esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Social RS-Based Proxies
#### Socio-Economic Status  
The socio-economic status of households is estimated based on the location information of the houses in the city and topographic area characteristics for social recovery and vulnerability assessments [33; 49]. Urban areas with poor road quality, little available infrastructure and economic development, little vegetation, and situated in hazard-prone areas are the most socially vulnerable areas. However, these proxies are not necessarily correct for all cases in urban areas. For example, not all people located in hazard-prone areas (e.g., steep slopes or lower elevations) have a low socio-economic status; in contrast, in some cases, there are luxury buildings located in areas with steeper slopes [33; 140] or close to a waterfront [141; 142]. Therefore, an effective way of using those proxies is to use them as an ensemble to describe the socio-economic status of an urban area. In addition, image-based texture serves as a proxy to extract homogeneity of an area [33; 143; 144]. It is a good complementary source of information for a settlement type extraction from images, e.g., slum areas, even using medium resolution images [145]. Similar to texture analysis, [PERSON], et al. [146] used irregular clusters of roofs in an urban area as a proxy to extract socially vulnerable areas, based on the fact that buildings poorer area are often not regularly constructed. A share of slum areas in a city compared to regular or high-income housing may serve as an indicator of social vulnerability. [PERSON] [90] also used the amount of green spaces around each building block to estimate the socio-economic status of householders based on the findings of [PERSON], et al. [147]. Accordingly, the occupants of buildings that are surrounded by more green spaces--estimated using land cover and land use information to extract socio-economic status--are less socially vulnerable to hazards.
# Electric Load Forecasting Based on Deep Ensemble Learning
###### Abstract  
Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.  
onvolutional neural network; bidirectional long short-term memory network; random forest; ensemble learning +
Footnote †: journal: Medical Image and Image Processing
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 2 Numerical Model  
A brief introduction to various numerical models is provided below, including the governing equation, the calculation of the free water surface, and the sedimentation scour model.  
In this study, turbulence was predicted by the renormalization group (RNG) turbulence model [17]. The governing partial differential equations were integrated over a series of control volumes covering the fluid domain with a staggered-grid finite difference method. The widely used semi-implicit method for press-linked equations (SIMPLE) procedure was included in the model for coupling the pressure and velocity. Further, explicit solver options were adopted in considering viscous stress, free surface pressure, and advection. Considering the complexity of flow around spur dikes, second order monotonicity preservation was employed in momentum advection. Finally, the fractional area volume obstacle representation (FAVOR) solution [18] was used to deal with model boundaries, and the volume of fluid (VOF) method proposed by [PERSON] and [PERSON] [19] was used to accurately predict the free surface.
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 1 Introduction
### Harris Hawk Optimization
The researchers have proposed energy-efficient wireless sensor networks using a hybrid Harris hawksalp swarm optimization algorithm in [36]. The approach uses a hybrid optimization algorithm combining the HHO and salp swarm techniques to optimize the network's energy consumption. Additionally, a mobile sink strategy reduces energy consumption by allowing the destination node to move toward the sensor nodes instead of the other way around. For PID controller optimization, researchers used HHO to control the speed of the DC motor [37]. Using the HHO algorithm for tuning the controllers improves performance in terms of steady-state error, rise time, overshoot, and settling time, resulting in better efficiency, accuracy, and stability of the motor control system. An overall detailed review of the HHO literature is given in Table 1.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 1 Introduction
### Motivation for Low Earth Orbits  
Lowering orbital altitudes is a clear advantage for small sats, as it directly reduces the minimum antenna area that complies with the pulse repetition frequency (PRF) constraints. Power requirements and cross track resolution are also improved in lower orbits [12]. The minimum antenna area \(A_{min}\) is a function of orbital height \(R\), incidence angle \(\theta\) and platform velocity \(v_{st}\), referenced in Equation (1).  
\[A_{min}\geq\frac{4v_{st}R\lambda tan(\theta)}{c} \tag{1}\]  
Additionally, a reduction on the antenna dimensions as much to fit the launcher envelope is mandatory. Therefore, an orbit height as low as possible is advantageous for the CP-SAR microsat. On the other hand, orbital altitudes lower than 500 km are subjected to atmospheric drag, considerably reducing the mission life time. Considering the requirements of the Indonesian and Japanese partners, the investigations concentrate on orbits with height around 500-600 km, accepting a shorter mission life span of at least one year. Therefore, the basis for the SAR sensor design was chosen to be at orbital altitudes of 500 km.  
This work presents the main design considerations for a compact SAR antenna, highlighting simulation and validation of the parabolic reflector antenna. Antenna pattern simulations were performed with the full wave simulation software 2018 CST Studio [13]. Simulations and antenna measurements considered a single feed antenna element. Future work shall concentrate in the development and performance verification of the feed assembly, and overall performance integrated with the parabolic reflector.  
This document is organized as follows: Section 2 describes the main considerations for the design of the deployable mesh reflector antenna; Section 3 describes the antenna effective gain estimation; Section 4 presents methods for the verification of reflector surface accuracy and subsequent results achieved; and Section 5 concludes with a summary of the current study and an outlook to future work.
## 2 Proposed Scheme
### Input Signal  
Taking into account that the proposed solution is oriented to operate in real time (generate and store the hash code immediately after recording the file), the first block of the proposed method is to record the audio file in the same Android application that performs the hash code calculation. For the recording process, the following parameters were defined in java: each audio is acquired with sampling frequency of 8000 Hz and 16 bits per sample, and it is stored in wav format. The number of samples and the duration will depend on the needs of the user. In addition, the _MediaRecorder_ library available in Android Studio was used to make the recordings.  
The result of the recording process is the signal \(S(n)\) (Equation (1)), where \(N\) is the number of samples of the signal.  
\[S\left(n\right),1\leq n\leq N \tag{1}\]
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Sentinel-1A Data
These images span from the flooding/transplanting stage to the heading/milking stage of paddy rice, a period at which changes in temporal backscatter of the rice crop are most diagnostic. The acquired images are all in dual polarization of vertical transmitted and vertical received (VV), and vertical transmitted and horizontal received (VH) channels. They are in a range and azimuth resolution of 5 m and 20 m, respectively [41]. All images from Sentinel satellite constellations are freely available to consumers and can be downloaded at [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)). Table 1 gives a summary of the characteristics of the 5 Sentinel-1A images used in this study. More details on Sentinel-1 products can be found in [41].
## 2 Materials and Methods  
### Coupled-Mode Equations  
The Jackiw-Rebbi modes can be solved from a tight bounding model, which is also known as coupled mode theory in optical waveguides. We consider the interface of two arrays with different masses, that is, different detuning of propagation constants. Then, the coupled mode equation can be given as  
\[-\mathrm{id}\mathrm{a}_{n}/\mathrm{d}\mathrm{z}=\mathrm{c}(\mathrm{a}_{n+1}+ \mathrm{a}_{n-1})-(-1)^{n}\sigma\mathrm{a}_{n}, \tag{1}\]  
where \(\mathrm{a}_{n}\) represents the field amplitude at \(n\)th waveguide, \(\mathrm{z}\) denotes coordinate along the wave propagation direction, \(\mathrm{c}\) is the coupling strength between adjacent waveguides, and \(\sigma\) is the mismatch of propagation constant. For left arrays (\(n<0\)), the detuning of propagation constant is \(\sigma=\sigma_{1}\), while we set \(\sigma=\sigma_{2}\) for right waveguide array. The eigenvalues \(\beta\) and related bound modes of Equation (1) is can be solved by setting \(-\mathrm{id}\mathrm{a}_{n}/\mathrm{d}\mathrm{z}=\beta\mathrm{a}_{n}=\mathrm{c} (\mathrm{a}_{n+1}+\mathrm{a}_{n-1})-(-1)^{n}\sigma\mathrm{a}_{n}\).  
According to [51], the solution of Jackiw-Rebbi modes at the interface can be divided into two cases. If \(-\sigma_{1}=\sigma_{2}=\sigma_{0}>0\), one has:  
\[\beta=\mathrm{c}-\left(\sigma_{0}^{2}+c^{2}\right)^{1/2}, \tag{2}\]the amplitudes satisfy the relation \(\mathrm{a_{2n-1}=a_{2n}}\). For the right sites, that is, \(\mathrm{n\geq 0}\), the amplitudes fulfill the relation \(\mathrm{a_{2n}/a_{2n+1}=-[\sigma_{0}/c+(1+\sigma_{0}^{2}/c^{2})^{1/2}]}\). For the left sites, that is, \(\mathrm{n<0}\), the amplitudes fulfill the relation \(\mathrm{a_{2n+1}/a_{2n}=-[\sigma_{0}/c+(1+\sigma_{0}^{2}/c^{2})^{1/2}]}\).  
If \(\sigma_{1}=-\sigma_{2}=\sigma_{0}>0\), one has:  
\[\beta=c+\left(\sigma_{0}^{2}+c^{2}\right)^{1/2}, \tag{3}\]  
at the same time, the amplitudes satisfy the relation \(\mathrm{a_{2n-1}=a_{2n}}\). For the right sites, that is, \(\mathrm{n\geq 0}\), the amplitude fulfills the relation \(\mathrm{a_{2n}/a_{2n+1}=[\sigma_{0}/c+(1+\sigma_{0}^{2}/c^{2})^{1/2}]}\). For the left sites, that is, \(\mathrm{n<0}\), the amplitude fulfills the relation \(\mathrm{a_{2n+1}/a_{2n}=[\sigma_{0}/c+(1+\sigma_{0}^{2}/c^{2})^{1/2}]}\).
## 1 Introduction  
The Tropical Rainfall Measuring Mission (TRMM) was a joint space project between the National Aeronautics and Space Administration (NASA) and the Japanese Aerospace Exploration Agency (JAXA) to measure tropical precipitation from a low-inclination orbit [1]. It was launched in November 1997, into a near circular non-sun-synchronous orbit with an inclination of 35\({}^{\circ}\) and at an altitude of 350 km, which was later boosted to 403 km in 2001. After providing important rainfall measurements of tropical precipitation for 17 years, the system was decommissioned on 15 April 2015, when the spacecraft depleted its fuel that was required for orbit maintenance.  
For TRMM, the primary instruments for measuring rainfall were microwave, namely: the passive TRMM Microwave Imager (TMI), and the active Precipitation Radar (PR), while this paper focuses on only the TMI radiometric calibration characteristics over this 17-year legacy period. For the TRMM constellation of cooperative satellites, the TRMM Project used a common rain retrieval algorithm (GPROF), which assumed all the radiometer sensors were radiometrically intercalibrated. For this purpose, TMI was used as the radiometric transfer standard to perform inter-satellite radiometric cross-calibration (XCAL) of the constellation, until it was decommissioned.  
For TMI to serve as the radiometric transfer standard, it was necessary to carefully assess the calibration accuracy and stability of this instrument, which is the goal of this paper. Previous to this research, there was an evolutionary development of the algorithm to process TMI sensor data to yield the desired earth scene brightness temperature (Tb) that resulted in several versions (V6 and V7) of the TMI 1B11 brightness data product [2; 3], which were largely based on a number of empirical ad-hoc corrections. To produce the legacy TMI Tb product, known as 1B11 Version-8 (V8) or GPM05, a new algorithm based on rigorous physical principles and on-orbit measurements over the entire 17-plus-year history of TMI measurements was developed. During this process, several important discoveries were made and corrections were implemented to mitigate these instrument anomalies.  
The Global Precipitation Measurement (GPM) mission launched in February 2014 will continue the legacy of TRMM to provide the next generation of global measurements of precipitation from space [4]. The GPM observatory also carries a multi-frequency microwave imager named the GPM Microwave Imager (GMI). The GMI on-orbit radiometric performance has been demonstrated to be excellent [5; 6]; therefore, it is the new calibration standard reference to which the TMI brightness temperature (Tb) datasets were inter-calibrated during their 13-month overlap period from March 2014 to March 2015.
## 2 Materials and Methods
### Experimental Design  
Two rates of XXpire (low and high, as described in the previous section) and one rate of Zylam were applied at three water volumes of 1870.8, 3741.6, and 7483.2 L/ha. The water volumes, 1870.8 and 3741.6 L/ ha, were sprayed on poinsettia foliage using a CO\({}_{2}\)-powered single boom (one nozzle) handheld sprayer at 206.8 kPa and are referred to as the \"foliar\" and \"spench\" treatments, respectively. To deliver spray volumes, 1870.8 (foliar) and 3741.6 (spench) L/ha, two different nozzle tips, TeeJet 8002 VS (yellow-colored tip) and TeeJet 8004 EVS (red-colored tip, TeeJet Technologies, Glendale Heights, IL, USA), respectively, were used. These two nozzle tips delivered insecticide solutions at 11.6 (foliar) and 18.3 (spench) mL/s, and hereafter, these water volumes are referred to as the foliar and spench treatments, respectively. The third water volume, 7483.2 L/ha, was applied as a soil drench, where 19.6 mL of the insecticide solution was slowly poured into the potting media of each pot using a graduated plastic container.  
The treatments included in the experiment were (1) XXpire low foliar, (2) XXpire low bench, (3) XXpire low drench, (4) XXpire high foliar, (5) XXpire high bench, (6) XXpire high drench, (7) Zylam foliar, (8) Zylam bench, (9) Zylam drench, and (10) nontreated check. The treatments were arranged in randomized complete block design, with eight plant replications. Poinsettia plants were placed 30.5 cm apart from each other on the greenhouse bench. The individual potted poinsettia plant was the experimental unit. The insecticide treatments were applied on 7 October 2020. CapSil (adjuvant) was added to all of the XXpire treatments, whereas Zylam treatments did not receive CapSil. Insecticide treatments were applied only once when the threshold of 10 _B. tabaci_ eggs or nymphs was achieved.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 5 Physical Layer
### Optimization Evaluation
#### 5.4.1 BCH1 Channel
Figure 11: Results for BCH1 channel considering the genie\(-\)aided approach. So, the selection of a benchmark point depends on the desirable overhead value. As an example, for an overhead of \(\rho_{\text{max}}=0.1\), the following points are eligible (see Figure 12): \((N_{\text{sub}}=128,T_{\text{CP}}=1)\), \((N_{\text{sub}}=256,T_{\text{CP}}=1)\), \((N_{\text{sub}}=256,T_{\text{CP}}=5)\), \((N_{\text{sub}}=512,T_{\text{CP}}=1)\), \((N_{\text{sub}}=512,T_{\text{CP}}=5)\), \((N_{\text{sub}}=512,T_{\text{CP}}=8)\), \((N_{\text{sub}}=512,T_{\text{CP}}=10)\), \((N_{\text{sub}}=512,T_{\text{CP}}=12)\). Considering only these points, we choose the parameters pair that has the lowest BER in Figure 11. For this case, the selected point is \((256,10)\). The above-mentioned procedure was performed for all overhead values considered in Table 4, and these results are shown in Table 5. As observed, all the parameters pair obtained by the optimization algorithm are the same as the benchmark points.
## 3 Methodology
### The Overall Structure of DHGAR  
To address the significant impact of environmental factors and the complex feature associations in wind power forecasting, the DHGAR model is proposed in this study. It learns the relationships between features by constructing a feature graph, which effectively utilizes diverse feature information. Additionally, since the association patterns between each feature and others may vary with the data, the proposed model employs dynamic graphs and node injection to dynamically learn the adjacency matrix parameters, thereby enhancing the stability of wind power predictions. Furthermore, the model's heterogeneous graph attention mechanism effectively filters information, further improving the prediction accuracy of the external GRU network.  
As shown in Figure 1, the proposed DHGAR model consists of three main components: a dynamic graph learning mechanism, heterogeneous graph attention, and a modified GRU network. First, the flow-aware dynamic graph learning mechanism improves the Data-Adaptive Graph Generation (DAGG) method by concatenating the current data stream into the node embedding matrix \(\rm E\) and transforming the weights through a linear layer. This enhancement allows the generated dynamic graph to better reflect node relationships under real-time data. Then, the generated dynamic graph and adjacency matrix are fed into the heterogeneous graph attention module to update and iterate node weights. The heterogeneous graph attention mechanism achieves the learning of heterogeneous attention weights by embedding an edge-type matrix, which better captures the influence of different feature types on wind power. Finally, the GRU network is used to predict wind power,with its multi-layer perceptual (MLP) hidden layer replaced by heterogeneous dynamic graph attention. The designed DHGAR model is able to effectively extract historical wind power characteristics and environmental factors and achieve stable and accurate wind power predictions.
## 4 Performance Evaluation for S Band Signals
### Performance Analysis  
Figure 4 shows the Gabor bandwidth and code tracking error curves of the above signals under the assumption that the receiver pre-filtering bandwidth \(B\) is equal to 16.5 MHz, tracking loop bandwidth \(B_{L}\) is 1 Hz and the correlation time spacing between the early and late reference signals \(d\) is 0.1 chip. As we see from Figure 4a, the Gabor bandwidth of B_M_2 RC(8) is approaching its maximum at around 16.5 MHz and greater than other modulations when \(B\) approximately exceeds 11.7 MHz.  
Meanwhile, the B_M_2 RC(8) gives the minimum code tracking error among the analyzed signals as seen in Figure 4b. Therefore, the B_M_2 RC(8) indicates a better code tracking performance than other candidates for 16.5 MHz of S band.  
An analysis of multi-path resistance performance for S band signals is estimated by multi-path error envelopes and running average multi-path errors in Figure 5, where MDR \(\widetilde{a}\) is fixed at \(-6\) dB and all other parameters are the same as the previous simulation. As seen in Figure 5, the minimum quantity of running average errors can be provided by B_M_2 RC(8) when the multi-path delay is changed from 0  
Figure 4: **(a)** Gabor bandwidth of modulation candidates for S band; **(b)** Code tracking errors of modulation candidates for S band.  
Figure 5: **(a)** Multi-path error envelopes of modulation candidates for S band; **(b)** Average multi-path errors of modulation candidates for S band.  
to 400 m, while it maintains the lowest multi-path error envelope amplitude when the multi-path delay is in the range of [0, 25 m]. We are able to deduce that B_M_2 RC(8) achieves the best multi-path mitigation performance among the above modulations as a result of cutting down the side lobe amplitude to a large extent.  
The anti-jamming performance in terms of \(Q_{\textit{Dcm=LUNW}}\) and \(Q_{\textit{Dcm,LUNS}}\) for the demodulation process, and \(Q_{\textit{CTANW}}\) and \(Q_{\textit{CTANS}}\) for the code tracking process are calculated in Table 1, where \(B\) and \(R\) are set as 16.5 MHz and 50 bps, respectively. Note that the differences among various signals are very slight and even less than 1.3 dB in each anti-jamming merit factor, thus they have similar or comparable anti-jamming ability performance.  
BPSK(1), BPSK(4), BPSK(8) and BOCc(1,1) have been investigated as possible signals in the S band for the Galileo system centered on a carrier frequency close to 2491 MHz with a 16.5 MHz receiver pre-filter [3,27]. The compatibility between the candidates in the future Compass system, including B_M_2 RC(8) and the Galileo candidates is shown in Table 2. We can observe that the spectral separation degree of B_M_2 RC(8) with BOCc(1,1), BPSK(4), and BPSK(8) is remarkably superior to the others, except for BOCc(4,4), because it contains less low frequency components within the receiver bandwidth that is likely to result in relatively low spectrum efficiency or high spectrum leakage. The compatibility between B_M_2 RC(8) and BPSK(1) is a bit worse than some of the candidates, yet it is still acceptable.