esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Shadow Detection and Removal  
The next step of our algorithm is the detection and removal of shadow regions from the foreground mask based on geometry feature information. The fundamental benefit of geometry features is that they deal specifically with the current input image. For example, they do not depend on a specific estimation of a background reference image. In our previous academic work, we applied a gradient-based texture correlation method in this step to remove shadow pixels and obtain improved results as compared to other state-of-the-art methods [23; 24].  
In general, in geometry-based applications, special attention is paid to the orientation, size, illumination source, and shape of shadows. For these reasons, a geometry-based method is used to distinguish shadow pixels from moving foreground objects [25]. The main hypothesis here is based on the fact that geometry properties impose scene limitations, for example, on particular target types, commonly walkers or animals (e.g., in an inside environment). For example, geometry properties might require objects and shadows to have an extraordinary orientation and one might expect a unique light source or background surface similarity.  
Initially, we investigate the vertical peaks on each blob to recognize possible heads and subsequently utilize these data to split the blobs into set of individual-shadow sets. Given an individual-shadow area, \(R\), its focal point of gravity, \((\overline{x},\overline{y})\), and orientation, \(\theta\), these are found as follows:  
\[(\overline{x},\overline{y})= \left(\frac{1}{|R|}\sum_{(x,y)\in R}x,\frac{1}{|R|}\sum_{(x,y) \in R}y\right) \tag{6}\] \[\theta= \frac{1}{2}\text{arctan}\bigg{(}\frac{2\mu_{1,1}}{\mu_{2,0}-\mu _{0,2}}\bigg{)} \tag{7}\]where \(|R|\) is the area of the region in pixels and \(\mu_{p,q}\) are the corresponding central moments. The point below the center of gravity with the maximum vertical change is considered to be the pixel where the shadow begins, and a segment oriented according to \(\theta\) is used to roughly split a candidate shadow region \(R_{2}\). Then, the following Gaussian model is built from the pixels in \(R_{2}\):  
\[G(s,t,g)=\exp\left[-\left(\frac{w_{s}s^{2}}{\sigma_{s}^{2}}+\frac{w_{t}t^{2}}{ \sigma_{t}^{2}}+\frac{w_{g}\big{(}g-\mu_{g}\big{)}^{2}}{\sigma_{g}^{2}}\right)\right] \tag{8}\]  
where \(s\) and \(t\) are the curved coordinates of each pixel, \(g\) is its gray level (\(g=I(s,t)\)), and \(w\) and \(\sigma^{2}\) are the weight and difference of every segment in the Gaussian model, respectively. This model outlines the brightness of the shadow regions and contains the coordinates where the shadow is estimated to be. When the model is assembled, every pixel in the original region \(R\) is classified as an object or shadow, as indicated by whether it concurs with the Gaussian model or not. We additionally enhanced the shadow removal task using a combination of local and global contrast image enhancement and median noise filtering applied to the foreground binary mask, where noise can be removed as described in the above chapters.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Overview of the SAR Satellite Missions
Following careful analyses, it was decided by ASI to deorbit the CSK-3 satellite after more than 13 years of operation, with a de-orbiting procedure beginning in May 2022. The second generation of COSMO-SkyMed (CSG) was developed to ensure an operational continuity to the mission. It includes four additional satellites, two of which were launched in December 2019 and February 2022 from Cape Canaveral (USA). The new CSG satellites are equipped with a multimode SAR sensor operating in X-band, with enhanced performance in terms of image quality and polarimetry (quadruple), acquisition mode, platform agility and data transmission. CSG satellites are positioned on the same orbital plane as the CSK satellites, with a 180\({}^{\circ}\) angle between CSG1 and CSG2, at an angle of 45\({}^{\circ}\) from the respective CSK satellites. Figure 10 shows the current orbital configuration of the COSMO-SkyMed satellites. The interferometric revisit time of each satellite is 16 days, exactly matching the repeat cycle. The CSG1 and CSG2 satellites can perform interferometric acquisitions every 8 days, since they are positioned at 180\({}^{\circ}\) from each other on the same orbit. In the Stripmap mode it is also possible to carry out interferometric acquisitions between different CSK and CSG satellites. Figure 11 shows a comparison of performance offered by the acquisition modes between the first generation (CSK, in orange) and the second generation (CSG, in blue) of COSMO-SkyMed. Figure 11: Comparison of SAR imaging types supported by CSK (orange)/CSG (blue), source ASI: Italian Space Agency [80]. Figure 10: CSK/CSG current orbital configuration. Regarding the future of the CSK System, the construction program for the 3 rd and 4 th satellites have recently been kicked off and these have been scheduled for launch in 2024 and 2025, respectively. A recent project will also provide the creation of the IRIDE constellation, which will become the most important multi-sensor European Earth Observation satellite space program at low altitude. This program is planned to be completed within the next five years (2027). IRIDE involves the use of SAR sensors operating in X-band and the possibility of an orbital choice, to be combined with the CSK/CSG constellation for interferometric applications, with a higher revisit time frequency. The project has been funded through the European funds of the Italian \"Piano Nazionale di Ripresa e Resilienza (PNRR)\" [83], as part of an agreement between ASI, ESA and the Italian Ministry for Innovation and Digital Transition (MITD). The constellation will also support civil protection and public administration bodies to monitor the hydrogeological instability of the environment, coasts, critical infrastructure, air quality and weather conditions.
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 3 Deep-Learning-Based Framework
### Methodology Framework
#### 3.2.3 Modified Faster R-CNN Models  
As shown in Figure 4, this network consists of two sub-networks: a basic convolutional network for image feature extraction and the region-proposal network for bounding-box prediction. To evaluate the effects of CNN feature extractors, the basic ZF network is used first. As originally proposed in [32], this model is taken as a baseline model in this paper. The ZF network [41], a variant of the AlexNet model, can map the extracted features to a synthesized image at the pixel level (termed DeConvNet), which makes it convenient to visualize the mechanism of CNN-based feature extraction. ZF network has five convolutional layers and two fully connected layers. Figure 4a illustrates this network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Class 1** & **Class 2** & **Class 3** \\ \hline \(M_{HT}(FRC,ZF)\) & Tornado & Tsunami & Earthquake \\ \(M_{DL}(FRC,ZF)\) & Minor & Moderate & Major \\ \hline \(M_{HT}(FRC,RN)\) & Tornado & Tsunami & Earthquake \\ \(M_{DL}(FRC,RN)\) & Minor & Moderate & Major \\ \hline \(M_{HT}(SSD,RN)\) & Tornado & Tsunami & Earthquake \\ \(M_{DL}(SSD,RN)\) & Minor & Moderate & Major \\ \hline \hline \end{tabular} Note: HT—hazard-type; DL—damage-level; FRC—Faster R-CNN; RN—ResNet-50; and SSD, Single Shot Multi-Box Detector.  
\end{table}
Table 1: Disaster-scene learning models and class prediction.  
In the second model, the ZF network is replaced by the ResNet-50 [42] as an enhanced feature extractor. In the ResNet family of networks, multiple nonlinear layers are used to approximate a residual function. This treatment significantly reduces the degradation phenomenon caused by introducing deeper layers. The ResNet layers are structured into blocks, and typical block stacks three layers: 1 \(\times\) 1 convolutional layer for reducing the dimension first, a 3 \(\times\) 3 convolutional layer, and another 1 \(\times\) 1 convolutional layer for restoring the dimension. Adopting batch-normalization (BN) in ResNet further enables improved generalization performance for such a large-scale deep network. A ResNet-50 network has four main clustered layers, which have 3, 4, 6, and 3 blocks in each cluster. As a result, ResNet-50 has 50 layers. Since the last layer of ResNet50 is a fully connected layer that cannot directly connect to the RPN stage in the Faster R-CNN architecture, the output from the 3 rd layer cluster of ResNet50 is fed to the RPN sub-network. The ROI layer is re-connected to the 4 th layer cluster of the ResNet-50, followed by the fully connected layers for bounding-box and label prediction. Figure 4b illustrates the resulting modified Faster R-CNN network.
# On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
## 3 Simulation Results and Analysis
### Device Design and Simulation
We synthesized 15,000 simulated spectral datasets based on a combination of Gaussian and Lorentzian functions and used this synthesized spectral dataset to train the model. Each simulated spectrum was composed of multiple function components, with the number of components randomly ranging from 1 to 10. Each function component was randomly set as either a Gaussian or Lorentzian function with a probability of 0.5. We then randomly set the position, height, and full width at half maximum (FWHM) for each peak. The position of each peak was randomly distributed between 1500 and 1600 nm; the heights were determined using uniformly distributed random numbers in the interval (0, 1); the FWHM is set by randomly selecting a value from a specific interval to serve as the FWHM of the current spectral peak. Finally, all function components are summed to generate the synthesized spectrum. We set three different ranges for the FWHM: (2 nm, 100 nm), (2 nm, 10 nm), and (1 nm, 2 nm). The additional generation of these spectra with smaller FWHMs is intended to enhance the model's ability to reconstruct narrow peaks (sparse spectra) in the spectrum. Before training the model, we randomly divided the synthesized spectral data into two parts with a ratio of 4:1, where the training set contained 12,000 samples and the validation set contained 3000 samples. Then, following the same method, we generated an additional 150 simulated spectra to serve as an independent test dataset. During model training, we employed the Adam optimizer with a batch size of 256 and trained for 10,000 epochs. The training was conducted on an NVIDIA GeForce RTX 4070 graphics (NVIDIA, Santa Clara, CA, USA) processing unit. After the training was completed, we chose to save the model that exhibited the lowest loss value on the validation set during the training process as the final spectral reconstruction model.
## 4 Proposed Detection Method
### Detection Performance  
Denote \(P_{f}=\text{Prob}\{\hat{\epsilon}\
eq\epsilon\}\) be the probability that the IFO is falsely detected. The probability of detection failure \(P_{f}\) of (25) in the AWGN channel is derived. Under the hypothesis that the local PSSS with ZC root index \(u_{n}\) is synchronized with the \(v\)-th shifted version of the received PSSS with ZC root index \(u_{i}\), equivalently, when \(v=\epsilon\) and \(n=i\) (hypothesis \(H_{1}\)), (23) is treated as a complex Gaussian RV based on the CLT. In this case, the mean is \(\mu=N_{p}E_{X}^{3}\) and the variance of (23) is calculated by  
\[\sigma_{1}^{2}=N_{p}E_{X}^{2}\sigma_{W}^{2}(4E_{X}^{2}+3\sigma_{W}^{2}) \tag{30}\]  
where \(\sigma_{W}^{2}=2E_{X}\sigma_{Z}^{2}+\sigma_{Z}^{4}\) is the variance of (17). When \(v\
eq\epsilon\) or \(n\
eq i\) (hypothesis \(H_{0}\)), namely, the signal is not synchronized, \(\Omega_{f}(v)\) in (24) has Gaussian distribution with zero mean and variance  
\[\sigma_{0}^{2}=N_{p}E_{X}^{6}+N_{p}E_{X}^{2}\sigma_{W}^{2}(4E_{X}^{2}+3\sigma_ {W}^{2}). \tag{31}\]  
Under hypothesis \(H_{1}\), the probability density function (PDF) of \(|\Omega_{f}(v)|\) can be given by  
\[p_{1}(y)=\frac{2y}{\sigma_{1}^{2}}e^{-\frac{y^{2}+y^{2}}{\sigma_{1}^{2}}}\;I_ {0}\left(\frac{2y\mu}{\sigma_{1}^{2}}\right) \tag{32}\]  
where \(I_{0}(\cdot)\) is the modified Bessel function of first kind and zero order. Under hypothesis \(H_{0}\), \(|\Omega_{f}(v)|\) has the Rayleigh distribution  
\[p_{0}(y)=\frac{2y}{\sigma_{0}^{2}}e^{-\frac{y^{2}}{\sigma_{0}^{2}}}. \tag{33}\]  
Since IFOs are equally likely, the probability that the IFO is correctly detected can be given by  
\[P_{d}=\int_{0}^{\infty}p_{1}(y)\left[\int_{0}^{y}p_{0}(x)dx\right]^{2M}dy. \tag{34}\]  
Substituting (32) and (33) to (34) yields  
\[P_{d}=\int_{0}^{\infty}\frac{2y}{\sigma_{1}^{2}}e^{-\frac{y^{2}+y^{2}}{\sigma_ {1}^{2}}}I_{0}\left(\frac{2y\mu}{\sigma_{1}^{2}}\right)\left[1-e^{-\frac{y^{2} }{\sigma_{0}^{2}}}\right]^{2M}dy. \tag{35}\]  
Using the binomial expansion, (35) can be further derived as  
\[P_{d}=e^{-\frac{y^{2}}{\sigma_{1}^{2}}}\sum_{m=0}^{2M}(-1)^{m}\binom{2M}{m} \int_{0}^{\infty}\frac{2y}{\sigma_{1}^{2}}e^{-y^{2}(1/\sigma_{1}^{2}+m/\sigma _{0}^{2})}I_{0}\left(\frac{2y\mu}{\sigma_{1}^{2}}\right)dy \tag{36}\]where the integral has a closed-form solution [31]. Let \(\gamma_{z}=E_{X}/\sigma_{Z}^{2}\) be the SNR. After some mathematical manipulations, the probability of detection failure \(P_{f}\) is obtained as  
\[P_{f}=1-\sum_{m=0}^{2M}(-1)^{m}\binom{2M}{m}\frac{1}{1+m\sigma_{1}^{2}/\sigma_{ 0}^{2}}e^{-\frac{m}{\sigma_{0}^{2}/\mu^{2}+m_{1}^{2}/\mu^{2}}}. \tag{37}\]  
with  
\[\frac{\sigma_{1}^{2}}{\mu^{2}}=\frac{\bar{\gamma}_{z}(4+3\bar{ \gamma}_{z})}{N_{p}}, \tag{38}\]  
and  
\[\frac{\sigma_{0}^{2}}{\mu^{2}}=\frac{1+\bar{\gamma}_{z}(4+3\bar{ \gamma}_{z})}{N_{p}}, \tag{39}\]  
where \(\bar{\gamma}_{z}=2/(\gamma_{z})+1/(\gamma_{z})^{2}\) and \(\sigma_{1}^{2}/\sigma_{0}^{2}\) is obtained on dividing (38) by (39).
## 4 Experiments
### Ablation Study  
To verify the effectiveness of each design in AOC-Caps, we conduct experiments for the ablation study. The ablation study focuses on (1) different forms of capsules and different routing methods; (2) types of pooling operations in the AOC-Layer; (3) the effect of attention modules. To test different forms of capsules and different routing methods, we choose the pooling operation type to be maximal and plug attention modules as introduced in Section 3.1. It should be noted that in dynamic routing, we do not add the decode term. As shown in Table 9, the AOC-Caps with matrix and EM routing outperforms the one with dynamic routing. It can be seen from Table 9 that different routing methods have a great impact on the results. For example, in the OrganMNIST_Axial dataset, the accuracy with EM routingis 93.1%, while the accuracy with dynamic routing is 80.2%. The AOC-Caps with EM routing also achieves better precision and recall, both of which are more than 10% higher. To test the effectiveness of different pooling operations, such as average and maximum, we keep the capsule form to matrix and EM routing. The attention modules are plugged into the AOC-Layer as introduced in Section 3.1. As shown in Table 10, the AOC-Caps with max-pooling outperforms the one with avg-pooling. What is more, the type of pooling operations has much less impact on the results than the routing methods. To test the effectiveness of the attention modules, we keep the capsule form to matrix and EM routing. The pooling type is set to be maximal. As shown in Table 11, the AOC-Caps with the attention module outperforms the one without the attention module. Attention modules have a greater impact on the results than the pooling operation type and less than the routing method. In Table 11, the AOC-Caps with attention modules achieves higher accuracy, precision and recall. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Names**} & \multicolumn{3}{c}{**AOC-Caps (AVG)**} & \multicolumn{6}{c}{**AOC-Caps (MAX)**} \\ \cline{2-11}  & **ACC** & **PRE** & **REC** & **F1** & **AUC** & **ACC** & **PRE** & **REC** & **F1** & **AUC** \\ \hline PathMNIST & 0.847 & 0.829 & 0.820 & 0.832 & 0.960 & **0.859** & **0.840** & **0.837** & **0.845** & **0.960** \\ \hline DermaMNIST & 0.764 & 0.447 & 0.380 & 0.403 & 0.860 & **0.786** & **0.485** & **0.417** & **0.447** & **0.890** \\ \hline OCTMNIST & 0.739 & 0.807 & 0.789 & 0.798 & 0.930 & **0.750** & **0.820** & 0.802 & 0.819 & 0.955 \\ \hline PneumoniaMNIST & 0.918 & 0.895 & 0.879 & 0.880 & 0.985 & **0.931** & **0.914** & 0.893 & 0.895 & 0.990 \\ \hline OrganMNIST\_Axial & 0.927 & 0.915 & 0.900 & 0.912 & 0.970 & **0.931** & **0.920** & **0.903** & **0.919** & **0.973** \\ \hline OrganMNIST\_Coronal & 0.886 & 0.889 & 0.878 & 0.883 & 0.960 & **0.907** & **0.911** & **0.904** & **0.904** & **0.965** \\ \hline OrganMNIST\_Sagittal & 0.769 & 0.718 & 0.702 & 0.710 & 0.970 & **0.783** & **0.731** & **0.717** & **0.723** & **0.980** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of different pooling operations in AOC-Layer.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion
### Energy Consumption  
The experimental results in the figures of energy consumption have been discussed. Figure 14a, expresses the variation of energy consumption in various protocols AOMR-LM, AOMDV, SRMP and AOMDV-ER. In AOMDV, it increases from 70 to 156 Joule; AOMR-LM varies from 60 to 100 J; SRMP increases from 80 to 170 J; and in AOMD-ER, there is an increase from 54 to 86 J. The AOMD-ER outperforms others because in this protocol flooding of packets are limited and only eligible nodes participate in the transmission of packets and remaining nodes wait and sense the network and act only when needed (recoiled technique).  
Figure 14b illustrates the energy consumption increase with packet size. In AOMR-LM, there is variation from 20 to 80 J; in SRMP, an increase from 42 to 119 J; and in AOMD-ER 18 to 62 J. The AOMD-ER performs best as it conserves energy while restricting flooding and follows the path that has less chance for link error.  
Figure 14c shows the power consumption increases with simulation time. The power consumption in AOMDV increases from 40 to 105 J, AOMR-LM varies from 20 to 80 J, SRMP increases from 42 to 119 J and AOMD-ER changes from 18 to 62 J. The result shows that performance of AOMD-ER is best among all, as it senses more and transmits less.  
Figure 14: (**a**) Best fit line on AODV-ER; (**b**) Best fit line on AODV-ER; (**c**) Best fit line on AODV-ER.  
Figure 13: (**a**) Throughput vs. Node Speed; (**b**) Throughput vs. Packet Size; (**c**) Throughput vs. Sim. time.  
The Experimental results in the figure of energy consumption have been discussed. Figure 13(a), expresses the variation of energy consumption in various protocols AOMR-LM, AOMDV, SRMP and AOMDV-ER.  
In AOMDV, there is an increase from 70 to 156 Joule; in AOMR-LM, a variation from 60 to 100 J; in SRMP there is an increase from 80 to 170 J; and in AOMD-ER as increase from 54 to 86 J. The AOMD-ER outperforms the others since, in this protocol, flooding of packets is limited and only eligible nodes do transmit the packets and the remaining wait and sense the network and act only when needed (recoiled technique).  
Figure 13(b) illustrates the energy consumption increase with packet size. In AOMR-LM varies from 20 to 80 J, in SRMP increases from 42 to 119 J and in AOMD-ER 18 to 62 J. The AOMD-ER performs best given it conserves energy while restricting flooding and follows the path that has less chance for link error.  
Figure 13(c) shows the power consumption increase with simulation time. The power consumption in AOMDV increases from 40 to 105 J, in AOMR-LM varies from 20 to 80 J, in SRMP increases from 42 to 119 J and in AOMD-ER from 18 to 62 J. The result shows that performance of AOMD-ER is best among all, as it senses more and transmits less.  
Figure 14(a)-c are also show the energy consumption in the different scenarios, which have already been discussed above.
## 2 Overall Analysis Approach and Implemented Methods
### Data and Assumptions on LH\({}_{2}\) Technology
#### 2.1.1 LH\({}_{2}\) Turbofan Engines  
The use of H\({}_{2}\) as fuel in the combustion chamber of a turbomachinery has been studied in [17; 19; 22; 25; 27; 28]. The combustion of H\({}_{2}\) is possible in a wider range of pressures and fuel/air mixtures than those allowed by JP4 (Figure 4 in [17]). Therefore, it is possible  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Study** & **Field of Study** & **Level of Detail** \\ \hline
[17; 20; 24; 25] & Aircraft design & Conceptual, aircraft level \\
[26] & Aircraft design & Conceptual, aircraft and systems level \\
[15; 28] & Engine & Experimental \\
[27] & Engine & Conceptual \\
[14] & Tank & Experimental \\
[16; 31; 32] & Tank & Conceptual \\
[29] & Tank & Conceptual and FEM analysis \\ \hline \hline \end{tabular}
\end{table}
Table 2: Level of detail of most relevant studies reported in state-of-the-art analyses.  
to convert ordinary turbofans to run on H\({}_{2}\), or design new engines for new operative conditions ([19]). In this work, the engine design has been preserved, following the \"retrofit\" approach proposed in [27], in order to minimise engine modifications.  
Despite the marginal variation in the engine design, there is the need of a heat exchanger (HE), in order to shift the hydrogen state from storage conditions to combustion chamber injection conditions. In [22, 27], some HE options have been proposed; however, in order to minimise the changes to the engine and the airframe, in this work, the HE will be considered located after the Low Pressure Turbine. For an engine with this configuration, delivering the same thrust of a JP4 fuelled engine, in [27] the authors indicate that the SFC decreases by 64.71% and Turbine Entry Temperature decreases by 31.5 K, with a significant improvement in engine life. The weight of the engine itself is the same as a conventional one, but the HE adds 1% to the overall engine weight ([27]).