esa-sceva/satcom-chunk-collection huggingface dataset:
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 3 Methods
### Adaptive Context Module  
In the encoder part, we develop a network based on the adaptive context module (ACM) to fully and effectively utilize both local-range and long-range semantic information interactions. In addition, ACM is used as a feature extraction tool to obtain multi-scale features via different shift windows in the hierarchy. We construct a one-short connection module (OCM) into the ACM to extract the local multi-scale information, as shown in Figure 2c. Two successive ACMs realize the partial window calculation self-attention, as shown in Figure 2b. Each window partitioning (WP) and shifted window partitioning (SWP) are used in ACMs to greatly reduce the computational complexity.  
Figure 2: (**a**) The overview of proposed BEAC-Net. (**b**) The architecture of two successive ACMs. (**c**) The detailed architecture of OCM.  
When ACM receives the features \(Y\in\mathbb{R}^{H\times W\times C}\) with a height of \(H\), a width of \(W\), and a channel of \(C\), it will calculate the inputs of the MSA using two parallel branches, where \(Q\) is query, \(K\) is key, and \(V\) is value. The multi-head attention self-attention uses global attention to learn features with good generalization performance. In the left part, feature Y is split into non-overlapping windows with a size of \(H\times W\) via (S)WP. Then, the features are reshaped as \(Y_{f}\in\mathbb{R}^{M\times C}\). A full connected layer is applied to acquire query \(Q\in\mathbb{R}^{M\times d}\), where in \(d=C/k\), \(k\) denotes the head number. The output of the concatenation is the input of the right part. In the right part, feature \(Y\) is first utilized to extract local-scale information using OCM. Similar to DenseNet, all features are concatenated once in the last feature map, which keeps the input size constant and enables the expansion of the new output channel in Figure 2c. Two consecutive ACMs consist of a shifted-window-based MSA with two layers of MLPs. LN layers are used before each MSA block and each MLP, and residual connections are used after each MSA and MLP. OCM consists of two \(1\times 1\) convolution layers and three \(3\times 3\) depthwise separable convolution layers as dilation rates \(r=\{1,2,3\}\). The features of all previous layers are sent as input to the separable \(3\times 3\) depthwise convolution. With \((y_{0},y_{1},\cdots,y_{m-1})\) as input, we obtain the following:  
\[y_{m}=C_{m}([y_{0},y,\ldots,y_{m-1}]) \tag{1}\]  
Figure 3: SRGAN network for image enhancement.  
where \(C_{m}\) is a multi-dimensional concatenate operation. We use the same operations on the left part for feature acquisition on the OCM to generate \(y_{l}\in\mathbb{R}^{M\times C}\), where \(K\in\mathbb{R}^{M\times d}\), \(V\in\mathbb{R}^{M\times d}\) are acquired from \(Y_{l}\). \(MSA\) is defined as follows:  
\[MSA(Q,K,V)=Softmax\left(\frac{QK^{T}}{\sqrt{d}}+B\right)V \tag{2}\]  
where \(Q,K,V\in\mathbb{R}^{M\times d}\) ; \(d\) is the query/key dimension; and \(B\) is the bias matrix, of which values are obtained from \(B\in\mathbb{R}^{M\times M}\).
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 5 Conclusions  
In this work, we propose the energy protocol EECMR for routing data packets in UWSNs. EECMR is a depth-based clustering protocol that uses the depth level of the node to select cluster head nodes and forwarder nodes for multi-hop routing. EECMR considers the residual energy of the node which elects cluster heads in turns. The nodes can change roles as cluster head, cluster member, and cluster relay. The cluster relay node forwards data from a deeper level to the sink. With the aid of a cluster relay, the energy consumption for transmission is decreased, leading to fewer dead nodes. The simulation results showed that EECMR achieves better performance in terms of higher residual energy, longer network lifetime, and higher received packets at the sink. Although the proposed protocol can properly select the cluster head and cluster relay according to the depth level and residual energy, the high energy consumption at the cluster relay between different depth levels may result in re-clustering or frequent re-clustering. However, the clustering protocol may cause high latency due to the multi-hop routing path. The different types of sensor nodes will have different data priorities, an issue which must be addressed in future work.  
In addition, several issues remain open for future work, including optimization of the network topology and implementation of the clustering protocol in firmware to support UWSN application in oceans. In our future work, we will implement the clustering protocols into firmware to investigate the practical performance.  
The main contributions of N.-T.N. and T.T.T.L. were creation of the main ideas and performance evaluation through extensive simulations. [PERSON] contributed to the manuscript preparation and designed the theoretical analysis. [PERSON] served as consultant of [PERSON] to discuss, create, and advise the main ideas and performance evaluations together. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results received funding from the Czech Ministry of Education under grant No. SP2020/65, conducted at the Technical University of Ostrava and partially by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project \"e-Infrastructure CZ-LM2018140\". This work was also supported by Saigon University, Vietnam, grant No. TD2020-17.  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflict of interest.  
Figure 6: Received packets at the sink: (**a**) Transmission range 200 m, 450 nodes in 1000 rounds; (**b**) Transmission range 200 m, nodes 50 to 450; (**c**) Transmission range 150 m, 450 nodes in 1000 rounds; (**d**) Transmission range 150 m, nodes 50 to 450.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 2 The Electric System of El Hierro
### The Generation System  
El Hierro, the most isolated landmass in the Canary Islands, has a population of more than 10,000 inhabitants and was designated a World Biosphere Reserve by UNESCO in 2000. The island's Sustainability Plan, approved in 1997, initiated its transition towards sustainable energy, supported by the environmental awareness of both the local community and government. A key project in this transition is the wind-hydro power initiative, managed by the Gorona del Viento consortium, which aims to meet 100% of energy needs from renewable sources. Traditionally, electricity was supplied by diesel engines with a capacity of around 13 MW. The commissioning of the Gorona del Viento wind-hydro plant in 2014 raised the island's installed power capacity to close to 40 MW.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Demand (GWh)** & **Diesel (\%)** & **Renewable \({}^{\text{1}}\) (\%)** \\ \hline
2013 & 44.02 & 99.53\% & 0.47\% \\
2014 & 42.08 & 97.45\% & 2.55\% \\
2015 & 42.99 & 80.10\% & 19.90\% \\
2016 & 44.6 &
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 1 Introduction
Compared to the ALE method, the S-ALE method combines structured meshes with adaptive technology, not only achieving efficient fluid domain modeling, but also significantly improving computational efficiency [10; 11]. When simulating blood FSI under physiological states, existing studies typically use the time-varying blood velocity curve as the boundary condition for the vessel inlet [12; 13; 15; 16; 20; 25]. However, relevant studies have shown that the distribution of blood flow velocity on vascular cross-sections exhibits significant non-uniformity [20; 23]. Using only a single velocity-time curve as a boundary condition is insufficient to reflect the complex hemodynamic characteristics under physiological states. Here, a displacement-time load boundary condition is employed to simulate the periodic systolic and diastolic movement of the left ventricle, thus more realistically reproducing the process of heart pumping. In this work, a numerical simulation analysis of the heart-aorta FSI under physiological states was conducted using the LS-DYNA explicit dynamics solver based on the S-ALE method. Through a comparative validation against the existing literature data, the applicability and reliability of the S-ALE method for simulating FSI problems in complex cardiovascular systems were confirmed. Based on the validated model, a preliminary application analysis of the heart-aorta FSI under different physiological states was performed.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 2 Related Work
### Simulations and Models for LoRa Environments  
Many simulation tools exist that allow repeatable and controlled tests in big scenarios with significant numbers of devices. ns-3 [26] is among the most popular ones, and it is widely used for research purposes in wired and wireless scenarios. It is continuously improved and extended in different ways, and it includes LoRa features. In this subsection, we will summarize some works proposing and testing different LoRa functionalities in ns-3.  
The authors of [27] extended the FUOTAsim simulator [8] to study a LoRaWAN scenario. Firmware updates were carried out using multiple gateways instead of one. They studied the benefits in terms of device energy consumption, update efficiency, update time, and number of corrupted and lost fragments.  
A LoRaWAN module was developed and tested for ns-3 in [28, 29]. It can be used to perform simulations of scenarios including Class A devices (one of the three classes of LoRaWAN devices [30]), gateways, and network servers. It is modular, so it can also be used for LoRa Phy simulations, without the need to implement the whole LoRaWAN stack. This is interesting in our case, since we are using MiWi instead of LoRaWAN, as explained previously.  
Some ns-3 signal propagation models have been proposed and tested for urban and city scenarios [31, 32]. The results of these papers show that Cost-231 and Okumura-Hata are the most fitted models for city scenarios. In addition, other propagation models for low-population scenarios have been included in ns-3, which were compared and summarized in [33]. This paper showed that Nagakami is one of the most realistic ones. Therefore, this is the model that is employed in the current study, given its suitability for industrial settings.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 4 Simulation Results
Thanks to the fact that PAPR of HABO-OFDM is superior to HACO-OFDM and ABO-OFDM, HABO-OFDM obtains significantly better BER performance. The variation of \(\langle E_{b(elce)}/N_{0}\rangle_{BER}\) when the proportion of electric energy allocated to subcarriers using QAM ranges from 0.1 to 0.9 is shown in Figure 6. We choose the size of QAM constellation and PAM constellation to be \(M\) and \(\sqrt{M}\). In this way, it is shown that the minimum \(\langle E_{b(elce)}/N_{0}\rangle_{BER}\) is obtained when the proportion of electric power allocated to subcarriers using QAM is 0.6 for all cases. In Figure 7, the \(\langle E_{b(elce)}/N_{0}\rangle_{BER}\) versus the proportion of electric power allocated to subcarriers using QAM is presented. The size of QAM constellation and PAM constellation is defined as \(M\) and \(\sqrt{M}\). The minimum \(\langle E_{b(elce)}/N_{0}\rangle_{BER}\) is achieved when the proportion of electric power on subcarriers using QAM is 0.7 in all cases. This is because, in  
Figure 4: CCDF curves of the PAPR for HACO-OFDM, ABO-OFDM, and HABO-OFDM. Figure 5: BER of HACO-OFDM, ABO-OFDM, and HABO-OFDM versus \(E_{b(elce)}/N_{0}\) when the clipping ratio is set to 9 dB. HABO-OFDM, the subcarriers that use QAM to modulate the transmitted signal occupy the majority. In Figure 8, the required \(E_{b(dec)}/N_{0}\) for the BER target of \(10^{-3}\) versus Bit rate/Normalized bandwidth [24] is presented. In order to make a fair comparison, the average electric power of each of the three schemes is set to be uniform. Since HACO-OFDM and HABO-OFDM are combined modulations of QAM and PAM, the distribution of electric power needs to be considered. The \(\langle E_{b(dec)}/N_{0}\rangle_{BER}\) versus the proportion of electric power allocated to subcarriers using QAM for HACO-OFDM and HABO-OFDM is shown in Figures 6 and 7, respectively. Therefore, the lowest values of \(\langle E_{b(dec)}/N_{0}\rangle_{BER}\) for HACO-OFDM and HABO-OFDM can be selected. From Figure 8, we see that the Bit rate/Normalized bandwidth of 3, 4.5, 6, 7.5, 9 for HACO-OFDM and ABO-OFDM and the Bit rate/Normalized bandwidth of 3.5, 5.25, 7, 8.75 for HABO-OFDM are shown. We can clearly obtain that when the Bit rate/Normalized bandwidth is in the range of 3 to 9, the \(E_{b(dec)}/N_{0}\) required for HACO-OFDM and ABO-OFDM to achieve the BER target of \(10^{-3}\) is almost the same, which means that their power efficiency is similar. Moreover, it is obvious that as the Bit rate/Normalized bandwidth increases, the \(E_{b(dec)}/N_{0}\) required for HABO-OFDM to achieve the BER target of \(10^{-3}\) will  
Figure 6: Comparison of \(\langle E_{b(dec)}/N_{0}\rangle_{BER}\) for HACO-OFDM for different proportions of electric power. Figure 7: Comparison of \(\langle E_{b(dec)}/N_{0}\rangle_{BER}\) for HABO-OFDM for different proportions of electric power. be significantly lower than the other two schemes, which shows that in a wide range of the Bit rate/Normalized bandwidth, HABO-OFDM exhibits higher power efficiency in comparison with HACO-OFDM and ABO-OFDM.
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 3 Proposed Method to Generate an Enhanced SPOH with Time-Division Comb Filtering
### Enhancing the Reconstructed Image of a SPOH with Time-Division Comb Filtering  
Mathematically, the comb filtering expressed in Equation (14) can be applied directly to reduce the down-sampling factor of the reconstructed image \(I^{\prime}_{D}(m,n)\). However, this cannot be realized in optical reconstruction. The reason is that when the phase-only hologram \(H_{P}(m,n)\) is shown on a SLM, the reconstructed image \(I^{\prime}_{D}(m,n)\) is displayed as an optical image. It is not possible to apply numerical computation on the optical image directly. To apply comb filtering, we propose the TDCF method shown in Figure 6, and described as follows. First, a phase-only sampled phase-only hologram, \(H_{P}(m,n)\), is generated. Next, three spatially translated versions of the phase-only hologram, \(H_{P}(m-T,n)\), \(H_{P}(m,n-T)\), and \(H_{P}(m-T,n-T)\), with \(T=\frac{\tau}{2}\) are obtained. Finally, these four versions of phase-only holograms are sequentially displayed at sufficiently high frame rate on the SLM. Each hologram shows a reconstructed image that is being translated by the same displacement as that of the hologram. This results in the SLM displaying four reconstructed images: \(I^{\prime}_{D}(m,n)\), \(I^{\prime}_{D}(m-T,n)\), \(I^{\prime}_{D}(m,n-T)\), and \(I^{\prime}_{D}(m-T,n-T)\) in rapid succession. These images will be summed into a single image \(I_{V}(m,n)\) due to persistence of vision of the human perception system. The observed image, as given by  
\[I_{V}(m,n)=0.25\times\left[I^{\prime}_{D}(m,n)+I^{\prime}_{D}(m-T,n)+I^{ \prime}_{D}(m,n-T)+I^{\prime}_{D}(m-T,n-T)\right] \tag{15}\]  
Figure 5: (a) Frequency spectrum of a signal in Figure 2a being down-sampled by \(\tau=4\) times (solid line), and the transfer function of a comb filter with delay \(T=\frac{\tau}{2}=2\) (dotted line), (b) frequency spectrum of the down-sampled signal after filtering with the comb filter.  
results in a reconstructed image with the down-sampling factor reduces by two times.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis
### Consistency of Cluster Heads  
The energy efficiency of the protocol is greatly influenced by the number of cluster heads in use. If the number of cluster heads is too less, the data transmission duration between sensor nodes and the cluster head will be too long, resulting in increased energy usage. Excessive data transmission by the cluster will also increase energy usage. As the cluster heads rose, the overall network energy demand increased.  
In Figure 19, we can see that ACO has many variances in the cluster head number since the cluster headcount is randomly reliant on the threshold function model, which is also random, which causes the cluster headcount to fluctuate a lot. The figure shows that the cluster headcount swings in the range of \(4\leq k\leq 19\) in the ACO and \(2\leq k\leq 18\) in the GWO, \(3\leq k\leq 17\) in PSO, but the suggested protocol has a headcount of \(3\leq k\leq 12\), which is the most appropriate as compared to others.
## 3 Experimental Results
### Air-Injected Enclosed Mist Spraying Smart Farm UAE Experiment Cultivation  
In this paper, compared to other cultivation methods such as nutrient solution culture and film culture, less water can be used, and the system is specialized for extreme outdoor environments with water scarcity and high adaptability. Therefore, experimental cultivation was conducted in the Middle East, in the United Arab Emirates, to confirm the cultivation potential, internal environmental control performance as shown in Figure 9, and water use efficiency. For this purpose, cultivation and nutrient solution data, electricity and water consumption data, and internal and external environmental data were collected. By analyzing the collected data, we aim to find more efficient environmental conditions. During the experiment, the entire system was set up as shown in Figure 1, and a Nutrient Mixer, Misting System, and Drainage Manager were installed using the existing method. Data were collected for one cultivation cycle of 21 days to analyze the efficiency of nutrient solution circulation and air circulation. As shown in Table 3, the total amount of water  
Figure 8: Representation of the difference in light supply quantity by color on one side of the vertical cultivation bed with installed artificial light in the vertical cultivator equipped with recirculation system. “A” Red indicates a relatively higher supply, while “B” green indicates a relatively lower supply of light.  
collected and reused from the air during one cultivation cycle was approximately 520 L, and the additional water required was measured to be 176 L.  
Assuming an average biomass of 100 g per plant and considering that leafy vegetables consist of 95% water in their bodies, the total number of crops that can be cultivated in an air-injected enclosed smart farm is calculated to be 1100 plants. Therefore, during the cultivation period, leafy vegetables would absorb approximately 105 L of water. The remaining 70 L can be attributed to water loss due to internal and external air circulation and system ingress. Minimizing such water loss could lead to innovative advancements in water efficiency, as only the water needed for absorption into plant bodies would be required, eliminating the need for further additional water. This demonstrates the potential for revolutionary advancements in water efficiency through innovative technologies.
## 4 Performance Analysis
### Workspace and Dexterity Performance Index in the 1T2R Motion Mode  
When the mechanism is a redundantly actuated parallel mechanism, the prismatic pairs of branched chains 1, 2, 3 and 4 are the driving pairs of the mechanism; when the mechanism is a non-redundantly actuated parallel mechanism, the prismatic pairs of branched chains 1, 2 and 4 are the driving pairs of the mechanism. In the 1T2R motion mode, the workspace performance index of the redundantly actuated parallel mechanism is 0.6126, and the workspace performance index of the non-redundantly actuated parallel mechanism is 0.6126. After introducing branched chains with actuation redundancy, the size and shape of the reachable workspace of the mechanism have not changed, as shown in Figure 5.  
It is calculated that the translational dexterity of the redundantly actuated parallel mechanism and the non-redundantly actuated parallel mechanism is always 1, indicating that the mechanism has complete dexterity in the translational degree of freedom and can meet the needs of flexible translation.  
Figure 5: The rotational dexterity index distribution in the workspace: (**a**) a redundantly actuated parallel mechanism and (**b**) a non-redundantly actuated parallel mechanism.  
The region with a low rotational dexterity performance is represented as a blue region. By comparing Figure 5a,b, it can be concluded that after introducing branched chains with actuation redundancy, the rotational dexterity performance index represented by the blue region of the redundantly actuated parallel mechanism is significantly higher than that of the non-redundantly actuated parallel mechanism, and the rotational dexterity performance of the mechanism is significantly improved. The region with a high rotational dexterity performance is represented as a yellow region. As the \(\alpha\) value increases, the rotational dexterity performance gradually decreases, which is reflected in the transition from yellow to green and blue.  
Under the initial parameters at \(Z=300\) mm, the distribution of the rotational dexterity index of the redundantly actuated parallel mechanism and non-redundantly actuated parallel mechanism is obtained, as shown in Figure 6. It can be seen from Figure 6 that the rotational dexterity index of the redundantly actuated parallel mechanism is higher than that of the non-redundantly actuated parallel mechanism. The rotational dexterity index of the redundantly actuated parallel mechanism is symmetrically distributed with respect to \(\beta=0^{\circ}\). As the \(\alpha\) value increases, the rotational dexterity performance gradually decreases.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 2 Instantaneous Frequency Estimation  
### Signal Model  
Ocean waves in real sea conditions can be modeled as a superposition of linear wave components derived from wave energy spectra such as JONSWAP and Pierson-Moskowitz [13]. These spectra describe how wave energy is distributed across different frequencies. Alternatively, the water surface elevation can be expressed as a single sinusoidal component with a time-dependent amplitude and phase.  
\[x(t)=A(t)\cos{(\overline{\omega}t+\epsilon(t))}, \tag{1}\]  
where \(A(t)\) is the slowly varying envelope amplitude of the wave; \(\overline{\omega}\) is the energy-weighted mean angular frequency of the wave spectrum; and \(\epsilon(t)\) represents the slowly varying phase.  
The instantaneous frequency of the wave signal is given by the following:  
\[\omega_{\text{inst}}(t)=\overline{\omega}+\frac{d\epsilon(t)}{dt}. \tag{2}\]