esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 3 Review Findings
### Analysis Domain
#### 3.2.3 Task  
In this review, all kinds of literature on the computer-aided bleeding detection approach were divided into three categories based on the task. The first category was the classification task for bleeding images or frames, the second was the segmentation task for bleeding zone identification, and the third was the combination of the classification and segmentation tasks.  
Classification is a supervised learning technique in both machine learning and deep learning that is used to categorize a given set of data into classes. A classification model learns from the given dataset and then classifies new observations into a number of classes or groups, such as 0 or 1, yes or no, bleeding image or non-bleeding image, etc. The study in [23] presented a machine learning algorithm to classify CE images into two categories, named bleeding and non-bleeding images. [PERSON] et al. developed a deep neural network for the classification of bleeding CE images [42]. A video frame classification model using SVM, which is a machine learning algorithm, was presented in [30] to classify bleeding frames in CE videos.  
Segmentation  
Segmentation is a technique in which an image is broken down into different subregions according to the extracted features. An image is a collection or set of different pixels. Similar types of pixels are grouped according to image segmentation. This technique helps in minimizing the complexity of the image in order to simplify further processing or analysis of the image. [PERSON] et al. [43] presented an automated segmentation technique based on SVM to detect bleeding regions in CE images. In other research on bleeding zone segmentation of CE images, a deep learning-based model, named Multi-Stage Attention-Unet, was proposed [44]. A time domain-based segmentation approach was provided by [PERSON] et al. [40] for locating bleeding in CE videos.  
Classification + Segmentation  
This category includes the classification and segmentation techniques at the same time to classify images and locate the region of similar types of pixels. In this review, several articles proposed to classify bleeding images and also to detect the bleeding region at the same time using both classification and segmentation techniques for the development of advanced computer-aided diagnosis systems. [PERSON] et al. presented a model based on Gaussian mixture model superpixels and SVM for automatic bleeding detection using CE images. First, the model classified bleeding and non-bleeding images, and then it applied the post-segmentation technique to detect the bleeding region in the bleeding image [45]. Two deep learning CNN-based models, AlexNet and SegNet, were presented in [46] to classify bleeding images and zones in CE images. In [36], a computer-aided scheme was presented for the classification of bleeding frames from CE videos, and then the post-segmentation technique was applied for the localization of the bleeding zone.
## 2 Multi-UAV Cooperative Persistent Monitoring Path Planning Model
### Min-Max Optimization for Multi-UAV Cooperative Monitoring  
#### 2.6.1 UAVs Monitoring Overdue Time Evaluation  
Let the maximum monitoring interval of a task node \(i\) between two adjacent monitoring events be the expected period of the node, denoted by \(T_{i}\), \(i\in V\). Ideally, for any time \(k\), the waiting time of node \(i\) should not exceed its expected period. That is  
\[0\leq f_{k,i}\leq T_{i},i\in V,k\in\{1,2,\ldots,K\} \tag{8}\]  
However, in practical applications, since the number of UAVs is far less than the quantity of the task nodes, it is inevitable that some nodes' monitoring will be overdue. The overdue time can be expressed as \(f_{k-1,i}+c-T_{i}\). Define the real monitoring period of the task node as \(P\in\mathbb{R}^{K\times N}\):  
\[p_{k,i}=\begin{cases}0,&i\in V,k=1\\ x_{k,i}(f_{k-1,i}+c),&i\in V,k\in\{2,3,\ldots,K\}\end{cases} \tag{9}\]  
The above equation indicates that when the UAV arrives at node \(i\) at time step \(k\), i.e., \(x_{k,i}=1\), the real monitoring period of this node is \(f_{k-1,i}+c\). Otherwise, \(p_{k,i}\) have no definition and it will be assigned to zero. Therefore, the maximum monitoring period of the task node \(i\) in the entire monitoring horizon is:  
\[\max_{k\in\{1,2,\ldots,K\}}p_{k,i} \tag{10}\]Then, the maximum overdue time of the task node \(i\) caused by exceeding its expected period \(T_{i}\) can be expressed as:  
\[\max\left\{0,\max_{k\in\{1,2,...,K\}}(p_{k,i}-T_{i})\right\} \tag{11}\]  
The following objective, \(J_{1}\), is proposed for optimization by minimizing the normalized maximum overdue time of all task nodes.  
\[\min_{Y,F}J_{1}=\max_{i\in V}\left(\frac{1}{T_{i}}\max\left\{0,\max_{k\in\{1,2,...,K\}}(p_{k,i}-T_{i})\right\}\right) \tag{12}\]
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System
### System Architecture and Description  
Figure 1 illustrates NEFOCAST's experimentation network for real-time wide-area rain-rate measurement (Figure 1a). Its relevant high level end-to-end architectural description is shown in Figure 1b. The experimentation network consists of a number of Earth stations, wherein each station is equipped with a SmartLNB device (see Section 2.1) interconnected to the satellite network. Some of the installations include also a TBR with a data logger, for validation purposes.  
The NEFOCAST Service Center collects information from the satellite network and the rain gauges, generating rain field maps that are then shared with a number of value added service providers (VASPs) that use them to offer dedicated services to end users.  
Figure 1: (a) NEFOCAST’s experimentation network for real-time wide-area rain-rate measurement and (b) its high-level end-to-end architectural description.
## 4 Application Example
### Energy Study of the Transformation House
#### 4.2.3 Replacing the Actual Transformer with a More Efficient One, with Less Power
Observing the total energy losses, indicated in Table 12, and the carbon dioxide emissions, in Table 13, it follows that the most suitable replacement transformer for the actual 1600 kVA must be an efficient transformer with a nominal power of 1000 kVA, since it is the one with the lowest total losses (Table 12) and carbon dioxide emissions (Table 13). Compared with the 1600 kVA inefficient transformer, annual energy savings of 19,037.95 kWh and annual savings in carbon dioxide emissions of 4588.14 kg are obtained.
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 3 Methods
### Point Cloud Normal Calculation and Optimization  
The calculation of normal vectors for point clouds is a local plane (or surface) fitting problem, where the normal vector of the plane (or surface) is the normal of the point cloud. For a point \(p_{i}\in P\), we first find the k nearest points \(P^{k}\) as the neighborhood of \(p_{i}\). By computing the covariance matrix of \(P^{k}\) and performing eigenvalue decomposition on the matrix, the normal vector \(\overrightarrow{\mathbf{n}}\) corresponds to the eigenvector with the smallest eigenvalue.  
In the process of computing normal vectors, the direction of the normal vector is ambiguous because the normal vector of a plane has two directions, as shown in Figure 11. The direction of the normal vector is an important factor affecting Poisson modeling. Therefore, it is necessary to optimize the normal vector direction.  
Usually, all normals are refined towards a predefined viewpoint \(v_{p}\) by requiring \(\overrightarrow{\mathbf{n_{i}}}\cdot(v_{p}-p_{i})>0\). However, this method is suitable for 2.5D point clouds which are captured from a single viewpoint. Tunnel point clouds are dynamically captured, and the above method does not work well for complex tunnel point clouds. In this paper, based on the extracted tunnel centerline, a dynamic viewpoint approach is adopted to optimize the normal vectors of the point cloud.  
For a point \(p\) in the tunnel point cloud, the corresponding Y-axis slice is first determined. The point \(P_{0}\) on the tunnel centerline corresponding to this slice is taken as the viewpoint of point \(p\). The normal direction is optimized accordingly to  
\[\overrightarrow{\mathbf{n_{i}}}\cdot(P_{0}-p_{i})<0 \tag{8}\]  
After this adjustment, all normal vectors of the tunnel point cloud point towards the outside of the tunnel, as shown in Figure 11.
## 3 Measurement Results
### Gesture Detection  
To measure four hand gestures consisting of an up /down motion, left/right motion, clockwise rotation, and counterclockwise rotation, the subject sat around 1.2 m away from the radar with their hand 1 m from the radar, as shown in Figure 12. Over 24 s, each gesture was first repeated three times after waiting four seconds between gestures. For the last gestures in each data set, the translation motion in Figure 12a was performed once at a time, consisting of up-hold-down-hold and left-hold-right-hold patterns, while the rotation motion in Figure 12b was increased to two rotations. The horizontal and vertical gesture motions spanned approximately 0.5 m, and the rotational gestures started 1 m away before rotating toward the radar with a diameter of about 0.3 m. Due to the shorter time window, the modulation signal and \(I/Q\) baseband signals of the gestures were recorded at 125 ksps as the oscilloscope could support the same amount of data over a shorter period.  
The PSK and PMCW baseband signals were demodulated, AC coupled, and joined to form a complex response. A set of discrete fast Fourier transforms (DFFT) was then taken from the data with a 1% data window and 50% overlap between windows along the response data for all three radar modes and all four gestures, shown in Figures 13-15. The responses of each gesture share identifying characteristics in the amplitude and frequency  
Figure 11: Measured respiration response for (**a**) CW mode, (**b**) PSK mode, and (**c**) PMCW mode. All tests were performed with the subject 1 m from the radar and show a strong correlation with the respiration belt response.  
Figure 12: Gesture measurement setup for (**a**) up/down and left/right motion and (**b**) clockwise and counterclockwise rotation. Target’s hand motions are approximately one meter away from the radar system for all tests.  
value of their spectrogram responses. While the up/down and left/right gestures are similar motions and span similar distances, the induced Doppler frequencies are slightly different due to the speed of the gesture. Both straight-line motions are distinctly different from the rotational gesture responses. For the latter, the subject's location was constant, so as their hand rotated there is a period when their hand reflected more of the transmitted wave and the induced signal is stronger. Since their hand passes through the same space in front of the radar, the timing of the increase in power/frequency is flipped when the rotation is reversed.  
Figure 14: PSK mode spectrograms for repeated hand gestures.  
Figure 13: CW mode spectrograms for repeated hand gestures.
## 2 Light Clock
### Time Measurement from Universal Frame of Reference
\[t=t_{1}+t_{2}=\frac{L_{1}}{c}+\frac{L_{2}}{c}=\frac{D_{x}(v)}{c-v}+\frac{D_{x}( v)}{c+v}=D_{x}(v)\frac{(c+v)+(c-v)}{(c-v)\ (c+v)}=\frac{2\ c\ D_{x}(v)}{c^{2}-v^{2}} \tag{10}\]  
\[t=\frac{2\ D_{x}(v)}{c}\frac{1}{1-\left(v/c\right)^{2}} \tag{11}\]  
During one cycle of the light clock, for the observer from the \(U^{\prime}\) system, the time elapses given by Equation (5), and for the observer from the universal frame of reference \(U\), the time elapses given by Equation (11). Therefore, these observers evaluate the time elapse differently. When we divide the pages of Equation (11) by Equation (5), then we obtain the formula for time dilation in the form of  
\[t=t^{\prime}\frac{D_{x}(v)}{D_{x}(0)}\frac{1}{1-\left(v/c\right)^{2}}=t^{ \prime}\frac{D_{x}(v)}{D_{x}^{\prime}}\frac{1}{1-\left(v/c\right)^{2}} \tag{12}\]  
Time dilation depends on the speed \(v\) with which the clock moves (i.e., on the speed of the \(U^{\prime}\) system in relation to the \(U\) system) and on how the longitudinal dimensions of bodies moving in relation to the light propagation medium contract (i.e., on the value of \(D_{x}(v)/D^{\prime}x\)). Time dilation also depends on the speed \(c\) with which the signal moves. From the derived Equation (12), it follows that  
Figure 3:0  
This means that if the same light clock in \(U^{\prime}\) system is seen by two observers, one is motionless in relation to the clock (inertial system \(U^{\prime}\)), and the other is motionless in relation to ether (universal frame of reference \(U\)), then for them, this clock measures the time differently. It results from the fact that for the observer from the \(U^{\prime}\) system, the considered clock is a time standard, while for the observer from the \(U\) system, the time standard is the motionless clocks in his \(U\) system. As the speed \(v\) with which the clock moves in relation to the universal frame of reference influences the speed of its ticking, the time standard of these two observers works differently. As a result, time dilation is a natural property of a light clock. The value \(c\) appearing in Equations (12) and (13) does not have to be the speed of light in a vacuum, but it can be the speed of any signal propagating in a medium, e.g., the speed of sound in air or water.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 2 Evolution of the Proposed Modified Quadratic Boost Converter
### Proposed Modified Quadratic Boost Converter with Integrated Dual Voltage Lift Cell
#### 2.4.2 Non-Ideal Voltage Gain Calculation Considering the Diode Voltage Drop  
The diode's voltage drop is one of the main factors for concern for the deviation of the output voltage from the actual voltage gain. For the non-isolated-based high gain quadratic boost converters, mainly in voltage boost cells-based QBC, there are more diodes to store the energy in the passive components (i.e., the extra passive components for boosting the voltage). The voltage reduction at each step is added together and reflected in the output voltage gain. The equivalent series resistance (ESR) value of each passive component was considered negligible. In the below section, the voltage gain is formulated considering the diode's forward voltage drop (i.e., \(V_{D}\)) following the same previous method as discussed in the earlier section.  
Mode 1 [0, DT]:  
Referring to Figure 6, during mode 1 the characteristics equations are as follows  
\[V_{L1}=V_{in}-V_{D} \tag{22}\]  
\[V_{\text{C1}}=V_{in}-2V_{D} \tag{23}\]  
\[V_{\text{L2}}=V_{\text{C3}} \tag{24}\]  
\[V_{\text{C2}}=V_{\text{C3}}+V_{\text{C4}}-V_{D} \tag{25}\]  
Mode 2 [DT, T]:  
Referring to Figure 7, during mode 2 the corresponding equations are as follows  
\[V_{L1}=V_{in}+V_{\text{C1}}-V_{\text{C3}}-V_{D} \tag{26}\]  
\[V_{L1}=2V_{in}-V_{\text{C3}}-V_{D} \tag{27}\]  
\[V_{\text{C4}}=-V_{L2}+V_{D} \tag{28}\]  
\[V_{L2}=V_{\text{C3}}+V_{\text{C2}}-V_{0}-V_{D} \tag{29}\]  
Now applying the volt-sec balance method to the inductor \(L_{1}\) the corresponding voltage gain was calculated and is shown below.  
\[V_{\text{C3}}=\frac{(2-d)}{(1-d)}V_{in}-\frac{(3-2d)}{(1-d)}V_{D} \tag{30}\]  
Applying the volt-sec method on Inductor \(L_{2}\) and the derived non-ideal voltage gain is shown as follows  
\[V_{0}=\frac{2\left(2-d\right)}{(1-d)^{2}}V_{in}-\frac{(7-6d+d^{2})}{(1-d)^{2} }V_{D} \tag{31}\]  
The related waveform of the ripple current of the inductor, ripple voltage of the capacitor, and the current waveform of the diode and switch are depicted in Figure 8.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
###### Abstract  
In a mature middle and lower Gharif field in Oman, uncertainties surrounding initial water saturation and early water breakthroughs of unknown sources and paths suggest the presence of significant bypassed oil. In order to determine the areas with remaining oil, petrophysical and logging data of seven wells were processed using Techlog software and imported into Petrel software for modelling and simulation. Porosity was calculated using the Electric Propagation Time log and was utilized to evaluate the presence of oil, particularly in the upper tight zone of the formation. Despite the low resistivity readings in the highly porous layers, caused by good network connectivity and high formation water salinity, the resistivity contrast was sufficient to differentiate them from the oil zone. However, the calculated water saturation (Sw) in the tight top oil zone was high, consistent with the observed water production in the field. To improve the match between production data and simulation results, sensitivity analyses were conducted on various permeability anisotropy and relative permeability values within the model. The analyses showed that core-derived permeability anisotropy (vertical to horizontal ratio of 1:1) yielded a better history match for water production compared to the conventionally used value of 1:10. Water saturation maps were generated at the start and the end of production to highlight saturation distribution within the reservoirs. The maps revealed that in the lower porous part, the oil was fully depleted around the wells but remained trapped in the undrilled areas.  
tight oil; low resistivity; permeability anisotropy; EPT +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Chloride Penetration  
In Figure 4, chloride penetration depth values are reported vs. time of exposure to 10% NaCl aqueous solution after saturation with water of concrete specimens. Figure 4 shows only data obtained  
Figure 3: Carbonation depth vs. time of air exposure.  
Figure 2: Compressive strength vs. curing time.  
after about 28-day water immersion due to initial instability phenomena (in practice, the chloride binding with cement matrix can interfere with the chloride migration [16]).  
[PERSON] et al. [13] demonstrated that chloride penetration depth (\(x\)) varies with immersion time (\(t\)) according to Equation (1), which can be obtained from the solution of [PERSON]'s second law under non-steady-state conditions for diffusion in a semi-finite solid:  
\[x=4\cdot\sqrt{D\cdot t} \tag{1}\]  
where \(D\) is the diffusion coefficient of Cl ions into wetted concrete pores, expressed as cm\({}^{2}\cdot\) s\({}^{-1}\cdot 10^{-8}\). The \(D\) values, coming from (1) by interpolation of the results reported in Figure 4, have been calculated (see Table 4). It can be noticed a significant beneficial effect due to fly ash addition on the chloride penetration depth, which has been measured for Rec + FA-0.6 and Nat + FA-0.6 concretes, respectively. As a matter of fact, Cl diffusion coefficients into these mixtures are 10 times less than those measured for the other concrete mixtures.  
[PERSON] et al. [13] found experimental results, which support the hypothesis that the different structure of pore surfaces created with the addition of pozzolanic materials played an important role in influencing concrete porosity while chloride ions penetrate it. In fact, if concrete is prepared by adding fly ash, chloride binding operated by the cement matrix significantly increases.  
A lower water/cement resulted beneficial in terms of hindering of chloride penetration. In fact, the Cl diffusion coefficient into the Nat-0.6 mixture is double than Rec-0.3, even if recycled instead of virgin aggregate was employed. Nevertheless, aggregate pore structure could have a significant effect on concrete permeability to chloride penetration (as you can see by comparing Nat + FA-0.6 and Rec + FA-0.6 mixtures in Figure 4). [PERSON] et al. [17] experimented several lightweight aggregate concretes and the conclusion was that concrete permeability depends more on cement matrix porosity than lightweight aggregate porosity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Nat-0.6** & **Nat + FA-0.6** & **Rec-0.3** & **Rec + FA-0.6** \\ \hline D (cm\({}^{2}\)s\({}^{-1}\cdot\)10\({}^{-6}\)) & 1.90 & 0.87 & 0.72 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chloride diffusion coefficients at 20 \({}^{\circ}\)C.  
Figure 4: Chloride penetration depth vs. immersion time.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Case Study Comparison  
In this section, PCAT is compared with the state-of-the-art schemes. Table 7 shows the assumptions of the considered schemes. In order to obtain a quantitative comparison of the state-of-the-art schemes, a case study is modeled by following the subsequent methodology. A network where each node has 10 neighbors is considered. The size of the secret material is set as follows: the length of the symmetric keys \(l_{k}=128\) bits, and the length of the public and private keys \(l_{pu}=l_{pr}=256\) bits, the size of the mask \(m=4\), the length of the hash \(l_{h}=20\) bits. The length of node IDs \(l_{ID}=\left\lceil\log_{256}n\right\rceil\) bytes and of the key IDs \(l_{RID}=1\) byte. The length of the signature in SSL is \(l_{sign}=64\). For 2C, RSDTMK and EG the best \(r\) and \(p\) are selected so that the upper bound to the memory storage is 5% of the RAM of Tmotes Sky (512 bytes). A connectivity \(\geq\)0.99 has been selected as a constraint for all the schemes. Since in the schemes based on random distribution (i.e., EG, 2C, 1-5C, RSDTMK) the connectivity is better with high values of \(r\) and since \(r\) affects the storage, the highest value of \(r\) compliant with the storage constraint has been selected. Finally, the highest value of \(p\) that is compliant with the connectivity constraint has been selected. A high value of \(\mu\) in RSDTMK increases the level of resilience, but also the memory storage. In order to provide a high level of resilience with limited drawbacks, \(\mu\) is set to 8. The resulting values are: for EG, \(p=198\), \(r=28\); for 2C, \(p=70\), \(r=19\); for 1-5C, \(p=160\), \(r=25\); and for RSDTMK, \(p=127\), \(r=24\).  
The proposed scheme always provides the best resilience, as with FPWK and SSL. LEAP+ and FLEAP also provide the best level of resilience if the nodes are not compromised during the initialization phase, otherwise their level of resilience is the poorest. The level of resilience for RSDTMK is close to the optimum if the nodes are not compromised during the initialization phase. An intermediate level of resilience is provided by 2C, 1-5C and EG. In particular, 1-5C is better than 2C and EG.