & set composed of road areas \\ \(d\) & distance between two points \\ \(d_{max},d_{min}\) & maximum and minimum communication range among elements \\ \(A_{com},A_{dis}\) & effective communication coverage area and affected region \\ \(\epsilon\) & denotes the exploration probability \\ \hline \end{tabular}
\end{table}
Table 2: Frequently used notations.  
Figure 3: Overall architecture of proposed DRL-based method.  
selects the location \(a_{t}\) with the highest value for deploying communication elements. After executing an action, the system receives an immediate reward through environmental feedback, which is then stored in the experience replay buffer. Each transition sample \(\{S_{t},a_{t},R_{t},S_{i+1}\}\) is stored using a prioritized experience replay mechanism. During training, the model maintains a separation between the parameters of the target network and the training network through an asynchronous parameter update strategy. Combined with the dual-network structure, this approach reduces the correlation in Q value estimation, thereby enhancing policy stability in complex and dynamic environments.