esa-sceva/satcom-chunk-collection huggingface dataset:
# A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas
###### Abstract  
Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize \"false detections\" caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy.  
oil spill; MODIS; Robust Satellite Techniques (RST); optical data; Cyprus; FAI 2017 9 128 doi:10.3390/rs9020128
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
## 2 Materials and Methods  
### Patient Selection and Patient Size Calculation  
Voxelized patient models of different sizes used in the Monte Carlo simulations were developed from the actual CT examinations. A total of 567 sets of chest diagnostic CT for Chinese adult patients were acquired on the same scanner (NeuViz 16, NMD, China) at Zhongye Hospital (Shanghai) from October to December 2017. The institutional review board approved and a waiver of informed consent was obtained. By considering the body-size effects on the organ doses and, taking the water equivalent diameter (_WED_) as a body-size indicator, CT image sequences from 18 adult patients (9 females and 9 males) with different body sizes were screened to create patient-specific voxelized chest models in this work. Each CT examination was performed with 120 kVp (HVL = 8.27 cm), 0.8631 pitch factor and 16 \(\times\) 1.25 mm collimation and 3 mm reconstructed image thickness with ATCM. All images were reconstructed under a relatively large field of view (FOV) to make the anatomical structure as complete as possible within the image coverage. The _WED_ was used as a patient size metric for dose estimates in the chest. It represented the diameter of a water cylinder in which the attenuation of X-rays was equivalent to that in the patient body. In this study, the DICOM of mid-chest, with the lowest portion of the sternum present, or the simultaneous view of all four heart chambers, was used to obtain the average CT number in the region of the entire patient cross-section without irrelevant objects. According to the definition in the AAPM 220 report, _WED_ can be calculated as follows:  
\[WED=2\sqrt{\left[\frac{1}{1000}\overline{CT(x,y)_{ROI}}+1\right]\frac{A_{ROI}} {\pi}} \tag{1}\]  
where \(\overline{CT(x,y)_{ROI}}\) is the mean _CT_ number in the region of interest (_ROI_), \(A_{ROI}\) is the total area of the _ROI_.
# Micro-Satellite Systems Design, Integration, and Flight
## 1 Introduction
### Background on Subsystems covered in This Manuscript  
The manuscript will review the two most influential systems responsible for meeting the mission goal, namely, the stabilization of the CubeSat and the radio frequency (RF) communication infrastructure between the picosatellite (ChipSat) and ground station.  
The first half of the manuscript will discuss the use of RF communication for robust, long-range, and low-power transmissions. For the Alpha project, the picosatellite payload was developed by Dr. [PERSON] [14; 15], (previously at Cornell). The satellite is printed on a thin Kapton substrate [16; 17] and only has a solar panel, processor, IMU, GPS, light sensor, and RF transceiver. The satellite is named ChipSat.  
The second half of the manuscript will discuss the ACS of a 1U CubeSat. This development was built off the progress of Armin [18] and [PERSON] [19] (previously at Cornell). For the Alpha mission, a CubeSat is used to transport our payload because of the affordability of the satellite type [20] and the simplicity the satellite structure offers. The one-unit (1U) CubeSat is a 10 cm by 10 cm by 11.35 cm sized satellite [21] with small rectangular rails on each of the corners. CubeSats can exist in several multiples of the one-unit size up to 12U [22]. The modular size standard of the CubeSat is chosen such that several of these satellites can be easily loaded onto a canisterized satellite dispenser (CSD) [23; 24]. The CDS dispenser is spring-loaded and fastened to the payload of a launch vehicle [25]. When activated, the CSD will eject the payloads into low-earth orbit (LEO).
## 4 Discussion
### Future Perspectives  
A number of activities are already planned for the future to further exploit and strengthen the current capabilities of the IREA-CNR infrastructure.  
In particular, the next MIPS acquisition campaigns are aimed at fully exploiting the L-band mode of the system. Specifically, L-band campaigns are planned for the near future to collect polarimetric SAR datasets for the retrieval of ground parameters such as soil moisture. Moreover, L-band repeat-pass InSAR acquisitions are planned to retrieve ground deformation fields through the use of the DInSAR technique [2].  
Furthermore, a number of actions supported by already assigned funds are currently being carried out to strengthen the infrastructure. In particular, as discussed in Section 2.2.3, the current data download and transfer procedures are quite time-consuming, because the hard disks where the data are downloaded at the end of the mission have to be manually transferred to the IREA-CNR laboratory. To speed up this overall procedure, in the near future, we plan to download and transfer the data using satellite Internet constellation links. Furthermore, as a part of the \"Next Generation EU Mission 4--ProjectIR0000032--ITINERIS\", a fully polarimetric pulsed multifrequency SAR system will be acquired. The latter will allow for the expansion of the operational scenarios of interest for the infrastructure, guaranteeing higher operational flight heights with respect to those currently achievable with the MIPS system based on the FMCW technology. In addition, within the framework of the same project, five computing nodes, which will be added to the IT platform described in Section 2.2.1, will be acquired to further increase its dedicated multi-core capabilities
## 3 Results and Discussion
### Degree of Esterification  
The application of pectin largely depends on its degree of esterification (DE). The results showed that the DE value of pectin from MAE (3\(A\) and 5 min of extraction) varied between 47.5 and 48.5%. No significant difference was observed in the DE value of pectin obtained with different extraction durations; they all belonged to low-methoxy pectin, as evidenced by the DE values, which were lower than 50%. According to this result and Figure 2, the effect of extraction time on DE values depends on the pretreatment used. The DE values of the samples obtained by MAUPE decreased from 44.7 \(\pm\) 0.5% to 42.9 \(\pm\) 0.5%. Cavitation and reaction by ionized groups produced during ultrasonic pretreatment can reduce the pectin DE [11]. Statistical analysis of the three factors, i.e., extraction time, ultrasonication time and ultrasonication power, showed that the DE value decreased with an increase in extraction pretreatment time and power (Figure 2). Moreover, the DE reduction was more substantial with the increase in extraction time when MAUPE was used. In this case, the time and power of extraction pretreatment had an equal effect on the DE value. It was previously reported that the DE value depends on the nature of the pectin source and extraction conditions [11]. [PERSON] et al. reported that the DE value of watermelon rind pectin extracted by MAE was 44.3-48.7% [21].
# A Photoplethysmogram Dataset for Emotional Analysis
## 2 Materials and Methods
### Frequency and Time Domain Analysis  
For emotion recognition, feature extraction is considered a key process [22, 28, 29]. Because emotional states are not easily observable in a time domain, signals are commonly transformed into frequency domains and analyzed. Following traditional methods [22, 29], we calculated the features in three different frequency bands: very-low-frequency (VLF, \(\leq\)0.04 Hz), low-frequency (LF, 0.04-0.15 Hz), and high-frequency (HF, 0.15-0.4 Hz). Frequency transforms were conducted using fast Fourier transform (FFT).  
Thirteen features (VLFa, LFa, HFa, TOTALa, VLFh, LFh, HFh, VLFP, LFp, HFp, LFn, HFn, and LFHF), proposed in [29], were calculated and analyzed for each emotional state. Table 3 lists the features and their descriptions. The features were derived from the frequency statistics of each band, using the area, peak point, percentage value, and highest power values.  
Six time-domain features were calculated based on studies in the literature [22, 29, 32]. The time-domain features were highly related to the NN intervals of heart rate variability. MeanNN, medianNN, and SDNN denote the mean, median, and standard deviation of the NN intervals of a signal, respectively. NN40 is the sum of NN intervals equal to or larger than 40 ms, and pNN40 denotes the value of NN40 divided by the number of total peaks (i.e., the number of heartbeats). RMSSD is the root mean square of the NN interval of a signal.
## 4 Experiments and Results
### Ablation Experiments  
#### 4.5.1 The Effects of Shallow-Deep Branch and GL-Deep Fusion  
As shown in Table 4, we designed three models: shallow-deep, shallow-shallow, and deep-deep. These three models differ in their design for the global backbone, local backbone, and fusion module. They are used to evaluate the impact of the shallow-deep branch collaborative strategy and GL-Deep Fusion structure on ultra-high resolution image segmentation. Note, the benchmark model, GLNet, is also included for comparison.  
On the DeepGlobe dataset, we conducted ablation studies to evaluate the impact of different network architectures on mIoU, GPU memory usage, and the frames per second (FPS) metric, which provides additional insight into the models' performance. As shown in Table 5, the deep-deep model, enhanced by the GL-Deep Fusion strategy, achieved a 1%  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Network Architectures** & **Global Backbone** & **Local Backbone** & **Fusion** \\ \hline GLNet (baseline) & ResNet50 & ResNet50 & FPN \\ Shallow-Deep & ResNet18 & ResNet50 & GL-Deep Fusion \\ Shallow-Shallow & ResNet18 & ResNet18 & GL-Deep Fusion \\ Deep–Deep & ResNet50 & ResNet50 & FPN + GL-Deep Fusion \\ \hline \hline \end{tabular}
\end{table}
Table 4: Illustrations of network architectures for various model designs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Local Inference**} & \multicolumn{2}{c}{**Global Inference**} \\  & **mIoU [\%]** & **Memory [MB]** & **mIoU [\%]** & **Memory [MB]** \\ \hline U-Net [8] & 37.3 & 949 & 38.4 & 5507 \\ ICNet [12] & 35.5 & 1195 & 40.2 & 2557 \\ PSPNet [10] & 53.3 & 1513 & 56.6 & 6289 \\ SegNet [11] & 60.8 & 1139 & 61.2 & 10,339 \\ DeepLabv4  & 63.1 & 1279 & 63.5 & 3199 \\ FCN-8s [45] & 64.3 & 1963 & 70.1 & 5227 \\ Mask2 Former [23] & 66.7 & 3458 & 70.3 & 23,577 \\ TransUnet [44] & 68.2 & 2436 & 70.2 & 6283 \\ \hline \hline \multicolumn{5}{c}{**Local and Global**} \\  & **mIoU [\%]** & **Memory [MB]** \\ \hline GLNet [20] (baseline) & 71.6 & \multicolumn{2}{c}{1865} \\ GLSNet & **72.4** & \multicolumn{2}{c}{**1414**} \\ \hline \hline \end{tabular}
\end{table}
Table 3: mIoU and inference GPU memory usage for predictions on the DeepGlobe test set.  
higher mIoU than the GLNet baseline. This confirms that our fusion approach contributes positively to segmentation accuracy.  
Notably, the Shallow-Shallow model, while using less complex ResNet18 for both branches, not only reduced GPU memory usage significantly to 1044 MB but also improved the FPS to 1.34, demonstrating its efficiency. The Shallow-Deep model, our primary choice for GLSNet, offers a balanced performance with a mIoU of 72.4%, memory usage of 1414 MB, and an FPS of 1.14, highlighting a good trade-off between accuracy and computational speed.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 4 Risk Classification of Evaluation Indicators  
### Geological Factors  
According to the above analysis, with the increase in mining depth, the risk of compound dynamic disasters increases rapidly. Therefore, the membership parameter of mining depth (U\({}_{11}\)) is set as 0, 350, 500 and 800 (m).  
Figure 3: Trapezoidal distribution function.  
The calculation formula of fault scale index is shown in Formula (13) [57] where \(T\) is the fault scale index; \(H_{i}\) is the drop of the \(i\)th fault; \(L_{i}\) is the strike length of the \(i\)th fault; \(n\) is the number of faults within the assessment range; and \(S\) is the area of the evaluation scope. Therefore, the membership parameter of U\({}_{12}\) is set as 0, 0.3, 0.6 and 0.9.  
\[T=\frac{\sum_{i=1}^{n}H_{i}\times L_{i}}{S} \tag{13}\]  
The characteristic parameter of roof rock stratum thickness is used to characterize the roof characteristics (Formula (14)) where \(h_{i}\) is the thickness of the \(i\)th rock stratum within 100 m of the roof and \(r_{i}\) is the weak surface decline coefficient of the given rock stratum. Therefore, the membership parameter of U\({}_{13}\) is set as 0, 50, 70 and 90 (m).  
\[L_{st}=\sum h_{i}r_{i} \tag{14}\]  
The change of coal seam thickness is a qualitative index [58]. Now, the qualitative index is classified into four levels according to the literature [59, 60, 61, 62]. The four levels correspond to no-change degree, small-change degree, medium-change degree and large-change degree. Therefore, the membership parameter of U\({}_{14}\) is set as 0, 1, 2 and 3. See Table 2 for details.  
The plane deformation coefficient was first proposed by former Soviet Union scholars when they studied the geological conditions of coal seam outburst, which is an index reflecting the complexity of fold structure [63]. See Formula (15) for its details, where \(K_{TC}\) is the plane deformation coefficient; \(h\) is the elevation difference of adjacent contour lines, m; \(\omega\) is the transformation angle of the contour near the center of the unit, \({}^{\circ}\); \(L\) is the average value of contour line spacing in the calculation unit, m; and \(S\) is the area of the calculation unit, m\({}^{2}\). The smaller the plane deformation coefficient is, the smaller the influence of folds on the change of tectonic stress in the assessed area is. According to the statistics of field survey data, the membership parameters of U\({}_{15}\) are determined as 0, 10\({}^{-5}\), 10\({}^{-3}\) and 10\({}^{-1}\).  
\[K_{TC}=h\cdot\omega/L\cdot S \tag{15}\]
## 6 Advantages and Disadvantages Analysis and Prospect of Non-Condensate Gas Injection
### Advantages and Disadvantages Analysis of Non-Condensate Gas Injection  
#### (1) Advantages  
In heavy-oil development, steam injection technology is the most widely used technology. However, due to the limitation of reservoir pressure and heat loss, this thermal recovery technology is not ideal for ultra-deep heavy-oil reservoirs with a buried depth greater than 2000 m. Non-condensate gas injection is an important way of tertiary oil recovery, which has many advantages in the exploitation of heavy-oil reservoirs. First of all, under the reservoir condition, the injected gas and heavy-oil contact many times to achieve dynamic miscibility, which has a significant viscosity-reduction effect on heavy oil. In recent years, people have started to pay more attention to CO\({}_{2}\) to improve the recovery of heavy-oil reservoirs. This technology can not only improve the recovery of heavy-oil reservoirs, but also solve the problem of CO\({}_{2}\) sequestration and inhibit the greenhouse effect [6; 99]. Second, the performance of non-condensate gas injection is almost not limited by reservoir depth. For deep heavy-oil reservoirs with thin formation and low permeability, it has better effect and higher economic benefits than thermal recovery. Water-sensitive reservoirs, bottom water reservoirs, and other reservoirs that are not suitable for water-flooding development can also be developed by injecting non condensable gas. Last, the non-condensate gas injection operation requires simple equipment.  
Figure 6: Schematic diagram of SAGP injection.  
Figure 7: Non-condensable gas displacement process.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 5 Development of 1-D CNN Models
### 1-D CNN Model Using SVD Responses
#### 5.2.3 Evaluation of the 1-D CNN Model Using SVD Responses
In summary, the CNN model trained with SVD responses demonstrated its efficacy in damage detection and monitoring. It maintained good performance even with noise levels up to 5%. This matched the performance of the CNN model trained with time-history responses. However, the model trained with SVD responses demonstrated better performance when dealing with untrained noise levels (6-10%). The computations were performed on a desktop computer (CPU--Intel Core i7-10700F 2.9 GHz). The training times for the CNN model using time-history and SVD data were 7 and 4 min, respectively. Both approaches demonstrate computational efficiency that is suitable for real-time monitoring applications. With the relatively short training times, models can be quickly updated or retrained when new data are available.
## 2 Proposed WSN Routing Scheme
### Protocol Operations
#### 2.2.2 Acknowledgement Method  
After the dissemination operation by the source and neighbor nodes is done, the base gets data in parts from several paths. The main idea of using different paths is to provide reliable transfer operations in case of breakdown of the major routes. To prevent package loss in these paths, acknowledgement signals are used. After receiving a package, the base decides the pheromone value to be added on that route by evaluating \(J^{k}_{\mathit{w}}\) in equation (3). Then, it forms an acknowledgement signal as in Figure 4 and broadcasts it towards to the source, using the same path which has been used for the associated package. This operation is illustrated in Figure 5.  
Figure 3: ACO parameters memorized in the nodes.  
Figure 2: Data package content.  
A node receiving an acknowledgement signal first checks the \(S\_N\) value. If the number is found in the node memory (tabu list) that has been inserted previously while disseminating associated packages, then this signal is broadcasted to other neighbors along the path. The node also updates the pheromone value by adding \(\Delta\tau^{k}\) specified in the signal by using equation (4). If the node ID is not found in the memory then this signal is discarded and no further operation is done. Thus, \(\Delta\tau^{k}\) is added along the routed path. The source node waits for the acknowledgement signal of each data package. In case of an absent acknowledgement signal, caused by errors in the path, that package should be disseminated again.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Performance of Image Inpainting  
Privacy-sensitive object detection was conducted on both the original and inpainted images. The re-detection rate was defined as the proportion of privacy-sensitive objects detected in the original image that were re-detected in the inpainted image. This metric assessed the performance of the inpainting methods in preserving privacy, with a lower re-detection rate indicating better privacy protection. For comparison, masking and noise addition algorithms were applied to privacy-sensitive areas. The masking algorithms used black, gray, and white pixels to cover these areas.  
Additionally, three types of noise addition algorithms were compared. The first method introduces Gaussian noise into the privacy-sensitive area, defined as:  
\[I_{\text{gaussian}}=I\odot(1-M)+(I+N(0,25))\odot M \tag{5}\]  
where \(I\) represents the original image, \(M\) is the mask indicating the privacy-sensitive region, \(\odot\) is the Hadamard product, and \(N(0,25)\) denotes Gaussian noise with a mean of 0 and a variance of 25. The other two methods randomly insert noise into 25% and 50% of the area, respectively.  
Table 2 shows that the proposed image inpainting method outperforms masking algorithms in most sequences, remaining competitive across the others. It also surpasses noise addition algorithms in all sequences. These results demonstrate that the method is more effective in handling privacy-sensitive areas, providing more robust privacy preservation for visual SLAM. Masking algorithms perform better than noise addition algorithms because they render privacy-sensitive objects invisible, whereas noise addition may still allow some sensitive areas to be identified.  
Figure 5 visualizes the generated images protected by different privacy-preservation algorithms alongside the original image. While masking algorithms offer a degree of privacy protection, they struggle to accurately restore privacy-sensitive areas. In contrast, the proposed method can accurately identify privacy-sensitive objects and realistically inpaint these regions while concealing their locations. Moreover, compared to noise addition algorithms, which introduce noise in the area where the privacy-sensitive objects are located, the proposed method can more thoroughly remove all privacy-sensitive information. This is because it first masks the area to ensure that the privacy-sensitive objects are invisible, and then realistic inpainting is performed on the masked area. The above processing not only enhances SLAM's privacy protection capabilities but also makes the restored image more conducive to subsequent SLAM positioning tasks.  
Figure 4: Comparison detection results on the test set of KITTI Object Detection Evaluation 2012.