esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 6 Conclusions  
In this survey paper, a review of the currently available low-power wireless communication protocols that can potentially be employed in WBAN systems is provided. More specifically, this survey paper provides a review of the current research in the area of WBANs with a specific focus on low-power consumption, transmission reliability, latency, data rates and security. A comparison of various energy-efficient and reliable wireless communication protocols are provided. This survey paper also considers the requirements and challenges of WBAN systems in a typical eHealthcare system in order to explore how such systems are able to effectively communicate with the home infrastructure. The imposed restrictions and requirements of WBAN systems are pointed out. The shortfalls of various WBAN systems in the residential environments are diagnosed and discussed andalso suggestions for developing more appropriate systems for residential eHealthcare are proposed. In this survey, WBANs are used to enable healthcare professionals to continuously monitor patients and elderly people in their own residential environments. In this way, abnormal conditions can be detected early which results in major improvements in the quality of patients' lives. This survey then investigates the future prospects of eHealthcare systems which includes the advantages, challenges and the fastest areas of growth in the near future. Finally, this survey concludes with a brief review of a number of currently published articles in the area of eHealthcare systems.  
This work was performed under the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1. University of Reading also provided funding to publish the result of this research in an open access journal.  
The authors declare no conflict of interest.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 4 Experiments and Discussion
### Experiments Settings and Metrics  
As described in Section 3, the proposed model aims to make a multi-step prediction of future traffic states according to historical observations. Following the typical settings in the baseline methods using the same datasets, we set both the length of historical observations and future predictions to one hour, i.e., \(P=Q=12\), to make fair comparison. When generating the time-evolving graphs, the embedding dimension \(d\) is set to 30 for METR-LA and 40 for PEMS-BAY. Since the length of each time slot is 5 min, the number of time slots in a day \(N_{t}=288\). As for the graph convolution module, the maximum hop of \(K\) is set to 2 for both datasets and \(\alpha\) is set to 0.01. The number of TGCGRU layers is set to two and the size of the hidden state in TGCGRU is set to 40 for METR-LA and 50 for PEMS-BAY. These hyper-parameters that are related to the proposed model are all selected via grid search. During the training process, the value \(\tau\) for scheduled sampling is set to 2000. The model is trained by Adam optimizer and the learning rate is initialized to 0.01 with a decay rate of 0.1. Mean absolute error (MAE) is selected as the loss function. The batch size of input data is set to 64 for METR-LA and 32 for PEMS-BAY. Early stopping strategy is employed. For each dataset, the experiment is repeated five times to obtain the average metrics. To evaluate the performance of traffic prediction, three types of metrics are used, including mean absolute error (MAE), root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).
## 4 Experimental Outcomes and Evaluations
### Graphical Representation of Accurateness of Epochs and Loss  
In machine learning, an epoch denotes a whole iteration over the entire training dataset throughout the training procedure. The training data are often partitioned into smaller batches, each of which is used to update the model's parameters. An era is considered complete after all batches have been processed. During each epoch, the model continually modifies its internal parameters by considering the input data and matching target labels to minimize the error or loss. The number of epochs defines the total number of iterations the model will perform on the training dataset. The training and testing datasets are used for gaining the accuracy and loss and they have been represented by Figure 5.
## 4 Conclusions  
This research paper focused on the valorization of RWG by using it as an alternative to scarce sand, in concrete. Different volume percentages of RWG varying from 0 to 50% were incorporated into concrete. Durability against alkali silica reaction and thermophysical and thermomechanical characterization have been experimentally carried out. The analysis of the SEM-EDS pictures showed that the incorporation of RWG with maximum size that did not exceed 1.25 mm in concrete did not produce deleterious alkali-silica reaction. The optical microscope characterization of MRGW50 indicated that only glass particles with pre-existing micro-cracks were cracked due to the ASR. Furthermore, the ASR tests on the mortar-based composite reinforced with different volume contents of RWG revealed that the expansion activity remained well below the limit expansion value of 0.15%. Regarding the compressive strength results, it increased respectively at 28 and 90 days of curing by up to 1.63% and 29% for 20% of volume content in concrete; beyond 20%, it diminished receptively by 30% and 3.2% due to the low adherence between cement paste and glass particles. It was also noticed that the compressive strength of RWG50 evaluated better with cure age. As for flexural strength, it decreased by around 34% for the highest volume content of RWG, at 28 days. Thereafter, the addition of RWG into concrete led to a gain in lightness of the manufactured composite by reducing its density by around 5%. Furthermore, the thermal conductivity and thermal effusivity decreased respectively by 36% and 8.06% at dry state and they diminished respectively by 44% and 21.28% at saturated state, related to RC. The calculated thermal diffusivity decreased by about 51% at both dry and saturated states, while the calculated volumetric thermal capacity increased by about 25% and 11% respectively at dry and saturated states. It may therefore be concluded that the elaborated composite by substitution of sand with RWG could be of use in the building construction as structural material. It is durable against the ASR and it can be of great interest in improving the thermal performance of buildings. Conceptualization, I.A., S.S. and F.-E.E. ; methodology, I.A., S.S. and F.-E.E. ; validation, I.A., S.S., F.-E.E. and L.B. ; formal analysis, I.A., S.S. and F.-E.E. ; investigation, I.A. and S.S.; resources, I.A. ; writing--original draft preparation, I.A., S.S. and F.-E.E. ; writing--review and editing, I.A. and L.B. ; supervision, L.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data of this paper will be available from corresponding author, L.B., on reasonable request. A big thank you to the National Center for Research and Studies on Water and Energy (Cadi Ayyad University, Morocco) for the technical and scientific support to this work. The authors declare no conflict of interest.
## 4 Results and Discussions
### Discussions  
In recent years, other scholars have also conducted similar research for the carbon emission efficiency in China. For instance, [PERSON] et al. found that urban carbon emission performance in China increased steadily from 1992-2013. However, the overall level of carbon emission performance remains low, and the spatial pattern of urban carbon emission performance in China can be described as \"high in the south and low in the north\" [70]. [PERSON] et al. found that the steady improvement for China's carbon emission efficiency is obvious, and there is an inverted U-shaped relationship between urbanization and carbon emission efficiency [71]. [PERSON] et al. took the Yangtze River Delta, the Pearl River Delta and the Beijing-Tianjin-Hebei Region as research objects, and found that regional industrial structure, energy efficiency, FDI(Foreign Direct Investment) dependence, and urbanization have a significant impact on the carbon emission efficiency [30].  
In recent years, other scholars have also conducted similar research for the relationship of carbon emissions, economic development, and environmental protection. However, such studies remain limited, and some related studies focus on the coordination degree of the 3E (energy-economy-environment) system. For instance, [PERSON] found that the coupling of carbon emissions, economic development, and environmental protection in Liaoning Province is still at a low level [72]. [PERSON] et al. found that economic growth is related to the environmental input, and there is an inverted U-shape relationship between the environmental input and carbon emissions [73]. [PERSON] et al. found that the coordination level of the 3E system is not optimistic, and there are 26 provinces at different levels of maladjustment in China [74]. [PERSON] et al. found that China's 3E system is in an intermediate coordination state, and that the contradiction between energy consumption and environmental protection is the biggest obstacle to the overall coordination of the 3E system [75].  
Studies about spatial pattern analysis of the coordination degree of carbon emissions, economic development, and environmental protection are still lacking, and some related studies focus on the spatial pattern of the coordination degree of the 3E system. For instance, [PERSON] et al. found that the spatial pattern of the coordination degree of China's 3E system shows a positive spatial correlation on the global perspective [76]. [PERSON] et al. analyzed the spatial distribution of the coordination degree of China's 3E system, and found the spatial pattern of the coordination degree from the eastern coastal areas to the surrounding provinces [77]. [PERSON] et al. found that the spatial correlation of the coordination level distribution of China's 3E system is weak, and the spatial distribution lacks the obvious positive effect of spatial agglomeration [78].
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 4 Performance Evaluation
### Performance Metrics  
The proposed mechanism is assessed based on the following performance metrics.  
* Energy consumption: Sensor nodes in WSNs are equipped with limited energy, which is mainly consumed in transmitting the data. To calculate the energy consumption, a first-order radio energy model is used [27].
* Data delivery delay: It represents the duration between the transmission of data from the sensor nodes to the sink and the subsequent reception of that data at the sink. For calculating delay, the data delivery model is adopted from [28].
* Data delivery ratio: It is described as follows: = \(\frac{data\ packet\ received\ at\ sink}{total\ number\ of\ data\ packets\ sent\ by\ sensor\ nodes}\)
# Incentive Price-Based Demand Response in Active Distribution Grids
## 3 Mathematical Modeling and Simulation Setup  
In this section, modeling of a balanced three-phase LV distribution grid of radial topology is presented. Let \(N+1\) be the total number of nodes of the LV grid including a slack node which represents the secondary side of the LV distribution transformer. Let the slack node be denoted as 0 and the nodes at which the loads are connected be denoted by the set \(\mathcal{N}\coloneqq\{1,\ldots,N\}\). As the LV grid is considered to be of radial topology, there will be \(N\) number of branches. Let the branches of this grid be represented by the set \(\mathcal{L}\) with \(N\times N\) elements which are arbitrarily numbered and are oriented outwards from the slack node.  
It is to be noted that the line resistances in distribution grids are higher than the reactances and needs to be included in the model [28]. The impedance of each branch \((i,j)\in\mathcal{L}\) is denoted as \(z_{ij}=r_{ij}+\mathrm{j}x_{ij}\), where \(r_{ij}\) and \(x_{ij}\) are the branch resistance and reactance respectively in pu. The shunt admittances at each node are not included in the model as their values are negligible due to short line lengths. Let \(\mathbf{R}_{db}=\mathrm{diag}(r_{ij})_{(i,j)\in\mathcal{L}}\) and \(\mathbf{X}_{db}=\mathrm{diag}(x_{ij})_{(i,j)\in\mathcal{L}}\) be the diagonal matrices with branch resistance and reactance values respectively.  
Let us consider the slack node voltage to be the reference, i.e., \(\arg(\bar{\tau}_{0})=0\). Let us denote the node-to-ground voltage and current injected at the \(N\) nodes by the complex  
Figure 2: Schematic diagram of distributed predictive control of DERs based on incentive prices.  
phasors \(\bar{v}_{n},\tilde{I}_{n}\) and be collected in the set \(\bar{\mathbf{v}}\coloneqq[\bar{v}_{1},\cdots,\bar{v}_{N}]^{\mathrm{T}}\) and \(\tilde{\mathbf{i}}\coloneqq\left[\tilde{I}_{1},\cdots,\tilde{I}_{N}\right]^{ \mathrm{T}}\) respectively. Let the vectors \(\mathbf{p}\) and \(\mathbf{q}\) contain the values of net active power and reactive power demand (power consumption minus generation) at each node and are of size \(N\).
# Joint Power Charging and Routing in Wireless
## 3 Optimization Algorithms
### Joint Optimization of Routing and Charging  
In order to find the best routing tree corresponding to the selected resident location, a straightforward solution is to conduct an exhaustive search by constructing all feasible routing trees. This approach, however, is infeasible for networks with a larger number of sensor nodes. The heuristic algorithm is based on decomposition. However, this approach may be suboptimal because of the fact that the routing tree and charging efficiency interact with each other and the routing and charging should be optimized in a compact form. Therefore, we apply the GA to integrate these two schemes to achieve the interaction between the charging efficiency and the routing tree.  
By simulating the process of evolution in the natural system, the GA can be considered as an adaptive heuristic search algorithm that is very suitable for providing a robust, near optimal solution for many real world NP-hard problems, and it is also widely applied for the performance optimization of WSNs, such as in network coverage control for WSNs [31; 32; 33], the scheduling problem [34; 35], the optimal sensor deployment problem [36; 37], and topology control [38]. This bio-inspired algorithm imitates the natural evolution of biological organisms to provide a robust, near-optimal solution for various problems. The GA is inherently an evolutionary process that involves individual encoding, selection, crossover, mutation, and replacement operations [39].
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 2 Properties and Synthesis of Graphene  
### Physical and Chemical Properties  
Graphene is the simplest form of carbon and the thinnest material ever produced. Graphene materials have high electrical and thermal conductivity, as well as excellent mechanical properties [31]. It is well known that the electrical and optical properties of graphene thin films are strongly affected by graphene grain size and boundary [32].  
Graphene is a material with high mechanical strength. Membrane-formed graphene can provide stronger support and adjustable spacing. Macroscopic membrane formation can increase the mechanical strength and controllability of graphene materials [33]. Graphene has a very high specific surface area (about 2675 m\({}^{2}\)/g), and the inherent capacitance of the original graphene is 0.21 F/m\({}^{2}\), which exceeds that of all carbon-based materials [34]. [PERSON] et al. showed through experiments that graphene has a very high specific surface area and is a highly efficient additive, promoting the heterogeneous nucleation of water [35]. The addition of a surfactant can improve the hydrophilicity and stability of nano-materials. [PERSON] et al. pointed out that graphene is hydrophobic using density functional theory [18]. The binding energy between water droplets and graphene is weaker than that between water molecules, so water droplets tend to absorb more water molecules than graphene. This phenomenon shows that graphene is hydrophobic. The existence of a micro-nanostructure on the surface of graphene enhances its hydrophobicity. Water molecules can easily pass between graphene sheets without attaching, and the water flux increases. In addition, due to the existence of hydrophobic properties, particles are not easily attached to the membrane surface, which can reduce the membrane fouling rate [33]. In addition to the above properties, graphene has many other properties, such as a high surface area (2630 m\({}^{2}\) g\({}^{-1}\)), high Young's modulus (\(\sim\)1100 GPa), carrier mobility at room temperature (\(\sim\)15,000 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)), good optical transparency (\(\sim\)97.7%), excellent electrical conductivity (3000-5000 W m\({}^{-1}\) K\({}^{-1}\)) and the quantum Hall effect [33]. GO is not as hydrophobic as graphene due to the abundance of oxygen-containing functional groups in the planes and edges. [PERSON] et al. prepared graphene oxide by oxidizing natural graphite and then reducing graphene by hydrazine hydrate [36]. The contact angle of water droplets on the surface of graphene oxide is 67.4\({}^{\circ}\), which indicates that GO is hydrophilic. This is due to the existence of oxygen-containing functional groups on GO, which can form hydrogen bonds with water molecules, and the existence of oxidized functional groups increases the surface hydrophilicity [37]. Graphene-based nanomaterials are different in size, surface area, roughness, hydrophilicity, dispersion and functionalization, all of which can affect the interaction between graphene-based nanomaterials and bacterial cells, either alone or mutually.
## 4 Experiment Results and Discussion
### Attacks in FL and FAT Strategies for Defense  
To illustrate insider attacks, as discussed in Section 3.3, we considered a scenario where attackers had knowledge of the IP address of the federated server and the SSL certificate, enabling them to participate in the federated learning (FL) system as clients. The attackers aimed to engage in data poisoning by transmitting corrupted gradients to the federated server, which were compromised by FGSM and PGD adversarial samples. The goal was to manipulate the global model into misclassifying accurate inputs once the aggregation process was completed. This scenario was depicted in Figure 7. To assess the impact of this attack on global models, we used three attackers (representing 75% of the total clients),two attackers (50% of the total clients), and one attacker (25% of the total clients) in our experimental setup.  
Table 3 contains an exhaustive list of federated training scenarios utilized in our experiments. It also included scenarios to observe the impact of the attacks, as well as to train a robust global model for FGSM and PGD attacks by implementing FAT defense mechanisms.
## 2 Conventional Stereophonic Image Enhancement  
In this section, we describe a conventional stereophonic image enhancement method applied to closely spaced omni-directional microphones based on a DS beamforming technique [13,21]. Figure 1 shows a block diagram of the conventional method. As shown in the figure, the DS beamformer of the conventional method compensates for the delay between the two channels, where the delay, \(n_{d}\), is determined depending on the distance, \(l\), between the two microphones. After that, a free-field response filter, \(h(n)\), [13] is applied to the beamformed signals to obtain the enhanced stereo signals, \(y_{L}(n)\) and \(y_{R}(n)\), respectively by:  
\[y_{L}(n)=h(n)*(x_{R}(n)-x_{L}(n-n_{d})) \tag{1}\]  
\[y_{R}(n)=h(n)*(x_{L}(n)-x_{R}(n-n_{d})) \tag{2}\]  
where \(*\) indicates the linear convolution operator, and \(x_{L}(n)\) and \(x_{R}(n)\) are the stereo audio sequences obtained by the omni-directional microphones.  
The directivity patterns for the beamformed signal of the left channel at 2.5 kHz and 4 kHz are depicted in Figure 2a,b, respectively. Note that the directivity patterns for the right channel are exactly the opposite of those for the left channel. As illustrated in the figure, the conventional method provides different directional responses depending on the frequency. That is, it has a cardioid and a super-cardioid directivity pattern for 2.5 and 4 kHz, respectively. It is expected that the stereophonic image of the audio signal at 2.5 kHz should be enhanced but that at 4 kHz could be distorted due to the negative rear lobe of the super-cardioid pattern [13]. To remedy this problem, a Wiener filter has been applied to the beamformed signal to reduce the negative rear-lobe effects [13]. Nevertheless, it was reported that the DS beamformer with a Wiener filter could not change the direction of a stereophonic image when audio recording was performed in a reverberant environment [13]. This is because the reverberation time differs from the frequency and the DS beamformer cannot be adapted to such reverberation time changes [14].  
Figure 1: Block diagram of a conventional stereophonic image enhancement method.  
Therefore, we propose a method that operates differently depending on the frequency, referred to as the frequency-dependent amplitude panning for stereophonic image enhancement (FDAP-SIE) method and compare the performance of our proposed method with that of the DS beamformer described in this section.
## 3 Materials  
The development of the present EO geospatial local service is scalable to other geomorphological complex areas, such as other mountain territories, and is based on the following datasets:  
-   Copernicus Sentinel-2A surface reflectance data to map the land cover components  
-   Copernicus Sentinel-1A and B SLC and GRD data to map urban and water components, respectively  
-   PlanetScope four band data to define a part of the training and validation set  
-   GIS updated datasets such as GNSS ground truth data to define both the training set and the validation set.  
Figure 1: EO geospatial service area of interest. Aosta Valley autonomous region in NW Italy (ED50 UTM 32N) EPSG:23032.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 5 Gap Solitons in Spinor SOC BEC  
Gap solitons (GS) are self-trapped modes existing in the spectral band gap of the system. The band gap generally is generated by certain types of spatially periodic potentials, such as Bragg gratings [69], wave guide arrays [21; 70] and photonic crystals [17] in optics, or the optical lattice in BEC [23; 24] and plasma medium [20; 71]. Combined with SOC, 1D and 2D BEC gap solitons can also be produced in the optical lattice [43; 44; 72; 73]. In these settings, the external potential, which creates the band structure, also exerts a spatial confinement on the solitons.  
On the other hand, theoretical and numerical analysis reveals that by adding a Zeeman splitting, the SOC spinor BEC can support gap solitons in free space, with the collaboration of a DDI. Without spatial modulation, the energy gap is opened solely by the Zeeman splitting, which is a crucial ingredient to the SOC realization in experiments [74]. Families of stable isotropic and anisotropic gap solitons exist at the edge of the band gap, depending on the orientation of the dipolar BEC [60].