esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### ROR Evaluation  
In Figure 11, the throughput at the sink is shown. The x-axis shows the number of nodes (SUs) in the network, which also signifies the amount of loads generated in the network. This is because, as the network density increases, the number of source nodes in the event area that inject packets into the network also increases. The throughput performance is shown for different PU activity patterns. It can be observed that the throughput increases with the number of the nodes in the network. Likewise, as the frequency of the PU activity increases, a corresponding drop in the throughput is also experienced. This is because increased PU activity decreases the time SUs have to route packets, which subsequently affect the throughput at the sink. However, ROR is still able to efficiently ensure reasonable throughput at the sink in the presence of PU activity. This property is better observed in the goodput performance of ROR discussed below.  
Figure 11: Throughput _vs_. number of CRSN nodes in the network.  
In Figure 12, the goodput performance is illustrated. This property is also shown at different PU activity patterns. In general, it can be observed that both PU activity and network load adversely affect the goodput performance, with PU activity having more of an effect than the network load. As the network load increases with the number of nodes in the network, eligible nodes in the priority region also increase. However, the effect of the implemented receiver contention prioritization scheme ensures the stability of the goodput as the load increases. Although the increase of nodes in the network is expected to increase the goodput, because of the increase of the opportunity for quality links, however, when more contending nodes compete in the priority regions, the probability of collision increases, which is responsible for the decline recorded at some points in the graph. Thus, it can be concluded that ROR ensures a stable reliability in the network of above 95% at moderate PU channel activity and network load. This reliability is greatly affected by PU activity and moderately affected by the network load. The performance in the throughput and goodput is exhibited in the end-to-end latency in the network. In Figure 13, the end-to-end latency is illustrated. Again, the end-to-end latency is shown for different PU activity patterns. The factors responsible for the gradual increase in the latency with network density are the increase in the number of eligible nodes participating in receiver contention and the PU activity. Thus, in ROR, latency increases with both network load and PU activity.
## 4 System Modelling Methodology
### HRIS-DMA Mutual Design Parameters  
Figure 2 shows an illustration of the HRIS-DMA collaborative transmission system. In this network, we considered an HRIS-aided downlink communication system operating over a THz frequency, where the \(p^{th}\) antenna of BS\({}_{1}\) communicates with multiple single-antenna users with the aid of an HRIS that consists of \(M\) elements. In this paper, we assume that obstacles block the direct links between the BS and the users in the presented network, as this is the most challenging scenario in conventional communication systems. To ensure simplicity of presentation and reveal fundamental design properties, the network shown in Figure 2 resembles a wireless communication network. In this network, we explain the concept of HRIS-DMA for only four blocked users UE\({}_{n}\), one BS, and one HRIS located on top of building number one B\({}_{1}\); all users are blocked from the BS by obstacles.  
The network in Figure 2 shows that the HRIS is located on top of B\({}_{1}\), and it is designed with \(M\) patches, each of which has a 100 cm\({}^{2}\) patch size, with 0.01 mm of air space between them. Additionally, the mutual HRIS design parameters, which are fixed through the testing process, are the HRIS element response, \(|\Gamma_{m}|=0.9\); signal power, \(P_{s}=45\) dBm; wavelength, \(\lambda=0.001\) m; HRIS antenna gain, \(G_{HRIS}=1\); and power radiation parameters, \(\varepsilon_{r}=\varepsilon_{t}=0.99\).  
As a start, we perform an initial test to check the signal redirection with full power as demonstrated in Figure 3. In this, we prove that the HRIS-DMA performs signal transmission with full signal power; additionally, the full signal beam is directed toward the user in the transmission area with different angles and different user locations. Moreover, to fully understand the maximum performance improvement offered by HRIS-DMA, it is anticipated that the BS will have access to the instantaneous and statistical channel state information CSI\({}_{ints,stat}\) for all channels, as analysing them is beyond the scope of this study.  
The next step is to optimise the HRIS-DMA, which was designed and controlled by an HRIS controller to transmit or reflect the incident signals simultaneously based on real-time user positions continuously refined through iterative updates.  
As shown in Figure 3, the new signal redirection keeps the signal power focused toward the intended user in one direction. It does not split the signal into the transmission and reflection areas as in the conventional T/R RIS.  
Figure 3: Redirecting user signal with focused beamforming.
## 3 ML for SatCom RRM: Architecture and Techniques
### Supervised Learning for RRM
#### 3.2.2 DNN-Assisted RRM: Beam Hopping  
On the learning point of view, traditional end-to-end learning approaches can help to provide a suboptimal solution to the cost function proposed in (6). However, it is generally only applicable to limited cases, e.g., a problem with few variables, non-strict or no constraints. It imposes difficulties for training and, therefore, can significantly increase the difficulty in achieving prediction accuracy. In that sense, the author in [30] explored a combined method for BH design to obtain the benefits of optimization and learning.  
A classifier is trained using a DNN to identify a small subset of promising snapshots. Promising snapshots means that they are with high probability to appear in the optimal illumination pattern. The feature vector consists of \(B\) binary elements corresponding to the number of beams, \(Y=[Y_{1},...,Y_{b},...,Y_{B}]\), where \(Y_{b}\) represents whether any snapshot with \(b\)th active beams is scheduled at the optimum (\(Y_{b}=1\)) or none of the snapshots of \(b\)th is used (\(Y_{b}=0\)).  
The training dataset contains two parts: the input parameters and the optimal or suboptimal labels. The data generation procedure is illustrated in Figure 5. The \(i\)th training set is denoted as \((X^{(i)},Y^{(i)})\). The input \(X^{(i)}\) is obtained by the \(i\)th realization in emulators,  
Figure 4: Deep neural network (DNN) to manage power and bandwidth allocation.  
which consists of the channel matrix \(H\), the traffic demand per beam \([D_{g}^{1},...,D_{g}^{B}]\) and the power per beam \([P_{g}^{1},...,P_{g}^{B}]\) in \(g\)th snapshot, where an adopted satellite emulator generates the beam coverage area and \(H\). The DNN accepts only real-valued inputs; thus, the original complex value in \(H\) is converted to real values. The labels are organized as feature vector \(Y^{(i)}\) for the \(i\)th realization. The DNN is trained to learn the mapping of the input to the optimal label. After being trained, the model can provide an efficiently predicted feature vector that can be used in conjunction with a conventional optimization algorithm.
## 2 Burned Area Mapping and Vegetation Indices
### Vegetation Indices  
Despite the capability of RBR to also record vegetation recovery after the event, to gather detailed information about biophysical parameters, specific index computation procedures were developed in the framework of Sentinel-2 data processing. These procedureswere supported by the evidence in the potential of Sentinel-2 red-edge spectral domain devoted band (BBA) data for retrieving leaf area index (LAI) derived parameters [64].  
Indices can be calculated with simple empirical regressions, which are efficient but imprecise, or by analyzing vegetation response using opensource toolboxes, e.g., SNAP [65], QGIS [66] equipped with a complex radiative transfer model inversions algorithm based on strong assumptions, and a turbid model conceptualization for vegetation [67]. [PERSON] and [PERSON] [67] implemented the SNAP exclusive biophysical parameter retrieval toolbox as a collection of backpropagation artificial neural networks (ANN), trained using a globally representative set of simulations from a canopy radiative transfer model (RT), resulting in PROSAIL. This model allows for both the simulation of the top of canopy reflectance and the computation of secondary biophysical variables closely related to each other, as a fraction of vegetation cover (fCover or FVC), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fAPAR).  
Leaf area index (LAI), a dimensionless ratio of the green leaf area, is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area canopy [67]. Remotely sensed vegetation LAI will include the green contribution of the forest understory. Furthermore, it refers to green parts of the canopy with leaf chlorophyll content higher than 15 ug \(\times\) cm\({}^{-2}\)[67]. fAPAR is a direct measure of the proportion of incoming photosynthetically active radiation (400-700 nm) absorbed by the canopy. In the toolbox model it directly derives from the radiative transfer model in the canopy. Regarding fCover, this parameter is an indicator of plant growth during early periods before the soil is fully canopy-covered. It corresponds to the gap fraction of vegetation for the nadir direction, and it is often used to separate vegetation and soil in energy balance processes for evapotranspiration computation. Similarly to LAI, both fCover and fAPAR are only sensitive for chlorophyll contents higher than 15 ug \(\times\) cm\({}^{-2}\).
###### Abstract  
This paper aims to investigate the effect of the void structure of porous asphalt concrete (PAC) on the sound absorption performance. The sound absorption coefficient (SAC) spectra of PAC with various voids were measured using the transfer function method and the air void structure was identified from Computed Tomography (CT) scanning images. The finite element model (FEM) of the void structure was established via CT cross-sectional images to research the noise reduction mechanism of PAC. At different frequencies, the sound pressure level distribution and SAC were calculated. The following are the main conclusions: (1) As the void content of PAC increases, the SAC spectrum curve becomes higher, more rightward and wider, while the peak values, their corresponding frequencies and mean values increase. (2) The SAC decreases with the average number of voids within each image and increases with the void equivalent diameter and fractal dimension; there exist linear relationships between these three parameters and the SAC. (3) The sound absorption capacity of the void with larger size and fractal dimension is higher than the smaller one and the voids connected to the upper and lower surfaces provide more noise reduction. (4) The FEMs of PAC can predict the SAC of PAC.  
porous asphalt concrete; sound absorption coefficients; microscopic void structure; FEM; noise reduction mechanism +
Footnote †: journal: Applied Sciences
# Risk-Based Hybrid Light-Weight Ship Structural Design Accounting for Carbon Footprint
## 7 Conclusions  
The study identified the best ship structural design solution concerning the ship service life, accounting for the impact of using AHS panels in a combination of the steel structure and developing a hybrid design solution. The ship structure starts the service life in intact, non-corroded conditions and progressively degrades and collapses if no repair occurs during service life. It has been found that the design solution AHS1 and AHS2, in which aluminium honeycomb panels replace the inner steel shell plates of a bulk carrier ship, enhances the corrosion degradation resistance, and reduce the lightweight. The achieved less lightweight is used to transport more cargo by keeping the same engine power and fuel with a cost of the ship like AHS1 and AHS2 and lower 11% that one of the steel solutions. However, the AHS1 design solution is the best among the other two analysed design solutions. The design solution AHS1 demonstrates a higher beta-reliability index of 1.7 at the time of the first repair in the 13 th year of the service life with a lower repair cost of 703 890 \(\upmu\)/yr. The second identified design solution is one of AH2, which has very similar characteristics of a beta-reliability index of 1.66 at the first repair time in the 12 th year of the service life with a repair cost of 836,555 \(\upmu\)/yr. The complete steel design solution showed the worst characteristics, where the beta-reliability index of 1.49 at the first repair time of the 10 th year with a repair cost of 1,569,745 \(\upmu\)/yr. The application of the aluminium honeycomb panels for different types of structural components of different topologies and loading remains to be further studied.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design** & \(\beta_{\text{t}}\) & **Repair Time,** & **Repair Cost,** \\
**Solution** & \(\beta_{\text{r}}\) & **yr.** & **€/yr.** \\ \hline Steel & 1.49 & 10.00 & 1,569,745 \\ AHS-1 & 1.70 & 13.00 & 703,890 \\ AHS-2 & 1.66 & 12.00 & 836,555 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Target beta reliability index at the first repair time and associated cost.  
Data Availability Statement: The data presented in this study are available within the article.  
The first author has been supported by the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering, financed by the Portuguese Foundation for Science and Technology (Fundacao para a Ciencia e Tecnologia-FCT) under contract UIDB/UIDP/00134/2020.  
The authors declare no conflict of interest.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 4 InSAR Datasets and Methods
### Interferogram Formation  
TerraSAR-X interferograms were produced using processing workflows that are well documented in the literature (e.g., [12]) and are implemented in the ISCE software. For Sentinel-1A imagery collected using the TOPS (Terrain Observation with Progressive Scans) [18] acquisition strategy, there are several variations to the interferogram processing sequence that must be taken to deburst and merge the swath whilst avoiding phase offsets.  
Firstly, Sentinel-1A IW SLCs that are processed with different versions of the instrument performance facility use different elevation antenna patterns, resulting in range-varying phase offsets that require correction at either the SLC or interferogram level [46]. Within ISCE, coregistration of the master and slave SLCs is then performed on a burst-by-burst, rather than full image basis, with the assistance of a digital surface model (we use a 30-m digital surface model derived from the ShuttleRadar Topography Mission: [47]) and precise orbits [48] that are released by ESA \(\sim\)20 days after each acquisition. Coarse offsets and interferograms are calculated for the regions of burst overlap only.  
As TOPS generates a high Doppler rate in azimuth, a very precise coregistration to one thousandth of a pixel in azimuth is required to avoid phase ramps across individual bursts and phase discontinuities between bursts [49; 50]. This is achieved by performing enhanced spectral diversity on the burst overlap interferograms [49] and is followed by estimation of the range misregistration using the amplitude correlation of the overlap regions. Next, fine offsets are estimated for each complete burst, and burst-by-burst interferograms are produced. The burst interferograms are then merged to produce a continuous sub-swath resulting in a strip map-like product.  
Interferograms for both datasets were filtered [51], unwrapped using the statistical-cost network-flow algorithm for phase unwrapping (SNAPHU) [52], geocoded and multi-looked (spatially averaged) to a final spatial resolution of \(\sim\)90 m (e.g., [53]). To facilitate comparisons between the two datasets, the unwrapped interferogram phase was lastly converted to vertical displacement using the look angle of each satellite [54]. This conversion is justified because the satellite line of sight is most sensitive to vertical displacements [55], and there is no evidence for differential horizontal ground motion across the Perth Basin between continuous GPS receivers HIL1 and PERT. Throughout this study, positive displacements therefore indicate ground movement towards the satellite (relative uplift), and negative displacements indicate movement away from the satellite (relative subsidence).
## 4 Reduce Redundancy in Trail-Based Search
### Transmission Redundancy in Trail-Based Search  
Due to the broadcasting manner of trail maintenance, there are multiple sensor nodes record the same footprint information that results in transmission redundancy. Although the _effective footprint_ proposed in TS can significantly reduce the redundancy in the search phase, the transmission redundancy still exists in two aspects: the redundant nodes that initiate the chase phase, and the redundant nodes involved in the transmission path of the chase phase.  
The simulations are executed to evaluate the transmission redundancy in the chase phase. There are 900 nodes uniformly deployed in 600 m \(\times\) 600 m area. The actor moves in the area that follows random waypoint model [27] with no pause time at a speed of 1 m/s. The TTL and the increment are both set to be 4 and the update interval \(T_{u}\) varies from 1 to 25 s.  
We firstly obtain the average number of hops in the chase phase, and then compare it with the number of packet transmission executed by sensor nodes. Ideally, if there is no transmission redundancy, the number of packet transmission will be the same as the number of hops. However, as shown in Figure 3, the number of packet transmission are 3-5.5 times over those in the optimal scenario.  
Moreover, Figure 3 shows that the transmission redundancy reduces with the growth of the update interval. The reason for this is that when the update interval is small, especially when \(T_{u}\leq 5\), the actor broadcasts footprint information frequently, such that more sensor nodes will receive the footprints. It leads to considerable redundancy of the footprints, and the redundancy of the footprints will eventually result in the redundancy of packet transmission.  
Although transmission redundancy can be controlled by increasing \(T_{u}\), simply increasing \(T_{u}\) may lead to the disconnection of the trail that impacts the deliver rate of packet transmission. Therefore, in Section 4.2, we propose a redundancy reduction scheme for TS, which has no relation with the update interval of the trail. The connectivity of the trail will be further studied in Section 5.1.
## 2 Materials and Methods  
### Chemicals  
C3G was obtained from Chemface (Wuhan, China). DPPH, ABTS, ascorbic acid, gallic acid, quercetin, Folin-Ciocalteu phenol reagent, potassium ferricyanide, potassium persulfate, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), acetic acid, 2,2-azobis(2-amidino propane), 2,4,6-tripyridyl-s-triazine, dihydrochloride, fluorescein sodium salt, sodium nitrite, sodium carbonate, and dimethyl sulfoxide were purchased from Sigma Aldrich Co. (St. Louis, MO, USA). Formic acid, acetonitrile, HCl, and methanol were purchased from J. T. Baker (Phillipsburg, NJ, USA). Trypsin-ethylenediaminettetraacetic acid (0.05%), penicillin/streptomycin, phosphate-buffered saline (pH 7.4), minimum essential medium (MEM), and fetal bovine serum were purchased from Gibco (Waltham, MA, USA). MTT assay was performed by DoGenBio (Seoul, Republic of Korea). Dinitrophenyl-human serum albumin (DNP-HSA), anti-DNP-IgE, and 4-nitrophenyl N-acetyl-B-d-glucosaminide, sodium bicarbonate, sodium carbonate, sodium citrate dihydrate, citric acid, and PP2 were purchased from Sigma Aldrich Co.(St. Louis, Mo, USA). All anti-bodies used for western blotting were obtained from Cell Signaling Technology (Danvers, MA, USA) and Santa Cruz Biotechnology (Santa Cruz, CA, USA). Bovine serum albumin and enhanced chemiluminescence (ECL) kits were purchased from Bio-Rad (Hercules, CA, USA).
# The Protection of Data Sharing for Privacy in Financial Vision
## 3 Methods
### Build a GAF-CNN Baseline Model  
We chose GAF-CNN instead of the CNN model because the candlestick charts contain time-series data. It is hard to find essential features in CNN models. However, the GAF-CNN model found the parts easily. We take the morning star pattern as an example (Figure 7). There is a downtrend, and the trend's end is the pattern of two long candlesticks sandwiching a short one. Figure 8 shows the features found in the second conversion layer of the GAF-CNN model. In the CNN model of Figure 9, the downtrend is not here, and some outputs have no attributes. The GAF-CNN model found some particular patterns. Hence, we chose the GAF-CNN model. After introducing the GAF-CNN model, we will introduce some methods that can use the model to define the training data.  
Figure 6: Experiment process.  
We elaborate a model using candlestick patterns and the time-series data as training data for the two phases. Firstly, the GASF method encodes time-series data. Secondly, a CNN model is trained using the encoded matrix. The CNN model has two convolutional layers and a fully connected layer [22]. We use the python package and optuna to search for the best parameters for 100 trains--the baseline model parameters in the Table 1. Moreover, we use the SGD optimizer with momentum.  
Figure 8: GAF-CNN output.  
Figure 7: An example of morning star pattern. The red indicates the candlestick’s close price is higher than the open price. The green shows the close price of these candlesticks is lower than the open price.  
Figure 9: CNN output.  
There are three steps to encoding time-series data with GAF, shown in the Figure 10[22]:  
**Step 1:**: Normalize time-series data between [0,1] using minimum-maximum scaling in Equation (2).  
\[\widetilde{x}_{i}=\frac{x_{i}-\min(X)}{\max(X)-\min(X)} \tag{2}\]
**Step 2:**: Represent the normalized time-series data with the polar coordinate system.  
\[\begin{split}\phi&=\arccos(\widetilde{x}_{i}),0\leq \widetilde{x}_{i}\leq 1,\widetilde{x}_{i}\in\widetilde{X}\\ r&=\frac{t_{i}}{N},t_{i}\in\mathbb{N}\end{split} \tag{3}\]
**Step 3:**: Adopting Equation (4), create a GASF matrix with the cosine of the summation of the angles.  
\[\begin{split}\text{GAF}&=\cos(\phi_{i}+\phi_{j})\\ &=\begin{bmatrix}\cos(\phi_{1}+\phi_{1})&\cdots&\cos(\phi_{1}+\phi_{n})\\ \cos(\phi_{2}+\phi_{1})&\cdots&\cos(\phi_{2}+\phi_{n})\\ \vdots&\ddots&\vdots\\ \cos(\phi_{n}+\phi_{1})&\cdots&\cos(\phi_{1}+\phi_{n})\end{bmatrix}\end{split} \tag{4}\]