esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 3 Results
### Flight Experiments and Results
#### 3.2.1 Design of Flight Experiments  
Four target points were set as shown in Table 3. The targets of control points were laid on the ground, and their geographical positions were measured by a DGPS device and the geo-location error of the target <0.2 m via the post-processing, which is much less than the geo-location error analyzed in Section 3.1. Thus, the target geo-graphical position can be viewed as the source of truth.  
The UAV's relative flight heights were set to 2500, 3000 and 3500 m. Five flight routes were set to achieve multiple positions and multiple angles to measure those targets as shown in Table 4. The routes of L1, L2 and L3 were used for the error solving and the routes of L4 and L5 were used to obtain samples for validation. The routes of L1, L3 and L5 have the same start and end waypoints with different relative flight height, and the route of L2 and L4 have the same start and end waypoints with different relative flight height too. The flight routes and target points are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**Route ID** & **Start Waypoint (B, L, H)** & **End Waypoint (B, L, H)** \\ \hline L1 & (124.5152661 E, 44.9594875 N, 2500) & (124.6667839 E, 44.9594875 N, 2500) \\ L2 & (124.5152661 E, 44.9406178 N, 3000) & (124.6667839 E, 44.9406178 N, 3000) \\ L3 & (124.5152661 E, 44.9594875 N, 3500) & (124.6667839 E, 44.9594875 N, 3500) \\ L4 & (124.5152661 E, 44.9406178 N, 3000) & (124.6667839 E, 44.9406178 N, 3000) \\ L5 & (124.5152661 E, 44.9594875 N, 3000) & (124.6667839 E, 44.9594875 N, 3000) \\ \hline \end{tabular}
\end{table}
Table 4: Position of flight routes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Target ID** & **Position (B, L, H)** & **Usage** \\ \hline P1 & (124.5797389 E, 44.9517639 N, 155 m) & For errors solving \\ P2 & (124.5818472 E, 44.9517556 N, 155 m) & For validation \\ P3 & (124.5809611 E, 44.9523944 N, 155 m) & For validation \\ P4 & (124.5810250 E, 44.9516056 N, 155 m) & For validation \\ \hline \end{tabular}
\end{table}
Table 3: Position of target points.  
Figure 9: The flight routes and target points of experiments.
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 1 Introduction
This motivates us to seek a suitable framework to adaptively learn the regions of interest in the input object, highlighting the structural features that are meaningful to the task, and thus improving the accuracy of the model prediction. Based on V-Net model and attention mechanism, this paper designs a segmentation algorithm for left atrial MR images which are mostly 3D data formats, different from classic algorithms such as FCN, U-Net, and other networks. The proposed method can use the interdependence between channel mappings, to emphasize the interdependent feature mapping, and improve the feature representation of specific semantics. Therefore, the model pays more attention to the salient features that are meaningful for specific tasks. The results demonstrate that our method achieves significant improvements in left atrial segmentation. In summary, this paper mainly makes the following contributions to the problem of how to make the network adaptively focus on the region of interest in the feature map:  
(1) We propose a 3D left atrium segmentation model based on the attention mechanism, Attention V-Net, to simulate the interdependence between channels. In contrast to the previous 2D segmentation models, it can fully use the information between the 3D sequences of medical images. It can adaptively learn to highlight the salient features that are useful for tasks in the image, thus effectively enhancing the ability of feature expression. (2) The proposed algorithm is applied to the semi-supervised framework of left atrium segmentation. The experimental results show that compared with the baseline, the proposed method obtains improved performance in terms of Dice, Jaccard, ASD, and 95 HD, and also outperforms other state-of-the-art semi-supervised methods. The rest of the paper is organized as follows. A brief review of the related works in left atrial segmentation and attention mechanism is given in Section 2. The architectures of the proposed Attention V-Net model is presented in Section 3. We present the experimental settings and the corresponding results in Section 4. Finally, we conclude this paper in Section 5.
## 5 Discussion
### Correlation Analysis between Surface Subsidence Time Series and Rainfall  
According to previous studies [41; 42], urban rainfall and urban surface subsidence have a certain relationship. The greater the rainfall the more stable or somewhat uplift the land will be. The surface subsidence caused by groundwater exploitation is effectively alleviated because the increase in rainfall can replenish the groundwater content.  
Shanghai is close to the ocean and is greatly affected by the ocean. Meanwhile, it is located in the subtropical monsoon climate. Thus, the precipitation times in Shanghai is longer, and the influence of rainfall on the change in groundwater level is greater.  
In this paper, the monthly precipitation information of two meteorological stations QX1 and QX2, which are located in Baoshan and Longhua of Shanghai in 2019, respectively. They are selected from the National Meteorological Data Network. In order to better analyze the correlation between the time series of surface subsidence and rainfall, the subsidence data used in this section are the average values of the subsidence of PSs within 200 m around the weather station. In this experiment, a buffer zone of 200 m was established, with two weather stations as the center. The PSs within 200 m around the two weather stations were extracted separately, and the mean subsidence of these PSs were obtained as the basis for comparison.  
The results of comparison between the two meteorological stations and the subsidence are shown in Figures 8 and 9. In the months with relatively more precipitation, the surface subsidence is significantly smaller or uplift, whereas in the months with relatively less precipitation, the surface subsidence is easy to occur significantly.  
Figure 7: Shanghai geological planing surface map. (Borehole (**a**) is shown on the left, borehole (**b**) is shown on the right, and red number indicates the bottom burial depth. The color is Quaternary Geology and the gray is deep bedrock).  
The 2018 rainy season in the middle and lower reaches of the Yangtze River begins in June. The rainfall data of Shanghai area show that the rainfall in Shanghai has increased significantly since June. As shown in Figures 8 and 9, the subsidence amount of the subsidence feature points located in the QX1 region significantly decreased, whereas the subsidence feature points located in the QX2 region showed a small amplitude of uplift, thereby confirming the above inference.  
The result shows that the seasonal variation of land subsidence is related to rainfall. We can combine the conclusion of linear relationship between subsidence and groundwater exploitation, which has been verified by previous studies. The over exploitation of groundwater will lead to surface subsidence, but the timely supplement of rainfall to groundwater will reduce the amount of subsidence effectively and can reduce the occurrence of excessive subsidence [43].
## 5 Experimental Analysis and Verification
### Comparison of RDCA, CAE, and CNN  
Considering the proposed RDCA integrate the advantages of CAE and CNN and these three methods can all extract the input feature automatically, to verify the validity  
Figure 10: The classification accuracies with varying noise.  
of the RDCA, we compare the proposed RDCA with some advanced feature extraction methods, including auto-encoder (AE), CAE [17], and DCNN [33]. The detailed structure of different methods is as follows.  
For traditional AE, the input matrix \(\mathbf{x}\) is directly flattened, i.e., the input layer has 32,768 neuron nodes. In this test, a single-layer (AE-1) and a three-layer AE (AE-3) model are used for comparison. We set the structure of 32,768 to 2000 for the AE-1. Therefore, it means that there are 2000 nodes in the second layer of the encoder. Similarly, the structure of AE-3 is set to 32,768 to 5000 and then 1000 to 512.  
For the CAE [17], an optimal single-layer CAE and three-layer CAE with L2 regularization are used to compare. The softmax classifier is used to classify both for AE and CAE.  
For the DCNN, a five-layer AlexNet with fewer parameters is used to classify time-frequency information directly [33]. The depth of each layer is set to 8, 16, 32, 64, and 128.  
In particular, the PQDs' signal is superimposed with the GN and the SNR is 20 dB. The quantitative performance and the test time are obtained, as listed in Table 2. The test time calculates the part of feature extraction and classification for all test sets (8960 samples), ignoring the time-frequency transform.  
It is shown in Table 2 that the proposed RDCA outperforms the AE, CAE, and DCNN. The performance of the RDCA-3 with the softmax classifier is lower than that with the SCNN. Particularly, the accuracy of RDCA-1 is better than DCNN, which indicates that the operation of refactoring contributes to the extraction of effective PQDs features. The performance of AE is not satisfactory due to the lack of convolution operations. On the other hand, the three-layer network always has a better classification result compared to the single-layer models. For the test time analysis, the AE and CAE take less time because the structures of models are simpler with fewer parameters. It can be witnessed that the total time of the RDCA models in Table 2 is similar to the CNN and CAE.
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 5 Nano-Delivery Systems of Bioactive Compounds
### Clinical Trials  
Curcumin is probably the most studied polyphenol in treatment of CVD, being an inhibitor of p300 histone acetyltransferase, which is associated with heart failure. Highly bioavailable curcumin has been developed as a nanoformulation commercially  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bioactive Compound** & **Nanoformulation** & **In Vitro/In Vivo Experimental Model** & **Main Results** & **Ref.** \\ \hline \multirow{6}{*}{Curcumin} & \multirow{4}{*}{Chitosan NPs} & In vitro/Ehrlich ascites carcinoma and in vivo animal model (albino mice) & Protection against myocardial injury and cardiac function, ameliorates EAC-induced cardiotoxicity. & [162] \\  & & & & \\ \cline{2-5}  & PEG-PDLLA (polyethylene glycol methyl ether-block-poly (D,  
known as Theracummin(r)[171]. This revolutionary formulation is a submicron crystal solid dispersion of curcumin, consisting of 10 \(w/w\)% curcumin, 2% other curcuminoids (such as dimethoxy-curcumin and bsidedmethoxycurcumin), 4% of gum ghatti, and 84% of water. In a clinical study performed with healthy participants, low (150 mg) and high (210 mg) doses of Theracummin(r) were administrated in order to evaluate plasma curcumin levels in a dose-dependent manner [172]. The study evidenced that nanoformulation increases plasma curcumin levels in a dose-dependent manner without saturating the absorption system. Another clinical study revealed that the treatment of hypertensive patients using 60 mg/day of Theracummin for 24 weeks significantly improved the parameters of diastolic function assessed by doppler echocardiography, which suggests that the nanoformulation improves left ventricular diastolic function without interfering with blood pressure in hypertensive patients [171].  
However, it is difficult to interpret whether the observed effect is due to the nanoparticle formulation or to curcumin itself, as the authors did not include a non-encapsulated formulation of curcumin as a control group. A comparison between curcumin nanoformulation and powder curcumin was performed by [PERSON] et al. [173] upon administration in a healthy participant, revealing that the bioavailability of Theracummin(r) orally administrated was 27-fold higher than that of curcumin powder, even at a low dosage (30 mg). It was concluded that Theracummin(r) shows higher bioavailability than currently available preparations (curcumin powder).  
Despite the large number of reported preclinical studies related to polyphenol nanoformulations, their transition to the clinical sector been proven to have several limitations, as it is well known that the concentrations of polyphenols proved to be effective in vitro or in small animal models are much higher than the required levels in human subjects. Moreover, the effectiveness of nano-nutraceutical products strongly depends on preserving the bioavailability of the bioactive compounds.
## 4 Discussion
### Sensor Position and Balance in Relation to Activity Type
#### 4.2.2 Torso  
In our results during static tasks, a greater total path length measured at the torso suggested unsecured balance. This emphasized the fact that the trunk has a crucial role in maintaining vertical posture and stabilizing the center of mass. Likewise, [PERSON] et al. (2010) [39] demonstrated that the proprioception of trunk muscles, when disturbed by vibrations, affected dynamic postural control.  
During dynamic tasks, trunk movements became more complex, and their influence on postural balance was more nuanced. The analysis revealed a complex interaction between trunk stability and mobility. Increased mean Y-acceleration was associated with better balance. This result may seem paradoxical, as another study by [PERSON] et al. (2021) [40] showed that increased lower back acceleration was generally associated with poorer balance.  
Notwithstanding, a previous study by [PERSON] and [PERSON] (2018) [41] on dynamic balance found that the balance skills depended on the tasks.  
The interpretation when considering the IMU placed at the torso was different from when the IMU was placed at the lower back. This suggested that the two sensor positions were complementary. In addition, [PERSON] et al. (2013) [42] demonstrated that the harmony of upper and lower trunk acceleration was a good predictor of falls in older adults, and that their measure of balance at the upper trunk, equivalent to the torso position in our study, could alone predict the incidence of falls well. Despite using different measures, their study is relevant to ours, as we also found that measures derived from torso IMUs were more discriminative than those from lower back IMUs.
# Hierarchical Parallel Evaluation of a Hamming Code
## 6 Conclusions  
The Hamming code is a well-known error-correcting code in which \(n=2^{m}-1\) data bits are secured by means of \(m=\log(n+1)\) parity bits, allowing to recover from any single bit-flip error. Each of the parity bits \(p_{1},\ldots,p_{m}\) is the result of the XORing of some subset \(S_{1},\ldots,S_{m}\) of the data bits, and these subsets are chosen so as to let any bit position be defined by the intersection of some of the \(S_{i}\). A straightforward implementation of the Hamming code thus needs to handle the entire block of \(n\) data bits to extract the parity bits; that is, all the data and parity bits have to reside in memory simultaneously, and this is also true for the decoding phase: data and parity bits are processed together to check if some single error has occurred.  
The contribution of this paper is the observation that this process may be parallelized in the sense of the hierarchy of processors defined in our model of Section 2.2. Actually, the entire process is summarized in Figure 2, depicting the basic building block of our suggested algorithm, which simply replicates the block in \(\log n\) layers, \(n/2^{i}\) times in the \(i\)th layer. The utterly simple work performed by such a block--the last two lines of Algorithm 1--is assigned to one of the processors and is the same for all the processors and all the layers, just with varying parameters. There is no need for any shared memory for the processors, nor is there any necessary inter-processor communication among the processors within the same layer. Processors of adjacent layers are connected by the hierarchy pattern described in Figure 1, where the outputs of a pair of neighboring processors of level \(i\) serve as input to a single processor of level \(i+1\), \(0\leq i<m\).  
**Author Contributions:** There was equal contribution of both authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results  
### Data Pre-Processing  
#### 4.1.1 Noise Cancellation: Fluctuation in the Sampling Data  
Only 0.83% of the data were omitted due to fluctuations in the sampling rate, meaning that these points had a sampling rate outside the normal range of sampling.
###### Abstract  
In this paper, we report on temperature dependence performances of a midwave infrared (MWIR) Ga-free InAs/InAsSb type-II superlattice (T2 SL) barrier (XBn) photodetector grown by molecular beam epitaxy on n-type GaSb substrate. The T2 SL structure, with a 3 \(\upmu\)m thick active region, was processed in a mesa device in order to perform dark current measurements and spectral photoresponse as a function of temperature. Analyses of these temperature dependence characterizations help us to improve the design of Ga-free T2 SL MWIR XBn detectors.  
Ga-free InAs/InAsSb type-II superlattice; XBn detector; midwave infrared; dark current; quantum efficiency
## 5 Discussion  
As previously mentioned, many deep learning algorithms have been used for the diagnosis of COVID-19 complications using CT or X ray images and they have achieved good classification performances; however, most of the results are obtained without knowledge of clinical characteristics of COVID-19. Moreover, the results analysis is only reported as metric values such as accuracy and AUC, without showing a clinical image that could be validated by radiologists. The aforementioned limitations constrain the translation of deep learning model into clinical practice. In this study, we proposed a new a 3D visualization of COVID-19 complications, which facilitate their interpretation by radiologists. An accurate training model requires sufficient annotated medical imaging data. On the other hand, high quantities of information from multiple sources are likely to overfit training and to lose the main clinical features. Not to mention that the collected data is heavily influenced by the instructions provided to the annotators. For this reason, the proposed framework achieved good performances for the different system's components, and at the same time, dealt with the reduced dataset used for training.  
Figure 14: Visual qualitative comparison of the infection segmentation results between the ground truth and our proposed model on four slices from different CT scans. First column: original CT scan. Second column: ground truth. Third column: predicted infection masks.  
For further evaluation, we quantitatively compared the performance of the proposed lungs and infection segmentation models with other state of the art methods that used the same publicly available dataset we used for training and testing, which renders the comparison to be interesting. We considered the works of [PERSON] et al. [41] who proposed a U-net based deep learning system, [PERSON] et al. [47], where a standard 3D U-Net architecture was implemented, [PERSON] [48], where two cascaded deep FCNs are connected sequentially to segment the lung organ and then the COVID-19 infection areas with an adjustment of U-net architecture as a backbone, and [PERSON] et [PERSON] [49], who developed their network with a two cascaded residual attention inception U-Net (RAIU-Net) model to generate lung contour maps and COVID-19 infected regions. Quantitative results, using the dice coefficient metric as a reference and including lungs and infection segmentation models, are reported in Table 6 below.  
The results prove the effectiveness of our models that outperformed the other listed methods by achieving the highest Dice Coefficient value for both lungs and infection segmentation models.
## 3 Study Area and Data
### Database Preparation and Analysis  
Based on the geological disaster investigation results of various counties and cities in the western region of Henan Province, landslide cataloging has been conducted. By the end of 2017, a total of 834 landslides had been recorded, categorized into two types: loess landslides and rocksslides (Figure 3). These landslides are mainly distributed in mountainous and hilly areas and along roadsides. Landslide susceptibility is determined based on the basic environmental conditions of an area to assess the spatial probability of landslides occurring. In accordance with basic environmental data and geological conditions, and with reference to the relevant literature analyzing the characteristics of landslide formation in the western region of Henan Province, a total of 10 environmental factors were selected: elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road [26]. GIS was used to extract raster layers of each environmental factor at a resolution of 30 m (Figure 4), and the _FR_ values of each environmental factor were calculated (Table 1). Figure 2: Photo of landslide site in the study area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environmental** & **Classes** & **\% Class** & **\% Landslide** & **FR** \\ \hline \multirow{8}{*}{Elevation} & 0–200 m & 8.59\% & 0.84\% & 0.098 \\  & 200–400 m & 22.21\% & 9.59\% & 0.432 \\  & 400–600 m & 19.24\% & 25.42\% & 1.321 \\  & 600–800 m & 14.91\% & 29.86\% & 2.003 \\ \multirow{8}{*}{Elevation} & 800–1000 m & 13.29\% & 22.06\% & 1.660 \\  & 1000–1200 m & 11.00\% & 7.31\% & 0.665 \\  & 1200–1400 m & 6.86\% & 3.96\% & 0.577 \\  & \(>\)1400 m & 3.90\% & 0.96\% & 0.246 \\ \hline \multirow{8}{*}{Slope} & 0\({}^{\circ}\)–10\({}^{\circ}\) & 43.49\% & 34.05\% & 0.783 \\  & 10\({}^{\circ}\)–20\({}^{\circ}\) & 27.85\% & 36.81\% & 1.322 \\ \cline{1-1}  & 20\({}^{\circ}\)–30\({}^{\circ}\) & 18.27\% & 22.78\% & 1.247 \\ \cline{1-1}  & 30\({}^{\circ}\)–40\({}^{\circ}\) & 8.48\% & 5.76\% & 0.679 \\ \cline{1-1}  & 40\({}^{\circ}\)–50\({}^{\circ}\) & 1.76\% & 0.60\% & 0.340 \\ \cline{1-1}  & 50\({}^{\circ}\) & 0.14\% & 0.00\% & 0.000 \\ \hline \multirow{8}{*}{Aspect} & Flat (–1) & 0.28\% & 0.00\% & 0.000 \\  & North (0–22.5, 337.5–360) & 12.77\% & 10.79\% & 0.845 \\ \cline{1-1}  & Northeast & 13.88\% & 13.55\% & 0.976 \\ \cline{1-1}  & East (67.5–112.5) & 12.89\% & 12.47\% & 0.967 \\ \cline{1-1}  & Southeast & 11.70\% & 16.07\% & 1.374 \\ \cline{1-1}  & South (157.5–202.5) & 12.69\% & 14.15\% & 1.115 \\ \cline{1-1}  & Southwest & 12.61\% & 13.67\% & 1.084 \\ \cline{1-1}  & West (247.5–292.5) & 11.69\% & 9.59\% & 0.820 \\ \cline{1-1}  & Northwest & 11.50\% & 9.71\% & 0.845 \\ \cline{1-1} \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency ratio of environmental factors. Figure 3: Topographic map and landslide distribution in western Henan Province.